diff --git a/.gitignore b/.gitignore index a57dc19..f3f0fe6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,20 +1,20 @@ -*.code-workspace -quavenv/* -*.pdf - -__pycache__/* -baselines/__pycache__/* -baselines/densratio/__pycache__/* -quacc/__pycache__/* -quacc/evaluation/__pycache__/* -quacc/method/__pycache__/* -tests/__pycache__/* - -*.coverage -.coverage - -scp_sync.py - -out/* -output/* +*.code-workspace +quavenv/* +*.pdf + +__pycache__/* +baselines/__pycache__/* +baselines/densratio/__pycache__/* +quacc/__pycache__/* +quacc/evaluation/__pycache__/* +quacc/method/__pycache__/* +tests/__pycache__/* + +*.coverage +.coverage + +scp_sync.py + +out/* +output/* !output/main/ \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json index b575dd7..abb8d43 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -1,25 +1,25 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - - { - "name": "main", - "type": "python", - "request": "launch", - "program": "C:\\Users\\Lorenzo Volpi\\source\\tesi\\quacc\\main.py", - "console": "integratedTerminal", - "justMyCode": true - }, - { - "name": "main_test", - "type": "python", - "request": "launch", - "program": "C:\\Users\\Lorenzo Volpi\\source\\tesi\\quacc\\main_test.py", - "console": "integratedTerminal", - "justMyCode": false - }, - ] +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + + { + "name": "main", + "type": "python", + "request": "launch", + "program": "C:\\Users\\Lorenzo Volpi\\source\\tesi\\quacc\\main.py", + "console": "integratedTerminal", + "justMyCode": true + }, + { + "name": "main_test", + "type": "python", + "request": "launch", + "program": "C:\\Users\\Lorenzo Volpi\\source\\tesi\\quacc\\main_test.py", + "console": "integratedTerminal", + "justMyCode": false + }, + ] } \ No newline at end of file diff --git a/.vscode/vscode-kanban.json b/.vscode/vscode-kanban.json index 7b6f95c..4f7c9ab 100644 --- a/.vscode/vscode-kanban.json +++ b/.vscode/vscode-kanban.json @@ -1,54 +1,54 @@ -{ - "todo": [ - { - "assignedTo": { - "name": "Lorenzo Volpi" - }, - "creation_time": "2023-10-28T14:33:36.069Z", - "id": "2", - "references": [], - "title": "Creare plot avg con training prevalence sull'asse x e media rispetto a test prevalence" - }, - { - "assignedTo": { - "name": "Lorenzo Volpi" - }, - "creation_time": "2023-10-28T14:32:37.610Z", - "id": "1", - "references": [], - "title": "Testare su imdb" - } - ], - "in-progress": [ - { - "assignedTo": { - "name": "Lorenzo Volpi" - }, - "creation_time": "2023-10-28T14:34:23.217Z", - "id": "3", - "references": [], - "title": "Relaizzare grid search per task specifico partedno da GridSearchQ" - }, - { - "assignedTo": { - "name": "Lorenzo Volpi" - }, - "creation_time": "2023-10-28T14:34:46.226Z", - "id": "4", - "references": [], - "title": "Aggingere estimator basati su PACC (quantificatore)" - } - ], - "testing": [], - "done": [ - { - "assignedTo": { - "name": "Lorenzo Volpi" - }, - "creation_time": "2023-10-28T14:35:12.683Z", - "id": "5", - "references": [], - "title": "Rework rappresentazione dati di report" - } - ] +{ + "todo": [ + { + "assignedTo": { + "name": "Lorenzo Volpi" + }, + "creation_time": "2023-10-28T14:33:36.069Z", + "id": "2", + "references": [], + "title": "Creare plot avg con training prevalence sull'asse x e media rispetto a test prevalence" + }, + { + "assignedTo": { + "name": "Lorenzo Volpi" + }, + "creation_time": "2023-10-28T14:32:37.610Z", + "id": "1", + "references": [], + "title": "Testare su imdb" + } + ], + "in-progress": [ + { + "assignedTo": { + "name": "Lorenzo Volpi" + }, + "creation_time": "2023-10-28T14:34:23.217Z", + "id": "3", + "references": [], + "title": "Relaizzare grid search per task specifico partedno da GridSearchQ" + }, + { + "assignedTo": { + "name": "Lorenzo Volpi" + }, + "creation_time": "2023-10-28T14:34:46.226Z", + "id": "4", + "references": [], + "title": "Aggingere estimator basati su PACC (quantificatore)" + } + ], + "testing": [], + "done": [ + { + "assignedTo": { + "name": "Lorenzo Volpi" + }, + "creation_time": "2023-10-28T14:35:12.683Z", + "id": "5", + "references": [], + "title": "Rework rappresentazione dati di report" + } + ] } \ No newline at end of file diff --git a/TODO.html b/TODO.html index 59f0412..60bf789 100644 --- a/TODO.html +++ b/TODO.html @@ -1,143 +1,143 @@ - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/TODO.md b/TODO.md index c1b47c7..3638c90 100644 --- a/TODO.md +++ b/TODO.md @@ -1,44 +1,44 @@ -- [x] aggiungere media tabelle -- [x] plot; 3 tipi (appunti + email + garg) -- [x] sistemare kfcv baseline -- [x] aggiungere metodo con CC oltre SLD -- [x] prendere classe più popolosa di rcv1, togliere negativi fino a raggiungere 50/50; poi fare subsampling con 9 training prvalences (da 0.1-0.9 a 0.9-0.1) -- [x] variare parametro recalibration in SLD - - -- [x] fix grafico diagonal - - seaborn example gallery -- [x] varianti recalib: bcts, SLD (provare exact_train_prev=False) -- [x] vedere cosa usa garg di validation size -- [x] per model selection testare il parametro c del classificatore, si esplora in np.logscale(-3,3, 7) oppure np.logscale(-4, 4, 9), parametro class_weight si esplora in None oppure "balanced"; va usato qp.model_selection.GridSearchQ in funzione di mae come errore, UPP come protocollo - - qp.train_test_split per avere v_train e v_val - - GridSearchQ( - model: BaseQuantifier, - param_grid: { - 'classifier__C': np.logspace(-3,3,7), - 'classifier__class_weight': [None, 'balanced'], - 'recalib': [None, 'bcts'] - }, - protocol: UPP(V_val, repeats=1000), - error = qp.error.mae, - refit=True, - timeout=-1, - n_jobs=-2, - verbose=True).fit(V_tr) -- [x] plot collettivo, con sulla x lo shift e prenda in considerazione tutti i training set, facendo la media sui 9 casi (ogni line è un metodo), risultati non ottimizzati e ottimizzati -- [x] salvare il best score ottenuto da ogni applicazione di GridSearchQ - - nel caso di bin fare media dei due best score -- [x] import baselines - -- [ ] importare mandoline - - mandoline può essere importato, ma richiedere uno slicing delle features a priori che devere essere realizzato ad hoc -- [ ] sistemare vecchie iw baselines - - non possono essere fixate perché dipendono da numpy -- [x] plot avg con train prevalence sull'asse x e media su test prevalecne -- [x] realizzare grid search per task specifico partendo da GridSearchQ -- [x] provare PACC come quantificatore -- [x] aggiungere etichette in shift plot -- [x] sistemare exact_train quapy -- [x] testare anche su imbd - +- [x] aggiungere media tabelle +- [x] plot; 3 tipi (appunti + email + garg) +- [x] sistemare kfcv baseline +- [x] aggiungere metodo con CC oltre SLD +- [x] prendere classe più popolosa di rcv1, togliere negativi fino a raggiungere 50/50; poi fare subsampling con 9 training prvalences (da 0.1-0.9 a 0.9-0.1) +- [x] variare parametro recalibration in SLD + + +- [x] fix grafico diagonal + - seaborn example gallery +- [x] varianti recalib: bcts, SLD (provare exact_train_prev=False) +- [x] vedere cosa usa garg di validation size +- [x] per model selection testare il parametro c del classificatore, si esplora in np.logscale(-3,3, 7) oppure np.logscale(-4, 4, 9), parametro class_weight si esplora in None oppure "balanced"; va usato qp.model_selection.GridSearchQ in funzione di mae come errore, UPP come protocollo + - qp.train_test_split per avere v_train e v_val + - GridSearchQ( + model: BaseQuantifier, + param_grid: { + 'classifier__C': np.logspace(-3,3,7), + 'classifier__class_weight': [None, 'balanced'], + 'recalib': [None, 'bcts'] + }, + protocol: UPP(V_val, repeats=1000), + error = qp.error.mae, + refit=True, + timeout=-1, + n_jobs=-2, + verbose=True).fit(V_tr) +- [x] plot collettivo, con sulla x lo shift e prenda in considerazione tutti i training set, facendo la media sui 9 casi (ogni line è un metodo), risultati non ottimizzati e ottimizzati +- [x] salvare il best score ottenuto da ogni applicazione di GridSearchQ + - nel caso di bin fare media dei due best score +- [x] import baselines + +- [ ] importare mandoline + - mandoline può essere importato, ma richiedere uno slicing delle features a priori che devere essere realizzato ad hoc +- [ ] sistemare vecchie iw baselines + - non possono essere fixate perché dipendono da numpy +- [x] plot avg con train prevalence sull'asse x e media su test prevalecne +- [x] realizzare grid search per task specifico partendo da GridSearchQ +- [x] provare PACC come quantificatore +- [x] aggiungere etichette in shift plot +- [x] sistemare exact_train quapy +- [x] testare anche su imbd + - [ ] rivedere nuove baselines \ No newline at end of file diff --git a/baselines/atc.py b/baselines/atc.py index 744c284..be93d24 100644 --- a/baselines/atc.py +++ b/baselines/atc.py @@ -1,44 +1,44 @@ -import numpy as np -from sklearn.metrics import f1_score - - -def get_entropy(probs): - return np.sum(np.multiply(probs, np.log(probs + 1e-20)), axis=1) - - -def get_max_conf(probs): - return np.max(probs, axis=-1) - - -def find_ATC_threshold(scores, labels): - sorted_idx = np.argsort(scores) - - sorted_scores = scores[sorted_idx] - sorted_labels = labels[sorted_idx] - - fp = np.sum(labels == 0) - fn = 0.0 - - min_fp_fn = np.abs(fp - fn) - thres = 0.0 - for i in range(len(labels)): - if sorted_labels[i] == 0: - fp -= 1 - else: - fn += 1 - - if np.abs(fp - fn) < min_fp_fn: - min_fp_fn = np.abs(fp - fn) - thres = sorted_scores[i] - - return min_fp_fn, thres - - -def get_ATC_acc(thres, scores): - return np.mean(scores >= thres) - - -def get_ATC_f1(thres, scores, probs): - preds = np.argmax(probs, axis=-1) - estim_y = np.abs(1 - (scores >= thres) ^ preds) - return f1_score(estim_y, preds) +import numpy as np +from sklearn.metrics import f1_score + + +def get_entropy(probs): + return np.sum(np.multiply(probs, np.log(probs + 1e-20)), axis=1) + + +def get_max_conf(probs): + return np.max(probs, axis=-1) + + +def find_ATC_threshold(scores, labels): + sorted_idx = np.argsort(scores) + + sorted_scores = scores[sorted_idx] + sorted_labels = labels[sorted_idx] + + fp = np.sum(labels == 0) + fn = 0.0 + + min_fp_fn = np.abs(fp - fn) + thres = 0.0 + for i in range(len(labels)): + if sorted_labels[i] == 0: + fp -= 1 + else: + fn += 1 + + if np.abs(fp - fn) < min_fp_fn: + min_fp_fn = np.abs(fp - fn) + thres = sorted_scores[i] + + return min_fp_fn, thres + + +def get_ATC_acc(thres, scores): + return np.mean(scores >= thres) + + +def get_ATC_f1(thres, scores, probs): + preds = np.argmax(probs, axis=-1) + estim_y = np.abs(1 - (scores >= thres) ^ preds) + return f1_score(estim_y, preds) diff --git a/baselines/densratio/RuLSIF.py b/baselines/densratio/RuLSIF.py index 504dd14..8ad6c2b 100644 --- a/baselines/densratio/RuLSIF.py +++ b/baselines/densratio/RuLSIF.py @@ -1,277 +1,277 @@ -""" -Relative Unconstrained Least-Squares Fitting (RuLSIF): A Python Implementation -References: - 'Change-point detection in time-series data by relative density-ratio estimation' - Song Liu, Makoto Yamada, Nigel Collier and Masashi Sugiyama, - Neural Networks 43 (2013) 72-83. - - 'A Least-squares Approach to Direct Importance Estimation' - Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama, - Journal of Machine Learning Research 10 (2009) 1391-1445. -""" - -from warnings import warn - -from numpy import ( - array, - asarray, - asmatrix, - diag, - diagflat, - empty, - exp, - inf, - log, - matrix, - multiply, - ones, - power, - sum, -) -from numpy.linalg import solve -from numpy.random import randint - -from .density_ratio import DensityRatio, KernelInfo -from .helpers import guvectorize_compute, np_float, to_ndarray - - -def RuLSIF(x, y, alpha, sigma_range, lambda_range, kernel_num=100, verbose=True): - """ - Estimation of the alpha-Relative Density Ratio p(x)/p_alpha(x) by RuLSIF - (Relative Unconstrained Least-Square Importance Fitting) - - p_alpha(x) = alpha * p(x) + (1 - alpha) * q(x) - - Arguments: - x (numpy.matrix): Sample from p(x). - y (numpy.matrix): Sample from q(x). - alpha (float): Mixture parameter. - sigma_range (list): Search range of Gaussian kernel bandwidth. - lambda_range (list): Search range of regularization parameter. - kernel_num (int): Number of kernels. (Default 100) - verbose (bool): Indicator to print messages (Default True) - - Returns: - densratio.DensityRatio object which has `compute_density_ratio()`. - """ - - # Number of samples. - nx = x.shape[0] - ny = y.shape[0] - - # Number of kernel functions. - kernel_num = min(kernel_num, nx) - - # Randomly take a subset of x, to identify centers for the kernels. - centers = x[randint(nx, size=kernel_num)] - - if verbose: - print("RuLSIF starting...") - - if len(sigma_range) == 1 and len(lambda_range) == 1: - sigma = sigma_range[0] - lambda_ = lambda_range[0] - else: - if verbose: - print("Searching for the optimal sigma and lambda...") - - # Grid-search cross-validation for optimal kernel and regularization parameters. - opt_params = search_sigma_and_lambda( - x, y, alpha, centers, sigma_range, lambda_range, verbose - ) - sigma = opt_params["sigma"] - lambda_ = opt_params["lambda"] - - if verbose: - print( - "Found optimal sigma = {:.3f}, lambda = {:.3f}.".format(sigma, lambda_) - ) - - if verbose: - print("Optimizing theta...") - - phi_x = compute_kernel_Gaussian(x, centers, sigma) - phi_y = compute_kernel_Gaussian(y, centers, sigma) - H = alpha * (phi_x.T.dot(phi_x) / nx) + (1 - alpha) * (phi_y.T.dot(phi_y) / ny) - h = phi_x.mean(axis=0).T - theta = asarray(solve(H + diag(array(lambda_).repeat(kernel_num)), h)).ravel() - - # No negative coefficients. - theta[theta < 0] = 0 - - # Compute the alpha-relative density ratio, at the given coordinates. - def alpha_density_ratio(coordinates): - # Evaluate the kernel at these coordinates, and take the dot-product with the weights. - coordinates = to_ndarray(coordinates) - phi_x = compute_kernel_Gaussian(coordinates, centers, sigma) - alpha_density_ratio = phi_x @ theta - - return alpha_density_ratio - - # Compute the approximate alpha-relative PE-divergence, given samples x and y from the respective distributions. - def alpha_PE_divergence(x, y): - # This is Y, in Reference 1. - x = to_ndarray(x) - - # Obtain alpha-relative density ratio at these points. - g_x = alpha_density_ratio(x) - - # This is Y', in Reference 1. - y = to_ndarray(y) - - # Obtain alpha-relative density ratio at these points. - g_y = alpha_density_ratio(y) - - # Compute the alpha-relative PE-divergence as given in Reference 1. - n = x.shape[0] - divergence = ( - -alpha * (g_x @ g_x) / 2 - (1 - alpha) * (g_y @ g_y) / 2 + g_x.sum(axis=0) - ) / n - 1.0 / 2 - return divergence - - # Compute the approximate alpha-relative KL-divergence, given samples x and y from the respective distributions. - def alpha_KL_divergence(x, y): - # This is Y, in Reference 1. - x = to_ndarray(x) - - # Obtain alpha-relative density ratio at these points. - g_x = alpha_density_ratio(x) - - # Compute the alpha-relative KL-divergence. - n = x.shape[0] - divergence = log(g_x).sum(axis=0) / n - return divergence - - alpha_PE = alpha_PE_divergence(x, y) - alpha_KL = alpha_KL_divergence(x, y) - - if verbose: - print("Approximate alpha-relative PE-divergence = {:03.2f}".format(alpha_PE)) - print("Approximate alpha-relative KL-divergence = {:03.2f}".format(alpha_KL)) - - kernel_info = KernelInfo( - kernel_type="Gaussian", kernel_num=kernel_num, sigma=sigma, centers=centers - ) - result = DensityRatio( - method="RuLSIF", - alpha=alpha, - theta=theta, - lambda_=lambda_, - alpha_PE=alpha_PE, - alpha_KL=alpha_KL, - kernel_info=kernel_info, - compute_density_ratio=alpha_density_ratio, - ) - - if verbose: - print("RuLSIF completed.") - - return result - - -# Grid-search cross-validation for the optimal parameters sigma and lambda by leave-one-out cross-validation. See Reference 2. -def search_sigma_and_lambda(x, y, alpha, centers, sigma_range, lambda_range, verbose): - nx = x.shape[0] - ny = y.shape[0] - n_min = min(nx, ny) - kernel_num = centers.shape[0] - - score_new = inf - sigma_new = 0 - lambda_new = 0 - - for sigma in sigma_range: - phi_x = compute_kernel_Gaussian(x, centers, sigma) # (nx, kernel_num) - phi_y = compute_kernel_Gaussian(y, centers, sigma) # (ny, kernel_num) - H = alpha * (phi_x.T @ phi_x / nx) + (1 - alpha) * ( - phi_y.T @ phi_y / ny - ) # (kernel_num, kernel_num) - h = phi_x.mean(axis=0).reshape(-1, 1) # (kernel_num, 1) - phi_x = phi_x[:n_min].T # (kernel_num, n_min) - phi_y = phi_y[:n_min].T # (kernel_num, n_min) - - for lambda_ in lambda_range: - B = H + diag( - array(lambda_ * (ny - 1) / ny).repeat(kernel_num) - ) # (kernel_num, kernel_num) - B_inv_X = solve(B, phi_y) # (kernel_num, n_min) - X_B_inv_X = multiply(phi_y, B_inv_X) # (kernel_num, n_min) - denom = ny * ones(n_min) - ones(kernel_num) @ X_B_inv_X # (n_min, ) - B0 = solve(B, h @ ones((1, n_min))) + B_inv_X @ diagflat( - h.T @ B_inv_X / denom - ) # (kernel_num, n_min) - B1 = solve(B, phi_x) + B_inv_X @ diagflat( - ones(kernel_num) @ multiply(phi_x, B_inv_X) - ) # (kernel_num, n_min) - B2 = (ny - 1) * (nx * B0 - B1) / (ny * (nx - 1)) # (kernel_num, n_min) - B2[B2 < 0] = 0 - r_y = multiply(phi_y, B2).sum(axis=0).T # (n_min, ) - r_x = multiply(phi_x, B2).sum(axis=0).T # (n_min, ) - - # Squared loss of RuLSIF, without regularization term. - # Directly related to the negative of the PE-divergence. - score = (r_y @ r_y / 2 - r_x.sum(axis=0)) / n_min - - if verbose: - print( - "sigma = %.5f, lambda = %.5f, score = %.5f" - % (sigma, lambda_, score) - ) - - if score < score_new: - score_new = score - sigma_new = sigma - lambda_new = lambda_ - - return {"sigma": sigma_new, "lambda": lambda_new} - - -def _compute_kernel_Gaussian(x_list, y_row, neg_gamma, res) -> None: - sq_norm = sum(power(x_list - y_row, 2), 1) - multiply(neg_gamma, sq_norm, res) - exp(res, res) - - -def _target_numpy_wrapper(x_list, y_list, neg_gamma): - res = empty((y_list.shape[0], x_list.shape[0]), np_float) - if isinstance(x_list, matrix) or isinstance(y_list, matrix): - res = asmatrix(res) - - for j, y_row in enumerate(y_list): - # `.T` aligns shapes for matrices, does nothing for 1D ndarray. - _compute_kernel_Gaussian(x_list, y_row, neg_gamma, res[j].T) - - return res - - -_compute_functions = {"numpy": _target_numpy_wrapper} -if guvectorize_compute: - _compute_functions.update( - { - key: guvectorize_compute(key)(_compute_kernel_Gaussian) - for key in ("cpu", "parallel") - } - ) - -_compute_function = _compute_functions[ - "cpu" if "cpu" in _compute_functions else "numpy" -] - - -# Returns a 2D numpy matrix of kernel evaluated at the gridpoints with coordinates from x_list and y_list. -def compute_kernel_Gaussian(x_list, y_list, sigma): - return _compute_function(x_list, y_list, -0.5 * sigma**-2).T - - -def set_compute_kernel_target(target: str) -> None: - global _compute_function - if target not in ("numpy", "cpu", "parallel"): - raise ValueError( - "'target' must be one of the following: 'numpy', 'cpu', or 'parallel'." - ) - - if target not in _compute_functions: - warn("'numba' not available; defaulting to 'numpy'.", ImportWarning) - target = "numpy" - - _compute_function = _compute_functions[target] +""" +Relative Unconstrained Least-Squares Fitting (RuLSIF): A Python Implementation +References: + 'Change-point detection in time-series data by relative density-ratio estimation' + Song Liu, Makoto Yamada, Nigel Collier and Masashi Sugiyama, + Neural Networks 43 (2013) 72-83. + + 'A Least-squares Approach to Direct Importance Estimation' + Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama, + Journal of Machine Learning Research 10 (2009) 1391-1445. +""" + +from warnings import warn + +from numpy import ( + array, + asarray, + asmatrix, + diag, + diagflat, + empty, + exp, + inf, + log, + matrix, + multiply, + ones, + power, + sum, +) +from numpy.linalg import solve +from numpy.random import randint + +from .density_ratio import DensityRatio, KernelInfo +from .helpers import guvectorize_compute, np_float, to_ndarray + + +def RuLSIF(x, y, alpha, sigma_range, lambda_range, kernel_num=100, verbose=True): + """ + Estimation of the alpha-Relative Density Ratio p(x)/p_alpha(x) by RuLSIF + (Relative Unconstrained Least-Square Importance Fitting) + + p_alpha(x) = alpha * p(x) + (1 - alpha) * q(x) + + Arguments: + x (numpy.matrix): Sample from p(x). + y (numpy.matrix): Sample from q(x). + alpha (float): Mixture parameter. + sigma_range (list): Search range of Gaussian kernel bandwidth. + lambda_range (list): Search range of regularization parameter. + kernel_num (int): Number of kernels. (Default 100) + verbose (bool): Indicator to print messages (Default True) + + Returns: + densratio.DensityRatio object which has `compute_density_ratio()`. + """ + + # Number of samples. + nx = x.shape[0] + ny = y.shape[0] + + # Number of kernel functions. + kernel_num = min(kernel_num, nx) + + # Randomly take a subset of x, to identify centers for the kernels. + centers = x[randint(nx, size=kernel_num)] + + if verbose: + print("RuLSIF starting...") + + if len(sigma_range) == 1 and len(lambda_range) == 1: + sigma = sigma_range[0] + lambda_ = lambda_range[0] + else: + if verbose: + print("Searching for the optimal sigma and lambda...") + + # Grid-search cross-validation for optimal kernel and regularization parameters. + opt_params = search_sigma_and_lambda( + x, y, alpha, centers, sigma_range, lambda_range, verbose + ) + sigma = opt_params["sigma"] + lambda_ = opt_params["lambda"] + + if verbose: + print( + "Found optimal sigma = {:.3f}, lambda = {:.3f}.".format(sigma, lambda_) + ) + + if verbose: + print("Optimizing theta...") + + phi_x = compute_kernel_Gaussian(x, centers, sigma) + phi_y = compute_kernel_Gaussian(y, centers, sigma) + H = alpha * (phi_x.T.dot(phi_x) / nx) + (1 - alpha) * (phi_y.T.dot(phi_y) / ny) + h = phi_x.mean(axis=0).T + theta = asarray(solve(H + diag(array(lambda_).repeat(kernel_num)), h)).ravel() + + # No negative coefficients. + theta[theta < 0] = 0 + + # Compute the alpha-relative density ratio, at the given coordinates. + def alpha_density_ratio(coordinates): + # Evaluate the kernel at these coordinates, and take the dot-product with the weights. + coordinates = to_ndarray(coordinates) + phi_x = compute_kernel_Gaussian(coordinates, centers, sigma) + alpha_density_ratio = phi_x @ theta + + return alpha_density_ratio + + # Compute the approximate alpha-relative PE-divergence, given samples x and y from the respective distributions. + def alpha_PE_divergence(x, y): + # This is Y, in Reference 1. + x = to_ndarray(x) + + # Obtain alpha-relative density ratio at these points. + g_x = alpha_density_ratio(x) + + # This is Y', in Reference 1. + y = to_ndarray(y) + + # Obtain alpha-relative density ratio at these points. + g_y = alpha_density_ratio(y) + + # Compute the alpha-relative PE-divergence as given in Reference 1. + n = x.shape[0] + divergence = ( + -alpha * (g_x @ g_x) / 2 - (1 - alpha) * (g_y @ g_y) / 2 + g_x.sum(axis=0) + ) / n - 1.0 / 2 + return divergence + + # Compute the approximate alpha-relative KL-divergence, given samples x and y from the respective distributions. + def alpha_KL_divergence(x, y): + # This is Y, in Reference 1. + x = to_ndarray(x) + + # Obtain alpha-relative density ratio at these points. + g_x = alpha_density_ratio(x) + + # Compute the alpha-relative KL-divergence. + n = x.shape[0] + divergence = log(g_x).sum(axis=0) / n + return divergence + + alpha_PE = alpha_PE_divergence(x, y) + alpha_KL = alpha_KL_divergence(x, y) + + if verbose: + print("Approximate alpha-relative PE-divergence = {:03.2f}".format(alpha_PE)) + print("Approximate alpha-relative KL-divergence = {:03.2f}".format(alpha_KL)) + + kernel_info = KernelInfo( + kernel_type="Gaussian", kernel_num=kernel_num, sigma=sigma, centers=centers + ) + result = DensityRatio( + method="RuLSIF", + alpha=alpha, + theta=theta, + lambda_=lambda_, + alpha_PE=alpha_PE, + alpha_KL=alpha_KL, + kernel_info=kernel_info, + compute_density_ratio=alpha_density_ratio, + ) + + if verbose: + print("RuLSIF completed.") + + return result + + +# Grid-search cross-validation for the optimal parameters sigma and lambda by leave-one-out cross-validation. See Reference 2. +def search_sigma_and_lambda(x, y, alpha, centers, sigma_range, lambda_range, verbose): + nx = x.shape[0] + ny = y.shape[0] + n_min = min(nx, ny) + kernel_num = centers.shape[0] + + score_new = inf + sigma_new = 0 + lambda_new = 0 + + for sigma in sigma_range: + phi_x = compute_kernel_Gaussian(x, centers, sigma) # (nx, kernel_num) + phi_y = compute_kernel_Gaussian(y, centers, sigma) # (ny, kernel_num) + H = alpha * (phi_x.T @ phi_x / nx) + (1 - alpha) * ( + phi_y.T @ phi_y / ny + ) # (kernel_num, kernel_num) + h = phi_x.mean(axis=0).reshape(-1, 1) # (kernel_num, 1) + phi_x = phi_x[:n_min].T # (kernel_num, n_min) + phi_y = phi_y[:n_min].T # (kernel_num, n_min) + + for lambda_ in lambda_range: + B = H + diag( + array(lambda_ * (ny - 1) / ny).repeat(kernel_num) + ) # (kernel_num, kernel_num) + B_inv_X = solve(B, phi_y) # (kernel_num, n_min) + X_B_inv_X = multiply(phi_y, B_inv_X) # (kernel_num, n_min) + denom = ny * ones(n_min) - ones(kernel_num) @ X_B_inv_X # (n_min, ) + B0 = solve(B, h @ ones((1, n_min))) + B_inv_X @ diagflat( + h.T @ B_inv_X / denom + ) # (kernel_num, n_min) + B1 = solve(B, phi_x) + B_inv_X @ diagflat( + ones(kernel_num) @ multiply(phi_x, B_inv_X) + ) # (kernel_num, n_min) + B2 = (ny - 1) * (nx * B0 - B1) / (ny * (nx - 1)) # (kernel_num, n_min) + B2[B2 < 0] = 0 + r_y = multiply(phi_y, B2).sum(axis=0).T # (n_min, ) + r_x = multiply(phi_x, B2).sum(axis=0).T # (n_min, ) + + # Squared loss of RuLSIF, without regularization term. + # Directly related to the negative of the PE-divergence. + score = (r_y @ r_y / 2 - r_x.sum(axis=0)) / n_min + + if verbose: + print( + "sigma = %.5f, lambda = %.5f, score = %.5f" + % (sigma, lambda_, score) + ) + + if score < score_new: + score_new = score + sigma_new = sigma + lambda_new = lambda_ + + return {"sigma": sigma_new, "lambda": lambda_new} + + +def _compute_kernel_Gaussian(x_list, y_row, neg_gamma, res) -> None: + sq_norm = sum(power(x_list - y_row, 2), 1) + multiply(neg_gamma, sq_norm, res) + exp(res, res) + + +def _target_numpy_wrapper(x_list, y_list, neg_gamma): + res = empty((y_list.shape[0], x_list.shape[0]), np_float) + if isinstance(x_list, matrix) or isinstance(y_list, matrix): + res = asmatrix(res) + + for j, y_row in enumerate(y_list): + # `.T` aligns shapes for matrices, does nothing for 1D ndarray. + _compute_kernel_Gaussian(x_list, y_row, neg_gamma, res[j].T) + + return res + + +_compute_functions = {"numpy": _target_numpy_wrapper} +if guvectorize_compute: + _compute_functions.update( + { + key: guvectorize_compute(key)(_compute_kernel_Gaussian) + for key in ("cpu", "parallel") + } + ) + +_compute_function = _compute_functions[ + "cpu" if "cpu" in _compute_functions else "numpy" +] + + +# Returns a 2D numpy matrix of kernel evaluated at the gridpoints with coordinates from x_list and y_list. +def compute_kernel_Gaussian(x_list, y_list, sigma): + return _compute_function(x_list, y_list, -0.5 * sigma**-2).T + + +def set_compute_kernel_target(target: str) -> None: + global _compute_function + if target not in ("numpy", "cpu", "parallel"): + raise ValueError( + "'target' must be one of the following: 'numpy', 'cpu', or 'parallel'." + ) + + if target not in _compute_functions: + warn("'numba' not available; defaulting to 'numpy'.", ImportWarning) + target = "numpy" + + _compute_function = _compute_functions[target] diff --git a/baselines/densratio/__init__.py b/baselines/densratio/__init__.py index 2990b7e..d2f0633 100644 --- a/baselines/densratio/__init__.py +++ b/baselines/densratio/__init__.py @@ -1,7 +1,7 @@ -from warnings import filterwarnings - -from .core import densratio -from .RuLSIF import set_compute_kernel_target - -filterwarnings("default", message="'numba'", category=ImportWarning, module="densratio") -__all__ = ["densratio", "set_compute_kernel_target"] +from warnings import filterwarnings + +from .core import densratio +from .RuLSIF import set_compute_kernel_target + +filterwarnings("default", message="'numba'", category=ImportWarning, module="densratio") +__all__ = ["densratio", "set_compute_kernel_target"] diff --git a/baselines/densratio/core.py b/baselines/densratio/core.py index c221419..5f7a96b 100644 --- a/baselines/densratio/core.py +++ b/baselines/densratio/core.py @@ -1,70 +1,70 @@ -""" -densratio.core -~~~~~~~~~~~~~~ - -Estimate Density Ratio p(x)/q(y) -""" - -from numpy import linspace - -from .helpers import to_ndarray -from .RuLSIF import RuLSIF - - -def densratio( - x, y, alpha=0, sigma_range="auto", lambda_range="auto", kernel_num=100, verbose=True -): - """Estimate alpha-mixture Density Ratio p(x)/(alpha*p(x) + (1 - alpha)*q(x)) - - Arguments: - x: sample from p(x). - y: sample from q(x). - alpha: Default 0 - corresponds to ordinary density ratio. - sigma_range: search range of Gaussian kernel bandwidth. - Default "auto" means 10^-3, 10^-2, ..., 10^9. - lambda_range: search range of regularization parameter for uLSIF. - Default "auto" means 10^-3, 10^-2, ..., 10^9. - kernel_num: number of kernels. Default 100. - verbose: indicator to print messages. Default True. - - Returns: - densratio.DensityRatio object which has `compute_density_ratio()`. - - Raises: - ValueError: if dimension of x != dimension of y - - Usage:: - >>> from scipy.stats import norm - >>> from densratio import densratio - - >>> x = norm.rvs(size=200, loc=1, scale=1./8) - >>> y = norm.rvs(size=200, loc=1, scale=1./2) - >>> result = densratio(x, y, alpha=0.7) - >>> print(result) - - >>> density_ratio = result.compute_density_ratio(y) - >>> print(density_ratio) - """ - - x = to_ndarray(x) - y = to_ndarray(y) - - if x.shape[1] != y.shape[1]: - raise ValueError("x and y must be same dimensions.") - - if isinstance(sigma_range, str) and sigma_range != "auto": - raise TypeError("Invalid value for sigma_range.") - - if isinstance(lambda_range, str) and lambda_range != "auto": - raise TypeError("Invalid value for lambda_range.") - - if sigma_range is None or (isinstance(sigma_range, str) and sigma_range == "auto"): - sigma_range = 10 ** linspace(-3, 9, 13) - - if lambda_range is None or ( - isinstance(lambda_range, str) and lambda_range == "auto" - ): - lambda_range = 10 ** linspace(-3, 9, 13) - - result = RuLSIF(x, y, alpha, sigma_range, lambda_range, kernel_num, verbose) - return result +""" +densratio.core +~~~~~~~~~~~~~~ + +Estimate Density Ratio p(x)/q(y) +""" + +from numpy import linspace + +from .helpers import to_ndarray +from .RuLSIF import RuLSIF + + +def densratio( + x, y, alpha=0, sigma_range="auto", lambda_range="auto", kernel_num=100, verbose=True +): + """Estimate alpha-mixture Density Ratio p(x)/(alpha*p(x) + (1 - alpha)*q(x)) + + Arguments: + x: sample from p(x). + y: sample from q(x). + alpha: Default 0 - corresponds to ordinary density ratio. + sigma_range: search range of Gaussian kernel bandwidth. + Default "auto" means 10^-3, 10^-2, ..., 10^9. + lambda_range: search range of regularization parameter for uLSIF. + Default "auto" means 10^-3, 10^-2, ..., 10^9. + kernel_num: number of kernels. Default 100. + verbose: indicator to print messages. Default True. + + Returns: + densratio.DensityRatio object which has `compute_density_ratio()`. + + Raises: + ValueError: if dimension of x != dimension of y + + Usage:: + >>> from scipy.stats import norm + >>> from densratio import densratio + + >>> x = norm.rvs(size=200, loc=1, scale=1./8) + >>> y = norm.rvs(size=200, loc=1, scale=1./2) + >>> result = densratio(x, y, alpha=0.7) + >>> print(result) + + >>> density_ratio = result.compute_density_ratio(y) + >>> print(density_ratio) + """ + + x = to_ndarray(x) + y = to_ndarray(y) + + if x.shape[1] != y.shape[1]: + raise ValueError("x and y must be same dimensions.") + + if isinstance(sigma_range, str) and sigma_range != "auto": + raise TypeError("Invalid value for sigma_range.") + + if isinstance(lambda_range, str) and lambda_range != "auto": + raise TypeError("Invalid value for lambda_range.") + + if sigma_range is None or (isinstance(sigma_range, str) and sigma_range == "auto"): + sigma_range = 10 ** linspace(-3, 9, 13) + + if lambda_range is None or ( + isinstance(lambda_range, str) and lambda_range == "auto" + ): + lambda_range = 10 ** linspace(-3, 9, 13) + + result = RuLSIF(x, y, alpha, sigma_range, lambda_range, kernel_num, verbose) + return result diff --git a/baselines/densratio/density_ratio.py b/baselines/densratio/density_ratio.py index b02a9e9..4905d41 100644 --- a/baselines/densratio/density_ratio.py +++ b/baselines/densratio/density_ratio.py @@ -1,88 +1,88 @@ -from pprint import pformat -from re import sub - - -class DensityRatio: - """Density Ratio.""" - - def __init__( - self, - method, - alpha, - theta, - lambda_, - alpha_PE, - alpha_KL, - kernel_info, - compute_density_ratio, - ): - self.method = method - self.alpha = alpha - self.theta = theta - self.lambda_ = lambda_ - self.alpha_PE = alpha_PE - self.alpha_KL = alpha_KL - self.kernel_info = kernel_info - self.compute_density_ratio = compute_density_ratio - - def __str__(self): - return """ -Method: %(method)s - -Alpha: %(alpha)s - -Kernel Information: -%(kernel_info)s - -Kernel Weights (theta): - %(theta)s - -Regularization Parameter (lambda): %(lambda_)s - -Alpha-Relative PE-Divergence: %(alpha_PE)s - -Alpha-Relative KL-Divergence: %(alpha_KL)s - -Function to Estimate Density Ratio: - compute_density_ratio(x) - -"""[ - 1:-1 - ] % dict( - method=self.method, - kernel_info=self.kernel_info, - alpha=self.alpha, - theta=my_format(self.theta), - lambda_=self.lambda_, - alpha_PE=self.alpha_PE, - alpha_KL=self.alpha_KL, - ) - - -class KernelInfo: - """Kernel Information.""" - - def __init__(self, kernel_type, kernel_num, sigma, centers): - self.kernel_type = kernel_type - self.kernel_num = kernel_num - self.sigma = sigma - self.centers = centers - - def __str__(self): - return """ - Kernel type: %(kernel_type)s - Number of kernels: %(kernel_num)s - Bandwidth(sigma): %(sigma)s - Centers: %(centers)s -"""[ - 1:-1 - ] % dict( - kernel_type=self.kernel_type, - kernel_num=self.kernel_num, - sigma=self.sigma, - centers=my_format(self.centers), - ) - - -def my_format(str): - return sub(r"\s+", " ", (pformat(str).split("\n")[0] + "..")) +from pprint import pformat +from re import sub + + +class DensityRatio: + """Density Ratio.""" + + def __init__( + self, + method, + alpha, + theta, + lambda_, + alpha_PE, + alpha_KL, + kernel_info, + compute_density_ratio, + ): + self.method = method + self.alpha = alpha + self.theta = theta + self.lambda_ = lambda_ + self.alpha_PE = alpha_PE + self.alpha_KL = alpha_KL + self.kernel_info = kernel_info + self.compute_density_ratio = compute_density_ratio + + def __str__(self): + return """ +Method: %(method)s + +Alpha: %(alpha)s + +Kernel Information: +%(kernel_info)s + +Kernel Weights (theta): + %(theta)s + +Regularization Parameter (lambda): %(lambda_)s + +Alpha-Relative PE-Divergence: %(alpha_PE)s + +Alpha-Relative KL-Divergence: %(alpha_KL)s + +Function to Estimate Density Ratio: + compute_density_ratio(x) + +"""[ + 1:-1 + ] % dict( + method=self.method, + kernel_info=self.kernel_info, + alpha=self.alpha, + theta=my_format(self.theta), + lambda_=self.lambda_, + alpha_PE=self.alpha_PE, + alpha_KL=self.alpha_KL, + ) + + +class KernelInfo: + """Kernel Information.""" + + def __init__(self, kernel_type, kernel_num, sigma, centers): + self.kernel_type = kernel_type + self.kernel_num = kernel_num + self.sigma = sigma + self.centers = centers + + def __str__(self): + return """ + Kernel type: %(kernel_type)s + Number of kernels: %(kernel_num)s + Bandwidth(sigma): %(sigma)s + Centers: %(centers)s +"""[ + 1:-1 + ] % dict( + kernel_type=self.kernel_type, + kernel_num=self.kernel_num, + sigma=self.sigma, + centers=my_format(self.centers), + ) + + +def my_format(str): + return sub(r"\s+", " ", (pformat(str).split("\n")[0] + "..")) diff --git a/baselines/densratio/helpers.py b/baselines/densratio/helpers.py index 08656f5..f1250ec 100644 --- a/baselines/densratio/helpers.py +++ b/baselines/densratio/helpers.py @@ -1,36 +1,36 @@ -from numpy import array, ndarray, result_type - -np_float = result_type(float) -try: - import numba as nb -except ModuleNotFoundError: - guvectorize_compute = None -else: - _nb_float = nb.from_dtype(np_float) - - def guvectorize_compute(target: str, *, cache: bool = True): - return nb.guvectorize( - [nb.void(_nb_float[:, :], _nb_float[:], _nb_float, _nb_float[:])], - "(m, p),(p),()->(m)", - nopython=True, - target=target, - cache=cache, - ) - - -def is_numeric(x): - return isinstance(x, int) or isinstance(x, float) - - -def to_ndarray(x): - if isinstance(x, ndarray): - if len(x.shape) == 1: - return x.reshape(-1, 1) - else: - return x - elif str(type(x)) == "": - return x.values - elif not x: - raise ValueError("Cannot transform to numpy.matrix.") - else: - return to_ndarray(array(x)) +from numpy import array, ndarray, result_type + +np_float = result_type(float) +try: + import numba as nb +except ModuleNotFoundError: + guvectorize_compute = None +else: + _nb_float = nb.from_dtype(np_float) + + def guvectorize_compute(target: str, *, cache: bool = True): + return nb.guvectorize( + [nb.void(_nb_float[:, :], _nb_float[:], _nb_float, _nb_float[:])], + "(m, p),(p),()->(m)", + nopython=True, + target=target, + cache=cache, + ) + + +def is_numeric(x): + return isinstance(x, int) or isinstance(x, float) + + +def to_ndarray(x): + if isinstance(x, ndarray): + if len(x.shape) == 1: + return x.reshape(-1, 1) + else: + return x + elif str(type(x)) == "": + return x.values + elif not x: + raise ValueError("Cannot transform to numpy.matrix.") + else: + return to_ndarray(array(x)) diff --git a/baselines/doc.py b/baselines/doc.py index 9b59883..3a78845 100644 --- a/baselines/doc.py +++ b/baselines/doc.py @@ -1,4 +1,4 @@ -import numpy as np - -def get_doc(probs1, probs2): +import numpy as np + +def get_doc(probs1, probs2): return np.mean(probs2) - np.mean(probs1) \ No newline at end of file diff --git a/baselines/impweight.py b/baselines/impweight.py index f144bce..198b3be 100644 --- a/baselines/impweight.py +++ b/baselines/impweight.py @@ -1,66 +1,66 @@ -import numpy as np -from scipy.sparse import issparse, vstack -from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import GridSearchCV -from sklearn.neighbors import KernelDensity - -from baselines import densratio -from baselines.pykliep import DensityRatioEstimator - - -def kliep(Xtr, ytr, Xte): - kliep = DensityRatioEstimator() - kliep.fit(Xtr, Xte) - return kliep.predict(Xtr) - - -def usilf(Xtr, ytr, Xte, alpha=0.0): - dense_ratio_obj = densratio(Xtr, Xte, alpha=alpha, verbose=False) - return dense_ratio_obj.compute_density_ratio(Xtr) - - -def logreg(Xtr, ytr, Xte): - # check "Direct Density Ratio Estimation for - # Large-scale Covariate Shift Adaptation", Eq.28 - - if issparse(Xtr): - X = vstack([Xtr, Xte]) - else: - X = np.concatenate([Xtr, Xte]) - - y = [0] * Xtr.shape[0] + [1] * Xte.shape[0] - - logreg = GridSearchCV( - LogisticRegression(), - param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]}, - n_jobs=-1, - ) - logreg.fit(X, y) - probs = logreg.predict_proba(Xtr) - prob_train, prob_test = probs[:, 0], probs[:, 1] - prior_train = Xtr.shape[0] - prior_test = Xte.shape[0] - w = (prior_train / prior_test) * (prob_test / prob_train) - return w - - -kdex2_params = {"bandwidth": np.logspace(-1, 1, 20)} - - -def kdex2_lltr(Xtr): - if issparse(Xtr): - Xtr = Xtr.toarray() - return GridSearchCV(KernelDensity(), kdex2_params).fit(Xtr).score_samples(Xtr) - - -def kdex2_weights(Xtr, Xte, log_likelihood_tr): - log_likelihood_te = ( - GridSearchCV(KernelDensity(), kdex2_params).fit(Xte).score_samples(Xtr) - ) - likelihood_tr = np.exp(log_likelihood_tr) - likelihood_te = np.exp(log_likelihood_te) - return likelihood_te / likelihood_tr - - -def get_acc(tr_preds, ytr, w): - return np.sum((1.0 * (tr_preds == ytr)) * w) / np.sum(w) +import numpy as np +from scipy.sparse import issparse, vstack +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import GridSearchCV +from sklearn.neighbors import KernelDensity + +from baselines import densratio +from baselines.pykliep import DensityRatioEstimator + + +def kliep(Xtr, ytr, Xte): + kliep = DensityRatioEstimator() + kliep.fit(Xtr, Xte) + return kliep.predict(Xtr) + + +def usilf(Xtr, ytr, Xte, alpha=0.0): + dense_ratio_obj = densratio(Xtr, Xte, alpha=alpha, verbose=False) + return dense_ratio_obj.compute_density_ratio(Xtr) + + +def logreg(Xtr, ytr, Xte): + # check "Direct Density Ratio Estimation for + # Large-scale Covariate Shift Adaptation", Eq.28 + + if issparse(Xtr): + X = vstack([Xtr, Xte]) + else: + X = np.concatenate([Xtr, Xte]) + + y = [0] * Xtr.shape[0] + [1] * Xte.shape[0] + + logreg = GridSearchCV( + LogisticRegression(), + param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]}, + n_jobs=-1, + ) + logreg.fit(X, y) + probs = logreg.predict_proba(Xtr) + prob_train, prob_test = probs[:, 0], probs[:, 1] + prior_train = Xtr.shape[0] + prior_test = Xte.shape[0] + w = (prior_train / prior_test) * (prob_test / prob_train) + return w + + +kdex2_params = {"bandwidth": np.logspace(-1, 1, 20)} + + +def kdex2_lltr(Xtr): + if issparse(Xtr): + Xtr = Xtr.toarray() + return GridSearchCV(KernelDensity(), kdex2_params).fit(Xtr).score_samples(Xtr) + + +def kdex2_weights(Xtr, Xte, log_likelihood_tr): + log_likelihood_te = ( + GridSearchCV(KernelDensity(), kdex2_params).fit(Xte).score_samples(Xtr) + ) + likelihood_tr = np.exp(log_likelihood_tr) + likelihood_te = np.exp(log_likelihood_te) + return likelihood_te / likelihood_tr + + +def get_acc(tr_preds, ytr, w): + return np.sum((1.0 * (tr_preds == ytr)) * w) / np.sum(w) diff --git a/baselines/models.py b/baselines/models.py index a0e8c35..6635f2b 100644 --- a/baselines/models.py +++ b/baselines/models.py @@ -1,140 +1,140 @@ -# import itertools -# from typing import Iterable - -# import quapy as qp -# import quapy.functional as F -# from densratio import densratio -# from quapy.method.aggregative import * -# from quapy.protocol import ( -# AbstractStochasticSeededProtocol, -# OnLabelledCollectionProtocol, -# ) -# from scipy.sparse import issparse, vstack -# from scipy.spatial.distance import cdist -# from scipy.stats import multivariate_normal -# from sklearn.linear_model import LogisticRegression -# from sklearn.model_selection import GridSearchCV -# from sklearn.neighbors import KernelDensity - -import time - -import numpy as np -import sklearn.metrics as metrics -from pykliep import DensityRatioEstimator -from quapy.protocol import APP -from scipy.sparse import issparse, vstack -from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import GridSearchCV -from sklearn.neighbors import KernelDensity - -import baselines.impweight as iw -from baselines.densratio import densratio -from quacc.dataset import Dataset - - -# --------------------------------------------------------------------------------------- -# Methods of "importance weight", e.g., by ratio density estimation (KLIEP, SILF, LogReg) -# --------------------------------------------------------------------------------------- -class ImportanceWeight: - def weights(self, Xtr, ytr, Xte): - ... - - -class KLIEP(ImportanceWeight): - def __init__(self): - pass - - def weights(self, Xtr, ytr, Xte): - kliep = DensityRatioEstimator() - kliep.fit(Xtr, Xte) - return kliep.predict(Xtr) - - -class USILF(ImportanceWeight): - def __init__(self, alpha=0.0): - self.alpha = alpha - - def weights(self, Xtr, ytr, Xte): - dense_ratio_obj = densratio(Xtr, Xte, alpha=self.alpha, verbose=False) - return dense_ratio_obj.compute_density_ratio(Xtr) - - -class LogReg(ImportanceWeight): - def __init__(self): - pass - - def weights(self, Xtr, ytr, Xte): - # check "Direct Density Ratio Estimation for - # Large-scale Covariate Shift Adaptation", Eq.28 - - if issparse(Xtr): - X = vstack([Xtr, Xte]) - else: - X = np.concatenate([Xtr, Xte]) - - y = [0] * Xtr.shape[0] + [1] * Xte.shape[0] - - logreg = GridSearchCV( - LogisticRegression(), - param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]}, - n_jobs=-1, - ) - logreg.fit(X, y) - probs = logreg.predict_proba(Xtr) - prob_train, prob_test = probs[:, 0], probs[:, 1] - prior_train = Xtr.shape[0] - prior_test = Xte.shape[0] - w = (prior_train / prior_test) * (prob_test / prob_train) - return w - - -class KDEx2(ImportanceWeight): - def __init__(self): - pass - - def weights(self, Xtr, ytr, Xte): - params = {"bandwidth": np.logspace(-1, 1, 20)} - log_likelihood_tr = ( - GridSearchCV(KernelDensity(), params).fit(Xtr).score_samples(Xtr) - ) - log_likelihood_te = ( - GridSearchCV(KernelDensity(), params).fit(Xte).score_samples(Xtr) - ) - likelihood_tr = np.exp(log_likelihood_tr) - likelihood_te = np.exp(log_likelihood_te) - return likelihood_te / likelihood_tr - - -if __name__ == "__main__": - # d = Dataset("rcv1", target="CCAT").get_raw() - d = Dataset("imdb", n_prevalences=1).get()[0] - - tstart = time.time() - lr = LogisticRegression() - lr.fit(*d.train.Xy) - val_preds = lr.predict(d.validation.X) - protocol = APP( - d.test, - n_prevalences=21, - repeats=1, - sample_size=100, - return_type="labelled_collection", - ) - - results = [] - for sample in protocol(): - wx = iw.kliep(d.validation.X, d.validation.y, sample.X) - test_preds = lr.predict(sample.X) - estim_acc = np.sum((1.0 * (val_preds == d.validation.y)) * wx) / np.sum(wx) - true_acc = metrics.accuracy_score(sample.y, test_preds) - results.append((sample.prevalence(), estim_acc, true_acc)) - - tend = time.time() - - for r in results: - print(*r) - - print(f"logreg finished [took {tend-tstart:.3f}s]") - import win11toast - - win11toast.notify("models.py", "Completed") +# import itertools +# from typing import Iterable + +# import quapy as qp +# import quapy.functional as F +# from densratio import densratio +# from quapy.method.aggregative import * +# from quapy.protocol import ( +# AbstractStochasticSeededProtocol, +# OnLabelledCollectionProtocol, +# ) +# from scipy.sparse import issparse, vstack +# from scipy.spatial.distance import cdist +# from scipy.stats import multivariate_normal +# from sklearn.linear_model import LogisticRegression +# from sklearn.model_selection import GridSearchCV +# from sklearn.neighbors import KernelDensity + +import time + +import numpy as np +import sklearn.metrics as metrics +from pykliep import DensityRatioEstimator +from quapy.protocol import APP +from scipy.sparse import issparse, vstack +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import GridSearchCV +from sklearn.neighbors import KernelDensity + +import baselines.impweight as iw +from baselines.densratio import densratio +from quacc.dataset import Dataset + + +# --------------------------------------------------------------------------------------- +# Methods of "importance weight", e.g., by ratio density estimation (KLIEP, SILF, LogReg) +# --------------------------------------------------------------------------------------- +class ImportanceWeight: + def weights(self, Xtr, ytr, Xte): + ... + + +class KLIEP(ImportanceWeight): + def __init__(self): + pass + + def weights(self, Xtr, ytr, Xte): + kliep = DensityRatioEstimator() + kliep.fit(Xtr, Xte) + return kliep.predict(Xtr) + + +class USILF(ImportanceWeight): + def __init__(self, alpha=0.0): + self.alpha = alpha + + def weights(self, Xtr, ytr, Xte): + dense_ratio_obj = densratio(Xtr, Xte, alpha=self.alpha, verbose=False) + return dense_ratio_obj.compute_density_ratio(Xtr) + + +class LogReg(ImportanceWeight): + def __init__(self): + pass + + def weights(self, Xtr, ytr, Xte): + # check "Direct Density Ratio Estimation for + # Large-scale Covariate Shift Adaptation", Eq.28 + + if issparse(Xtr): + X = vstack([Xtr, Xte]) + else: + X = np.concatenate([Xtr, Xte]) + + y = [0] * Xtr.shape[0] + [1] * Xte.shape[0] + + logreg = GridSearchCV( + LogisticRegression(), + param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]}, + n_jobs=-1, + ) + logreg.fit(X, y) + probs = logreg.predict_proba(Xtr) + prob_train, prob_test = probs[:, 0], probs[:, 1] + prior_train = Xtr.shape[0] + prior_test = Xte.shape[0] + w = (prior_train / prior_test) * (prob_test / prob_train) + return w + + +class KDEx2(ImportanceWeight): + def __init__(self): + pass + + def weights(self, Xtr, ytr, Xte): + params = {"bandwidth": np.logspace(-1, 1, 20)} + log_likelihood_tr = ( + GridSearchCV(KernelDensity(), params).fit(Xtr).score_samples(Xtr) + ) + log_likelihood_te = ( + GridSearchCV(KernelDensity(), params).fit(Xte).score_samples(Xtr) + ) + likelihood_tr = np.exp(log_likelihood_tr) + likelihood_te = np.exp(log_likelihood_te) + return likelihood_te / likelihood_tr + + +if __name__ == "__main__": + # d = Dataset("rcv1", target="CCAT").get_raw() + d = Dataset("imdb", n_prevalences=1).get()[0] + + tstart = time.time() + lr = LogisticRegression() + lr.fit(*d.train.Xy) + val_preds = lr.predict(d.validation.X) + protocol = APP( + d.test, + n_prevalences=21, + repeats=1, + sample_size=100, + return_type="labelled_collection", + ) + + results = [] + for sample in protocol(): + wx = iw.kliep(d.validation.X, d.validation.y, sample.X) + test_preds = lr.predict(sample.X) + estim_acc = np.sum((1.0 * (val_preds == d.validation.y)) * wx) / np.sum(wx) + true_acc = metrics.accuracy_score(sample.y, test_preds) + results.append((sample.prevalence(), estim_acc, true_acc)) + + tend = time.time() + + for r in results: + print(*r) + + print(f"logreg finished [took {tend-tstart:.3f}s]") + import win11toast + + win11toast.notify("models.py", "Completed") diff --git a/baselines/pykliep.py b/baselines/pykliep.py index 8c67ea4..caad427 100644 --- a/baselines/pykliep.py +++ b/baselines/pykliep.py @@ -1,221 +1,221 @@ -import warnings - -import numpy as np -from scipy.sparse import csr_matrix - - -class DensityRatioEstimator: - """ - Class to accomplish direct density estimation implementing the original KLIEP - algorithm from Direct Importance Estimation with Model Selection - and Its Application to Covariate Shift Adaptation by Sugiyama et al. - - The training set is distributed via - train ~ p(x) - and the test set is distributed via - test ~ q(x). - - The KLIEP algorithm and its variants approximate w(x) = q(x) / p(x) directly. The predict function returns the - estimate of w(x). The function w(x) can serve as sample weights for the training set during - training to modify the expectation function that the model's loss function is optimized via, - i.e. - - E_{x ~ w(x)p(x)} loss(x) = E_{x ~ q(x)} loss(x). - - Usage : - The fit method is used to run the KLIEP algorithm using LCV and returns value of J - trained on the entire training/test set with the best sigma found. - Use the predict method on the training set to determine the sample weights from the KLIEP algorithm. - """ - - def __init__( - self, - max_iter=5000, - num_params=[0.1, 0.2], - epsilon=1e-4, - cv=3, - sigmas=[0.01, 0.1, 0.25, 0.5, 0.75, 1], - random_state=None, - verbose=0, - ): - """ - Direct density estimation using an inner LCV loop to estimate the proper model. Can be used with sklearn - cross validation methods with or without storing the inner CV. To use a standard grid search. - - - max_iter : Number of iterations to perform - num_params : List of number of test set vectors used to construct the approximation for inner LCV. - Must be a float. Original paper used 10%, i.e. =.1 - sigmas : List of sigmas to be used in inner LCV loop. - epsilon : Additive factor in the iterative algorithm for numerical stability. - """ - self.max_iter = max_iter - self.num_params = num_params - self.epsilon = epsilon - self.verbose = verbose - self.sigmas = sigmas - self.cv = cv - self.random_state = 0 - - def fit(self, X_train, X_test, alpha_0=None): - """Uses cross validation to select sigma as in the original paper (LCV). - In a break from sklearn convention, y=X_test. - The parameter cv corresponds to R in the original paper. - Once found, the best sigma is used to train on the full set.""" - - # LCV loop, shuffle a copy in place for performance. - cv = self.cv - chunk = int(X_test.shape[0] / float(cv)) - if self.random_state is not None: - np.random.seed(self.random_state) - # if isinstance(X_test, csr_matrix): - # X_test_shuffled = X_test.toarray() - # else: - # X_test_shuffled = X_test.copy() - X_test_shuffled = X_test.copy() - - X_test_index = np.arange(X_test_shuffled.shape[0]) - np.random.shuffle(X_test_index) - X_test_shuffled = X_test_shuffled[X_test_index, :] - - j_scores = {} - - if type(self.sigmas) != list: - self.sigmas = [self.sigmas] - - if type(self.num_params) != list: - self.num_params = [self.num_params] - - if len(self.sigmas) * len(self.num_params) > 1: - # Inner LCV loop - for num_param in self.num_params: - for sigma in self.sigmas: - j_scores[(num_param, sigma)] = np.zeros(cv) - for k in range(1, cv + 1): - if self.verbose > 0: - print("Training: sigma: %s R: %s" % (sigma, k)) - X_test_fold = X_test_shuffled[(k - 1) * chunk : k * chunk, :] - j_scores[(num_param, sigma)][k - 1] = self._fit( - X_train=X_train, - X_test=X_test_fold, - num_parameters=num_param, - sigma=sigma, - ) - j_scores[(num_param, sigma)] = np.mean(j_scores[(num_param, sigma)]) - - sorted_scores = sorted( - [x for x in j_scores.items() if np.isfinite(x[1])], - key=lambda x: x[1], - reverse=True, - ) - if len(sorted_scores) == 0: - warnings.warn("LCV failed to converge for all values of sigma.") - return self - self._sigma = sorted_scores[0][0][1] - self._num_parameters = sorted_scores[0][0][0] - self._j_scores = sorted_scores - else: - self._sigma = self.sigmas[0] - self._num_parameters = self.num_params[0] - # best sigma - self._j = self._fit( - X_train=X_train, - X_test=X_test_shuffled, - num_parameters=self._num_parameters, - sigma=self._sigma, - ) - - return self # Compatibility with sklearn - - def _fit(self, X_train, X_test, num_parameters, sigma, alpha_0=None): - """Fits the estimator with the given parameters w-hat and returns J""" - - num_parameters = num_parameters - - if type(num_parameters) == float: - num_parameters = int(X_test.shape[0] * num_parameters) - - self._select_param_vectors( - X_test=X_test, sigma=sigma, num_parameters=num_parameters - ) - - # if isinstance(X_train, csr_matrix): - # X_train = X_train.toarray() - X_train = self._reshape_X(X_train) - X_test = self._reshape_X(X_test) - - if alpha_0 is None: - alpha_0 = np.ones(shape=(num_parameters, 1)) / float(num_parameters) - - self._find_alpha( - X_train=X_train, - X_test=X_test, - num_parameters=num_parameters, - epsilon=self.epsilon, - alpha_0=alpha_0, - sigma=sigma, - ) - - return self._calculate_j(X_test, sigma=sigma) - - def _calculate_j(self, X_test, sigma): - pred = self.predict(X_test, sigma=sigma) + 0.0000001 - log = np.log(pred).sum() - return log / (X_test.shape[0]) - - def score(self, X_test): - """Return the J score, similar to sklearn's API""" - return self._calculate_j(X_test=X_test, sigma=self._sigma) - - @staticmethod - def _reshape_X(X): - """Reshape input from mxn to mx1xn to take advantage of numpy broadcasting.""" - if len(X.shape) != 3: - return X.reshape((X.shape[0], 1, X.shape[1])) - return X - - def _select_param_vectors(self, X_test, sigma, num_parameters): - """X_test is the test set. b is the number of parameters.""" - indices = np.random.choice(X_test.shape[0], size=num_parameters, replace=False) - self._test_vectors = X_test[indices, :].copy() - self._phi_fitted = True - - def _phi(self, X, sigma=None): - if sigma is None: - sigma = self._sigma - - if self._phi_fitted: - return np.exp( - -np.sum((X - self._test_vectors) ** 2, axis=-1) / (2 * sigma**2) - ) - raise Exception("Phi not fitted.") - - def _find_alpha(self, alpha_0, X_train, X_test, num_parameters, sigma, epsilon): - A = np.zeros(shape=(X_test.shape[0], num_parameters)) - b = np.zeros(shape=(num_parameters, 1)) - - A = self._phi(X_test, sigma) - b = self._phi(X_train, sigma).sum(axis=0) / X_train.shape[0] - b = b.reshape((num_parameters, 1)) - - out = alpha_0.copy() - for k in range(self.max_iter): - mat = np.dot(A, out) - mat += 0.000000001 - out += epsilon * np.dot(np.transpose(A), 1.0 / mat) - out += b * ( - ((1 - np.dot(np.transpose(b), out)) / np.dot(np.transpose(b), b)) - ) - out = np.maximum(0, out) - out /= np.dot(np.transpose(b), out) - - self._alpha = out - self._fitted = True - - def predict(self, X, sigma=None): - """Equivalent of w(X) from the original paper.""" - - X = self._reshape_X(X) - if not self._fitted: - raise Exception("Not fitted!") - return np.dot(self._phi(X, sigma=sigma), self._alpha).reshape((X.shape[0],)) +import warnings + +import numpy as np +from scipy.sparse import csr_matrix + + +class DensityRatioEstimator: + """ + Class to accomplish direct density estimation implementing the original KLIEP + algorithm from Direct Importance Estimation with Model Selection + and Its Application to Covariate Shift Adaptation by Sugiyama et al. + + The training set is distributed via + train ~ p(x) + and the test set is distributed via + test ~ q(x). + + The KLIEP algorithm and its variants approximate w(x) = q(x) / p(x) directly. The predict function returns the + estimate of w(x). The function w(x) can serve as sample weights for the training set during + training to modify the expectation function that the model's loss function is optimized via, + i.e. + + E_{x ~ w(x)p(x)} loss(x) = E_{x ~ q(x)} loss(x). + + Usage : + The fit method is used to run the KLIEP algorithm using LCV and returns value of J + trained on the entire training/test set with the best sigma found. + Use the predict method on the training set to determine the sample weights from the KLIEP algorithm. + """ + + def __init__( + self, + max_iter=5000, + num_params=[0.1, 0.2], + epsilon=1e-4, + cv=3, + sigmas=[0.01, 0.1, 0.25, 0.5, 0.75, 1], + random_state=None, + verbose=0, + ): + """ + Direct density estimation using an inner LCV loop to estimate the proper model. Can be used with sklearn + cross validation methods with or without storing the inner CV. To use a standard grid search. + + + max_iter : Number of iterations to perform + num_params : List of number of test set vectors used to construct the approximation for inner LCV. + Must be a float. Original paper used 10%, i.e. =.1 + sigmas : List of sigmas to be used in inner LCV loop. + epsilon : Additive factor in the iterative algorithm for numerical stability. + """ + self.max_iter = max_iter + self.num_params = num_params + self.epsilon = epsilon + self.verbose = verbose + self.sigmas = sigmas + self.cv = cv + self.random_state = 0 + + def fit(self, X_train, X_test, alpha_0=None): + """Uses cross validation to select sigma as in the original paper (LCV). + In a break from sklearn convention, y=X_test. + The parameter cv corresponds to R in the original paper. + Once found, the best sigma is used to train on the full set.""" + + # LCV loop, shuffle a copy in place for performance. + cv = self.cv + chunk = int(X_test.shape[0] / float(cv)) + if self.random_state is not None: + np.random.seed(self.random_state) + # if isinstance(X_test, csr_matrix): + # X_test_shuffled = X_test.toarray() + # else: + # X_test_shuffled = X_test.copy() + X_test_shuffled = X_test.copy() + + X_test_index = np.arange(X_test_shuffled.shape[0]) + np.random.shuffle(X_test_index) + X_test_shuffled = X_test_shuffled[X_test_index, :] + + j_scores = {} + + if type(self.sigmas) != list: + self.sigmas = [self.sigmas] + + if type(self.num_params) != list: + self.num_params = [self.num_params] + + if len(self.sigmas) * len(self.num_params) > 1: + # Inner LCV loop + for num_param in self.num_params: + for sigma in self.sigmas: + j_scores[(num_param, sigma)] = np.zeros(cv) + for k in range(1, cv + 1): + if self.verbose > 0: + print("Training: sigma: %s R: %s" % (sigma, k)) + X_test_fold = X_test_shuffled[(k - 1) * chunk : k * chunk, :] + j_scores[(num_param, sigma)][k - 1] = self._fit( + X_train=X_train, + X_test=X_test_fold, + num_parameters=num_param, + sigma=sigma, + ) + j_scores[(num_param, sigma)] = np.mean(j_scores[(num_param, sigma)]) + + sorted_scores = sorted( + [x for x in j_scores.items() if np.isfinite(x[1])], + key=lambda x: x[1], + reverse=True, + ) + if len(sorted_scores) == 0: + warnings.warn("LCV failed to converge for all values of sigma.") + return self + self._sigma = sorted_scores[0][0][1] + self._num_parameters = sorted_scores[0][0][0] + self._j_scores = sorted_scores + else: + self._sigma = self.sigmas[0] + self._num_parameters = self.num_params[0] + # best sigma + self._j = self._fit( + X_train=X_train, + X_test=X_test_shuffled, + num_parameters=self._num_parameters, + sigma=self._sigma, + ) + + return self # Compatibility with sklearn + + def _fit(self, X_train, X_test, num_parameters, sigma, alpha_0=None): + """Fits the estimator with the given parameters w-hat and returns J""" + + num_parameters = num_parameters + + if type(num_parameters) == float: + num_parameters = int(X_test.shape[0] * num_parameters) + + self._select_param_vectors( + X_test=X_test, sigma=sigma, num_parameters=num_parameters + ) + + # if isinstance(X_train, csr_matrix): + # X_train = X_train.toarray() + X_train = self._reshape_X(X_train) + X_test = self._reshape_X(X_test) + + if alpha_0 is None: + alpha_0 = np.ones(shape=(num_parameters, 1)) / float(num_parameters) + + self._find_alpha( + X_train=X_train, + X_test=X_test, + num_parameters=num_parameters, + epsilon=self.epsilon, + alpha_0=alpha_0, + sigma=sigma, + ) + + return self._calculate_j(X_test, sigma=sigma) + + def _calculate_j(self, X_test, sigma): + pred = self.predict(X_test, sigma=sigma) + 0.0000001 + log = np.log(pred).sum() + return log / (X_test.shape[0]) + + def score(self, X_test): + """Return the J score, similar to sklearn's API""" + return self._calculate_j(X_test=X_test, sigma=self._sigma) + + @staticmethod + def _reshape_X(X): + """Reshape input from mxn to mx1xn to take advantage of numpy broadcasting.""" + if len(X.shape) != 3: + return X.reshape((X.shape[0], 1, X.shape[1])) + return X + + def _select_param_vectors(self, X_test, sigma, num_parameters): + """X_test is the test set. b is the number of parameters.""" + indices = np.random.choice(X_test.shape[0], size=num_parameters, replace=False) + self._test_vectors = X_test[indices, :].copy() + self._phi_fitted = True + + def _phi(self, X, sigma=None): + if sigma is None: + sigma = self._sigma + + if self._phi_fitted: + return np.exp( + -np.sum((X - self._test_vectors) ** 2, axis=-1) / (2 * sigma**2) + ) + raise Exception("Phi not fitted.") + + def _find_alpha(self, alpha_0, X_train, X_test, num_parameters, sigma, epsilon): + A = np.zeros(shape=(X_test.shape[0], num_parameters)) + b = np.zeros(shape=(num_parameters, 1)) + + A = self._phi(X_test, sigma) + b = self._phi(X_train, sigma).sum(axis=0) / X_train.shape[0] + b = b.reshape((num_parameters, 1)) + + out = alpha_0.copy() + for k in range(self.max_iter): + mat = np.dot(A, out) + mat += 0.000000001 + out += epsilon * np.dot(np.transpose(A), 1.0 / mat) + out += b * ( + ((1 - np.dot(np.transpose(b), out)) / np.dot(np.transpose(b), b)) + ) + out = np.maximum(0, out) + out /= np.dot(np.transpose(b), out) + + self._alpha = out + self._fitted = True + + def predict(self, X, sigma=None): + """Equivalent of w(X) from the original paper.""" + + X = self._reshape_X(X) + if not self._fitted: + raise Exception("Not fitted!") + return np.dot(self._phi(X, sigma=sigma), self._alpha).reshape((X.shape[0],)) diff --git a/baselines/rca.py b/baselines/rca.py index d85b8d8..2ce307a 100644 --- a/baselines/rca.py +++ b/baselines/rca.py @@ -1,14 +1,14 @@ -import numpy as np -from sklearn import clone -from sklearn.base import BaseEstimator - - -def clone_fit(c_model: BaseEstimator, data, labels): - c_model2 = clone(c_model) - c_model2.fit(data, labels) - return c_model2 - -def get_score(pred1, pred2, labels): - return np.mean((pred1 == labels).astype(int) - (pred2 == labels).astype(int)) - - +import numpy as np +from sklearn import clone +from sklearn.base import BaseEstimator + + +def clone_fit(c_model: BaseEstimator, data, labels): + c_model2 = clone(c_model) + c_model2.fit(data, labels) + return c_model2 + +def get_score(pred1, pred2, labels): + return np.mean((pred1 == labels).astype(int) - (pred2 == labels).astype(int)) + + diff --git a/conf.yaml b/conf.yaml index fb49814..8ff1057 100644 --- a/conf.yaml +++ b/conf.yaml @@ -1,233 +1,233 @@ -debug_conf: &debug_conf - global: - METRICS: - - acc - DATASET_N_PREVS: 5 - DATASET_PREVS: - # - 0.2 - - 0.5 - # - 0.8 - - confs: - - DATASET_NAME: rcv1 - DATASET_TARGET: CCAT - - plot_confs: - debug: - PLOT_ESTIMATORS: - - mulmc_sld - - atc_mc - PLOT_STDEV: true - -mc_conf: &mc_conf - global: - METRICS: - - acc - DATASET_N_PREVS: 9 - DATASET_DIR_UPDATE: true - - confs: - - DATASET_NAME: rcv1 - DATASET_TARGET: CCAT - # - DATASET_NAME: imdb - - plot_confs: - debug3: - PLOT_ESTIMATORS: - - binmc_sld - - mulmc_sld - - binne_sld - - mulne_sld - - bin_sld_gs - - mul_sld_gs - - atc_mc - PLOT_STDEV: true - -test_conf: &test_conf - global: - METRICS: - - acc - - f1 - DATASET_N_PREVS: 9 - - confs: - - DATASET_NAME: rcv1 - DATASET_TARGET: CCAT - # - DATASET_NAME: imdb - - plot_confs: - gs_vs_gsq: - PLOT_ESTIMATORS: - - bin_sld - - bin_sld_gs - - bin_sld_gsq - - mul_sld - - mul_sld_gs - - mul_sld_gsq - gs_vs_atc: - PLOT_ESTIMATORS: - - bin_sld - - bin_sld_gs - - mul_sld - - mul_sld_gs - - atc_mc - - atc_ne - sld_vs_pacc: - PLOT_ESTIMATORS: - - bin_sld - - bin_sld_gs - - mul_sld - - mul_sld_gs - - bin_pacc - - bin_pacc_gs - - mul_pacc - - mul_pacc_gs - - atc_mc - - atc_ne - pacc_vs_atc: - PLOT_ESTIMATORS: - - bin_pacc - - bin_pacc_gs - - mul_pacc - - mul_pacc_gs - - atc_mc - - atc_ne - -main_conf: &main_conf - - global: - METRICS: - - acc - - f1 - DATASET_N_PREVS: 9 - DATASET_DIR_UPDATE: true - - confs: - - DATASET_NAME: rcv1 - DATASET_TARGET: CCAT - - DATASET_NAME: imdb - confs_next: - - DATASET_NAME: rcv1 - DATASET_TARGET: GCAT - - DATASET_NAME: rcv1 - DATASET_TARGET: MCAT - - plot_confs: - gs_vs_qgs: - PLOT_ESTIMATORS: - - mul_sld_gs - - bin_sld_gs - - mul_sld_gsq - - bin_sld_gsq - - atc_mc - - atc_ne - PLOT_STDEV: true - plot_confs_completed: - max_conf_vs_atc_pacc: - PLOT_ESTIMATORS: - - bin_pacc - - binmc_pacc - - mul_pacc - - mulmc_pacc - - atc_mc - PLOT_STDEV: true - max_conf_vs_entropy_pacc: - PLOT_ESTIMATORS: - - binmc_pacc - - binne_pacc - - mulmc_pacc - - mulne_pacc - - atc_mc - PLOT_STDEV: true - gs_vs_atc: - PLOT_ESTIMATORS: - - mul_sld_gs - - bin_sld_gs - - mul_pacc_gs - - bin_pacc_gs - - atc_mc - - atc_ne - PLOT_STDEV: true - gs_vs_all: - PLOT_ESTIMATORS: - - mul_sld_gs - - bin_sld_gs - - mul_pacc_gs - - bin_pacc_gs - - atc_mc - - doc_feat - - kfcv - PLOT_STDEV: true - gs_vs_qgs: - PLOT_ESTIMATORS: - - mul_sld_gs - - bin_sld_gs - - mul_sld_gsq - - bin_sld_gsq - - atc_mc - - atc_ne - PLOT_STDEV: true - cc_vs_other: - PLOT_ESTIMATORS: - - mul_cc - - bin_cc - - mul_sld - - bin_sld - - mul_pacc - - bin_pacc - PLOT_STDEV: true - max_conf_vs_atc: - PLOT_ESTIMATORS: - - bin_sld - - binmc_sld - - mul_sld - - mulmc_sld - - atc_mc - PLOT_STDEV: true - max_conf_vs_entropy: - PLOT_ESTIMATORS: - - binmc_sld - - binne_sld - - mulmc_sld - - mulne_sld - - atc_mc - PLOT_STDEV: true - sld_vs_pacc: - PLOT_ESTIMATORS: - - bin_sld - - mul_sld - - bin_pacc - - mul_pacc - - atc_mc - PLOT_STDEV: true - plot_confs_other: - best_vs_atc: - PLOT_ESTIMATORS: - - mul_sld_bcts - - mul_sld_gs - - bin_sld_bcts - - bin_sld_gs - - atc_mc - - atc_ne - all_vs_atc: - PLOT_ESTIMATORS: - - bin_sld - - bin_sld_bcts - - bin_sld_gs - - mul_sld - - mul_sld_bcts - - mul_sld_gs - - atc_mc - - atc_ne - best_vs_all: - PLOT_ESTIMATORS: - - bin_sld_bcts - - bin_sld_gs - - mul_sld_bcts - - mul_sld_gs - - kfcv - - atc_mc - - atc_ne - - doc_feat - +debug_conf: &debug_conf + global: + METRICS: + - acc + DATASET_N_PREVS: 5 + DATASET_PREVS: + # - 0.2 + - 0.5 + # - 0.8 + + confs: + - DATASET_NAME: rcv1 + DATASET_TARGET: CCAT + + plot_confs: + debug: + PLOT_ESTIMATORS: + - mulmc_sld + - atc_mc + PLOT_STDEV: true + +mc_conf: &mc_conf + global: + METRICS: + - acc + DATASET_N_PREVS: 9 + DATASET_DIR_UPDATE: true + + confs: + - DATASET_NAME: rcv1 + DATASET_TARGET: CCAT + # - DATASET_NAME: imdb + + plot_confs: + debug3: + PLOT_ESTIMATORS: + - binmc_sld + - mulmc_sld + - binne_sld + - mulne_sld + - bin_sld_gs + - mul_sld_gs + - atc_mc + PLOT_STDEV: true + +test_conf: &test_conf + global: + METRICS: + - acc + - f1 + DATASET_N_PREVS: 9 + + confs: + - DATASET_NAME: rcv1 + DATASET_TARGET: CCAT + # - DATASET_NAME: imdb + + plot_confs: + gs_vs_gsq: + PLOT_ESTIMATORS: + - bin_sld + - bin_sld_gs + - bin_sld_gsq + - mul_sld + - mul_sld_gs + - mul_sld_gsq + gs_vs_atc: + PLOT_ESTIMATORS: + - bin_sld + - bin_sld_gs + - mul_sld + - mul_sld_gs + - atc_mc + - atc_ne + sld_vs_pacc: + PLOT_ESTIMATORS: + - bin_sld + - bin_sld_gs + - mul_sld + - mul_sld_gs + - bin_pacc + - bin_pacc_gs + - mul_pacc + - mul_pacc_gs + - atc_mc + - atc_ne + pacc_vs_atc: + PLOT_ESTIMATORS: + - bin_pacc + - bin_pacc_gs + - mul_pacc + - mul_pacc_gs + - atc_mc + - atc_ne + +main_conf: &main_conf + + global: + METRICS: + - acc + - f1 + DATASET_N_PREVS: 9 + DATASET_DIR_UPDATE: true + + confs: + - DATASET_NAME: rcv1 + DATASET_TARGET: CCAT + - DATASET_NAME: imdb + confs_next: + - DATASET_NAME: rcv1 + DATASET_TARGET: GCAT + - DATASET_NAME: rcv1 + DATASET_TARGET: MCAT + + plot_confs: + gs_vs_qgs: + PLOT_ESTIMATORS: + - mul_sld_gs + - bin_sld_gs + - mul_sld_gsq + - bin_sld_gsq + - atc_mc + - atc_ne + PLOT_STDEV: true + plot_confs_completed: + max_conf_vs_atc_pacc: + PLOT_ESTIMATORS: + - bin_pacc + - binmc_pacc + - mul_pacc + - mulmc_pacc + - atc_mc + PLOT_STDEV: true + max_conf_vs_entropy_pacc: + PLOT_ESTIMATORS: + - binmc_pacc + - binne_pacc + - mulmc_pacc + - mulne_pacc + - atc_mc + PLOT_STDEV: true + gs_vs_atc: + PLOT_ESTIMATORS: + - mul_sld_gs + - bin_sld_gs + - mul_pacc_gs + - bin_pacc_gs + - atc_mc + - atc_ne + PLOT_STDEV: true + gs_vs_all: + PLOT_ESTIMATORS: + - mul_sld_gs + - bin_sld_gs + - mul_pacc_gs + - bin_pacc_gs + - atc_mc + - doc_feat + - kfcv + PLOT_STDEV: true + gs_vs_qgs: + PLOT_ESTIMATORS: + - mul_sld_gs + - bin_sld_gs + - mul_sld_gsq + - bin_sld_gsq + - atc_mc + - atc_ne + PLOT_STDEV: true + cc_vs_other: + PLOT_ESTIMATORS: + - mul_cc + - bin_cc + - mul_sld + - bin_sld + - mul_pacc + - bin_pacc + PLOT_STDEV: true + max_conf_vs_atc: + PLOT_ESTIMATORS: + - bin_sld + - binmc_sld + - mul_sld + - mulmc_sld + - atc_mc + PLOT_STDEV: true + max_conf_vs_entropy: + PLOT_ESTIMATORS: + - binmc_sld + - binne_sld + - mulmc_sld + - mulne_sld + - atc_mc + PLOT_STDEV: true + sld_vs_pacc: + PLOT_ESTIMATORS: + - bin_sld + - mul_sld + - bin_pacc + - mul_pacc + - atc_mc + PLOT_STDEV: true + plot_confs_other: + best_vs_atc: + PLOT_ESTIMATORS: + - mul_sld_bcts + - mul_sld_gs + - bin_sld_bcts + - bin_sld_gs + - atc_mc + - atc_ne + all_vs_atc: + PLOT_ESTIMATORS: + - bin_sld + - bin_sld_bcts + - bin_sld_gs + - mul_sld + - mul_sld_bcts + - mul_sld_gs + - atc_mc + - atc_ne + best_vs_all: + PLOT_ESTIMATORS: + - bin_sld_bcts + - bin_sld_gs + - mul_sld_bcts + - mul_sld_gs + - kfcv + - atc_mc + - atc_ne + - doc_feat + exec: *main_conf \ No newline at end of file diff --git a/out_imdb.md b/out_imdb.md index d3bd704..0d5144f 100644 --- a/out_imdb.md +++ b/out_imdb.md @@ -1,445 +1,445 @@ - -
target: default
-
train: [0.5 0.5]
-
validation: [0.5 0.5]
-
evaluate_binary: 277.300s
-
evaluate_multiclass: 139.986s
-
kfcv: 98.625s
-
atc_mc: 93.304s
-
atc_ne: 91.201s
-
doc_feat: 29.930s
-
rca_score: 1018.341s
-
rca_star_score: 1013.733s
-
tot: 1054.413s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01540.01770.02490.02910.02910.02480.27050.2413
(0.05, 0.95)0.03090.02840.02520.03000.03000.02470.27960.2504
(0.1, 0.9)0.03090.03020.02510.02790.02790.02500.27220.2430
(0.15, 0.85)0.03100.03390.02450.02690.02690.02440.26840.2392
(0.2, 0.8)0.04110.04070.02590.02920.02920.02570.27240.2432
(0.25, 0.75)0.03810.03760.02620.03190.03190.02590.27010.2409
(0.3, 0.7)0.04420.04520.02540.02730.02730.02560.26500.2358
(0.35, 0.65)0.04800.04980.02360.02570.02570.02350.26400.2347
(0.4, 0.6)0.04010.04310.02220.02960.02960.02200.26540.2361
(0.45, 0.55)0.05510.05580.02430.02950.02950.02460.18380.1551
(0.5, 0.5)0.04990.05130.03080.03190.03190.03090.14720.1202
(0.55, 0.45)0.05380.05420.02780.03290.03290.02800.17170.1459
(0.6, 0.4)0.04760.04840.02580.02980.02980.02590.24340.2147
(0.65, 0.35)0.04470.04740.02870.03320.03320.02880.26320.2340
(0.7, 0.3)0.03880.03970.02950.03280.03280.02960.26590.2367
(0.75, 0.25)0.03360.03990.02410.02930.02930.02440.26120.2320
(0.8, 0.2)0.04070.04470.02660.03030.03030.02710.26010.2309
(0.85, 0.15)0.03830.04230.02190.02780.02780.02200.26700.2378
(0.9, 0.1)0.03510.03870.02440.02750.02750.02450.26180.2326
(0.95, 0.05)0.02380.02630.02690.02960.02960.02720.26020.2310
(1.0, 0.0)0.01180.02020.02410.02790.02790.02440.25710.2279
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00880.01000.05800.01830.0183
(0.05, 0.95)0.01750.01590.06050.01930.0193
(0.1, 0.9)0.01840.01760.05320.01890.0189
(0.15, 0.85)0.01880.02040.04750.01800.0180
(0.2, 0.8)0.02690.02660.04550.02060.0206
(0.25, 0.75)0.02650.02610.04010.02420.0242
(0.3, 0.7)0.03280.03360.03310.02080.0208
(0.35, 0.65)0.03860.03940.03070.02110.0211
(0.4, 0.6)0.03430.03710.02730.02650.0265
(0.45, 0.55)0.05110.05120.02310.02750.0275
(0.5, 0.5)0.05170.05290.03060.03190.0319
(0.55, 0.45)0.05840.05830.03080.03540.0354
(0.6, 0.4)0.05900.05990.03630.03570.0357
(0.65, 0.35)0.06350.06620.05060.04400.0440
(0.7, 0.3)0.05960.06380.06540.04570.0457
(0.75, 0.25)0.06270.07440.09640.04610.0461
(0.8, 0.2)0.09090.09990.14000.06290.0629
(0.85, 0.15)0.10520.11260.18290.07270.0727
(0.9, 0.1)0.13770.14810.28390.12150.1215
(0.95, 0.05)0.13050.14500.45920.20370.2037
(1.0, 0.0)0.10920.13870.88180.52670.5267
+ +
target: default
+
train: [0.5 0.5]
+
validation: [0.5 0.5]
+
evaluate_binary: 277.300s
+
evaluate_multiclass: 139.986s
+
kfcv: 98.625s
+
atc_mc: 93.304s
+
atc_ne: 91.201s
+
doc_feat: 29.930s
+
rca_score: 1018.341s
+
rca_star_score: 1013.733s
+
tot: 1054.413s
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01540.01770.02490.02910.02910.02480.27050.2413
(0.05, 0.95)0.03090.02840.02520.03000.03000.02470.27960.2504
(0.1, 0.9)0.03090.03020.02510.02790.02790.02500.27220.2430
(0.15, 0.85)0.03100.03390.02450.02690.02690.02440.26840.2392
(0.2, 0.8)0.04110.04070.02590.02920.02920.02570.27240.2432
(0.25, 0.75)0.03810.03760.02620.03190.03190.02590.27010.2409
(0.3, 0.7)0.04420.04520.02540.02730.02730.02560.26500.2358
(0.35, 0.65)0.04800.04980.02360.02570.02570.02350.26400.2347
(0.4, 0.6)0.04010.04310.02220.02960.02960.02200.26540.2361
(0.45, 0.55)0.05510.05580.02430.02950.02950.02460.18380.1551
(0.5, 0.5)0.04990.05130.03080.03190.03190.03090.14720.1202
(0.55, 0.45)0.05380.05420.02780.03290.03290.02800.17170.1459
(0.6, 0.4)0.04760.04840.02580.02980.02980.02590.24340.2147
(0.65, 0.35)0.04470.04740.02870.03320.03320.02880.26320.2340
(0.7, 0.3)0.03880.03970.02950.03280.03280.02960.26590.2367
(0.75, 0.25)0.03360.03990.02410.02930.02930.02440.26120.2320
(0.8, 0.2)0.04070.04470.02660.03030.03030.02710.26010.2309
(0.85, 0.15)0.03830.04230.02190.02780.02780.02200.26700.2378
(0.9, 0.1)0.03510.03870.02440.02750.02750.02450.26180.2326
(0.95, 0.05)0.02380.02630.02690.02960.02960.02720.26020.2310
(1.0, 0.0)0.01180.02020.02410.02790.02790.02440.25710.2279
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00880.01000.05800.01830.0183
(0.05, 0.95)0.01750.01590.06050.01930.0193
(0.1, 0.9)0.01840.01760.05320.01890.0189
(0.15, 0.85)0.01880.02040.04750.01800.0180
(0.2, 0.8)0.02690.02660.04550.02060.0206
(0.25, 0.75)0.02650.02610.04010.02420.0242
(0.3, 0.7)0.03280.03360.03310.02080.0208
(0.35, 0.65)0.03860.03940.03070.02110.0211
(0.4, 0.6)0.03430.03710.02730.02650.0265
(0.45, 0.55)0.05110.05120.02310.02750.0275
(0.5, 0.5)0.05170.05290.03060.03190.0319
(0.55, 0.45)0.05840.05830.03080.03540.0354
(0.6, 0.4)0.05900.05990.03630.03570.0357
(0.65, 0.35)0.06350.06620.05060.04400.0440
(0.7, 0.3)0.05960.06380.06540.04570.0457
(0.75, 0.25)0.06270.07440.09640.04610.0461
(0.8, 0.2)0.09090.09990.14000.06290.0629
(0.85, 0.15)0.10520.11260.18290.07270.0727
(0.9, 0.1)0.13770.14810.28390.12150.1215
(0.95, 0.05)0.13050.14500.45920.20370.2037
(1.0, 0.0)0.10920.13870.88180.52670.5267
diff --git a/out_rcv1.md b/out_rcv1.md index 17c6121..c5ac70e 100644 --- a/out_rcv1.md +++ b/out_rcv1.md @@ -1,17355 +1,17355 @@ - -
target: C12
-
train: [0.98358389 0.01641611]
-
validation: [0.98349892 0.01650108]
-
evaluate_binary: 258.363s
-
evaluate_multiclass: 116.180s
-
kfcv: 104.789s
-
atc_mc: 154.996s
-
atc_ne: 144.298s
-
doc_feat: 88.728s
-
rca_score: 909.590s
-
rca_star_score: 899.370s
-
tot: 951.474s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04920.04930.93750.51580.51580.9361NaNNaN
(0.05, 0.95)0.09670.09680.89000.49700.49700.8887NaNNaN
(0.1, 0.9)0.14320.14380.84290.46830.46830.84160.85300.8561
(0.15, 0.85)0.19630.19660.79000.42860.42860.7888NaNNaN
(0.2, 0.8)0.24020.23990.74640.40800.40800.7452NaNNaN
(0.25, 0.75)0.28960.28790.69680.38060.38060.6957NaNNaN
(0.3, 0.7)0.33860.32560.64780.35870.35870.6468NaNNaN
(0.35, 0.65)0.38040.33420.60530.33750.33750.6043NaNNaN
(0.4, 0.6)0.42460.33030.55750.30660.30660.5566NaNNaN
(0.45, 0.55)0.43460.29080.51030.28710.28710.5095NaNNaN
(0.5, 0.5)0.41710.27610.46120.25270.25270.4604NaNNaN
(0.55, 0.45)0.38030.24470.41400.22690.22690.4133NaNNaN
(0.6, 0.4)0.33440.20730.36850.20630.20630.3678NaNNaN
(0.65, 0.35)0.28660.17850.31900.17820.17820.3184NaNNaN
(0.7, 0.3)0.23390.14380.27180.14980.14980.2713NaNNaN
(0.75, 0.25)0.17810.11040.22340.12740.12740.2229NaNNaN
(0.8, 0.2)0.12750.07610.17660.10270.10270.1762NaNNaN
(0.85, 0.15)0.09000.05460.12970.07310.07310.1293NaNNaN
(0.9, 0.1)0.04940.03620.08180.04930.04930.0815NaNNaN
(0.95, 0.05)0.02760.02290.03420.02380.02380.0340NaNNaN
(1.0, 0.0)0.00180.00130.01320.00230.00230.0134NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.09290.09300.57120.09170.0917
(0.05, 0.95)0.09300.09310.57110.09010.0901
(0.1, 0.9)0.09100.09220.57200.08920.0892
(0.15, 0.85)0.10240.10320.56100.10050.1005
(0.2, 0.8)0.09500.09510.56910.09220.0922
(0.25, 0.75)0.09990.10040.56380.09840.0984
(0.3, 0.7)0.10420.10430.55990.10070.1007
(0.35, 0.65)0.09040.09130.57290.08860.0886
(0.4, 0.6)0.09150.09200.57220.08890.0889
(0.45, 0.55)0.08940.09070.57350.08940.0894
(0.5, 0.5)0.09570.09650.56770.09450.0945
(0.55, 0.45)0.09270.09510.56910.09060.0906
(0.6, 0.4)0.08530.08540.57880.08260.0826
(0.65, 0.35)0.09400.09510.56910.09550.0955
(0.7, 0.3)0.09190.09340.57080.09340.0934
(0.75, 0.25)0.09810.09920.56500.09680.0968
(0.8, 0.2)0.09280.09290.57130.09290.0929
(0.85, 0.15)0.08470.08570.57830.08590.0859
(0.9, 0.1)0.08580.08660.57740.08390.0839
(0.95, 0.05)0.08370.08710.57470.08950.0895
(1.0, 0.0)0.00000.00000.66420.00000.0000
- -
target: C13
-
train: [0.95913254 0.04086746]
-
validation: [0.95904968 0.04095032]
-
evaluate_binary: 293.415s
-
evaluate_multiclass: 130.949s
-
kfcv: 164.741s
-
atc_mc: 163.221s
-
atc_ne: 98.244s
-
doc_feat: 127.361s
-
rca_score: 661.548s
-
rca_star_score: 635.802s
-
tot: 705.253s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01100.01800.94120.52120.52120.9413NaNNaN
(0.05, 0.95)0.06330.07020.88890.49620.49620.8891NaNNaN
(0.1, 0.9)0.10930.11410.84330.46130.46130.8436NaNNaN
(0.15, 0.85)0.15830.15500.79370.43400.43400.7940NaNNaN
(0.2, 0.8)0.20580.18520.74450.40340.40340.7449NaNNaN
(0.25, 0.75)0.23670.19550.69590.37990.37990.6963NaNNaN
(0.3, 0.7)0.24700.20190.64580.35120.35120.6463NaNNaN
(0.35, 0.65)0.23680.21230.59660.31790.31790.5971NaNNaN
(0.4, 0.6)0.20960.18710.54760.30490.30490.5482NaNNaN
(0.45, 0.55)0.18980.17290.49870.26770.26770.4994NaNNaN
(0.5, 0.5)0.16980.13740.44880.24450.24450.4495NaNNaN
(0.55, 0.45)0.15310.15040.40180.22090.22090.4026NaNNaN
(0.6, 0.4)0.12730.10820.35230.19480.19480.3531NaNNaN
(0.65, 0.35)0.12240.11310.30230.16100.16100.3032NaNNaN
(0.7, 0.3)0.08740.07170.25310.13660.13660.2540NaNNaN
(0.75, 0.25)0.06890.06160.20410.11300.11300.2051NaNNaN
(0.8, 0.2)0.05530.04930.15560.08330.08330.1567NaNNaN
(0.85, 0.15)0.04650.04930.10840.06040.06040.1095NaNNaN
(0.9, 0.1)0.03800.03610.05750.03510.03510.0587NaNNaN
(0.95, 0.05)0.02870.02840.00880.01370.01370.0100NaNNaN
(1.0, 0.0)0.01400.01230.04030.02610.02610.0390NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.02130.03500.47040.03570.0357
(0.05, 0.95)0.02830.04180.46360.04250.0425
(0.1, 0.9)0.02230.03510.47080.03530.0353
(0.15, 0.85)0.02330.03660.46940.03670.0367
(0.2, 0.8)0.02410.03680.46920.03690.0369
(0.25, 0.75)0.02180.03560.47040.03570.0357
(0.3, 0.7)0.02690.03800.46790.03820.0382
(0.35, 0.65)0.02370.03890.46670.03940.0394
(0.4, 0.6)0.03080.03850.46690.03920.0392
(0.45, 0.55)0.02450.03800.46730.03880.0388
(0.5, 0.5)0.02650.04090.46460.04160.0416
(0.55, 0.45)0.02540.03340.47240.03370.0337
(0.6, 0.4)0.02550.03470.47080.03540.0354
(0.65, 0.35)0.02800.03950.46560.04050.0405
(0.7, 0.3)0.03020.03980.46510.04100.0410
(0.75, 0.25)0.02780.04010.46330.04290.0429
(0.8, 0.2)0.02460.03770.46790.03830.0383
(0.85, 0.15)0.01200.02360.48500.02110.0211
(0.9, 0.1)0.02980.04550.46670.03940.0394
(0.95, 0.05)0.02690.03380.47950.02670.0267
(1.0, 0.0)0.00000.02530.50610.00000.0000
- -
target: C15
-
train: [0.81950924 0.18049076]
-
validation: [0.81943844 0.18056156]
-
evaluate_binary: 329.208s
-
evaluate_multiclass: 130.846s
-
kfcv: 170.297s
-
atc_mc: 170.842s
-
atc_ne: 107.158s
-
doc_feat: 71.484s
-
rca_score: 1210.284s
-
rca_star_score: 1189.765s
-
tot: 1265.086s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00660.01430.17960.06130.06130.18280.55850.5360
(0.05, 0.95)0.04000.04040.17740.06710.06710.18060.55880.5363
(0.1, 0.9)0.04590.04610.16370.05960.05960.16700.57070.5483
(0.15, 0.85)0.03750.03790.14560.05160.05160.14900.53380.5155
(0.2, 0.8)0.04300.04250.14390.05520.05520.14730.40910.3967
(0.25, 0.75)0.03260.03250.12410.05050.05050.12760.24680.2473
(0.3, 0.7)0.03510.03800.11510.04820.04820.11860.14030.1543
(0.35, 0.65)0.02910.02990.10700.04680.04680.11060.10550.1264
(0.4, 0.6)0.02990.02940.09780.03740.03740.10140.08050.1025
(0.45, 0.55)0.03300.03040.08350.03850.03850.08720.04740.0695
(0.5, 0.5)0.03180.03350.07130.03350.03350.07490.01960.0393
(0.55, 0.45)0.03300.03150.05730.02990.02990.06110.01810.0171
(0.6, 0.4)0.02770.02620.04980.02930.02930.05340.03610.0183
(0.65, 0.35)0.03020.03210.04440.02910.02910.04790.05030.0306
(0.7, 0.3)0.02990.02690.02940.02270.02270.03250.07020.0476
(0.75, 0.25)0.02610.02480.02170.02370.02370.02410.08230.0597
(0.8, 0.2)0.03010.02890.01680.02050.02050.01820.09110.0686
(0.85, 0.15)0.02420.02180.01560.01890.01890.01530.10180.0793
(0.9, 0.1)0.02230.01930.01860.01460.01460.01590.11510.0926
(0.95, 0.05)0.01820.01410.02680.01090.01090.02280.12590.1034
(1.0, 0.0)0.01190.00860.03750.00930.00930.0334NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00440.00930.04780.02680.0268
(0.05, 0.95)0.02130.02240.05350.02970.0297
(0.1, 0.9)0.02600.02690.05250.02800.0280
(0.15, 0.85)0.02140.02270.04790.02710.0271
(0.2, 0.8)0.02740.02770.05440.02980.0298
(0.25, 0.75)0.02160.02240.04900.03000.0300
(0.3, 0.7)0.02460.02820.05120.03060.0306
(0.35, 0.65)0.02260.02440.05360.03360.0336
(0.4, 0.6)0.02530.02580.05560.02820.0282
(0.45, 0.55)0.03130.02940.05380.03480.0348
(0.5, 0.5)0.03280.03540.05390.03310.0331
(0.55, 0.45)0.03780.03650.05040.03530.0353
(0.6, 0.4)0.03480.03370.05490.03740.0374
(0.65, 0.35)0.04320.04810.06560.04280.0428
(0.7, 0.3)0.05480.04880.05850.04460.0446
(0.75, 0.25)0.05690.05350.06330.05470.0547
(0.8, 0.2)0.07670.07700.07430.05760.0576
(0.85, 0.15)0.08670.07810.08430.07260.0726
(0.9, 0.1)0.10970.10000.09420.08070.0807
(0.95, 0.05)0.18110.15140.13110.11930.1193
(1.0, 0.0)0.03630.03630.92180.10030.1003
- -
target: C151
-
train: [0.89778815 0.10221185]
-
validation: [0.89779698 0.10220302]
-
evaluate_binary: 355.746s
-
evaluate_multiclass: 136.969s
-
kfcv: 195.146s
-
atc_mc: 193.365s
-
atc_ne: 187.934s
-
doc_feat: 73.112s
-
rca_score: 1235.567s
-
rca_star_score: 1236.092s
-
tot: 1294.815s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00030.01080.22360.08270.08270.22390.60960.5989
(0.05, 0.95)0.04650.04750.22160.08610.08610.22200.52290.5149
(0.1, 0.9)0.05670.05870.20620.07190.07190.20660.42470.4188
(0.15, 0.85)0.04440.04680.18460.06710.06710.18510.29190.2922
(0.2, 0.8)0.04530.04580.17840.06810.06810.17900.18580.1927
(0.25, 0.75)0.03350.04020.16520.06240.06240.16580.18300.1919
(0.3, 0.7)0.03040.03200.15170.05610.05610.15240.16000.1702
(0.35, 0.65)0.02780.03330.13480.05090.05090.13560.14120.1516
(0.4, 0.6)0.02480.02630.12460.04790.04790.12550.12350.1341
(0.45, 0.55)0.02570.02850.10620.04130.04130.10710.09880.1099
(0.5, 0.5)0.02710.03250.10510.04920.04920.10610.08780.0995
(0.55, 0.45)0.02370.03150.09120.04260.04260.09230.06690.0787
(0.6, 0.4)0.02300.02890.07800.03910.03910.07910.04800.0598
(0.65, 0.35)0.02360.02570.05950.03420.03420.06070.02870.0368
(0.7, 0.3)0.02550.03010.05360.02850.02850.05480.02040.0257
(0.75, 0.25)0.02020.02470.03470.01930.01930.03600.01860.0156
(0.8, 0.2)0.02360.02700.02880.02220.02220.03010.02060.0150
(0.85, 0.15)0.02060.02230.01730.01900.01900.01850.03220.0234
(0.9, 0.1)0.01790.01880.01260.01430.01430.01280.04600.0355
(0.95, 0.05)0.01310.01120.01450.00990.00990.01350.05880.0481
(1.0, 0.0)0.00420.00290.02600.00520.00520.0243NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.00710.05740.03360.0336
(0.05, 0.95)0.02330.02500.06480.03850.0385
(0.1, 0.9)0.02950.03320.06330.03270.0327
(0.15, 0.85)0.02450.02810.05670.03300.0330
(0.2, 0.8)0.02650.02870.06180.03550.0355
(0.25, 0.75)0.02130.02790.06290.03910.0391
(0.3, 0.7)0.02060.02330.06170.03460.0346
(0.35, 0.65)0.02120.02700.05730.03610.0361
(0.4, 0.6)0.01970.02240.05990.03560.0356
(0.45, 0.55)0.02310.02710.05510.03720.0372
(0.5, 0.5)0.02700.03450.06810.04630.0463
(0.55, 0.45)0.02640.03710.06760.04530.0453
(0.6, 0.4)0.02940.03830.06750.04820.0482
(0.65, 0.35)0.03490.03890.06570.04930.0493
(0.7, 0.3)0.04450.05360.07590.04840.0484
(0.75, 0.25)0.04240.05330.06280.04220.0422
(0.8, 0.2)0.06330.07240.08190.06030.0603
(0.85, 0.15)0.07570.08220.08830.06860.0686
(0.9, 0.1)0.10510.10820.10590.08280.0828
(0.95, 0.05)0.16440.13730.13690.12130.1213
(1.0, 0.0)0.00000.00050.91190.04730.0473
- -
target: C1511
-
train: [0.98280629 0.01719371]
-
validation: [0.98272138 0.01727862]
-
evaluate_binary: 301.572s
-
evaluate_multiclass: 149.682s
-
kfcv: 102.539s
-
atc_mc: 99.622s
-
atc_ne: 92.903s
-
doc_feat: 78.631s
-
rca_score: 1097.941s
-
rca_star_score: 1093.801s
-
tot: 1139.243s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00010.10990.87510.60870.60870.87490.88420.8890
(0.05, 0.95)0.05010.15820.82660.57030.57030.82640.83580.8405
(0.1, 0.9)0.10010.20010.78470.54530.54530.78460.79380.7986
(0.15, 0.85)0.14900.24090.73590.51020.51020.73590.74510.7498
(0.2, 0.8)0.17710.23740.69530.48310.48310.69540.70450.7092
(0.25, 0.75)0.16700.21840.65320.45390.45390.65340.66230.6671
(0.3, 0.7)0.15030.20260.60660.42030.42030.60690.61570.6205
(0.35, 0.65)0.12210.18190.55820.38100.38100.55850.56730.5721
(0.4, 0.6)0.09870.15870.51360.35880.35880.51400.52270.5275
(0.45, 0.55)0.09180.14030.47520.32960.32960.47570.48430.4891
(0.5, 0.5)0.07290.11320.43350.30670.30670.43410.44260.4474
(0.55, 0.45)0.06140.10570.38590.26830.26830.3866NaNNaN
(0.6, 0.4)0.05150.08650.34180.23960.23960.3425NaNNaN
(0.65, 0.35)0.03930.07290.29550.20440.20440.2963NaNNaN
(0.7, 0.3)0.03870.06070.25200.17830.17830.2529NaNNaN
(0.75, 0.25)0.03840.05230.20710.14910.14910.2081NaNNaN
(0.8, 0.2)0.03050.04070.16410.11920.11920.1652NaNNaN
(0.85, 0.15)0.02900.03050.11910.09000.09000.1203NaNNaN
(0.9, 0.1)0.02730.02630.07670.06110.06110.0779NaNNaN
(0.95, 0.05)0.02320.02060.03060.02890.02890.0319NaNNaN
(1.0, 0.0)0.00360.00250.01340.00270.00270.0120NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.19650.46180.09870.0987
(0.05, 0.95)0.00940.20310.45510.10250.1025
(0.1, 0.9)0.01870.19960.45940.12230.1223
(0.15, 0.85)0.02870.20710.45140.11010.1101
(0.2, 0.8)0.03320.20040.45820.10450.1045
(0.25, 0.75)0.03230.19610.46280.11140.1114
(0.3, 0.7)0.03370.19910.45820.11020.1102
(0.35, 0.65)0.02980.20840.44840.12330.1233
(0.4, 0.6)0.02660.21020.44720.11400.1140
(0.45, 0.55)0.02890.19500.46240.12590.1259
(0.5, 0.5)0.02240.18610.47210.12510.1251
(0.55, 0.45)0.02270.19480.46240.13220.1322
(0.6, 0.4)0.02550.19500.46280.13240.1324
(0.65, 0.35)0.02210.19620.45460.14510.1451
(0.7, 0.3)0.02220.19140.46080.14600.1460
(0.75, 0.25)0.03260.19480.45850.14490.1449
(0.8, 0.2)0.03180.18230.46880.16520.1652
(0.85, 0.15)0.04570.17690.46170.18270.1827
(0.9, 0.1)0.05750.15140.49530.16410.1641
(0.95, 0.05)0.13870.16400.48050.17290.1729
(1.0, 0.0)0.03990.04220.66050.00000.0000
- -
target: C152
-
train: [0.91662347 0.08337653]
-
validation: [0.91663067 0.08336933]
-
evaluate_binary: 225.296s
-
evaluate_multiclass: 165.847s
-
kfcv: 126.786s
-
atc_mc: 102.362s
-
atc_ne: 91.713s
-
doc_feat: 74.154s
-
rca_score: 1219.516s
-
rca_star_score: 1213.375s
-
tot: 1270.692s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00050.01530.46990.17580.17580.47090.48780.5104
(0.05, 0.95)0.05170.04960.43230.15310.15310.43330.45610.4786
(0.1, 0.9)0.08180.07070.40370.13570.13570.40480.42740.4502
(0.15, 0.85)0.08800.07140.39160.14260.14260.39280.41360.4363
(0.2, 0.8)0.07800.06950.36250.13730.13730.36390.38510.4076
(0.25, 0.75)0.06930.05730.33120.11810.11810.33260.35310.3754
(0.3, 0.7)0.06760.05560.30820.11140.11140.30970.32870.3508
(0.35, 0.65)0.06110.05170.28050.10420.10420.28210.30010.3221
(0.4, 0.6)0.05510.04770.25890.09180.09180.26060.27660.2984
(0.45, 0.55)0.04840.04100.23770.09900.09900.23950.25450.2763
(0.5, 0.5)0.04760.04070.20660.07730.07730.20850.22200.2438
(0.55, 0.45)0.04570.04100.18170.06270.06270.18370.19570.2174
(0.6, 0.4)0.04210.03730.16150.06590.06590.16360.17460.1964
(0.65, 0.35)0.03070.03520.13220.05730.05730.13440.14510.1670
(0.7, 0.3)0.02930.03070.10640.04550.04550.10870.11910.1410
(0.75, 0.25)0.03410.03420.08120.04480.04480.08360.09390.1158
(0.8, 0.2)0.02870.02900.05790.03560.03560.06040.07060.0924
(0.85, 0.15)0.02680.02790.03210.02710.02710.03450.04390.0653
(0.9, 0.1)0.02770.02610.01320.01820.01820.01450.02030.0398
(0.95, 0.05)0.02140.01950.02090.01510.01510.0188NaNNaN
(1.0, 0.0)0.01020.00870.04540.01240.01240.0425NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00050.01410.13260.04850.0485
(0.05, 0.95)0.02460.02600.12180.04930.0493
(0.1, 0.9)0.03770.03740.11860.04690.0469
(0.15, 0.85)0.04210.03830.13280.06070.0607
(0.2, 0.8)0.03950.04130.12860.04920.0492
(0.25, 0.75)0.03980.03710.12170.04730.0473
(0.3, 0.7)0.04060.03660.12520.05370.0537
(0.35, 0.65)0.03830.03590.12400.05810.0581
(0.4, 0.6)0.03760.03740.12750.06170.0617
(0.45, 0.55)0.03660.03450.13590.06430.0643
(0.5, 0.5)0.03850.03980.12620.06310.0631
(0.55, 0.45)0.04790.04570.12780.07340.0734
(0.6, 0.4)0.04790.04460.13940.06970.0697
(0.65, 0.35)0.04400.05770.13240.08530.0853
(0.7, 0.3)0.04250.05160.13100.08100.0810
(0.75, 0.25)0.07140.07180.14100.09100.0910
(0.8, 0.2)0.07720.07800.15110.10240.1024
(0.85, 0.15)0.09750.10350.15390.12190.1219
(0.9, 0.1)0.14700.13680.16940.16880.1688
(0.95, 0.05)0.24200.20450.21750.21850.2185
(1.0, 0.0)0.12060.07750.78030.02450.0245
- -
target: C17
-
train: [0.94936928 0.05063072]
-
validation: [0.94937365 0.05062635]
-
evaluate_binary: 398.040s
-
evaluate_multiclass: 149.938s
-
kfcv: 165.629s
-
atc_mc: 170.907s
-
atc_ne: 103.307s
-
doc_feat: 77.863s
-
rca_score: 1203.842s
-
rca_star_score: 1177.692s
-
tot: 1248.888s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.11620.05880.65630.19360.19360.65690.67630.6893
(0.05, 0.95)0.14550.12970.62180.17860.17860.62250.64110.6542
(0.1, 0.9)0.18210.19140.59170.17590.17590.59260.61030.6234
(0.15, 0.85)0.21690.21280.55700.16180.16180.55800.57510.5883
(0.2, 0.8)0.22530.22170.51530.14200.14200.51630.53310.5463
(0.25, 0.75)0.20430.20980.47860.13160.13160.47980.49610.5094
(0.3, 0.7)0.18610.19270.45450.14110.14110.45580.47140.4848
(0.35, 0.65)0.17150.17390.41920.12630.12630.42060.43600.4494
(0.4, 0.6)0.15140.15340.38510.12440.12440.38660.40180.4152
(0.45, 0.55)0.15730.14690.34900.10970.10970.35060.36570.3790
(0.5, 0.5)0.13960.12350.31340.09700.09700.31510.33000.3434
(0.55, 0.45)0.11910.11770.27840.09020.09020.28020.29490.3083
(0.6, 0.4)0.09740.10360.24090.07340.07340.24280.25740.2708
(0.65, 0.35)0.08900.08620.21030.06700.06700.21230.22680.2402
(0.7, 0.3)0.07050.06640.16910.05590.05590.17120.18560.1990
(0.75, 0.25)0.06110.06430.13850.04920.04920.14070.15500.1684
(0.8, 0.2)0.05170.04900.10460.03930.03930.10690.12110.1345
(0.85, 0.15)0.04110.03930.07110.02990.02990.07350.08760.1010
(0.9, 0.1)0.03060.03190.03440.02390.02390.0369NaNNaN
(0.95, 0.05)0.02160.02300.00940.01400.01400.0093NaNNaN
(1.0, 0.0)0.00640.00570.03380.00570.00570.0310NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.16550.07470.25940.21680.2168
(0.05, 0.95)0.15700.12280.25890.23370.2337
(0.1, 0.9)0.15780.16280.26550.22690.2269
(0.15, 0.85)0.16930.16730.26510.22570.2257
(0.2, 0.8)0.18750.18660.25520.22570.2257
(0.25, 0.75)0.17520.18800.25200.23500.2350
(0.3, 0.7)0.16780.18520.26810.21750.2175
(0.35, 0.65)0.17250.18040.26860.21920.2192
(0.4, 0.6)0.17630.18130.26980.22220.2222
(0.45, 0.55)0.21900.18780.26670.21830.2183
(0.5, 0.5)0.21000.16820.26730.22790.2279
(0.55, 0.45)0.19420.18490.26710.22530.2253
(0.6, 0.4)0.17330.18250.25810.25260.2526
(0.65, 0.35)0.19500.17930.27070.24240.2424
(0.7, 0.3)0.19070.16850.24840.23530.2353
(0.75, 0.25)0.19230.17870.26400.24270.2427
(0.8, 0.2)0.19290.16780.27060.25050.2505
(0.85, 0.15)0.20100.18250.28190.25130.2513
(0.9, 0.1)0.22610.18530.27500.27270.2727
(0.95, 0.05)0.26600.22210.27640.34080.3408
(1.0, 0.0)0.06840.05350.72910.01000.0100
- -
target: C172
-
train: [0.98773112 0.01226888]
-
validation: [0.98764579 0.01235421]
-
evaluate_binary: 369.199s
-
evaluate_multiclass: 215.109s
-
kfcv: 103.301s
-
atc_mc: 107.211s
-
atc_ne: 167.924s
-
doc_feat: 135.162s
-
rca_score: 1176.166s
-
rca_star_score: 1152.207s
-
tot: 1217.302s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.30960.05550.68200.21760.21760.67960.68740.6923
(0.05, 0.95)0.34910.11310.64220.19930.19930.63990.64750.6525
(0.1, 0.9)0.37850.17240.61160.19550.19550.60940.61690.6218
(0.15, 0.85)0.40720.22550.58230.19570.19570.58020.58730.5922
(0.2, 0.8)0.44810.28910.54350.17710.17710.54150.54850.5533
(0.25, 0.75)0.47690.36740.50970.16870.16870.50780.51450.5193
(0.3, 0.7)0.51560.44510.47430.15830.15830.47250.47900.4838
(0.35, 0.65)0.54340.51190.44780.15680.15680.44610.45220.4569
(0.4, 0.6)0.57480.56990.40690.12930.12930.40530.41110.4158
(0.45, 0.55)0.60880.57300.37620.13170.13170.37470.38020.3848
(0.5, 0.5)0.61590.53630.33500.11060.11060.33360.33880.3435
(0.55, 0.45)0.57630.49390.30250.09920.09920.30120.30590.3105
(0.6, 0.4)0.52420.43660.26930.09250.09250.26810.27250.2771
(0.65, 0.35)0.45250.37350.23590.07870.07870.23480.23880.2434
(0.7, 0.3)0.38190.32050.19630.06860.06860.19530.19920.2038
(0.75, 0.25)0.31170.26520.15940.05130.05130.15850.16220.1668
(0.8, 0.2)0.23030.18840.13360.05410.05410.13280.13630.1409
(0.85, 0.15)0.16640.13910.09570.03600.03600.09500.09840.1030
(0.9, 0.1)0.09940.08830.05990.02740.02740.0593NaNNaN
(0.95, 0.05)0.04650.03510.03000.01910.01910.0295NaNNaN
(1.0, 0.0)0.00150.00160.00710.00020.00020.0075NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.46980.06930.31750.42010.4201
(0.05, 0.95)0.47600.09850.31090.42520.4252
(0.1, 0.9)0.46970.13200.31520.42750.4275
(0.15, 0.85)0.46130.15840.32250.40440.4044
(0.2, 0.8)0.47040.20060.31710.42150.4215
(0.25, 0.75)0.46130.26950.31740.42420.4242
(0.3, 0.7)0.46830.34180.31650.41340.4134
(0.35, 0.65)0.45640.39910.33080.41030.4103
(0.4, 0.6)0.45240.44820.31930.42360.4236
(0.45, 0.55)0.45350.44500.32800.41150.4115
(0.5, 0.5)0.46770.45410.31280.42990.4299
(0.55, 0.45)0.44400.45920.31810.41430.4143
(0.6, 0.4)0.45360.45540.32150.41220.4122
(0.65, 0.35)0.44280.44290.32520.40790.4079
(0.7, 0.3)0.46260.45180.31100.41560.4156
(0.75, 0.25)0.47030.45470.29650.43910.4391
(0.8, 0.2)0.38800.39810.34090.39750.3975
(0.85, 0.15)0.43000.40580.32270.42170.4217
(0.9, 0.1)0.42350.40520.31210.44730.4473
(0.95, 0.05)0.33250.28500.41580.35160.3516
(1.0, 0.0)0.01000.03910.78950.00000.0000
- -
target: C18
-
train: [0.9368412 0.0631588]
-
validation: [0.93684665 0.06315335]
-
evaluate_binary: 335.349s
-
evaluate_multiclass: 166.267s
-
kfcv: 103.911s
-
atc_mc: 97.746s
-
atc_ne: 91.184s
-
doc_feat: 75.425s
-
rca_score: 1144.163s
-
rca_star_score: 1141.640s
-
tot: 1192.245s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01210.05140.63020.12230.12230.63070.65710.6765
(0.05, 0.95)0.06190.09850.60040.12300.12300.60100.62720.6466
(0.1, 0.9)0.09800.11970.56450.11100.11100.56520.59130.6108
(0.15, 0.85)0.09960.12220.53100.10820.10820.53190.55780.5772
(0.2, 0.8)0.09740.11090.49960.09790.09790.50060.52640.5458
(0.25, 0.75)0.09260.11290.46420.09160.09160.46530.49100.5104
(0.3, 0.7)0.08540.10180.42940.08800.08800.43060.45620.4756
(0.35, 0.65)0.07260.09350.39300.08160.08160.39440.41980.4392
(0.4, 0.6)0.07250.08560.35720.07450.07450.35870.38400.4034
(0.45, 0.55)0.06580.08120.32000.06510.06510.32160.34680.3662
(0.5, 0.5)0.06150.07480.29790.06930.06930.29960.32470.3441
(0.55, 0.45)0.05470.06030.26080.06550.06550.26270.28760.3070
(0.6, 0.4)0.05350.05650.22770.05550.05550.22970.25450.2739
(0.65, 0.35)0.04820.04960.19300.04650.04650.19510.21980.2392
(0.7, 0.3)0.03720.05070.16130.03870.03870.16350.18810.2075
(0.75, 0.25)0.03010.03520.12510.03240.03240.12750.15190.1713
(0.8, 0.2)0.03150.03840.09270.02940.02940.09520.11950.1389
(0.85, 0.15)0.02890.03020.05600.02510.02510.05860.08280.1022
(0.9, 0.1)0.02110.02160.02760.02300.02300.0301NaNNaN
(0.95, 0.05)0.01910.01730.01180.01520.01520.0102NaNNaN
(1.0, 0.0)0.00900.00780.04420.00860.00860.0412NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.01560.06270.21750.22060.2206
(0.05, 0.95)0.03550.08160.22310.21280.2128
(0.1, 0.9)0.04950.08630.22040.20980.2098
(0.15, 0.85)0.05220.09710.22050.20880.2088
(0.2, 0.8)0.05320.08760.22530.21310.2131
(0.25, 0.75)0.05090.09740.22240.21580.2158
(0.3, 0.7)0.05830.10140.22150.21670.2167
(0.35, 0.65)0.05000.10340.21740.22320.2232
(0.4, 0.6)0.07130.10700.21350.22380.2238
(0.45, 0.55)0.06270.10420.20650.22600.2260
(0.5, 0.5)0.07370.12090.23140.21480.2148
(0.55, 0.45)0.06550.09040.22500.19860.1986
(0.6, 0.4)0.07740.10570.22630.21820.2182
(0.65, 0.35)0.08240.11430.22870.21770.2177
(0.7, 0.3)0.06450.14570.23550.23280.2328
(0.75, 0.25)0.06950.14150.22680.22990.2299
(0.8, 0.2)0.09320.16540.23810.24370.2437
(0.85, 0.15)0.13280.17780.22850.26420.2642
(0.9, 0.1)0.13400.16420.29490.22800.2280
(0.95, 0.05)0.20250.22790.29150.27690.2769
(1.0, 0.0)0.08470.10030.70470.02670.0267
- -
target: C181
-
train: [0.94798687 0.05201313]
-
validation: [0.94790497 0.05209503]
-
evaluate_binary: 381.404s
-
evaluate_multiclass: 222.049s
-
kfcv: 197.485s
-
atc_mc: 198.730s
-
atc_ne: 199.850s
-
doc_feat: 79.764s
-
rca_score: 1126.500s
-
rca_star_score: 1125.388s
-
tot: 1178.360s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03250.04830.70900.17600.17600.70810.73810.7500
(0.05, 0.95)0.08630.09690.65840.15310.15310.65760.68750.6994
(0.1, 0.9)0.14190.14470.63320.15470.15470.63250.66230.6742
(0.15, 0.85)0.18470.15380.59450.13960.13960.59390.62360.6355
(0.2, 0.8)0.16680.15070.55630.13810.13810.55590.58540.5973
(0.25, 0.75)0.14960.14050.51890.12570.12570.51860.54800.5599
(0.3, 0.7)0.12920.12500.48580.12380.12380.48560.51490.5268
(0.35, 0.65)0.11820.11090.43990.10680.10680.43980.46900.4809
(0.4, 0.6)0.12000.10350.40570.09830.09830.40570.43480.4467
(0.45, 0.55)0.10490.09550.36980.09340.09340.37000.39890.4108
(0.5, 0.5)0.09240.07960.33610.09130.09130.33640.36520.3771
(0.55, 0.45)0.08560.07090.30020.08380.08380.30060.32930.3412
(0.6, 0.4)0.08030.06830.25950.06980.06980.26000.28860.3005
(0.65, 0.35)0.07260.05940.22110.05580.05580.22170.25020.2621
(0.7, 0.3)0.05340.04720.18610.05450.05450.18690.21520.2271
(0.75, 0.25)0.04700.04410.14510.03880.03880.14600.17420.1861
(0.8, 0.2)0.04040.03630.11400.03660.03660.11500.14310.1550
(0.85, 0.15)0.03020.02780.07460.03330.03330.0757NaNNaN
(0.9, 0.1)0.02700.02380.03480.02040.02040.0360NaNNaN
(0.95, 0.05)0.02160.01990.00820.01470.01470.0077NaNNaN
(1.0, 0.0)0.00700.00550.03930.00880.00880.0378NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.04930.06600.27350.22240.2224
(0.05, 0.95)0.07140.08250.25600.24300.2430
(0.1, 0.9)0.10060.10520.27260.23380.2338
(0.15, 0.85)0.14110.10920.27040.23700.2370
(0.2, 0.8)0.12470.11460.26980.23880.2388
(0.25, 0.75)0.11290.11240.26930.22990.2299
(0.3, 0.7)0.09820.10640.27850.22680.2268
(0.35, 0.65)0.09810.09950.26110.21530.2153
(0.4, 0.6)0.13010.10440.26760.24130.2413
(0.45, 0.55)0.11700.10300.27000.22190.2219
(0.5, 0.5)0.12400.10380.28030.21240.2124
(0.55, 0.45)0.13780.10750.28490.21800.2180
(0.6, 0.4)0.15600.12920.27780.23650.2365
(0.65, 0.35)0.15830.11200.27260.24230.2423
(0.7, 0.3)0.12670.10950.28500.23350.2335
(0.75, 0.25)0.12680.12890.26910.23490.2349
(0.8, 0.2)0.16660.15410.30750.22360.2236
(0.85, 0.15)0.14350.13990.31070.24850.2485
(0.9, 0.1)0.20170.16810.29140.25820.2582
(0.95, 0.05)0.20700.19510.35030.22980.2298
(1.0, 0.0)0.03640.07940.67130.01000.0100
- -
target: C21
-
train: [0.96578538 0.03421462]
-
validation: [0.96570194 0.03429806]
-
evaluate_binary: 311.430s
-
evaluate_multiclass: 127.753s
-
kfcv: 169.599s
-
atc_mc: 171.895s
-
atc_ne: 99.774s
-
doc_feat: 132.229s
-
rca_score: 928.435s
-
rca_star_score: 903.426s
-
tot: 975.778s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04240.04920.90720.49140.49140.90700.93790.9402
(0.05, 0.95)0.08900.09480.86380.47790.47790.86370.89450.8968
(0.1, 0.9)0.12220.13880.81940.46040.46040.81930.85010.8524
(0.15, 0.85)0.18170.17390.77080.42300.42300.77080.80150.8038
(0.2, 0.8)0.21630.18880.72030.40590.40590.7204NaNNaN
(0.25, 0.75)0.23010.17840.67520.36960.36960.6753NaNNaN
(0.3, 0.7)0.22840.17060.62780.34530.34530.62800.65850.6608
(0.35, 0.65)0.19280.14270.57950.32320.32320.5797NaNNaN
(0.4, 0.6)0.16190.11680.53460.30000.30000.5349NaNNaN
(0.45, 0.55)0.16250.11880.48390.26700.26700.4843NaNNaN
(0.5, 0.5)0.14080.10150.43800.23430.23430.4384NaNNaN
(0.55, 0.45)0.10770.07410.39370.21920.21920.3942NaNNaN
(0.6, 0.4)0.09490.07180.34570.19310.19310.3463NaNNaN
(0.65, 0.35)0.06640.04920.29800.16980.16980.2986NaNNaN
(0.7, 0.3)0.06170.04630.25090.13870.13870.2516NaNNaN
(0.75, 0.25)0.05290.04460.20230.11690.11690.2031NaNNaN
(0.8, 0.2)0.04400.04030.15640.09240.09240.1572NaNNaN
(0.85, 0.15)0.04140.03930.10700.06260.06260.1079NaNNaN
(0.9, 0.1)0.03140.03420.06200.03940.03940.0630NaNNaN
(0.95, 0.05)0.02710.02510.01490.01850.01850.0159NaNNaN
(1.0, 0.0)0.00660.00540.03270.01420.01420.0316NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.07900.09150.42900.11090.1109
(0.05, 0.95)0.07840.08910.43550.10280.1028
(0.1, 0.9)0.05220.08840.44100.09920.0992
(0.15, 0.85)0.07850.09320.43870.10200.1020
(0.2, 0.8)0.07670.10020.43170.10780.1078
(0.25, 0.75)0.07420.09690.43590.10360.1036
(0.3, 0.7)0.07630.09860.43550.10400.1040
(0.35, 0.65)0.07310.09660.43300.10770.1077
(0.4, 0.6)0.06320.08720.43980.09910.0991
(0.45, 0.55)0.08100.09600.42860.11060.1106
(0.5, 0.5)0.07650.09200.43260.10570.1057
(0.55, 0.45)0.05880.07760.44360.09630.0963
(0.6, 0.4)0.06450.08370.44090.10060.1006
(0.65, 0.35)0.06700.08350.43900.10170.1017
(0.7, 0.3)0.06780.07960.44170.09680.0968
(0.75, 0.25)0.07440.08670.43310.10630.1063
(0.8, 0.2)0.07640.07470.44390.09680.0968
(0.85, 0.15)0.08060.09030.41930.12140.1214
(0.9, 0.1)0.06880.07930.45150.08920.0892
(0.95, 0.05)0.05530.07140.46210.08460.0846
(1.0, 0.0)0.00970.01030.54070.00000.0000
- -
target: C24
-
train: [0.96016935 0.03983065]
-
validation: [0.96017279 0.03982721]
-
evaluate_binary: 341.390s
-
evaluate_multiclass: 134.762s
-
kfcv: 115.097s
-
atc_mc: 166.466s
-
atc_ne: 156.922s
-
doc_feat: 127.605s
-
rca_score: 1002.111s
-
rca_star_score: 1016.135s
-
tot: 1070.690s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04650.04830.87910.42000.42000.87820.91180.9157
(0.05, 0.95)0.09270.09620.83130.39480.39480.83050.86400.8679
(0.1, 0.9)0.14340.14530.77980.37440.37440.77910.81250.8164
(0.15, 0.85)0.18940.17660.73120.34530.34530.73050.76390.7678
(0.2, 0.8)0.20800.17840.69550.33080.33080.69490.72820.7321
(0.25, 0.75)0.20160.16200.64090.30530.30530.64040.67360.6775
(0.3, 0.7)0.19010.15260.60050.28580.28580.60010.63320.6371
(0.35, 0.65)0.17570.13940.55410.25520.25520.55370.58680.5907
(0.4, 0.6)0.14900.11300.51020.24780.24780.51000.54290.5468
(0.45, 0.55)0.14170.11450.46030.21390.21390.46010.49300.4969
(0.5, 0.5)0.10980.08130.41820.19790.19790.4181NaNNaN
(0.55, 0.45)0.09160.07370.37200.17770.17770.3720NaNNaN
(0.6, 0.4)0.08070.06330.32660.15160.15160.32670.35930.3632
(0.65, 0.35)0.06070.04990.28210.13720.13720.28230.31480.3187
(0.7, 0.3)0.04950.04180.23650.11220.11220.2367NaNNaN
(0.75, 0.25)0.04500.03940.19130.09200.09200.1916NaNNaN
(0.8, 0.2)0.02970.03200.14400.07210.07210.1444NaNNaN
(0.85, 0.15)0.02990.03310.09970.05050.05050.1002NaNNaN
(0.9, 0.1)0.02750.02940.05320.03420.03420.0538NaNNaN
(0.95, 0.05)0.02250.02270.00980.01600.01600.0104NaNNaN
(1.0, 0.0)0.00570.00450.03620.01120.01120.0355NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.08440.08680.42050.14510.1451
(0.05, 0.95)0.08590.09090.41670.15100.1510
(0.1, 0.9)0.09460.09980.40620.15930.1593
(0.15, 0.85)0.10190.11120.40000.16620.1662
(0.2, 0.8)0.08590.10900.41970.14840.1484
(0.25, 0.75)0.10530.11270.39960.16290.1629
(0.3, 0.7)0.09300.10630.41180.15490.1549
(0.35, 0.65)0.09880.10690.40930.15540.1554
(0.4, 0.6)0.09910.09890.41320.14840.1484
(0.45, 0.55)0.11270.11600.40000.16870.1687
(0.5, 0.5)0.09400.09460.41130.15280.1528
(0.55, 0.45)0.08030.09670.40750.16140.1614
(0.6, 0.4)0.10850.10720.40710.16030.1603
(0.65, 0.35)0.08470.10350.41130.15520.1552
(0.7, 0.3)0.08590.08890.40980.15750.1575
(0.75, 0.25)0.09270.10030.41530.15350.1535
(0.8, 0.2)0.09530.10510.40060.16060.1606
(0.85, 0.15)0.09570.10260.41180.15630.1563
(0.9, 0.1)0.12920.14860.39440.17560.1756
(0.95, 0.05)0.13110.15740.41790.15440.1544
(1.0, 0.0)0.00010.03200.57480.00000.0000
- -
target: C31
-
train: [0.95429411 0.04570589]
-
validation: [0.95429806 0.04570194]
-
evaluate_binary: 242.965s
-
evaluate_multiclass: 161.716s
-
kfcv: 142.016s
-
atc_mc: 101.146s
-
atc_ne: 92.376s
-
doc_feat: 76.202s
-
rca_score: 834.193s
-
rca_star_score: 832.758s
-
tot: 884.843s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03650.03780.91670.52800.52800.91600.95520.9591
(0.05, 0.95)0.08910.08910.86740.50520.50520.8668NaNNaN
(0.1, 0.9)0.13200.13220.82300.47350.47350.8225NaNNaN
(0.15, 0.85)0.17620.17130.77750.45060.45060.7770NaNNaN
(0.2, 0.8)0.22290.19750.72840.41660.41660.7280NaNNaN
(0.25, 0.75)0.24900.18890.68050.40110.40110.6802NaNNaN
(0.3, 0.7)0.25410.18790.62790.35060.35060.6276NaNNaN
(0.35, 0.65)0.23240.16450.58110.33470.33470.5809NaNNaN
(0.4, 0.6)0.21330.15330.53140.30600.30600.5313NaNNaN
(0.45, 0.55)0.18870.13170.48670.28590.28590.4866NaNNaN
(0.5, 0.5)0.16420.11480.43660.25920.25920.4366NaNNaN
(0.55, 0.45)0.14680.10100.39160.22930.22930.3917NaNNaN
(0.6, 0.4)0.11870.08320.34240.20010.20010.3425NaNNaN
(0.65, 0.35)0.09520.06440.29520.17610.17610.2954NaNNaN
(0.7, 0.3)0.07790.05640.24510.14520.14520.2454NaNNaN
(0.75, 0.25)0.06460.05030.19810.11770.11770.1984NaNNaN
(0.8, 0.2)0.05270.04530.15130.09430.09430.1517NaNNaN
(0.85, 0.15)0.04250.03720.10210.06250.06250.1026NaNNaN
(0.9, 0.1)0.03830.03680.05470.03580.03580.0552NaNNaN
(0.95, 0.05)0.03190.02920.00680.01620.01620.0072NaNNaN
(1.0, 0.0)0.00580.00450.04200.01680.01680.0413NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.06960.07190.48540.07480.0748
(0.05, 0.95)0.07840.07850.48300.07810.0781
(0.1, 0.9)0.06860.07070.49020.06840.0684
(0.15, 0.85)0.06090.06480.49580.06430.0643
(0.2, 0.8)0.06430.06840.49380.06700.0670
(0.25, 0.75)0.06450.06720.49430.06640.0664
(0.3, 0.7)0.07150.07700.48260.07820.0782
(0.35, 0.65)0.07220.07500.48560.07500.0750
(0.4, 0.6)0.07150.07750.48070.07850.0785
(0.45, 0.55)0.06150.06850.49110.07040.0704
(0.5, 0.5)0.07220.07580.48370.07870.0787
(0.55, 0.45)0.06180.06360.49540.06730.0673
(0.6, 0.4)0.06340.06630.49100.07000.0700
(0.65, 0.35)0.05540.06180.49550.06490.0649
(0.7, 0.3)0.06790.06930.48520.07770.0777
(0.75, 0.25)0.05630.05810.49150.07130.0713
(0.8, 0.2)0.05100.05280.50340.06010.0601
(0.85, 0.15)0.06010.06190.49650.06510.0651
(0.9, 0.1)0.05150.05780.50600.05750.0575
(0.95, 0.05)0.07690.09070.49110.07240.0724
(1.0, 0.0)0.00000.00990.56350.00000.0000
- -
target: C42
-
train: [0.98522551 0.01477449]
-
validation: [0.98514039 0.01485961]
-
evaluate_binary: 216.862s
-
evaluate_multiclass: 148.154s
-
kfcv: 142.768s
-
atc_mc: 101.557s
-
atc_ne: 93.067s
-
doc_feat: 77.781s
-
rca_score: 983.309s
-
rca_star_score: 974.280s
-
tot: 1025.959s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00000.07880.90710.33900.33900.90660.91880.9209
(0.05, 0.95)0.05000.12240.86350.32050.32050.86300.87520.8773
(0.1, 0.9)0.10000.16400.82200.30770.30770.82160.83370.8358
(0.15, 0.85)0.14990.20910.77680.28700.28700.77650.78850.7906
(0.2, 0.8)0.19990.26010.72540.26510.26510.72520.73710.7392
(0.25, 0.75)0.24980.30460.67700.25350.25350.6768NaNNaN
(0.3, 0.7)0.29950.33000.63120.23180.23180.6311NaNNaN
(0.35, 0.65)0.34650.30910.58880.22120.22120.5888NaNNaN
(0.4, 0.6)0.36360.28810.54120.19740.19740.54130.55290.5550
(0.45, 0.55)0.34510.26370.49450.18180.18180.4947NaNNaN
(0.5, 0.5)0.32020.24890.44540.15990.15990.4456NaNNaN
(0.55, 0.45)0.26950.20910.39930.14810.14810.3996NaNNaN
(0.6, 0.4)0.22110.16590.35670.14120.14120.3571NaNNaN
(0.65, 0.35)0.19280.14340.31100.11810.11810.3115NaNNaN
(0.7, 0.3)0.16330.12430.26330.09740.09740.2639NaNNaN
(0.75, 0.25)0.12580.09340.21810.08190.08190.2187NaNNaN
(0.8, 0.2)0.08700.07200.16880.06210.06210.1695NaNNaN
(0.85, 0.15)0.05510.04550.12400.04570.04570.1248NaNNaN
(0.9, 0.1)0.03340.03240.07790.03120.03120.0788NaNNaN
(0.95, 0.05)0.01920.01710.03220.01560.01560.0332NaNNaN
(1.0, 0.0)0.00050.00040.01370.00200.00200.0127NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00010.14510.42510.14460.1446
(0.05, 0.95)0.00660.14050.42960.14030.1403
(0.1, 0.9)0.01230.13150.43880.13150.1315
(0.15, 0.85)0.01820.12930.44120.12820.1282
(0.2, 0.8)0.02620.13990.43070.13870.1387
(0.25, 0.75)0.03380.14440.42620.14360.1436
(0.3, 0.7)0.04070.14410.42660.14360.1436
(0.35, 0.65)0.04440.13450.43620.13450.1345
(0.4, 0.6)0.04910.13780.43290.13610.1361
(0.45, 0.55)0.04990.13900.43160.13910.1391
(0.5, 0.5)0.05300.14760.42300.14670.1467
(0.55, 0.45)0.05210.14930.42120.14950.1495
(0.6, 0.4)0.04330.13440.43610.13280.1328
(0.65, 0.35)0.04130.13100.43940.13130.1313
(0.7, 0.3)0.04460.13800.43230.13830.1383
(0.75, 0.25)0.03850.12990.44030.12900.1290
(0.8, 0.2)0.03950.15420.41570.15500.1550
(0.85, 0.15)0.03060.14160.42770.14300.1430
(0.9, 0.1)0.03380.13270.42910.14160.1416
(0.95, 0.05)0.04990.10010.44730.12700.1270
(1.0, 0.0)0.01000.01040.57070.00000.0000
- -
target: E12
-
train: [0.97071021 0.02928979]
-
validation: [0.97062635 0.02937365]
-
evaluate_binary: 270.181s
-
evaluate_multiclass: 123.959s
-
kfcv: 166.426s
-
atc_mc: 163.725s
-
atc_ne: 159.501s
-
doc_feat: 79.566s
-
rca_score: 940.772s
-
rca_star_score: 955.992s
-
tot: 1005.211s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00020.05830.90970.47630.47630.90800.93210.9356
(0.05, 0.95)0.05010.10580.86090.45490.45490.85930.88330.8868
(0.1, 0.9)0.10040.15200.81800.43500.43500.81650.84040.8439
(0.15, 0.85)0.15090.20070.77000.41230.41230.76860.79240.7959
(0.2, 0.8)0.20030.23340.72580.38270.38270.7245NaNNaN
(0.25, 0.75)0.24340.24560.67630.35560.35560.6751NaNNaN
(0.3, 0.7)0.26320.23850.62910.32710.32710.6279NaNNaN
(0.35, 0.65)0.25500.22490.58240.30690.30690.58130.60480.6083
(0.4, 0.6)0.22760.19950.53700.28700.28700.53600.55940.5629
(0.45, 0.55)0.21390.18660.48880.25990.25990.4879NaNNaN
(0.5, 0.5)0.18690.16150.44290.23900.23900.4421NaNNaN
(0.55, 0.45)0.16240.14340.39490.21130.21130.3942NaNNaN
(0.6, 0.4)0.14170.13090.34640.18890.18890.3458NaNNaN
(0.65, 0.35)0.12550.10950.30270.15680.15680.3021NaNNaN
(0.7, 0.3)0.10860.09320.25570.13460.13460.2552NaNNaN
(0.75, 0.25)0.07740.07190.20810.11220.11220.2077NaNNaN
(0.8, 0.2)0.06360.05590.16250.08670.08670.1622NaNNaN
(0.85, 0.15)0.05220.04490.11690.06600.06600.1167NaNNaN
(0.9, 0.1)0.03600.03250.06840.04140.04140.0683NaNNaN
(0.95, 0.05)0.02230.02000.02120.01720.01720.0212NaNNaN
(1.0, 0.0)0.00500.00360.02580.00820.00820.0258NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00030.10830.48800.11630.1163
(0.05, 0.95)0.00610.10930.48450.12130.1213
(0.1, 0.9)0.01190.10850.49170.11410.1141
(0.15, 0.85)0.01880.11530.48920.11740.1174
(0.2, 0.8)0.02280.11020.49540.11050.1105
(0.25, 0.75)0.02890.11660.48900.11730.1173
(0.3, 0.7)0.03170.11650.48830.11730.1173
(0.35, 0.65)0.03170.11680.48860.11700.1170
(0.4, 0.6)0.02960.11240.49330.11410.1141
(0.45, 0.55)0.03140.11090.48900.11560.1156
(0.5, 0.5)0.03180.10750.49130.11370.1137
(0.55, 0.45)0.03210.11390.48780.11850.1185
(0.6, 0.4)0.03160.12050.47940.12550.1255
(0.65, 0.35)0.03050.10650.49220.11250.1125
(0.7, 0.3)0.03140.10050.49360.11450.1145
(0.75, 0.25)0.02870.11060.48850.11710.1171
(0.8, 0.2)0.02930.09390.50130.10640.1064
(0.85, 0.15)0.03250.07580.51660.09150.0915
(0.9, 0.1)0.04840.09140.50420.10390.1039
(0.95, 0.05)0.07170.08070.50520.10290.1029
(1.0, 0.0)0.00000.01700.60810.00000.0000
- -
target: E21
-
train: [0.94582685 0.05417315]
-
validation: [0.94574514 0.05425486]
-
evaluate_binary: 339.478s
-
evaluate_multiclass: 127.963s
-
kfcv: 161.870s
-
atc_mc: 171.427s
-
atc_ne: 103.308s
-
doc_feat: 77.827s
-
rca_score: 1211.965s
-
rca_star_score: 1184.838s
-
tot: 1259.208s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00390.03470.55570.19410.19410.55590.57400.5870
(0.05, 0.95)0.05260.08220.53590.19910.19910.53620.55240.5655
(0.1, 0.9)0.10470.12720.50950.17770.17770.50990.52420.5373
(0.15, 0.85)0.12210.13370.47420.16590.16590.47470.48770.5010
(0.2, 0.8)0.11110.11670.43900.15380.15380.43960.45180.4651
(0.25, 0.75)0.10220.11360.41220.14750.14750.41290.42320.4365
(0.3, 0.7)0.08860.10040.38320.13980.13980.38400.39330.4066
(0.35, 0.65)0.08500.09470.35710.13200.13200.35800.36530.3787
(0.4, 0.6)0.06660.08000.31900.10400.10400.32000.32650.3399
(0.45, 0.55)0.05600.06740.29600.11300.11300.29720.30200.3155
(0.5, 0.5)0.05180.05760.26340.10040.10040.26470.26880.2824
(0.55, 0.45)0.04660.05070.23000.08000.08000.23130.23500.2486
(0.6, 0.4)0.03720.03950.20290.07870.07870.20440.20760.2212
(0.65, 0.35)0.03650.03890.17700.06830.06830.17860.18160.1953
(0.7, 0.3)0.02960.02850.14690.05990.05990.14860.15150.1651
(0.75, 0.25)0.03150.03000.11660.04980.04980.11840.12120.1348
(0.8, 0.2)0.02940.03070.08880.04150.04150.09070.09340.1070
(0.85, 0.15)0.02810.03090.05760.02810.02810.05960.06220.0758
(0.9, 0.1)0.02160.02410.02940.02370.02370.03140.03390.0474
(0.95, 0.05)0.01560.01600.00930.01410.01410.0099NaNNaN
(1.0, 0.0)0.00180.00140.03010.00350.00350.0278NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00420.03640.21930.06010.0601
(0.05, 0.95)0.02370.05630.22900.05590.0559
(0.1, 0.9)0.05060.08170.23260.07040.0704
(0.15, 0.85)0.05630.08680.22720.06080.0608
(0.2, 0.8)0.05260.07770.22050.06490.0649
(0.25, 0.75)0.05380.08400.22310.06470.0647
(0.3, 0.7)0.05050.08060.22490.06840.0684
(0.35, 0.65)0.05100.08030.22960.06310.0631
(0.4, 0.6)0.04730.07900.21620.08180.0818
(0.45, 0.55)0.04090.07200.22750.08050.0805
(0.5, 0.5)0.04660.06720.22180.08440.0844
(0.55, 0.45)0.05200.06660.21220.08660.0866
(0.6, 0.4)0.04600.06090.21770.08300.0830
(0.65, 0.35)0.05220.06760.22780.08320.0832
(0.7, 0.3)0.06330.05980.22610.09290.0929
(0.75, 0.25)0.09740.07510.22340.11470.1147
(0.8, 0.2)0.08930.08900.23320.11920.1192
(0.85, 0.15)0.12720.12960.22990.13800.1380
(0.9, 0.1)0.15350.16090.24110.17760.1776
(0.95, 0.05)0.20680.20450.29480.19930.1993
(1.0, 0.0)0.00460.00230.80260.00500.0050
- -
target: E211
-
train: [0.98246069 0.01753931]
-
validation: [0.98237581 0.01762419]
-
evaluate_binary: 320.478s
-
evaluate_multiclass: 123.957s
-
kfcv: 112.571s
-
atc_mc: 170.547s
-
atc_ne: 167.429s
-
doc_feat: 129.581s
-
rca_score: 1011.800s
-
rca_star_score: 1025.452s
-
tot: 1076.046s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.09450.09670.88610.41950.41950.88590.89830.9018
(0.05, 0.95)0.13650.13780.84450.39360.39360.84440.85670.8602
(0.1, 0.9)0.17520.17950.80390.38400.38400.80390.81610.8196
(0.15, 0.85)0.22350.22610.75780.35590.35590.75790.77000.7735
(0.2, 0.8)0.26900.27030.71150.34760.34760.71170.72370.7272
(0.25, 0.75)0.31460.30560.66350.31790.31790.66380.67570.6792
(0.3, 0.7)0.35630.30800.62150.29170.29170.62180.63370.6372
(0.35, 0.65)0.37970.29420.57620.28060.28060.5766NaNNaN
(0.4, 0.6)0.36960.27830.52930.25230.25230.52980.54150.5450
(0.45, 0.55)0.33430.25330.48300.23140.23140.48360.49520.4987
(0.5, 0.5)0.30640.23500.43560.19830.19830.4363NaNNaN
(0.55, 0.45)0.27490.21030.39230.18440.18440.3931NaNNaN
(0.6, 0.4)0.23710.18320.34640.16940.16940.3473NaNNaN
(0.65, 0.35)0.20100.15570.30290.14550.14550.3039NaNNaN
(0.7, 0.3)0.16310.12700.25670.12920.12920.2578NaNNaN
(0.75, 0.25)0.13390.10610.20980.09780.09780.2109NaNNaN
(0.8, 0.2)0.09040.07150.16580.08220.08220.1670NaNNaN
(0.85, 0.15)0.05820.04820.11930.06050.06050.1206NaNNaN
(0.9, 0.1)0.03300.02980.07440.03880.03880.0758NaNNaN
(0.95, 0.05)0.02300.02340.02920.02110.02110.0307NaNNaN
(1.0, 0.0)0.00140.00110.01560.00230.00230.0140NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.17090.17480.42430.16500.1650
(0.05, 0.95)0.16550.16760.43040.15480.1548
(0.1, 0.9)0.15240.16030.43990.15250.1525
(0.15, 0.85)0.15850.16340.43810.15230.1523
(0.2, 0.8)0.15840.16380.43670.15100.1510
(0.25, 0.75)0.15990.16930.43140.15380.1538
(0.3, 0.7)0.15650.16270.43890.15160.1516
(0.35, 0.65)0.15480.15970.44000.14800.1480
(0.4, 0.6)0.15940.16510.43620.15220.1522
(0.45, 0.55)0.15790.16740.43290.15430.1543
(0.5, 0.5)0.16690.17390.42690.16300.1630
(0.55, 0.45)0.15690.16410.43460.15170.1517
(0.6, 0.4)0.16020.16750.43190.15570.1557
(0.65, 0.35)0.14870.15890.44020.14810.1481
(0.7, 0.3)0.14900.16090.43660.16120.1612
(0.75, 0.25)0.15580.16870.42810.15660.1566
(0.8, 0.2)0.14980.15510.43950.15660.1566
(0.85, 0.15)0.14840.15010.42750.15970.1597
(0.9, 0.1)0.14910.14640.43360.16530.1653
(0.95, 0.05)0.15000.14350.44080.16350.1635
(1.0, 0.0)0.00990.01000.60200.00000.0000
- -
target: E212
-
train: [0.96319336 0.03680664]
-
validation: [0.96311015 0.03688985]
-
evaluate_binary: 233.509s
-
evaluate_multiclass: 178.371s
-
kfcv: 145.769s
-
atc_mc: 124.033s
-
atc_ne: 91.719s
-
doc_feat: 78.459s
-
rca_score: 1204.775s
-
rca_star_score: 1188.268s
-
tot: 1247.499s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00100.03410.48600.14110.14110.48600.49930.5061
(0.05, 0.95)0.05250.09230.45390.12090.12090.45400.46800.4747
(0.1, 0.9)0.10100.14330.43790.12630.12630.43810.45040.4573
(0.15, 0.85)0.14820.18610.40970.11140.11140.41000.42150.4286
(0.2, 0.8)0.17060.17690.38100.10990.10990.38140.39170.3989
(0.25, 0.75)0.17060.17180.36160.10690.10690.36210.37070.3780
(0.3, 0.7)0.15070.15510.33620.09370.09370.33680.34380.3511
(0.35, 0.65)0.13850.14270.31550.09860.09860.31620.32120.3285
(0.4, 0.6)0.12390.13500.28410.08300.08300.28490.28840.2956
(0.45, 0.55)0.11330.11650.26190.07540.07540.26280.26420.2711
(0.5, 0.5)0.08500.09390.23060.06950.06950.23160.23130.2380
(0.55, 0.45)0.07510.08190.20470.05880.05880.20580.20370.2102
(0.6, 0.4)0.07550.07520.18730.05950.05950.18850.18460.1910
(0.65, 0.35)0.06480.06810.16030.04910.04910.16160.15710.1634
(0.7, 0.3)0.04890.04800.13610.04930.04930.13750.13240.1387
(0.75, 0.25)0.03710.03930.10710.04180.04180.10860.10330.1096
(0.8, 0.2)0.03130.03470.08240.03380.03380.08400.07860.0849
(0.85, 0.15)0.02650.02760.05790.02550.02550.05960.05410.0604
(0.9, 0.1)0.02520.02380.03390.01940.01940.03560.03020.0363
(0.95, 0.05)0.01660.01540.01210.01280.01280.0133NaNNaN
(1.0, 0.0)0.00380.00300.01710.00200.00200.0151NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00100.03190.18210.05460.0546
(0.05, 0.95)0.02510.06300.17530.07290.0729
(0.1, 0.9)0.04670.09030.18470.06120.0612
(0.15, 0.85)0.06920.11760.18140.07140.0714
(0.2, 0.8)0.08290.11470.17750.06470.0647
(0.25, 0.75)0.08750.11880.18380.06050.0605
(0.3, 0.7)0.08390.11670.18420.06530.0653
(0.35, 0.65)0.08130.11520.19070.06450.0645
(0.4, 0.6)0.08060.12030.18230.07360.0736
(0.45, 0.55)0.08190.11260.18730.07960.0796
(0.5, 0.5)0.06540.10170.17690.07590.0759
(0.55, 0.45)0.06640.09950.17450.08970.0897
(0.6, 0.4)0.08140.10070.19060.07970.0797
(0.65, 0.35)0.07550.10350.18970.09000.0900
(0.7, 0.3)0.07010.08640.19310.09190.0919
(0.75, 0.25)0.07600.09570.18300.10430.1043
(0.8, 0.2)0.08160.09890.18330.11870.1187
(0.85, 0.15)0.09740.10870.19210.13850.1385
(0.9, 0.1)0.12920.13080.20950.17050.1705
(0.95, 0.05)0.23270.18030.24520.22420.2242
(1.0, 0.0)0.00000.00970.84410.00000.0000
- -
target: E41
-
train: [0.98064628 0.01935372]
-
validation: [0.98056156 0.01943844]
-
evaluate_binary: 279.480s
-
evaluate_multiclass: 130.254s
-
kfcv: 167.427s
-
atc_mc: 141.283s
-
atc_ne: 169.435s
-
doc_feat: 82.077s
-
rca_score: 1007.269s
-
rca_star_score: 1022.263s
-
tot: 1072.119s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00000.09530.88220.27380.27380.88060.89620.8990
(0.05, 0.95)0.05000.14180.83890.27000.27000.83740.85290.8557
(0.1, 0.9)0.09980.18470.79610.25810.25810.79470.81010.8129
(0.15, 0.85)0.14990.23250.74910.23370.23370.74770.76310.7659
(0.2, 0.8)0.19960.28150.70030.22100.22100.69900.71430.7171
(0.25, 0.75)0.24960.31120.66170.22390.22390.66050.67570.6785
(0.3, 0.7)0.29590.31540.61170.19560.19560.6106NaNNaN
(0.35, 0.65)0.31830.29410.56730.18130.18130.56630.58130.5841
(0.4, 0.6)0.30310.27360.52380.16620.16620.52290.53780.5406
(0.45, 0.55)0.27300.24640.47750.15560.15560.47670.49150.4943
(0.5, 0.5)0.24400.21640.43540.14220.14220.43470.44940.4522
(0.55, 0.45)0.21570.19310.38970.12870.12870.3891NaNNaN
(0.6, 0.4)0.18180.16700.34190.10600.10600.3413NaNNaN
(0.65, 0.35)0.15090.13820.29920.09760.09760.2987NaNNaN
(0.7, 0.3)0.12200.11290.25320.08620.08620.2528NaNNaN
(0.75, 0.25)0.08680.08080.20720.07030.07030.2069NaNNaN
(0.8, 0.2)0.06320.05770.16250.05380.05380.1623NaNNaN
(0.85, 0.15)0.04230.03850.11890.03930.03930.1188NaNNaN
(0.9, 0.1)0.02570.02690.07300.02840.02840.0730NaNNaN
(0.95, 0.05)0.01900.01940.02790.01470.01470.0280NaNNaN
(1.0, 0.0)0.00060.00050.01660.00200.00200.0164NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00010.17100.43930.18010.1801
(0.05, 0.95)0.00820.17420.44210.17620.1762
(0.1, 0.9)0.01600.17030.44630.17230.1723
(0.15, 0.85)0.02460.17550.44290.17770.1777
(0.2, 0.8)0.03410.18570.43470.18460.1846
(0.25, 0.75)0.04010.17150.44870.17050.1705
(0.3, 0.7)0.05000.18120.43820.18050.1805
(0.35, 0.65)0.05500.17880.43950.17920.1792
(0.4, 0.6)0.05500.17810.44280.17570.1757
(0.45, 0.55)0.05440.17920.43990.17680.1768
(0.5, 0.5)0.05180.16890.44940.16930.1693
(0.55, 0.45)0.05140.17200.44740.16890.1689
(0.6, 0.4)0.05190.17870.43620.18160.1816
(0.65, 0.35)0.04420.16950.44780.17160.1716
(0.7, 0.3)0.04370.16850.44290.17620.1762
(0.75, 0.25)0.04030.16650.43830.17980.1798
(0.8, 0.2)0.03800.15230.43930.17800.1780
(0.85, 0.15)0.03150.14000.45320.16520.1652
(0.9, 0.1)0.02960.12250.45140.16710.1671
(0.95, 0.05)0.08540.16860.45230.16900.1690
(1.0, 0.0)0.02000.01980.62130.00000.0000
- -
target: E51
-
train: [0.97235182 0.02764818]
-
validation: [0.97226782 0.02773218]
-
evaluate_binary: 347.216s
-
evaluate_multiclass: 198.698s
-
kfcv: 107.965s
-
atc_mc: 165.906s
-
atc_ne: 106.609s
-
doc_feat: 77.541s
-
rca_score: 937.639s
-
rca_star_score: 952.963s
-
tot: 1000.226s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03110.05850.90860.47200.47200.90880.93150.9347
(0.05, 0.95)0.07360.09810.87030.45100.45100.8705NaNNaN
(0.1, 0.9)0.12260.15130.81810.43030.43030.81840.84100.8442
(0.15, 0.85)0.16480.19310.77070.40840.40840.77110.79360.7968
(0.2, 0.8)0.20770.22170.72080.38030.38030.72120.74370.7469
(0.25, 0.75)0.22840.24060.67820.34940.34940.67870.70110.7043
(0.3, 0.7)0.20610.23090.63100.33490.33490.6316NaNNaN
(0.35, 0.65)0.19080.23930.58400.30430.30430.5847NaNNaN
(0.4, 0.6)0.17260.23720.53650.27740.27740.5372NaNNaN
(0.45, 0.55)0.14620.19500.48830.25260.25260.4891NaNNaN
(0.5, 0.5)0.12170.19620.44660.24610.24610.4475NaNNaN
(0.55, 0.45)0.09940.15050.39640.21240.21240.3974NaNNaN
(0.6, 0.4)0.08180.15110.34920.18880.18880.3502NaNNaN
(0.65, 0.35)0.06640.14880.30160.16440.16440.3027NaNNaN
(0.7, 0.3)0.05090.08160.25590.13760.13760.2571NaNNaN
(0.75, 0.25)0.03440.06400.20640.11370.11370.2076NaNNaN
(0.8, 0.2)0.03170.04780.16190.09070.09070.1632NaNNaN
(0.85, 0.15)0.03090.03420.11460.06660.06660.1160NaNNaN
(0.9, 0.1)0.02890.02890.06790.04140.04140.0694NaNNaN
(0.95, 0.05)0.02400.02320.02020.02020.02020.0217NaNNaN
(1.0, 0.0)0.00260.00260.02570.00680.00680.0241NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.05760.10850.42970.11530.1153
(0.05, 0.95)0.04930.09550.44550.10120.1012
(0.1, 0.9)0.05330.10800.43560.11070.1107
(0.15, 0.85)0.04520.10840.43470.10710.1071
(0.2, 0.8)0.05210.11610.42810.11580.1158
(0.25, 0.75)0.04970.10670.43740.10540.1054
(0.3, 0.7)0.05750.10760.43690.10740.1074
(0.35, 0.65)0.05780.10650.43700.10870.1087
(0.4, 0.6)0.05180.10350.43520.10900.1090
(0.45, 0.55)0.04680.10440.43190.11030.1103
(0.5, 0.5)0.04090.09050.44930.09520.0952
(0.55, 0.45)0.04600.09400.43780.10880.1088
(0.6, 0.4)0.04860.08950.43680.10490.1049
(0.65, 0.35)0.04180.09770.43400.11290.1129
(0.7, 0.3)0.04260.08490.44100.10480.1048
(0.75, 0.25)0.03940.09150.42440.12050.1205
(0.8, 0.2)0.04650.07850.44220.10930.1093
(0.85, 0.15)0.04840.07820.44130.10250.1025
(0.9, 0.1)0.08220.10070.44580.09960.0996
(0.95, 0.05)0.11290.13450.43050.12890.1289
(1.0, 0.0)0.00000.03330.55140.00000.0000
- -
target: ECAT
-
train: [0.85104545 0.14895455]
-
validation: [0.85097192 0.14902808]
-
evaluate_binary: 264.961s
-
evaluate_multiclass: 139.511s
-
kfcv: 164.571s
-
atc_mc: 164.474s
-
atc_ne: 104.597s
-
doc_feat: 120.243s
-
rca_score: 1226.111s
-
rca_star_score: 1194.784s
-
tot: 1281.006s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01950.02630.40860.12410.12410.41240.36480.3803
(0.05, 0.95)0.06140.06830.38280.11360.11360.38670.35230.3775
(0.1, 0.9)0.06730.07260.36320.11440.11440.36720.36000.3912
(0.15, 0.85)0.06690.07080.34350.10880.10880.34760.35490.3896
(0.2, 0.8)0.06640.07230.32360.10720.10720.32780.33150.3654
(0.25, 0.75)0.05660.05850.29410.10030.10030.29840.29490.3291
(0.3, 0.7)0.05420.05500.27270.08940.08940.27710.26870.3028
(0.35, 0.65)0.05100.04830.24280.08370.08370.24730.23650.2706
(0.4, 0.6)0.04670.04750.21960.07150.07150.22420.21100.2450
(0.45, 0.55)0.04600.04800.19840.07010.07010.20310.18910.2232
(0.5, 0.5)0.04300.04120.17000.05680.05680.17480.16030.1943
(0.55, 0.45)0.04800.04130.14210.04450.04450.14700.13220.1662
(0.6, 0.4)0.04030.04010.12020.04520.04520.12520.11030.1443
(0.65, 0.35)0.04050.03920.09430.03980.03980.09940.08440.1184
(0.7, 0.3)0.03620.03590.07350.03520.03520.07870.06360.0976
(0.75, 0.25)0.03900.03930.05460.03480.03480.05940.04580.0775
(0.8, 0.2)0.03920.03690.03410.02930.02930.03870.02620.0564
(0.85, 0.15)0.03160.03000.01860.02500.02500.02010.01840.0321
(0.9, 0.1)0.02650.02300.02090.01930.01930.01800.02810.0151
(0.95, 0.05)0.02350.02200.04050.01720.01720.03500.05040.0189
(1.0, 0.0)0.00920.00850.06580.01560.01560.0600NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.01830.02380.14480.05200.0520
(0.05, 0.95)0.03860.04510.14300.04970.0497
(0.1, 0.9)0.04350.04970.14710.04950.0495
(0.15, 0.85)0.04560.04920.15130.05270.0527
(0.2, 0.8)0.05050.05480.15680.05040.0504
(0.25, 0.75)0.04670.04710.15000.04920.0492
(0.3, 0.7)0.04880.04760.15410.05720.0572
(0.35, 0.65)0.05350.04740.14790.05770.0577
(0.4, 0.6)0.04920.04820.14830.05840.0584
(0.45, 0.55)0.04900.05110.15350.06250.0625
(0.5, 0.5)0.05640.05000.14690.07300.0730
(0.55, 0.45)0.07370.05540.13890.08270.0827
(0.6, 0.4)0.06280.05740.14240.07520.0752
(0.65, 0.35)0.08250.06460.13890.08930.0893
(0.7, 0.3)0.07380.06970.14350.08980.0898
(0.75, 0.25)0.10890.09500.15870.09370.0937
(0.8, 0.2)0.13240.11010.17360.10570.1057
(0.85, 0.15)0.13570.11690.18370.11130.1113
(0.9, 0.1)0.18050.14090.19260.13640.1364
(0.95, 0.05)0.27620.24470.25620.19960.1996
(1.0, 0.0)0.03560.07910.82650.09280.0928
- -
target: G15
-
train: [0.9843615 0.0156385]
-
validation: [0.98427646 0.01572354]
-
evaluate_binary: 353.823s
-
evaluate_multiclass: 120.493s
-
kfcv: 151.945s
-
atc_mc: 155.115s
-
atc_ne: 92.674s
-
doc_feat: 76.970s
-
rca_score: 1121.730s
-
rca_star_score: 1112.820s
-
tot: 1165.059s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.13940.13130.84620.26770.26770.84520.85670.8599
(0.05, 0.95)0.17830.17370.80440.24590.24590.80350.81490.8181
(0.1, 0.9)0.21990.21490.76550.24060.24060.76470.77600.7792
(0.15, 0.85)0.26660.26490.71780.22220.22220.71710.72830.7315
(0.2, 0.8)0.31290.30970.67190.20810.20810.67130.68240.6856
(0.25, 0.75)0.35550.33970.63000.19590.19590.62950.64050.6437
(0.3, 0.7)0.39390.34260.58140.17230.17230.58100.59190.5951
(0.35, 0.65)0.39780.33300.54280.16170.16170.54250.55330.5565
(0.4, 0.6)0.37210.32470.50130.15720.15720.50120.51180.5150
(0.45, 0.55)0.34330.29490.45380.12770.12770.45380.46430.4675
(0.5, 0.5)0.30820.28930.42000.13380.13380.42010.43050.4337
(0.55, 0.45)0.27920.28980.36690.11000.11000.36710.37740.3806
(0.6, 0.4)0.24890.25890.32660.09620.09620.32690.33710.3403
(0.65, 0.35)0.21290.22170.28650.08660.08660.28690.29700.3002
(0.7, 0.3)0.17380.18220.24310.07320.07320.24360.25360.2568
(0.75, 0.25)0.14430.13950.19980.05760.05760.2004NaNNaN
(0.8, 0.2)0.10720.11550.15850.05110.05110.1593NaNNaN
(0.85, 0.15)0.07970.09060.11380.04040.04040.1147NaNNaN
(0.9, 0.1)0.04920.04870.07120.02740.02740.0722NaNNaN
(0.95, 0.05)0.02410.02460.02910.01640.01640.0302NaNNaN
(1.0, 0.0)0.00180.00160.01370.00160.00160.0125NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.24280.22750.37020.21310.2131
(0.05, 0.95)0.23560.22660.37220.20630.2063
(0.1, 0.9)0.23320.22360.37960.19780.1978
(0.15, 0.85)0.23960.23620.37150.20740.2074
(0.2, 0.8)0.24550.24140.36650.20850.2085
(0.25, 0.75)0.24570.24420.36810.21250.2125
(0.3, 0.7)0.25070.25090.35620.22150.2215
(0.35, 0.65)0.24540.24420.36540.20820.2082
(0.4, 0.6)0.24300.24110.36880.20730.2073
(0.45, 0.55)0.25500.24910.35640.22450.2245
(0.5, 0.5)0.22520.22260.38280.19440.1944
(0.55, 0.45)0.25880.25160.35060.22830.2283
(0.6, 0.4)0.24290.24110.35840.22160.2216
(0.65, 0.35)0.23770.23330.36870.21240.2124
(0.7, 0.3)0.23150.22770.36730.21600.2160
(0.75, 0.25)0.23400.22010.36710.22170.2217
(0.8, 0.2)0.20460.20610.37820.20730.2073
(0.85, 0.15)0.23350.21940.36250.22750.2275
(0.9, 0.1)0.20540.19630.36760.23550.2355
(0.95, 0.05)0.17760.14200.40640.20390.2039
(1.0, 0.0)0.00000.01160.61450.00000.0000
- -
target: GCAT
-
train: [0.69889407 0.30110593]
-
validation: [0.69892009 0.30107991]
-
evaluate_binary: 267.282s
-
evaluate_multiclass: 139.971s
-
kfcv: 180.088s
-
atc_mc: 180.896s
-
atc_ne: 172.480s
-
doc_feat: 138.047s
-
rca_score: 1202.504s
-
rca_star_score: 1201.723s
-
tot: 1263.883s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01210.01550.07620.02930.02930.07680.52840.5080
(0.05, 0.95)0.03200.02850.07290.03200.03200.07350.53190.5115
(0.1, 0.9)0.02900.02790.06540.03520.03520.06610.53950.5191
(0.15, 0.85)0.02670.02550.05880.02630.02630.05950.54600.5256
(0.2, 0.8)0.02440.02730.05030.02490.02490.05110.55490.5345
(0.25, 0.75)0.02570.02630.05280.02650.02650.05370.54760.5268
(0.3, 0.7)0.02920.02980.04580.02580.02580.04650.52000.4984
(0.35, 0.65)0.03120.03260.04590.03010.03010.04670.37380.3537
(0.4, 0.6)0.03450.03120.03480.02460.02460.03560.20860.1981
(0.45, 0.55)0.03180.02890.03090.02070.02070.03180.03960.0461
(0.5, 0.5)0.03100.03270.03070.02510.02510.03150.07540.0612
(0.55, 0.45)0.03120.03120.02480.02510.02510.02540.15360.1338
(0.6, 0.4)0.03130.03040.02090.02330.02330.02140.19200.1716
(0.65, 0.35)0.02450.02480.01800.01820.01820.01850.19870.1784
(0.7, 0.3)0.02820.02630.01630.01900.01900.01630.20660.1863
(0.75, 0.25)0.02610.02580.01710.01520.01520.01720.20470.1844
(0.8, 0.2)0.02470.02370.01790.01870.01870.01750.21520.1949
(0.85, 0.15)0.02230.02140.01770.01650.01650.01710.21750.1972
(0.9, 0.1)0.02350.02230.02000.01540.01540.01910.22240.2021
(0.95, 0.05)0.01610.01430.02580.01300.01300.02450.23180.2115
(1.0, 0.0)0.00780.00940.02970.01220.01220.0282NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00710.00910.01510.01630.0163
(0.05, 0.95)0.01840.01630.01670.01920.0192
(0.1, 0.9)0.01750.01680.01800.02170.0217
(0.15, 0.85)0.01720.01610.01670.01730.0173
(0.2, 0.8)0.01620.01810.01660.01650.0165
(0.25, 0.75)0.01810.01870.01960.01870.0187
(0.3, 0.7)0.02300.02300.02110.02050.0205
(0.35, 0.65)0.02610.02660.02470.02510.0251
(0.4, 0.6)0.03150.02770.02340.02390.0239
(0.45, 0.55)0.03150.02850.02170.02260.0226
(0.5, 0.5)0.03360.03480.02890.02790.0279
(0.55, 0.45)0.03930.03840.02850.03060.0306
(0.6, 0.4)0.04420.04180.03060.03250.0325
(0.65, 0.35)0.03850.03840.03320.02860.0286
(0.7, 0.3)0.05250.04840.03430.03580.0358
(0.75, 0.25)0.05820.05690.05200.03290.0329
(0.8, 0.2)0.06860.06360.05080.05000.0500
(0.85, 0.15)0.08540.08030.07100.05630.0563
(0.9, 0.1)0.12580.11500.10430.07750.0775
(0.95, 0.05)0.18700.15460.14050.10320.1032
(1.0, 0.0)0.05900.09030.94170.40250.4025
- -
target: GCRIM
-
train: [0.95109729 0.04890271]
-
validation: [0.95101512 0.04898488]
-
evaluate_binary: 351.759s
-
evaluate_multiclass: 190.396s
-
kfcv: 163.725s
-
atc_mc: 109.508s
-
atc_ne: 103.056s
-
doc_feat: 131.868s
-
rca_score: 1163.895s
-
rca_star_score: 1137.033s
-
tot: 1206.507s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04790.07200.65500.13940.13940.65430.66930.6846
(0.05, 0.95)0.09550.12200.61940.13550.13550.61890.63370.6489
(0.1, 0.9)0.13400.16450.58630.12820.12820.58590.60060.6158
(0.15, 0.85)0.16370.18130.54650.11580.11580.54620.56080.5760
(0.2, 0.8)0.15600.17710.51570.11850.11850.51550.53000.5452
(0.25, 0.75)0.17250.17630.49000.10610.10610.48990.50430.5195
(0.3, 0.7)0.14640.15320.44590.09440.09440.44590.46020.4754
(0.35, 0.65)0.13790.14180.41120.09500.09500.41130.42550.4407
(0.4, 0.6)0.11900.12720.37570.08580.08580.37600.39000.4052
(0.45, 0.55)0.10580.11130.34430.07520.07520.34470.35860.3738
(0.5, 0.5)0.09640.09830.30680.06510.06510.30730.32110.3363
(0.55, 0.45)0.09070.08860.27210.05840.05840.27270.28640.3016
(0.6, 0.4)0.06380.07080.24290.06250.06250.24360.25720.2724
(0.65, 0.35)0.06130.05970.21210.05670.05670.21290.22640.2416
(0.7, 0.3)0.05490.05060.17070.04210.04210.17170.18500.2002
(0.75, 0.25)0.03950.04320.14430.04320.04320.14540.15860.1738
(0.8, 0.2)0.03400.03060.10850.03280.03280.10970.12280.1380
(0.85, 0.15)0.02800.02540.07240.02690.02690.07370.08670.1019
(0.9, 0.1)0.02120.02110.03850.02020.02020.0399NaNNaN
(0.95, 0.05)0.01710.01780.00900.01390.01390.0093NaNNaN
(1.0, 0.0)0.00350.00290.03130.00500.00500.0296NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.06900.09010.27810.21290.2129
(0.05, 0.95)0.08450.11080.27630.20690.2069
(0.1, 0.9)0.08980.12620.27790.20980.2098
(0.15, 0.85)0.10800.14050.27040.21360.2136
(0.2, 0.8)0.10410.14390.27570.20740.2074
(0.25, 0.75)0.14470.15590.28860.21920.2192
(0.3, 0.7)0.13860.14910.27340.22140.2214
(0.35, 0.65)0.14450.15000.27280.21730.2173
(0.4, 0.6)0.13040.14360.27070.21900.2190
(0.45, 0.55)0.12880.14000.27680.22380.2238
(0.5, 0.5)0.13790.13820.26800.21930.2193
(0.55, 0.45)0.16240.14590.26670.22210.2221
(0.6, 0.4)0.11140.13090.28150.22030.2203
(0.65, 0.35)0.12490.13310.29300.20730.2073
(0.7, 0.3)0.17160.14190.26860.23970.2397
(0.75, 0.25)0.13370.14580.30310.21030.2103
(0.8, 0.2)0.14520.14610.30200.23720.2372
(0.85, 0.15)0.18600.16040.29970.23930.2393
(0.9, 0.1)0.19460.19350.31370.27010.2701
(0.95, 0.05)0.25050.24550.34020.28790.2879
(1.0, 0.0)0.01040.03710.75210.00000.0000
- -
target: GDIP
-
train: [0.95662692 0.04337308]
-
validation: [0.95663067 0.04336933]
-
evaluate_binary: 413.043s
-
evaluate_multiclass: 184.927s
-
kfcv: 111.445s
-
atc_mc: 110.721s
-
atc_ne: 109.317s
-
doc_feat: 127.169s
-
rca_score: 1106.672s
-
rca_star_score: 1122.975s
-
tot: 1177.098s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.05790.09360.77700.39060.39060.77680.79460.8082
(0.05, 0.95)0.09840.13940.73880.37350.37350.73870.75640.7700
(0.1, 0.9)0.13890.18280.70360.35920.35920.70360.72120.7348
(0.15, 0.85)0.17560.19740.65800.33760.33760.65810.67560.6892
(0.2, 0.8)0.17320.18810.62130.33210.33210.62160.63890.6525
(0.25, 0.75)0.15630.17610.58740.31350.31350.58780.60500.6186
(0.3, 0.7)0.14340.16380.53860.27990.27990.53910.55620.5698
(0.35, 0.65)0.12230.14370.49830.25860.25860.49890.51590.5295
(0.4, 0.6)0.10920.13220.46320.24680.24680.46390.48080.4944
(0.45, 0.55)0.08940.11770.41750.21600.21600.41830.43510.4487
(0.5, 0.5)0.08250.10690.37540.18860.18860.37630.39300.4066
(0.55, 0.45)0.06110.08010.33330.18120.18120.33440.35090.3645
(0.6, 0.4)0.05660.06340.29380.15680.15680.29500.31140.3250
(0.65, 0.35)0.04760.05630.25330.13880.13880.25460.27090.2845
(0.7, 0.3)0.03380.04150.21300.11870.11870.21440.23060.2442
(0.75, 0.25)0.03020.03220.17250.09660.09660.17410.19010.2037
(0.8, 0.2)0.02830.02690.13310.08180.08180.1348NaNNaN
(0.85, 0.15)0.02680.02860.09200.05750.05750.0938NaNNaN
(0.9, 0.1)0.02180.02590.04770.03250.03250.0496NaNNaN
(0.95, 0.05)0.01700.01690.01070.01800.01800.0120NaNNaN
(1.0, 0.0)0.00500.00380.03080.00490.00490.0286NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.09330.14470.39760.15300.1530
(0.05, 0.95)0.09360.15370.40080.13920.1392
(0.1, 0.9)0.09280.16930.40970.14400.1440
(0.15, 0.85)0.12100.17990.40200.14510.1451
(0.2, 0.8)0.13760.18500.40840.13330.1333
(0.25, 0.75)0.12260.18150.42170.13610.1361
(0.3, 0.7)0.12310.17700.40560.14180.1418
(0.35, 0.65)0.11970.17520.40780.14160.1416
(0.4, 0.6)0.12180.17870.41960.13830.1383
(0.45, 0.55)0.09720.17230.41010.13940.1394
(0.5, 0.5)0.10570.17360.40640.16260.1626
(0.55, 0.45)0.10860.16110.40180.14050.1405
(0.6, 0.4)0.12790.15120.40710.15480.1548
(0.65, 0.35)0.11070.14230.40520.14090.1409
(0.7, 0.3)0.11280.14370.40640.16150.1615
(0.75, 0.25)0.13230.13370.41180.17280.1728
(0.8, 0.2)0.12940.11320.42420.16970.1697
(0.85, 0.15)0.17550.15550.42680.20840.2084
(0.9, 0.1)0.17690.17600.38120.24720.2472
(0.95, 0.05)0.20560.20660.42100.25430.2543
(1.0, 0.0)0.01010.04400.71730.00670.0067
- -
target: GPOL
-
train: [0.92889234 0.07110766]
-
validation: [0.9288121 0.0711879]
-
evaluate_binary: 269.509s
-
evaluate_multiclass: 195.577s
-
kfcv: 109.578s
-
atc_mc: 112.040s
-
atc_ne: 171.498s
-
doc_feat: 127.471s
-
rca_score: 1224.001s
-
rca_star_score: 1196.316s
-
tot: 1275.440s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00020.03480.55520.19190.19190.55620.57130.5899
(0.05, 0.95)0.04940.08800.53120.18800.18800.53240.54560.5643
(0.1, 0.9)0.08970.12410.51090.17710.17710.51210.52360.5424
(0.15, 0.85)0.08970.11960.46090.16060.16060.46230.47410.4929
(0.2, 0.8)0.08650.11980.44880.15530.15530.45030.46070.4795
(0.25, 0.75)0.07890.11170.41300.13520.13520.41460.42490.4436
(0.3, 0.7)0.06750.10240.38480.13710.13710.38650.39660.4154
(0.35, 0.65)0.05790.08390.34880.12470.12470.35070.36060.3793
(0.4, 0.6)0.04780.07090.32300.11240.11240.32500.33470.3535
(0.45, 0.55)0.04550.06710.28860.09650.09650.29070.30030.3191
(0.5, 0.5)0.03450.05280.25440.08400.08400.25660.26610.2849
(0.55, 0.45)0.03990.05270.22990.07900.07900.23220.24160.2604
(0.6, 0.4)0.03540.05070.20630.07420.07420.20880.21800.2368
(0.65, 0.35)0.02890.03830.16430.06430.06430.16690.17600.1948
(0.7, 0.3)0.03230.04060.14410.05570.05570.14680.15580.1746
(0.75, 0.25)0.02780.03010.10980.04580.04580.11260.12150.1403
(0.8, 0.2)0.03020.03590.08600.03600.03600.08890.09770.1165
(0.85, 0.15)0.02220.02680.04970.02780.02780.05280.06140.0802
(0.9, 0.1)0.02630.02690.02290.02010.02010.02520.03340.0521
(0.95, 0.05)0.01880.01910.01370.01490.01490.0116NaNNaN
(1.0, 0.0)0.01160.01030.03750.00980.00980.0341NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.03730.21910.07850.0785
(0.05, 0.95)0.02020.06410.22550.07580.0758
(0.1, 0.9)0.03670.08480.23660.07440.0744
(0.15, 0.85)0.03940.08490.21420.08120.0812
(0.2, 0.8)0.03920.09020.23560.08060.0806
(0.25, 0.75)0.03790.09010.22870.09430.0943
(0.3, 0.7)0.03460.09040.23080.08470.0847
(0.35, 0.65)0.03150.07730.22300.07900.0790
(0.4, 0.6)0.02880.07310.23050.09450.0945
(0.45, 0.55)0.03120.07570.22250.10390.1039
(0.5, 0.5)0.02620.06890.21550.10020.1002
(0.55, 0.45)0.03350.07060.22710.10770.1077
(0.6, 0.4)0.03700.07960.24460.10740.1074
(0.65, 0.35)0.03360.06520.21040.10630.1063
(0.7, 0.3)0.05230.08240.24100.13490.1349
(0.75, 0.25)0.05080.07220.23150.11760.1176
(0.8, 0.2)0.06760.09510.26000.13000.1300
(0.85, 0.15)0.07480.10200.23410.13740.1374
(0.9, 0.1)0.14140.14960.26100.21210.2121
(0.95, 0.05)0.20850.21070.26910.22860.2286
(1.0, 0.0)0.09380.15260.79050.07490.0749
- -
target: GVIO
-
train: [0.95187489 0.04812511]
-
validation: [0.95179266 0.04820734]
-
evaluate_binary: 348.091s
-
evaluate_multiclass: 119.628s
-
kfcv: 101.483s
-
atc_mc: 99.585s
-
atc_ne: 93.777s
-
doc_feat: 105.866s
-
rca_score: 1212.214s
-
rca_star_score: 1206.863s
-
tot: 1254.673s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.25610.17060.63990.17720.17720.63980.64850.6616
(0.05, 0.95)0.30100.20670.61320.16040.16040.61320.62130.6345
(0.1, 0.9)0.33860.23690.57860.15930.15930.57870.58650.5997
(0.15, 0.85)0.33430.24070.54170.14860.14860.54190.54950.5627
(0.2, 0.8)0.31170.22640.51700.15230.15230.51740.52460.5378
(0.25, 0.75)0.30210.22260.48100.13330.13330.48150.48850.5018
(0.3, 0.7)0.28560.21240.44280.11530.11530.44340.45030.4636
(0.35, 0.65)0.25960.19060.41220.11820.11820.41300.41970.4329
(0.4, 0.6)0.23020.17680.38060.10860.10860.38150.38810.4013
(0.45, 0.55)0.19450.14630.34480.10860.10860.34580.35230.3655
(0.5, 0.5)0.18490.14710.31680.09500.09500.31790.32430.3375
(0.55, 0.45)0.15680.12310.27380.07940.07940.27510.28130.2945
(0.6, 0.4)0.14390.11570.24200.07420.07420.24340.24950.2627
(0.65, 0.35)0.11620.09430.20820.06510.06510.20970.21570.2289
(0.7, 0.3)0.09480.07540.17280.05450.05450.17450.18030.1935
(0.75, 0.25)0.08120.06760.13710.04510.04510.13890.14460.1578
(0.8, 0.2)0.05930.05030.11130.04170.04170.11320.11880.1320
(0.85, 0.15)0.04660.04020.07380.03230.03230.07580.08130.0945
(0.9, 0.1)0.03030.02620.04070.02100.02100.0428NaNNaN
(0.95, 0.05)0.02180.01920.00970.01290.01290.0110NaNNaN
(1.0, 0.0)0.00620.00500.02730.00590.00590.0249NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.36540.22740.29580.16300.1630
(0.05, 0.95)0.38830.23150.30370.17710.1771
(0.1, 0.9)0.39760.23100.30290.17320.1732
(0.15, 0.85)0.36970.22920.29870.17280.1728
(0.2, 0.8)0.35780.22110.31080.16150.1615
(0.25, 0.75)0.37810.23840.30830.17030.1703
(0.3, 0.7)0.38080.23990.30170.18110.1811
(0.35, 0.65)0.38260.23230.30780.17180.1718
(0.4, 0.6)0.37060.24140.31200.16920.1692
(0.45, 0.55)0.34290.21750.30800.15200.1520
(0.5, 0.5)0.35410.23900.32220.17180.1718
(0.55, 0.45)0.35110.24040.30060.16660.1666
(0.6, 0.4)0.37610.26130.30750.18770.1877
(0.65, 0.35)0.33950.24290.30630.17310.1731
(0.7, 0.3)0.33030.23220.30310.17540.1754
(0.75, 0.25)0.36590.27580.29160.19000.1900
(0.8, 0.2)0.33300.26450.33600.19860.1986
(0.85, 0.15)0.33680.26710.31770.21870.2187
(0.9, 0.1)0.31290.27310.33360.22630.2263
(0.95, 0.05)0.33850.29610.32690.27670.2767
(1.0, 0.0)0.02010.04120.79100.00670.0067
- -
target: GVOTE
-
train: [0.9850527 0.0149473]
-
validation: [0.985054 0.014946]
-
evaluate_binary: 411.703s
-
evaluate_multiclass: 176.863s
-
kfcv: 110.327s
-
atc_mc: 169.946s
-
atc_ne: 103.675s
-
doc_feat: 82.976s
-
rca_score: 939.116s
-
rca_star_score: 952.807s
-
tot: 999.321s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04600.06430.92420.67300.67300.92340.93130.9356
(0.05, 0.95)0.08880.10530.88320.65600.65600.8825NaNNaN
(0.1, 0.9)0.13410.15240.83600.61890.61890.83540.84310.8474
(0.15, 0.85)0.17990.19650.79170.59080.59080.7912NaNNaN
(0.2, 0.8)0.23410.24520.73960.55010.55010.7391NaNNaN
(0.25, 0.75)0.27960.26400.69300.51330.51330.69260.70010.7044
(0.3, 0.7)0.32360.25210.64870.48210.48210.6484NaNNaN
(0.35, 0.65)0.34180.24140.59960.44430.44430.5994NaNNaN
(0.4, 0.6)0.31610.21890.55170.41610.41610.5516NaNNaN
(0.45, 0.55)0.29270.19950.50610.37820.37820.5060NaNNaN
(0.5, 0.5)0.26660.18460.45690.34050.34050.4569NaNNaN
(0.55, 0.45)0.23550.15880.41370.31130.31130.4138NaNNaN
(0.6, 0.4)0.20780.14490.36340.27070.27070.3636NaNNaN
(0.65, 0.35)0.17230.11870.31640.23770.23770.3166NaNNaN
(0.7, 0.3)0.14250.09310.27270.20800.20800.2730NaNNaN
(0.75, 0.25)0.10640.06810.22500.17490.17490.2254NaNNaN
(0.8, 0.2)0.08640.05750.17610.13600.13600.1766NaNNaN
(0.85, 0.15)0.05210.03480.12970.10140.10140.1303NaNNaN
(0.9, 0.1)0.02870.02240.08230.06830.06830.0830NaNNaN
(0.95, 0.05)0.02080.01920.03620.03600.03600.0369NaNNaN
(1.0, 0.0)0.00110.00100.01140.00040.00040.0106NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.08550.11970.57440.09490.0949
(0.05, 0.95)0.07790.10920.58510.08750.0875
(0.1, 0.9)0.07450.10910.58510.09020.0902
(0.15, 0.85)0.07140.10360.59080.07970.0797
(0.2, 0.8)0.08640.11420.58020.08590.0859
(0.25, 0.75)0.08380.11330.58100.08160.0816
(0.3, 0.7)0.08290.10670.58770.08430.0843
(0.35, 0.65)0.08540.11140.58300.08550.0855
(0.4, 0.6)0.08720.11460.57980.09370.0937
(0.45, 0.55)0.08670.10940.58490.09220.0922
(0.5, 0.5)0.08890.11720.57710.08510.0851
(0.55, 0.45)0.07740.10270.59130.09020.0902
(0.6, 0.4)0.08230.11630.57760.09630.0963
(0.65, 0.35)0.07970.11550.57730.10150.1015
(0.7, 0.3)0.07150.09730.59630.08930.0893
(0.75, 0.25)0.07620.09890.59530.09540.0954
(0.8, 0.2)0.09300.11050.58170.11160.1116
(0.85, 0.15)0.08470.10230.58800.10280.1028
(0.9, 0.1)0.07620.10290.58200.11250.1125
(0.95, 0.05)0.07260.06480.61720.08240.0824
(1.0, 0.0)0.00000.00000.69440.00000.0000
- -
target: GWEA
-
train: [0.99421116 0.00578884]
-
validation: [0.99412527 0.00587473]
-
evaluate_binary: 262.208s
-
evaluate_multiclass: 108.019s
-
kfcv: 97.840s
-
atc_mc: 135.028s
-
atc_ne: 94.500s
-
doc_feat: 82.704s
-
rca_score: 374.076s
-
rca_star_score: 374.355s
-
tot: 415.864s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00650.00730.98740.64090.64090.9869NaNNaN
(0.05, 0.95)0.05480.05550.93920.61980.61980.9387NaNNaN
(0.1, 0.9)0.10410.10520.88940.58770.58770.8890NaNNaN
(0.15, 0.85)0.15380.15470.83990.54880.54880.8395NaNNaN
(0.2, 0.8)0.20380.20490.78950.52670.52670.7892NaNNaN
(0.25, 0.75)0.25550.25650.73760.48430.48430.7373NaNNaN
(0.3, 0.7)0.30400.30260.68940.45790.45790.6892NaNNaN
(0.35, 0.65)0.35330.33240.64020.41870.41870.6400NaNNaN
(0.4, 0.6)0.40190.32060.59130.39060.39060.5912NaNNaN
(0.45, 0.55)0.45210.28950.54130.36090.36090.5412NaNNaN
(0.5, 0.5)0.49980.25520.49140.32640.32640.4914NaNNaN
(0.55, 0.45)0.52260.22080.44160.29600.29600.4416NaNNaN
(0.6, 0.4)0.47670.18980.39210.26310.26310.3922NaNNaN
(0.65, 0.35)0.41220.15680.34280.22550.22550.3429NaNNaN
(0.7, 0.3)0.33730.12200.29260.19110.19110.2928NaNNaN
(0.75, 0.25)0.25510.08400.24310.16400.16400.2433NaNNaN
(0.8, 0.2)0.18450.05610.19250.13200.13200.1928NaNNaN
(0.85, 0.15)0.11980.03270.14400.09740.09740.1443NaNNaN
(0.9, 0.1)0.05980.02110.09380.06530.06530.0942NaNNaN
(0.95, 0.05)0.02700.02210.04440.03370.03370.0448NaNNaN
(1.0, 0.0)0.00010.00000.00530.00020.00020.0048NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.01270.01430.59700.01430.0143
(0.05, 0.95)0.01000.01140.60000.01140.0114
(0.1, 0.9)0.00920.01160.59980.01160.0116
(0.15, 0.85)0.00930.01110.60030.01110.0111
(0.2, 0.8)0.01010.01280.59860.01280.0128
(0.25, 0.75)0.01550.01850.59290.01850.0185
(0.3, 0.7)0.01240.01480.59660.01480.0148
(0.35, 0.65)0.01160.01360.59780.01360.0136
(0.4, 0.6)0.00870.01110.60030.01110.0111
(0.45, 0.55)0.01070.01210.59930.01210.0121
(0.5, 0.5)0.01100.01280.59860.01280.0128
(0.55, 0.45)0.01170.01340.59800.01340.0134
(0.6, 0.4)0.01210.01260.59880.01260.0126
(0.65, 0.35)0.00860.01050.60080.01050.0105
(0.7, 0.3)0.01130.01350.59790.01350.0135
(0.75, 0.25)0.01090.01210.59920.01210.0121
(0.8, 0.2)0.01940.02080.59050.02090.0209
(0.85, 0.15)0.00860.00860.60280.00860.0086
(0.9, 0.1)0.01360.01640.59500.01640.0164
(0.95, 0.05)0.00740.00910.60140.01000.0100
(1.0, 0.0)0.00000.00020.61140.00000.0000
- -
target: M11
-
train: [0.94409884 0.05590116]
-
validation: [0.94410367 0.05589633]
-
evaluate_binary: 350.246s
-
evaluate_multiclass: 186.170s
-
kfcv: 106.680s
-
atc_mc: 169.230s
-
atc_ne: 99.756s
-
doc_feat: 75.310s
-
rca_score: 1245.803s
-
rca_star_score: 1216.185s
-
tot: 1288.407s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00230.02190.34910.08630.08630.34860.44690.4354
(0.05, 0.95)0.05100.07230.32780.07440.07440.32740.35270.3499
(0.1, 0.9)0.09200.10520.31210.07440.07440.31180.29400.3077
(0.15, 0.85)0.09050.10090.28200.05920.05920.28180.25800.2777
(0.2, 0.8)0.08740.10530.26770.06310.06310.26760.25950.2801
(0.25, 0.75)0.08160.09370.25270.05940.05940.25270.25970.2814
(0.3, 0.7)0.06990.07890.22980.05130.05130.23000.23620.2575
(0.35, 0.65)0.06820.07960.21500.05320.05320.21530.21670.2376
(0.4, 0.6)0.05730.06350.19640.04830.04830.19680.19230.2133
(0.45, 0.55)0.05020.06060.18230.04690.04690.18280.17240.1934
(0.5, 0.5)0.04550.05420.15760.03790.03790.15820.14500.1660
(0.55, 0.45)0.03860.04260.14070.03560.03560.14140.12570.1467
(0.6, 0.4)0.03930.04500.12710.03810.03810.12790.11060.1315
(0.65, 0.35)0.03280.03880.11390.03600.03600.11480.09690.1176
(0.7, 0.3)0.03760.04040.09320.03560.03560.09420.07600.0966
(0.75, 0.25)0.02720.02730.07300.02820.02820.07420.05600.0764
(0.8, 0.2)0.03110.02470.05500.03000.03000.05630.03820.0583
(0.85, 0.15)0.02340.02320.03640.02230.02230.03780.02190.0397
(0.9, 0.1)0.01920.01830.01820.01600.01600.01940.01240.0210
(0.95, 0.05)0.01650.01350.00880.01170.01170.0089NaNNaN
(1.0, 0.0)0.00610.00550.01860.00330.00330.0169NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00180.01700.11400.03790.0379
(0.05, 0.95)0.02500.04260.11190.03870.0387
(0.1, 0.9)0.04610.06160.11410.04160.0416
(0.15, 0.85)0.04750.06170.10410.03970.0397
(0.2, 0.8)0.04990.07090.10740.04850.0485
(0.25, 0.75)0.04960.06630.11020.04700.0470
(0.3, 0.7)0.04690.05970.10540.04540.0454
(0.35, 0.65)0.05120.06710.10900.05580.0558
(0.4, 0.6)0.04380.05660.10880.04730.0473
(0.45, 0.55)0.04250.06060.11360.05050.0505
(0.5, 0.5)0.04290.05960.10510.05500.0550
(0.55, 0.45)0.04140.05210.10590.04910.0491
(0.6, 0.4)0.04750.06220.11490.05990.0599
(0.65, 0.35)0.04640.06500.12820.07020.0702
(0.7, 0.3)0.06660.07800.12510.07320.0732
(0.75, 0.25)0.06180.06300.12080.07170.0717
(0.8, 0.2)0.11690.08160.12400.09620.0962
(0.85, 0.15)0.11620.09820.12680.09940.0994
(0.9, 0.1)0.12450.10440.12860.11000.1100
(0.95, 0.05)0.22840.17080.15570.17460.1746
(1.0, 0.0)0.03680.06640.88680.05000.0500
- -
target: M12
-
train: [0.9683774 0.0316226]
-
validation: [0.96838013 0.03161987]
-
evaluate_binary: 388.733s
-
evaluate_multiclass: 144.432s
-
kfcv: 199.371s
-
atc_mc: 127.456s
-
atc_ne: 206.927s
-
doc_feat: 172.047s
-
rca_score: 1212.021s
-
rca_star_score: 1206.772s
-
tot: 1256.777s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00300.03280.65410.14570.14570.65410.66750.6782
(0.05, 0.95)0.05360.08380.62550.13920.13920.62560.63870.6493
(0.1, 0.9)0.10400.14320.58070.12440.12440.58090.59380.6045
(0.15, 0.85)0.15000.18200.55330.11650.11650.55360.56610.5768
(0.2, 0.8)0.14690.16350.50930.10680.10680.50980.52200.5327
(0.25, 0.75)0.13940.16820.48280.10980.10980.48340.49540.5061
(0.3, 0.7)0.12750.14940.45200.10010.10010.45270.46460.4752
(0.35, 0.65)0.11070.13210.42090.09920.09920.42170.43340.4440
(0.4, 0.6)0.09680.11880.38200.08660.08660.38300.39450.4051
(0.45, 0.55)0.08500.10340.35210.08280.08280.35320.36450.3752
(0.5, 0.5)0.07420.09140.31330.07350.07350.31450.32570.3363
(0.55, 0.45)0.05940.07390.27870.07250.07250.28000.29110.3017
(0.6, 0.4)0.05140.06660.24260.05340.05340.24400.25500.2656
(0.65, 0.35)0.04980.06250.22050.06630.06630.22210.23290.2435
(0.7, 0.3)0.03690.05010.18440.05090.05090.18610.19680.2074
(0.75, 0.25)0.03790.03830.14630.04170.04170.14810.15870.1693
(0.8, 0.2)0.02750.03270.11280.03610.03610.11480.12520.1358
(0.85, 0.15)0.02440.02830.07650.02390.02390.07860.08890.0995
(0.9, 0.1)0.02050.02370.04460.02440.02440.0468NaNNaN
(0.95, 0.05)0.01610.01790.01440.01590.01590.0160NaNNaN
(1.0, 0.0)0.00210.00220.02240.00330.00330.0199NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00370.03910.22490.22810.2281
(0.05, 0.95)0.02350.06300.23110.23200.2320
(0.1, 0.9)0.04430.09560.21820.23640.2364
(0.15, 0.85)0.06280.12090.22600.24520.2452
(0.2, 0.8)0.06530.10970.21180.23010.2301
(0.25, 0.75)0.06850.12800.22230.23740.2374
(0.3, 0.7)0.06560.11930.22710.23120.2312
(0.35, 0.65)0.06660.11830.23200.23350.2335
(0.4, 0.6)0.05960.11280.22330.23110.2311
(0.45, 0.55)0.06570.11380.23130.24050.2405
(0.5, 0.5)0.05920.10570.22150.24020.2402
(0.55, 0.45)0.06170.10500.22010.23220.2322
(0.6, 0.4)0.05930.10890.21220.27150.2715
(0.65, 0.35)0.06420.11800.24970.20500.2050
(0.7, 0.3)0.06210.11810.24540.23670.2367
(0.75, 0.25)0.10270.11300.22730.23250.2325
(0.8, 0.2)0.08390.12120.23400.23420.2342
(0.85, 0.15)0.12760.15500.21510.26050.2605
(0.9, 0.1)0.14230.17240.24650.25510.2551
(0.95, 0.05)0.19280.20590.32030.28700.2870
(1.0, 0.0)0.01670.03930.71090.01000.0100
- -
target: M13
-
train: [0.93105236 0.06894764]
-
validation: [0.93105832 0.06894168]
-
evaluate_binary: 325.372s
-
evaluate_multiclass: 189.027s
-
kfcv: 110.743s
-
atc_mc: 176.696s
-
atc_ne: 105.095s
-
doc_feat: 77.559s
-
rca_score: 1219.115s
-
rca_star_score: 1234.910s
-
tot: 1284.729s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00100.02130.36850.11150.11150.36670.44270.4345
(0.05, 0.95)0.05340.07570.33610.08870.08870.33440.39530.3930
(0.1, 0.9)0.09020.11360.32870.09750.09750.32710.31730.3267
(0.15, 0.85)0.09030.11470.30390.09140.09140.30250.28320.2987
(0.2, 0.8)0.07720.09620.27630.08420.08420.27500.27090.2862
(0.25, 0.75)0.07560.09620.27080.08700.08700.26960.27450.2912
(0.3, 0.7)0.06080.07630.24950.08580.08580.24840.24980.2662
(0.35, 0.65)0.05650.07500.23030.08140.08140.22930.22530.2418
(0.4, 0.6)0.05020.07130.21220.06310.06310.21130.20070.2172
(0.45, 0.55)0.04800.06140.18440.06010.06010.18360.16950.1861
(0.5, 0.5)0.03870.04950.17500.05740.05740.17440.15500.1716
(0.55, 0.45)0.03740.04880.15410.05040.05040.15350.13210.1488
(0.6, 0.4)0.02920.03750.13050.04130.04130.13010.10750.1242
(0.65, 0.35)0.03000.03660.11730.04580.04580.11700.09380.1105
(0.7, 0.3)0.02820.03500.09400.03660.03660.09380.07050.0872
(0.75, 0.25)0.02730.03260.07070.02940.02940.07060.04730.0638
(0.8, 0.2)0.02550.03000.05900.02880.02880.05900.03600.0521
(0.85, 0.15)0.01980.02420.03890.02430.02430.03910.01930.0322
(0.9, 0.1)0.01980.02290.01840.01710.01710.01860.01320.0139
(0.95, 0.05)0.01520.01520.00960.01330.01330.00950.02550.0114
(1.0, 0.0)0.00520.00470.02050.00380.00380.0200NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00080.01700.14340.03480.0348
(0.05, 0.95)0.02720.04580.13280.03270.0327
(0.1, 0.9)0.04490.06860.14300.03240.0324
(0.15, 0.85)0.04660.07220.13800.03940.0394
(0.2, 0.8)0.04270.06440.13040.04350.0435
(0.25, 0.75)0.04490.06880.14340.04310.0431
(0.3, 0.7)0.03840.05850.14200.04740.0474
(0.35, 0.65)0.03900.06290.14270.04700.0470
(0.4, 0.6)0.03690.06570.14400.04010.0401
(0.45, 0.55)0.03930.05920.13190.04170.0417
(0.5, 0.5)0.03430.05340.14710.04080.0408
(0.55, 0.45)0.03930.06090.14550.05350.0535
(0.6, 0.4)0.03510.05200.13720.05510.0551
(0.65, 0.35)0.04180.05840.15110.06080.0608
(0.7, 0.3)0.04800.06160.14470.06380.0638
(0.75, 0.25)0.06070.07220.13210.05930.0593
(0.8, 0.2)0.07840.08470.15950.07170.0717
(0.85, 0.15)0.07760.09470.16300.10190.1019
(0.9, 0.1)0.11810.13200.15370.10780.1078
(0.95, 0.05)0.20880.17830.18680.18600.1860
(1.0, 0.0)0.03990.07280.89900.12000.1200
- -
target: M131
-
train: [0.95930534 0.04069466]
-
validation: [0.95922246 0.04077754]
-
evaluate_binary: 301.469s
-
evaluate_multiclass: 233.836s
-
kfcv: 197.763s
-
atc_mc: 208.329s
-
atc_ne: 203.979s
-
doc_feat: 76.945s
-
rca_score: 1221.431s
-
rca_star_score: 1220.546s
-
tot: 1273.127s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00080.02050.38720.06720.06720.38800.41220.4139
(0.05, 0.95)0.05060.07030.37310.06840.06840.37410.37590.3844
(0.1, 0.9)0.09440.10740.34490.05780.05780.34590.34480.3558
(0.15, 0.85)0.10500.10470.33460.06130.06130.33580.34300.3545
(0.2, 0.8)0.09370.09090.30510.05770.05770.30640.31690.3284
(0.25, 0.75)0.08480.08440.29140.05500.05500.29280.30170.3135
(0.3, 0.7)0.06400.06610.26510.05360.05360.26660.27440.2862
(0.35, 0.65)0.06260.06090.25030.05270.05270.25190.25640.2684
(0.4, 0.6)0.05470.05680.23160.05620.05620.23340.23460.2467
(0.45, 0.55)0.04490.04580.20240.04540.04540.20430.20380.2159
(0.5, 0.5)0.03880.04140.19150.04730.04730.19350.18950.2014
(0.55, 0.45)0.03220.03210.16520.04060.04060.16730.16150.1734
(0.6, 0.4)0.02690.03170.14320.03800.03800.14540.13800.1498
(0.65, 0.35)0.02040.02390.12500.03530.03530.12740.11910.1309
(0.7, 0.3)0.02360.02660.10930.03560.03560.11180.10290.1148
(0.75, 0.25)0.02580.03140.08390.02540.02540.08650.07750.0893
(0.8, 0.2)0.02000.02750.06260.02440.02440.06530.05620.0680
(0.85, 0.15)0.02370.02500.04470.02160.02160.04750.03830.0501
(0.9, 0.1)0.01940.02120.02650.01880.01880.02920.02110.0315
(0.95, 0.05)0.01490.01410.00740.01150.01150.00860.00880.0096
(1.0, 0.0)0.00260.00190.01720.00250.00250.0140NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00060.01660.11160.06470.0647
(0.05, 0.95)0.02420.04160.11690.06150.0615
(0.1, 0.9)0.04610.06390.11030.07450.0745
(0.15, 0.85)0.05300.06460.11970.06880.0688
(0.2, 0.8)0.05040.05960.11110.06780.0678
(0.25, 0.75)0.04870.06010.11800.06840.0684
(0.3, 0.7)0.04010.05190.11170.06930.0693
(0.35, 0.65)0.04280.05160.11820.06890.0689
(0.4, 0.6)0.04110.05220.12170.07430.0743
(0.45, 0.55)0.03750.04570.11050.08010.0801
(0.5, 0.5)0.03560.04810.12530.07550.0755
(0.55, 0.45)0.03260.04060.11500.07630.0763
(0.6, 0.4)0.03470.04710.11150.08680.0868
(0.65, 0.35)0.02640.04040.11870.08280.0828
(0.7, 0.3)0.04480.05730.13120.08760.0876
(0.75, 0.25)0.06000.07640.12050.09820.0982
(0.8, 0.2)0.05370.07730.11670.09920.0992
(0.85, 0.15)0.10140.10410.13020.12020.1202
(0.9, 0.1)0.11090.12880.15930.14790.1479
(0.95, 0.05)0.20590.18090.15320.18410.1841
(1.0, 0.0)0.04360.06270.85580.05330.0533
- -
target: M132
-
train: [0.96984621 0.03015379]
-
validation: [0.96976242 0.03023758]
-
evaluate_binary: 321.525s
-
evaluate_multiclass: 206.725s
-
kfcv: 173.106s
-
atc_mc: 170.535s
-
atc_ne: 107.990s
-
doc_feat: 84.375s
-
rca_score: 1216.493s
-
rca_star_score: 1232.990s
-
tot: 1283.130s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03210.05550.50600.12700.12700.50570.51170.5245
(0.05, 0.95)0.10240.11500.48990.12080.12080.48970.50040.5133
(0.1, 0.9)0.12530.16360.45430.10830.10830.45420.46470.4774
(0.15, 0.85)0.18810.20760.42930.10520.10520.42940.43830.4511
(0.2, 0.8)0.20690.21700.40930.09960.09960.40950.41600.4288
(0.25, 0.75)0.18930.20360.37720.08890.08890.37750.38330.3959
(0.3, 0.7)0.16760.17710.35430.10160.10160.35480.35950.3721
(0.35, 0.65)0.16220.17070.32240.08190.08190.32300.32670.3392
(0.4, 0.6)0.14140.15310.30190.08610.08610.30260.30530.3178
(0.45, 0.55)0.13940.14570.27600.07050.07050.27680.27890.2914
(0.5, 0.5)0.13120.12900.25150.06810.06810.25250.25400.2665
(0.55, 0.45)0.09840.10050.21610.05620.05620.21720.21850.2311
(0.6, 0.4)0.09450.09700.19180.05430.05430.19300.19410.2066
(0.65, 0.35)0.08580.08350.16300.05240.05240.16430.16520.1777
(0.7, 0.3)0.06230.06630.13990.04080.04080.14140.14210.1546
(0.75, 0.25)0.05760.06040.11390.03680.03680.11550.11610.1286
(0.8, 0.2)0.04550.04350.08960.03410.03410.09130.09180.1043
(0.85, 0.15)0.02940.03310.06530.02820.02820.06720.06750.0800
(0.9, 0.1)0.02570.02840.03510.01950.01950.03700.03720.0496
(0.95, 0.05)0.01960.01780.01190.01210.01210.0131NaNNaN
(1.0, 0.0)0.00410.00320.01650.00200.00200.0144NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.03570.05510.15850.08760.0876
(0.05, 0.95)0.08200.08960.16810.08920.0892
(0.1, 0.9)0.07380.11190.15880.08680.0868
(0.15, 0.85)0.11970.14340.16100.09490.0949
(0.2, 0.8)0.13680.15920.16760.10090.1009
(0.25, 0.75)0.12820.15790.16150.10530.1053
(0.3, 0.7)0.12210.14660.16630.08690.0869
(0.35, 0.65)0.13890.15560.15820.10100.1010
(0.4, 0.6)0.11770.14680.16770.09430.0943
(0.45, 0.55)0.14620.16080.16840.10580.1058
(0.5, 0.5)0.15650.16030.17320.10470.1047
(0.55, 0.45)0.13540.13550.15590.10540.1054
(0.6, 0.4)0.15470.15760.15960.10120.1012
(0.65, 0.35)0.16740.15700.15350.12420.1242
(0.7, 0.3)0.14000.15550.16350.11210.1121
(0.75, 0.25)0.15010.15830.16390.12090.1209
(0.8, 0.2)0.16580.15390.17740.12390.1239
(0.85, 0.15)0.13830.17480.19780.13160.1316
(0.9, 0.1)0.20170.20290.17950.17780.1778
(0.95, 0.05)0.29410.25090.21940.22080.2208
(1.0, 0.0)0.01020.00320.80250.00000.0000
- -
target: M14
-
train: [0.8902713 0.1097287]
-
validation: [0.89019438 0.10980562]
-
evaluate_binary: 221.946s
-
evaluate_multiclass: 119.692s
-
kfcv: 142.085s
-
atc_mc: 136.098s
-
atc_ne: 89.356s
-
doc_feat: 74.912s
-
rca_score: 1226.222s
-
rca_star_score: 1225.357s
-
tot: 1281.699s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00290.01590.22470.04380.04380.22660.61370.5902
(0.05, 0.95)0.04180.05040.20600.04440.04440.20790.63240.6089
(0.1, 0.9)0.03980.05010.19150.04060.04060.19360.64400.6210
(0.15, 0.85)0.03510.04870.18590.04380.04380.18800.60440.5828
(0.2, 0.8)0.03300.04290.17520.03960.03960.17740.49980.4828
(0.25, 0.75)0.02370.03680.15840.04090.04090.16080.31070.3069
(0.3, 0.7)0.02250.02860.14570.03470.03470.14820.20720.2193
(0.35, 0.65)0.02530.02990.14380.03960.03960.14630.14170.1629
(0.4, 0.6)0.01940.02920.12140.03340.03340.12400.11200.1338
(0.45, 0.55)0.01840.02490.10210.03380.03380.10490.07950.1015
(0.5, 0.5)0.01800.02100.09740.02820.02820.10020.05560.0783
(0.55, 0.45)0.02120.02570.08510.03210.03210.08800.03500.0571
(0.6, 0.4)0.02010.02680.07170.02650.02650.07470.02130.0396
(0.65, 0.35)0.02010.02550.06590.02390.02390.06900.02030.0339
(0.7, 0.3)0.01720.02540.04640.02450.02450.04960.02260.0223
(0.75, 0.25)0.01980.02260.03650.02190.02190.03980.02590.0157
(0.8, 0.2)0.01770.02060.02550.02070.02070.02840.03480.0178
(0.85, 0.15)0.01720.01650.01470.01800.01800.01650.04870.0270
(0.9, 0.1)0.01630.01730.01070.01290.01290.01190.05540.0321
(0.95, 0.05)0.01180.01080.01350.01070.01070.0113NaNNaN
(1.0, 0.0)0.00310.00250.02480.00370.00370.0210NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00210.01060.06720.02320.0232
(0.05, 0.95)0.02130.02850.06330.02960.0296
(0.1, 0.9)0.02110.02930.06150.03030.0303
(0.15, 0.85)0.01980.03110.06680.03070.0307
(0.2, 0.8)0.01990.02870.06800.02870.0287
(0.25, 0.75)0.01550.02710.06450.03410.0341
(0.3, 0.7)0.01580.02250.06530.03130.0313
(0.35, 0.65)0.01960.02560.07470.03090.0309
(0.4, 0.6)0.01590.02730.06550.03500.0350
(0.45, 0.55)0.01720.02470.05830.03360.0336
(0.5, 0.5)0.01800.02340.06580.03420.0342
(0.55, 0.45)0.02600.03240.06840.04640.0464
(0.6, 0.4)0.02790.03790.06530.04150.0415
(0.65, 0.35)0.03310.04280.07860.04040.0404
(0.7, 0.3)0.03120.04730.06780.05010.0501
(0.75, 0.25)0.04480.05260.07210.05420.0542
(0.8, 0.2)0.04680.05700.07610.06410.0641
(0.85, 0.15)0.06890.06140.07330.07430.0743
(0.9, 0.1)0.12050.10450.10670.08840.0884
(0.95, 0.05)0.15650.12380.15190.12890.1289
(1.0, 0.0)0.03530.06440.92210.13800.1380
- -
target: M141
-
train: [0.93485398 0.06514602]
-
validation: [0.93485961 0.06514039]
-
evaluate_binary: 300.410s
-
evaluate_multiclass: 211.143s
-
kfcv: 106.019s
-
atc_mc: 193.164s
-
atc_ne: 168.442s
-
doc_feat: 154.162s
-
rca_score: 1231.179s
-
rca_star_score: 1227.842s
-
tot: 1275.118s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00630.02560.29210.05680.05680.29150.59470.5748
(0.05, 0.95)0.05290.05920.26840.04500.04500.26800.60350.5845
(0.1, 0.9)0.07790.08750.25300.04610.04610.25270.53700.5222
(0.15, 0.85)0.07390.08180.23620.04560.04560.23600.43050.4262
(0.2, 0.8)0.07150.07740.21960.03800.03800.21950.25050.2598
(0.25, 0.75)0.06310.07120.21290.03980.03980.21290.21630.2345
(0.3, 0.7)0.05200.05860.18150.03620.03620.18170.18490.2042
(0.35, 0.65)0.04940.06030.18320.04020.04020.18350.18470.2052
(0.4, 0.6)0.04540.05070.16860.03800.03800.16900.16280.1837
(0.45, 0.55)0.03970.04390.14420.03160.03160.14470.13360.1546
(0.5, 0.5)0.04110.04360.13710.03780.03780.13770.11940.1403
(0.55, 0.45)0.02880.03510.12150.03190.03190.12230.09970.1205
(0.6, 0.4)0.03190.03100.10150.02970.02970.10240.07790.0986
(0.65, 0.35)0.02260.02610.09310.03110.03110.09410.06810.0886
(0.7, 0.3)0.02350.02590.07040.02280.02280.07150.04540.0656
(0.75, 0.25)0.02210.02290.05700.02200.02200.05820.03250.0519
(0.8, 0.2)0.01950.02020.04240.01940.01940.04370.02150.0373
(0.85, 0.15)0.02170.02260.02610.01910.01910.02740.01550.0226
(0.9, 0.1)0.01790.01630.01450.01400.01400.01570.01680.0123
(0.95, 0.05)0.01440.01140.01040.01060.01060.01020.02770.0127
(1.0, 0.0)0.00280.00200.01990.00210.00210.0181NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00490.01840.09010.04230.0423
(0.05, 0.95)0.02710.03280.08380.03640.0364
(0.1, 0.9)0.04100.05180.08360.04220.0422
(0.15, 0.85)0.04150.05090.08260.03970.0397
(0.2, 0.8)0.04300.05120.08090.03830.0383
(0.25, 0.75)0.04000.05050.08900.03630.0363
(0.3, 0.7)0.03490.04500.07360.04990.0499
(0.35, 0.65)0.03550.05060.09070.04920.0492
(0.4, 0.6)0.03660.04610.09180.03720.0372
(0.45, 0.55)0.03690.04540.08080.04900.0490
(0.5, 0.5)0.04320.04870.09340.05260.0526
(0.55, 0.45)0.03100.04480.09230.04310.0431
(0.6, 0.4)0.04370.04440.08680.05120.0512
(0.65, 0.35)0.03310.04280.10020.05720.0572
(0.7, 0.3)0.04580.05120.08690.05530.0553
(0.75, 0.25)0.05970.05680.09170.06860.0686
(0.8, 0.2)0.07050.06660.09750.06850.0685
(0.85, 0.15)0.10790.10450.09860.09050.0905
(0.9, 0.1)0.15450.11620.11460.09800.0980
(0.95, 0.05)0.24560.15970.15720.14790.1479
(1.0, 0.0)0.01000.01820.90350.02670.0267
- -
target: M142
-
train: [0.98660791 0.01339209]
-
validation: [0.98652268 0.01347732]
-
evaluate_binary: 360.311s
-
evaluate_multiclass: 147.946s
-
kfcv: 160.268s
-
atc_mc: 107.097s
-
atc_ne: 160.737s
-
doc_feat: 79.718s
-
rca_score: 1250.102s
-
rca_star_score: 1217.368s
-
tot: 1290.194s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00070.05030.68060.23520.23520.68000.68670.6920
(0.05, 0.95)0.05070.09970.63860.21920.21920.63810.64450.6499
(0.1, 0.9)0.10060.14830.60760.20600.20600.60720.61330.6187
(0.15, 0.85)0.15040.19080.57840.20070.20070.57810.58380.5892
(0.2, 0.8)0.19750.23260.54310.18940.18940.54290.54840.5536
(0.25, 0.75)0.20960.27430.49670.16170.16170.49660.50200.5073
(0.3, 0.7)0.18610.32310.47130.16900.16900.47140.47630.4816
(0.35, 0.65)0.16750.37440.43490.14470.14470.43510.43990.4452
(0.4, 0.6)0.15050.43060.40450.14140.14140.40480.40930.4146
(0.45, 0.55)0.14080.48070.36930.13110.13110.36970.37400.3793
(0.5, 0.5)0.11120.51540.33450.11780.11780.33510.33910.3444
(0.55, 0.45)0.09060.50290.29540.09880.09880.29610.29990.3053
(0.6, 0.4)0.07680.46230.25900.08720.08720.25980.26360.2689
(0.65, 0.35)0.05780.39500.22280.07300.07300.22370.22730.2326
(0.7, 0.3)0.04630.32320.19290.06770.06770.19390.19740.2027
(0.75, 0.25)0.03230.26910.16010.06050.06050.16120.16450.1699
(0.8, 0.2)0.02530.19710.12390.04620.04620.12510.12830.1337
(0.85, 0.15)0.01890.12440.09050.03600.03600.09190.09490.1003
(0.9, 0.1)0.01670.05920.05740.02580.02580.0589NaNNaN
(0.95, 0.05)0.01370.02310.02360.01520.01520.0252NaNNaN
(1.0, 0.0)0.00000.00000.00980.00000.00000.0081NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00090.06220.24370.23920.2392
(0.05, 0.95)0.01930.08340.23430.24260.2426
(0.1, 0.9)0.03760.10450.24000.23880.2388
(0.15, 0.85)0.05620.11400.24620.23780.2378
(0.2, 0.8)0.07480.12160.24490.23560.2356
(0.25, 0.75)0.08730.13410.22690.25000.2500
(0.3, 0.7)0.07920.14990.24070.22980.2298
(0.35, 0.65)0.07720.17530.23780.24580.2458
(0.4, 0.6)0.07450.20420.24540.23140.2314
(0.45, 0.55)0.07820.23230.24270.21860.2186
(0.5, 0.5)0.06870.26650.24390.23370.2337
(0.55, 0.45)0.06230.28480.23130.25860.2586
(0.6, 0.4)0.06000.28820.22540.25630.2563
(0.65, 0.35)0.05290.28820.21630.26980.2698
(0.7, 0.3)0.05220.26740.23160.23840.2384
(0.75, 0.25)0.05030.27180.23610.24680.2468
(0.8, 0.2)0.05610.25450.22700.27290.2729
(0.85, 0.15)0.05960.22280.22780.28010.2801
(0.9, 0.1)0.11130.20370.23970.30040.3004
(0.95, 0.05)0.16410.21510.27900.33350.3335
(1.0, 0.0)0.00000.00000.71460.00000.0000
- -
target: M143
-
train: [0.97382063 0.02617937]
-
validation: [0.97382289 0.02617711]
-
evaluate_binary: 473.804s
-
evaluate_multiclass: 228.844s
-
kfcv: 156.644s
-
atc_mc: 102.505s
-
atc_ne: 101.586s
-
doc_feat: 79.162s
-
rca_score: 1232.456s
-
rca_star_score: 1207.609s
-
tot: 1277.000s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.30040.02730.44140.15470.15470.44120.44710.4553
(0.05, 0.95)0.36970.08970.41270.15110.15110.41260.41900.4283
(0.1, 0.9)0.36780.15640.39060.12700.12700.39060.39890.4083
(0.15, 0.85)0.39590.22150.36340.12380.12380.36350.37190.3814
(0.2, 0.8)0.42990.28870.34890.11960.11960.34910.35580.3653
(0.25, 0.75)0.35290.32750.32880.12600.12600.32920.33430.3437
(0.3, 0.7)0.36010.35410.30450.11160.11160.30500.30860.3179
(0.35, 0.65)0.28990.31110.28030.10780.10780.28090.28260.2920
(0.4, 0.6)0.31320.31750.25920.09440.09440.25990.25960.2689
(0.45, 0.55)0.28180.28470.23990.08580.08580.24070.23830.2474
(0.5, 0.5)0.22730.23260.20830.07700.07700.20920.20600.2151
(0.55, 0.45)0.18700.20040.19210.07700.07700.19320.18810.1971
(0.6, 0.4)0.17180.16900.16690.06450.06450.16810.16210.1710
(0.65, 0.35)0.15950.14730.14800.06000.06000.14930.14280.1517
(0.7, 0.3)0.11260.11180.12350.04990.04990.12490.11810.1270
(0.75, 0.25)0.09640.08620.09790.04240.04240.09950.09250.1014
(0.8, 0.2)0.07670.06910.07900.03310.03310.08070.07360.0825
(0.85, 0.15)0.05670.04910.05530.02500.02500.05710.04990.0588
(0.9, 0.1)0.03270.03280.03440.02030.02030.03620.02930.0378
(0.95, 0.05)0.01610.01540.01480.01340.01340.0162NaNNaN
(1.0, 0.0)0.00140.00150.01110.00070.00070.0090NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.37440.02390.15290.04200.0420
(0.05, 0.95)0.43120.05930.14760.04330.0433
(0.1, 0.9)0.39290.10050.14780.03900.0390
(0.15, 0.85)0.40420.14340.14320.04680.0468
(0.2, 0.8)0.44930.19250.15140.04210.0421
(0.25, 0.75)0.38370.22120.15400.04980.0498
(0.3, 0.7)0.42860.24600.15180.04860.0486
(0.35, 0.65)0.37500.23280.15030.05440.0544
(0.4, 0.6)0.44970.25650.15260.05900.0590
(0.45, 0.55)0.44150.24880.15790.06100.0610
(0.5, 0.5)0.39400.22630.14320.05670.0567
(0.55, 0.45)0.36220.21630.15400.05920.0592
(0.6, 0.4)0.40270.21140.14840.07200.0720
(0.65, 0.35)0.43410.20890.15770.07250.0725
(0.7, 0.3)0.36350.18840.15210.07320.0732
(0.75, 0.25)0.39530.18130.14530.08400.0840
(0.8, 0.2)0.37440.17590.15870.08020.0802
(0.85, 0.15)0.39570.17560.15400.09090.0909
(0.9, 0.1)0.38410.18670.17560.13440.1344
(0.95, 0.05)0.35830.18990.23740.18360.1836
(1.0, 0.0)0.00000.02000.85890.02000.0200
- -
target: MCAT
-
train: [0.74589597 0.25410403]
-
validation: [0.74591793 0.25408207]
-
evaluate_binary: 239.003s
-
evaluate_multiclass: 125.654s
-
kfcv: 109.511s
-
atc_mc: 133.150s
-
atc_ne: 93.708s
-
doc_feat: 96.904s
-
rca_score: 1237.329s
-
rca_star_score: 1220.138s
-
tot: 1304.368s
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01160.01700.11980.04300.04300.12370.53870.5136
(0.05, 0.95)0.02470.02970.11420.04340.04340.11820.54430.5192
(0.1, 0.9)0.02620.03070.10650.04210.04210.11050.55200.5269
(0.15, 0.85)0.02560.03240.09640.03790.03790.10050.56210.5370
(0.2, 0.8)0.02350.02710.08630.03730.03730.09040.57190.5468
(0.25, 0.75)0.02440.02670.08640.04120.04120.09050.55060.5259
(0.3, 0.7)0.02370.02750.07640.03810.03810.08060.44550.4240
(0.35, 0.65)0.02340.02690.06320.03110.03110.06740.25150.2414
(0.4, 0.6)0.02590.02890.06180.03160.03160.06610.06080.0744
(0.45, 0.55)0.02090.02460.05400.02970.02970.05840.02010.0327
(0.5, 0.5)0.02530.03070.04350.02990.02990.04780.05430.0336
(0.55, 0.45)0.02780.02950.03890.02480.02480.04280.10600.0805
(0.6, 0.4)0.02230.02540.02910.02430.02430.03260.13370.1085
(0.65, 0.35)0.02540.02780.02860.02590.02590.03150.14250.1175
(0.7, 0.3)0.02490.02630.02090.02110.02110.02310.15490.1299
(0.75, 0.25)0.02640.02610.02020.02250.02250.02120.16010.1351
(0.8, 0.2)0.02320.02290.01540.01660.01660.01460.16940.1444
(0.85, 0.15)0.02290.02280.01610.01650.01650.01360.17740.1524
(0.9, 0.1)0.02020.01770.01940.01400.01400.01550.18460.1596
(0.95, 0.05)0.02240.02050.02630.01350.01350.02150.19280.1678
(1.0, 0.0)0.01150.01210.03200.01000.01000.0270NaNNaN
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00730.01030.02990.02090.0209
(0.05, 0.95)0.01310.01640.03070.02190.0219
(0.1, 0.9)0.01460.01790.03320.02310.0231
(0.15, 0.85)0.01530.02000.03080.02220.0222
(0.2, 0.8)0.01500.01750.02860.02380.0238
(0.25, 0.75)0.01640.01840.03580.02580.0258
(0.3, 0.7)0.01740.02050.03570.02810.0281
(0.35, 0.65)0.01870.02180.03180.02430.0243
(0.4, 0.6)0.02190.02490.03760.02750.0275
(0.45, 0.55)0.01940.02320.03770.02800.0280
(0.5, 0.5)0.02600.03210.03660.03220.0322
(0.55, 0.45)0.03190.03380.04190.03040.0304
(0.6, 0.4)0.02890.03280.03970.03180.0318
(0.65, 0.35)0.03760.04080.05100.04010.0401
(0.7, 0.3)0.04280.04530.04980.03770.0377
(0.75, 0.25)0.05390.05240.05930.04720.0472
(0.8, 0.2)0.06260.06040.06080.04580.0458
(0.85, 0.15)0.07640.07670.07350.05940.0594
(0.9, 0.1)0.10710.09260.09080.07440.0744
(0.95, 0.05)0.20760.18720.14090.12100.1210
(1.0, 0.0)0.13820.14900.93690.32680.3268
+ +
target: C12
+
train: [0.98358389 0.01641611]
+
validation: [0.98349892 0.01650108]
+
evaluate_binary: 258.363s
+
evaluate_multiclass: 116.180s
+
kfcv: 104.789s
+
atc_mc: 154.996s
+
atc_ne: 144.298s
+
doc_feat: 88.728s
+
rca_score: 909.590s
+
rca_star_score: 899.370s
+
tot: 951.474s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04920.04930.93750.51580.51580.9361NaNNaN
(0.05, 0.95)0.09670.09680.89000.49700.49700.8887NaNNaN
(0.1, 0.9)0.14320.14380.84290.46830.46830.84160.85300.8561
(0.15, 0.85)0.19630.19660.79000.42860.42860.7888NaNNaN
(0.2, 0.8)0.24020.23990.74640.40800.40800.7452NaNNaN
(0.25, 0.75)0.28960.28790.69680.38060.38060.6957NaNNaN
(0.3, 0.7)0.33860.32560.64780.35870.35870.6468NaNNaN
(0.35, 0.65)0.38040.33420.60530.33750.33750.6043NaNNaN
(0.4, 0.6)0.42460.33030.55750.30660.30660.5566NaNNaN
(0.45, 0.55)0.43460.29080.51030.28710.28710.5095NaNNaN
(0.5, 0.5)0.41710.27610.46120.25270.25270.4604NaNNaN
(0.55, 0.45)0.38030.24470.41400.22690.22690.4133NaNNaN
(0.6, 0.4)0.33440.20730.36850.20630.20630.3678NaNNaN
(0.65, 0.35)0.28660.17850.31900.17820.17820.3184NaNNaN
(0.7, 0.3)0.23390.14380.27180.14980.14980.2713NaNNaN
(0.75, 0.25)0.17810.11040.22340.12740.12740.2229NaNNaN
(0.8, 0.2)0.12750.07610.17660.10270.10270.1762NaNNaN
(0.85, 0.15)0.09000.05460.12970.07310.07310.1293NaNNaN
(0.9, 0.1)0.04940.03620.08180.04930.04930.0815NaNNaN
(0.95, 0.05)0.02760.02290.03420.02380.02380.0340NaNNaN
(1.0, 0.0)0.00180.00130.01320.00230.00230.0134NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.05, 0.95)0.09300.09310.57110.09010.0901
(0.1, 0.9)0.09100.09220.57200.08920.0892
(0.15, 0.85)0.10240.10320.56100.10050.1005
(0.2, 0.8)0.09500.09510.56910.09220.0922
(0.25, 0.75)0.09990.10040.56380.09840.0984
(0.3, 0.7)0.10420.10430.55990.10070.1007
(0.35, 0.65)0.09040.09130.57290.08860.0886
(0.4, 0.6)0.09150.09200.57220.08890.0889
(0.45, 0.55)0.08940.09070.57350.08940.0894
(0.5, 0.5)0.09570.09650.56770.09450.0945
(0.55, 0.45)0.09270.09510.56910.09060.0906
(0.6, 0.4)0.08530.08540.57880.08260.0826
(0.65, 0.35)0.09400.09510.56910.09550.0955
(0.7, 0.3)0.09190.09340.57080.09340.0934
(0.75, 0.25)0.09810.09920.56500.09680.0968
(0.8, 0.2)0.09280.09290.57130.09290.0929
(0.85, 0.15)0.08470.08570.57830.08590.0859
(0.9, 0.1)0.08580.08660.57740.08390.0839
(0.95, 0.05)0.08370.08710.57470.08950.0895
(1.0, 0.0)0.00000.00000.66420.00000.0000
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train: [0.95913254 0.04086746]
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validation: [0.95904968 0.04095032]
+
evaluate_binary: 293.415s
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evaluate_multiclass: 130.949s
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kfcv: 164.741s
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atc_mc: 163.221s
+
atc_ne: 98.244s
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doc_feat: 127.361s
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rca_score: 661.548s
+
rca_star_score: 635.802s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01100.01800.94120.52120.52120.9413NaNNaN
(0.05, 0.95)0.06330.07020.88890.49620.49620.8891NaNNaN
(0.1, 0.9)0.10930.11410.84330.46130.46130.8436NaNNaN
(0.15, 0.85)0.15830.15500.79370.43400.43400.7940NaNNaN
(0.2, 0.8)0.20580.18520.74450.40340.40340.7449NaNNaN
(0.25, 0.75)0.23670.19550.69590.37990.37990.6963NaNNaN
(0.3, 0.7)0.24700.20190.64580.35120.35120.6463NaNNaN
(0.35, 0.65)0.23680.21230.59660.31790.31790.5971NaNNaN
(0.4, 0.6)0.20960.18710.54760.30490.30490.5482NaNNaN
(0.45, 0.55)0.18980.17290.49870.26770.26770.4994NaNNaN
(0.5, 0.5)0.16980.13740.44880.24450.24450.4495NaNNaN
(0.55, 0.45)0.15310.15040.40180.22090.22090.4026NaNNaN
(0.6, 0.4)0.12730.10820.35230.19480.19480.3531NaNNaN
(0.65, 0.35)0.12240.11310.30230.16100.16100.3032NaNNaN
(0.7, 0.3)0.08740.07170.25310.13660.13660.2540NaNNaN
(0.75, 0.25)0.06890.06160.20410.11300.11300.2051NaNNaN
(0.8, 0.2)0.05530.04930.15560.08330.08330.1567NaNNaN
(0.85, 0.15)0.04650.04930.10840.06040.06040.1095NaNNaN
(0.9, 0.1)0.03800.03610.05750.03510.03510.0587NaNNaN
(0.95, 0.05)0.02870.02840.00880.01370.01370.0100NaNNaN
(1.0, 0.0)0.01400.01230.04030.02610.02610.0390NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.02130.03500.47040.03570.0357
(0.05, 0.95)0.02830.04180.46360.04250.0425
(0.1, 0.9)0.02230.03510.47080.03530.0353
(0.15, 0.85)0.02330.03660.46940.03670.0367
(0.2, 0.8)0.02410.03680.46920.03690.0369
(0.25, 0.75)0.02180.03560.47040.03570.0357
(0.3, 0.7)0.02690.03800.46790.03820.0382
(0.35, 0.65)0.02370.03890.46670.03940.0394
(0.4, 0.6)0.03080.03850.46690.03920.0392
(0.45, 0.55)0.02450.03800.46730.03880.0388
(0.5, 0.5)0.02650.04090.46460.04160.0416
(0.55, 0.45)0.02540.03340.47240.03370.0337
(0.6, 0.4)0.02550.03470.47080.03540.0354
(0.65, 0.35)0.02800.03950.46560.04050.0405
(0.7, 0.3)0.03020.03980.46510.04100.0410
(0.75, 0.25)0.02780.04010.46330.04290.0429
(0.8, 0.2)0.02460.03770.46790.03830.0383
(0.85, 0.15)0.01200.02360.48500.02110.0211
(0.9, 0.1)0.02980.04550.46670.03940.0394
(0.95, 0.05)0.02690.03380.47950.02670.0267
(1.0, 0.0)0.00000.02530.50610.00000.0000
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+
train: [0.81950924 0.18049076]
+
validation: [0.81943844 0.18056156]
+
evaluate_binary: 329.208s
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evaluate_multiclass: 130.846s
+
kfcv: 170.297s
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atc_mc: 170.842s
+
atc_ne: 107.158s
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doc_feat: 71.484s
+
rca_score: 1210.284s
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rca_star_score: 1189.765s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.04000.04040.17740.06710.06710.18060.55880.5363
(0.1, 0.9)0.04590.04610.16370.05960.05960.16700.57070.5483
(0.15, 0.85)0.03750.03790.14560.05160.05160.14900.53380.5155
(0.2, 0.8)0.04300.04250.14390.05520.05520.14730.40910.3967
(0.25, 0.75)0.03260.03250.12410.05050.05050.12760.24680.2473
(0.3, 0.7)0.03510.03800.11510.04820.04820.11860.14030.1543
(0.35, 0.65)0.02910.02990.10700.04680.04680.11060.10550.1264
(0.4, 0.6)0.02990.02940.09780.03740.03740.10140.08050.1025
(0.45, 0.55)0.03300.03040.08350.03850.03850.08720.04740.0695
(0.5, 0.5)0.03180.03350.07130.03350.03350.07490.01960.0393
(0.55, 0.45)0.03300.03150.05730.02990.02990.06110.01810.0171
(0.6, 0.4)0.02770.02620.04980.02930.02930.05340.03610.0183
(0.65, 0.35)0.03020.03210.04440.02910.02910.04790.05030.0306
(0.7, 0.3)0.02990.02690.02940.02270.02270.03250.07020.0476
(0.75, 0.25)0.02610.02480.02170.02370.02370.02410.08230.0597
(0.8, 0.2)0.03010.02890.01680.02050.02050.01820.09110.0686
(0.85, 0.15)0.02420.02180.01560.01890.01890.01530.10180.0793
(0.9, 0.1)0.02230.01930.01860.01460.01460.01590.11510.0926
(0.95, 0.05)0.01820.01410.02680.01090.01090.02280.12590.1034
(1.0, 0.0)0.01190.00860.03750.00930.00930.0334NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00440.00930.04780.02680.0268
(0.05, 0.95)0.02130.02240.05350.02970.0297
(0.1, 0.9)0.02600.02690.05250.02800.0280
(0.15, 0.85)0.02140.02270.04790.02710.0271
(0.2, 0.8)0.02740.02770.05440.02980.0298
(0.25, 0.75)0.02160.02240.04900.03000.0300
(0.3, 0.7)0.02460.02820.05120.03060.0306
(0.35, 0.65)0.02260.02440.05360.03360.0336
(0.4, 0.6)0.02530.02580.05560.02820.0282
(0.45, 0.55)0.03130.02940.05380.03480.0348
(0.5, 0.5)0.03280.03540.05390.03310.0331
(0.55, 0.45)0.03780.03650.05040.03530.0353
(0.6, 0.4)0.03480.03370.05490.03740.0374
(0.65, 0.35)0.04320.04810.06560.04280.0428
(0.7, 0.3)0.05480.04880.05850.04460.0446
(0.75, 0.25)0.05690.05350.06330.05470.0547
(0.8, 0.2)0.07670.07700.07430.05760.0576
(0.85, 0.15)0.08670.07810.08430.07260.0726
(0.9, 0.1)0.10970.10000.09420.08070.0807
(0.95, 0.05)0.18110.15140.13110.11930.1193
(1.0, 0.0)0.03630.03630.92180.10030.1003
+ +
target: C151
+
train: [0.89778815 0.10221185]
+
validation: [0.89779698 0.10220302]
+
evaluate_binary: 355.746s
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evaluate_multiclass: 136.969s
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kfcv: 195.146s
+
atc_mc: 193.365s
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atc_ne: 187.934s
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doc_feat: 73.112s
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rca_score: 1235.567s
+
rca_star_score: 1236.092s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00030.01080.22360.08270.08270.22390.60960.5989
(0.05, 0.95)0.04650.04750.22160.08610.08610.22200.52290.5149
(0.1, 0.9)0.05670.05870.20620.07190.07190.20660.42470.4188
(0.15, 0.85)0.04440.04680.18460.06710.06710.18510.29190.2922
(0.2, 0.8)0.04530.04580.17840.06810.06810.17900.18580.1927
(0.25, 0.75)0.03350.04020.16520.06240.06240.16580.18300.1919
(0.3, 0.7)0.03040.03200.15170.05610.05610.15240.16000.1702
(0.35, 0.65)0.02780.03330.13480.05090.05090.13560.14120.1516
(0.4, 0.6)0.02480.02630.12460.04790.04790.12550.12350.1341
(0.45, 0.55)0.02570.02850.10620.04130.04130.10710.09880.1099
(0.5, 0.5)0.02710.03250.10510.04920.04920.10610.08780.0995
(0.55, 0.45)0.02370.03150.09120.04260.04260.09230.06690.0787
(0.6, 0.4)0.02300.02890.07800.03910.03910.07910.04800.0598
(0.65, 0.35)0.02360.02570.05950.03420.03420.06070.02870.0368
(0.7, 0.3)0.02550.03010.05360.02850.02850.05480.02040.0257
(0.75, 0.25)0.02020.02470.03470.01930.01930.03600.01860.0156
(0.8, 0.2)0.02360.02700.02880.02220.02220.03010.02060.0150
(0.85, 0.15)0.02060.02230.01730.01900.01900.01850.03220.0234
(0.9, 0.1)0.01790.01880.01260.01430.01430.01280.04600.0355
(0.95, 0.05)0.01310.01120.01450.00990.00990.01350.05880.0481
(1.0, 0.0)0.00420.00290.02600.00520.00520.0243NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.00710.05740.03360.0336
(0.05, 0.95)0.02330.02500.06480.03850.0385
(0.1, 0.9)0.02950.03320.06330.03270.0327
(0.15, 0.85)0.02450.02810.05670.03300.0330
(0.2, 0.8)0.02650.02870.06180.03550.0355
(0.25, 0.75)0.02130.02790.06290.03910.0391
(0.3, 0.7)0.02060.02330.06170.03460.0346
(0.35, 0.65)0.02120.02700.05730.03610.0361
(0.4, 0.6)0.01970.02240.05990.03560.0356
(0.45, 0.55)0.02310.02710.05510.03720.0372
(0.5, 0.5)0.02700.03450.06810.04630.0463
(0.55, 0.45)0.02640.03710.06760.04530.0453
(0.6, 0.4)0.02940.03830.06750.04820.0482
(0.65, 0.35)0.03490.03890.06570.04930.0493
(0.7, 0.3)0.04450.05360.07590.04840.0484
(0.75, 0.25)0.04240.05330.06280.04220.0422
(0.8, 0.2)0.06330.07240.08190.06030.0603
(0.85, 0.15)0.07570.08220.08830.06860.0686
(0.9, 0.1)0.10510.10820.10590.08280.0828
(0.95, 0.05)0.16440.13730.13690.12130.1213
(1.0, 0.0)0.00000.00050.91190.04730.0473
+ +
target: C1511
+
train: [0.98280629 0.01719371]
+
validation: [0.98272138 0.01727862]
+
evaluate_binary: 301.572s
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evaluate_multiclass: 149.682s
+
kfcv: 102.539s
+
atc_mc: 99.622s
+
atc_ne: 92.903s
+
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+
rca_score: 1097.941s
+
rca_star_score: 1093.801s
+
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00010.10990.87510.60870.60870.87490.88420.8890
(0.05, 0.95)0.05010.15820.82660.57030.57030.82640.83580.8405
(0.1, 0.9)0.10010.20010.78470.54530.54530.78460.79380.7986
(0.15, 0.85)0.14900.24090.73590.51020.51020.73590.74510.7498
(0.2, 0.8)0.17710.23740.69530.48310.48310.69540.70450.7092
(0.25, 0.75)0.16700.21840.65320.45390.45390.65340.66230.6671
(0.3, 0.7)0.15030.20260.60660.42030.42030.60690.61570.6205
(0.35, 0.65)0.12210.18190.55820.38100.38100.55850.56730.5721
(0.4, 0.6)0.09870.15870.51360.35880.35880.51400.52270.5275
(0.45, 0.55)0.09180.14030.47520.32960.32960.47570.48430.4891
(0.5, 0.5)0.07290.11320.43350.30670.30670.43410.44260.4474
(0.55, 0.45)0.06140.10570.38590.26830.26830.3866NaNNaN
(0.6, 0.4)0.05150.08650.34180.23960.23960.3425NaNNaN
(0.65, 0.35)0.03930.07290.29550.20440.20440.2963NaNNaN
(0.7, 0.3)0.03870.06070.25200.17830.17830.2529NaNNaN
(0.75, 0.25)0.03840.05230.20710.14910.14910.2081NaNNaN
(0.8, 0.2)0.03050.04070.16410.11920.11920.1652NaNNaN
(0.85, 0.15)0.02900.03050.11910.09000.09000.1203NaNNaN
(0.9, 0.1)0.02730.02630.07670.06110.06110.0779NaNNaN
(0.95, 0.05)0.02320.02060.03060.02890.02890.0319NaNNaN
(1.0, 0.0)0.00360.00250.01340.00270.00270.0120NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.19650.46180.09870.0987
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(0.1, 0.9)0.01870.19960.45940.12230.1223
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binmulkfcvatc_mcatc_ne
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(0.3, 0.7)0.04060.03660.12520.05370.0537
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(0.8, 0.2)0.07720.07800.15110.10240.1024
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(0.9, 0.1)0.14700.13680.16940.16880.1688
(0.95, 0.05)0.24200.20450.21750.21850.2185
(1.0, 0.0)0.12060.07750.78030.02450.0245
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binmulkfcvatc_mcatc_ne
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(0.25, 0.75)0.17520.18800.25200.23500.2350
(0.3, 0.7)0.16780.18520.26810.21750.2175
(0.35, 0.65)0.17250.18040.26860.21920.2192
(0.4, 0.6)0.17630.18130.26980.22220.2222
(0.45, 0.55)0.21900.18780.26670.21830.2183
(0.5, 0.5)0.21000.16820.26730.22790.2279
(0.55, 0.45)0.19420.18490.26710.22530.2253
(0.6, 0.4)0.17330.18250.25810.25260.2526
(0.65, 0.35)0.19500.17930.27070.24240.2424
(0.7, 0.3)0.19070.16850.24840.23530.2353
(0.75, 0.25)0.19230.17870.26400.24270.2427
(0.8, 0.2)0.19290.16780.27060.25050.2505
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(0.9, 0.1)0.22610.18530.27500.27270.2727
(0.95, 0.05)0.26600.22210.27640.34080.3408
(1.0, 0.0)0.06840.05350.72910.01000.0100
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.95, 0.05)0.04650.03510.03000.01910.01910.0295NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.35, 0.65)0.45640.39910.33080.41030.4103
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(0.5, 0.5)0.46770.45410.31280.42990.4299
(0.55, 0.45)0.44400.45920.31810.41430.4143
(0.6, 0.4)0.45360.45540.32150.41220.4122
(0.65, 0.35)0.44280.44290.32520.40790.4079
(0.7, 0.3)0.46260.45180.31100.41560.4156
(0.75, 0.25)0.47030.45470.29650.43910.4391
(0.8, 0.2)0.38800.39810.34090.39750.3975
(0.85, 0.15)0.43000.40580.32270.42170.4217
(0.9, 0.1)0.42350.40520.31210.44730.4473
(0.95, 0.05)0.33250.28500.41580.35160.3516
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(0.7, 0.3)0.03720.05070.16130.03870.03870.16350.18810.2075
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(0.9, 0.1)0.02110.02160.02760.02300.02300.0301NaNNaN
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(0.1, 0.9)0.04950.08630.22040.20980.2098
(0.15, 0.85)0.05220.09710.22050.20880.2088
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(0.25, 0.75)0.05090.09740.22240.21580.2158
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(0.35, 0.65)0.05000.10340.21740.22320.2232
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(0.45, 0.55)0.06270.10420.20650.22600.2260
(0.5, 0.5)0.07370.12090.23140.21480.2148
(0.55, 0.45)0.06550.09040.22500.19860.1986
(0.6, 0.4)0.07740.10570.22630.21820.2182
(0.65, 0.35)0.08240.11430.22870.21770.2177
(0.7, 0.3)0.06450.14570.23550.23280.2328
(0.75, 0.25)0.06950.14150.22680.22990.2299
(0.8, 0.2)0.09320.16540.23810.24370.2437
(0.85, 0.15)0.13280.17780.22850.26420.2642
(0.9, 0.1)0.13400.16420.29490.22800.2280
(0.95, 0.05)0.20250.22790.29150.27690.2769
(1.0, 0.0)0.08470.10030.70470.02670.0267
+ +
target: C181
+
train: [0.94798687 0.05201313]
+
validation: [0.94790497 0.05209503]
+
evaluate_binary: 381.404s
+
evaluate_multiclass: 222.049s
+
kfcv: 197.485s
+
atc_mc: 198.730s
+
atc_ne: 199.850s
+
doc_feat: 79.764s
+
rca_score: 1126.500s
+
rca_star_score: 1125.388s
+
tot: 1178.360s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03250.04830.70900.17600.17600.70810.73810.7500
(0.05, 0.95)0.08630.09690.65840.15310.15310.65760.68750.6994
(0.1, 0.9)0.14190.14470.63320.15470.15470.63250.66230.6742
(0.15, 0.85)0.18470.15380.59450.13960.13960.59390.62360.6355
(0.2, 0.8)0.16680.15070.55630.13810.13810.55590.58540.5973
(0.25, 0.75)0.14960.14050.51890.12570.12570.51860.54800.5599
(0.3, 0.7)0.12920.12500.48580.12380.12380.48560.51490.5268
(0.35, 0.65)0.11820.11090.43990.10680.10680.43980.46900.4809
(0.4, 0.6)0.12000.10350.40570.09830.09830.40570.43480.4467
(0.45, 0.55)0.10490.09550.36980.09340.09340.37000.39890.4108
(0.5, 0.5)0.09240.07960.33610.09130.09130.33640.36520.3771
(0.55, 0.45)0.08560.07090.30020.08380.08380.30060.32930.3412
(0.6, 0.4)0.08030.06830.25950.06980.06980.26000.28860.3005
(0.65, 0.35)0.07260.05940.22110.05580.05580.22170.25020.2621
(0.7, 0.3)0.05340.04720.18610.05450.05450.18690.21520.2271
(0.75, 0.25)0.04700.04410.14510.03880.03880.14600.17420.1861
(0.8, 0.2)0.04040.03630.11400.03660.03660.11500.14310.1550
(0.85, 0.15)0.03020.02780.07460.03330.03330.0757NaNNaN
(0.9, 0.1)0.02700.02380.03480.02040.02040.0360NaNNaN
(0.95, 0.05)0.02160.01990.00820.01470.01470.0077NaNNaN
(1.0, 0.0)0.00700.00550.03930.00880.00880.0378NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.04930.06600.27350.22240.2224
(0.05, 0.95)0.07140.08250.25600.24300.2430
(0.1, 0.9)0.10060.10520.27260.23380.2338
(0.15, 0.85)0.14110.10920.27040.23700.2370
(0.2, 0.8)0.12470.11460.26980.23880.2388
(0.25, 0.75)0.11290.11240.26930.22990.2299
(0.3, 0.7)0.09820.10640.27850.22680.2268
(0.35, 0.65)0.09810.09950.26110.21530.2153
(0.4, 0.6)0.13010.10440.26760.24130.2413
(0.45, 0.55)0.11700.10300.27000.22190.2219
(0.5, 0.5)0.12400.10380.28030.21240.2124
(0.55, 0.45)0.13780.10750.28490.21800.2180
(0.6, 0.4)0.15600.12920.27780.23650.2365
(0.65, 0.35)0.15830.11200.27260.24230.2423
(0.7, 0.3)0.12670.10950.28500.23350.2335
(0.75, 0.25)0.12680.12890.26910.23490.2349
(0.8, 0.2)0.16660.15410.30750.22360.2236
(0.85, 0.15)0.14350.13990.31070.24850.2485
(0.9, 0.1)0.20170.16810.29140.25820.2582
(0.95, 0.05)0.20700.19510.35030.22980.2298
(1.0, 0.0)0.03640.07940.67130.01000.0100
+ +
target: C21
+
train: [0.96578538 0.03421462]
+
validation: [0.96570194 0.03429806]
+
evaluate_binary: 311.430s
+
evaluate_multiclass: 127.753s
+
kfcv: 169.599s
+
atc_mc: 171.895s
+
atc_ne: 99.774s
+
doc_feat: 132.229s
+
rca_score: 928.435s
+
rca_star_score: 903.426s
+
tot: 975.778s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04240.04920.90720.49140.49140.90700.93790.9402
(0.05, 0.95)0.08900.09480.86380.47790.47790.86370.89450.8968
(0.1, 0.9)0.12220.13880.81940.46040.46040.81930.85010.8524
(0.15, 0.85)0.18170.17390.77080.42300.42300.77080.80150.8038
(0.2, 0.8)0.21630.18880.72030.40590.40590.7204NaNNaN
(0.25, 0.75)0.23010.17840.67520.36960.36960.6753NaNNaN
(0.3, 0.7)0.22840.17060.62780.34530.34530.62800.65850.6608
(0.35, 0.65)0.19280.14270.57950.32320.32320.5797NaNNaN
(0.4, 0.6)0.16190.11680.53460.30000.30000.5349NaNNaN
(0.45, 0.55)0.16250.11880.48390.26700.26700.4843NaNNaN
(0.5, 0.5)0.14080.10150.43800.23430.23430.4384NaNNaN
(0.55, 0.45)0.10770.07410.39370.21920.21920.3942NaNNaN
(0.6, 0.4)0.09490.07180.34570.19310.19310.3463NaNNaN
(0.65, 0.35)0.06640.04920.29800.16980.16980.2986NaNNaN
(0.7, 0.3)0.06170.04630.25090.13870.13870.2516NaNNaN
(0.75, 0.25)0.05290.04460.20230.11690.11690.2031NaNNaN
(0.8, 0.2)0.04400.04030.15640.09240.09240.1572NaNNaN
(0.85, 0.15)0.04140.03930.10700.06260.06260.1079NaNNaN
(0.9, 0.1)0.03140.03420.06200.03940.03940.0630NaNNaN
(0.95, 0.05)0.02710.02510.01490.01850.01850.0159NaNNaN
(1.0, 0.0)0.00660.00540.03270.01420.01420.0316NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.07900.09150.42900.11090.1109
(0.05, 0.95)0.07840.08910.43550.10280.1028
(0.1, 0.9)0.05220.08840.44100.09920.0992
(0.15, 0.85)0.07850.09320.43870.10200.1020
(0.2, 0.8)0.07670.10020.43170.10780.1078
(0.25, 0.75)0.07420.09690.43590.10360.1036
(0.3, 0.7)0.07630.09860.43550.10400.1040
(0.35, 0.65)0.07310.09660.43300.10770.1077
(0.4, 0.6)0.06320.08720.43980.09910.0991
(0.45, 0.55)0.08100.09600.42860.11060.1106
(0.5, 0.5)0.07650.09200.43260.10570.1057
(0.55, 0.45)0.05880.07760.44360.09630.0963
(0.6, 0.4)0.06450.08370.44090.10060.1006
(0.65, 0.35)0.06700.08350.43900.10170.1017
(0.7, 0.3)0.06780.07960.44170.09680.0968
(0.75, 0.25)0.07440.08670.43310.10630.1063
(0.8, 0.2)0.07640.07470.44390.09680.0968
(0.85, 0.15)0.08060.09030.41930.12140.1214
(0.9, 0.1)0.06880.07930.45150.08920.0892
(0.95, 0.05)0.05530.07140.46210.08460.0846
(1.0, 0.0)0.00970.01030.54070.00000.0000
+ +
target: C24
+
train: [0.96016935 0.03983065]
+
validation: [0.96017279 0.03982721]
+
evaluate_binary: 341.390s
+
evaluate_multiclass: 134.762s
+
kfcv: 115.097s
+
atc_mc: 166.466s
+
atc_ne: 156.922s
+
doc_feat: 127.605s
+
rca_score: 1002.111s
+
rca_star_score: 1016.135s
+
tot: 1070.690s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04650.04830.87910.42000.42000.87820.91180.9157
(0.05, 0.95)0.09270.09620.83130.39480.39480.83050.86400.8679
(0.1, 0.9)0.14340.14530.77980.37440.37440.77910.81250.8164
(0.15, 0.85)0.18940.17660.73120.34530.34530.73050.76390.7678
(0.2, 0.8)0.20800.17840.69550.33080.33080.69490.72820.7321
(0.25, 0.75)0.20160.16200.64090.30530.30530.64040.67360.6775
(0.3, 0.7)0.19010.15260.60050.28580.28580.60010.63320.6371
(0.35, 0.65)0.17570.13940.55410.25520.25520.55370.58680.5907
(0.4, 0.6)0.14900.11300.51020.24780.24780.51000.54290.5468
(0.45, 0.55)0.14170.11450.46030.21390.21390.46010.49300.4969
(0.5, 0.5)0.10980.08130.41820.19790.19790.4181NaNNaN
(0.55, 0.45)0.09160.07370.37200.17770.17770.3720NaNNaN
(0.6, 0.4)0.08070.06330.32660.15160.15160.32670.35930.3632
(0.65, 0.35)0.06070.04990.28210.13720.13720.28230.31480.3187
(0.7, 0.3)0.04950.04180.23650.11220.11220.2367NaNNaN
(0.75, 0.25)0.04500.03940.19130.09200.09200.1916NaNNaN
(0.8, 0.2)0.02970.03200.14400.07210.07210.1444NaNNaN
(0.85, 0.15)0.02990.03310.09970.05050.05050.1002NaNNaN
(0.9, 0.1)0.02750.02940.05320.03420.03420.0538NaNNaN
(0.95, 0.05)0.02250.02270.00980.01600.01600.0104NaNNaN
(1.0, 0.0)0.00570.00450.03620.01120.01120.0355NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.08440.08680.42050.14510.1451
(0.05, 0.95)0.08590.09090.41670.15100.1510
(0.1, 0.9)0.09460.09980.40620.15930.1593
(0.15, 0.85)0.10190.11120.40000.16620.1662
(0.2, 0.8)0.08590.10900.41970.14840.1484
(0.25, 0.75)0.10530.11270.39960.16290.1629
(0.3, 0.7)0.09300.10630.41180.15490.1549
(0.35, 0.65)0.09880.10690.40930.15540.1554
(0.4, 0.6)0.09910.09890.41320.14840.1484
(0.45, 0.55)0.11270.11600.40000.16870.1687
(0.5, 0.5)0.09400.09460.41130.15280.1528
(0.55, 0.45)0.08030.09670.40750.16140.1614
(0.6, 0.4)0.10850.10720.40710.16030.1603
(0.65, 0.35)0.08470.10350.41130.15520.1552
(0.7, 0.3)0.08590.08890.40980.15750.1575
(0.75, 0.25)0.09270.10030.41530.15350.1535
(0.8, 0.2)0.09530.10510.40060.16060.1606
(0.85, 0.15)0.09570.10260.41180.15630.1563
(0.9, 0.1)0.12920.14860.39440.17560.1756
(0.95, 0.05)0.13110.15740.41790.15440.1544
(1.0, 0.0)0.00010.03200.57480.00000.0000
+ +
target: C31
+
train: [0.95429411 0.04570589]
+
validation: [0.95429806 0.04570194]
+
evaluate_binary: 242.965s
+
evaluate_multiclass: 161.716s
+
kfcv: 142.016s
+
atc_mc: 101.146s
+
atc_ne: 92.376s
+
doc_feat: 76.202s
+
rca_score: 834.193s
+
rca_star_score: 832.758s
+
tot: 884.843s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03650.03780.91670.52800.52800.91600.95520.9591
(0.05, 0.95)0.08910.08910.86740.50520.50520.8668NaNNaN
(0.1, 0.9)0.13200.13220.82300.47350.47350.8225NaNNaN
(0.15, 0.85)0.17620.17130.77750.45060.45060.7770NaNNaN
(0.2, 0.8)0.22290.19750.72840.41660.41660.7280NaNNaN
(0.25, 0.75)0.24900.18890.68050.40110.40110.6802NaNNaN
(0.3, 0.7)0.25410.18790.62790.35060.35060.6276NaNNaN
(0.35, 0.65)0.23240.16450.58110.33470.33470.5809NaNNaN
(0.4, 0.6)0.21330.15330.53140.30600.30600.5313NaNNaN
(0.45, 0.55)0.18870.13170.48670.28590.28590.4866NaNNaN
(0.5, 0.5)0.16420.11480.43660.25920.25920.4366NaNNaN
(0.55, 0.45)0.14680.10100.39160.22930.22930.3917NaNNaN
(0.6, 0.4)0.11870.08320.34240.20010.20010.3425NaNNaN
(0.65, 0.35)0.09520.06440.29520.17610.17610.2954NaNNaN
(0.7, 0.3)0.07790.05640.24510.14520.14520.2454NaNNaN
(0.75, 0.25)0.06460.05030.19810.11770.11770.1984NaNNaN
(0.8, 0.2)0.05270.04530.15130.09430.09430.1517NaNNaN
(0.85, 0.15)0.04250.03720.10210.06250.06250.1026NaNNaN
(0.9, 0.1)0.03830.03680.05470.03580.03580.0552NaNNaN
(0.95, 0.05)0.03190.02920.00680.01620.01620.0072NaNNaN
(1.0, 0.0)0.00580.00450.04200.01680.01680.0413NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.06960.07190.48540.07480.0748
(0.05, 0.95)0.07840.07850.48300.07810.0781
(0.1, 0.9)0.06860.07070.49020.06840.0684
(0.15, 0.85)0.06090.06480.49580.06430.0643
(0.2, 0.8)0.06430.06840.49380.06700.0670
(0.25, 0.75)0.06450.06720.49430.06640.0664
(0.3, 0.7)0.07150.07700.48260.07820.0782
(0.35, 0.65)0.07220.07500.48560.07500.0750
(0.4, 0.6)0.07150.07750.48070.07850.0785
(0.45, 0.55)0.06150.06850.49110.07040.0704
(0.5, 0.5)0.07220.07580.48370.07870.0787
(0.55, 0.45)0.06180.06360.49540.06730.0673
(0.6, 0.4)0.06340.06630.49100.07000.0700
(0.65, 0.35)0.05540.06180.49550.06490.0649
(0.7, 0.3)0.06790.06930.48520.07770.0777
(0.75, 0.25)0.05630.05810.49150.07130.0713
(0.8, 0.2)0.05100.05280.50340.06010.0601
(0.85, 0.15)0.06010.06190.49650.06510.0651
(0.9, 0.1)0.05150.05780.50600.05750.0575
(0.95, 0.05)0.07690.09070.49110.07240.0724
(1.0, 0.0)0.00000.00990.56350.00000.0000
+ +
target: C42
+
train: [0.98522551 0.01477449]
+
validation: [0.98514039 0.01485961]
+
evaluate_binary: 216.862s
+
evaluate_multiclass: 148.154s
+
kfcv: 142.768s
+
atc_mc: 101.557s
+
atc_ne: 93.067s
+
doc_feat: 77.781s
+
rca_score: 983.309s
+
rca_star_score: 974.280s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00000.07880.90710.33900.33900.90660.91880.9209
(0.05, 0.95)0.05000.12240.86350.32050.32050.86300.87520.8773
(0.1, 0.9)0.10000.16400.82200.30770.30770.82160.83370.8358
(0.15, 0.85)0.14990.20910.77680.28700.28700.77650.78850.7906
(0.2, 0.8)0.19990.26010.72540.26510.26510.72520.73710.7392
(0.25, 0.75)0.24980.30460.67700.25350.25350.6768NaNNaN
(0.3, 0.7)0.29950.33000.63120.23180.23180.6311NaNNaN
(0.35, 0.65)0.34650.30910.58880.22120.22120.5888NaNNaN
(0.4, 0.6)0.36360.28810.54120.19740.19740.54130.55290.5550
(0.45, 0.55)0.34510.26370.49450.18180.18180.4947NaNNaN
(0.5, 0.5)0.32020.24890.44540.15990.15990.4456NaNNaN
(0.55, 0.45)0.26950.20910.39930.14810.14810.3996NaNNaN
(0.6, 0.4)0.22110.16590.35670.14120.14120.3571NaNNaN
(0.65, 0.35)0.19280.14340.31100.11810.11810.3115NaNNaN
(0.7, 0.3)0.16330.12430.26330.09740.09740.2639NaNNaN
(0.75, 0.25)0.12580.09340.21810.08190.08190.2187NaNNaN
(0.8, 0.2)0.08700.07200.16880.06210.06210.1695NaNNaN
(0.85, 0.15)0.05510.04550.12400.04570.04570.1248NaNNaN
(0.9, 0.1)0.03340.03240.07790.03120.03120.0788NaNNaN
(0.95, 0.05)0.01920.01710.03220.01560.01560.0332NaNNaN
(1.0, 0.0)0.00050.00040.01370.00200.00200.0127NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00010.14510.42510.14460.1446
(0.05, 0.95)0.00660.14050.42960.14030.1403
(0.1, 0.9)0.01230.13150.43880.13150.1315
(0.15, 0.85)0.01820.12930.44120.12820.1282
(0.2, 0.8)0.02620.13990.43070.13870.1387
(0.25, 0.75)0.03380.14440.42620.14360.1436
(0.3, 0.7)0.04070.14410.42660.14360.1436
(0.35, 0.65)0.04440.13450.43620.13450.1345
(0.4, 0.6)0.04910.13780.43290.13610.1361
(0.45, 0.55)0.04990.13900.43160.13910.1391
(0.5, 0.5)0.05300.14760.42300.14670.1467
(0.55, 0.45)0.05210.14930.42120.14950.1495
(0.6, 0.4)0.04330.13440.43610.13280.1328
(0.65, 0.35)0.04130.13100.43940.13130.1313
(0.7, 0.3)0.04460.13800.43230.13830.1383
(0.75, 0.25)0.03850.12990.44030.12900.1290
(0.8, 0.2)0.03950.15420.41570.15500.1550
(0.85, 0.15)0.03060.14160.42770.14300.1430
(0.9, 0.1)0.03380.13270.42910.14160.1416
(0.95, 0.05)0.04990.10010.44730.12700.1270
(1.0, 0.0)0.01000.01040.57070.00000.0000
+ +
target: E12
+
train: [0.97071021 0.02928979]
+
validation: [0.97062635 0.02937365]
+
evaluate_binary: 270.181s
+
evaluate_multiclass: 123.959s
+
kfcv: 166.426s
+
atc_mc: 163.725s
+
atc_ne: 159.501s
+
doc_feat: 79.566s
+
rca_score: 940.772s
+
rca_star_score: 955.992s
+
tot: 1005.211s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00020.05830.90970.47630.47630.90800.93210.9356
(0.05, 0.95)0.05010.10580.86090.45490.45490.85930.88330.8868
(0.1, 0.9)0.10040.15200.81800.43500.43500.81650.84040.8439
(0.15, 0.85)0.15090.20070.77000.41230.41230.76860.79240.7959
(0.2, 0.8)0.20030.23340.72580.38270.38270.7245NaNNaN
(0.25, 0.75)0.24340.24560.67630.35560.35560.6751NaNNaN
(0.3, 0.7)0.26320.23850.62910.32710.32710.6279NaNNaN
(0.35, 0.65)0.25500.22490.58240.30690.30690.58130.60480.6083
(0.4, 0.6)0.22760.19950.53700.28700.28700.53600.55940.5629
(0.45, 0.55)0.21390.18660.48880.25990.25990.4879NaNNaN
(0.5, 0.5)0.18690.16150.44290.23900.23900.4421NaNNaN
(0.55, 0.45)0.16240.14340.39490.21130.21130.3942NaNNaN
(0.6, 0.4)0.14170.13090.34640.18890.18890.3458NaNNaN
(0.65, 0.35)0.12550.10950.30270.15680.15680.3021NaNNaN
(0.7, 0.3)0.10860.09320.25570.13460.13460.2552NaNNaN
(0.75, 0.25)0.07740.07190.20810.11220.11220.2077NaNNaN
(0.8, 0.2)0.06360.05590.16250.08670.08670.1622NaNNaN
(0.85, 0.15)0.05220.04490.11690.06600.06600.1167NaNNaN
(0.9, 0.1)0.03600.03250.06840.04140.04140.0683NaNNaN
(0.95, 0.05)0.02230.02000.02120.01720.01720.0212NaNNaN
(1.0, 0.0)0.00500.00360.02580.00820.00820.0258NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00030.10830.48800.11630.1163
(0.05, 0.95)0.00610.10930.48450.12130.1213
(0.1, 0.9)0.01190.10850.49170.11410.1141
(0.15, 0.85)0.01880.11530.48920.11740.1174
(0.2, 0.8)0.02280.11020.49540.11050.1105
(0.25, 0.75)0.02890.11660.48900.11730.1173
(0.3, 0.7)0.03170.11650.48830.11730.1173
(0.35, 0.65)0.03170.11680.48860.11700.1170
(0.4, 0.6)0.02960.11240.49330.11410.1141
(0.45, 0.55)0.03140.11090.48900.11560.1156
(0.5, 0.5)0.03180.10750.49130.11370.1137
(0.55, 0.45)0.03210.11390.48780.11850.1185
(0.6, 0.4)0.03160.12050.47940.12550.1255
(0.65, 0.35)0.03050.10650.49220.11250.1125
(0.7, 0.3)0.03140.10050.49360.11450.1145
(0.75, 0.25)0.02870.11060.48850.11710.1171
(0.8, 0.2)0.02930.09390.50130.10640.1064
(0.85, 0.15)0.03250.07580.51660.09150.0915
(0.9, 0.1)0.04840.09140.50420.10390.1039
(0.95, 0.05)0.07170.08070.50520.10290.1029
(1.0, 0.0)0.00000.01700.60810.00000.0000
+ +
target: E21
+
train: [0.94582685 0.05417315]
+
validation: [0.94574514 0.05425486]
+
evaluate_binary: 339.478s
+
evaluate_multiclass: 127.963s
+
kfcv: 161.870s
+
atc_mc: 171.427s
+
atc_ne: 103.308s
+
doc_feat: 77.827s
+
rca_score: 1211.965s
+
rca_star_score: 1184.838s
+
tot: 1259.208s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00390.03470.55570.19410.19410.55590.57400.5870
(0.05, 0.95)0.05260.08220.53590.19910.19910.53620.55240.5655
(0.1, 0.9)0.10470.12720.50950.17770.17770.50990.52420.5373
(0.15, 0.85)0.12210.13370.47420.16590.16590.47470.48770.5010
(0.2, 0.8)0.11110.11670.43900.15380.15380.43960.45180.4651
(0.25, 0.75)0.10220.11360.41220.14750.14750.41290.42320.4365
(0.3, 0.7)0.08860.10040.38320.13980.13980.38400.39330.4066
(0.35, 0.65)0.08500.09470.35710.13200.13200.35800.36530.3787
(0.4, 0.6)0.06660.08000.31900.10400.10400.32000.32650.3399
(0.45, 0.55)0.05600.06740.29600.11300.11300.29720.30200.3155
(0.5, 0.5)0.05180.05760.26340.10040.10040.26470.26880.2824
(0.55, 0.45)0.04660.05070.23000.08000.08000.23130.23500.2486
(0.6, 0.4)0.03720.03950.20290.07870.07870.20440.20760.2212
(0.65, 0.35)0.03650.03890.17700.06830.06830.17860.18160.1953
(0.7, 0.3)0.02960.02850.14690.05990.05990.14860.15150.1651
(0.75, 0.25)0.03150.03000.11660.04980.04980.11840.12120.1348
(0.8, 0.2)0.02940.03070.08880.04150.04150.09070.09340.1070
(0.85, 0.15)0.02810.03090.05760.02810.02810.05960.06220.0758
(0.9, 0.1)0.02160.02410.02940.02370.02370.03140.03390.0474
(0.95, 0.05)0.01560.01600.00930.01410.01410.0099NaNNaN
(1.0, 0.0)0.00180.00140.03010.00350.00350.0278NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00420.03640.21930.06010.0601
(0.05, 0.95)0.02370.05630.22900.05590.0559
(0.1, 0.9)0.05060.08170.23260.07040.0704
(0.15, 0.85)0.05630.08680.22720.06080.0608
(0.2, 0.8)0.05260.07770.22050.06490.0649
(0.25, 0.75)0.05380.08400.22310.06470.0647
(0.3, 0.7)0.05050.08060.22490.06840.0684
(0.35, 0.65)0.05100.08030.22960.06310.0631
(0.4, 0.6)0.04730.07900.21620.08180.0818
(0.45, 0.55)0.04090.07200.22750.08050.0805
(0.5, 0.5)0.04660.06720.22180.08440.0844
(0.55, 0.45)0.05200.06660.21220.08660.0866
(0.6, 0.4)0.04600.06090.21770.08300.0830
(0.65, 0.35)0.05220.06760.22780.08320.0832
(0.7, 0.3)0.06330.05980.22610.09290.0929
(0.75, 0.25)0.09740.07510.22340.11470.1147
(0.8, 0.2)0.08930.08900.23320.11920.1192
(0.85, 0.15)0.12720.12960.22990.13800.1380
(0.9, 0.1)0.15350.16090.24110.17760.1776
(0.95, 0.05)0.20680.20450.29480.19930.1993
(1.0, 0.0)0.00460.00230.80260.00500.0050
+ +
target: E211
+
train: [0.98246069 0.01753931]
+
validation: [0.98237581 0.01762419]
+
evaluate_binary: 320.478s
+
evaluate_multiclass: 123.957s
+
kfcv: 112.571s
+
atc_mc: 170.547s
+
atc_ne: 167.429s
+
doc_feat: 129.581s
+
rca_score: 1011.800s
+
rca_star_score: 1025.452s
+
tot: 1076.046s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.09450.09670.88610.41950.41950.88590.89830.9018
(0.05, 0.95)0.13650.13780.84450.39360.39360.84440.85670.8602
(0.1, 0.9)0.17520.17950.80390.38400.38400.80390.81610.8196
(0.15, 0.85)0.22350.22610.75780.35590.35590.75790.77000.7735
(0.2, 0.8)0.26900.27030.71150.34760.34760.71170.72370.7272
(0.25, 0.75)0.31460.30560.66350.31790.31790.66380.67570.6792
(0.3, 0.7)0.35630.30800.62150.29170.29170.62180.63370.6372
(0.35, 0.65)0.37970.29420.57620.28060.28060.5766NaNNaN
(0.4, 0.6)0.36960.27830.52930.25230.25230.52980.54150.5450
(0.45, 0.55)0.33430.25330.48300.23140.23140.48360.49520.4987
(0.5, 0.5)0.30640.23500.43560.19830.19830.4363NaNNaN
(0.55, 0.45)0.27490.21030.39230.18440.18440.3931NaNNaN
(0.6, 0.4)0.23710.18320.34640.16940.16940.3473NaNNaN
(0.65, 0.35)0.20100.15570.30290.14550.14550.3039NaNNaN
(0.7, 0.3)0.16310.12700.25670.12920.12920.2578NaNNaN
(0.75, 0.25)0.13390.10610.20980.09780.09780.2109NaNNaN
(0.8, 0.2)0.09040.07150.16580.08220.08220.1670NaNNaN
(0.85, 0.15)0.05820.04820.11930.06050.06050.1206NaNNaN
(0.9, 0.1)0.03300.02980.07440.03880.03880.0758NaNNaN
(0.95, 0.05)0.02300.02340.02920.02110.02110.0307NaNNaN
(1.0, 0.0)0.00140.00110.01560.00230.00230.0140NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.17090.17480.42430.16500.1650
(0.05, 0.95)0.16550.16760.43040.15480.1548
(0.1, 0.9)0.15240.16030.43990.15250.1525
(0.15, 0.85)0.15850.16340.43810.15230.1523
(0.2, 0.8)0.15840.16380.43670.15100.1510
(0.25, 0.75)0.15990.16930.43140.15380.1538
(0.3, 0.7)0.15650.16270.43890.15160.1516
(0.35, 0.65)0.15480.15970.44000.14800.1480
(0.4, 0.6)0.15940.16510.43620.15220.1522
(0.45, 0.55)0.15790.16740.43290.15430.1543
(0.5, 0.5)0.16690.17390.42690.16300.1630
(0.55, 0.45)0.15690.16410.43460.15170.1517
(0.6, 0.4)0.16020.16750.43190.15570.1557
(0.65, 0.35)0.14870.15890.44020.14810.1481
(0.7, 0.3)0.14900.16090.43660.16120.1612
(0.75, 0.25)0.15580.16870.42810.15660.1566
(0.8, 0.2)0.14980.15510.43950.15660.1566
(0.85, 0.15)0.14840.15010.42750.15970.1597
(0.9, 0.1)0.14910.14640.43360.16530.1653
(0.95, 0.05)0.15000.14350.44080.16350.1635
(1.0, 0.0)0.00990.01000.60200.00000.0000
+ +
target: E212
+
train: [0.96319336 0.03680664]
+
validation: [0.96311015 0.03688985]
+
evaluate_binary: 233.509s
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evaluate_multiclass: 178.371s
+
kfcv: 145.769s
+
atc_mc: 124.033s
+
atc_ne: 91.719s
+
doc_feat: 78.459s
+
rca_score: 1204.775s
+
rca_star_score: 1188.268s
+
tot: 1247.499s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00100.03410.48600.14110.14110.48600.49930.5061
(0.05, 0.95)0.05250.09230.45390.12090.12090.45400.46800.4747
(0.1, 0.9)0.10100.14330.43790.12630.12630.43810.45040.4573
(0.15, 0.85)0.14820.18610.40970.11140.11140.41000.42150.4286
(0.2, 0.8)0.17060.17690.38100.10990.10990.38140.39170.3989
(0.25, 0.75)0.17060.17180.36160.10690.10690.36210.37070.3780
(0.3, 0.7)0.15070.15510.33620.09370.09370.33680.34380.3511
(0.35, 0.65)0.13850.14270.31550.09860.09860.31620.32120.3285
(0.4, 0.6)0.12390.13500.28410.08300.08300.28490.28840.2956
(0.45, 0.55)0.11330.11650.26190.07540.07540.26280.26420.2711
(0.5, 0.5)0.08500.09390.23060.06950.06950.23160.23130.2380
(0.55, 0.45)0.07510.08190.20470.05880.05880.20580.20370.2102
(0.6, 0.4)0.07550.07520.18730.05950.05950.18850.18460.1910
(0.65, 0.35)0.06480.06810.16030.04910.04910.16160.15710.1634
(0.7, 0.3)0.04890.04800.13610.04930.04930.13750.13240.1387
(0.75, 0.25)0.03710.03930.10710.04180.04180.10860.10330.1096
(0.8, 0.2)0.03130.03470.08240.03380.03380.08400.07860.0849
(0.85, 0.15)0.02650.02760.05790.02550.02550.05960.05410.0604
(0.9, 0.1)0.02520.02380.03390.01940.01940.03560.03020.0363
(0.95, 0.05)0.01660.01540.01210.01280.01280.0133NaNNaN
(1.0, 0.0)0.00380.00300.01710.00200.00200.0151NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00100.03190.18210.05460.0546
(0.05, 0.95)0.02510.06300.17530.07290.0729
(0.1, 0.9)0.04670.09030.18470.06120.0612
(0.15, 0.85)0.06920.11760.18140.07140.0714
(0.2, 0.8)0.08290.11470.17750.06470.0647
(0.25, 0.75)0.08750.11880.18380.06050.0605
(0.3, 0.7)0.08390.11670.18420.06530.0653
(0.35, 0.65)0.08130.11520.19070.06450.0645
(0.4, 0.6)0.08060.12030.18230.07360.0736
(0.45, 0.55)0.08190.11260.18730.07960.0796
(0.5, 0.5)0.06540.10170.17690.07590.0759
(0.55, 0.45)0.06640.09950.17450.08970.0897
(0.6, 0.4)0.08140.10070.19060.07970.0797
(0.65, 0.35)0.07550.10350.18970.09000.0900
(0.7, 0.3)0.07010.08640.19310.09190.0919
(0.75, 0.25)0.07600.09570.18300.10430.1043
(0.8, 0.2)0.08160.09890.18330.11870.1187
(0.85, 0.15)0.09740.10870.19210.13850.1385
(0.9, 0.1)0.12920.13080.20950.17050.1705
(0.95, 0.05)0.23270.18030.24520.22420.2242
(1.0, 0.0)0.00000.00970.84410.00000.0000
+ +
target: E41
+
train: [0.98064628 0.01935372]
+
validation: [0.98056156 0.01943844]
+
evaluate_binary: 279.480s
+
evaluate_multiclass: 130.254s
+
kfcv: 167.427s
+
atc_mc: 141.283s
+
atc_ne: 169.435s
+
doc_feat: 82.077s
+
rca_score: 1007.269s
+
rca_star_score: 1022.263s
+
tot: 1072.119s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00000.09530.88220.27380.27380.88060.89620.8990
(0.05, 0.95)0.05000.14180.83890.27000.27000.83740.85290.8557
(0.1, 0.9)0.09980.18470.79610.25810.25810.79470.81010.8129
(0.15, 0.85)0.14990.23250.74910.23370.23370.74770.76310.7659
(0.2, 0.8)0.19960.28150.70030.22100.22100.69900.71430.7171
(0.25, 0.75)0.24960.31120.66170.22390.22390.66050.67570.6785
(0.3, 0.7)0.29590.31540.61170.19560.19560.6106NaNNaN
(0.35, 0.65)0.31830.29410.56730.18130.18130.56630.58130.5841
(0.4, 0.6)0.30310.27360.52380.16620.16620.52290.53780.5406
(0.45, 0.55)0.27300.24640.47750.15560.15560.47670.49150.4943
(0.5, 0.5)0.24400.21640.43540.14220.14220.43470.44940.4522
(0.55, 0.45)0.21570.19310.38970.12870.12870.3891NaNNaN
(0.6, 0.4)0.18180.16700.34190.10600.10600.3413NaNNaN
(0.65, 0.35)0.15090.13820.29920.09760.09760.2987NaNNaN
(0.7, 0.3)0.12200.11290.25320.08620.08620.2528NaNNaN
(0.75, 0.25)0.08680.08080.20720.07030.07030.2069NaNNaN
(0.8, 0.2)0.06320.05770.16250.05380.05380.1623NaNNaN
(0.85, 0.15)0.04230.03850.11890.03930.03930.1188NaNNaN
(0.9, 0.1)0.02570.02690.07300.02840.02840.0730NaNNaN
(0.95, 0.05)0.01900.01940.02790.01470.01470.0280NaNNaN
(1.0, 0.0)0.00060.00050.01660.00200.00200.0164NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00010.17100.43930.18010.1801
(0.05, 0.95)0.00820.17420.44210.17620.1762
(0.1, 0.9)0.01600.17030.44630.17230.1723
(0.15, 0.85)0.02460.17550.44290.17770.1777
(0.2, 0.8)0.03410.18570.43470.18460.1846
(0.25, 0.75)0.04010.17150.44870.17050.1705
(0.3, 0.7)0.05000.18120.43820.18050.1805
(0.35, 0.65)0.05500.17880.43950.17920.1792
(0.4, 0.6)0.05500.17810.44280.17570.1757
(0.45, 0.55)0.05440.17920.43990.17680.1768
(0.5, 0.5)0.05180.16890.44940.16930.1693
(0.55, 0.45)0.05140.17200.44740.16890.1689
(0.6, 0.4)0.05190.17870.43620.18160.1816
(0.65, 0.35)0.04420.16950.44780.17160.1716
(0.7, 0.3)0.04370.16850.44290.17620.1762
(0.75, 0.25)0.04030.16650.43830.17980.1798
(0.8, 0.2)0.03800.15230.43930.17800.1780
(0.85, 0.15)0.03150.14000.45320.16520.1652
(0.9, 0.1)0.02960.12250.45140.16710.1671
(0.95, 0.05)0.08540.16860.45230.16900.1690
(1.0, 0.0)0.02000.01980.62130.00000.0000
+ +
target: E51
+
train: [0.97235182 0.02764818]
+
validation: [0.97226782 0.02773218]
+
evaluate_binary: 347.216s
+
evaluate_multiclass: 198.698s
+
kfcv: 107.965s
+
atc_mc: 165.906s
+
atc_ne: 106.609s
+
doc_feat: 77.541s
+
rca_score: 937.639s
+
rca_star_score: 952.963s
+
tot: 1000.226s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.03110.05850.90860.47200.47200.90880.93150.9347
(0.05, 0.95)0.07360.09810.87030.45100.45100.8705NaNNaN
(0.1, 0.9)0.12260.15130.81810.43030.43030.81840.84100.8442
(0.15, 0.85)0.16480.19310.77070.40840.40840.77110.79360.7968
(0.2, 0.8)0.20770.22170.72080.38030.38030.72120.74370.7469
(0.25, 0.75)0.22840.24060.67820.34940.34940.67870.70110.7043
(0.3, 0.7)0.20610.23090.63100.33490.33490.6316NaNNaN
(0.35, 0.65)0.19080.23930.58400.30430.30430.5847NaNNaN
(0.4, 0.6)0.17260.23720.53650.27740.27740.5372NaNNaN
(0.45, 0.55)0.14620.19500.48830.25260.25260.4891NaNNaN
(0.5, 0.5)0.12170.19620.44660.24610.24610.4475NaNNaN
(0.55, 0.45)0.09940.15050.39640.21240.21240.3974NaNNaN
(0.6, 0.4)0.08180.15110.34920.18880.18880.3502NaNNaN
(0.65, 0.35)0.06640.14880.30160.16440.16440.3027NaNNaN
(0.7, 0.3)0.05090.08160.25590.13760.13760.2571NaNNaN
(0.75, 0.25)0.03440.06400.20640.11370.11370.2076NaNNaN
(0.8, 0.2)0.03170.04780.16190.09070.09070.1632NaNNaN
(0.85, 0.15)0.03090.03420.11460.06660.06660.1160NaNNaN
(0.9, 0.1)0.02890.02890.06790.04140.04140.0694NaNNaN
(0.95, 0.05)0.02400.02320.02020.02020.02020.0217NaNNaN
(1.0, 0.0)0.00260.00260.02570.00680.00680.0241NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.05760.10850.42970.11530.1153
(0.05, 0.95)0.04930.09550.44550.10120.1012
(0.1, 0.9)0.05330.10800.43560.11070.1107
(0.15, 0.85)0.04520.10840.43470.10710.1071
(0.2, 0.8)0.05210.11610.42810.11580.1158
(0.25, 0.75)0.04970.10670.43740.10540.1054
(0.3, 0.7)0.05750.10760.43690.10740.1074
(0.35, 0.65)0.05780.10650.43700.10870.1087
(0.4, 0.6)0.05180.10350.43520.10900.1090
(0.45, 0.55)0.04680.10440.43190.11030.1103
(0.5, 0.5)0.04090.09050.44930.09520.0952
(0.55, 0.45)0.04600.09400.43780.10880.1088
(0.6, 0.4)0.04860.08950.43680.10490.1049
(0.65, 0.35)0.04180.09770.43400.11290.1129
(0.7, 0.3)0.04260.08490.44100.10480.1048
(0.75, 0.25)0.03940.09150.42440.12050.1205
(0.8, 0.2)0.04650.07850.44220.10930.1093
(0.85, 0.15)0.04840.07820.44130.10250.1025
(0.9, 0.1)0.08220.10070.44580.09960.0996
(0.95, 0.05)0.11290.13450.43050.12890.1289
(1.0, 0.0)0.00000.03330.55140.00000.0000
+ +
target: ECAT
+
train: [0.85104545 0.14895455]
+
validation: [0.85097192 0.14902808]
+
evaluate_binary: 264.961s
+
evaluate_multiclass: 139.511s
+
kfcv: 164.571s
+
atc_mc: 164.474s
+
atc_ne: 104.597s
+
doc_feat: 120.243s
+
rca_score: 1226.111s
+
rca_star_score: 1194.784s
+
tot: 1281.006s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01950.02630.40860.12410.12410.41240.36480.3803
(0.05, 0.95)0.06140.06830.38280.11360.11360.38670.35230.3775
(0.1, 0.9)0.06730.07260.36320.11440.11440.36720.36000.3912
(0.15, 0.85)0.06690.07080.34350.10880.10880.34760.35490.3896
(0.2, 0.8)0.06640.07230.32360.10720.10720.32780.33150.3654
(0.25, 0.75)0.05660.05850.29410.10030.10030.29840.29490.3291
(0.3, 0.7)0.05420.05500.27270.08940.08940.27710.26870.3028
(0.35, 0.65)0.05100.04830.24280.08370.08370.24730.23650.2706
(0.4, 0.6)0.04670.04750.21960.07150.07150.22420.21100.2450
(0.45, 0.55)0.04600.04800.19840.07010.07010.20310.18910.2232
(0.5, 0.5)0.04300.04120.17000.05680.05680.17480.16030.1943
(0.55, 0.45)0.04800.04130.14210.04450.04450.14700.13220.1662
(0.6, 0.4)0.04030.04010.12020.04520.04520.12520.11030.1443
(0.65, 0.35)0.04050.03920.09430.03980.03980.09940.08440.1184
(0.7, 0.3)0.03620.03590.07350.03520.03520.07870.06360.0976
(0.75, 0.25)0.03900.03930.05460.03480.03480.05940.04580.0775
(0.8, 0.2)0.03920.03690.03410.02930.02930.03870.02620.0564
(0.85, 0.15)0.03160.03000.01860.02500.02500.02010.01840.0321
(0.9, 0.1)0.02650.02300.02090.01930.01930.01800.02810.0151
(0.95, 0.05)0.02350.02200.04050.01720.01720.03500.05040.0189
(1.0, 0.0)0.00920.00850.06580.01560.01560.0600NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.01830.02380.14480.05200.0520
(0.05, 0.95)0.03860.04510.14300.04970.0497
(0.1, 0.9)0.04350.04970.14710.04950.0495
(0.15, 0.85)0.04560.04920.15130.05270.0527
(0.2, 0.8)0.05050.05480.15680.05040.0504
(0.25, 0.75)0.04670.04710.15000.04920.0492
(0.3, 0.7)0.04880.04760.15410.05720.0572
(0.35, 0.65)0.05350.04740.14790.05770.0577
(0.4, 0.6)0.04920.04820.14830.05840.0584
(0.45, 0.55)0.04900.05110.15350.06250.0625
(0.5, 0.5)0.05640.05000.14690.07300.0730
(0.55, 0.45)0.07370.05540.13890.08270.0827
(0.6, 0.4)0.06280.05740.14240.07520.0752
(0.65, 0.35)0.08250.06460.13890.08930.0893
(0.7, 0.3)0.07380.06970.14350.08980.0898
(0.75, 0.25)0.10890.09500.15870.09370.0937
(0.8, 0.2)0.13240.11010.17360.10570.1057
(0.85, 0.15)0.13570.11690.18370.11130.1113
(0.9, 0.1)0.18050.14090.19260.13640.1364
(0.95, 0.05)0.27620.24470.25620.19960.1996
(1.0, 0.0)0.03560.07910.82650.09280.0928
+ +
target: G15
+
train: [0.9843615 0.0156385]
+
validation: [0.98427646 0.01572354]
+
evaluate_binary: 353.823s
+
evaluate_multiclass: 120.493s
+
kfcv: 151.945s
+
atc_mc: 155.115s
+
atc_ne: 92.674s
+
doc_feat: 76.970s
+
rca_score: 1121.730s
+
rca_star_score: 1112.820s
+
tot: 1165.059s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.13940.13130.84620.26770.26770.84520.85670.8599
(0.05, 0.95)0.17830.17370.80440.24590.24590.80350.81490.8181
(0.1, 0.9)0.21990.21490.76550.24060.24060.76470.77600.7792
(0.15, 0.85)0.26660.26490.71780.22220.22220.71710.72830.7315
(0.2, 0.8)0.31290.30970.67190.20810.20810.67130.68240.6856
(0.25, 0.75)0.35550.33970.63000.19590.19590.62950.64050.6437
(0.3, 0.7)0.39390.34260.58140.17230.17230.58100.59190.5951
(0.35, 0.65)0.39780.33300.54280.16170.16170.54250.55330.5565
(0.4, 0.6)0.37210.32470.50130.15720.15720.50120.51180.5150
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(0.8, 0.2)0.10720.11550.15850.05110.05110.1593NaNNaN
(0.85, 0.15)0.07970.09060.11380.04040.04040.1147NaNNaN
(0.9, 0.1)0.04920.04870.07120.02740.02740.0722NaNNaN
(0.95, 0.05)0.02410.02460.02910.01640.01640.0302NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.15, 0.85)0.23960.23620.37150.20740.2074
(0.2, 0.8)0.24550.24140.36650.20850.2085
(0.25, 0.75)0.24570.24420.36810.21250.2125
(0.3, 0.7)0.25070.25090.35620.22150.2215
(0.35, 0.65)0.24540.24420.36540.20820.2082
(0.4, 0.6)0.24300.24110.36880.20730.2073
(0.45, 0.55)0.25500.24910.35640.22450.2245
(0.5, 0.5)0.22520.22260.38280.19440.1944
(0.55, 0.45)0.25880.25160.35060.22830.2283
(0.6, 0.4)0.24290.24110.35840.22160.2216
(0.65, 0.35)0.23770.23330.36870.21240.2124
(0.7, 0.3)0.23150.22770.36730.21600.2160
(0.75, 0.25)0.23400.22010.36710.22170.2217
(0.8, 0.2)0.20460.20610.37820.20730.2073
(0.85, 0.15)0.23350.21940.36250.22750.2275
(0.9, 0.1)0.20540.19630.36760.23550.2355
(0.95, 0.05)0.17760.14200.40640.20390.2039
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.03200.02850.07290.03200.03200.07350.53190.5115
(0.1, 0.9)0.02900.02790.06540.03520.03520.06610.53950.5191
(0.15, 0.85)0.02670.02550.05880.02630.02630.05950.54600.5256
(0.2, 0.8)0.02440.02730.05030.02490.02490.05110.55490.5345
(0.25, 0.75)0.02570.02630.05280.02650.02650.05370.54760.5268
(0.3, 0.7)0.02920.02980.04580.02580.02580.04650.52000.4984
(0.35, 0.65)0.03120.03260.04590.03010.03010.04670.37380.3537
(0.4, 0.6)0.03450.03120.03480.02460.02460.03560.20860.1981
(0.45, 0.55)0.03180.02890.03090.02070.02070.03180.03960.0461
(0.5, 0.5)0.03100.03270.03070.02510.02510.03150.07540.0612
(0.55, 0.45)0.03120.03120.02480.02510.02510.02540.15360.1338
(0.6, 0.4)0.03130.03040.02090.02330.02330.02140.19200.1716
(0.65, 0.35)0.02450.02480.01800.01820.01820.01850.19870.1784
(0.7, 0.3)0.02820.02630.01630.01900.01900.01630.20660.1863
(0.75, 0.25)0.02610.02580.01710.01520.01520.01720.20470.1844
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(0.85, 0.15)0.02230.02140.01770.01650.01650.01710.21750.1972
(0.9, 0.1)0.02350.02230.02000.01540.01540.01910.22240.2021
(0.95, 0.05)0.01610.01430.02580.01300.01300.02450.23180.2115
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.01750.01680.01800.02170.0217
(0.15, 0.85)0.01720.01610.01670.01730.0173
(0.2, 0.8)0.01620.01810.01660.01650.0165
(0.25, 0.75)0.01810.01870.01960.01870.0187
(0.3, 0.7)0.02300.02300.02110.02050.0205
(0.35, 0.65)0.02610.02660.02470.02510.0251
(0.4, 0.6)0.03150.02770.02340.02390.0239
(0.45, 0.55)0.03150.02850.02170.02260.0226
(0.5, 0.5)0.03360.03480.02890.02790.0279
(0.55, 0.45)0.03930.03840.02850.03060.0306
(0.6, 0.4)0.04420.04180.03060.03250.0325
(0.65, 0.35)0.03850.03840.03320.02860.0286
(0.7, 0.3)0.05250.04840.03430.03580.0358
(0.75, 0.25)0.05820.05690.05200.03290.0329
(0.8, 0.2)0.06860.06360.05080.05000.0500
(0.85, 0.15)0.08540.08030.07100.05630.0563
(0.9, 0.1)0.12580.11500.10430.07750.0775
(0.95, 0.05)0.18700.15460.14050.10320.1032
(1.0, 0.0)0.05900.09030.94170.40250.4025
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train: [0.95109729 0.04890271]
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validation: [0.95101512 0.04898488]
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kfcv: 163.725s
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atc_ne: 103.056s
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rca_score: 1163.895s
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rca_star_score: 1137.033s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.09550.12200.61940.13550.13550.61890.63370.6489
(0.1, 0.9)0.13400.16450.58630.12820.12820.58590.60060.6158
(0.15, 0.85)0.16370.18130.54650.11580.11580.54620.56080.5760
(0.2, 0.8)0.15600.17710.51570.11850.11850.51550.53000.5452
(0.25, 0.75)0.17250.17630.49000.10610.10610.48990.50430.5195
(0.3, 0.7)0.14640.15320.44590.09440.09440.44590.46020.4754
(0.35, 0.65)0.13790.14180.41120.09500.09500.41130.42550.4407
(0.4, 0.6)0.11900.12720.37570.08580.08580.37600.39000.4052
(0.45, 0.55)0.10580.11130.34430.07520.07520.34470.35860.3738
(0.5, 0.5)0.09640.09830.30680.06510.06510.30730.32110.3363
(0.55, 0.45)0.09070.08860.27210.05840.05840.27270.28640.3016
(0.6, 0.4)0.06380.07080.24290.06250.06250.24360.25720.2724
(0.65, 0.35)0.06130.05970.21210.05670.05670.21290.22640.2416
(0.7, 0.3)0.05490.05060.17070.04210.04210.17170.18500.2002
(0.75, 0.25)0.03950.04320.14430.04320.04320.14540.15860.1738
(0.8, 0.2)0.03400.03060.10850.03280.03280.10970.12280.1380
(0.85, 0.15)0.02800.02540.07240.02690.02690.07370.08670.1019
(0.9, 0.1)0.02120.02110.03850.02020.02020.0399NaNNaN
(0.95, 0.05)0.01710.01780.00900.01390.01390.0093NaNNaN
(1.0, 0.0)0.00350.00290.03130.00500.00500.0296NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.08980.12620.27790.20980.2098
(0.15, 0.85)0.10800.14050.27040.21360.2136
(0.2, 0.8)0.10410.14390.27570.20740.2074
(0.25, 0.75)0.14470.15590.28860.21920.2192
(0.3, 0.7)0.13860.14910.27340.22140.2214
(0.35, 0.65)0.14450.15000.27280.21730.2173
(0.4, 0.6)0.13040.14360.27070.21900.2190
(0.45, 0.55)0.12880.14000.27680.22380.2238
(0.5, 0.5)0.13790.13820.26800.21930.2193
(0.55, 0.45)0.16240.14590.26670.22210.2221
(0.6, 0.4)0.11140.13090.28150.22030.2203
(0.65, 0.35)0.12490.13310.29300.20730.2073
(0.7, 0.3)0.17160.14190.26860.23970.2397
(0.75, 0.25)0.13370.14580.30310.21030.2103
(0.8, 0.2)0.14520.14610.30200.23720.2372
(0.85, 0.15)0.18600.16040.29970.23930.2393
(0.9, 0.1)0.19460.19350.31370.27010.2701
(0.95, 0.05)0.25050.24550.34020.28790.2879
(1.0, 0.0)0.01040.03710.75210.00000.0000
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.09840.13940.73880.37350.37350.73870.75640.7700
(0.1, 0.9)0.13890.18280.70360.35920.35920.70360.72120.7348
(0.15, 0.85)0.17560.19740.65800.33760.33760.65810.67560.6892
(0.2, 0.8)0.17320.18810.62130.33210.33210.62160.63890.6525
(0.25, 0.75)0.15630.17610.58740.31350.31350.58780.60500.6186
(0.3, 0.7)0.14340.16380.53860.27990.27990.53910.55620.5698
(0.35, 0.65)0.12230.14370.49830.25860.25860.49890.51590.5295
(0.4, 0.6)0.10920.13220.46320.24680.24680.46390.48080.4944
(0.45, 0.55)0.08940.11770.41750.21600.21600.41830.43510.4487
(0.5, 0.5)0.08250.10690.37540.18860.18860.37630.39300.4066
(0.55, 0.45)0.06110.08010.33330.18120.18120.33440.35090.3645
(0.6, 0.4)0.05660.06340.29380.15680.15680.29500.31140.3250
(0.65, 0.35)0.04760.05630.25330.13880.13880.25460.27090.2845
(0.7, 0.3)0.03380.04150.21300.11870.11870.21440.23060.2442
(0.75, 0.25)0.03020.03220.17250.09660.09660.17410.19010.2037
(0.8, 0.2)0.02830.02690.13310.08180.08180.1348NaNNaN
(0.85, 0.15)0.02680.02860.09200.05750.05750.0938NaNNaN
(0.9, 0.1)0.02180.02590.04770.03250.03250.0496NaNNaN
(0.95, 0.05)0.01700.01690.01070.01800.01800.0120NaNNaN
(1.0, 0.0)0.00500.00380.03080.00490.00490.0286NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.09280.16930.40970.14400.1440
(0.15, 0.85)0.12100.17990.40200.14510.1451
(0.2, 0.8)0.13760.18500.40840.13330.1333
(0.25, 0.75)0.12260.18150.42170.13610.1361
(0.3, 0.7)0.12310.17700.40560.14180.1418
(0.35, 0.65)0.11970.17520.40780.14160.1416
(0.4, 0.6)0.12180.17870.41960.13830.1383
(0.45, 0.55)0.09720.17230.41010.13940.1394
(0.5, 0.5)0.10570.17360.40640.16260.1626
(0.55, 0.45)0.10860.16110.40180.14050.1405
(0.6, 0.4)0.12790.15120.40710.15480.1548
(0.65, 0.35)0.11070.14230.40520.14090.1409
(0.7, 0.3)0.11280.14370.40640.16150.1615
(0.75, 0.25)0.13230.13370.41180.17280.1728
(0.8, 0.2)0.12940.11320.42420.16970.1697
(0.85, 0.15)0.17550.15550.42680.20840.2084
(0.9, 0.1)0.17690.17600.38120.24720.2472
(0.95, 0.05)0.20560.20660.42100.25430.2543
(1.0, 0.0)0.01010.04400.71730.00670.0067
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.1, 0.9)0.08970.12410.51090.17710.17710.51210.52360.5424
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(0.2, 0.8)0.08650.11980.44880.15530.15530.45030.46070.4795
(0.25, 0.75)0.07890.11170.41300.13520.13520.41460.42490.4436
(0.3, 0.7)0.06750.10240.38480.13710.13710.38650.39660.4154
(0.35, 0.65)0.05790.08390.34880.12470.12470.35070.36060.3793
(0.4, 0.6)0.04780.07090.32300.11240.11240.32500.33470.3535
(0.45, 0.55)0.04550.06710.28860.09650.09650.29070.30030.3191
(0.5, 0.5)0.03450.05280.25440.08400.08400.25660.26610.2849
(0.55, 0.45)0.03990.05270.22990.07900.07900.23220.24160.2604
(0.6, 0.4)0.03540.05070.20630.07420.07420.20880.21800.2368
(0.65, 0.35)0.02890.03830.16430.06430.06430.16690.17600.1948
(0.7, 0.3)0.03230.04060.14410.05570.05570.14680.15580.1746
(0.75, 0.25)0.02780.03010.10980.04580.04580.11260.12150.1403
(0.8, 0.2)0.03020.03590.08600.03600.03600.08890.09770.1165
(0.85, 0.15)0.02220.02680.04970.02780.02780.05280.06140.0802
(0.9, 0.1)0.02630.02690.02290.02010.02010.02520.03340.0521
(0.95, 0.05)0.01880.01910.01370.01490.01490.0116NaNNaN
(1.0, 0.0)0.01160.01030.03750.00980.00980.0341NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00020.03730.21910.07850.0785
(0.05, 0.95)0.02020.06410.22550.07580.0758
(0.1, 0.9)0.03670.08480.23660.07440.0744
(0.15, 0.85)0.03940.08490.21420.08120.0812
(0.2, 0.8)0.03920.09020.23560.08060.0806
(0.25, 0.75)0.03790.09010.22870.09430.0943
(0.3, 0.7)0.03460.09040.23080.08470.0847
(0.35, 0.65)0.03150.07730.22300.07900.0790
(0.4, 0.6)0.02880.07310.23050.09450.0945
(0.45, 0.55)0.03120.07570.22250.10390.1039
(0.5, 0.5)0.02620.06890.21550.10020.1002
(0.55, 0.45)0.03350.07060.22710.10770.1077
(0.6, 0.4)0.03700.07960.24460.10740.1074
(0.65, 0.35)0.03360.06520.21040.10630.1063
(0.7, 0.3)0.05230.08240.24100.13490.1349
(0.75, 0.25)0.05080.07220.23150.11760.1176
(0.8, 0.2)0.06760.09510.26000.13000.1300
(0.85, 0.15)0.07480.10200.23410.13740.1374
(0.9, 0.1)0.14140.14960.26100.21210.2121
(0.95, 0.05)0.20850.21070.26910.22860.2286
(1.0, 0.0)0.09380.15260.79050.07490.0749
+ +
target: GVIO
+
train: [0.95187489 0.04812511]
+
validation: [0.95179266 0.04820734]
+
evaluate_binary: 348.091s
+
evaluate_multiclass: 119.628s
+
kfcv: 101.483s
+
atc_mc: 99.585s
+
atc_ne: 93.777s
+
doc_feat: 105.866s
+
rca_score: 1212.214s
+
rca_star_score: 1206.863s
+
tot: 1254.673s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.25610.17060.63990.17720.17720.63980.64850.6616
(0.05, 0.95)0.30100.20670.61320.16040.16040.61320.62130.6345
(0.1, 0.9)0.33860.23690.57860.15930.15930.57870.58650.5997
(0.15, 0.85)0.33430.24070.54170.14860.14860.54190.54950.5627
(0.2, 0.8)0.31170.22640.51700.15230.15230.51740.52460.5378
(0.25, 0.75)0.30210.22260.48100.13330.13330.48150.48850.5018
(0.3, 0.7)0.28560.21240.44280.11530.11530.44340.45030.4636
(0.35, 0.65)0.25960.19060.41220.11820.11820.41300.41970.4329
(0.4, 0.6)0.23020.17680.38060.10860.10860.38150.38810.4013
(0.45, 0.55)0.19450.14630.34480.10860.10860.34580.35230.3655
(0.5, 0.5)0.18490.14710.31680.09500.09500.31790.32430.3375
(0.55, 0.45)0.15680.12310.27380.07940.07940.27510.28130.2945
(0.6, 0.4)0.14390.11570.24200.07420.07420.24340.24950.2627
(0.65, 0.35)0.11620.09430.20820.06510.06510.20970.21570.2289
(0.7, 0.3)0.09480.07540.17280.05450.05450.17450.18030.1935
(0.75, 0.25)0.08120.06760.13710.04510.04510.13890.14460.1578
(0.8, 0.2)0.05930.05030.11130.04170.04170.11320.11880.1320
(0.85, 0.15)0.04660.04020.07380.03230.03230.07580.08130.0945
(0.9, 0.1)0.03030.02620.04070.02100.02100.0428NaNNaN
(0.95, 0.05)0.02180.01920.00970.01290.01290.0110NaNNaN
(1.0, 0.0)0.00620.00500.02730.00590.00590.0249NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.36540.22740.29580.16300.1630
(0.05, 0.95)0.38830.23150.30370.17710.1771
(0.1, 0.9)0.39760.23100.30290.17320.1732
(0.15, 0.85)0.36970.22920.29870.17280.1728
(0.2, 0.8)0.35780.22110.31080.16150.1615
(0.25, 0.75)0.37810.23840.30830.17030.1703
(0.3, 0.7)0.38080.23990.30170.18110.1811
(0.35, 0.65)0.38260.23230.30780.17180.1718
(0.4, 0.6)0.37060.24140.31200.16920.1692
(0.45, 0.55)0.34290.21750.30800.15200.1520
(0.5, 0.5)0.35410.23900.32220.17180.1718
(0.55, 0.45)0.35110.24040.30060.16660.1666
(0.6, 0.4)0.37610.26130.30750.18770.1877
(0.65, 0.35)0.33950.24290.30630.17310.1731
(0.7, 0.3)0.33030.23220.30310.17540.1754
(0.75, 0.25)0.36590.27580.29160.19000.1900
(0.8, 0.2)0.33300.26450.33600.19860.1986
(0.85, 0.15)0.33680.26710.31770.21870.2187
(0.9, 0.1)0.31290.27310.33360.22630.2263
(0.95, 0.05)0.33850.29610.32690.27670.2767
(1.0, 0.0)0.02010.04120.79100.00670.0067
+ +
target: GVOTE
+
train: [0.9850527 0.0149473]
+
validation: [0.985054 0.014946]
+
evaluate_binary: 411.703s
+
evaluate_multiclass: 176.863s
+
kfcv: 110.327s
+
atc_mc: 169.946s
+
atc_ne: 103.675s
+
doc_feat: 82.976s
+
rca_score: 939.116s
+
rca_star_score: 952.807s
+
tot: 999.321s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04600.06430.92420.67300.67300.92340.93130.9356
(0.05, 0.95)0.08880.10530.88320.65600.65600.8825NaNNaN
(0.1, 0.9)0.13410.15240.83600.61890.61890.83540.84310.8474
(0.15, 0.85)0.17990.19650.79170.59080.59080.7912NaNNaN
(0.2, 0.8)0.23410.24520.73960.55010.55010.7391NaNNaN
(0.25, 0.75)0.27960.26400.69300.51330.51330.69260.70010.7044
(0.3, 0.7)0.32360.25210.64870.48210.48210.6484NaNNaN
(0.35, 0.65)0.34180.24140.59960.44430.44430.5994NaNNaN
(0.4, 0.6)0.31610.21890.55170.41610.41610.5516NaNNaN
(0.45, 0.55)0.29270.19950.50610.37820.37820.5060NaNNaN
(0.5, 0.5)0.26660.18460.45690.34050.34050.4569NaNNaN
(0.55, 0.45)0.23550.15880.41370.31130.31130.4138NaNNaN
(0.6, 0.4)0.20780.14490.36340.27070.27070.3636NaNNaN
(0.65, 0.35)0.17230.11870.31640.23770.23770.3166NaNNaN
(0.7, 0.3)0.14250.09310.27270.20800.20800.2730NaNNaN
(0.75, 0.25)0.10640.06810.22500.17490.17490.2254NaNNaN
(0.8, 0.2)0.08640.05750.17610.13600.13600.1766NaNNaN
(0.85, 0.15)0.05210.03480.12970.10140.10140.1303NaNNaN
(0.9, 0.1)0.02870.02240.08230.06830.06830.0830NaNNaN
(0.95, 0.05)0.02080.01920.03620.03600.03600.0369NaNNaN
(1.0, 0.0)0.00110.00100.01140.00040.00040.0106NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.08550.11970.57440.09490.0949
(0.05, 0.95)0.07790.10920.58510.08750.0875
(0.1, 0.9)0.07450.10910.58510.09020.0902
(0.15, 0.85)0.07140.10360.59080.07970.0797
(0.2, 0.8)0.08640.11420.58020.08590.0859
(0.25, 0.75)0.08380.11330.58100.08160.0816
(0.3, 0.7)0.08290.10670.58770.08430.0843
(0.35, 0.65)0.08540.11140.58300.08550.0855
(0.4, 0.6)0.08720.11460.57980.09370.0937
(0.45, 0.55)0.08670.10940.58490.09220.0922
(0.5, 0.5)0.08890.11720.57710.08510.0851
(0.55, 0.45)0.07740.10270.59130.09020.0902
(0.6, 0.4)0.08230.11630.57760.09630.0963
(0.65, 0.35)0.07970.11550.57730.10150.1015
(0.7, 0.3)0.07150.09730.59630.08930.0893
(0.75, 0.25)0.07620.09890.59530.09540.0954
(0.8, 0.2)0.09300.11050.58170.11160.1116
(0.85, 0.15)0.08470.10230.58800.10280.1028
(0.9, 0.1)0.07620.10290.58200.11250.1125
(0.95, 0.05)0.07260.06480.61720.08240.0824
(1.0, 0.0)0.00000.00000.69440.00000.0000
+ +
target: GWEA
+
train: [0.99421116 0.00578884]
+
validation: [0.99412527 0.00587473]
+
evaluate_binary: 262.208s
+
evaluate_multiclass: 108.019s
+
kfcv: 97.840s
+
atc_mc: 135.028s
+
atc_ne: 94.500s
+
doc_feat: 82.704s
+
rca_score: 374.076s
+
rca_star_score: 374.355s
+
tot: 415.864s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00650.00730.98740.64090.64090.9869NaNNaN
(0.05, 0.95)0.05480.05550.93920.61980.61980.9387NaNNaN
(0.1, 0.9)0.10410.10520.88940.58770.58770.8890NaNNaN
(0.15, 0.85)0.15380.15470.83990.54880.54880.8395NaNNaN
(0.2, 0.8)0.20380.20490.78950.52670.52670.7892NaNNaN
(0.25, 0.75)0.25550.25650.73760.48430.48430.7373NaNNaN
(0.3, 0.7)0.30400.30260.68940.45790.45790.6892NaNNaN
(0.35, 0.65)0.35330.33240.64020.41870.41870.6400NaNNaN
(0.4, 0.6)0.40190.32060.59130.39060.39060.5912NaNNaN
(0.45, 0.55)0.45210.28950.54130.36090.36090.5412NaNNaN
(0.5, 0.5)0.49980.25520.49140.32640.32640.4914NaNNaN
(0.55, 0.45)0.52260.22080.44160.29600.29600.4416NaNNaN
(0.6, 0.4)0.47670.18980.39210.26310.26310.3922NaNNaN
(0.65, 0.35)0.41220.15680.34280.22550.22550.3429NaNNaN
(0.7, 0.3)0.33730.12200.29260.19110.19110.2928NaNNaN
(0.75, 0.25)0.25510.08400.24310.16400.16400.2433NaNNaN
(0.8, 0.2)0.18450.05610.19250.13200.13200.1928NaNNaN
(0.85, 0.15)0.11980.03270.14400.09740.09740.1443NaNNaN
(0.9, 0.1)0.05980.02110.09380.06530.06530.0942NaNNaN
(0.95, 0.05)0.02700.02210.04440.03370.03370.0448NaNNaN
(1.0, 0.0)0.00010.00000.00530.00020.00020.0048NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.01270.01430.59700.01430.0143
(0.05, 0.95)0.01000.01140.60000.01140.0114
(0.1, 0.9)0.00920.01160.59980.01160.0116
(0.15, 0.85)0.00930.01110.60030.01110.0111
(0.2, 0.8)0.01010.01280.59860.01280.0128
(0.25, 0.75)0.01550.01850.59290.01850.0185
(0.3, 0.7)0.01240.01480.59660.01480.0148
(0.35, 0.65)0.01160.01360.59780.01360.0136
(0.4, 0.6)0.00870.01110.60030.01110.0111
(0.45, 0.55)0.01070.01210.59930.01210.0121
(0.5, 0.5)0.01100.01280.59860.01280.0128
(0.55, 0.45)0.01170.01340.59800.01340.0134
(0.6, 0.4)0.01210.01260.59880.01260.0126
(0.65, 0.35)0.00860.01050.60080.01050.0105
(0.7, 0.3)0.01130.01350.59790.01350.0135
(0.75, 0.25)0.01090.01210.59920.01210.0121
(0.8, 0.2)0.01940.02080.59050.02090.0209
(0.85, 0.15)0.00860.00860.60280.00860.0086
(0.9, 0.1)0.01360.01640.59500.01640.0164
(0.95, 0.05)0.00740.00910.60140.01000.0100
(1.0, 0.0)0.00000.00020.61140.00000.0000
+ +
target: M11
+
train: [0.94409884 0.05590116]
+
validation: [0.94410367 0.05589633]
+
evaluate_binary: 350.246s
+
evaluate_multiclass: 186.170s
+
kfcv: 106.680s
+
atc_mc: 169.230s
+
atc_ne: 99.756s
+
doc_feat: 75.310s
+
rca_score: 1245.803s
+
rca_star_score: 1216.185s
+
tot: 1288.407s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.00230.02190.34910.08630.08630.34860.44690.4354
(0.05, 0.95)0.05100.07230.32780.07440.07440.32740.35270.3499
(0.1, 0.9)0.09200.10520.31210.07440.07440.31180.29400.3077
(0.15, 0.85)0.09050.10090.28200.05920.05920.28180.25800.2777
(0.2, 0.8)0.08740.10530.26770.06310.06310.26760.25950.2801
(0.25, 0.75)0.08160.09370.25270.05940.05940.25270.25970.2814
(0.3, 0.7)0.06990.07890.22980.05130.05130.23000.23620.2575
(0.35, 0.65)0.06820.07960.21500.05320.05320.21530.21670.2376
(0.4, 0.6)0.05730.06350.19640.04830.04830.19680.19230.2133
(0.45, 0.55)0.05020.06060.18230.04690.04690.18280.17240.1934
(0.5, 0.5)0.04550.05420.15760.03790.03790.15820.14500.1660
(0.55, 0.45)0.03860.04260.14070.03560.03560.14140.12570.1467
(0.6, 0.4)0.03930.04500.12710.03810.03810.12790.11060.1315
(0.65, 0.35)0.03280.03880.11390.03600.03600.11480.09690.1176
(0.7, 0.3)0.03760.04040.09320.03560.03560.09420.07600.0966
(0.75, 0.25)0.02720.02730.07300.02820.02820.07420.05600.0764
(0.8, 0.2)0.03110.02470.05500.03000.03000.05630.03820.0583
(0.85, 0.15)0.02340.02320.03640.02230.02230.03780.02190.0397
(0.9, 0.1)0.01920.01830.01820.01600.01600.01940.01240.0210
(0.95, 0.05)0.01650.01350.00880.01170.01170.0089NaNNaN
(1.0, 0.0)0.00610.00550.01860.00330.00330.0169NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.04610.06160.11410.04160.0416
(0.15, 0.85)0.04750.06170.10410.03970.0397
(0.2, 0.8)0.04990.07090.10740.04850.0485
(0.25, 0.75)0.04960.06630.11020.04700.0470
(0.3, 0.7)0.04690.05970.10540.04540.0454
(0.35, 0.65)0.05120.06710.10900.05580.0558
(0.4, 0.6)0.04380.05660.10880.04730.0473
(0.45, 0.55)0.04250.06060.11360.05050.0505
(0.5, 0.5)0.04290.05960.10510.05500.0550
(0.55, 0.45)0.04140.05210.10590.04910.0491
(0.6, 0.4)0.04750.06220.11490.05990.0599
(0.65, 0.35)0.04640.06500.12820.07020.0702
(0.7, 0.3)0.06660.07800.12510.07320.0732
(0.75, 0.25)0.06180.06300.12080.07170.0717
(0.8, 0.2)0.11690.08160.12400.09620.0962
(0.85, 0.15)0.11620.09820.12680.09940.0994
(0.9, 0.1)0.12450.10440.12860.11000.1100
(0.95, 0.05)0.22840.17080.15570.17460.1746
(1.0, 0.0)0.03680.06640.88680.05000.0500
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train: [0.9683774 0.0316226]
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validation: [0.96838013 0.03161987]
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evaluate_binary: 388.733s
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evaluate_multiclass: 144.432s
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kfcv: 199.371s
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atc_mc: 127.456s
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atc_ne: 206.927s
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rca_score: 1212.021s
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rca_star_score: 1206.772s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.05360.08380.62550.13920.13920.62560.63870.6493
(0.1, 0.9)0.10400.14320.58070.12440.12440.58090.59380.6045
(0.15, 0.85)0.15000.18200.55330.11650.11650.55360.56610.5768
(0.2, 0.8)0.14690.16350.50930.10680.10680.50980.52200.5327
(0.25, 0.75)0.13940.16820.48280.10980.10980.48340.49540.5061
(0.3, 0.7)0.12750.14940.45200.10010.10010.45270.46460.4752
(0.35, 0.65)0.11070.13210.42090.09920.09920.42170.43340.4440
(0.4, 0.6)0.09680.11880.38200.08660.08660.38300.39450.4051
(0.45, 0.55)0.08500.10340.35210.08280.08280.35320.36450.3752
(0.5, 0.5)0.07420.09140.31330.07350.07350.31450.32570.3363
(0.55, 0.45)0.05940.07390.27870.07250.07250.28000.29110.3017
(0.6, 0.4)0.05140.06660.24260.05340.05340.24400.25500.2656
(0.65, 0.35)0.04980.06250.22050.06630.06630.22210.23290.2435
(0.7, 0.3)0.03690.05010.18440.05090.05090.18610.19680.2074
(0.75, 0.25)0.03790.03830.14630.04170.04170.14810.15870.1693
(0.8, 0.2)0.02750.03270.11280.03610.03610.11480.12520.1358
(0.85, 0.15)0.02440.02830.07650.02390.02390.07860.08890.0995
(0.9, 0.1)0.02050.02370.04460.02440.02440.0468NaNNaN
(0.95, 0.05)0.01610.01790.01440.01590.01590.0160NaNNaN
(1.0, 0.0)0.00210.00220.02240.00330.00330.0199NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.04430.09560.21820.23640.2364
(0.15, 0.85)0.06280.12090.22600.24520.2452
(0.2, 0.8)0.06530.10970.21180.23010.2301
(0.25, 0.75)0.06850.12800.22230.23740.2374
(0.3, 0.7)0.06560.11930.22710.23120.2312
(0.35, 0.65)0.06660.11830.23200.23350.2335
(0.4, 0.6)0.05960.11280.22330.23110.2311
(0.45, 0.55)0.06570.11380.23130.24050.2405
(0.5, 0.5)0.05920.10570.22150.24020.2402
(0.55, 0.45)0.06170.10500.22010.23220.2322
(0.6, 0.4)0.05930.10890.21220.27150.2715
(0.65, 0.35)0.06420.11800.24970.20500.2050
(0.7, 0.3)0.06210.11810.24540.23670.2367
(0.75, 0.25)0.10270.11300.22730.23250.2325
(0.8, 0.2)0.08390.12120.23400.23420.2342
(0.85, 0.15)0.12760.15500.21510.26050.2605
(0.9, 0.1)0.14230.17240.24650.25510.2551
(0.95, 0.05)0.19280.20590.32030.28700.2870
(1.0, 0.0)0.01670.03930.71090.01000.0100
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train: [0.93105236 0.06894764]
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validation: [0.93105832 0.06894168]
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evaluate_binary: 325.372s
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evaluate_multiclass: 189.027s
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kfcv: 110.743s
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atc_ne: 105.095s
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rca_score: 1219.115s
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rca_star_score: 1234.910s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.05340.07570.33610.08870.08870.33440.39530.3930
(0.1, 0.9)0.09020.11360.32870.09750.09750.32710.31730.3267
(0.15, 0.85)0.09030.11470.30390.09140.09140.30250.28320.2987
(0.2, 0.8)0.07720.09620.27630.08420.08420.27500.27090.2862
(0.25, 0.75)0.07560.09620.27080.08700.08700.26960.27450.2912
(0.3, 0.7)0.06080.07630.24950.08580.08580.24840.24980.2662
(0.35, 0.65)0.05650.07500.23030.08140.08140.22930.22530.2418
(0.4, 0.6)0.05020.07130.21220.06310.06310.21130.20070.2172
(0.45, 0.55)0.04800.06140.18440.06010.06010.18360.16950.1861
(0.5, 0.5)0.03870.04950.17500.05740.05740.17440.15500.1716
(0.55, 0.45)0.03740.04880.15410.05040.05040.15350.13210.1488
(0.6, 0.4)0.02920.03750.13050.04130.04130.13010.10750.1242
(0.65, 0.35)0.03000.03660.11730.04580.04580.11700.09380.1105
(0.7, 0.3)0.02820.03500.09400.03660.03660.09380.07050.0872
(0.75, 0.25)0.02730.03260.07070.02940.02940.07060.04730.0638
(0.8, 0.2)0.02550.03000.05900.02880.02880.05900.03600.0521
(0.85, 0.15)0.01980.02420.03890.02430.02430.03910.01930.0322
(0.9, 0.1)0.01980.02290.01840.01710.01710.01860.01320.0139
(0.95, 0.05)0.01520.01520.00960.01330.01330.00950.02550.0114
(1.0, 0.0)0.00520.00470.02050.00380.00380.0200NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.04490.06860.14300.03240.0324
(0.15, 0.85)0.04660.07220.13800.03940.0394
(0.2, 0.8)0.04270.06440.13040.04350.0435
(0.25, 0.75)0.04490.06880.14340.04310.0431
(0.3, 0.7)0.03840.05850.14200.04740.0474
(0.35, 0.65)0.03900.06290.14270.04700.0470
(0.4, 0.6)0.03690.06570.14400.04010.0401
(0.45, 0.55)0.03930.05920.13190.04170.0417
(0.5, 0.5)0.03430.05340.14710.04080.0408
(0.55, 0.45)0.03930.06090.14550.05350.0535
(0.6, 0.4)0.03510.05200.13720.05510.0551
(0.65, 0.35)0.04180.05840.15110.06080.0608
(0.7, 0.3)0.04800.06160.14470.06380.0638
(0.75, 0.25)0.06070.07220.13210.05930.0593
(0.8, 0.2)0.07840.08470.15950.07170.0717
(0.85, 0.15)0.07760.09470.16300.10190.1019
(0.9, 0.1)0.11810.13200.15370.10780.1078
(0.95, 0.05)0.20880.17830.18680.18600.1860
(1.0, 0.0)0.03990.07280.89900.12000.1200
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train: [0.95930534 0.04069466]
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evaluate_binary: 301.469s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.05, 0.95)0.05060.07030.37310.06840.06840.37410.37590.3844
(0.1, 0.9)0.09440.10740.34490.05780.05780.34590.34480.3558
(0.15, 0.85)0.10500.10470.33460.06130.06130.33580.34300.3545
(0.2, 0.8)0.09370.09090.30510.05770.05770.30640.31690.3284
(0.25, 0.75)0.08480.08440.29140.05500.05500.29280.30170.3135
(0.3, 0.7)0.06400.06610.26510.05360.05360.26660.27440.2862
(0.35, 0.65)0.06260.06090.25030.05270.05270.25190.25640.2684
(0.4, 0.6)0.05470.05680.23160.05620.05620.23340.23460.2467
(0.45, 0.55)0.04490.04580.20240.04540.04540.20430.20380.2159
(0.5, 0.5)0.03880.04140.19150.04730.04730.19350.18950.2014
(0.55, 0.45)0.03220.03210.16520.04060.04060.16730.16150.1734
(0.6, 0.4)0.02690.03170.14320.03800.03800.14540.13800.1498
(0.65, 0.35)0.02040.02390.12500.03530.03530.12740.11910.1309
(0.7, 0.3)0.02360.02660.10930.03560.03560.11180.10290.1148
(0.75, 0.25)0.02580.03140.08390.02540.02540.08650.07750.0893
(0.8, 0.2)0.02000.02750.06260.02440.02440.06530.05620.0680
(0.85, 0.15)0.02370.02500.04470.02160.02160.04750.03830.0501
(0.9, 0.1)0.01940.02120.02650.01880.01880.02920.02110.0315
(0.95, 0.05)0.01490.01410.00740.01150.01150.00860.00880.0096
(1.0, 0.0)0.00260.00190.01720.00250.00250.0140NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.04610.06390.11030.07450.0745
(0.15, 0.85)0.05300.06460.11970.06880.0688
(0.2, 0.8)0.05040.05960.11110.06780.0678
(0.25, 0.75)0.04870.06010.11800.06840.0684
(0.3, 0.7)0.04010.05190.11170.06930.0693
(0.35, 0.65)0.04280.05160.11820.06890.0689
(0.4, 0.6)0.04110.05220.12170.07430.0743
(0.45, 0.55)0.03750.04570.11050.08010.0801
(0.5, 0.5)0.03560.04810.12530.07550.0755
(0.55, 0.45)0.03260.04060.11500.07630.0763
(0.6, 0.4)0.03470.04710.11150.08680.0868
(0.65, 0.35)0.02640.04040.11870.08280.0828
(0.7, 0.3)0.04480.05730.13120.08760.0876
(0.75, 0.25)0.06000.07640.12050.09820.0982
(0.8, 0.2)0.05370.07730.11670.09920.0992
(0.85, 0.15)0.10140.10410.13020.12020.1202
(0.9, 0.1)0.11090.12880.15930.14790.1479
(0.95, 0.05)0.20590.18090.15320.18410.1841
(1.0, 0.0)0.04360.06270.85580.05330.0533
+ +
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+
train: [0.96984621 0.03015379]
+
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+
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.1, 0.9)0.12530.16360.45430.10830.10830.45420.46470.4774
(0.15, 0.85)0.18810.20760.42930.10520.10520.42940.43830.4511
(0.2, 0.8)0.20690.21700.40930.09960.09960.40950.41600.4288
(0.25, 0.75)0.18930.20360.37720.08890.08890.37750.38330.3959
(0.3, 0.7)0.16760.17710.35430.10160.10160.35480.35950.3721
(0.35, 0.65)0.16220.17070.32240.08190.08190.32300.32670.3392
(0.4, 0.6)0.14140.15310.30190.08610.08610.30260.30530.3178
(0.45, 0.55)0.13940.14570.27600.07050.07050.27680.27890.2914
(0.5, 0.5)0.13120.12900.25150.06810.06810.25250.25400.2665
(0.55, 0.45)0.09840.10050.21610.05620.05620.21720.21850.2311
(0.6, 0.4)0.09450.09700.19180.05430.05430.19300.19410.2066
(0.65, 0.35)0.08580.08350.16300.05240.05240.16430.16520.1777
(0.7, 0.3)0.06230.06630.13990.04080.04080.14140.14210.1546
(0.75, 0.25)0.05760.06040.11390.03680.03680.11550.11610.1286
(0.8, 0.2)0.04550.04350.08960.03410.03410.09130.09180.1043
(0.85, 0.15)0.02940.03310.06530.02820.02820.06720.06750.0800
(0.9, 0.1)0.02570.02840.03510.01950.01950.03700.03720.0496
(0.95, 0.05)0.01960.01780.01190.01210.01210.0131NaNNaN
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binmulkfcvatc_mcatc_ne
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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binmulkfcvatc_mcatc_ne
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(0.3, 0.7)0.01580.02250.06530.03130.0313
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(0.55, 0.45)0.02600.03240.06840.04640.0464
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(0.65, 0.35)0.03310.04280.07860.04040.0404
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(0.8, 0.2)0.04680.05700.07610.06410.0641
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(0.9, 0.1)0.12050.10450.10670.08840.0884
(0.95, 0.05)0.15650.12380.15190.12890.1289
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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binmulkfcvatc_mcatc_ne
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(0.25, 0.75)0.04000.05050.08900.03630.0363
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(0.4, 0.6)0.03660.04610.09180.03720.0372
(0.45, 0.55)0.03690.04540.08080.04900.0490
(0.5, 0.5)0.04320.04870.09340.05260.0526
(0.55, 0.45)0.03100.04480.09230.04310.0431
(0.6, 0.4)0.04370.04440.08680.05120.0512
(0.65, 0.35)0.03310.04280.10020.05720.0572
(0.7, 0.3)0.04580.05120.08690.05530.0553
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(0.8, 0.2)0.07050.06660.09750.06850.0685
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(0.9, 0.1)0.15450.11620.11460.09800.0980
(0.95, 0.05)0.24560.15970.15720.14790.1479
(1.0, 0.0)0.01000.01820.90350.02670.0267
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.03760.10450.24000.23880.2388
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(0.2, 0.8)0.07480.12160.24490.23560.2356
(0.25, 0.75)0.08730.13410.22690.25000.2500
(0.3, 0.7)0.07920.14990.24070.22980.2298
(0.35, 0.65)0.07720.17530.23780.24580.2458
(0.4, 0.6)0.07450.20420.24540.23140.2314
(0.45, 0.55)0.07820.23230.24270.21860.2186
(0.5, 0.5)0.06870.26650.24390.23370.2337
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(0.6, 0.4)0.06000.28820.22540.25630.2563
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(0.7, 0.3)0.05220.26740.23160.23840.2384
(0.75, 0.25)0.05030.27180.23610.24680.2468
(0.8, 0.2)0.05610.25450.22700.27290.2729
(0.85, 0.15)0.05960.22280.22780.28010.2801
(0.9, 0.1)0.11130.20370.23970.30040.3004
(0.95, 0.05)0.16410.21510.27900.33350.3335
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
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(0.6, 0.4)0.17180.16900.16690.06450.06450.16810.16210.1710
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(0.7, 0.3)0.11260.11180.12350.04990.04990.12490.11810.1270
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(0.85, 0.15)0.05670.04910.05530.02500.02500.05710.04990.0588
(0.9, 0.1)0.03270.03280.03440.02030.02030.03620.02930.0378
(0.95, 0.05)0.01610.01540.01480.01340.01340.0162NaNNaN
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binmulkfcvatc_mcatc_ne
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(0.1, 0.9)0.39290.10050.14780.03900.0390
(0.15, 0.85)0.40420.14340.14320.04680.0468
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(0.25, 0.75)0.38370.22120.15400.04980.0498
(0.3, 0.7)0.42860.24600.15180.04860.0486
(0.35, 0.65)0.37500.23280.15030.05440.0544
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(0.45, 0.55)0.44150.24880.15790.06100.0610
(0.5, 0.5)0.39400.22630.14320.05670.0567
(0.55, 0.45)0.36220.21630.15400.05920.0592
(0.6, 0.4)0.40270.21140.14840.07200.0720
(0.65, 0.35)0.43410.20890.15770.07250.0725
(0.7, 0.3)0.36350.18840.15210.07320.0732
(0.75, 0.25)0.39530.18130.14530.08400.0840
(0.8, 0.2)0.37440.17590.15870.08020.0802
(0.85, 0.15)0.39570.17560.15400.09090.0909
(0.9, 0.1)0.38410.18670.17560.13440.1344
(0.95, 0.05)0.35830.18990.23740.18360.1836
(1.0, 0.0)0.00000.02000.85890.02000.0200
+ +
target: MCAT
+
train: [0.74589597 0.25410403]
+
validation: [0.74591793 0.25408207]
+
evaluate_binary: 239.003s
+
evaluate_multiclass: 125.654s
+
kfcv: 109.511s
+
atc_mc: 133.150s
+
atc_ne: 93.708s
+
doc_feat: 96.904s
+
rca_score: 1237.329s
+
rca_star_score: 1220.138s
+
tot: 1304.368s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.01160.01700.11980.04300.04300.12370.53870.5136
(0.05, 0.95)0.02470.02970.11420.04340.04340.11820.54430.5192
(0.1, 0.9)0.02620.03070.10650.04210.04210.11050.55200.5269
(0.15, 0.85)0.02560.03240.09640.03790.03790.10050.56210.5370
(0.2, 0.8)0.02350.02710.08630.03730.03730.09040.57190.5468
(0.25, 0.75)0.02440.02670.08640.04120.04120.09050.55060.5259
(0.3, 0.7)0.02370.02750.07640.03810.03810.08060.44550.4240
(0.35, 0.65)0.02340.02690.06320.03110.03110.06740.25150.2414
(0.4, 0.6)0.02590.02890.06180.03160.03160.06610.06080.0744
(0.45, 0.55)0.02090.02460.05400.02970.02970.05840.02010.0327
(0.5, 0.5)0.02530.03070.04350.02990.02990.04780.05430.0336
(0.55, 0.45)0.02780.02950.03890.02480.02480.04280.10600.0805
(0.6, 0.4)0.02230.02540.02910.02430.02430.03260.13370.1085
(0.65, 0.35)0.02540.02780.02860.02590.02590.03150.14250.1175
(0.7, 0.3)0.02490.02630.02090.02110.02110.02310.15490.1299
(0.75, 0.25)0.02640.02610.02020.02250.02250.02120.16010.1351
(0.8, 0.2)0.02320.02290.01540.01660.01660.01460.16940.1444
(0.85, 0.15)0.02290.02280.01610.01650.01650.01360.17740.1524
(0.9, 0.1)0.02020.01770.01940.01400.01400.01550.18460.1596
(0.95, 0.05)0.02240.02050.02630.01350.01350.02150.19280.1678
(1.0, 0.0)0.01150.01210.03200.01000.01000.0270NaNNaN
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.00730.01030.02990.02090.0209
(0.05, 0.95)0.01310.01640.03070.02190.0219
(0.1, 0.9)0.01460.01790.03320.02310.0231
(0.15, 0.85)0.01530.02000.03080.02220.0222
(0.2, 0.8)0.01500.01750.02860.02380.0238
(0.25, 0.75)0.01640.01840.03580.02580.0258
(0.3, 0.7)0.01740.02050.03570.02810.0281
(0.35, 0.65)0.01870.02180.03180.02430.0243
(0.4, 0.6)0.02190.02490.03760.02750.0275
(0.45, 0.55)0.01940.02320.03770.02800.0280
(0.5, 0.5)0.02600.03210.03660.03220.0322
(0.55, 0.45)0.03190.03380.04190.03040.0304
(0.6, 0.4)0.02890.03280.03970.03180.0318
(0.65, 0.35)0.03760.04080.05100.04010.0401
(0.7, 0.3)0.04280.04530.04980.03770.0377
(0.75, 0.25)0.05390.05240.05930.04720.0472
(0.8, 0.2)0.06260.06040.06080.04580.0458
(0.85, 0.15)0.07640.07670.07350.05940.0594
(0.9, 0.1)0.10710.09260.09080.07440.0744
(0.95, 0.05)0.20760.18720.14090.12100.1210
(1.0, 0.0)0.13820.14900.93690.32680.3268
diff --git a/out_spambase.md b/out_spambase.md index e8ad65b..8686607 100644 --- a/out_spambase.md +++ b/out_spambase.md @@ -1,445 +1,445 @@ - -
target: default
-
train: [0.60621118 0.39378882]
-
validation: [0.60559006 0.39440994]
-
evaluate_binary: 31.883s
-
evaluate_multiclass: 24.748s
-
kfcv: 23.957s
-
atc_mc: 36.062s
-
atc_ne: 37.123s
-
doc_feat: 7.063s
-
rca_score: 148.420s
-
rca_star_score: 145.690s
-
tot: 149.118s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04110.09070.02080.02670.02670.02040.11060.1059
(0.05, 0.95)0.03920.08970.02160.02660.02660.02110.05230.0510
(0.1, 0.9)0.03710.08910.02320.02670.02670.02270.03470.0354
(0.15, 0.85)0.04640.08530.02260.02570.02570.02220.03150.0341
(0.2, 0.8)0.04140.07570.02020.02490.02490.02000.02800.0302
(0.25, 0.75)0.04680.07680.02040.02500.02500.02010.03350.0376
(0.3, 0.7)0.03840.07390.02010.02520.02520.02000.03490.0410
(0.35, 0.65)0.03860.07150.01980.02390.02390.01960.03760.0448
(0.4, 0.6)0.03920.06570.01990.02490.02490.01970.03150.0391
(0.45, 0.55)0.03800.06790.02130.02580.02580.02120.03580.0450
(0.5, 0.5)0.04000.06700.02180.02280.02280.02170.04410.0550
(0.55, 0.45)0.04030.06860.02030.02370.02370.02000.03980.0507
(0.6, 0.4)0.04320.06250.02010.02450.02450.02000.03700.0487
(0.65, 0.35)0.03840.06200.01950.02360.02360.01950.03560.0460
(0.7, 0.3)0.03040.05700.02360.02270.02270.02360.03020.0396
(0.75, 0.25)0.03210.06140.01870.02730.02730.01870.03320.0439
(0.8, 0.2)0.03000.05550.02210.02300.02300.02220.02870.0340
(0.85, 0.15)0.03250.05400.02240.02290.02290.02250.03420.0360
(0.9, 0.1)0.02620.05180.02110.02380.02380.02110.04830.0469
(0.95, 0.05)0.02430.05760.01970.02400.02400.01960.08060.0746
(1.0, 0.0)0.01460.05970.02310.02440.02440.02320.16000.1515
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.02390.04770.03450.01620.0162
(0.05, 0.95)0.02350.04960.03200.01690.0169
(0.1, 0.9)0.02300.05200.02890.01710.0171
(0.15, 0.85)0.03080.05280.02740.01710.0171
(0.2, 0.8)0.02860.04900.02910.01860.0186
(0.25, 0.75)0.03460.05340.02550.01860.0186
(0.3, 0.7)0.02990.05450.02320.02050.0205
(0.35, 0.65)0.03350.05660.02170.02110.0211
(0.4, 0.6)0.03600.05620.02170.02260.0226
(0.45, 0.55)0.03720.06260.02130.02460.0246
(0.5, 0.5)0.04370.06770.02230.02410.0241
(0.55, 0.45)0.04860.07620.02410.02690.0269
(0.6, 0.4)0.05720.07790.02900.03120.0312
(0.65, 0.35)0.05800.08660.03400.03410.0341
(0.7, 0.3)0.05460.09190.04200.03740.0374
(0.75, 0.25)0.06360.11610.06890.05330.0533
(0.8, 0.2)0.07500.11920.07680.05600.0560
(0.85, 0.15)0.10310.15800.12440.07280.0728
(0.9, 0.1)0.11750.24120.18850.11000.1100
(0.95, 0.05)0.18770.34340.35790.20530.2053
(1.0, 0.0)0.27170.31360.91780.62640.6264
+ +
target: default
+
train: [0.60621118 0.39378882]
+
validation: [0.60559006 0.39440994]
+
evaluate_binary: 31.883s
+
evaluate_multiclass: 24.748s
+
kfcv: 23.957s
+
atc_mc: 36.062s
+
atc_ne: 37.123s
+
doc_feat: 7.063s
+
rca_score: 148.420s
+
rca_star_score: 145.690s
+
tot: 149.118s
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binmulkfcvatc_mcatc_nedoc_featrcarca_star
(0.0, 1.0)0.04110.09070.02080.02670.02670.02040.11060.1059
(0.05, 0.95)0.03920.08970.02160.02660.02660.02110.05230.0510
(0.1, 0.9)0.03710.08910.02320.02670.02670.02270.03470.0354
(0.15, 0.85)0.04640.08530.02260.02570.02570.02220.03150.0341
(0.2, 0.8)0.04140.07570.02020.02490.02490.02000.02800.0302
(0.25, 0.75)0.04680.07680.02040.02500.02500.02010.03350.0376
(0.3, 0.7)0.03840.07390.02010.02520.02520.02000.03490.0410
(0.35, 0.65)0.03860.07150.01980.02390.02390.01960.03760.0448
(0.4, 0.6)0.03920.06570.01990.02490.02490.01970.03150.0391
(0.45, 0.55)0.03800.06790.02130.02580.02580.02120.03580.0450
(0.5, 0.5)0.04000.06700.02180.02280.02280.02170.04410.0550
(0.55, 0.45)0.04030.06860.02030.02370.02370.02000.03980.0507
(0.6, 0.4)0.04320.06250.02010.02450.02450.02000.03700.0487
(0.65, 0.35)0.03840.06200.01950.02360.02360.01950.03560.0460
(0.7, 0.3)0.03040.05700.02360.02270.02270.02360.03020.0396
(0.75, 0.25)0.03210.06140.01870.02730.02730.01870.03320.0439
(0.8, 0.2)0.03000.05550.02210.02300.02300.02220.02870.0340
(0.85, 0.15)0.03250.05400.02240.02290.02290.02250.03420.0360
(0.9, 0.1)0.02620.05180.02110.02380.02380.02110.04830.0469
(0.95, 0.05)0.02430.05760.01970.02400.02400.01960.08060.0746
(1.0, 0.0)0.01460.05970.02310.02440.02440.02320.16000.1515
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binmulkfcvatc_mcatc_ne
(0.0, 1.0)0.02390.04770.03450.01620.0162
(0.05, 0.95)0.02350.04960.03200.01690.0169
(0.1, 0.9)0.02300.05200.02890.01710.0171
(0.15, 0.85)0.03080.05280.02740.01710.0171
(0.2, 0.8)0.02860.04900.02910.01860.0186
(0.25, 0.75)0.03460.05340.02550.01860.0186
(0.3, 0.7)0.02990.05450.02320.02050.0205
(0.35, 0.65)0.03350.05660.02170.02110.0211
(0.4, 0.6)0.03600.05620.02170.02260.0226
(0.45, 0.55)0.03720.06260.02130.02460.0246
(0.5, 0.5)0.04370.06770.02230.02410.0241
(0.55, 0.45)0.04860.07620.02410.02690.0269
(0.6, 0.4)0.05720.07790.02900.03120.0312
(0.65, 0.35)0.05800.08660.03400.03410.0341
(0.7, 0.3)0.05460.09190.04200.03740.0374
(0.75, 0.25)0.06360.11610.06890.05330.0533
(0.8, 0.2)0.07500.11920.07680.05600.0560
(0.85, 0.15)0.10310.15800.12440.07280.0728
(0.9, 0.1)0.11750.24120.18850.11000.1100
(0.95, 0.05)0.18770.34340.35790.20530.2053
(1.0, 0.0)0.27170.31360.91780.62640.6264
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-version = "0.1.0" -description = "" -authors = ["Lorenzo Volpi "] -readme = "README.md" - -[tool.poetry.dependencies] -python = "^3.11" -quapy = "^0.1.7" -pandas = "^2.0.3" -jinja2 = "^3.1.2" -pyyaml = "^6.0.1" -logging = "^0.4.9.6" - -[tool.poetry.scripts] -main = "quacc.main:main" -comp = "quacc.main:estimate_comparison" -tohost = "scp_sync:scp_sync_to_host" - - -[tool.poetry.group.dev.dependencies] -pytest = "^7.4.0" -pylance = "^0.5.9" -pytest-mock = "^3.11.1" -pytest-cov = "^4.1.0" -win11toast = "^0.32" -tabulate = "^0.9.0" -paramiko = "^3.3.1" - -[tool.pytest.ini_options] -addopts = "--cov=quacc --capture=tee-sys" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[virtualenvs] -in-project = true - +[tool.poetry] +name = "quacc" +version = "0.1.0" +description = "" +authors = ["Lorenzo Volpi "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.11" +quapy = "^0.1.7" +pandas = "^2.0.3" +jinja2 = "^3.1.2" +pyyaml = "^6.0.1" +logging = "^0.4.9.6" + +[tool.poetry.scripts] +main = "quacc.main:main" +comp = "quacc.main:estimate_comparison" +tohost = "scp_sync:scp_sync_to_host" + + +[tool.poetry.group.dev.dependencies] +pytest = "^7.4.0" +pylance = "^0.5.9" +pytest-mock = "^3.11.1" +pytest-cov = "^4.1.0" +win11toast = "^0.32" +tabulate = "^0.9.0" +paramiko = "^3.3.1" + +[tool.pytest.ini_options] +addopts = "--cov=quacc --capture=tee-sys" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[virtualenvs] +in-project = true + diff --git a/quacc.log b/quacc.log index 5dbaffd..e2f6455 100644 --- a/quacc.log +++ b/quacc.log @@ -1,4671 +1,4671 @@ ----------------------------------------------------------------------------------------------------- -30/10/23 14:14:05| INFO: dataset imdb ----------------------------------------------------------------------------------------------------- -30/10/23 14:14:24| INFO: dataset imdb -30/10/23 14:14:31| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:14:35| WARNING: Method ref failed. Exception: 'dict' object has no attribute 'Keys' -30/10/23 14:14:35| WARNING: Method atc_mc failed. Exception: 'dict' object has no attribute 'Keys' -30/10/23 14:14:35| WARNING: Method atc_ne failed. Exception: 'dict' object has no attribute 'Keys' -30/10/23 14:14:42| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:14:52| WARNING: Method mul_sld failed. Exception: 'dict' object has no attribute 'Keys' -30/10/23 14:14:52| INFO: Dataset sample 0.90 of dataset imdb finished [took 21.1198s] -30/10/23 14:14:52| WARNING: Dataset sample 0.90 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 14:14:52| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable -30/10/23 14:14:52| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 14:16:15| INFO: dataset imdb -30/10/23 14:16:22| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:16:34| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:16:36| INFO: ref finished [took 11.6636s] -30/10/23 14:16:39| INFO: atc_mc finished [took 14.8672s] -30/10/23 14:16:39| INFO: atc_ne finished [took 14.8614s] -30/10/23 14:16:49| INFO: mul_sld finished [took 24.6212s] -30/10/23 14:16:49| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.7805s] -30/10/23 14:16:49| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 14:17:02| INFO: ref finished [took 13.0129s] -30/10/23 14:17:06| INFO: atc_mc finished [took 16.0277s] -30/10/23 14:17:06| INFO: atc_ne finished [took 16.1381s] -30/10/23 14:17:17| INFO: mul_sld finished [took 28.1917s] -30/10/23 14:17:23| INFO: mul_sld_bcts finished [took 33.5628s] -30/10/23 14:17:23| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.3680s] -30/10/23 14:17:23| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 14:17:36| INFO: ref finished [took 12.5930s] -30/10/23 14:17:40| INFO: atc_mc finished [took 16.1461s] -30/10/23 14:17:40| INFO: atc_ne finished [took 16.1788s] -30/10/23 14:17:52| INFO: mul_sld finished [took 28.5367s] -30/10/23 14:18:00| INFO: mul_sld_bcts finished [took 36.0452s] -30/10/23 14:18:00| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.7488s] -30/10/23 14:18:00| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 14:18:13| INFO: ref finished [took 12.3910s] -30/10/23 14:18:17| INFO: atc_mc finished [took 15.8804s] -30/10/23 14:18:17| INFO: atc_ne finished [took 15.7115s] -30/10/23 14:18:32| INFO: mul_sld_bcts finished [took 31.9997s] -30/10/23 14:18:34| INFO: mul_sld finished [took 33.3735s] -30/10/23 14:18:34| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.9557s] -30/10/23 14:18:34| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 14:18:44| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:18:46| INFO: ref finished [took 11.7451s] -30/10/23 14:18:50| INFO: atc_mc finished [took 15.2294s] -30/10/23 14:18:50| INFO: atc_ne finished [took 15.1239s] -30/10/23 14:18:55| INFO: mul_sld finished [took 21.3092s] -30/10/23 14:18:55| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.7186s] -30/10/23 14:18:55| ERROR: Configuration imdb_1prevs failed. Exception: 'mul_sld_bcts' -30/10/23 14:18:55| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 14:32:36| INFO: dataset imdb -30/10/23 14:32:43| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:32:56| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:32:58| INFO: ref finished [took 12.0197s] -30/10/23 14:33:01| INFO: atc_mc finished [took 15.0884s] -30/10/23 14:33:01| INFO: atc_ne finished [took 15.0503s] -30/10/23 14:33:10| INFO: mul_sld finished [took 24.4470s] -30/10/23 14:33:10| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.6099s] -30/10/23 14:33:10| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 14:33:23| INFO: ref finished [took 12.1812s] -30/10/23 14:33:27| INFO: atc_mc finished [took 15.5589s] -30/10/23 14:33:27| INFO: atc_ne finished [took 15.5283s] -30/10/23 14:33:38| INFO: mul_sld finished [took 27.1282s] -30/10/23 14:33:44| INFO: mul_sld_bcts finished [took 33.1098s] -30/10/23 14:33:44| INFO: Dataset sample 0.80 of dataset imdb finished [took 33.9196s] -30/10/23 14:33:44| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 14:33:57| INFO: ref finished [took 12.5959s] -30/10/23 14:34:01| INFO: atc_mc finished [took 15.9389s] -30/10/23 14:34:01| INFO: atc_ne finished [took 16.0795s] -30/10/23 14:34:13| INFO: mul_sld finished [took 28.1568s] -30/10/23 14:34:20| INFO: mul_sld_bcts finished [took 35.7147s] -30/10/23 14:34:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.3828s] -30/10/23 14:34:20| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 14:34:33| INFO: ref finished [took 12.2399s] -30/10/23 14:34:37| INFO: atc_mc finished [took 15.4570s] -30/10/23 14:34:37| INFO: atc_ne finished [took 15.5302s] -30/10/23 14:34:52| INFO: mul_sld_bcts finished [took 31.1972s] -30/10/23 14:34:54| INFO: mul_sld finished [took 32.9409s] -30/10/23 14:34:54| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.5034s] -30/10/23 14:34:54| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 14:35:04| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:35:06| INFO: ref finished [took 11.6742s] -30/10/23 14:35:09| INFO: atc_mc finished [took 14.8324s] -30/10/23 14:35:10| INFO: atc_ne finished [took 14.8661s] -30/10/23 14:35:15| INFO: mul_sld finished [took 21.1356s] -30/10/23 14:35:15| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.5814s] -30/10/23 14:35:15| ERROR: Configuration imdb_1prevs failed. Exception: ('acc', None) -30/10/23 14:35:15| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 14:37:47| INFO: dataset imdb -30/10/23 14:37:54| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:38:07| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:38:09| INFO: ref finished [took 12.0443s] -30/10/23 14:38:12| INFO: atc_mc finished [took 14.8929s] -30/10/23 14:38:12| INFO: atc_ne finished [took 15.0431s] -30/10/23 14:38:21| INFO: mul_sld finished [took 24.7987s] -30/10/23 14:38:21| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.0182s] -30/10/23 14:38:21| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 14:38:35| INFO: ref finished [took 12.4504s] -30/10/23 14:38:38| INFO: atc_mc finished [took 16.1560s] -30/10/23 14:38:39| INFO: atc_ne finished [took 16.1785s] -30/10/23 14:38:49| INFO: mul_sld finished [took 27.0617s] -30/10/23 14:38:55| INFO: mul_sld_bcts finished [took 32.7384s] -30/10/23 14:38:55| INFO: Dataset sample 0.80 of dataset imdb finished [took 33.5347s] -30/10/23 14:38:55| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 14:39:08| INFO: ref finished [took 12.4381s] -30/10/23 14:39:11| INFO: atc_mc finished [took 15.6709s] -30/10/23 14:39:11| INFO: atc_ne finished [took 15.7319s] -30/10/23 14:39:23| INFO: mul_sld finished [took 27.9301s] -30/10/23 14:39:31| INFO: mul_sld_bcts finished [took 35.5094s] -30/10/23 14:39:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.1333s] -30/10/23 14:39:31| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 14:39:44| INFO: ref finished [took 12.0382s] -30/10/23 14:39:47| INFO: atc_mc finished [took 15.0164s] -30/10/23 14:39:47| INFO: atc_ne finished [took 15.1080s] -30/10/23 14:40:02| INFO: mul_sld_bcts finished [took 30.9659s] -30/10/23 14:40:04| INFO: mul_sld finished [took 32.9418s] -30/10/23 14:40:04| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.4681s] -30/10/23 14:40:04| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 14:40:14| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:40:17| INFO: ref finished [took 11.8501s] -30/10/23 14:40:20| INFO: atc_mc finished [took 14.8473s] -30/10/23 14:40:21| INFO: atc_ne finished [took 15.2000s] -30/10/23 14:40:26| INFO: mul_sld finished [took 21.4799s] -30/10/23 14:40:26| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.9220s] -30/10/23 14:40:26| ERROR: Configuration imdb_1prevs failed. Exception: NDFrame.droplevel() got an unexpected keyword argument 'index' -30/10/23 14:40:26| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 14:42:13| INFO: dataset imdb -30/10/23 14:42:20| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:42:33| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:42:34| INFO: ref finished [took 12.1951s] -30/10/23 14:42:38| INFO: atc_ne finished [took 15.3431s] -30/10/23 14:42:38| INFO: atc_mc finished [took 15.4508s] -30/10/23 14:42:47| INFO: mul_sld finished [took 25.0246s] -30/10/23 14:42:47| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.1381s] -30/10/23 14:42:47| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 14:43:00| INFO: ref finished [took 12.3269s] -30/10/23 14:43:04| INFO: atc_ne finished [took 15.9216s] -30/10/23 14:43:04| INFO: atc_mc finished [took 16.1140s] -30/10/23 14:43:16| INFO: mul_sld finished [took 28.0575s] -30/10/23 14:43:22| INFO: mul_sld_bcts finished [took 33.9201s] -30/10/23 14:43:22| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.7703s] -30/10/23 14:43:22| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 14:43:35| INFO: ref finished [took 12.6508s] -30/10/23 14:43:39| INFO: atc_mc finished [took 16.0527s] -30/10/23 14:43:39| INFO: atc_ne finished [took 16.0515s] -30/10/23 14:43:50| INFO: mul_sld finished [took 28.1061s] -30/10/23 14:43:57| INFO: mul_sld_bcts finished [took 34.9278s] -30/10/23 14:43:57| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.6587s] -30/10/23 14:43:57| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 14:44:10| INFO: ref finished [took 12.0801s] -30/10/23 14:44:14| INFO: atc_mc finished [took 15.4685s] -30/10/23 14:44:14| INFO: atc_ne finished [took 15.4165s] -30/10/23 14:44:29| INFO: mul_sld_bcts finished [took 31.5628s] -30/10/23 14:44:31| INFO: mul_sld finished [took 33.3113s] -30/10/23 14:44:31| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.8828s] -30/10/23 14:44:31| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 14:44:41| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:44:44| INFO: ref finished [took 11.6822s] -30/10/23 14:44:47| INFO: atc_mc finished [took 14.8091s] -30/10/23 14:44:47| INFO: atc_ne finished [took 14.7900s] -30/10/23 14:44:53| INFO: mul_sld finished [took 21.0390s] -30/10/23 14:44:53| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.4515s] -30/10/23 14:44:53| ERROR: Configuration imdb_1prevs failed. Exception: 'function' object has no attribute 'index' -30/10/23 14:44:53| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 14:46:34| INFO: dataset imdb -30/10/23 14:46:41| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 14:46:54| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:46:56| INFO: ref finished [took 12.5001s] -30/10/23 14:46:59| INFO: atc_mc finished [took 15.5415s] -30/10/23 14:46:59| INFO: atc_ne finished [took 15.6358s] -30/10/23 14:47:08| INFO: mul_sld finished [took 24.5102s] -30/10/23 14:47:08| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.5553s] -30/10/23 14:47:08| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 14:47:21| INFO: ref finished [took 12.0997s] -30/10/23 14:47:24| INFO: atc_mc finished [took 15.4285s] -30/10/23 14:47:24| INFO: atc_ne finished [took 15.5599s] -30/10/23 14:47:36| INFO: mul_sld finished [took 27.6146s] -30/10/23 14:47:43| INFO: mul_sld_bcts finished [took 34.2610s] -30/10/23 14:47:43| INFO: Dataset sample 0.80 of dataset imdb finished [took 35.0096s] -30/10/23 14:47:43| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 14:47:56| INFO: ref finished [took 12.1238s] -30/10/23 14:48:00| INFO: atc_mc finished [took 15.6990s] -30/10/23 14:48:00| INFO: atc_ne finished [took 15.8708s] -30/10/23 14:48:11| INFO: mul_sld finished [took 28.0048s] -30/10/23 14:48:20| INFO: mul_sld_bcts finished [took 36.1524s] -30/10/23 14:48:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.8480s] -30/10/23 14:48:20| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 14:48:32| INFO: ref finished [took 11.3690s] -30/10/23 14:48:35| INFO: atc_mc finished [took 14.3092s] -30/10/23 14:48:35| INFO: atc_ne finished [took 14.4043s] -30/10/23 14:48:51| INFO: mul_sld_bcts finished [took 30.2595s] -30/10/23 14:48:52| INFO: mul_sld finished [took 31.4270s] -30/10/23 14:48:52| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.9598s] -30/10/23 14:48:52| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 14:49:02| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 14:49:04| INFO: ref finished [took 12.1449s] -30/10/23 14:49:08| INFO: atc_mc finished [took 15.0332s] -30/10/23 14:49:08| INFO: atc_ne finished [took 15.3463s] -30/10/23 14:49:13| INFO: mul_sld finished [took 21.2802s] -30/10/23 14:49:13| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.7079s] -30/10/23 14:49:14| ERROR: Configuration imdb_1prevs failed. Exception: unsupported operand type(s) for -: 'list' and 'list' -30/10/23 14:49:14| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 15:10:08| INFO: dataset imdb -30/10/23 15:10:14| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 15:10:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 15:10:27| INFO: ref finished [took 10.8100s] -30/10/23 15:10:30| INFO: atc_mc finished [took 13.5996s] -30/10/23 15:10:30| INFO: atc_ne finished [took 13.6110s] -30/10/23 15:10:39| INFO: mul_sld finished [took 22.7361s] -30/10/23 15:10:39| INFO: Dataset sample 0.90 of dataset imdb finished [took 24.8056s] -30/10/23 15:10:39| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 15:10:51| INFO: ref finished [took 10.9293s] -30/10/23 15:10:54| INFO: atc_mc finished [took 13.8377s] -30/10/23 15:10:54| INFO: atc_ne finished [took 13.9983s] -30/10/23 15:11:05| INFO: mul_sld finished [took 25.1977s] -30/10/23 15:11:11| INFO: mul_sld_bcts finished [took 31.1124s] -30/10/23 15:11:11| INFO: Dataset sample 0.80 of dataset imdb finished [took 31.8294s] -30/10/23 15:11:11| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 15:11:23| INFO: ref finished [took 11.0056s] -30/10/23 15:11:26| INFO: atc_mc finished [took 14.3946s] -30/10/23 15:11:27| INFO: atc_ne finished [took 14.6355s] -30/10/23 15:11:38| INFO: mul_sld finished [took 26.2697s] -30/10/23 15:11:45| INFO: mul_sld_bcts finished [took 33.8992s] -30/10/23 15:11:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 34.4963s] -30/10/23 15:11:45| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 15:11:57| INFO: ref finished [took 10.9836s] -30/10/23 15:12:00| INFO: atc_mc finished [took 13.8378s] -30/10/23 15:12:00| INFO: atc_ne finished [took 13.8318s] -30/10/23 15:12:16| INFO: mul_sld_bcts finished [took 29.9813s] -30/10/23 15:12:17| INFO: mul_sld finished [took 30.7175s] -30/10/23 15:12:17| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.2508s] -30/10/23 15:12:17| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 15:12:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 15:12:28| INFO: ref finished [took 10.4376s] -30/10/23 15:12:31| INFO: atc_ne finished [took 13.3510s] -30/10/23 15:12:31| INFO: atc_mc finished [took 13.5172s] -30/10/23 15:12:37| INFO: mul_sld finished [took 19.7440s] -30/10/23 15:12:37| INFO: Dataset sample 0.10 of dataset imdb finished [took 20.1519s] -30/10/23 15:12:37| ERROR: Configuration imdb_1prevs failed. Exception: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' -30/10/23 15:12:37| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 17:12:41| INFO: dataset imdb -30/10/23 17:12:48| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 17:13:01| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 17:13:03| INFO: ref finished [took 12.6699s] -30/10/23 17:13:07| INFO: atc_ne finished [took 15.6073s] -30/10/23 17:13:07| INFO: atc_mc finished [took 15.6695s] -30/10/23 17:13:15| INFO: mul_sld finished [took 24.8617s] -30/10/23 17:13:15| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.6018s] -30/10/23 17:13:15| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 17:13:29| INFO: ref finished [took 12.6205s] -30/10/23 17:13:33| INFO: atc_mc finished [took 16.2005s] -30/10/23 17:13:33| INFO: atc_ne finished [took 16.2091s] -30/10/23 17:13:43| INFO: mul_sld finished [took 27.1113s] -30/10/23 17:13:49| INFO: mul_sld_bcts finished [took 33.3939s] -30/10/23 17:13:49| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.1222s] -30/10/23 17:13:49| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 17:14:04| INFO: ref finished [took 13.2345s] -30/10/23 17:14:07| INFO: atc_mc finished [took 16.5475s] -30/10/23 17:14:07| INFO: atc_ne finished [took 16.6557s] -30/10/23 17:14:19| INFO: mul_sld finished [took 28.8817s] -30/10/23 17:14:27| INFO: mul_sld_bcts finished [took 36.5726s] -30/10/23 17:14:27| INFO: Dataset sample 0.50 of dataset imdb finished [took 37.2057s] -30/10/23 17:14:27| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 17:14:39| INFO: ref finished [took 11.7051s] -30/10/23 17:14:42| INFO: atc_mc finished [took 14.8335s] -30/10/23 17:14:43| INFO: atc_ne finished [took 15.0826s] -30/10/23 17:14:59| INFO: mul_sld_bcts finished [took 31.7685s] -30/10/23 17:15:00| INFO: mul_sld finished [took 33.2861s] -30/10/23 17:15:00| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.8225s] -30/10/23 17:15:00| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 17:15:11| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 17:15:13| INFO: ref finished [took 12.0927s] -30/10/23 17:15:17| INFO: atc_mc finished [took 15.4201s] -30/10/23 17:15:17| INFO: atc_ne finished [took 15.5212s] -30/10/23 17:15:23| INFO: mul_sld finished [took 21.7236s] -30/10/23 17:15:23| INFO: Dataset sample 0.10 of dataset imdb finished [took 22.2065s] -30/10/23 17:15:23| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) -30/10/23 17:15:23| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 17:16:39| INFO: dataset imdb -30/10/23 17:16:46| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 17:16:58| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 17:17:00| INFO: ref finished [took 11.7575s] -30/10/23 17:17:03| INFO: atc_ne finished [took 14.7709s] -30/10/23 17:17:03| INFO: atc_mc finished [took 14.8925s] -30/10/23 17:17:12| INFO: mul_sld finished [took 23.7037s] -30/10/23 17:17:12| INFO: Dataset sample 0.90 of dataset imdb finished [took 25.8491s] -30/10/23 17:17:12| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 17:17:25| INFO: ref finished [took 12.2081s] -30/10/23 17:17:28| INFO: atc_ne finished [took 15.3145s] -30/10/23 17:17:28| INFO: atc_mc finished [took 15.5166s] -30/10/23 17:17:39| INFO: mul_sld finished [took 26.7520s] -30/10/23 17:17:45| INFO: mul_sld_bcts finished [took 32.0850s] -30/10/23 17:17:45| INFO: Dataset sample 0.80 of dataset imdb finished [took 32.8702s] -30/10/23 17:17:45| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 17:17:57| INFO: ref finished [took 11.9494s] -30/10/23 17:18:01| INFO: atc_mc finished [took 15.3034s] -30/10/23 17:18:01| INFO: atc_ne finished [took 15.3254s] -30/10/23 17:18:12| INFO: mul_sld finished [took 27.2902s] -30/10/23 17:18:20| INFO: mul_sld_bcts finished [took 34.4237s] -30/10/23 17:18:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.1216s] -30/10/23 17:18:20| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 17:18:32| INFO: ref finished [took 11.7945s] -30/10/23 17:18:35| INFO: atc_mc finished [took 14.9218s] -30/10/23 17:18:36| INFO: atc_ne finished [took 14.9745s] -30/10/23 17:18:51| INFO: mul_sld_bcts finished [took 30.7287s] -30/10/23 17:18:53| INFO: mul_sld finished [took 32.5641s] -30/10/23 17:18:53| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.0982s] -30/10/23 17:18:53| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 17:19:02| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 17:19:05| INFO: ref finished [took 11.4568s] -30/10/23 17:19:08| INFO: atc_mc finished [took 14.4778s] -30/10/23 17:19:08| INFO: atc_ne finished [took 14.5099s] -30/10/23 17:19:14| INFO: mul_sld finished [took 20.5183s] -30/10/23 17:19:14| INFO: Dataset sample 0.10 of dataset imdb finished [took 20.9251s] -30/10/23 17:19:14| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) -30/10/23 17:19:14| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- -30/10/23 19:57:49| INFO: dataset imdb -30/10/23 19:58:00| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 19:58:11| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 19:58:22| INFO: ref finished [took 20.9010s] -30/10/23 19:58:29| INFO: atc_ne finished [took 27.8453s] -30/10/23 19:58:29| INFO: atc_mc finished [took 28.1079s] -30/10/23 19:58:37| INFO: mul_sld finished [took 36.1699s] -30/10/23 19:58:37| INFO: Dataset sample 0.90 of dataset imdb finished [took 36.7140s] -30/10/23 19:58:37| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 19:59:01| INFO: ref finished [took 23.2803s] -30/10/23 19:59:09| INFO: atc_ne finished [took 31.1099s] -30/10/23 19:59:09| INFO: atc_mc finished [took 31.5916s] -30/10/23 19:59:19| INFO: mul_sld finished [took 41.5113s] -30/10/23 19:59:24| INFO: mul_sld_bcts finished [took 46.6603s] -30/10/23 19:59:24| INFO: Dataset sample 0.80 of dataset imdb finished [took 47.4989s] -30/10/23 19:59:24| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 19:59:49| INFO: ref finished [took 23.6312s] -30/10/23 19:59:57| INFO: atc_ne finished [took 31.5195s] -30/10/23 19:59:57| INFO: atc_mc finished [took 31.8197s] -30/10/23 20:00:08| INFO: mul_sld finished [took 42.8675s] -30/10/23 20:00:15| INFO: mul_sld_bcts finished [took 50.5527s] -30/10/23 20:00:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 51.3659s] -30/10/23 20:00:16| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 20:00:41| INFO: ref finished [took 24.2178s] -30/10/23 20:00:48| INFO: atc_mc finished [took 31.9886s] -30/10/23 20:00:49| INFO: atc_ne finished [took 32.1537s] -30/10/23 20:01:03| INFO: mul_sld_bcts finished [took 46.2477s] -30/10/23 20:01:07| INFO: mul_sld finished [took 50.8912s] -30/10/23 20:01:07| INFO: Dataset sample 0.20 of dataset imdb finished [took 51.4589s] -30/10/23 20:01:07| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 20:01:18| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 20:01:30| INFO: ref finished [took 22.6404s] -30/10/23 20:01:38| INFO: atc_mc finished [took 29.8371s] -30/10/23 20:01:38| INFO: atc_ne finished [took 30.2098s] -30/10/23 20:01:41| INFO: mul_sld finished [took 33.6271s] -30/10/23 20:01:41| INFO: Dataset sample 0.10 of dataset imdb finished [took 34.1993s] -30/10/23 20:01:42| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) -30/10/23 20:01:42| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 20:05:04| INFO: dataset imdb -30/10/23 20:05:14| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 20:05:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 20:05:38| INFO: ref finished [took 22.7241s] -30/10/23 20:05:45| INFO: atc_mc finished [took 29.9191s] -30/10/23 20:05:45| INFO: atc_ne finished [took 29.8405s] -30/10/23 20:05:52| INFO: mul_sld finished [took 37.4045s] -30/10/23 20:05:52| INFO: Dataset sample 0.90 of dataset imdb finished [took 37.9554s] -30/10/23 20:05:52| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 20:06:17| INFO: ref finished [took 23.2465s] -30/10/23 20:06:25| INFO: atc_ne finished [took 31.0138s] -30/10/23 20:06:25| INFO: atc_mc finished [took 31.1341s] -30/10/23 20:06:34| INFO: mul_sld finished [took 40.8777s] -30/10/23 20:06:40| INFO: mul_sld_bcts finished [took 46.7083s] -30/10/23 20:06:40| INFO: Dataset sample 0.80 of dataset imdb finished [took 47.5062s] -30/10/23 20:06:40| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:07:05| INFO: ref finished [took 24.3375s] -30/10/23 20:07:15| INFO: atc_mc finished [took 33.8014s] -30/10/23 20:07:15| INFO: atc_ne finished [took 33.7355s] -30/10/23 20:07:25| INFO: mul_sld finished [took 44.2891s] -30/10/23 20:07:32| INFO: mul_sld_bcts finished [took 51.2404s] -30/10/23 20:07:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 51.9917s] -30/10/23 20:07:32| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 20:07:55| INFO: ref finished [took 21.6828s] -30/10/23 20:08:01| INFO: atc_mc finished [took 28.2369s] -30/10/23 20:08:01| INFO: atc_ne finished [took 28.4328s] -30/10/23 20:08:15| INFO: mul_sld_bcts finished [took 41.9176s] -30/10/23 20:08:18| INFO: mul_sld finished [took 45.4999s] -30/10/23 20:08:18| INFO: Dataset sample 0.20 of dataset imdb finished [took 46.0301s] -30/10/23 20:08:18| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 20:08:28| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 20:08:38| INFO: ref finished [took 19.4082s] -30/10/23 20:08:45| INFO: atc_mc finished [took 26.2343s] -30/10/23 20:08:45| INFO: atc_ne finished [took 26.2322s] -30/10/23 20:08:48| INFO: mul_sld finished [took 29.8392s] -30/10/23 20:08:48| INFO: Dataset sample 0.10 of dataset imdb finished [took 30.3563s] ----------------------------------------------------------------------------------------------------- -30/10/23 20:29:28| INFO: dataset imdb -30/10/23 20:29:38| INFO: Dataset sample 0.50 of dataset imdb started - 30/10/23 20:29:59| INFO: ref finished [took 19.1581s] - 30/10/23 20:30:06| INFO: atc_mc finished [took 26.3398s] - 30/10/23 20:30:07| INFO: atc_ne finished [took 26.4359s] -30/10/23 20:30:07| INFO: Dataset sample 0.50 of dataset imdb finished [took 28.7984s] ----------------------------------------------------------------------------------------------------- -30/10/23 20:31:50| INFO: dataset imdb -30/10/23 20:32:00| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:33:59|INFO: ref finished [took 118.1306s] ----------------------------------------------------------------------------------------------------- -30/10/23 20:36:06| INFO: dataset imdb -30/10/23 20:36:17| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:38:52|WARNING: Method ref failed. Exception: "['acc_score' 'f1_score' 'ref'] not in index" -30/10/23 20:41:28|WARNING: Method atc_mc failed. Exception: "['acc' 'acc_score' 'atc_mc' 'f1' 'f1_score'] not in index" -30/10/23 20:41:32|WARNING: Method atc_ne failed. Exception: "['acc' 'acc_score' 'atc_ne' 'f1' 'f1_score'] not in index" -30/10/23 20:41:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 315.4626s] -30/10/23 20:41:32| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:41:32| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable -30/10/23 20:41:32| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 20:41:43| INFO: dataset imdb -30/10/23 20:41:54| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:42:26| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) -30/10/23 20:43:01| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:43:08| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:43:08| INFO: Dataset sample 0.50 of dataset imdb finished [took 73.6011s] -30/10/23 20:43:08| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:43:08| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable -30/10/23 20:43:08| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 20:44:25| INFO: dataset imdb -30/10/23 20:44:35| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:44:37| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) -30/10/23 20:44:37| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:44:38| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:44:38| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6758s] -30/10/23 20:44:38| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:44:38| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable -30/10/23 20:44:38| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable ----------------------------------------------------------------------------------------------------- -30/10/23 20:47:08| INFO: dataset imdb -30/10/23 20:47:18| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:47:20| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) -30/10/23 20:47:21| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:47:21| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:47:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6147s] -30/10/23 20:47:21| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:47:21| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 20:50:07| INFO: dataset imdb -30/10/23 20:50:17| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:50:19| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) -30/10/23 20:50:20| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:50:20| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:50:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5897s] -30/10/23 20:50:20| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:50:20| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 20:51:29| INFO: dataset imdb -30/10/23 20:51:39| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:51:42| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) -30/10/23 20:51:42| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:51:42| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) -30/10/23 20:51:42| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5821s] -30/10/23 20:51:42| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:51:42| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 20:56:28| INFO: dataset imdb -30/10/23 20:56:38| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:56:40| WARNING: Method ref failed. Exception: cannot reshape array of size 1 into shape (1,2) -30/10/23 20:56:40| WARNING: Method atc_mc failed. Exception: cannot reshape array of size 1 into shape (1,4) -30/10/23 20:56:40| WARNING: Method atc_ne failed. Exception: cannot reshape array of size 1 into shape (1,4) -30/10/23 20:56:40| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6150s] -30/10/23 20:56:40| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:56:40| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 20:57:13| INFO: dataset imdb -30/10/23 20:57:23| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 20:59:40| WARNING: Method ref failed. Exception: cannot reshape array of size 1 into shape (1,2) -30/10/23 20:59:51| WARNING: Method atc_mc failed. Exception: cannot reshape array of size 1 into shape (1,4) -30/10/23 20:59:52| WARNING: Method atc_ne failed. Exception: cannot reshape array of size 1 into shape (1,4) -30/10/23 20:59:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 149.2395s] -30/10/23 20:59:52| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 20:59:52| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 21:00:04| INFO: dataset imdb -30/10/23 21:00:14| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:01:33| INFO: ref finished [took 78.2917s] -30/10/23 21:01:42| INFO: atc_mc finished [took 86.9003s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:01:59| INFO: dataset imdb -30/10/23 21:02:09| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:04:09| INFO: dataset imdb -30/10/23 21:04:19| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:06:25| INFO: dataset imdb -30/10/23 21:06:35| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:07:33| INFO: dataset imdb -30/10/23 21:07:43| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:07:45| WARNING: Method ref failed. Exception: setting an array element with a sequence. -30/10/23 21:07:45| WARNING: Method atc_mc failed. Exception: setting an array element with a sequence. -30/10/23 21:07:45| WARNING: Method atc_ne failed. Exception: setting an array element with a sequence. -30/10/23 21:07:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5382s] -30/10/23 21:07:45| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate -30/10/23 21:07:45| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate ----------------------------------------------------------------------------------------------------- -30/10/23 21:09:07| INFO: dataset imdb -30/10/23 21:09:16| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:10:48| INFO: dataset imdb -30/10/23 21:10:58| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:18:53| INFO: dataset imdb -30/10/23 21:19:03| INFO: Dataset sample 0.50 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 21:22:03| INFO: dataset imdb -30/10/23 21:22:12| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:22:31| INFO: ref finished [took 17.0861s] -30/10/23 21:22:37| INFO: atc_mc finished [took 23.6279s] -30/10/23 21:22:38| INFO: atc_ne finished [took 23.7395s] -30/10/23 21:22:38| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.2007s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:29:55| INFO: dataset imdb -30/10/23 21:30:05| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:30:23| INFO: ref finished [took 16.7801s] -30/10/23 21:30:30| INFO: atc_mc finished [took 23.5645s] -30/10/23 21:30:30| INFO: atc_ne finished [took 23.5639s] -30/10/23 21:30:30| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.0459s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:33:45| INFO: dataset imdb -30/10/23 21:33:55| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:34:13| INFO: ref finished [took 17.0169s] -30/10/23 21:34:20| INFO: atc_mc finished [took 23.4725s] -30/10/23 21:34:20| INFO: atc_ne finished [took 23.5928s] -30/10/23 21:34:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9542s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:37:32| INFO: dataset imdb -30/10/23 21:37:39| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:37:49| INFO: ref finished [took 8.9050s] -30/10/23 21:37:52| INFO: atc_mc finished [took 11.7412s] -30/10/23 21:37:52| INFO: atc_ne finished [took 11.7256s] -30/10/23 21:37:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.9758s] -30/10/23 21:37:53| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices ----------------------------------------------------------------------------------------------------- -30/10/23 21:39:14| INFO: dataset imdb -30/10/23 21:39:21| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:39:31| INFO: ref finished [took 8.5615s] -30/10/23 21:39:34| INFO: atc_mc finished [took 11.4156s] -30/10/23 21:39:34| INFO: atc_ne finished [took 11.4156s] -30/10/23 21:39:34| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.7024s] -30/10/23 21:39:35| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices ----------------------------------------------------------------------------------------------------- -30/10/23 21:40:51| INFO: dataset imdb -30/10/23 21:41:01| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:41:19| INFO: ref finished [took 16.7164s] -30/10/23 21:41:26| INFO: atc_mc finished [took 23.3181s] -30/10/23 21:41:26| INFO: atc_ne finished [took 23.4811s] -30/10/23 21:41:26| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9698s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:43:25| INFO: dataset imdb -30/10/23 21:43:35| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:43:53| INFO: ref finished [took 16.9333s] -30/10/23 21:44:00| INFO: atc_mc finished [took 23.4183s] -30/10/23 21:44:00| INFO: atc_ne finished [took 23.4274s] -30/10/23 21:44:00| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9308s] -30/10/23 21:44:19| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices ----------------------------------------------------------------------------------------------------- -30/10/23 21:45:16| INFO: dataset imdb -30/10/23 21:45:26| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:45:44| INFO: ref finished [took 17.6768s] -30/10/23 21:45:51| INFO: atc_mc finished [took 24.3756s] -30/10/23 21:45:52| INFO: atc_ne finished [took 24.5307s] -30/10/23 21:45:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.8971s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:48:20| INFO: dataset imdb -30/10/23 21:48:27| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:48:36| INFO: ref finished [took 8.6456s] -30/10/23 21:48:39| INFO: atc_mc finished [took 11.2686s] -30/10/23 21:48:39| INFO: atc_ne finished [took 11.3112s] -30/10/23 21:48:39| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.5747s] -30/10/23 21:48:40| ERROR: Configuration imdb_1prevs failed. Exception: NDFrame.droplevel() got an unexpected keyword argument 'index' ----------------------------------------------------------------------------------------------------- -30/10/23 21:49:49| INFO: dataset imdb -30/10/23 21:49:55| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:50:05| INFO: ref finished [took 8.6556s] -30/10/23 21:50:08| INFO: atc_mc finished [took 11.6953s] -30/10/23 21:50:08| INFO: atc_ne finished [took 11.6000s] -30/10/23 21:50:08| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8578s] -30/10/23 21:50:09| ERROR: Configuration imdb_1prevs failed. Exception: 'NoneType' object has no attribute 'groupby' ----------------------------------------------------------------------------------------------------- -30/10/23 21:50:57| INFO: dataset imdb -30/10/23 21:51:07| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:51:25| INFO: ref finished [took 17.0426s] -30/10/23 21:51:31| INFO: atc_mc finished [took 23.5734s] -30/10/23 21:51:31| INFO: atc_ne finished [took 23.5276s] -30/10/23 21:51:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.8200s] ----------------------------------------------------------------------------------------------------- -30/10/23 21:55:21| INFO: dataset imdb -30/10/23 21:55:27| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:55:37| INFO: ref finished [took 8.8453s] -30/10/23 21:55:40| INFO: atc_mc finished [took 11.5585s] -30/10/23 21:55:40| INFO: atc_ne finished [took 11.5871s] -30/10/23 21:55:40| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8416s] -30/10/23 21:55:41| ERROR: Configuration imdb_1prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' ----------------------------------------------------------------------------------------------------- -30/10/23 21:57:00| INFO: dataset imdb -30/10/23 21:57:06| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:57:16| INFO: ref finished [took 8.5540s] -30/10/23 21:57:19| INFO: atc_mc finished [took 11.4482s] -30/10/23 21:57:19| INFO: atc_ne finished [took 11.5399s] -30/10/23 21:57:19| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.7681s] -30/10/23 21:57:20| ERROR: Configuration imdb_1prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' ----------------------------------------------------------------------------------------------------- -30/10/23 21:57:38| INFO: dataset imdb -30/10/23 21:57:45| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 21:57:55| INFO: ref finished [took 8.7982s] -30/10/23 21:57:58| INFO: atc_mc finished [took 11.4787s] -30/10/23 21:57:58| INFO: atc_ne finished [took 11.5419s] -30/10/23 21:57:58| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8803s] ----------------------------------------------------------------------------------------------------- -30/10/23 22:00:05| INFO: dataset imdb -30/10/23 22:00:12| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 22:00:21| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:00:22| INFO: ref finished [took 10.0983s] -30/10/23 22:00:25| INFO: atc_mc finished [took 13.0928s] -30/10/23 22:00:26| INFO: atc_ne finished [took 13.1088s] -30/10/23 22:00:34| INFO: mul_sld finished [took 22.3228s] -30/10/23 22:00:34| INFO: Dataset sample 0.90 of dataset imdb finished [took 22.7020s] -30/10/23 22:00:34| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 22:00:46| INFO: ref finished [took 10.5937s] -30/10/23 22:00:49| INFO: atc_mc finished [took 13.5008s] -30/10/23 22:00:49| INFO: atc_ne finished [took 13.7521s] -30/10/23 22:01:00| INFO: mul_sld finished [took 25.0319s] -30/10/23 22:01:06| INFO: mul_sld_bcts finished [took 31.0525s] -30/10/23 22:01:06| INFO: Dataset sample 0.80 of dataset imdb finished [took 31.7700s] -30/10/23 22:01:06| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 22:01:17| INFO: ref finished [took 10.6316s] -30/10/23 22:01:21| INFO: atc_ne finished [took 14.1054s] -30/10/23 22:01:21| INFO: atc_mc finished [took 14.4357s] -30/10/23 22:01:33| INFO: mul_sld finished [took 26.6800s] -30/10/23 22:01:41| INFO: mul_sld_bcts finished [took 34.4745s] -30/10/23 22:01:41| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.1450s] -30/10/23 22:01:41| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 22:01:53| INFO: ref finished [took 10.7413s] -30/10/23 22:01:56| INFO: atc_ne finished [took 13.5169s] -30/10/23 22:01:56| INFO: atc_mc finished [took 13.5849s] -30/10/23 22:02:11| INFO: mul_sld_bcts finished [took 29.3981s] -30/10/23 22:02:12| INFO: mul_sld finished [took 30.6705s] -30/10/23 22:02:12| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.2089s] -30/10/23 22:02:12| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 22:02:22| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:02:24| INFO: ref finished [took 10.3435s] -30/10/23 22:02:26| INFO: atc_mc finished [took 13.0763s] -30/10/23 22:02:27| INFO: atc_ne finished [took 13.2013s] -30/10/23 22:02:32| INFO: mul_sld finished [took 19.2237s] -30/10/23 22:02:32| INFO: Dataset sample 0.10 of dataset imdb finished [took 19.7097s] ----------------------------------------------------------------------------------------------------- -30/10/23 22:07:59| INFO: dataset imdb -30/10/23 22:08:07| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 22:08:10| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:08:11| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:08:11| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:08:11| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 22:08:18| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:08:20| INFO: ref finished [took 11.3765s] -30/10/23 22:08:23| INFO: atc_mc finished [took 14.2141s] -30/10/23 22:08:23| INFO: atc_ne finished [took 14.0568s] -30/10/23 22:08:31| INFO: mul_sld finished [took 23.9496s] -30/10/23 22:08:31| INFO: Dataset sample 0.90 of dataset imdb finished [took 24.5121s] -30/10/23 22:08:31| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 22:08:48| INFO: ref finished [took 14.8939s] -30/10/23 22:08:52| INFO: atc_mc finished [took 18.5014s] -30/10/23 22:08:52| INFO: atc_ne finished [took 18.4609s] -30/10/23 22:09:05| INFO: mul_sld finished [took 32.9898s] -30/10/23 22:09:12| INFO: mul_sld_bcts finished [took 39.3492s] -30/10/23 22:11:48| INFO: bin_sld_bcts finished [took 195.8293s] -30/10/23 22:11:49| INFO: bin_sld finished [took 196.6861s] -30/10/23 22:12:44| INFO: mul_sld_gs finished [took 250.9835s] -30/10/23 22:16:16| INFO: bin_sld_gs finished [took 462.9748s] -30/10/23 22:16:16| INFO: Dataset sample 0.80 of dataset imdb finished [took 464.4318s] -30/10/23 22:16:16| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 22:16:33| INFO: ref finished [took 15.2921s] -30/10/23 22:16:37| INFO: atc_mc finished [took 18.9592s] -30/10/23 22:16:37| INFO: atc_ne finished [took 19.1317s] -30/10/23 22:16:50| INFO: mul_sld finished [took 33.4304s] -30/10/23 22:16:59| INFO: mul_sld_bcts finished [took 42.3496s] -30/10/23 22:19:33| INFO: bin_sld finished [took 196.1571s] -30/10/23 22:19:36| INFO: bin_sld_bcts finished [took 199.7857s] -30/10/23 22:20:39| INFO: mul_sld_gs finished [took 261.6674s] -30/10/23 22:23:46| INFO: bin_sld_gs finished [took 449.3788s] -30/10/23 22:23:46| INFO: Dataset sample 0.50 of dataset imdb finished [took 450.7045s] -30/10/23 22:23:46| INFO: Dataset sample 0.20 of dataset imdb started -30/10/23 22:24:05| INFO: ref finished [took 16.4122s] -30/10/23 22:24:09| INFO: atc_mc finished [took 20.4920s] -30/10/23 22:24:09| INFO: atc_ne finished [took 20.3723s] -30/10/23 22:24:28| INFO: mul_sld_bcts finished [took 40.3400s] -30/10/23 22:24:30| INFO: mul_sld finished [took 43.2311s] -30/10/23 22:27:16| INFO: bin_sld_bcts finished [took 208.6113s] -30/10/23 22:27:21| INFO: bin_sld finished [took 214.1596s] -30/10/23 22:28:17| INFO: mul_sld_gs finished [took 269.1075s] -30/10/23 22:34:19| INFO: bin_sld_gs finished [took 630.9727s] -30/10/23 22:34:19| INFO: Dataset sample 0.20 of dataset imdb finished [took 632.2728s] -30/10/23 22:34:19| INFO: Dataset sample 0.10 of dataset imdb started -30/10/23 22:34:23| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 22:34:23| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -30/10/23 22:34:26| WARNING: Method bin_sld_bcts failed. Exception: Cannot have number of splits n_splits=5 greater than the number of samples: n_samples=4. -30/10/23 22:34:31| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:34:34| INFO: ref finished [took 13.7988s] -30/10/23 22:34:37| INFO: atc_mc finished [took 16.7490s] -30/10/23 22:34:38| INFO: atc_ne finished [took 16.7307s] -30/10/23 22:34:43| INFO: mul_sld finished [took 23.6079s] -30/10/23 22:36:42| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -30/10/23 22:36:42| INFO: Dataset sample 0.10 of dataset imdb finished [took 143.1097s] ----------------------------------------------------------------------------------------------------- -30/10/23 22:49:25| INFO: dataset imdb -30/10/23 22:49:37| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 22:49:42| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 22:49:43| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:49:43| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:49:43| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:49:51| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:50:02| INFO: ref finished [took 22.5398s] -30/10/23 22:50:09| INFO: atc_mc finished [took 29.3095s] -30/10/23 22:50:09| INFO: atc_ne finished [took 29.2984s] -30/10/23 22:50:16| INFO: mul_sld finished [took 37.6287s] -30/10/23 22:50:16| INFO: Dataset sample 0.90 of dataset imdb finished [took 38.3452s] ----------------------------------------------------------------------------------------------------- -30/10/23 22:53:57| INFO: dataset imdb -30/10/23 22:54:09| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 22:54:13| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 22:54:14| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:54:15| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:54:15| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 22:54:22| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 22:54:33| INFO: ref finished [took 22.4225s] -30/10/23 22:54:40| INFO: atc_ne finished [took 29.0085s] -30/10/23 22:54:41| INFO: atc_mc finished [took 29.6620s] -30/10/23 22:54:48| INFO: mul_sld finished [took 37.9580s] -30/10/23 22:54:48| INFO: Dataset sample 0.90 of dataset imdb finished [took 38.6632s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:02:33| INFO: dataset imdb -30/10/23 23:02:45| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 23:02:50| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 23:02:51| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:02:52| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:02:52| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:02:59| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 23:03:10| INFO: ref finished [took 23.4021s] -30/10/23 23:03:17| INFO: atc_mc finished [took 30.1849s] -30/10/23 23:03:18| INFO: atc_ne finished [took 30.4116s] -30/10/23 23:03:25| INFO: mul_sld finished [took 38.6513s] -30/10/23 23:03:25| INFO: Dataset sample 0.90 of dataset imdb finished [took 39.3497s] -30/10/23 23:07:32| INFO: Dataset sample 0.80 of dataset imdb started ----------------------------------------------------------------------------------------------------- -30/10/23 23:08:15| INFO: dataset imdb -30/10/23 23:08:26| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:08:51| INFO: ref finished [took 23.6855s] -30/10/23 23:08:59| INFO: atc_mc finished [took 31.1520s] -30/10/23 23:08:59| INFO: atc_ne finished [took 31.1659s] -30/10/23 23:09:10| INFO: mul_sld finished [took 42.2066s] -30/10/23 23:09:21| INFO: mul_sld_bcts finished [took 52.9631s] -30/10/23 23:09:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.5286s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:14:11| INFO: dataset imdb -30/10/23 23:14:22| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:14:47| INFO: ref finished [took 22.8152s] -30/10/23 23:14:55| INFO: atc_mc finished [took 31.2100s] -30/10/23 23:14:55| INFO: atc_ne finished [took 31.2325s] -30/10/23 23:15:06| INFO: mul_sld finished [took 42.5389s] -30/10/23 23:15:16| INFO: mul_sld_bcts finished [took 52.7119s] -30/10/23 23:15:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.2106s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:16:16| INFO: dataset imdb -30/10/23 23:16:27| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:16:51| INFO: ref finished [took 22.6482s] -30/10/23 23:17:00| INFO: atc_ne finished [took 30.5701s] -30/10/23 23:17:00| INFO: atc_mc finished [took 30.9988s] -30/10/23 23:17:10| INFO: mul_sld finished [took 41.9572s] -30/10/23 23:17:21| INFO: mul_sld_bcts finished [took 52.6091s] -30/10/23 23:17:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.1182s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:20:27| INFO: dataset imdb -30/10/23 23:20:38| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:21:02| INFO: ref finished [took 22.7779s] -30/10/23 23:21:10| INFO: atc_mc finished [took 30.4191s] -30/10/23 23:21:10| INFO: atc_ne finished [took 30.8097s] -30/10/23 23:21:20| INFO: mul_sld finished [took 41.5927s] -30/10/23 23:21:32| INFO: mul_sld_bcts finished [took 52.6374s] -30/10/23 23:21:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.1125s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:24:11| INFO: dataset imdb -30/10/23 23:24:22| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:24:46| INFO: ref finished [took 23.2007s] -30/10/23 23:24:54| INFO: atc_ne finished [took 30.9437s] -30/10/23 23:24:55| INFO: atc_mc finished [took 31.6008s] -30/10/23 23:25:05| INFO: mul_sld finished [took 42.0673s] -30/10/23 23:25:16| INFO: mul_sld_bcts finished [took 52.6228s] -30/10/23 23:25:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.0611s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:33:01| INFO: dataset imdb -30/10/23 23:33:11| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:33:36| INFO: ref finished [took 22.9215s] -30/10/23 23:33:44| INFO: atc_mc finished [took 30.5897s] -30/10/23 23:33:44| INFO: atc_ne finished [took 30.4788s] -30/10/23 23:33:55| INFO: mul_sld finished [took 42.0598s] -30/10/23 23:34:05| INFO: mul_sld_bcts finished [took 52.1772s] -30/10/23 23:34:05| INFO: Dataset sample 0.50 of dataset imdb finished [took 53.6878s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:38:11| INFO: dataset imdb -30/10/23 23:38:22| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:38:47| INFO: ref finished [took 22.8046s] -30/10/23 23:38:56| INFO: atc_mc finished [took 31.5660s] -30/10/23 23:38:56| INFO: atc_ne finished [took 31.5269s] -30/10/23 23:39:06| INFO: mul_sld finished [took 42.2553s] -30/10/23 23:39:16| INFO: mul_sld_bcts finished [took 52.2602s] -30/10/23 23:39:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 53.7890s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:46:40| INFO: dataset imdb -30/10/23 23:46:51| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:47:16| INFO: ref finished [took 22.8069s] -30/10/23 23:47:24| INFO: atc_mc finished [took 30.7916s] -30/10/23 23:47:24| INFO: atc_ne finished [took 30.8668s] -30/10/23 23:47:35| INFO: mul_sld finished [took 42.2809s] -30/10/23 23:47:45| INFO: mul_sld_bcts finished [took 52.5498s] -30/10/23 23:47:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.0424s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:50:43| INFO: dataset imdb -30/10/23 23:50:50| INFO: Dataset sample 0.50 of dataset imdb started -30/10/23 23:51:04| INFO: ref finished [took 12.0863s] -30/10/23 23:51:07| INFO: atc_ne finished [took 15.0218s] -30/10/23 23:51:08| INFO: atc_mc finished [took 15.7900s] -30/10/23 23:51:20| INFO: mul_sld finished [took 28.7221s] -30/10/23 23:51:31| INFO: mul_sld_bcts finished [took 39.4698s] -30/10/23 23:51:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 40.8506s] ----------------------------------------------------------------------------------------------------- -30/10/23 23:52:29| INFO: dataset imdb -30/10/23 23:52:37| INFO: Dataset sample 0.90 of dataset imdb started -30/10/23 23:52:40| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:52:41| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:52:41| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -30/10/23 23:52:41| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -30/10/23 23:52:48| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -30/10/23 23:52:50| INFO: ref finished [took 12.4800s] -30/10/23 23:52:53| INFO: atc_mc finished [took 15.1770s] -30/10/23 23:52:54| INFO: atc_ne finished [took 15.2184s] -30/10/23 23:53:02| INFO: mul_sld finished [took 24.9402s] -30/10/23 23:53:02| INFO: Dataset sample 0.90 of dataset imdb finished [took 25.4588s] -30/10/23 23:53:02| INFO: Dataset sample 0.80 of dataset imdb started -30/10/23 23:53:20| INFO: ref finished [took 16.3699s] -30/10/23 23:53:25| INFO: atc_ne finished [took 20.5069s] -30/10/23 23:53:25| INFO: atc_mc finished [took 20.7398s] -30/10/23 23:53:38| INFO: mul_sld finished [took 35.3572s] -30/10/23 23:53:45| INFO: mul_sld_bcts finished [took 41.8712s] -30/10/23 23:56:35| INFO: bin_sld finished [took 212.1758s] -30/10/23 23:56:36| INFO: bin_sld_bcts finished [took 213.3641s] -30/10/23 23:57:38| INFO: mul_sld_gs finished [took 274.6360s] -31/10/23 00:01:13| INFO: bin_sld_gs finished [took 490.0221s] -31/10/23 00:01:13| INFO: Dataset sample 0.80 of dataset imdb finished [took 491.4099s] -31/10/23 00:01:13| INFO: Dataset sample 0.50 of dataset imdb started -31/10/23 00:01:32| INFO: ref finished [took 17.1003s] -31/10/23 00:01:37| INFO: atc_ne finished [took 21.2159s] -31/10/23 00:01:37| INFO: atc_mc finished [took 21.6794s] -31/10/23 00:01:51| INFO: mul_sld finished [took 37.3507s] -31/10/23 00:02:01| INFO: mul_sld_bcts finished [took 46.7227s] -31/10/23 00:04:46| INFO: bin_sld finished [took 211.9902s] -31/10/23 00:04:48| INFO: bin_sld_bcts finished [took 213.3398s] -31/10/23 00:05:55| INFO: mul_sld_gs finished [took 279.4401s] -31/10/23 00:08:56| INFO: bin_sld_gs finished [took 461.6571s] -31/10/23 00:08:56| INFO: Dataset sample 0.50 of dataset imdb finished [took 462.8616s] -31/10/23 00:08:56| INFO: Dataset sample 0.20 of dataset imdb started -31/10/23 00:09:15| INFO: ref finished [took 17.3643s] -31/10/23 00:09:20| INFO: atc_mc finished [took 21.0373s] -31/10/23 00:09:20| INFO: atc_ne finished [took 21.2599s] -31/10/23 00:09:38| INFO: mul_sld_bcts finished [took 41.0473s] -31/10/23 00:09:41| INFO: mul_sld finished [took 43.7800s] -31/10/23 00:12:30| INFO: bin_sld_bcts finished [took 212.8639s] -31/10/23 00:12:32| INFO: bin_sld finished [took 215.5704s] -31/10/23 00:13:29| INFO: mul_sld_gs finished [took 270.7454s] -31/10/23 00:19:19| INFO: bin_sld_gs finished [took 621.7089s] -31/10/23 00:19:19| INFO: Dataset sample 0.20 of dataset imdb finished [took 623.1501s] -31/10/23 00:19:19| INFO: Dataset sample 0.10 of dataset imdb started -31/10/23 00:19:24| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken -31/10/23 00:19:24| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -31/10/23 00:19:26| WARNING: Method bin_sld_bcts failed. Exception: Cannot have number of splits n_splits=5 greater than the number of samples: n_samples=4. -31/10/23 00:19:31| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 -31/10/23 00:19:35| INFO: ref finished [took 13.7926s] -31/10/23 00:19:38| INFO: atc_mc finished [took 16.8128s] -31/10/23 00:19:39| INFO: atc_ne finished [took 16.9032s] -31/10/23 00:19:44| INFO: mul_sld finished [took 23.7188s] -31/10/23 00:21:43| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -31/10/23 00:21:43| INFO: Dataset sample 0.10 of dataset imdb finished [took 143.1001s] ----------------------------------------------------------------------------------------------------- -31/10/23 01:36:56| INFO: dataset imdb -31/10/23 01:37:04| INFO: Dataset sample 0.90 of dataset imdb started -31/10/23 01:37:13| WARNING: Method bin_sld_bcts failed. Exception: fun: nan - hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> - jac: array([ 1.06687127, -0.00373246, 0.00373246]) - message: 'ABNORMAL_TERMINATION_IN_LNSRCH' - nfev: 53 - nit: 34 - njev: 53 - status: 2 - success: False - x: array([ 0.11536329, -12.93833991, 12.93833991]) -31/10/23 01:37:24| INFO: ref finished [took 15.9844s] -31/10/23 01:37:28| INFO: atc_mc finished [took 19.7000s] -31/10/23 01:37:28| INFO: atc_ne finished [took 19.4612s] -31/10/23 01:37:39| INFO: mul_sld finished [took 33.2999s] -31/10/23 01:37:49| INFO: mul_sld_bcts finished [took 43.1402s] -31/10/23 01:40:23| INFO: bin_sld finished [took 197.7518s] -31/10/23 01:41:23| INFO: mul_sld_gs finished [took 256.4496s] -31/10/23 01:42:49| INFO: bin_sld_gs finished [took 342.7515s] -31/10/23 01:42:49| INFO: Dataset sample 0.90 of dataset imdb finished [took 344.8637s] -31/10/23 01:42:49| INFO: Dataset sample 0.50 of dataset imdb started -31/10/23 01:43:10| INFO: ref finished [took 17.6503s] -31/10/23 01:43:15| INFO: atc_mc finished [took 21.9510s] -31/10/23 01:43:15| INFO: atc_ne finished [took 21.5680s] -31/10/23 01:43:29| INFO: mul_sld finished [took 38.2515s] -31/10/23 01:43:41| INFO: mul_sld_bcts finished [took 48.9560s] -31/10/23 01:46:29| INFO: bin_sld_bcts finished [took 217.8464s] -31/10/23 01:46:29| INFO: bin_sld finished [took 218.6211s] -31/10/23 01:47:51| INFO: mul_sld_gs finished [took 298.6694s] -31/10/23 01:50:25| INFO: bin_sld_gs finished [took 452.8300s] -31/10/23 01:50:25| INFO: Dataset sample 0.50 of dataset imdb finished [took 455.3596s] -31/10/23 01:50:28| ERROR: Configuration imdb_2prevs failed. Exception: could not broadcast input array from shape (2100,7) into shape (2100,) ----------------------------------------------------------------------------------------------------- -31/10/23 02:13:21| INFO: dataset imdb -31/10/23 02:13:29| INFO: Dataset sample 0.90 of dataset imdb started -31/10/23 02:13:37| WARNING: Method bin_sld_bcts failed. Exception: fun: nan - hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> - jac: array([ 1.06687127, -0.00373246, 0.00373246]) - message: 'ABNORMAL_TERMINATION_IN_LNSRCH' - nfev: 53 - nit: 34 - njev: 53 - status: 2 - success: False - x: array([ 0.11536329, -12.93833991, 12.93833991]) -31/10/23 02:13:48| INFO: ref finished [took 16.5509s] -31/10/23 02:13:52| INFO: atc_mc finished [took 20.3138s] -31/10/23 02:13:52| INFO: atc_ne finished [took 20.1191s] -31/10/23 02:14:02| INFO: mul_sld finished [took 32.5158s] -31/10/23 02:14:12| INFO: mul_sld_bcts finished [took 42.0654s] -31/10/23 02:16:44| INFO: bin_sld finished [took 193.9189s] -31/10/23 02:17:44| INFO: mul_sld_gs finished [took 252.9066s] -31/10/23 02:19:11| INFO: bin_sld_gs finished [took 339.9813s] -31/10/23 02:19:11| INFO: Dataset sample 0.90 of dataset imdb finished [took 341.6967s] -31/10/23 02:19:11| INFO: Dataset sample 0.50 of dataset imdb started -31/10/23 02:19:30| INFO: ref finished [took 16.1334s] -31/10/23 02:19:35| INFO: atc_mc finished [took 20.5691s] -31/10/23 02:19:35| INFO: atc_ne finished [took 20.0126s] -31/10/23 02:19:49| INFO: mul_sld finished [took 36.4597s] -31/10/23 02:20:02| INFO: mul_sld_bcts finished [took 48.7131s] -31/10/23 02:22:38| INFO: bin_sld finished [took 205.8577s] -31/10/23 02:22:41| INFO: bin_sld_bcts finished [took 208.1999s] -31/10/23 02:23:58| INFO: mul_sld_gs finished [took 284.9247s] -31/10/23 02:26:26| INFO: bin_sld_gs finished [took 432.5665s] -31/10/23 02:26:26| INFO: Dataset sample 0.50 of dataset imdb finished [took 435.0679s] ----------------------------------------------------------------------------------------------------- -31/10/23 03:05:44| INFO: dataset rcv1_CCAT -31/10/23 03:05:49| INFO: Dataset sample 0.90 of dataset rcv1_CCAT started -31/10/23 03:06:59| INFO: kfcv finished [took 59.0143s] -31/10/23 03:06:59| INFO: ref finished [took 56.9074s] -31/10/23 03:07:03| INFO: doc_feat finished [took 49.0683s] -31/10/23 03:07:05| INFO: atc_mc finished [took 59.3988s] -31/10/23 03:07:07| INFO: atc_ne finished [took 58.0283s] -31/10/23 03:07:09| INFO: mul_sld finished [took 76.8284s] -31/10/23 03:07:19| INFO: mul_sld_bcts finished [took 84.0129s] -31/10/23 03:09:51| INFO: bin_sld_bcts finished [took 237.9395s] -31/10/23 03:09:53| INFO: bin_sld finished [took 242.1415s] -31/10/23 03:10:13| INFO: mul_sld_gs finished [took 255.3743s] -31/10/23 03:13:59| INFO: bin_sld_gs finished [took 483.0217s] -31/10/23 03:13:59| INFO: Dataset sample 0.90 of dataset rcv1_CCAT finished [took 489.9328s] -31/10/23 03:13:59| INFO: Dataset sample 0.80 of dataset rcv1_CCAT started -31/10/23 03:15:04| INFO: ref finished [took 53.0703s] -31/10/23 03:15:05| INFO: kfcv finished [took 55.9779s] -31/10/23 03:15:05| INFO: doc_feat finished [took 48.1315s] -31/10/23 03:15:10| INFO: atc_mc finished [took 56.8062s] -31/10/23 03:15:11| INFO: atc_ne finished [took 55.9933s] -31/10/23 03:15:20| INFO: mul_sld finished [took 77.2840s] -31/10/23 03:15:25| INFO: mul_sld_bcts finished [took 80.0502s] -31/10/23 03:17:55| INFO: bin_sld finished [took 233.0173s] -31/10/23 03:17:55| INFO: bin_sld_bcts finished [took 231.2358s] -31/10/23 03:18:59| INFO: mul_sld_gs finished [took 291.7573s] -31/10/23 03:21:50| INFO: bin_sld_gs finished [took 463.8743s] -31/10/23 03:21:50| INFO: Dataset sample 0.80 of dataset rcv1_CCAT finished [took 470.4706s] -31/10/23 03:21:50| INFO: Dataset sample 0.70 of dataset rcv1_CCAT started -31/10/23 03:22:52| INFO: doc_feat finished [took 46.9563s] -31/10/23 03:22:53| INFO: ref finished [took 52.8185s] -31/10/23 03:22:54| INFO: kfcv finished [took 55.3202s] -31/10/23 03:22:57| INFO: atc_mc finished [took 55.1482s] -31/10/23 03:22:58| INFO: atc_ne finished [took 54.7420s] -31/10/23 03:23:09| INFO: mul_sld finished [took 76.8111s] -31/10/23 03:23:14| INFO: mul_sld_bcts finished [took 80.0460s] -31/10/23 03:25:43| INFO: bin_sld finished [took 231.7146s] -31/10/23 03:25:44| INFO: bin_sld_bcts finished [took 230.9954s] -31/10/23 03:26:53| INFO: mul_sld_gs finished [took 296.5824s] -31/10/23 03:29:12| INFO: bin_sld_gs finished [took 437.2666s] -31/10/23 03:29:12| INFO: Dataset sample 0.70 of dataset rcv1_CCAT finished [took 442.6584s] -31/10/23 03:29:12| INFO: Dataset sample 0.60 of dataset rcv1_CCAT started -31/10/23 03:30:14| INFO: doc_feat finished [took 47.2841s] -31/10/23 03:30:15| INFO: ref finished [took 52.5819s] -31/10/23 03:30:16| INFO: kfcv finished [took 54.9735s] -31/10/23 03:30:19| INFO: atc_mc finished [took 55.5994s] -31/10/23 03:30:20| INFO: atc_ne finished [took 55.0062s] -31/10/23 03:30:30| INFO: mul_sld finished [took 75.3263s] -31/10/23 03:30:37| INFO: mul_sld_bcts finished [took 80.4052s] -31/10/23 03:33:04| INFO: bin_sld finished [took 229.9416s] -31/10/23 03:33:05| INFO: bin_sld_bcts finished [took 229.0971s] -31/10/23 03:34:12| INFO: mul_sld_gs finished [took 292.9916s] -31/10/23 03:37:15| INFO: bin_sld_gs finished [took 477.2157s] -31/10/23 03:37:15| INFO: Dataset sample 0.60 of dataset rcv1_CCAT finished [took 482.5150s] -31/10/23 03:37:15| INFO: Dataset sample 0.50 of dataset rcv1_CCAT started -31/10/23 03:38:17| INFO: doc_feat finished [took 47.6798s] -31/10/23 03:38:17| INFO: ref finished [took 52.2283s] -31/10/23 03:38:18| INFO: kfcv finished [took 54.3535s] -31/10/23 03:38:22| INFO: atc_mc finished [took 55.4316s] -31/10/23 03:38:23| INFO: atc_ne finished [took 55.3697s] -31/10/23 03:38:32| INFO: mul_sld finished [took 74.3762s] -31/10/23 03:38:39| INFO: mul_sld_bcts finished [took 79.7216s] -31/10/23 03:41:05| INFO: bin_sld finished [took 228.4963s] -31/10/23 03:41:08| INFO: bin_sld_bcts finished [took 230.0901s] -31/10/23 03:42:09| INFO: mul_sld_gs finished [took 287.8477s] -31/10/23 03:45:08| INFO: bin_sld_gs finished [took 467.2633s] -31/10/23 03:45:08| INFO: Dataset sample 0.50 of dataset rcv1_CCAT finished [took 472.6090s] -31/10/23 03:45:08| INFO: Dataset sample 0.40 of dataset rcv1_CCAT started -31/10/23 03:46:08| INFO: doc_feat finished [took 47.0674s] -31/10/23 03:46:09| INFO: ref finished [took 51.5844s] -31/10/23 03:46:09| INFO: kfcv finished [took 53.6481s] -31/10/23 03:46:14| INFO: atc_mc finished [took 55.3679s] -31/10/23 03:46:14| INFO: atc_ne finished [took 54.6174s] -31/10/23 03:46:21| INFO: mul_sld finished [took 71.5925s] -31/10/23 03:46:29| INFO: mul_sld_bcts finished [took 77.5938s] -31/10/23 03:48:55| INFO: bin_sld finished [took 226.3217s] -31/10/23 03:48:57| INFO: bin_sld_bcts finished [took 226.5561s] -31/10/23 03:50:04| INFO: mul_sld_gs finished [took 289.8958s] -31/10/23 03:53:13| INFO: bin_sld_gs finished [took 479.9650s] -31/10/23 03:53:13| INFO: Dataset sample 0.40 of dataset rcv1_CCAT finished [took 485.0438s] -31/10/23 03:53:13| INFO: Dataset sample 0.30 of dataset rcv1_CCAT started -31/10/23 03:54:15| INFO: doc_feat finished [took 47.1959s] -31/10/23 03:54:16| INFO: ref finished [took 52.7452s] -31/10/23 03:54:17| INFO: kfcv finished [took 55.3715s] -31/10/23 03:54:20| INFO: atc_mc finished [took 55.5749s] -31/10/23 03:54:21| INFO: atc_ne finished [took 54.8719s] -31/10/23 03:54:29| INFO: mul_sld finished [took 74.1932s] -31/10/23 03:54:37| INFO: mul_sld_bcts finished [took 80.1150s] -31/10/23 03:57:01| INFO: bin_sld finished [took 227.2338s] -31/10/23 03:57:06| INFO: bin_sld_bcts finished [took 229.7342s] -31/10/23 03:58:13| INFO: mul_sld_gs finished [took 293.4750s] -31/10/23 04:00:50| INFO: bin_sld_gs finished [took 451.3322s] -31/10/23 04:00:50| INFO: Dataset sample 0.30 of dataset rcv1_CCAT finished [took 456.9583s] -31/10/23 04:00:50| INFO: Dataset sample 0.20 of dataset rcv1_CCAT started -31/10/23 04:01:54| INFO: doc_feat finished [took 48.9670s] -31/10/23 04:01:54| INFO: ref finished [took 54.3281s] -31/10/23 04:01:55| INFO: kfcv finished [took 57.0798s] -31/10/23 04:01:59| INFO: atc_mc finished [took 57.5663s] -31/10/23 04:02:00| INFO: atc_ne finished [took 57.1756s] -31/10/23 04:02:07| INFO: mul_sld finished [took 74.8552s] -31/10/23 04:02:14| INFO: mul_sld_bcts finished [took 79.8097s] -31/10/23 04:04:43| INFO: bin_sld finished [took 231.6926s] -31/10/23 04:04:43| INFO: bin_sld_bcts finished [took 229.9267s] -31/10/23 04:05:23| INFO: mul_sld_gs finished [took 266.8226s] -31/10/23 04:08:36| INFO: bin_sld_gs finished [took 460.9384s] -31/10/23 04:08:36| INFO: Dataset sample 0.20 of dataset rcv1_CCAT finished [took 466.5653s] -31/10/23 04:08:36| INFO: Dataset sample 0.10 of dataset rcv1_CCAT started -31/10/23 04:08:46| WARNING: Method mul_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -31/10/23 04:08:46| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -31/10/23 04:08:55| WARNING: Method mul_sld_bcts failed. Exception: fun: nan - hess_inv: <5x5 LbfgsInvHessProduct with dtype=float64> - jac: array([nan, nan, nan, nan, nan]) - message: 'ABNORMAL_TERMINATION_IN_LNSRCH' - nfev: 21 - nit: 0 - njev: 21 - status: 2 - success: False - x: array([1., 0., 0., 0., 0.]) -31/10/23 04:09:33| INFO: doc_feat finished [took 42.6167s] -31/10/23 04:09:33| INFO: ref finished [took 46.6961s] -31/10/23 04:09:33| INFO: kfcv finished [took 48.7570s] -31/10/23 04:09:37| INFO: atc_mc finished [took 49.6198s] -31/10/23 04:09:38| INFO: atc_ne finished [took 49.1195s] -31/10/23 04:09:42| INFO: mul_sld finished [took 63.1364s] -31/10/23 04:11:02| WARNING: Method bin_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -31/10/23 04:12:05| INFO: bin_sld finished [took 207.4063s] -31/10/23 04:12:05| INFO: Dataset sample 0.10 of dataset rcv1_CCAT finished [took 208.7423s] -31/10/23 04:12:16| ERROR: Configuration rcv1_CCAT_9prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' ----------------------------------------------------------------------------------------------------- -31/10/23 11:30:20| INFO: dataset rcv1_CCAT -31/10/23 11:30:26| INFO: Dataset sample 0.90 of dataset rcv1_CCAT started -31/10/23 11:31:32| INFO: doc_feat finished [took 48.1486s] -31/10/23 11:31:32| INFO: ref finished [took 53.9235s] -31/10/23 11:31:33| INFO: kfcv finished [took 56.4175s] -31/10/23 11:31:37| INFO: atc_mc finished [took 57.2963s] -31/10/23 11:31:39| INFO: atc_ne finished [took 56.1470s] -31/10/23 11:31:43| INFO: mul_sld finished [took 74.0703s] -31/10/23 11:31:50| INFO: mul_sld_bcts finished [took 78.8253s] -31/10/23 11:34:16| INFO: bin_sld_bcts finished [took 225.7409s] -31/10/23 11:34:18| INFO: bin_sld finished [took 229.9705s] -31/10/23 11:34:42| INFO: mul_sld_gs finished [took 247.4756s] -31/10/23 11:38:30| INFO: bin_sld_gs finished [took 477.2173s] -31/10/23 11:38:30| INFO: Dataset sample 0.90 of dataset rcv1_CCAT finished [took 483.7632s] -31/10/23 11:38:30| INFO: Dataset sample 0.80 of dataset rcv1_CCAT started -31/10/23 11:39:32| INFO: doc_feat finished [took 47.0343s] -31/10/23 11:39:33| INFO: ref finished [took 52.5674s] -31/10/23 11:39:33| INFO: kfcv finished [took 54.5521s] -31/10/23 11:39:38| INFO: atc_mc finished [took 55.5394s] -31/10/23 11:39:38| INFO: atc_ne finished [took 54.9616s] -31/10/23 11:39:48| INFO: mul_sld finished [took 75.9068s] -31/10/23 11:39:53| INFO: mul_sld_bcts finished [took 78.1581s] -31/10/23 11:42:22| INFO: bin_sld finished [took 230.5536s] -31/10/23 11:42:23| INFO: bin_sld_bcts finished [took 229.2262s] -31/10/23 11:43:29| INFO: mul_sld_gs finished [took 291.7013s] -31/10/23 11:46:14| INFO: bin_sld_gs finished [took 457.9059s] -31/10/23 11:46:14| INFO: Dataset sample 0.80 of dataset rcv1_CCAT finished [took 463.5174s] -31/10/23 11:46:14| INFO: Dataset sample 0.70 of dataset rcv1_CCAT started -31/10/23 11:47:17| INFO: doc_feat finished [took 46.4490s] -31/10/23 11:47:17| INFO: ref finished [took 52.6852s] -31/10/23 11:47:17| INFO: kfcv finished [took 55.0158s] -31/10/23 11:47:21| INFO: atc_mc finished [took 55.4861s] -31/10/23 11:47:22| INFO: atc_ne finished [took 54.9236s] -31/10/23 11:47:32| INFO: mul_sld finished [took 75.5717s] -31/10/23 11:47:38| INFO: mul_sld_bcts finished [took 80.0893s] -31/10/23 11:50:02| INFO: bin_sld finished [took 226.8402s] -31/10/23 11:50:05| INFO: bin_sld_bcts finished [took 227.7311s] -31/10/23 11:51:15| INFO: mul_sld_gs finished [took 294.0087s] -31/10/23 11:53:36| INFO: bin_sld_gs finished [took 436.3031s] -31/10/23 11:53:36| INFO: Dataset sample 0.70 of dataset rcv1_CCAT finished [took 441.8200s] -31/10/23 11:53:36| INFO: Dataset sample 0.60 of dataset rcv1_CCAT started -31/10/23 11:54:37| INFO: doc_feat finished [took 47.1550s] -31/10/23 11:54:38| INFO: ref finished [took 52.2980s] -31/10/23 11:54:39| INFO: kfcv finished [took 54.5489s] -31/10/23 11:54:42| INFO: atc_mc finished [took 55.2076s] -31/10/23 11:54:43| INFO: atc_ne finished [took 54.6137s] -31/10/23 11:54:53| INFO: mul_sld finished [took 74.8407s] -31/10/23 11:54:59| INFO: mul_sld_bcts finished [took 79.4977s] -31/10/23 11:57:24| INFO: bin_sld finished [took 226.9209s] -31/10/23 11:57:26| INFO: bin_sld_bcts finished [took 227.3112s] -31/10/23 11:58:29| INFO: mul_sld_gs finished [took 286.1947s] -31/10/23 12:01:41| INFO: bin_sld_gs finished [took 479.8610s] -31/10/23 12:01:41| INFO: Dataset sample 0.60 of dataset rcv1_CCAT finished [took 485.3472s] -31/10/23 12:01:41| INFO: Dataset sample 0.50 of dataset rcv1_CCAT started -31/10/23 12:02:42| INFO: doc_feat finished [took 46.7340s] -31/10/23 12:02:42| INFO: ref finished [took 51.5482s] -31/10/23 12:02:43| INFO: kfcv finished [took 53.9559s] -31/10/23 12:02:47| INFO: atc_mc finished [took 54.7558s] -31/10/23 12:02:48| INFO: atc_ne finished [took 54.5216s] -31/10/23 12:02:57| INFO: mul_sld finished [took 73.4013s] -31/10/23 12:03:04| INFO: mul_sld_bcts finished [took 78.9197s] -31/10/23 12:05:30| INFO: bin_sld finished [took 227.3887s] -31/10/23 12:05:31| INFO: bin_sld_bcts finished [took 226.6540s] -31/10/23 12:06:37| INFO: mul_sld_gs finished [took 289.2631s] -31/10/23 12:09:30| INFO: bin_sld_gs finished [took 463.2754s] -31/10/23 12:09:30| INFO: Dataset sample 0.50 of dataset rcv1_CCAT finished [took 468.6356s] -31/10/23 12:09:30| INFO: Dataset sample 0.40 of dataset rcv1_CCAT started -31/10/23 12:10:30| INFO: doc_feat finished [took 47.0178s] -31/10/23 12:10:31| INFO: ref finished [took 51.8808s] -31/10/23 12:10:31| INFO: kfcv finished [took 53.6165s] -31/10/23 12:10:36| INFO: atc_mc finished [took 55.8052s] -31/10/23 12:10:36| INFO: atc_ne finished [took 55.0541s] -31/10/23 12:10:44| INFO: mul_sld finished [took 72.1431s] -31/10/23 12:10:52| INFO: mul_sld_bcts finished [took 78.0435s] -31/10/23 12:13:18| INFO: bin_sld finished [took 227.6364s] -31/10/23 12:13:20| INFO: bin_sld_bcts finished [took 227.4485s] -31/10/23 12:14:23| INFO: mul_sld_gs finished [took 287.2824s] -31/10/23 12:17:20| INFO: bin_sld_gs finished [took 465.4084s] -31/10/23 12:17:20| INFO: Dataset sample 0.40 of dataset rcv1_CCAT finished [took 470.5362s] -31/10/23 12:17:20| INFO: Dataset sample 0.30 of dataset rcv1_CCAT started -31/10/23 12:18:22| INFO: doc_feat finished [took 46.7203s] -31/10/23 12:18:24| INFO: ref finished [took 52.8941s] -31/10/23 12:18:24| INFO: kfcv finished [took 55.1602s] -31/10/23 12:18:27| INFO: atc_mc finished [took 55.1296s] -31/10/23 12:18:29| INFO: atc_ne finished [took 54.8176s] -31/10/23 12:18:36| INFO: mul_sld finished [took 73.6368s] -31/10/23 12:18:44| INFO: mul_sld_bcts finished [took 79.2444s] -31/10/23 12:21:09| INFO: bin_sld finished [took 227.4633s] -31/10/23 12:21:11| INFO: bin_sld_bcts finished [took 227.8848s] -31/10/23 12:22:18| INFO: mul_sld_gs finished [took 290.8750s] -31/10/23 12:25:01| INFO: bin_sld_gs finished [took 455.0492s] -31/10/23 12:25:01| INFO: Dataset sample 0.30 of dataset rcv1_CCAT finished [took 460.7077s] -31/10/23 12:25:01| INFO: Dataset sample 0.20 of dataset rcv1_CCAT started -31/10/23 12:26:04| INFO: doc_feat finished [took 48.7419s] -31/10/23 12:26:05| INFO: ref finished [took 53.9956s] -31/10/23 12:26:06| INFO: kfcv finished [took 56.7159s] -31/10/23 12:26:10| INFO: atc_mc finished [took 57.0141s] -31/10/23 12:26:11| INFO: atc_ne finished [took 56.6235s] -31/10/23 12:26:18| INFO: mul_sld finished [took 74.9361s] -31/10/23 12:26:24| INFO: mul_sld_bcts finished [took 78.6411s] -31/10/23 12:28:51| INFO: bin_sld finished [took 228.5964s] -31/10/23 12:28:51| INFO: bin_sld_bcts finished [took 226.9077s] -31/10/23 12:29:34| INFO: mul_sld_gs finished [took 265.9319s] -31/10/23 12:32:39| INFO: bin_sld_gs finished [took 452.9439s] -31/10/23 12:32:39| INFO: Dataset sample 0.20 of dataset rcv1_CCAT finished [took 458.4924s] -31/10/23 12:32:39| INFO: Dataset sample 0.10 of dataset rcv1_CCAT started -31/10/23 12:32:49| WARNING: Method mul_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -31/10/23 12:32:49| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -31/10/23 12:32:57| WARNING: Method mul_sld_bcts failed. Exception: fun: nan - hess_inv: <5x5 LbfgsInvHessProduct with dtype=float64> - jac: array([nan, nan, nan, nan, nan]) - message: 'ABNORMAL_TERMINATION_IN_LNSRCH' - nfev: 21 - nit: 0 - njev: 21 - status: 2 - success: False - x: array([1., 0., 0., 0., 0.]) -31/10/23 12:33:33| INFO: doc_feat finished [took 40.8855s] -31/10/23 12:33:34| INFO: ref finished [took 44.7933s] -31/10/23 12:33:34| INFO: kfcv finished [took 47.0146s] -31/10/23 12:33:38| INFO: atc_mc finished [took 47.7008s] -31/10/23 12:33:39| INFO: atc_ne finished [took 47.4664s] -31/10/23 12:33:42| INFO: mul_sld finished [took 60.5341s] -31/10/23 12:35:06| WARNING: Method bin_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -31/10/23 12:36:11| INFO: bin_sld finished [took 210.5128s] -31/10/23 12:36:11| INFO: Dataset sample 0.10 of dataset rcv1_CCAT finished [took 211.7476s] ----------------------------------------------------------------------------------------------------- -31/10/23 13:07:34| INFO: dataset imdb_2prevs -31/10/23 13:07:41| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 13:07:55| INFO: ref finished [took 12.2932s] -31/10/23 13:07:58| INFO: atc_mc finished [took 15.8781s] -31/10/23 13:07:58| INFO: atc_ne finished [took 15.8256s] -31/10/23 13:08:08| INFO: mul_sld finished [took 25.6841s] -31/10/23 13:08:18| INFO: mul_sld_bcts finished [took 35.3498s] -31/10/23 13:08:18| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 36.3540s] -31/10/23 13:08:18| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 13:08:32| INFO: ref finished [took 12.8011s] -31/10/23 13:08:36| INFO: atc_mc finished [took 16.7266s] -31/10/23 13:08:36| INFO: atc_ne finished [took 16.9577s] -31/10/23 13:08:49| INFO: mul_sld finished [took 30.1948s] -31/10/23 13:09:00| INFO: mul_sld_bcts finished [took 41.0998s] -31/10/23 13:09:00| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 42.6008s] -31/10/23 13:09:00| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' ----------------------------------------------------------------------------------------------------- -31/10/23 13:10:27| INFO: dataset imdb_2prevs -31/10/23 13:10:34| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 13:10:47| INFO: ref finished [took 11.6569s] -31/10/23 13:10:51| INFO: atc_mc finished [took 15.6380s] -31/10/23 13:10:51| INFO: atc_ne finished [took 15.5430s] -31/10/23 13:11:00| INFO: mul_sld finished [took 24.9236s] -31/10/23 13:11:10| INFO: mul_sld_bcts finished [took 34.5252s] -31/10/23 13:11:10| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 35.4874s] -31/10/23 13:11:10| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 13:11:23| INFO: ref finished [took 11.5602s] -31/10/23 13:11:26| INFO: atc_ne finished [took 14.3888s] -31/10/23 13:11:26| INFO: atc_mc finished [took 14.5643s] -31/10/23 13:11:39| INFO: mul_sld finished [took 27.8023s] -31/10/23 13:11:51| INFO: mul_sld_bcts finished [took 39.2892s] -31/10/23 13:11:51| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.6914s] -31/10/23 13:11:51| DEBUG: ['COMP_ESTIMATORS', 'DATASET_DIR_UPDATE', 'DATASET_NAME', 'DATASET_N_PREVS', 'DATASET_PREVS', 'DATASET_TARGET', 'METRICS', 'OUT_DIR', 'OUT_DIR_NAME', 'PLOT_DIR_NAME', 'PLOT_ESTIMATORS', 'PLOT_OUT_DIR', 'PLOT_STDEV', 'PROTOCOL_N_PREVS', 'PROTOCOL_REPEATS', 'SAMPLE_SIZE', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slotnames__', '__str__', '__subclasshook__', '__weakref__', '_current_conf', '_default', '_environ__getdict', '_environ__setdict', '_instance', '_keys', 'confs', 'exec', 'get_confs', 'get_plot_confs', 'load_conf', 'plot_confs'] -31/10/23 13:11:51| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' ----------------------------------------------------------------------------------------------------- -31/10/23 13:12:56| INFO: dataset imdb_2prevs -31/10/23 13:13:03| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 13:13:15| INFO: ref finished [took 11.2578s] -31/10/23 13:13:19| INFO: atc_mc finished [took 14.7895s] -31/10/23 13:13:19| INFO: atc_ne finished [took 14.8637s] -31/10/23 13:13:28| INFO: mul_sld finished [took 24.2622s] -31/10/23 13:13:38| INFO: mul_sld_bcts finished [took 34.0592s] -31/10/23 13:13:38| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.9907s] -31/10/23 13:13:38| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 13:13:52| INFO: ref finished [took 12.2334s] -31/10/23 13:13:56| INFO: atc_ne finished [took 16.1148s] -31/10/23 13:13:56| INFO: atc_mc finished [took 16.4135s] -31/10/23 13:14:10| INFO: mul_sld finished [took 30.7003s] -31/10/23 13:14:21| INFO: mul_sld_bcts finished [took 41.5915s] -31/10/23 13:14:21| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 43.0339s] -31/10/23 13:14:21| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '__getdict' ----------------------------------------------------------------------------------------------------- -31/10/23 14:05:25| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '_current_conf' ----------------------------------------------------------------------------------------------------- -31/10/23 14:06:00| INFO: dataset imdb_2prevs -31/10/23 14:06:07| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:06:19| INFO: ref finished [took 10.8776s] -31/10/23 14:06:23| INFO: atc_ne finished [took 14.0744s] -31/10/23 14:06:23| INFO: atc_mc finished [took 14.4000s] -31/10/23 14:06:33| INFO: mul_sld finished [took 24.5149s] -31/10/23 14:06:42| INFO: mul_sld_bcts finished [took 33.9116s] -31/10/23 14:06:42| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.8416s] -31/10/23 14:06:42| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:06:55| INFO: ref finished [took 11.2125s] -31/10/23 14:06:59| INFO: atc_ne finished [took 14.9235s] -31/10/23 14:06:59| INFO: atc_mc finished [took 15.1623s] -31/10/23 14:07:12| INFO: mul_sld finished [took 28.3463s] -31/10/23 14:07:23| INFO: mul_sld_bcts finished [took 39.0377s] -31/10/23 14:07:23| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.4312s] -31/10/23 14:07:23| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '__getdict' ----------------------------------------------------------------------------------------------------- -31/10/23 14:09:14| INFO: dataset imdb_2prevs -31/10/23 14:09:22| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:09:33| INFO: ref finished [took 10.6213s] -31/10/23 14:09:37| INFO: atc_mc finished [took 14.2004s] -31/10/23 14:09:37| INFO: atc_ne finished [took 14.2574s] -31/10/23 14:09:46| INFO: mul_sld finished [took 23.8084s] -31/10/23 14:09:56| INFO: mul_sld_bcts finished [took 33.4634s] -31/10/23 14:09:56| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.4023s] -31/10/23 14:09:56| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:10:09| INFO: ref finished [took 11.0452s] -31/10/23 14:10:12| INFO: atc_mc finished [took 14.0363s] -31/10/23 14:10:12| INFO: atc_ne finished [took 14.2492s] -31/10/23 14:10:25| INFO: mul_sld finished [took 27.1464s] -31/10/23 14:10:35| INFO: mul_sld_bcts finished [took 37.7957s] -31/10/23 14:10:35| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 39.1750s] -31/10/23 14:10:35| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' ----------------------------------------------------------------------------------------------------- -31/10/23 14:14:00| INFO: dataset imdb_2prevs -31/10/23 14:14:07| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:14:19| INFO: ref finished [took 11.0176s] -31/10/23 14:14:22| INFO: atc_mc finished [took 14.0422s] -31/10/23 14:14:23| INFO: atc_ne finished [took 14.2169s] -31/10/23 14:14:31| INFO: mul_sld finished [took 23.7014s] -31/10/23 14:14:42| INFO: mul_sld_bcts finished [took 33.7536s] -31/10/23 14:14:42| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.7003s] -31/10/23 14:14:42| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:14:54| INFO: ref finished [took 11.0187s] -31/10/23 14:14:57| INFO: atc_mc finished [took 14.0175s] -31/10/23 14:14:58| INFO: atc_ne finished [took 14.4154s] -31/10/23 14:15:10| INFO: mul_sld finished [took 27.5946s] -31/10/23 14:15:21| INFO: mul_sld_bcts finished [took 38.0464s] -31/10/23 14:15:21| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 39.4594s] -31/10/23 14:15:21| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts', 'ref'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': None, 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': None, 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 14:15:21| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' ----------------------------------------------------------------------------------------------------- -31/10/23 14:30:59| INFO: dataset imdb_2prevs -31/10/23 14:31:10| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:31:33| INFO: ref finished [took 22.2885s] -31/10/23 14:31:41| INFO: atc_mc finished [took 29.8328s] -31/10/23 14:31:41| INFO: atc_ne finished [took 30.1421s] -31/10/23 14:31:46| INFO: mul_sld finished [took 35.8373s] -31/10/23 14:31:57| INFO: mul_sld_bcts finished [took 46.5130s] -31/10/23 14:31:57| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 47.5379s] -31/10/23 14:31:57| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:32:24| INFO: ref finished [took 24.3912s] -31/10/23 14:32:31| INFO: atc_mc finished [took 31.2488s] -31/10/23 14:32:31| INFO: atc_ne finished [took 31.5120s] -31/10/23 14:32:43| INFO: mul_sld finished [took 44.6372s] -31/10/23 14:32:54| INFO: mul_sld_bcts finished [took 54.5749s] -31/10/23 14:32:54| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 56.2358s] -31/10/23 14:32:54| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_ne', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 14:32:58| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_ne', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} ----------------------------------------------------------------------------------------------------- -31/10/23 14:37:38| INFO: dataset imdb_2prevs -31/10/23 14:37:45| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:37:58| INFO: ref finished [took 11.4397s] -31/10/23 14:38:01| INFO: atc_mc finished [took 14.6411s] -31/10/23 14:38:01| INFO: atc_ne finished [took 14.8218s] -31/10/23 14:38:11| INFO: mul_sld finished [took 24.4862s] -31/10/23 14:38:20| INFO: mul_sld_bcts finished [took 33.9900s] -31/10/23 14:38:20| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.9816s] -31/10/23 14:38:20| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:38:33| INFO: ref finished [took 11.3787s] -31/10/23 14:38:36| INFO: atc_mc finished [took 14.4366s] -31/10/23 14:38:36| INFO: atc_ne finished [took 14.3365s] -31/10/23 14:38:50| INFO: mul_sld finished [took 28.1585s] -31/10/23 14:39:00| INFO: mul_sld_bcts finished [took 38.5508s] -31/10/23 14:39:00| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.0193s] -31/10/23 14:39:00| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld', 'ref', 'mul_sld_bcts', 'atc_mc'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 14:39:03| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld', 'ref', 'mul_sld_bcts', 'atc_mc'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} ----------------------------------------------------------------------------------------------------- -31/10/23 14:44:15| INFO: dataset imdb_2prevs -31/10/23 14:44:22| INFO: Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:44:35| INFO: ref finished [took 11.5902s] -31/10/23 14:44:38| INFO: atc_mc finished [took 14.7915s] -31/10/23 14:44:39| INFO: atc_ne finished [took 14.8611s] -31/10/23 14:44:48| INFO: mul_sld finished [took 24.7670s] -31/10/23 14:44:58| INFO: mul_sld_bcts finished [took 34.3171s] -31/10/23 14:44:58| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 35.3088s] -31/10/23 14:44:58| INFO: Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:45:11| INFO: ref finished [took 11.8887s] -31/10/23 14:45:15| INFO: atc_mc finished [took 15.2624s] -31/10/23 14:45:15| INFO: atc_ne finished [took 15.2085s] -31/10/23 14:45:28| INFO: mul_sld finished [took 28.5408s] -31/10/23 14:45:38| INFO: mul_sld_bcts finished [took 38.9976s] -31/10/23 14:45:38| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.5854s] -31/10/23 14:45:38| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 14:45:41| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} ----------------------------------------------------------------------------------------------------- -31/10/23 14:55:18| INFO dataset imdb_2prevs -31/10/23 14:55:26| INFO Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 14:55:37| INFO ref finished [took 10.1990s] -31/10/23 14:55:40| INFO atc_mc finished [took 13.4778s] -31/10/23 14:55:41| INFO atc_ne finished [took 13.5559s] -31/10/23 14:55:50| INFO mul_sld finished [took 23.0450s] -31/10/23 14:55:59| INFO mul_sld_bcts finished [took 32.4582s] -31/10/23 14:55:59| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.3679s] -31/10/23 14:55:59| INFO Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 14:56:11| INFO ref finished [took 10.3415s] -31/10/23 14:56:14| INFO atc_mc finished [took 13.4638s] -31/10/23 14:56:14| INFO atc_ne finished [took 13.4791s] -31/10/23 14:56:27| INFO mul_sld finished [took 26.3298s] -31/10/23 14:56:38| INFO mul_sld_bcts finished [took 37.2449s] -31/10/23 14:56:38| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.6430s] -31/10/23 14:56:38| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld', 'atc_ne'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 14:56:41| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld', 'atc_ne'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} ----------------------------------------------------------------------------------------------------- -31/10/23 15:00:19| INFO dataset imdb_2prevs -31/10/23 15:00:26| INFO Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 15:00:38| INFO ref finished [took 10.7099s] -31/10/23 15:00:41| INFO atc_ne finished [took 13.7392s] -31/10/23 15:00:41| INFO atc_mc finished [took 13.9108s] -31/10/23 15:00:50| INFO mul_sld finished [took 23.3628s] -31/10/23 15:01:00| INFO mul_sld_bcts finished [took 33.0440s] -31/10/23 15:01:00| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.9805s] -31/10/23 15:01:00| INFO Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 15:01:12| INFO ref finished [took 10.7020s] -31/10/23 15:01:15| INFO atc_mc finished [took 13.9521s] -31/10/23 15:01:15| INFO atc_ne finished [took 13.8623s] -31/10/23 15:01:28| INFO mul_sld finished [took 26.8476s] -31/10/23 15:01:39| INFO mul_sld_bcts finished [took 37.4291s] -31/10/23 15:01:39| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.8721s] -31/10/23 15:01:39| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['ref', 'atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} -31/10/23 15:01:42| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['ref', 'atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} ----------------------------------------------------------------------------------------------------- -31/10/23 15:02:43| INFO dataset imdb_2prevs -31/10/23 15:02:50| INFO Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 15:03:02| INFO ref finished [took 10.5528s] -31/10/23 15:03:05| INFO atc_mc finished [took 13.7838s] -31/10/23 15:03:05| INFO atc_ne finished [took 13.6736s] -31/10/23 15:03:14| INFO mul_sld finished [took 23.2705s] -31/10/23 15:03:24| INFO mul_sld_bcts finished [took 32.8493s] -31/10/23 15:03:24| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.7917s] -31/10/23 15:03:24| INFO Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 15:03:36| INFO ref finished [took 10.4338s] -31/10/23 15:03:39| INFO atc_mc finished [took 13.5140s] -31/10/23 15:03:39| INFO atc_ne finished [took 13.5920s] -31/10/23 15:03:52| INFO mul_sld finished [took 26.7677s] -31/10/23 15:04:03| INFO mul_sld_bcts finished [took 37.2882s] -31/10/23 15:04:03| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.7014s] ----------------------------------------------------------------------------------------------------- -31/10/23 17:01:56| INFO dataset imdb_2prevs -31/10/23 17:02:03| INFO Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 17:02:16| INFO ref finished [took 11.7260s] -31/10/23 17:02:19| INFO atc_mc finished [took 14.9332s] -31/10/23 17:02:20| INFO atc_ne finished [took 14.9267s] -31/10/23 17:02:29| INFO mul_sld finished [took 25.0825s] -31/10/23 17:02:39| INFO mul_sld_bcts finished [took 35.1456s] -31/10/23 17:02:39| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 36.1167s] -31/10/23 17:02:39| INFO Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 17:02:53| INFO ref finished [took 12.1990s] -31/10/23 17:02:57| INFO atc_mc finished [took 15.9130s] -31/10/23 17:02:57| INFO atc_ne finished [took 15.8122s] -31/10/23 17:03:10| INFO mul_sld finished [took 29.0681s] -31/10/23 17:03:21| INFO mul_sld_bcts finished [took 40.0346s] -31/10/23 17:03:21| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 41.6137s] ----------------------------------------------------------------------------------------------------- -31/10/23 17:04:35| INFO dataset imdb_2prevs -31/10/23 17:04:42| INFO Dataset sample 0.10 of dataset imdb_2prevs started -31/10/23 17:04:56| INFO ref finished [took 11.5027s] -31/10/23 17:05:00| INFO atc_mc finished [took 15.1600s] -31/10/23 17:05:00| INFO atc_ne finished [took 15.0072s] -31/10/23 17:05:09| INFO mul_sld finished [took 24.7931s] -31/10/23 17:05:19| INFO mul_sld_bcts finished [took 34.6305s] -31/10/23 17:05:19| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 37.1778s] -31/10/23 17:05:19| INFO Dataset sample 0.50 of dataset imdb_2prevs started -31/10/23 17:05:33| INFO ref finished [took 12.2649s] -31/10/23 17:05:36| INFO atc_mc finished [took 15.5987s] -31/10/23 17:05:37| INFO atc_ne finished [took 15.8214s] -31/10/23 17:05:50| INFO mul_sld finished [took 29.3523s] -31/10/23 17:06:00| INFO mul_sld_bcts finished [took 39.8376s] -31/10/23 17:06:00| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 41.2888s] ----------------------------------------------------------------------------------------------------- -31/10/23 20:19:37| INFO dataset imdb_1prevs -31/10/23 20:19:48| INFO Dataset sample 0.50 of dataset imdb_1prevs started -31/10/23 20:20:07| INFO ref finished [took 17.4125s] ----------------------------------------------------------------------------------------------------- -31/10/23 20:20:50| INFO dataset imdb_1prevs -31/10/23 20:21:01| INFO Dataset sample 0.50 of dataset imdb_1prevs started -31/10/23 20:21:19| INFO ref finished [took 17.0717s] ----------------------------------------------------------------------------------------------------- -31/10/23 20:22:05| INFO dataset imdb_1prevs -31/10/23 20:22:15| INFO Dataset sample 0.50 of dataset imdb_1prevs started -31/10/23 20:22:35| INFO ref finished [took 18.4752s] ----------------------------------------------------------------------------------------------------- -31/10/23 20:23:38| INFO dataset imdb_1prevs -31/10/23 20:23:48| INFO Dataset sample 0.50 of dataset imdb_1prevs started -31/10/23 20:24:08| INFO ref finished [took 18.3216s] ----------------------------------------------------------------------------------------------------- -01/11/23 13:07:19| INFO dataset imdb_1prevs -01/11/23 13:07:27| INFO Dataset sample 0.50 of dataset imdb_1prevs started -01/11/23 13:07:27| ERROR Evaluation over imdb_1prevs failed. Exception: 'Invalid estimator: estimator mul_sld_gs does not exist' -01/11/23 13:07:27| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value ----------------------------------------------------------------------------------------------------- -03/11/23 20:54:19| INFO dataset rcv1_CCAT_9prevs -03/11/23 20:54:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -03/11/23 20:54:28| WARNING Method mul_sld_gs failed. Exception: Invalid parameter 'quantifier' for estimator EMQ(classifier=LogisticRegression()). Valid parameters are: ['classifier', 'exact_train_prev', 'recalib']. -03/11/23 20:54:29| WARNING Method mul_sld failed. Exception: evaluation_report() got an unexpected keyword argument 'protocor' -03/11/23 20:54:30| WARNING Method bin_sld_gs failed. Exception: Invalid parameter 'quantifier' for estimator EMQ(classifier=LogisticRegression()). Valid parameters are: ['classifier', 'exact_train_prev', 'recalib']. -03/11/23 20:55:09| INFO ref finished [took 38.5179s] ----------------------------------------------------------------------------------------------------- -03/11/23 21:28:36| INFO dataset rcv1_CCAT_9prevs -03/11/23 21:28:41| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -03/11/23 21:28:45| WARNING Method mul_sld failed. Exception: evaluation_report() got an unexpected keyword argument 'protocor' ----------------------------------------------------------------------------------------------------- -03/11/23 21:31:03| INFO dataset rcv1_CCAT_9prevs -03/11/23 21:31:08| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -03/11/23 21:31:59| INFO ref finished [took 45.6616s] -03/11/23 21:32:03| INFO atc_mc finished [took 48.4360s] -03/11/23 21:32:07| INFO atc_ne finished [took 51.0515s] -03/11/23 21:32:23| INFO mul_sld finished [took 72.9229s] -03/11/23 21:34:43| INFO bin_sld finished [took 213.9538s] -03/11/23 21:36:27| INFO mul_sld_gs finished [took 314.9357s] -03/11/23 21:40:50| INFO bin_sld_gs finished [took 579.2530s] -03/11/23 21:40:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 582.5876s] -03/11/23 21:40:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -03/11/23 21:41:39| INFO ref finished [took 43.7409s] -03/11/23 21:41:43| INFO atc_mc finished [took 46.4580s] -03/11/23 21:41:44| INFO atc_ne finished [took 46.4267s] -03/11/23 21:41:54| INFO mul_sld finished [took 61.3005s] -03/11/23 21:44:18| INFO bin_sld finished [took 206.3680s] -03/11/23 21:45:59| INFO mul_sld_gs finished [took 304.4726s] -03/11/23 21:50:33| INFO bin_sld_gs finished [took 579.3455s] -03/11/23 21:50:33| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 582.4808s] -03/11/23 21:50:33| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -03/11/23 21:51:22| INFO ref finished [took 43.6853s] -03/11/23 21:51:26| INFO atc_mc finished [took 47.1366s] -03/11/23 21:51:30| INFO atc_ne finished [took 49.4868s] -03/11/23 21:51:34| INFO mul_sld finished [took 59.0964s] -03/11/23 21:53:59| INFO bin_sld finished [took 205.0248s] -03/11/23 21:55:50| INFO mul_sld_gs finished [took 312.5630s] -03/11/23 22:00:27| INFO bin_sld_gs finished [took 591.1460s] -03/11/23 22:00:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 594.3163s] -03/11/23 22:00:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -03/11/23 22:01:15| INFO ref finished [took 43.3806s] -03/11/23 22:01:19| INFO atc_mc finished [took 46.6674s] -03/11/23 22:01:21| INFO atc_ne finished [took 47.1220s] -03/11/23 22:01:28| INFO mul_sld finished [took 58.6799s] -03/11/23 22:03:53| INFO bin_sld finished [took 204.7659s] -03/11/23 22:05:39| INFO mul_sld_gs finished [took 307.8811s] -03/11/23 22:10:32| INFO bin_sld_gs finished [took 601.9995s] -03/11/23 22:10:32| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 604.8406s] -03/11/23 22:10:32| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -03/11/23 22:11:20| INFO ref finished [took 42.8256s] -03/11/23 22:11:25| INFO atc_mc finished [took 46.9203s] -03/11/23 22:11:28| INFO atc_ne finished [took 49.3042s] -03/11/23 22:11:34| INFO mul_sld finished [took 60.2744s] -03/11/23 22:13:59| INFO bin_sld finished [took 205.7078s] -03/11/23 22:15:45| INFO mul_sld_gs finished [took 309.0888s] -03/11/23 22:20:32| INFO bin_sld_gs finished [took 596.5102s] -03/11/23 22:20:32| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 599.5067s] -03/11/23 22:20:32| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -03/11/23 22:21:20| INFO ref finished [took 43.1698s] -03/11/23 22:21:24| INFO atc_mc finished [took 46.5768s] -03/11/23 22:21:25| INFO atc_ne finished [took 46.3408s] -03/11/23 22:21:34| INFO mul_sld finished [took 60.8070s] -03/11/23 22:23:58| INFO bin_sld finished [took 205.3362s] -03/11/23 22:25:44| INFO mul_sld_gs finished [took 308.1859s] -03/11/23 22:30:44| INFO bin_sld_gs finished [took 609.5468s] -03/11/23 22:30:44| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 612.5803s] -03/11/23 22:30:44| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -03/11/23 22:31:32| INFO ref finished [took 43.2949s] -03/11/23 22:31:37| INFO atc_mc finished [took 46.3686s] -03/11/23 22:31:40| INFO atc_ne finished [took 49.2242s] -03/11/23 22:31:47| INFO mul_sld finished [took 60.9437s] -03/11/23 22:34:11| INFO bin_sld finished [took 205.9299s] -03/11/23 22:35:56| INFO mul_sld_gs finished [took 308.2738s] -03/11/23 22:40:36| INFO bin_sld_gs finished [took 588.7918s] -03/11/23 22:40:36| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 591.8830s] -03/11/23 22:40:36| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -03/11/23 22:41:24| INFO ref finished [took 43.3321s] -03/11/23 22:41:29| INFO atc_mc finished [took 46.8041s] -03/11/23 22:41:29| INFO atc_ne finished [took 46.5810s] -03/11/23 22:41:38| INFO mul_sld finished [took 60.2962s] -03/11/23 22:44:07| INFO bin_sld finished [took 209.6435s] -03/11/23 22:45:44| INFO mul_sld_gs finished [took 304.4809s] -03/11/23 22:50:39| INFO bin_sld_gs finished [took 599.5588s] -03/11/23 22:50:39| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 602.5720s] -03/11/23 22:50:39| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -03/11/23 22:51:26| INFO ref finished [took 42.4313s] -03/11/23 22:51:30| INFO atc_mc finished [took 45.5261s] -03/11/23 22:51:34| INFO atc_ne finished [took 48.4488s] -03/11/23 22:51:47| INFO mul_sld finished [took 66.4801s] -03/11/23 22:54:08| INFO bin_sld finished [took 208.4272s] -03/11/23 22:55:49| INFO mul_sld_gs finished [took 306.4505s] -03/11/23 23:00:15| INFO bin_sld_gs finished [took 573.7761s] -03/11/23 23:00:15| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 576.7586s] ----------------------------------------------------------------------------------------------------- -03/11/23 23:33:15| INFO dataset imdb_1prevs -03/11/23 23:33:22| INFO Dataset sample 0.50 of dataset imdb_1prevs started -03/11/23 23:33:22| ERROR Evaluation over imdb_1prevs failed. Exception: 'function' object is not iterable -03/11/23 23:33:22| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value ----------------------------------------------------------------------------------------------------- -03/11/23 23:34:15| INFO dataset imdb_1prevs -03/11/23 23:34:23| INFO Dataset sample 0.50 of dataset imdb_1prevs started -03/11/23 23:34:35| INFO atc_mc finished [took 11.5081s] -03/11/23 23:34:45| INFO ref finished [took 8.7754s] -03/11/23 23:34:47| INFO mul_sld finished [took 22.9651s] -03/11/23 23:34:47| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 23.9721s] -03/11/23 23:34:47| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: 'ref' ----------------------------------------------------------------------------------------------------- -03/11/23 23:36:10| INFO dataset imdb_1prevs -03/11/23 23:36:30| INFO Dataset sample 0.50 of dataset imdb_1prevs started -03/11/23 23:38:02| INFO atc_mc finished [took 56.2957s] -03/11/23 23:38:03| INFO mul_sld finished [took 57.6237s] -03/11/23 23:38:40| INFO ref finished [took 37.7811s] -03/11/23 23:38:40| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 130.9031s] -03/11/23 23:38:42| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: 'ref' ----------------------------------------------------------------------------------------------------- -03/11/23 23:39:32| INFO dataset imdb_1prevs -03/11/23 23:39:42| INFO Dataset sample 0.50 of dataset imdb_1prevs started -03/11/23 23:40:08| INFO atc_mc finished [took 24.7110s] -03/11/23 23:40:23| INFO mul_sld finished [took 40.2345s] -03/11/23 23:40:26| INFO ref finished [took 17.8417s] -03/11/23 23:40:26| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 44.8087s] ----------------------------------------------------------------------------------------------------- -03/11/23 23:41:18| INFO dataset imdb_1prevs -03/11/23 23:41:28| INFO Dataset sample 0.50 of dataset imdb_1prevs started -03/11/23 23:41:54| INFO atc_mc finished [took 24.0569s] -03/11/23 23:42:03| INFO mul_sld finished [took 33.3390s] -03/11/23 23:42:12| INFO ref finished [took 16.9551s] -03/11/23 23:42:12| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 43.2484s] ----------------------------------------------------------------------------------------------------- -04/11/23 00:03:17| ERROR Evaluation over imdb_1prevs failed. Exception: CompEstimatorName_.__init__() missing 1 required positional argument: 'ce' -04/11/23 00:03:17| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value ----------------------------------------------------------------------------------------------------- -04/11/23 00:03:50| ERROR Evaluation over imdb_1prevs failed. Exception: 'CompEstimator' object has no attribute '_CompEstimatorName___get' -04/11/23 00:03:50| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value ----------------------------------------------------------------------------------------------------- -04/11/23 00:04:42| INFO dataset imdb_1prevs -04/11/23 00:04:53| INFO Dataset sample 0.50 of dataset imdb_1prevs started -04/11/23 00:05:13| INFO ref finished [took 19.2363s] -04/11/23 00:05:20| INFO atc_mc finished [took 26.4278s] -04/11/23 00:05:29| INFO mul_sld finished [took 35.3110s] -04/11/23 00:05:29| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 36.4422s] ----------------------------------------------------------------------------------------------------- -04/11/23 00:19:43| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:19:49| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:19:53| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:19:55| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:19:57| WARNING Method bin_pacc failed. Exception: PACC.__init__() got an unexpected keyword argument 'recalib' -04/11/23 00:19:59| WARNING Method mul_pacc failed. Exception: PACC.__init__() got an unexpected keyword argument 'recalib' -04/11/23 00:20:00| WARNING Method bin_pacc_gs failed. Exception: Invalid parameter 'recalib' for estimator PACC(classifier=LogisticRegression(), n_jobs=1). Valid parameters are: ['classifier', 'n_jobs', 'val_split']. -04/11/23 00:20:01| WARNING Method mul_pacc_gs failed. Exception: Invalid parameter 'recalib' for estimator PACC(classifier=LogisticRegression(), n_jobs=1). Valid parameters are: ['classifier', 'n_jobs', 'val_split']. ----------------------------------------------------------------------------------------------------- -04/11/23 00:22:45| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:22:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:22:54| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:22:55| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. ----------------------------------------------------------------------------------------------------- -04/11/23 00:28:11| INFO dataset rcv1_CCAT_9prevs ----------------------------------------------------------------------------------------------------- -04/11/23 00:29:39| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:29:45| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:29:49| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:29:51| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:30:39| WARNING Method mul_pacc_gs failed. Exception: evaluation_report() got an unexpected keyword argument 'method_name' -04/11/23 00:31:00| INFO ref finished [took 60.5788s] -04/11/23 00:31:09| INFO atc_mc finished [took 64.6156s] -04/11/23 00:31:09| INFO mul_pacc finished [took 75.1821s] -04/11/23 00:31:12| INFO atc_ne finished [took 62.8665s] -04/11/23 00:31:24| INFO mul_sld finished [took 96.8624s] ----------------------------------------------------------------------------------------------------- -04/11/23 00:33:26| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:33:31| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:33:35| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. -04/11/23 00:33:37| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. ----------------------------------------------------------------------------------------------------- -04/11/23 00:38:42| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:38:48| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:38:51| WARNING Method bin_sld_qgs failed. Exception: ExtendedCollection.extend_collection() missing 1 required positional argument: 'pred_proba' -04/11/23 00:38:52| WARNING Method mul_sld_qgs failed. Exception: ExtendedCollection.extend_collection() missing 1 required positional argument: 'pred_proba' -04/11/23 00:39:41| WARNING Method mul_pacc_gs failed. Exception: evaluation_report() got an unexpected keyword argument 'method_name' ----------------------------------------------------------------------------------------------------- -04/11/23 00:46:33| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:46:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:46:40| WARNING Method bin_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:41| WARNING Method mul_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:42| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' -04/11/23 00:46:43| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' -04/11/23 00:46:44| WARNING Method bin_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:45| WARNING Method mul_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:46| WARNING Method bin_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:47| WARNING Method mul_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:47| WARNING Method bin_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:46:48| WARNING Method mul_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:27| INFO ref finished [took 37.5294s] -04/11/23 00:47:31| INFO atc_mc finished [took 40.5777s] -04/11/23 00:47:32| INFO atc_ne finished [took 40.7565s] -04/11/23 00:47:32| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 52.8106s] -04/11/23 00:47:32| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 00:47:33| WARNING Method bin_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:34| WARNING Method mul_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:35| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' -04/11/23 00:47:36| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' -04/11/23 00:47:37| WARNING Method bin_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:38| WARNING Method mul_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:39| WARNING Method bin_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:39| WARNING Method mul_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:40| WARNING Method bin_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() -04/11/23 00:47:41| WARNING Method mul_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ----------------------------------------------------------------------------------------------------- -04/11/23 00:48:05| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:48:10| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:48:13| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' -04/11/23 00:48:14| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' ----------------------------------------------------------------------------------------------------- -04/11/23 00:49:18| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:49:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:49:27| WARNING Method bin_sld_qgs failed. Exception: GridSearchQ.__init__() missing 1 required positional argument: 'model' -04/11/23 00:49:28| WARNING Method mul_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. ----------------------------------------------------------------------------------------------------- -04/11/23 00:51:27| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:51:32| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:51:36| WARNING Method bin_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. -04/11/23 00:51:37| WARNING Method mul_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. ----------------------------------------------------------------------------------------------------- -04/11/23 00:54:47| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:54:53| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 00:54:57| WARNING Method bin_sld_qgs failed. Exception: a must be greater than 0 unless no samples are taken -04/11/23 00:54:58| WARNING Method mul_sld_qgs failed. Exception: a must be greater than 0 unless no samples are taken ----------------------------------------------------------------------------------------------------- -04/11/23 00:58:47| INFO dataset rcv1_CCAT_9prevs -04/11/23 00:58:52| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 01:00:04| INFO ref finished [took 61.6328s] -04/11/23 01:00:11| INFO atc_mc finished [took 65.4916s] -04/11/23 01:00:13| INFO atc_ne finished [took 63.2288s] -04/11/23 01:00:14| INFO mul_pacc finished [took 75.5101s] -04/11/23 01:00:30| INFO mul_sld finished [took 96.6656s] -04/11/23 01:00:41| INFO mul_pacc_gs finished [took 99.7211s] -04/11/23 01:03:02| INFO bin_pacc finished [took 244.6260s] -04/11/23 01:03:07| INFO bin_sld finished [took 254.3478s] -04/11/23 01:04:51| INFO mul_sld_gs finished [took 354.7477s] -04/11/23 01:05:02| INFO bin_pacc_gs finished [took 362.1808s] -04/11/23 01:09:24| INFO bin_sld_gs finished [took 628.6714s] -04/11/23 01:09:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 631.8421s] -04/11/23 01:09:24| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 01:10:39| INFO ref finished [took 63.5158s] -04/11/23 01:10:44| INFO atc_mc finished [took 66.4279s] -04/11/23 01:10:46| INFO mul_pacc finished [took 75.3281s] -04/11/23 01:10:47| INFO atc_ne finished [took 67.5374s] -04/11/23 01:10:52| INFO mul_sld finished [took 86.6592s] -04/11/23 01:11:19| INFO mul_pacc_gs finished [took 104.6374s] -04/11/23 01:13:58| INFO bin_sld finished [took 273.4932s] -04/11/23 01:14:01| INFO bin_pacc finished [took 271.3481s] -04/11/23 01:15:42| INFO mul_sld_gs finished [took 374.2416s] -04/11/23 01:16:01| INFO bin_pacc_gs finished [took 388.0839s] -04/11/23 01:20:29| INFO bin_sld_gs finished [took 661.9729s] -04/11/23 01:20:29| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 665.2874s] -04/11/23 01:20:29| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -04/11/23 01:21:46| INFO ref finished [took 63.8544s] -04/11/23 01:21:50| INFO atc_mc finished [took 66.6917s] -04/11/23 01:21:52| INFO atc_ne finished [took 65.0860s] -04/11/23 01:21:53| INFO mul_pacc finished [took 77.2630s] -04/11/23 01:21:55| INFO mul_sld finished [took 83.3146s] -04/11/23 01:22:23| INFO mul_pacc_gs finished [took 102.3761s] -04/11/23 01:24:47| INFO bin_pacc finished [took 252.0964s] -04/11/23 01:24:49| INFO bin_sld finished [took 258.6998s] -04/11/23 01:26:37| INFO mul_sld_gs finished [took 363.7500s] -04/11/23 01:26:49| INFO bin_pacc_gs finished [took 370.5817s] -04/11/23 01:31:27| INFO bin_sld_gs finished [took 654.3921s] -04/11/23 01:31:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 658.0041s] -04/11/23 01:31:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -04/11/23 01:32:33| INFO ref finished [took 55.7749s] -04/11/23 01:32:38| INFO atc_mc finished [took 59.4190s] -04/11/23 01:32:40| INFO atc_ne finished [took 59.5155s] -04/11/23 01:32:42| INFO mul_pacc finished [took 68.8994s] -04/11/23 01:32:44| INFO mul_sld finished [took 74.6470s] -04/11/23 01:33:09| INFO mul_pacc_gs finished [took 92.6473s] -04/11/23 01:35:32| INFO bin_pacc finished [took 239.7541s] -04/11/23 01:35:34| INFO bin_sld finished [took 245.7504s] -04/11/23 01:37:19| INFO mul_sld_gs finished [took 348.1188s] -04/11/23 01:37:30| INFO bin_pacc_gs finished [took 355.4729s] -04/11/23 01:42:07| INFO bin_sld_gs finished [took 636.8598s] -04/11/23 01:42:07| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 639.9201s] -04/11/23 01:42:07| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -04/11/23 01:43:14| INFO ref finished [took 56.1531s] -04/11/23 01:43:19| INFO atc_mc finished [took 59.7473s] -04/11/23 01:43:20| INFO atc_ne finished [took 59.0606s] -04/11/23 01:43:23| INFO mul_pacc finished [took 69.4266s] -04/11/23 01:43:25| INFO mul_sld finished [took 76.3328s] -04/11/23 01:43:49| INFO mul_pacc_gs finished [took 92.3926s] -04/11/23 01:46:05| INFO bin_pacc finished [took 233.1877s] -04/11/23 01:46:08| INFO bin_sld finished [took 239.8757s] -04/11/23 01:47:51| INFO mul_sld_gs finished [took 339.5911s] -04/11/23 01:48:00| INFO bin_pacc_gs finished [took 345.7788s] -04/11/23 01:52:44| INFO bin_sld_gs finished [took 633.8407s] -04/11/23 01:52:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 637.0648s] -04/11/23 01:52:44| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -04/11/23 01:53:52| INFO ref finished [took 57.4958s] -04/11/23 01:53:57| INFO atc_mc finished [took 60.9998s] -04/11/23 01:53:58| INFO atc_ne finished [took 60.4847s] -04/11/23 01:54:01| INFO mul_pacc finished [took 70.5216s] -04/11/23 01:54:04| INFO mul_sld finished [took 78.2910s] -04/11/23 01:54:27| INFO mul_pacc_gs finished [took 94.4726s] -04/11/23 01:56:48| INFO bin_pacc finished [took 238.5969s] -04/11/23 01:56:50| INFO bin_sld finished [took 244.5679s] -04/11/23 01:58:31| INFO mul_sld_gs finished [took 342.4843s] -04/11/23 01:58:44| INFO bin_pacc_gs finished [took 352.8264s] -04/11/23 02:03:32| INFO bin_sld_gs finished [took 644.7046s] -04/11/23 02:03:32| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 647.8055s] -04/11/23 02:03:32| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -04/11/23 02:04:37| INFO ref finished [took 55.4488s] -04/11/23 02:04:42| INFO atc_mc finished [took 59.2634s] -04/11/23 02:04:44| INFO atc_ne finished [took 59.1371s] -04/11/23 02:04:46| INFO mul_pacc finished [took 68.0960s] -04/11/23 02:04:50| INFO mul_sld finished [took 76.4282s] -04/11/23 02:05:12| INFO mul_pacc_gs finished [took 91.7735s] -04/11/23 02:07:30| INFO bin_pacc finished [took 232.7650s] -04/11/23 02:07:36| INFO bin_sld finished [took 242.4077s] -04/11/23 02:09:14| INFO mul_sld_gs finished [took 338.1418s] -04/11/23 02:09:26| INFO bin_pacc_gs finished [took 347.2033s] -04/11/23 02:13:59| INFO bin_sld_gs finished [took 624.6098s] -04/11/23 02:13:59| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 627.7979s] -04/11/23 02:13:59| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -04/11/23 02:15:05| INFO ref finished [took 55.1962s] -04/11/23 02:15:10| INFO atc_mc finished [took 59.0907s] -04/11/23 02:15:11| INFO atc_ne finished [took 59.1531s] -04/11/23 02:15:13| INFO mul_pacc finished [took 67.6705s] -04/11/23 02:15:17| INFO mul_sld finished [took 75.4559s] -04/11/23 02:15:41| INFO mul_pacc_gs finished [took 92.4901s] -04/11/23 02:17:59| INFO bin_pacc finished [took 233.8600s] -04/11/23 02:18:04| INFO bin_sld finished [took 243.2382s] -04/11/23 02:19:40| INFO mul_sld_gs finished [took 336.0961s] -04/11/23 02:19:51| INFO bin_pacc_gs finished [took 344.4075s] -04/11/23 02:24:30| INFO bin_sld_gs finished [took 627.6209s] -04/11/23 02:24:30| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 630.8251s] -04/11/23 02:24:30| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -04/11/23 02:25:35| INFO ref finished [took 54.8513s] -04/11/23 02:25:40| INFO atc_mc finished [took 58.8528s] -04/11/23 02:25:41| INFO atc_ne finished [took 58.6035s] -04/11/23 02:25:43| INFO mul_pacc finished [took 66.9030s] -04/11/23 02:25:57| INFO mul_sld finished [took 84.2072s] -04/11/23 02:26:10| INFO mul_pacc_gs finished [took 91.0973s] -04/11/23 02:28:31| INFO bin_pacc finished [took 235.7331s] -04/11/23 02:28:35| INFO bin_sld finished [took 243.6260s] -04/11/23 02:30:09| INFO mul_sld_gs finished [took 334.4842s] -04/11/23 02:30:22| INFO bin_pacc_gs finished [took 344.6874s] -04/11/23 02:34:46| INFO bin_sld_gs finished [took 612.1219s] -04/11/23 02:34:46| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 615.2004s] ----------------------------------------------------------------------------------------------------- -04/11/23 02:57:35| INFO dataset rcv1_CCAT_9prevs -04/11/23 02:57:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 02:57:47| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 02:58:59| INFO ref finished [took 64.5948s] -04/11/23 02:59:06| INFO atc_mc finished [took 69.5808s] -04/11/23 02:59:12| INFO mul_pacc finished [took 82.8518s] -04/11/23 02:59:13| INFO atc_ne finished [took 72.1303s] -04/11/23 02:59:26| INFO mul_sld finished [took 103.4201s] -04/11/23 02:59:30| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -04/11/23 02:59:41| INFO mul_pacc_gs finished [took 109.4672s] -04/11/23 03:01:59| INFO bin_pacc finished [took 251.3945s] -04/11/23 03:02:02| INFO bin_sld finished [took 260.0226s] -04/11/23 03:03:35| INFO mul_sld_gs finished [took 350.1705s] -04/11/23 03:03:48| INFO bin_pacc_gs finished [took 357.9668s] -04/11/23 03:07:59| INFO bin_sld_gs finished [took 615.8087s] -04/11/23 03:07:59| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 620.4985s] -04/11/23 03:07:59| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 03:08:06| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 03:09:17| INFO ref finished [took 64.4692s] -04/11/23 03:09:25| INFO atc_mc finished [took 71.3766s] -04/11/23 03:09:27| INFO atc_ne finished [took 71.0947s] -04/11/23 03:09:28| INFO mul_pacc finished [took 80.0201s] -04/11/23 03:09:31| INFO mul_sld finished [took 89.4295s] -04/11/23 03:09:55| INFO mul_pacc_gs finished [took 104.7292s] -04/11/23 03:12:25| INFO bin_sld finished [took 263.6824s] -04/11/23 03:12:25| INFO bin_pacc finished [took 258.6502s] -04/11/23 03:14:01| INFO mul_sld_gs finished [took 357.3344s] -04/11/23 03:14:14| INFO bin_sld_gsq finished [took 369.1636s] -04/11/23 03:14:22| INFO bin_pacc_gs finished [took 372.8646s] -04/11/23 03:18:40| INFO bin_sld_gs finished [took 636.9190s] -04/11/23 03:18:40| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 640.2322s] -04/11/23 03:18:40| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -04/11/23 03:18:46| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 03:19:58| INFO ref finished [took 65.9462s] -04/11/23 03:20:02| INFO atc_mc finished [took 68.5710s] -04/11/23 03:20:04| INFO atc_ne finished [took 68.9466s] -04/11/23 03:20:06| INFO mul_pacc finished [took 77.9039s] -04/11/23 03:20:06| INFO mul_sld finished [took 84.0917s] -04/11/23 03:20:37| INFO mul_pacc_gs finished [took 106.2536s] -04/11/23 03:23:04| INFO bin_pacc finished [took 257.4211s] -04/11/23 03:23:05| INFO bin_sld finished [took 264.3442s] -04/11/23 03:24:49| INFO mul_sld_gs finished [took 365.1691s] -04/11/23 03:25:01| INFO bin_pacc_gs finished [took 371.9184s] -04/11/23 03:25:02| INFO bin_sld_gsq finished [took 377.0442s] -04/11/23 03:29:37| INFO bin_sld_gs finished [took 654.0366s] -04/11/23 03:29:37| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 657.0840s] -04/11/23 03:29:37| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -04/11/23 03:29:42| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 03:30:51| INFO ref finished [took 62.7217s] -04/11/23 03:30:58| INFO atc_mc finished [took 67.8613s] -04/11/23 03:31:00| INFO atc_ne finished [took 68.5026s] -04/11/23 03:31:03| INFO mul_sld finished [took 83.8857s] -04/11/23 03:31:03| INFO mul_pacc finished [took 78.6340s] -04/11/23 03:31:30| INFO mul_pacc_gs finished [took 103.4683s] -04/11/23 03:34:00| INFO bin_sld finished [took 262.4457s] -04/11/23 03:34:02| INFO bin_pacc finished [took 258.2247s] -04/11/23 03:35:44| INFO mul_sld_gs finished [took 363.8135s] -04/11/23 03:35:58| INFO bin_pacc_gs finished [took 372.0485s] -04/11/23 03:36:05| INFO bin_sld_gsq finished [took 382.9585s] -04/11/23 03:40:39| INFO bin_sld_gs finished [took 659.6222s] -04/11/23 03:40:39| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 662.5763s] -04/11/23 03:40:39| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -04/11/23 03:40:45| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 03:41:56| INFO ref finished [took 64.5923s] -04/11/23 03:42:01| INFO atc_mc finished [took 68.0148s] -04/11/23 03:42:03| INFO atc_ne finished [took 68.3119s] -04/11/23 03:42:04| INFO mul_pacc finished [took 76.9397s] -04/11/23 03:42:07| INFO mul_sld finished [took 85.5363s] -04/11/23 03:42:34| INFO mul_pacc_gs finished [took 103.4448s] -04/11/23 03:45:01| INFO bin_sld finished [took 260.0814s] -04/11/23 03:45:03| INFO bin_pacc finished [took 256.9386s] -04/11/23 03:46:45| INFO mul_sld_gs finished [took 361.5910s] -04/11/23 03:47:01| INFO bin_pacc_gs finished [took 371.9657s] -04/11/23 03:47:13| INFO bin_sld_gsq finished [took 388.2498s] -04/11/23 03:51:40| INFO bin_sld_gs finished [took 657.4008s] -04/11/23 03:51:40| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 660.5115s] -04/11/23 03:51:40| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -04/11/23 03:51:46| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 03:52:54| INFO ref finished [took 61.9225s] -04/11/23 03:53:00| INFO atc_mc finished [took 66.3156s] -04/11/23 03:53:02| INFO atc_ne finished [took 66.5025s] -04/11/23 03:53:04| INFO mul_pacc finished [took 75.8808s] -04/11/23 03:53:06| INFO mul_sld finished [took 84.3204s] -04/11/23 03:53:33| INFO mul_pacc_gs finished [took 102.5763s] -04/11/23 03:56:04| INFO bin_sld finished [took 263.2781s] -04/11/23 03:56:04| INFO bin_pacc finished [took 257.7298s] -04/11/23 03:57:44| INFO mul_sld_gs finished [took 359.7910s] -04/11/23 03:58:00| INFO bin_pacc_gs finished [took 371.3848s] -04/11/23 03:58:11| INFO bin_sld_gsq finished [took 386.0904s] -04/11/23 04:02:50| INFO bin_sld_gs finished [took 667.6623s] -04/11/23 04:02:50| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 670.7255s] -04/11/23 04:02:50| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -04/11/23 04:02:57| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 04:04:05| INFO ref finished [took 62.3256s] -04/11/23 04:04:13| INFO atc_mc finished [took 68.9525s] -04/11/23 04:04:15| INFO atc_ne finished [took 68.8750s] -04/11/23 04:04:16| INFO mul_pacc finished [took 77.5049s] -04/11/23 04:04:19| INFO mul_sld finished [took 86.0694s] -04/11/23 04:04:45| INFO mul_pacc_gs finished [took 103.3513s] -04/11/23 04:07:15| INFO bin_pacc finished [took 257.6456s] -04/11/23 04:07:16| INFO bin_sld finished [took 263.9914s] -04/11/23 04:08:55| INFO mul_sld_gs finished [took 360.5634s] -04/11/23 04:09:12| INFO bin_pacc_gs finished [took 372.2665s] -04/11/23 04:09:18| INFO bin_sld_gsq finished [took 381.8311s] -04/11/23 04:13:39| INFO bin_sld_gs finished [took 645.3599s] -04/11/23 04:13:39| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 648.5328s] -04/11/23 04:13:39| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -04/11/23 04:13:45| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 04:14:51| INFO ref finished [took 59.8110s] -04/11/23 04:14:58| INFO atc_mc finished [took 65.2666s] -04/11/23 04:14:59| INFO atc_ne finished [took 64.5173s] -04/11/23 04:15:01| INFO mul_pacc finished [took 73.8332s] -04/11/23 04:15:04| INFO mul_sld finished [took 82.3509s] -04/11/23 04:15:29| INFO mul_pacc_gs finished [took 99.3541s] -04/11/23 04:18:00| INFO bin_pacc finished [took 254.3308s] -04/11/23 04:18:03| INFO bin_sld finished [took 262.3008s] -04/11/23 04:19:40| INFO mul_sld_gs finished [took 357.1229s] -04/11/23 04:19:57| INFO bin_pacc_gs finished [took 368.4516s] -04/11/23 04:20:03| INFO bin_sld_gsq finished [took 378.7658s] -04/11/23 04:24:37| INFO bin_sld_gs finished [took 655.1931s] -04/11/23 04:24:37| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 658.3505s] -04/11/23 04:24:37| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -04/11/23 04:24:43| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' -04/11/23 04:25:49| INFO ref finished [took 59.4546s] -04/11/23 04:25:55| INFO atc_mc finished [took 63.5805s] -04/11/23 04:25:58| INFO atc_ne finished [took 63.2985s] -04/11/23 04:25:58| INFO mul_pacc finished [took 72.5198s] -04/11/23 04:26:11| INFO mul_sld finished [took 91.7136s] -04/11/23 04:26:27| INFO mul_pacc_gs finished [took 98.8722s] -04/11/23 04:28:57| INFO bin_pacc finished [took 252.8144s] -04/11/23 04:29:02| INFO bin_sld finished [took 263.8013s] -04/11/23 04:30:35| INFO mul_sld_gs finished [took 353.3693s] -04/11/23 04:30:51| INFO bin_sld_gsq finished [took 368.8564s] -04/11/23 04:30:54| INFO bin_pacc_gs finished [took 367.5592s] -04/11/23 04:35:11| INFO bin_sld_gs finished [took 630.6700s] -04/11/23 04:35:11| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 633.7494s] ----------------------------------------------------------------------------------------------------- -04/11/23 19:09:42| INFO dataset rcv1_CCAT_9prevs -04/11/23 19:09:47| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 19:10:28| INFO ref finished [took 36.0351s] -04/11/23 19:10:32| INFO atc_mc finished [took 38.9507s] -04/11/23 19:10:35| INFO mulmc_sld finished [took 43.7869s] -04/11/23 19:10:50| INFO mul_sld finished [took 60.8007s] -04/11/23 19:10:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 62.9600s] -04/11/23 19:10:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 19:11:29| INFO ref finished [took 36.3632s] -04/11/23 19:11:34| INFO atc_mc finished [took 39.5928s] -04/11/23 19:11:36| INFO mulmc_sld finished [took 44.2915s] -04/11/23 19:11:44| INFO mul_sld finished [took 52.6727s] -04/11/23 19:11:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 54.0362s] -04/11/23 19:11:44| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -04/11/23 19:12:24| INFO ref finished [took 36.4303s] -04/11/23 19:12:27| INFO atc_mc finished [took 39.2329s] -04/11/23 19:12:30| INFO mulmc_sld finished [took 43.6247s] -04/11/23 19:12:36| INFO mul_sld finished [took 50.2041s] -04/11/23 19:12:36| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 51.6412s] -04/11/23 19:12:36| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -04/11/23 19:13:16| INFO ref finished [took 36.7551s] -04/11/23 19:13:19| INFO atc_mc finished [took 39.2806s] -04/11/23 19:13:21| INFO mulmc_sld finished [took 43.6120s] -04/11/23 19:13:27| INFO mul_sld finished [took 50.4446s] -04/11/23 19:13:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 51.6672s] -04/11/23 19:13:27| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -04/11/23 19:14:07| INFO ref finished [took 35.8789s] -04/11/23 19:14:11| INFO atc_mc finished [took 39.2168s] -04/11/23 19:14:13| INFO mulmc_sld finished [took 43.4580s] -04/11/23 19:14:20| INFO mul_sld finished [took 51.2902s] -04/11/23 19:14:20| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 52.6303s] -04/11/23 19:14:20| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -04/11/23 19:15:00| INFO ref finished [took 36.3735s] -04/11/23 19:15:04| INFO atc_mc finished [took 39.7035s] -04/11/23 19:15:06| INFO mulmc_sld finished [took 43.6364s] -04/11/23 19:15:13| INFO mul_sld finished [took 52.0138s] -04/11/23 19:15:13| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 53.3303s] -04/11/23 19:15:13| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -04/11/23 19:15:54| INFO ref finished [took 37.3366s] -04/11/23 19:15:57| INFO atc_mc finished [took 39.8921s] -04/11/23 19:16:00| INFO mulmc_sld finished [took 44.5159s] -04/11/23 19:16:08| INFO mul_sld finished [took 53.0806s] -04/11/23 19:16:08| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 54.4117s] -04/11/23 19:16:08| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -04/11/23 19:16:47| INFO ref finished [took 35.7800s] -04/11/23 19:16:50| INFO atc_mc finished [took 38.4484s] -04/11/23 19:16:53| INFO mulmc_sld finished [took 42.7405s] -04/11/23 19:17:01| INFO mul_sld finished [took 51.5556s] -04/11/23 19:17:01| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 52.9684s] -04/11/23 19:17:01| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -04/11/23 19:17:39| INFO ref finished [took 35.0919s] -04/11/23 19:17:43| INFO atc_mc finished [took 38.1718s] -04/11/23 19:17:45| INFO mulmc_sld finished [took 42.4413s] -04/11/23 19:17:59| INFO mul_sld finished [took 57.0766s] -04/11/23 19:17:59| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 58.3668s] ----------------------------------------------------------------------------------------------------- -04/11/23 19:42:38| INFO dataset rcv1_CCAT_9prevs -04/11/23 19:42:43| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 19:43:27| INFO ref finished [took 38.7664s] -04/11/23 19:43:31| INFO atc_mc finished [took 42.4000s] -04/11/23 19:43:33| INFO mulmc_sld finished [took 47.0913s] -04/11/23 19:43:34| INFO binmc_sld finished [took 47.1675s] -04/11/23 19:43:49| INFO mul_sld finished [took 64.1382s] -04/11/23 19:46:00| INFO bin_sld finished [took 195.9822s] -04/11/23 19:46:00| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 197.2916s] -04/11/23 19:46:00| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 19:46:44| INFO ref finished [took 38.5976s] -04/11/23 19:46:48| INFO atc_mc finished [took 41.9465s] -04/11/23 19:46:49| INFO mulmc_sld finished [took 46.2205s] -04/11/23 19:46:51| INFO binmc_sld finished [took 46.7475s] -04/11/23 19:46:58| INFO mul_sld finished [took 56.3552s] -04/11/23 19:49:14| INFO bin_sld finished [took 193.2923s] -04/11/23 19:49:14| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 194.6251s] -04/11/23 19:49:14| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -04/11/23 19:49:58| INFO ref finished [took 38.3754s] -04/11/23 19:50:02| INFO atc_mc finished [took 41.0091s] -04/11/23 19:50:03| INFO mulmc_sld finished [took 45.6205s] -04/11/23 19:50:05| INFO binmc_sld finished [took 46.1852s] -04/11/23 19:50:10| INFO mul_sld finished [took 52.9704s] -04/11/23 19:52:27| INFO bin_sld finished [took 190.6101s] -04/11/23 19:52:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 192.0378s] -04/11/23 19:52:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -04/11/23 19:53:10| INFO ref finished [took 38.4467s] -04/11/23 19:53:13| INFO atc_mc finished [took 41.2602s] -04/11/23 19:53:15| INFO mulmc_sld finished [took 45.7496s] -04/11/23 19:53:16| INFO binmc_sld finished [took 45.5531s] -04/11/23 19:53:21| INFO mul_sld finished [took 52.5067s] -04/11/23 19:55:38| INFO bin_sld finished [took 190.7744s] -04/11/23 19:55:38| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 191.9715s] -04/11/23 19:55:39| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -04/11/23 19:56:21| INFO ref finished [took 37.9420s] -04/11/23 19:56:26| INFO atc_mc finished [took 41.2056s] -04/11/23 19:56:27| INFO mulmc_sld finished [took 45.7577s] -04/11/23 19:56:28| INFO binmc_sld finished [took 45.6411s] -04/11/23 19:56:34| INFO mul_sld finished [took 53.5219s] -04/11/23 19:58:51| INFO bin_sld finished [took 191.1772s] -04/11/23 19:58:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 192.4566s] -04/11/23 19:58:51| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -04/11/23 19:59:34| INFO ref finished [took 37.8604s] -04/11/23 19:59:38| INFO atc_mc finished [took 41.0334s] -04/11/23 19:59:39| INFO mulmc_sld finished [took 45.1999s] -04/11/23 19:59:40| INFO binmc_sld finished [took 45.4846s] -04/11/23 19:59:47| INFO mul_sld finished [took 54.3166s] -04/11/23 20:02:04| INFO bin_sld finished [took 191.4002s] -04/11/23 20:02:04| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 192.6275s] -04/11/23 20:02:04| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -04/11/23 20:02:48| INFO ref finished [took 38.8313s] -04/11/23 20:02:52| INFO atc_mc finished [took 42.1162s] -04/11/23 20:02:54| INFO mulmc_sld finished [took 47.0413s] -04/11/23 20:02:55| INFO binmc_sld finished [took 46.8891s] -04/11/23 20:03:02| INFO mul_sld finished [took 55.8821s] -04/11/23 20:05:19| INFO bin_sld finished [took 193.7571s] -04/11/23 20:05:19| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 195.2404s] -04/11/23 20:05:19| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -04/11/23 20:06:03| INFO ref finished [took 38.7982s] -04/11/23 20:06:06| INFO atc_mc finished [took 41.6213s] -04/11/23 20:06:08| INFO mulmc_sld finished [took 46.2646s] -04/11/23 20:06:09| INFO binmc_sld finished [took 46.2453s] -04/11/23 20:06:16| INFO mul_sld finished [took 54.8621s] -04/11/23 20:08:35| INFO bin_sld finished [took 194.5226s] -04/11/23 20:08:35| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 195.9251s] -04/11/23 20:08:35| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -04/11/23 20:09:18| INFO ref finished [took 38.3873s] -04/11/23 20:09:22| INFO atc_mc finished [took 41.2537s] -04/11/23 20:09:24| INFO mulmc_sld finished [took 46.2211s] -04/11/23 20:09:25| INFO binmc_sld finished [took 46.6421s] -04/11/23 20:09:38| INFO mul_sld finished [took 60.9539s] -04/11/23 20:11:51| INFO bin_sld finished [took 195.1888s] -04/11/23 20:11:51| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 196.4776s] ----------------------------------------------------------------------------------------------------- -04/11/23 20:56:32| INFO dataset rcv1_CCAT_9prevs -04/11/23 20:56:37| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -04/11/23 20:57:33| INFO ref finished [took 49.2697s] -04/11/23 20:57:38| INFO atc_mc finished [took 53.2068s] -04/11/23 20:57:39| INFO mulmc_sld finished [took 58.6224s] -04/11/23 20:58:59| INFO mulmc_sld_gs finished [took 136.0930s] -04/11/23 21:00:30| INFO binmc_sld finished [took 230.3290s] -04/11/23 21:02:12| INFO mul_sld_gs finished [took 333.4899s] -04/11/23 21:06:49| INFO bin_sld_gs finished [took 610.5751s] -04/11/23 21:06:54| INFO binmc_sld_gs finished [took 612.8900s] -04/11/23 21:06:55| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 617.6873s] -04/11/23 21:06:55| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -04/11/23 21:07:52| INFO ref finished [took 49.8077s] -04/11/23 21:07:56| INFO atc_mc finished [took 53.3303s] -04/11/23 21:07:57| INFO mulmc_sld finished [took 58.9345s] -04/11/23 21:09:17| INFO mulmc_sld_gs finished [took 136.5258s] -04/11/23 21:10:51| INFO binmc_sld finished [took 233.4049s] -04/11/23 21:12:35| INFO mul_sld_gs finished [took 338.2751s] -04/11/23 21:17:38| INFO bin_sld_gs finished [took 641.8524s] -04/11/23 21:18:19| INFO binmc_sld_gs finished [took 679.9471s] -04/11/23 21:18:19| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 684.7098s] -04/11/23 21:18:19| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -04/11/23 21:19:24| INFO ref finished [took 55.3767s] -04/11/23 21:19:28| INFO mulmc_sld finished [took 64.2789s] -04/11/23 21:19:29| INFO atc_mc finished [took 59.5610s] -04/11/23 21:20:57| INFO mulmc_sld_gs finished [took 150.1392s] -04/11/23 21:22:36| INFO binmc_sld finished [took 253.0960s] -04/11/23 21:24:16| INFO mul_sld_gs finished [took 354.6283s] -04/11/23 21:29:15| INFO bin_sld_gs finished [took 654.3325s] -04/11/23 21:29:50| INFO binmc_sld_gs finished [took 684.5074s] -04/11/23 21:29:50| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 690.4897s] -04/11/23 21:29:50| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -04/11/23 21:30:45| INFO ref finished [took 48.2647s] -04/11/23 21:30:51| INFO atc_mc finished [took 52.2724s] -04/11/23 21:30:51| INFO mulmc_sld finished [took 57.5142s] -04/11/23 21:32:07| INFO mulmc_sld_gs finished [took 131.4908s] -04/11/23 21:33:38| INFO binmc_sld finished [took 224.9620s] -04/11/23 21:35:22| INFO mul_sld_gs finished [took 329.9053s] -04/11/23 21:40:25| INFO bin_sld_gs finished [took 634.4342s] -04/11/23 21:41:08| INFO binmc_sld_gs finished [took 673.6071s] -04/11/23 21:41:08| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 678.4725s] -04/11/23 21:41:08| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -04/11/23 21:42:03| INFO ref finished [took 47.4381s] -04/11/23 21:42:08| INFO atc_mc finished [took 51.3566s] -04/11/23 21:42:09| INFO mulmc_sld finished [took 56.6180s] -04/11/23 21:43:23| INFO mulmc_sld_gs finished [took 128.6413s] -04/11/23 21:44:54| INFO binmc_sld finished [took 222.7951s] -04/11/23 21:46:39| INFO mul_sld_gs finished [took 328.8118s] -04/11/23 21:51:37| INFO bin_sld_gs finished [took 627.4937s] -04/11/23 21:52:17| INFO binmc_sld_gs finished [took 663.8116s] -04/11/23 21:52:17| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 668.8948s] -04/11/23 21:52:17| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -04/11/23 21:53:12| INFO ref finished [took 47.6269s] -04/11/23 21:53:16| INFO atc_mc finished [took 51.1109s] -04/11/23 21:53:17| INFO mulmc_sld finished [took 56.5728s] -04/11/23 21:54:31| INFO mulmc_sld_gs finished [took 128.0358s] -04/11/23 21:56:00| INFO binmc_sld finished [took 220.0811s] -04/11/23 21:57:46| INFO mul_sld_gs finished [took 327.0856s] -04/11/23 22:02:58| INFO bin_sld_gs finished [took 639.3432s] -04/11/23 22:03:48| INFO binmc_sld_gs finished [took 686.2326s] -04/11/23 22:03:48| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 690.9677s] -04/11/23 22:03:48| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -04/11/23 22:04:42| INFO ref finished [took 47.2804s] -04/11/23 22:04:48| INFO atc_mc finished [took 51.6888s] -04/11/23 22:04:48| INFO mulmc_sld finished [took 56.1465s] -04/11/23 22:06:06| INFO mulmc_sld_gs finished [took 132.4278s] -04/11/23 22:07:33| INFO binmc_sld finished [took 221.9299s] -04/11/23 22:09:19| INFO mul_sld_gs finished [took 329.1446s] -04/11/23 22:14:09| INFO bin_sld_gs finished [took 619.3584s] -04/11/23 22:14:32| INFO binmc_sld_gs finished [took 638.7326s] -04/11/23 22:14:32| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 643.6278s] -04/11/23 22:14:32| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -04/11/23 22:15:26| INFO ref finished [took 47.3139s] -04/11/23 22:15:30| INFO atc_mc finished [took 50.8602s] -04/11/23 22:15:32| INFO mulmc_sld finished [took 56.5107s] -04/11/23 22:16:47| INFO mulmc_sld_gs finished [took 129.5292s] -04/11/23 22:18:22| INFO binmc_sld finished [took 226.9238s] -04/11/23 22:20:02| INFO mul_sld_gs finished [took 327.7014s] -04/11/23 22:24:57| INFO bin_sld_gs finished [took 624.4254s] -04/11/23 22:25:13| INFO binmc_sld_gs finished [took 636.2675s] -04/11/23 22:25:13| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 641.0382s] -04/11/23 22:25:13| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -04/11/23 22:26:07| INFO ref finished [took 47.3224s] -04/11/23 22:26:12| INFO atc_mc finished [took 51.1828s] -04/11/23 22:26:13| INFO mulmc_sld finished [took 56.6133s] -04/11/23 22:27:30| INFO mulmc_sld_gs finished [took 131.3662s] -04/11/23 22:29:05| INFO binmc_sld finished [took 229.3002s] -04/11/23 22:30:38| INFO mul_sld_gs finished [took 323.5271s] -04/11/23 22:35:21| INFO bin_sld_gs finished [took 606.6430s] -04/11/23 22:35:30| INFO binmc_sld_gs finished [took 612.5966s] -04/11/23 22:35:30| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 617.3109s] ----------------------------------------------------------------------------------------------------- -04/11/23 22:49:37| ERROR Evaluation over rcv1_CCAT_3prevs failed. Exception: 'Invalid estimator: estimator binmc_sld_gs does not exist' -04/11/23 22:49:37| ERROR Failed while saving configuration rcv1_CCAT_debug of rcv1_CCAT_3prevs. Exception: cannot access local variable 'dr' where it is not associated with a value ----------------------------------------------------------------------------------------------------- -04/11/23 22:50:07| INFO dataset rcv1_CCAT_3prevs -04/11/23 22:50:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started ----------------------------------------------------------------------------------------------------- -04/11/23 22:55:55| INFO dataset rcv1_CCAT_3prevs -04/11/23 22:55:59| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 22:56:48| INFO ref finished [took 44.4275s] ----------------------------------------------------------------------------------------------------- -04/11/23 22:56:59| INFO dataset rcv1_CCAT_3prevs -04/11/23 22:57:03| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 22:57:09| WARNING Method mul_sld_gs failed. Exception: '>=' not supported between instances of 'TypeError' and 'int' -04/11/23 22:57:17| WARNING Method bin_sld_gs failed. Exception: '>=' not supported between instances of 'TypeError' and 'int' ----------------------------------------------------------------------------------------------------- -04/11/23 22:58:04| INFO dataset rcv1_CCAT_3prevs -04/11/23 22:58:09| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 22:58:58| INFO ref finished [took 43.7541s] -04/11/23 22:59:05| INFO atc_mc finished [took 50.0628s] ----------------------------------------------------------------------------------------------------- -04/11/23 23:01:22| INFO dataset rcv1_CCAT_3prevs -04/11/23 23:01:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 23:02:16| INFO ref finished [took 43.9765s] -04/11/23 23:02:23| INFO atc_mc finished [took 50.5568s] ----------------------------------------------------------------------------------------------------- -04/11/23 23:09:33| INFO dataset rcv1_CCAT_3prevs -04/11/23 23:09:37| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 23:09:38| WARNING Method binmc_sld failed. Exception: classifier and pred_proba cannot be both None -04/11/23 23:09:39| WARNING Method mulmc_sld failed. Exception: classifier and pred_proba cannot be both None -04/11/23 23:09:40| WARNING Method bin_sld_gs failed. Exception: no combination of hyperparameters seem to work -04/11/23 23:09:41| WARNING Method mul_sld_gs failed. Exception: no combination of hyperparameters seem to work ----------------------------------------------------------------------------------------------------- -04/11/23 23:10:23| INFO dataset rcv1_CCAT_3prevs -04/11/23 23:10:28| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 23:11:15| INFO ref finished [took 42.4887s] -04/11/23 23:11:20| INFO atc_mc finished [took 45.6262s] -04/11/23 23:11:21| INFO mulmc_sld finished [took 50.9790s] -04/11/23 23:13:57| INFO binmc_sld finished [took 208.3159s] ----------------------------------------------------------------------------------------------------- -04/11/23 23:16:22| INFO dataset rcv1_CCAT_3prevs -04/11/23 23:16:26| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started -04/11/23 23:17:12| INFO ref finished [took 40.5978s] -04/11/23 23:17:16| INFO atc_mc finished [took 43.6933s] -04/11/23 23:17:17| INFO mulmc_sld finished [took 49.0808s] -04/11/23 23:19:53| INFO binmc_sld finished [took 205.5731s] -04/11/23 23:22:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00672) [took 354.1411s] -04/11/23 23:23:05| INFO mul_sld_gs finished [took 394.8240s] -04/11/23 23:30:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00891) [took 852.1465s] -04/11/23 23:33:44| INFO bin_sld_gs finished [took 1035.2071s] -04/11/23 23:33:44| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs finished [took 1038.1845s] -04/11/23 23:33:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_3prevs started -04/11/23 23:34:33| INFO ref finished [took 43.6409s] -04/11/23 23:34:37| INFO atc_mc finished [took 46.7818s] -04/11/23 23:34:38| INFO mulmc_sld finished [took 51.3459s] -04/11/23 23:37:15| INFO binmc_sld finished [took 209.5746s] -04/11/23 23:39:48| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00553) [took 359.3210s] -04/11/23 23:40:28| INFO mul_sld_gs finished [took 399.5320s] -04/11/23 23:48:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': None} (score=0.01058) [took 855.1289s] -04/11/23 23:51:06| INFO bin_sld_gs finished [took 1038.6344s] -04/11/23 23:51:06| INFO Dataset sample 0.50 of dataset rcv1_CCAT_3prevs finished [took 1041.6478s] -04/11/23 23:51:06| INFO Dataset sample 0.60 of dataset rcv1_CCAT_3prevs started -04/11/23 23:51:51| INFO ref finished [took 40.0694s] -04/11/23 23:51:55| INFO atc_mc finished [took 42.4882s] -04/11/23 23:51:56| INFO mulmc_sld finished [took 47.7936s] -04/11/23 23:54:29| INFO binmc_sld finished [took 201.3777s] -04/11/23 23:57:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00429) [took 352.7820s] -04/11/23 23:57:43| INFO mul_sld_gs finished [took 392.5201s] -05/11/23 00:05:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00552) [took 851.9361s] -05/11/23 00:08:24| INFO bin_sld_gs finished [took 1034.7353s] -05/11/23 00:08:24| INFO Dataset sample 0.60 of dataset rcv1_CCAT_3prevs finished [took 1037.8033s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:11:07| INFO dataset rcv1_CCAT_3prevs -05/11/23 00:11:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started ----------------------------------------------------------------------------------------------------- -05/11/23 00:28:39| INFO dataset imdb_3prevs -05/11/23 00:28:46| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 00:28:55| INFO ref finished [took 8.7347s] -05/11/23 00:28:58| INFO atc_mc finished [took 11.6376s] -05/11/23 00:28:59| INFO mulmc_sld finished [took 13.5476s] -05/11/23 00:28:59| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 13.9513s] -05/11/23 00:28:59| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 00:29:09| INFO ref finished [took 8.7049s] -05/11/23 00:29:12| INFO atc_mc finished [took 11.6170s] -05/11/23 00:29:14| INFO mulmc_sld finished [took 13.7416s] -05/11/23 00:29:14| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.2842s] -05/11/23 00:29:14| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 00:29:23| INFO ref finished [took 8.6894s] -05/11/23 00:29:26| INFO atc_mc finished [took 11.5275s] -05/11/23 00:29:28| INFO mulmc_sld finished [took 13.6977s] -05/11/23 00:29:28| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2742s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:34:12| INFO dataset imdb_3prevs -05/11/23 00:34:22| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 00:34:41| INFO ref finished [took 17.6558s] -05/11/23 00:34:46| INFO mulmc_sld finished [took 22.9646s] -05/11/23 00:34:48| INFO atc_mc finished [took 24.5871s] -05/11/23 00:34:48| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 25.3179s] -05/11/23 00:34:48| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 00:35:05| INFO ref finished [took 17.2188s] -05/11/23 00:35:10| INFO mulmc_sld finished [took 22.2420s] -05/11/23 00:35:12| INFO atc_mc finished [took 23.9752s] -05/11/23 00:35:12| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 24.7688s] -05/11/23 00:35:12| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 00:35:33| INFO ref finished [took 20.0731s] -05/11/23 00:35:38| INFO mulmc_sld finished [took 24.8736s] -05/11/23 00:35:40| INFO atc_mc finished [took 27.0318s] -05/11/23 00:35:40| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 27.8108s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:39:57| INFO dataset imdb_1prevs -05/11/23 00:40:07| INFO Dataset sample 0.50 of dataset imdb_1prevs started -05/11/23 00:40:26| INFO ref finished [took 17.4863s] -05/11/23 00:40:31| INFO mulmc_sld finished [took 22.5384s] -05/11/23 00:40:33| INFO atc_mc finished [took 24.2747s] -05/11/23 00:40:33| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 25.6430s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:41:36| INFO dataset imdb_2prevs -05/11/23 00:41:46| INFO Dataset sample 0.20 of dataset imdb_2prevs started -05/11/23 00:42:05| INFO ref finished [took 17.6637s] -05/11/23 00:42:10| INFO mulmc_sld finished [took 22.6956s] -05/11/23 00:42:11| INFO atc_mc finished [took 24.3529s] -05/11/23 00:42:11| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 25.0708s] -05/11/23 00:42:11| INFO Dataset sample 0.80 of dataset imdb_2prevs started -05/11/23 00:42:29| INFO ref finished [took 17.2818s] -05/11/23 00:42:34| INFO mulmc_sld finished [took 22.4054s] -05/11/23 00:42:36| INFO atc_mc finished [took 23.9392s] -05/11/23 00:42:36| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.7193s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:45:54| INFO dataset imdb_2prevs -05/11/23 00:46:04| INFO Dataset sample 0.20 of dataset imdb_2prevs started -05/11/23 00:46:22| INFO ref finished [took 17.2217s] -05/11/23 00:46:27| INFO mulmc_sld finished [took 22.2712s] -05/11/23 00:46:28| INFO atc_mc finished [took 23.7770s] -05/11/23 00:46:28| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 24.5092s] -05/11/23 00:46:28| INFO Dataset sample 0.80 of dataset imdb_2prevs started -05/11/23 00:46:46| INFO ref finished [took 17.1303s] -05/11/23 00:46:51| INFO mulmc_sld finished [took 22.5084s] -05/11/23 00:46:53| INFO atc_mc finished [took 23.9160s] -05/11/23 00:46:53| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.6992s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:51:06| INFO dataset imdb_2prevs -05/11/23 00:51:16| INFO Dataset sample 0.20 of dataset imdb_2prevs started -05/11/23 00:51:33| INFO ref finished [took 17.0670s] -05/11/23 00:51:38| INFO mulmc_sld finished [took 22.3141s] -05/11/23 00:51:40| INFO atc_mc finished [took 23.7219s] -05/11/23 00:51:40| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 24.4385s] -05/11/23 00:51:40| INFO Dataset sample 0.80 of dataset imdb_2prevs started -05/11/23 00:51:58| INFO ref finished [took 17.1894s] -05/11/23 00:52:03| INFO mulmc_sld finished [took 22.4247s] -05/11/23 00:52:04| INFO atc_mc finished [took 23.6032s] -05/11/23 00:52:04| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.3674s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:53:32| INFO dataset imdb_3prevs -05/11/23 00:53:39| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 00:53:48| INFO ref finished [took 8.8062s] -05/11/23 00:53:51| INFO atc_mc finished [took 11.7173s] -05/11/23 00:53:53| INFO mulmc_sld finished [took 13.8761s] -05/11/23 00:53:53| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.3147s] -05/11/23 00:53:53| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 00:54:03| INFO ref finished [took 8.9071s] -05/11/23 00:54:06| INFO atc_mc finished [took 11.7005s] -05/11/23 00:54:08| INFO mulmc_sld finished [took 13.6266s] -05/11/23 00:54:08| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.1625s] -05/11/23 00:54:08| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 00:54:17| INFO ref finished [took 8.7680s] -05/11/23 00:54:20| INFO atc_mc finished [took 11.4957s] -05/11/23 00:54:22| INFO mulmc_sld finished [took 13.5719s] -05/11/23 00:54:22| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.1564s] ----------------------------------------------------------------------------------------------------- -05/11/23 00:57:53| INFO dataset imdb_3prevs -05/11/23 00:57:59| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 00:58:08| INFO ref finished [took 8.7497s] -05/11/23 00:58:12| INFO atc_mc finished [took 11.6903s] -05/11/23 00:58:13| INFO mulmc_sld finished [took 13.6731s] -05/11/23 00:58:13| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.1073s] -05/11/23 00:58:13| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 00:58:23| INFO ref finished [took 8.7718s] -05/11/23 00:58:26| INFO atc_mc finished [took 11.7653s] -05/11/23 00:58:28| INFO mulmc_sld finished [took 13.9184s] -05/11/23 00:58:28| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.4270s] -05/11/23 00:58:28| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 00:58:37| INFO ref finished [took 8.8129s] -05/11/23 00:58:40| INFO atc_mc finished [took 11.7267s] -05/11/23 00:58:42| INFO mulmc_sld finished [took 13.6726s] -05/11/23 00:58:42| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2387s] ----------------------------------------------------------------------------------------------------- -05/11/23 01:04:04| INFO dataset imdb_3prevs -05/11/23 01:04:10| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 01:04:20| INFO ref finished [took 8.7879s] -05/11/23 01:04:23| INFO atc_mc finished [took 11.8757s] -05/11/23 01:04:25| INFO mulmc_sld finished [took 13.8698s] -05/11/23 01:04:25| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.2927s] -05/11/23 01:04:25| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 01:04:34| INFO ref finished [took 8.9200s] -05/11/23 01:04:37| INFO atc_mc finished [took 11.9555s] -05/11/23 01:04:39| INFO mulmc_sld finished [took 13.9860s] -05/11/23 01:04:39| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.5339s] -05/11/23 01:04:39| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 01:04:49| INFO ref finished [took 8.8757s] -05/11/23 01:04:52| INFO atc_mc finished [took 11.8222s] -05/11/23 01:04:53| INFO mulmc_sld finished [took 13.7034s] -05/11/23 01:04:53| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2710s] ----------------------------------------------------------------------------------------------------- -05/11/23 01:08:05| INFO dataset rcv1_CCAT_9prevs -05/11/23 01:08:09| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 01:08:55| INFO ref finished [took 40.9427s] -05/11/23 01:09:00| INFO atc_mc finished [took 44.2152s] -05/11/23 01:09:01| INFO mulmc_sld finished [took 49.6089s] -05/11/23 01:11:38| INFO bin_sld finished [took 207.5917s] -05/11/23 01:13:47| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00663) [took 333.7044s] -05/11/23 01:14:30| INFO mul_sld_gs finished [took 376.3503s] -05/11/23 01:20:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00619) [took 751.2730s] -05/11/23 01:23:45| INFO bin_sld_gs finished [took 932.2941s] -05/11/23 01:23:45| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 935.3228s] -05/11/23 01:23:45| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 01:24:30| INFO ref finished [took 39.9821s] -05/11/23 01:24:34| INFO atc_mc finished [took 43.3585s] -05/11/23 01:24:36| INFO mulmc_sld finished [took 48.6404s] -05/11/23 01:27:08| INFO bin_sld finished [took 202.3970s] -05/11/23 01:29:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00699) [took 328.6883s] -05/11/23 01:30:00| INFO mul_sld_gs finished [took 371.2150s] -05/11/23 01:36:40| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00780) [took 771.8150s] -05/11/23 01:39:44| INFO bin_sld_gs finished [took 956.5831s] -05/11/23 01:39:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 959.5214s] -05/11/23 01:39:44| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 01:40:38| INFO ref finished [took 46.9727s] -05/11/23 01:40:42| INFO atc_mc finished [took 49.6456s] -05/11/23 01:40:43| INFO mulmc_sld finished [took 55.1784s] -05/11/23 01:43:16| INFO bin_sld finished [took 209.5653s] -05/11/23 01:45:30| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00720) [took 340.0613s] -05/11/23 01:46:09| INFO mul_sld_gs finished [took 379.3695s] -05/11/23 01:53:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00810) [took 813.3118s] -05/11/23 01:56:25| INFO bin_sld_gs finished [took 996.4380s] -05/11/23 01:56:25| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1000.8297s] -05/11/23 01:56:25| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 01:57:11| INFO ref finished [took 40.2515s] -05/11/23 01:57:15| INFO atc_mc finished [took 43.5348s] -05/11/23 01:57:15| INFO mulmc_sld finished [took 48.1622s] -05/11/23 01:59:46| INFO bin_sld finished [took 200.0955s] -05/11/23 02:02:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00644) [took 331.2368s] -05/11/23 02:02:40| INFO mul_sld_gs finished [took 370.8879s] -05/11/23 02:10:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.01269) [took 813.7272s] -05/11/23 02:13:04| INFO bin_sld_gs finished [took 995.6098s] -05/11/23 02:13:04| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 998.5647s] -05/11/23 02:13:04| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 02:13:50| INFO ref finished [took 41.4181s] -05/11/23 02:13:55| INFO atc_mc finished [took 44.8414s] -05/11/23 02:13:55| INFO mulmc_sld finished [took 49.5767s] -05/11/23 02:16:27| INFO bin_sld finished [took 201.8696s] -05/11/23 02:18:33| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00636) [took 325.5401s] -05/11/23 02:19:17| INFO mul_sld_gs finished [took 368.9682s] -05/11/23 02:26:26| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00670) [took 799.7618s] -05/11/23 02:29:29| INFO bin_sld_gs finished [took 982.2921s] -05/11/23 02:29:29| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 985.2925s] -05/11/23 02:29:29| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 02:30:14| INFO ref finished [took 40.3341s] -05/11/23 02:30:18| INFO atc_mc finished [took 43.3032s] -05/11/23 02:30:19| INFO mulmc_sld finished [took 47.8507s] -05/11/23 02:32:51| INFO bin_sld finished [took 200.9647s] -05/11/23 02:34:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00596) [took 321.5172s] -05/11/23 02:35:33| INFO mul_sld_gs finished [took 360.5222s] -05/11/23 02:43:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00544) [took 829.7314s] -05/11/23 02:46:23| INFO bin_sld_gs finished [took 1011.3917s] -05/11/23 02:46:23| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1014.2514s] -05/11/23 02:46:23| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 02:47:09| INFO ref finished [took 40.4272s] -05/11/23 02:47:13| INFO atc_mc finished [took 43.8966s] -05/11/23 02:47:14| INFO mulmc_sld finished [took 48.4437s] -05/11/23 02:49:47| INFO bin_sld finished [took 202.6013s] -05/11/23 02:51:57| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00653) [took 329.4236s] -05/11/23 02:52:36| INFO mul_sld_gs finished [took 368.7426s] -05/11/23 02:59:51| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01370) [took 804.3215s] -05/11/23 03:02:54| INFO bin_sld_gs finished [took 987.5377s] -05/11/23 03:02:54| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 990.4607s] -05/11/23 03:02:54| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 03:03:40| INFO ref finished [took 41.5104s] -05/11/23 03:03:44| INFO atc_mc finished [took 44.1770s] -05/11/23 03:03:46| INFO mulmc_sld finished [took 49.7176s] -05/11/23 03:06:27| INFO bin_sld finished [took 211.4985s] -05/11/23 03:08:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00822) [took 334.7029s] -05/11/23 03:09:16| INFO mul_sld_gs finished [took 377.8219s] -05/11/23 03:16:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00984) [took 805.4146s] -05/11/23 03:19:28| INFO bin_sld_gs finished [took 991.0520s] -05/11/23 03:19:28| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 994.1016s] -05/11/23 03:19:28| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 03:20:18| INFO ref finished [took 44.1663s] -05/11/23 03:20:22| INFO atc_mc finished [took 47.3231s] -05/11/23 03:20:23| INFO mulmc_sld finished [took 53.1243s] -05/11/23 03:23:10| INFO bin_sld finished [took 220.4921s] -05/11/23 03:25:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00811) [took 338.9143s] -05/11/23 03:25:56| INFO mul_sld_gs finished [took 383.9350s] -05/11/23 03:32:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': None} (score=0.00954) [took 792.6190s] -05/11/23 03:35:44| INFO bin_sld_gs finished [took 973.2397s] -05/11/23 03:35:44| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 976.4502s] -05/11/23 03:35:57| INFO dataset imbd_9prevs -05/11/23 03:35:57| ERROR Evaluation over imbd_9prevs failed. Exception: 'imbd' ----------------------------------------------------------------------------------------------------- -05/11/23 09:42:24| INFO dataset imdb_9prevs -05/11/23 09:42:30| INFO Dataset sample 0.10 of dataset imdb_9prevs started -05/11/23 09:42:44| INFO ref finished [took 10.6450s] -05/11/23 09:42:47| INFO atc_mc finished [took 13.9369s] -05/11/23 09:42:49| INFO mulmc_sld finished [took 16.6526s] -05/11/23 09:42:56| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -05/11/23 09:45:26| INFO bin_sld finished [took 173.8798s] -05/11/23 09:47:18| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.03792) [took 285.1339s] -05/11/23 09:47:33| INFO mul_sld_gs finished [took 300.3243s] -05/11/23 09:47:33| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 302.7180s] -05/11/23 09:47:33| INFO Dataset sample 0.20 of dataset imdb_9prevs started -05/11/23 09:47:46| INFO ref finished [took 12.0501s] -05/11/23 09:47:50| INFO atc_mc finished [took 15.0907s] -05/11/23 09:47:52| INFO mulmc_sld finished [took 17.8502s] -05/11/23 09:50:42| INFO bin_sld finished [took 188.2533s] -05/11/23 09:53:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.01067) [took 328.6088s] -05/11/23 09:53:19| INFO mul_sld_gs finished [took 345.1926s] -05/11/23 10:00:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00923) [took 798.1316s] -05/11/23 10:03:34| INFO bin_sld_gs finished [took 960.1696s] -05/11/23 10:03:34| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 960.9823s] -05/11/23 10:03:34| INFO Dataset sample 0.30 of dataset imdb_9prevs started -05/11/23 10:03:46| INFO ref finished [took 10.8585s] -05/11/23 10:03:49| INFO atc_mc finished [took 13.6836s] -05/11/23 10:03:51| INFO mulmc_sld finished [took 15.8085s] -05/11/23 10:06:39| INFO bin_sld finished [took 183.7435s] -05/11/23 10:09:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00707) [took 326.5308s] -05/11/23 10:09:15| INFO mul_sld_gs finished [took 339.3412s] -05/11/23 10:16:51| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01094) [took 796.0895s] -05/11/23 10:19:27| INFO bin_sld_gs finished [took 952.0793s] -05/11/23 10:19:27| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 953.0580s] -05/11/23 10:19:27| INFO Dataset sample 0.40 of dataset imdb_9prevs started -05/11/23 10:19:39| INFO ref finished [took 10.8707s] -05/11/23 10:19:42| INFO atc_mc finished [took 13.9089s] -05/11/23 10:19:44| INFO mulmc_sld finished [took 15.9994s] -05/11/23 10:22:17| INFO bin_sld finished [took 168.9998s] -05/11/23 10:24:35| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00999) [took 306.5297s] -05/11/23 10:24:47| INFO mul_sld_gs finished [took 318.6584s] -05/11/23 10:32:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.01176) [took 782.7189s] -05/11/23 10:35:07| INFO bin_sld_gs finished [took 939.3830s] -05/11/23 10:35:07| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 940.2365s] -05/11/23 10:35:07| INFO Dataset sample 0.50 of dataset imdb_9prevs started -05/11/23 10:35:19| INFO ref finished [took 10.1160s] -05/11/23 10:35:22| INFO atc_mc finished [took 13.6292s] -05/11/23 10:35:24| INFO mulmc_sld finished [took 15.7949s] -05/11/23 10:38:04| INFO bin_sld finished [took 176.0746s] -05/11/23 10:40:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01008) [took 319.8045s] -05/11/23 10:40:45| INFO mul_sld_gs finished [took 336.8792s] -05/11/23 10:48:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00942) [took 802.9266s] -05/11/23 10:51:09| INFO bin_sld_gs finished [took 961.0002s] -05/11/23 10:51:09| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 961.8311s] -05/11/23 10:51:09| INFO Dataset sample 0.60 of dataset imdb_9prevs started -05/11/23 10:51:21| INFO ref finished [took 10.7059s] -05/11/23 10:51:24| INFO atc_mc finished [took 13.8934s] -05/11/23 10:51:26| INFO mulmc_sld finished [took 16.0295s] -05/11/23 10:54:09| INFO bin_sld finished [took 179.1806s] -05/11/23 10:56:31| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00906) [took 320.5350s] -05/11/23 10:56:45| INFO mul_sld_gs finished [took 334.5485s] -05/11/23 11:04:55| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01228) [took 824.5702s] -05/11/23 11:07:33| INFO bin_sld_gs finished [took 983.3806s] -05/11/23 11:07:33| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 984.1981s] -05/11/23 11:07:34| INFO Dataset sample 0.70 of dataset imdb_9prevs started -05/11/23 11:07:45| INFO ref finished [took 10.7034s] -05/11/23 11:07:49| INFO atc_mc finished [took 14.0668s] -05/11/23 11:07:51| INFO mulmc_sld finished [took 16.2499s] ----------------------------------------------------------------------------------------------------- -05/11/23 11:09:19| INFO dataset rcv1_CCAT_9prevs -05/11/23 11:09:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started ----------------------------------------------------------------------------------------------------- -05/11/23 11:10:40| INFO dataset rcv1_CCAT_9prevs -05/11/23 11:10:44| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 11:11:21| INFO ref finished [took 34.7944s] -05/11/23 11:11:25| INFO atc_mc finished [took 37.6168s] -05/11/23 11:11:36| INFO mulmc_sld finished [took 51.0883s] -05/11/23 11:11:36| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 52.3442s] -05/11/23 11:11:36| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 11:12:14| INFO ref finished [took 35.0033s] -05/11/23 11:12:17| INFO atc_mc finished [took 37.7761s] -05/11/23 11:12:24| INFO mulmc_sld finished [took 46.2195s] -05/11/23 11:12:24| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 47.5446s] -05/11/23 11:12:24| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 11:13:01| INFO ref finished [took 35.1077s] -05/11/23 11:13:05| INFO atc_mc finished [took 37.7889s] -05/11/23 11:13:12| INFO mulmc_sld finished [took 46.6515s] -05/11/23 11:13:12| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 48.0359s] -05/11/23 11:13:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 11:13:49| INFO ref finished [took 35.0214s] -05/11/23 11:13:53| INFO atc_mc finished [took 37.9480s] -05/11/23 11:14:00| INFO mulmc_sld finished [took 46.4140s] -05/11/23 11:14:00| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 47.5164s] -05/11/23 11:14:00| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 11:14:37| INFO ref finished [took 35.2699s] -05/11/23 11:14:41| INFO atc_mc finished [took 37.9490s] -05/11/23 11:14:49| INFO mulmc_sld finished [took 47.7005s] -05/11/23 11:14:49| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 49.0189s] -05/11/23 11:14:49| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 11:15:26| INFO ref finished [took 35.2350s] -05/11/23 11:15:30| INFO atc_mc finished [took 38.6364s] -05/11/23 11:15:39| INFO mulmc_sld finished [took 48.8860s] -05/11/23 11:15:39| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 50.1097s] -05/11/23 11:15:39| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 11:16:16| INFO ref finished [took 35.0322s] -05/11/23 11:16:20| INFO atc_mc finished [took 38.4809s] -05/11/23 11:16:29| INFO mulmc_sld finished [took 48.6466s] -05/11/23 11:16:29| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 50.0372s] -05/11/23 11:16:29| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 11:17:06| INFO ref finished [took 35.2988s] -05/11/23 11:17:10| INFO atc_mc finished [took 38.3390s] -05/11/23 11:17:18| INFO mulmc_sld finished [took 47.8829s] -05/11/23 11:17:18| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 49.2700s] -05/11/23 11:17:18| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 11:17:56| INFO ref finished [took 35.2614s] -05/11/23 11:17:59| INFO atc_mc finished [took 38.1131s] -05/11/23 11:18:08| INFO mulmc_sld finished [took 49.0925s] -05/11/23 11:18:09| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 50.4765s] ----------------------------------------------------------------------------------------------------- -05/11/23 11:26:35| INFO dataset rcv1_CCAT_9prevs -05/11/23 11:26:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 11:27:17| INFO ref finished [took 35.3305s] -05/11/23 11:27:21| INFO atc_mc finished [took 37.9469s] -05/11/23 11:27:28| INFO mulmc_sld finished [took 46.9769s] -05/11/23 11:27:28| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 48.3022s] -05/11/23 11:27:28| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 11:28:05| INFO ref finished [took 35.2459s] -05/11/23 11:28:09| INFO atc_mc finished [took 38.1660s] -05/11/23 11:28:15| INFO mulmc_sld finished [took 46.3832s] -05/11/23 11:28:15| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 47.7328s] -05/11/23 11:28:15| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 11:28:53| INFO ref finished [took 35.4919s] -05/11/23 11:28:57| INFO atc_mc finished [took 38.1023s] -05/11/23 11:29:03| INFO mulmc_sld finished [took 46.4657s] -05/11/23 11:29:03| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 47.8578s] -05/11/23 11:29:03| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 11:29:41| INFO ref finished [took 35.3209s] -05/11/23 11:29:45| INFO atc_mc finished [took 38.3693s] -05/11/23 11:29:51| INFO mulmc_sld finished [took 46.5707s] -05/11/23 11:29:51| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 47.7036s] -05/11/23 11:29:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 11:30:28| INFO ref finished [took 35.0276s] -05/11/23 11:30:32| INFO atc_mc finished [took 38.1508s] -05/11/23 11:30:40| INFO mulmc_sld finished [took 47.6215s] -05/11/23 11:30:40| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 48.9244s] -05/11/23 11:30:40| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 11:31:17| INFO ref finished [took 35.3308s] -05/11/23 11:31:21| INFO atc_mc finished [took 38.0629s] -05/11/23 11:31:29| INFO mulmc_sld finished [took 47.8783s] -05/11/23 11:31:29| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 49.1655s] -05/11/23 11:31:29| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 11:32:07| INFO ref finished [took 35.1485s] -05/11/23 11:32:10| INFO atc_mc finished [took 38.1974s] -05/11/23 11:32:18| INFO mulmc_sld finished [took 47.6056s] -05/11/23 11:32:18| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 48.9545s] -05/11/23 11:32:18| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 11:32:56| INFO ref finished [took 35.1879s] -05/11/23 11:32:59| INFO atc_mc finished [took 38.1684s] -05/11/23 11:33:07| INFO mulmc_sld finished [took 47.5635s] -05/11/23 11:33:07| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 48.9364s] -05/11/23 11:33:07| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 11:33:45| INFO ref finished [took 35.5855s] -05/11/23 11:33:48| INFO atc_mc finished [took 38.1206s] -05/11/23 11:33:54| INFO mulmc_sld finished [took 45.1957s] -05/11/23 11:33:54| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 46.5129s] ----------------------------------------------------------------------------------------------------- -05/11/23 12:02:53| INFO dataset rcv1_CCAT_9prevs -05/11/23 12:02:58| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 12:03:01| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 -05/11/23 12:03:37| INFO ref finished [took 34.9513s] -05/11/23 12:03:41| INFO atc_mc finished [took 37.7710s] -05/11/23 12:03:47| INFO mulmc_sld finished [took 47.2144s] -05/11/23 12:03:56| INFO mul_sld finished [took 56.8347s] -05/11/23 12:03:56| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 58.1052s] -05/11/23 12:03:56| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 12:03:59| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 -05/11/23 12:04:36| INFO ref finished [took 35.5921s] -05/11/23 12:04:40| INFO atc_mc finished [took 38.4587s] -05/11/23 12:04:46| INFO mulmc_sld finished [took 47.5756s] -05/11/23 12:04:47| INFO mul_sld finished [took 50.0690s] -05/11/23 12:04:47| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 51.3609s] -05/11/23 12:04:47| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 12:04:50| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 -05/11/23 12:05:28| INFO ref finished [took 36.0399s] -05/11/23 12:05:31| INFO atc_mc finished [took 38.9414s] -05/11/23 12:05:38| INFO mulmc_sld finished [took 48.4594s] -05/11/23 12:05:38| INFO mul_sld finished [took 49.4355s] -05/11/23 12:05:38| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 50.7799s] -05/11/23 12:05:38| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 12:05:41| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 ----------------------------------------------------------------------------------------------------- -05/11/23 12:06:13| INFO dataset rcv1_CCAT_9prevs -05/11/23 12:06:18| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 12:07:03| INFO ref finished [took 41.7793s] -05/11/23 12:07:10| INFO atc_mc finished [took 48.0537s] -05/11/23 12:07:38| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 ----------------------------------------------------------------------------------------------------- -05/11/23 12:08:00| INFO dataset rcv1_CCAT_9prevs -05/11/23 12:08:04| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 12:08:47| INFO ref finished [took 37.5352s] -05/11/23 12:08:50| INFO atc_mc finished [took 40.2843s] -05/11/23 12:08:55| INFO mulne_sld finished [took 47.4558s] -05/11/23 12:08:56| INFO mulmc_sld finished [took 49.8247s] -05/11/23 12:09:05| INFO mul_sld finished [took 59.5033s] -05/11/23 12:09:05| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 60.7605s] -05/11/23 12:09:05| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 12:09:47| INFO ref finished [took 37.4891s] -05/11/23 12:09:52| INFO atc_mc finished [took 40.9763s] -05/11/23 12:09:58| INFO mulmc_sld finished [took 50.3687s] -05/11/23 12:09:59| INFO mulne_sld finished [took 50.8494s] -05/11/23 12:10:00| INFO mul_sld finished [took 53.7955s] -05/11/23 12:10:00| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 55.1095s] -05/11/23 12:10:00| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 12:10:44| INFO ref finished [took 39.3665s] -05/11/23 12:10:49| INFO atc_mc finished [took 43.1884s] -05/11/23 12:10:55| INFO mul_sld finished [took 53.2533s] -05/11/23 12:10:55| INFO mulmc_sld finished [took 52.6179s] -05/11/23 12:10:56| INFO mulne_sld finished [took 52.7117s] -05/11/23 12:10:56| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 56.0058s] -05/11/23 12:10:56| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 12:11:40| INFO ref finished [took 39.1357s] -05/11/23 12:11:44| INFO atc_mc finished [took 42.7168s] -05/11/23 12:11:50| INFO mul_sld finished [took 53.1250s] -05/11/23 12:11:51| INFO mulmc_sld finished [took 52.6875s] -05/11/23 12:11:51| INFO mulne_sld finished [took 51.9871s] -05/11/23 12:11:51| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 55.0715s] -05/11/23 12:11:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 12:12:34| INFO ref finished [took 38.0624s] -05/11/23 12:12:38| INFO atc_mc finished [took 40.9414s] -05/11/23 12:12:45| INFO mul_sld finished [took 52.0220s] -05/11/23 12:12:46| INFO mulmc_sld finished [took 52.0904s] -05/11/23 12:12:47| INFO mulne_sld finished [took 52.2011s] -05/11/23 12:12:47| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 55.6901s] -05/11/23 12:12:47| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 12:13:29| INFO ref finished [took 37.9734s] -05/11/23 12:13:34| INFO atc_mc finished [took 41.4316s] -05/11/23 12:13:41| INFO mulmc_sld finished [took 51.9276s] -05/11/23 12:13:42| INFO mul_sld finished [took 53.8232s] -05/11/23 12:13:43| INFO mulne_sld finished [took 52.4359s] -05/11/23 12:13:43| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 55.5737s] -05/11/23 12:13:43| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 12:14:25| INFO ref finished [took 38.1687s] -05/11/23 12:14:29| INFO atc_mc finished [took 40.6142s] -05/11/23 12:14:37| INFO mulmc_sld finished [took 52.4191s] -05/11/23 12:14:38| INFO mul_sld finished [took 53.7962s] -05/11/23 12:14:38| INFO mulne_sld finished [took 52.1544s] -05/11/23 12:14:38| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 55.4465s] -05/11/23 12:14:38| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 12:15:21| INFO ref finished [took 38.4494s] -05/11/23 12:15:25| INFO atc_mc finished [took 40.9944s] -05/11/23 12:15:32| INFO mulmc_sld finished [took 51.8551s] -05/11/23 12:15:33| INFO mul_sld finished [took 53.4409s] -05/11/23 12:15:33| INFO mulne_sld finished [took 51.7256s] -05/11/23 12:15:33| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 55.1132s] -05/11/23 12:15:33| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 12:16:16| INFO ref finished [took 38.2838s] -05/11/23 12:16:20| INFO atc_mc finished [took 41.3532s] -05/11/23 12:16:24| INFO mulmc_sld finished [took 49.1257s] -05/11/23 12:16:26| INFO mulne_sld finished [took 49.8205s] -05/11/23 12:16:34| INFO mul_sld finished [took 59.1323s] -05/11/23 12:16:34| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 60.4191s] ----------------------------------------------------------------------------------------------------- -05/11/23 12:23:45| INFO dataset rcv1_CCAT_9prevs -05/11/23 12:23:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 12:24:32| INFO ref finished [took 38.5638s] -05/11/23 12:24:36| INFO atc_mc finished [took 41.5043s] -05/11/23 12:27:13| INFO binmc_sld finished [took 201.7673s] -05/11/23 12:27:14| INFO bin_sld finished [took 203.3515s] -05/11/23 12:27:17| INFO binne_sld finished [took 204.1403s] -05/11/23 12:27:17| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 207.1212s] -05/11/23 12:27:17| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 12:27:59| INFO ref finished [took 37.8990s] -05/11/23 12:28:02| INFO atc_mc finished [took 40.1947s] -05/11/23 12:30:36| INFO bin_sld finished [took 197.7408s] -05/11/23 12:30:37| INFO binne_sld finished [took 197.0819s] -05/11/23 12:30:37| INFO binmc_sld finished [took 198.5545s] -05/11/23 12:30:37| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 200.7762s] -05/11/23 12:30:37| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 12:31:19| INFO ref finished [took 37.3749s] -05/11/23 12:31:23| INFO atc_mc finished [took 40.9120s] -05/11/23 12:33:54| INFO binmc_sld finished [took 194.5722s] -05/11/23 12:33:55| INFO bin_sld finished [took 195.8961s] -05/11/23 12:33:56| INFO binne_sld finished [took 194.9605s] -05/11/23 12:33:56| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 198.1296s] -05/11/23 12:33:56| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 12:34:37| INFO ref finished [took 37.5561s] -05/11/23 12:34:40| INFO atc_mc finished [took 40.3775s] -05/11/23 12:37:09| INFO bin_sld finished [took 192.3652s] -05/11/23 12:37:12| INFO binne_sld finished [took 193.4666s] -05/11/23 12:37:12| INFO binmc_sld finished [took 194.3499s] -05/11/23 12:37:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 196.3675s] -05/11/23 12:37:12| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 12:37:53| INFO ref finished [took 36.5043s] -05/11/23 12:37:57| INFO atc_mc finished [took 40.0038s] -05/11/23 12:40:26| INFO bin_sld finished [took 192.4518s] -05/11/23 12:40:26| INFO binne_sld finished [took 191.2727s] -05/11/23 12:40:26| INFO binmc_sld finished [took 192.3339s] -05/11/23 12:40:26| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 194.5333s] -05/11/23 12:40:27| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 12:41:07| INFO ref finished [took 36.6809s] -05/11/23 12:41:11| INFO atc_mc finished [took 39.9520s] -05/11/23 12:43:40| INFO bin_sld finished [took 192.1873s] -05/11/23 12:43:40| INFO binmc_sld finished [took 191.7820s] -05/11/23 12:43:41| INFO binne_sld finished [took 191.9164s] -05/11/23 12:43:41| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 194.8818s] -05/11/23 12:43:41| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 12:44:22| INFO ref finished [took 36.9564s] -05/11/23 12:44:26| INFO atc_mc finished [took 40.1293s] -05/11/23 12:46:55| INFO bin_sld finished [took 192.4960s] -05/11/23 12:46:56| INFO binmc_sld finished [took 192.8281s] -05/11/23 12:46:58| INFO binne_sld finished [took 193.1524s] -05/11/23 12:46:58| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 196.2697s] -05/11/23 12:46:58| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 12:47:39| INFO ref finished [took 37.2831s] -05/11/23 12:47:42| INFO atc_mc finished [took 39.7258s] -05/11/23 12:50:16| INFO binmc_sld finished [took 195.9783s] -05/11/23 12:50:16| INFO binne_sld finished [took 195.2592s] -05/11/23 12:50:16| INFO bin_sld finished [took 197.4676s] -05/11/23 12:50:16| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 198.8232s] -05/11/23 12:50:16| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 12:50:58| INFO ref finished [took 37.4054s] -05/11/23 12:51:02| INFO atc_mc finished [took 40.4573s] -05/11/23 12:53:36| INFO bin_sld finished [took 198.0953s] -05/11/23 12:53:36| INFO binmc_sld finished [took 197.8028s] -05/11/23 12:53:37| INFO binne_sld finished [took 197.3027s] -05/11/23 12:53:37| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 200.2560s] ----------------------------------------------------------------------------------------------------- -05/11/23 13:29:43| INFO dataset rcv1_CCAT_9prevs -05/11/23 13:29:47| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -05/11/23 13:30:43| INFO ref finished [took 47.3558s] -05/11/23 13:30:48| INFO atc_mc finished [took 50.8788s] -05/11/23 13:30:52| INFO mulne_sld finished [took 60.4851s] -05/11/23 13:30:53| INFO mulmc_sld finished [took 63.4717s] -05/11/23 13:33:31| INFO binmc_sld finished [took 222.0328s] -05/11/23 13:33:33| INFO binne_sld finished [took 223.0449s] -05/11/23 13:43:18| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': 'max_conf'} (score=0.00644) [took 803.9708s] -05/11/23 13:44:01| INFO mul_sld_gs finished [took 847.1261s] -05/11/23 13:49:04| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00589) [took 1151.2473s] -05/11/23 13:52:06| INFO bin_sld_gs finished [took 1333.4736s] -05/11/23 13:52:06| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 1338.9046s] -05/11/23 13:52:06| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -05/11/23 13:53:00| INFO ref finished [took 45.3095s] -05/11/23 13:53:04| INFO atc_mc finished [took 48.2659s] -05/11/23 13:53:08| INFO mulmc_sld finished [took 58.9237s] -05/11/23 13:53:11| INFO mulne_sld finished [took 59.5712s] -05/11/23 13:55:46| INFO binmc_sld finished [took 218.1315s] -05/11/23 13:55:51| INFO binne_sld finished [took 220.8543s] -05/11/23 14:05:34| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00699) [took 800.6256s] -05/11/23 14:06:16| INFO mul_sld_gs finished [took 842.1616s] -05/11/23 14:12:14| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00768) [took 1201.3712s] -05/11/23 14:15:15| INFO bin_sld_gs finished [took 1382.8113s] -05/11/23 14:15:15| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 1388.8622s] -05/11/23 14:15:15| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -05/11/23 14:16:11| INFO ref finished [took 46.8666s] -05/11/23 14:16:15| INFO atc_mc finished [took 49.6779s] -05/11/23 14:16:19| INFO mulmc_sld finished [took 61.0610s] -05/11/23 14:16:22| INFO mulne_sld finished [took 62.2089s] -05/11/23 14:19:02| INFO binmc_sld finished [took 225.5737s] -05/11/23 14:19:03| INFO binne_sld finished [took 223.9017s] -05/11/23 14:28:50| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00756) [took 806.7930s] -05/11/23 14:29:32| INFO mul_sld_gs finished [took 848.7630s] -05/11/23 14:36:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00781) [took 1240.9138s] -05/11/23 14:39:04| INFO bin_sld_gs finished [took 1422.5520s] -05/11/23 14:39:04| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1428.8824s] -05/11/23 14:39:04| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -05/11/23 14:39:58| INFO ref finished [took 45.7514s] -05/11/23 14:40:02| INFO atc_mc finished [took 48.3888s] -05/11/23 14:40:05| INFO mulmc_sld finished [took 59.0537s] -05/11/23 14:40:09| INFO mulne_sld finished [took 60.9189s] -05/11/23 14:42:42| INFO binne_sld finished [took 214.5464s] -05/11/23 14:42:44| INFO binmc_sld finished [took 218.8429s] -05/11/23 14:52:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00984) [took 792.5474s] -05/11/23 14:53:05| INFO mul_sld_gs finished [took 834.1824s] -05/11/23 14:59:56| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.01112) [took 1247.0092s] -05/11/23 15:02:57| INFO bin_sld_gs finished [took 1427.5051s] -05/11/23 15:02:57| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1432.9172s] -05/11/23 15:02:57| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -05/11/23 15:03:49| INFO ref finished [took 44.4148s] -05/11/23 15:03:54| INFO atc_mc finished [took 47.7566s] -05/11/23 15:04:00| INFO mulmc_sld finished [took 60.5480s] -05/11/23 15:04:03| INFO mulne_sld finished [took 61.2226s] -05/11/23 15:06:30| INFO binmc_sld finished [took 211.9647s] -05/11/23 15:06:32| INFO binne_sld finished [took 211.4312s] -05/11/23 15:16:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.00571) [took 776.6085s] -05/11/23 15:16:42| INFO mul_sld_gs finished [took 817.9358s] -05/11/23 15:23:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00653) [took 1221.6531s] -05/11/23 15:26:23| INFO bin_sld_gs finished [took 1400.9688s] -05/11/23 15:26:23| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 1406.4620s] -05/11/23 15:26:23| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -05/11/23 15:27:16| INFO ref finished [took 44.3988s] -05/11/23 15:27:21| INFO atc_mc finished [took 48.5589s] -05/11/23 15:27:27| INFO mulmc_sld finished [took 61.4269s] -05/11/23 15:27:29| INFO mulne_sld finished [took 61.8292s] -05/11/23 15:29:55| INFO binmc_sld finished [took 210.1585s] -05/11/23 15:29:59| INFO binne_sld finished [took 212.0930s] -05/11/23 15:39:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.00616) [took 771.6071s] -05/11/23 15:40:03| INFO mul_sld_gs finished [took 813.2905s] -05/11/23 15:47:04| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00544) [took 1234.9832s] -05/11/23 15:50:10| INFO bin_sld_gs finished [took 1421.7775s] -05/11/23 15:50:10| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1427.0062s] -05/11/23 15:50:10| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -05/11/23 15:51:11| INFO ref finished [took 49.7682s] -05/11/23 15:51:19| INFO atc_mc finished [took 54.2855s] -05/11/23 15:51:22| INFO mulmc_sld finished [took 68.7688s] -05/11/23 15:51:26| INFO mulne_sld finished [took 69.3711s] -05/11/23 15:54:07| INFO binmc_sld finished [took 234.7962s] -05/11/23 15:54:09| INFO binne_sld finished [took 234.6444s] -05/11/23 16:03:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': 'entropy'} (score=0.00765) [took 811.6704s] -05/11/23 16:04:34| INFO mul_sld_gs finished [took 854.8196s] -05/11/23 16:11:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.01234) [took 1252.4784s] -05/11/23 16:14:10| INFO bin_sld_gs finished [took 1431.7446s] -05/11/23 16:14:10| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 1439.1145s] -05/11/23 16:14:10| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -05/11/23 16:15:02| INFO ref finished [took 44.0970s] -05/11/23 16:15:07| INFO atc_mc finished [took 48.2871s] -05/11/23 16:15:13| INFO mulmc_sld finished [took 61.0461s] -05/11/23 16:15:15| INFO mulne_sld finished [took 60.6375s] -05/11/23 16:17:46| INFO binmc_sld finished [took 215.1734s] -05/11/23 16:17:49| INFO binne_sld finished [took 215.7846s] -05/11/23 16:27:15| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00822) [took 778.5688s] -05/11/23 16:27:56| INFO mul_sld_gs finished [took 819.2615s] -05/11/23 16:34:16| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'entropy'} (score=0.00894) [took 1200.6639s] -05/11/23 16:37:21| INFO bin_sld_gs finished [took 1385.9035s] -05/11/23 16:37:21| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 1391.5055s] -05/11/23 16:37:21| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -05/11/23 16:38:13| INFO ref finished [took 44.7046s] -05/11/23 16:38:18| INFO atc_mc finished [took 48.7802s] -05/11/23 16:38:21| INFO mulmc_sld finished [took 57.4163s] -05/11/23 16:38:24| INFO mulne_sld finished [took 58.9847s] -05/11/23 16:40:59| INFO binmc_sld finished [took 216.7311s] -05/11/23 16:41:01| INFO binne_sld finished [took 216.5312s] -05/11/23 16:50:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00808) [took 758.6896s] -05/11/23 16:50:46| INFO mul_sld_gs finished [took 798.8038s] -05/11/23 16:56:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'entropy'} (score=0.00604) [took 1154.7043s] -05/11/23 16:59:39| INFO bin_sld_gs finished [took 1332.5521s] -05/11/23 16:59:39| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 1337.7947s] ----------------------------------------------------------------------------------------------------- -05/11/23 20:08:46| ERROR estimate comparison failed. Exceprion: 'environ' object has no attribute 'OUT_PATH' ----------------------------------------------------------------------------------------------------- -05/11/23 20:09:08| ERROR estimate comparison failed. Exceprion: 'environ' object has no attribute 'OUT_PATH' ----------------------------------------------------------------------------------------------------- -05/11/23 20:09:27| INFO dataset imdb_3prevs -05/11/23 20:09:34| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 20:09:44| INFO ref finished [took 8.9550s] -05/11/23 20:09:47| INFO atc_mc finished [took 11.8923s] -05/11/23 20:09:56| INFO mulmc_sld finished [took 21.3196s] -05/11/23 20:09:56| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 21.7709s] -05/11/23 20:09:56| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 20:10:05| INFO ref finished [took 8.6116s] -05/11/23 20:10:08| INFO atc_mc finished [took 11.6880s] -05/11/23 20:10:16| INFO mulmc_sld finished [took 19.7793s] -05/11/23 20:10:16| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.3246s] -05/11/23 20:10:16| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 20:10:26| INFO ref finished [took 8.6654s] -05/11/23 20:10:29| INFO atc_mc finished [took 11.6975s] -05/11/23 20:10:35| INFO mulmc_sld finished [took 18.1478s] -05/11/23 20:10:35| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.7200s] ----------------------------------------------------------------------------------------------------- -05/11/23 20:11:42| INFO dataset imdb_3prevs -05/11/23 20:11:49| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 20:11:58| INFO ref finished [took 8.7146s] -05/11/23 20:12:02| INFO atc_mc finished [took 11.9672s] -05/11/23 20:12:10| INFO mulmc_sld finished [took 20.7824s] -05/11/23 20:12:10| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 21.2293s] -05/11/23 20:12:10| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 20:12:19| INFO ref finished [took 8.5867s] -05/11/23 20:12:23| INFO atc_mc finished [took 11.6542s] -05/11/23 20:12:30| INFO mulmc_sld finished [took 19.6709s] -05/11/23 20:12:30| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.1802s] -05/11/23 20:12:30| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 20:12:40| INFO ref finished [took 8.7231s] -05/11/23 20:12:43| INFO atc_mc finished [took 11.8244s] -05/11/23 20:12:49| INFO mulmc_sld finished [took 18.0420s] -05/11/23 20:12:49| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.6102s] ----------------------------------------------------------------------------------------------------- -05/11/23 20:14:32| INFO dataset imdb_3prevs -05/11/23 20:14:39| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 20:14:48| INFO ref finished [took 8.6247s] -05/11/23 20:14:51| INFO atc_mc finished [took 11.6363s] -05/11/23 20:15:00| INFO mulmc_sld finished [took 20.4634s] -05/11/23 20:15:00| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 20.9026s] -05/11/23 20:15:00| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 20:15:09| INFO ref finished [took 8.5219s] -05/11/23 20:15:12| INFO atc_mc finished [took 11.6739s] -05/11/23 20:15:20| INFO mulmc_sld finished [took 19.8454s] -05/11/23 20:15:20| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.3705s] -05/11/23 20:15:20| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 20:15:29| INFO ref finished [took 8.5948s] -05/11/23 20:15:32| INFO atc_mc finished [took 11.7465s] -05/11/23 20:15:39| INFO mulmc_sld finished [took 17.9276s] -05/11/23 20:15:39| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.4893s] ----------------------------------------------------------------------------------------------------- -05/11/23 20:16:10| INFO dataset imdb_3prevs -05/11/23 20:16:17| INFO Dataset sample 0.20 of dataset imdb_3prevs started -05/11/23 20:16:26| INFO ref finished [took 8.3736s] -05/11/23 20:16:29| INFO atc_mc finished [took 11.3995s] -05/11/23 20:16:38| INFO mulmc_sld finished [took 20.4916s] -05/11/23 20:16:38| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 20.9187s] -05/11/23 20:16:38| INFO Dataset sample 0.50 of dataset imdb_3prevs started -05/11/23 20:16:47| INFO ref finished [took 8.4368s] -05/11/23 20:16:50| INFO atc_mc finished [took 11.4889s] -05/11/23 20:16:58| INFO mulmc_sld finished [took 19.6803s] -05/11/23 20:16:58| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.2091s] -05/11/23 20:16:58| INFO Dataset sample 0.80 of dataset imdb_3prevs started -05/11/23 20:17:08| INFO ref finished [took 8.9281s] -05/11/23 20:17:11| INFO atc_mc finished [took 11.9333s] -05/11/23 20:17:17| INFO mulmc_sld finished [took 18.2367s] -05/11/23 20:17:17| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.8309s] ----------------------------------------------------------------------------------------------------- -06/11/23 01:34:48| INFO dataset imdb_3prevs -06/11/23 01:34:59| INFO Dataset sample 0.20 of dataset imdb_3prevs started -06/11/23 01:35:18| INFO ref finished [took 18.0987s] -06/11/23 01:35:24| INFO atc_mc finished [took 24.9118s] -06/11/23 01:35:31| INFO mulmc_sld finished [took 32.0631s] -06/11/23 01:35:31| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 32.6119s] -06/11/23 01:35:31| INFO Dataset sample 0.50 of dataset imdb_3prevs started -06/11/23 01:35:51| INFO ref finished [took 18.7770s] -06/11/23 01:35:58| INFO atc_mc finished [took 25.5592s] -06/11/23 01:36:04| INFO mulmc_sld finished [took 31.9103s] -06/11/23 01:36:04| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 32.5205s] -06/11/23 01:36:04| INFO Dataset sample 0.80 of dataset imdb_3prevs started -06/11/23 01:36:23| INFO ref finished [took 18.5730s] -06/11/23 01:36:31| INFO atc_mc finished [took 25.8019s] -06/11/23 01:36:33| INFO mulmc_sld finished [took 28.9526s] -06/11/23 01:36:33| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 29.5292s] ----------------------------------------------------------------------------------------------------- -06/11/23 02:06:40| INFO dataset imdb_3prevs -06/11/23 02:06:47| INFO Dataset sample 0.20 of dataset imdb_3prevs started -06/11/23 02:06:56| INFO ref finished [took 9.0989s] -06/11/23 02:06:59| INFO atc_mc finished [took 12.2513s] -06/11/23 03:01:43| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'quantifier__exact_train_prev': False, 'confidence': 'max_conf'} (score=0.00738) [took 3296.0714s] -06/11/23 03:01:56| INFO mul_sld_gs finished [took 3309.2417s] -06/11/23 03:01:56| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 3309.7038s] -06/11/23 03:01:56| INFO Dataset sample 0.50 of dataset imdb_3prevs started -06/11/23 03:02:06| INFO ref finished [took 8.5518s] -06/11/23 03:02:09| INFO atc_mc finished [took 11.4390s] -06/11/23 03:54:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00899) [took 3146.2364s] -06/11/23 03:54:37| INFO mul_sld_gs finished [took 3159.8209s] -06/11/23 03:54:37| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 3160.3546s] -06/11/23 03:54:37| INFO Dataset sample 0.80 of dataset imdb_3prevs started -06/11/23 03:54:46| INFO ref finished [took 8.2678s] -06/11/23 03:54:48| INFO atc_mc finished [took 11.0430s] -06/11/23 04:47:50| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': False, 'confidence': 'entropy'} (score=0.00770) [took 3193.1812s] -06/11/23 04:48:04| INFO mul_sld_gs finished [took 3206.9550s] -06/11/23 04:48:04| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 3207.5040s] ----------------------------------------------------------------------------------------------------- -06/11/23 05:14:48| INFO dataset rcv1_CCAT_9prevs -06/11/23 05:14:53| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -06/11/23 05:15:55| INFO ref finished [took 48.0242s] -06/11/23 05:16:01| INFO atc_mc finished [took 51.7851s] -06/11/23 05:16:04| INFO mul_pacc finished [took 58.4704s] -06/11/23 05:16:04| INFO mulne_sld finished [took 62.7354s] -06/11/23 05:16:04| INFO mulmc_sld finished [took 66.2593s] -06/11/23 05:16:14| INFO mul_sld finished [took 78.2483s] -06/11/23 05:18:40| INFO bin_pacc finished [took 217.0012s] -06/11/23 05:18:43| INFO bin_sld finished [took 227.8835s] -06/11/23 05:18:43| INFO binne_sld finished [took 223.2764s] -06/11/23 05:18:44| INFO binmc_sld finished [took 226.7324s] -06/11/23 05:18:44| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 230.5906s] -06/11/23 05:18:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -06/11/23 05:19:44| INFO ref finished [took 49.5147s] -06/11/23 05:19:51| INFO atc_mc finished [took 54.8022s] -06/11/23 05:19:53| INFO mul_pacc finished [took 60.3260s] -06/11/23 05:19:55| INFO mulmc_sld finished [took 67.0280s] -06/11/23 05:19:56| INFO mul_sld finished [took 70.4092s] -06/11/23 05:19:58| INFO mulne_sld finished [took 67.3468s] -06/11/23 05:22:30| INFO bin_sld finished [took 224.7344s] -06/11/23 05:22:30| INFO bin_pacc finished [took 218.3044s] -06/11/23 05:22:30| INFO binmc_sld finished [took 223.3607s] -06/11/23 05:22:33| INFO binne_sld finished [took 223.6042s] -06/11/23 05:22:33| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 229.0745s] -06/11/23 05:22:33| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -06/11/23 05:23:32| INFO ref finished [took 48.1565s] -06/11/23 05:23:37| INFO atc_mc finished [took 52.1124s] -06/11/23 05:23:40| INFO mul_pacc finished [took 58.0112s] -06/11/23 05:23:40| INFO mul_sld finished [took 65.2727s] -06/11/23 05:23:42| INFO mulmc_sld finished [took 64.5943s] -06/11/23 05:23:43| INFO mulne_sld finished [took 63.9053s] -06/11/23 05:26:13| INFO bin_sld finished [took 218.6511s] -06/11/23 05:26:16| INFO bin_pacc finished [took 215.1485s] -06/11/23 05:26:17| INFO binne_sld finished [took 218.6855s] -06/11/23 05:26:17| INFO binmc_sld finished [took 221.2605s] -06/11/23 05:26:17| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 224.5608s] -06/11/23 05:26:17| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -06/11/23 05:27:15| INFO ref finished [took 48.2181s] -06/11/23 05:27:21| INFO atc_mc finished [took 52.3420s] -06/11/23 05:27:23| INFO mul_pacc finished [took 57.1950s] -06/11/23 05:27:24| INFO mul_sld finished [took 64.4722s] -06/11/23 05:27:26| INFO mulmc_sld finished [took 64.1870s] -06/11/23 05:27:27| INFO mulne_sld finished [took 63.7407s] -06/11/23 05:29:52| INFO bin_sld finished [took 213.1913s] -06/11/23 05:29:53| INFO bin_pacc finished [took 208.1322s] -06/11/23 05:29:53| INFO binmc_sld finished [took 212.6473s] -06/11/23 05:29:57| INFO binne_sld finished [took 214.5243s] -06/11/23 05:29:57| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 219.5765s] -06/11/23 05:29:57| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -06/11/23 05:30:55| INFO ref finished [took 47.7289s] -06/11/23 05:31:01| INFO atc_mc finished [took 52.1531s] -06/11/23 05:31:03| INFO mul_pacc finished [took 57.3804s] -06/11/23 05:31:06| INFO mul_sld finished [took 66.9237s] -06/11/23 05:31:06| INFO mulmc_sld finished [took 65.3230s] -06/11/23 05:31:09| INFO mulne_sld finished [took 65.6645s] -06/11/23 05:33:33| INFO bin_sld finished [took 214.3242s] -06/11/23 05:33:34| INFO bin_pacc finished [took 209.3862s] -06/11/23 05:33:35| INFO binmc_sld finished [took 214.4687s] -06/11/23 05:33:37| INFO binne_sld finished [took 214.7267s] -06/11/23 05:33:37| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 220.0212s] -06/11/23 05:33:37| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -06/11/23 05:34:35| INFO ref finished [took 48.0021s] -06/11/23 05:34:41| INFO atc_mc finished [took 52.2171s] -06/11/23 05:34:43| INFO mul_pacc finished [took 57.2348s] -06/11/23 05:34:46| INFO mul_sld finished [took 67.0899s] -06/11/23 05:34:47| INFO mulmc_sld finished [took 66.1078s] -06/11/23 05:34:49| INFO mulne_sld finished [took 66.0237s] -06/11/23 05:37:13| INFO bin_sld finished [took 214.9942s] -06/11/23 05:37:13| INFO binmc_sld finished [took 213.1574s] -06/11/23 05:37:14| INFO bin_pacc finished [took 209.1347s] -06/11/23 05:37:17| INFO binne_sld finished [took 214.9703s] -06/11/23 05:37:17| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 220.1235s] -06/11/23 05:37:17| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -06/11/23 05:38:15| INFO ref finished [took 47.8227s] -06/11/23 05:38:20| INFO atc_mc finished [took 51.9364s] -06/11/23 05:38:23| INFO mul_pacc finished [took 56.9053s] -06/11/23 05:38:27| INFO mul_sld finished [took 67.4535s] -06/11/23 05:38:27| INFO mulmc_sld finished [took 65.5956s] -06/11/23 05:38:30| INFO mulne_sld finished [took 66.0476s] -06/11/23 05:40:55| INFO bin_pacc finished [took 210.0633s] -06/11/23 05:40:56| INFO binmc_sld finished [took 215.3452s] -06/11/23 05:40:56| INFO bin_sld finished [took 217.8091s] -06/11/23 05:40:59| INFO binne_sld finished [took 216.8970s] -06/11/23 05:40:59| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 222.2971s] -06/11/23 05:40:59| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -06/11/23 05:41:57| INFO ref finished [took 47.6970s] -06/11/23 05:42:03| INFO atc_mc finished [took 52.0893s] -06/11/23 05:42:05| INFO mul_pacc finished [took 56.6428s] -06/11/23 05:42:09| INFO mul_sld finished [took 66.8810s] -06/11/23 05:42:09| INFO mulmc_sld finished [took 65.8427s] -06/11/23 05:42:11| INFO mulne_sld finished [took 64.8594s] -06/11/23 05:44:36| INFO bin_pacc finished [took 208.7884s] -06/11/23 05:44:38| INFO bin_sld finished [took 216.6052s] -06/11/23 05:44:38| INFO binmc_sld finished [took 215.5486s] -06/11/23 05:44:43| INFO binne_sld finished [took 217.9926s] -06/11/23 05:44:43| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 223.2270s] -06/11/23 05:44:43| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -06/11/23 05:45:40| INFO ref finished [took 48.0710s] -06/11/23 05:45:46| INFO atc_mc finished [took 52.0992s] -06/11/23 05:45:48| INFO mul_pacc finished [took 56.6568s] -06/11/23 05:45:49| INFO mulmc_sld finished [took 61.7314s] -06/11/23 05:45:52| INFO mulne_sld finished [took 62.7505s] -06/11/23 05:45:59| INFO mul_sld finished [took 73.7681s] -06/11/23 05:48:18| INFO bin_pacc finished [took 208.2267s] -06/11/23 05:48:23| INFO bin_sld finished [took 218.9333s] -06/11/23 05:48:24| INFO binmc_sld finished [took 218.0032s] -06/11/23 05:48:27| INFO binne_sld finished [took 219.2450s] -06/11/23 05:48:27| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 224.3446s] -06/11/23 05:49:49| INFO dataset imdb_9prevs -06/11/23 05:49:57| INFO Dataset sample 0.10 of dataset imdb_9prevs started -06/11/23 05:50:12| INFO ref finished [took 13.3064s] -06/11/23 05:50:17| INFO atc_mc finished [took 17.3508s] -06/11/23 05:50:19| INFO mul_pacc finished [took 20.0802s] -06/11/23 05:50:22| INFO mulne_sld finished [took 23.6723s] -06/11/23 05:50:24| INFO mulmc_sld finished [took 25.5159s] -06/11/23 05:50:39| INFO mul_sld finished [took 40.7099s] -06/11/23 05:52:55| INFO bin_pacc finished [took 176.3728s] -06/11/23 05:53:05| INFO binmc_sld finished [took 186.8240s] -06/11/23 05:53:06| INFO binne_sld finished [took 187.6585s] -06/11/23 05:53:07| INFO bin_sld finished [took 189.1728s] -06/11/23 05:53:07| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 189.6034s] -06/11/23 05:53:07| INFO Dataset sample 0.20 of dataset imdb_9prevs started -06/11/23 05:53:22| INFO ref finished [took 13.2778s] -06/11/23 05:53:26| INFO atc_mc finished [took 17.4491s] -06/11/23 05:53:28| INFO mul_pacc finished [took 19.9359s] -06/11/23 05:53:40| INFO mulmc_sld finished [took 31.6686s] -06/11/23 05:53:44| INFO mulne_sld finished [took 35.2085s] -06/11/23 05:53:44| INFO mul_sld finished [took 36.2502s] -06/11/23 05:56:05| INFO bin_pacc finished [took 177.0225s] -06/11/23 05:56:13| INFO binmc_sld finished [took 185.4811s] -06/11/23 05:56:15| INFO bin_sld finished [took 187.1039s] -06/11/23 05:56:16| INFO binne_sld finished [took 187.3163s] -06/11/23 05:56:16| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 188.4781s] -06/11/23 05:56:16| INFO Dataset sample 0.30 of dataset imdb_9prevs started -06/11/23 05:56:31| INFO ref finished [took 13.4513s] -06/11/23 05:56:36| INFO atc_mc finished [took 18.1025s] -06/11/23 05:56:38| INFO mul_pacc finished [took 20.3997s] -06/11/23 05:56:45| INFO mulmc_sld finished [took 28.4298s] -06/11/23 05:56:46| INFO mulne_sld finished [took 28.8678s] -06/11/23 05:56:46| INFO mul_sld finished [took 29.5573s] -06/11/23 05:59:11| INFO bin_pacc finished [took 174.0262s] -06/11/23 05:59:17| INFO binmc_sld finished [took 180.1998s] -06/11/23 05:59:18| INFO binne_sld finished [took 181.2200s] -06/11/23 05:59:19| INFO bin_sld finished [took 182.1672s] -06/11/23 05:59:19| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 183.0515s] -06/11/23 05:59:19| INFO Dataset sample 0.40 of dataset imdb_9prevs started -06/11/23 05:59:34| INFO ref finished [took 13.5163s] -06/11/23 05:59:39| INFO atc_mc finished [took 17.9856s] -06/11/23 05:59:41| INFO mul_pacc finished [took 20.7441s] -06/11/23 05:59:49| INFO mulmc_sld finished [took 29.2747s] -06/11/23 05:59:50| INFO mulne_sld finished [took 29.6624s] -06/11/23 05:59:50| INFO mul_sld finished [took 30.3432s] -06/11/23 06:02:17| INFO bin_pacc finished [took 176.7354s] -06/11/23 06:02:19| INFO binmc_sld finished [took 179.9981s] -06/11/23 06:02:21| INFO bin_sld finished [took 181.6844s] -06/11/23 06:02:22| INFO binne_sld finished [took 182.0846s] -06/11/23 06:02:22| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 183.2033s] -06/11/23 06:02:22| INFO Dataset sample 0.50 of dataset imdb_9prevs started -06/11/23 06:02:37| INFO ref finished [took 13.4688s] -06/11/23 06:02:42| INFO atc_mc finished [took 18.0218s] -06/11/23 06:02:44| INFO mul_pacc finished [took 20.5800s] -06/11/23 06:02:52| INFO mulmc_sld finished [took 29.0192s] -06/11/23 06:02:52| INFO mul_sld finished [took 29.4403s] -06/11/23 06:02:52| INFO mulne_sld finished [took 29.1611s] -06/11/23 06:05:19| INFO bin_pacc finished [took 175.5125s] -06/11/23 06:05:23| INFO binmc_sld finished [took 180.0427s] -06/11/23 06:05:25| INFO binne_sld finished [took 182.5814s] -06/11/23 06:05:26| INFO bin_sld finished [took 183.2892s] -06/11/23 06:05:26| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 183.8611s] -06/11/23 06:05:26| INFO Dataset sample 0.60 of dataset imdb_9prevs started -06/11/23 06:05:41| INFO ref finished [took 13.4643s] -06/11/23 06:05:45| INFO atc_mc finished [took 17.9768s] -06/11/23 06:05:48| INFO mul_pacc finished [took 20.7525s] -06/11/23 06:05:55| INFO mulmc_sld finished [took 28.8234s] -06/11/23 06:05:55| INFO mulne_sld finished [took 28.6537s] -06/11/23 06:05:56| INFO mul_sld finished [took 29.6167s] -06/11/23 06:08:24| INFO bin_pacc finished [took 176.5335s] -06/11/23 06:08:27| INFO binmc_sld finished [took 180.4803s] -06/11/23 06:08:28| INFO bin_sld finished [took 181.6676s] -06/11/23 06:08:29| INFO binne_sld finished [took 182.0534s] -06/11/23 06:08:29| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 183.0240s] -06/11/23 06:08:29| INFO Dataset sample 0.70 of dataset imdb_9prevs started -06/11/23 06:08:44| INFO ref finished [took 13.7566s] -06/11/23 06:08:49| INFO atc_mc finished [took 17.9495s] -06/11/23 06:08:51| INFO mul_pacc finished [took 20.5859s] -06/11/23 06:08:57| INFO mulmc_sld finished [took 27.4370s] -06/11/23 06:08:58| INFO mul_sld finished [took 28.3224s] -06/11/23 06:08:58| INFO mulne_sld finished [took 28.1390s] -06/11/23 06:11:26| INFO bin_pacc finished [took 175.7412s] -06/11/23 06:11:31| INFO binmc_sld finished [took 181.4310s] -06/11/23 06:11:32| INFO binne_sld finished [took 182.0095s] -06/11/23 06:11:33| INFO bin_sld finished [took 183.6520s] -06/11/23 06:11:33| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 184.2005s] -06/11/23 06:11:33| INFO Dataset sample 0.80 of dataset imdb_9prevs started -06/11/23 06:11:48| INFO ref finished [took 13.5418s] -06/11/23 06:11:53| INFO atc_mc finished [took 17.8150s] -06/11/23 06:11:55| INFO mul_pacc finished [took 20.4761s] -06/11/23 06:12:01| INFO mulmc_sld finished [took 27.2741s] -06/11/23 06:12:02| INFO mulne_sld finished [took 27.2693s] -06/11/23 06:12:02| INFO mul_sld finished [took 28.3364s] -06/11/23 06:14:30| INFO bin_pacc finished [took 175.7637s] -06/11/23 06:14:37| INFO binmc_sld finished [took 183.2422s] -06/11/23 06:14:38| INFO bin_sld finished [took 184.1064s] -06/11/23 06:14:39| INFO binne_sld finished [took 184.9073s] -06/11/23 06:14:39| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 186.2580s] -06/11/23 06:14:39| INFO Dataset sample 0.90 of dataset imdb_9prevs started -06/11/23 06:14:41| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 06:14:41| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 06:14:52| INFO ref finished [took 11.6315s] -06/11/23 06:14:56| INFO atc_mc finished [took 15.3068s] -06/11/23 06:15:01| INFO mulne_sld finished [took 21.1133s] -06/11/23 06:15:02| INFO mulmc_sld finished [took 22.2375s] -06/11/23 06:15:08| INFO mul_sld finished [took 27.8149s] -06/11/23 06:17:32| INFO binne_sld finished [took 171.8722s] -06/11/23 06:17:32| INFO bin_sld finished [took 172.4710s] -06/11/23 06:17:33| INFO binmc_sld finished [took 172.8193s] -06/11/23 06:17:33| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 173.4411s] -06/11/23 06:18:54| INFO dataset rcv1_GCAT_9prevs -06/11/23 06:18:59| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs started -06/11/23 06:19:11| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 06:19:11| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 06:19:54| INFO ref finished [took 42.0769s] -06/11/23 06:19:59| INFO atc_mc finished [took 45.5011s] -06/11/23 06:20:14| INFO mulne_sld finished [took 66.0516s] -06/11/23 06:20:15| INFO mul_sld finished [took 73.1171s] -06/11/23 06:20:17| INFO mulmc_sld finished [took 72.1930s] -06/11/23 06:22:23| INFO bin_sld finished [took 203.0368s] -06/11/23 06:22:27| INFO binmc_sld finished [took 203.2975s] -06/11/23 06:22:29| INFO binne_sld finished [took 202.7501s] -06/11/23 06:22:29| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs finished [took 210.2201s] -06/11/23 06:22:29| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs started -06/11/23 06:23:26| INFO ref finished [took 46.6022s] -06/11/23 06:23:31| INFO atc_mc finished [took 50.3293s] -06/11/23 06:23:33| INFO mul_pacc finished [took 54.9265s] -06/11/23 06:23:46| INFO mul_sld finished [took 74.9035s] -06/11/23 06:23:52| INFO mulne_sld finished [took 76.2697s] -06/11/23 06:23:54| INFO mulmc_sld finished [took 80.8754s] -06/11/23 06:26:06| INFO bin_pacc finished [took 209.7751s] -06/11/23 06:26:08| INFO bin_sld finished [took 217.8889s] -06/11/23 06:26:13| INFO binmc_sld finished [took 220.7753s] -06/11/23 06:26:14| INFO binne_sld finished [took 219.7510s] -06/11/23 06:26:14| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs finished [took 224.9268s] -06/11/23 06:26:14| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs started -06/11/23 06:27:10| INFO ref finished [took 46.4938s] -06/11/23 06:27:16| INFO atc_mc finished [took 50.5904s] -06/11/23 06:27:18| INFO mul_pacc finished [took 55.4949s] -06/11/23 06:27:26| INFO mulmc_sld finished [took 67.7140s] -06/11/23 06:27:26| INFO mul_sld finished [took 70.0891s] -06/11/23 06:27:28| INFO mulne_sld finished [took 68.1806s] -06/11/23 06:29:50| INFO bin_pacc finished [took 208.6091s] -06/11/23 06:29:51| INFO binmc_sld finished [took 213.7985s] -06/11/23 06:29:51| INFO bin_sld finished [took 215.8158s] -06/11/23 06:29:55| INFO binne_sld finished [took 215.5523s] -06/11/23 06:29:55| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs finished [took 220.4589s] -06/11/23 06:29:55| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs started -06/11/23 06:30:51| INFO ref finished [took 46.3752s] -06/11/23 06:30:56| INFO atc_mc finished [took 50.7062s] -06/11/23 06:30:58| INFO mul_pacc finished [took 55.2260s] -06/11/23 06:31:01| INFO mul_sld finished [took 64.2359s] -06/11/23 06:31:02| INFO mulmc_sld finished [took 63.5099s] -06/11/23 06:31:04| INFO mulne_sld finished [took 62.9188s] -06/11/23 06:33:29| INFO bin_sld finished [took 213.2716s] -06/11/23 06:33:30| INFO bin_pacc finished [took 208.6574s] -06/11/23 06:33:31| INFO binmc_sld finished [took 213.1856s] -06/11/23 06:33:33| INFO binne_sld finished [took 213.2771s] -06/11/23 06:33:33| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs finished [took 218.1742s] -06/11/23 06:33:33| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs started -06/11/23 06:34:29| INFO ref finished [took 46.6793s] -06/11/23 06:34:34| INFO atc_mc finished [took 50.9915s] -06/11/23 06:34:37| INFO mul_pacc finished [took 55.9725s] -06/11/23 06:34:38| INFO mul_sld finished [took 63.1317s] -06/11/23 06:34:40| INFO mulmc_sld finished [took 62.7473s] -06/11/23 06:34:41| INFO mulne_sld finished [took 62.1303s] -06/11/23 06:37:08| INFO bin_pacc finished [took 207.7854s] -06/11/23 06:37:08| INFO bin_sld finished [took 213.7945s] -06/11/23 06:37:08| INFO binmc_sld finished [took 212.6207s] -06/11/23 06:37:12| INFO binne_sld finished [took 213.8742s] -06/11/23 06:37:12| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs finished [took 218.7265s] -06/11/23 06:37:12| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs started -06/11/23 06:38:08| INFO ref finished [took 46.6057s] -06/11/23 06:38:14| INFO atc_mc finished [took 51.1055s] -06/11/23 06:38:15| INFO mul_pacc finished [took 55.5338s] -06/11/23 06:38:17| INFO mul_sld finished [took 63.2113s] -06/11/23 06:38:18| INFO mulmc_sld finished [took 62.2265s] -06/11/23 06:38:20| INFO mulne_sld finished [took 61.9918s] -06/11/23 06:40:46| INFO bin_pacc finished [took 207.5094s] -06/11/23 06:40:46| INFO bin_sld finished [took 213.6350s] -06/11/23 06:40:47| INFO binmc_sld finished [took 212.8363s] -06/11/23 06:40:49| INFO binne_sld finished [took 212.2587s] -06/11/23 06:40:49| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs finished [took 217.1976s] -06/11/23 06:40:49| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs started -06/11/23 06:41:44| INFO ref finished [took 45.9582s] -06/11/23 06:41:50| INFO atc_mc finished [took 50.0401s] -06/11/23 06:41:54| INFO mul_sld finished [took 62.6045s] -06/11/23 06:41:54| INFO mulmc_sld finished [took 61.4168s] -06/11/23 06:41:56| INFO mulne_sld finished [took 61.3708s] -06/11/23 06:42:00| INFO mul_pacc finished [took 62.6486s] -06/11/23 06:44:23| INFO bin_sld finished [took 212.5992s] -06/11/23 06:44:23| INFO bin_pacc finished [took 207.5241s] -06/11/23 06:44:24| INFO binmc_sld finished [took 212.2794s] -06/11/23 06:44:27| INFO binne_sld finished [took 212.8325s] -06/11/23 06:44:27| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs finished [took 217.7909s] -06/11/23 06:44:27| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs started -06/11/23 06:45:23| INFO ref finished [took 46.6997s] -06/11/23 06:45:28| INFO atc_mc finished [took 50.6417s] -06/11/23 06:45:30| INFO mul_sld finished [took 61.5352s] -06/11/23 06:45:31| INFO mul_pacc finished [took 55.9055s] -06/11/23 06:45:31| INFO mulmc_sld finished [took 60.6608s] -06/11/23 06:45:33| INFO mulne_sld finished [took 60.1616s] -06/11/23 06:48:01| INFO bin_pacc finished [took 207.7543s] -06/11/23 06:48:02| INFO bin_sld finished [took 213.7056s] -06/11/23 06:48:03| INFO binmc_sld finished [took 213.7901s] -06/11/23 06:48:04| INFO binne_sld finished [took 212.4421s] -06/11/23 06:48:04| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs finished [took 217.4465s] -06/11/23 06:48:04| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs started -06/11/23 06:48:06| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 06:48:07| WARNING Method binmc_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 06:48:09| WARNING Method binne_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 06:48:11| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 06:48:13| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 06:48:49| INFO ref finished [took 36.4085s] -06/11/23 06:48:53| INFO atc_mc finished [took 39.1380s] -06/11/23 06:48:54| INFO mulmc_sld finished [took 46.0254s] -06/11/23 06:48:55| INFO mulne_sld finished [took 45.1935s] -06/11/23 06:49:00| INFO mul_sld finished [took 53.9145s] -06/11/23 06:49:00| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs finished [took 55.9159s] -06/11/23 06:50:22| INFO dataset rcv1_MCAT_9prevs -06/11/23 06:50:27| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs started -06/11/23 06:51:29| INFO ref finished [took 47.8430s] -06/11/23 06:51:34| INFO atc_mc finished [took 51.2418s] -06/11/23 06:51:36| INFO mul_pacc finished [took 56.8770s] -06/11/23 06:52:00| INFO mulne_sld finished [took 85.8579s] -06/11/23 06:52:00| INFO mul_sld finished [took 90.6401s] -06/11/23 06:52:12| INFO mulmc_sld finished [took 100.3728s] -06/11/23 06:54:15| INFO bin_pacc finished [took 217.8843s] -06/11/23 06:54:15| INFO bin_sld finished [took 226.6925s] -06/11/23 06:54:17| INFO binne_sld finished [took 224.5785s] -06/11/23 06:54:17| INFO binmc_sld finished [took 226.9490s] -06/11/23 06:54:17| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs finished [took 229.9256s] -06/11/23 06:54:17| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs started -06/11/23 06:55:14| INFO ref finished [took 46.7323s] -06/11/23 06:55:20| INFO atc_mc finished [took 51.0126s] -06/11/23 06:55:22| INFO mul_pacc finished [took 55.8357s] -06/11/23 06:55:23| INFO mulmc_sld finished [took 62.0464s] -06/11/23 06:55:24| INFO mul_sld finished [took 64.8106s] -06/11/23 06:55:25| INFO mulne_sld finished [took 61.6750s] -06/11/23 06:57:56| INFO bin_pacc finished [took 210.8901s] -06/11/23 06:57:56| INFO bin_sld finished [took 217.3461s] -06/11/23 06:57:57| INFO binmc_sld finished [took 216.6599s] -06/11/23 06:58:00| INFO binne_sld finished [took 216.9668s] -06/11/23 06:58:00| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs finished [took 222.3450s] -06/11/23 06:58:00| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs started -06/11/23 06:58:57| INFO ref finished [took 47.5989s] -06/11/23 06:59:02| INFO atc_mc finished [took 51.5080s] -06/11/23 06:59:04| INFO mul_pacc finished [took 56.1671s] -06/11/23 06:59:09| INFO mulmc_sld finished [took 65.0229s] -06/11/23 06:59:10| INFO mul_sld finished [took 68.8366s] -06/11/23 06:59:11| INFO mulne_sld finished [took 65.2964s] -06/11/23 07:01:39| INFO bin_pacc finished [took 212.3570s] -06/11/23 07:01:40| INFO bin_sld finished [took 219.3886s] -06/11/23 07:01:42| INFO binmc_sld finished [took 219.1471s] -06/11/23 07:01:43| INFO binne_sld finished [took 218.3714s] -06/11/23 07:01:43| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs finished [took 223.2305s] -06/11/23 07:01:43| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs started -06/11/23 07:02:40| INFO ref finished [took 47.3513s] -06/11/23 07:02:45| INFO atc_mc finished [took 51.3858s] -06/11/23 07:02:47| INFO mul_pacc finished [took 56.3829s] -06/11/23 07:02:50| INFO mul_sld finished [took 64.9257s] -06/11/23 07:02:50| INFO mulmc_sld finished [took 63.6515s] -06/11/23 07:02:52| INFO mulne_sld finished [took 63.8008s] -06/11/23 07:05:22| INFO bin_pacc finished [took 211.8418s] -06/11/23 07:05:22| INFO binmc_sld finished [took 216.4950s] -06/11/23 07:05:22| INFO bin_sld finished [took 218.2730s] -06/11/23 07:05:25| INFO binne_sld finished [took 217.6016s] -06/11/23 07:05:25| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs finished [took 222.4416s] -06/11/23 07:05:25| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs started -06/11/23 07:06:22| INFO ref finished [took 47.3783s] -06/11/23 07:06:27| INFO atc_mc finished [took 51.1924s] -06/11/23 07:06:30| INFO mul_pacc finished [took 56.3115s] -06/11/23 07:06:34| INFO mul_sld finished [took 66.6559s] -06/11/23 07:06:34| INFO mulmc_sld finished [took 65.2448s] -06/11/23 07:06:37| INFO mulne_sld finished [took 65.6557s] -06/11/23 07:09:03| INFO binmc_sld finished [took 214.4549s] -06/11/23 07:09:03| INFO bin_sld finished [took 216.8097s] -06/11/23 07:09:04| INFO bin_pacc finished [took 211.9484s] -06/11/23 07:09:06| INFO binne_sld finished [took 215.5010s] -06/11/23 07:09:06| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs finished [took 220.4788s] -06/11/23 07:09:06| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs started -06/11/23 07:10:03| INFO ref finished [took 47.0882s] -06/11/23 07:10:08| INFO atc_mc finished [took 51.1826s] -06/11/23 07:10:10| INFO mul_pacc finished [took 55.8766s] -06/11/23 07:10:14| INFO mulmc_sld finished [took 64.7175s] -06/11/23 07:10:15| INFO mul_sld finished [took 67.2892s] -06/11/23 07:10:17| INFO mulne_sld finished [took 64.9305s] -06/11/23 07:12:40| INFO bin_pacc finished [took 207.6921s] -06/11/23 07:12:41| INFO binmc_sld finished [took 212.3821s] -06/11/23 07:12:41| INFO bin_sld finished [took 214.5241s] -06/11/23 07:12:43| INFO binne_sld finished [took 212.5115s] -06/11/23 07:12:43| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs finished [took 217.3597s] -06/11/23 07:12:43| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs started -06/11/23 07:13:39| INFO ref finished [took 46.5374s] -06/11/23 07:13:45| INFO atc_mc finished [took 51.0121s] -06/11/23 07:13:47| INFO mul_pacc finished [took 55.4950s] -06/11/23 07:13:52| INFO mulmc_sld finished [took 64.7651s] -06/11/23 07:13:52| INFO mul_sld finished [took 67.0632s] -06/11/23 07:13:54| INFO mulne_sld finished [took 65.2533s] -06/11/23 07:16:18| INFO bin_pacc finished [took 207.9541s] -06/11/23 07:16:19| INFO bin_sld finished [took 214.6495s] -06/11/23 07:16:19| INFO binmc_sld finished [took 213.2167s] -06/11/23 07:16:24| INFO binne_sld finished [took 215.5646s] -06/11/23 07:16:24| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs finished [took 220.4851s] -06/11/23 07:16:24| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs started -06/11/23 07:17:20| INFO ref finished [took 46.7655s] -06/11/23 07:17:25| INFO atc_mc finished [took 50.9430s] -06/11/23 07:17:27| INFO mul_pacc finished [took 55.4948s] -06/11/23 07:17:44| INFO mul_sld finished [took 78.5002s] -06/11/23 07:17:46| INFO mulmc_sld finished [took 78.7519s] -06/11/23 07:17:48| INFO mulne_sld finished [took 78.4293s] -06/11/23 07:19:59| INFO bin_pacc finished [took 208.5200s] -06/11/23 07:20:02| INFO bin_sld finished [took 216.9046s] -06/11/23 07:20:03| INFO binmc_sld finished [took 216.2736s] -06/11/23 07:20:03| INFO binne_sld finished [took 214.9573s] -06/11/23 07:20:03| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs finished [took 219.7592s] -06/11/23 07:20:03| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs started -06/11/23 07:20:05| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 07:20:06| WARNING Method binmc_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 07:20:08| WARNING Method binne_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 07:20:10| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 07:20:12| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 07:20:48| INFO ref finished [took 36.6985s] -06/11/23 07:20:52| INFO atc_mc finished [took 39.8292s] -06/11/23 07:20:55| INFO mulmc_sld finished [took 48.0943s] -06/11/23 07:20:56| INFO mul_sld finished [took 50.2138s] -06/11/23 07:20:57| INFO mulne_sld finished [took 47.9755s] -06/11/23 07:20:57| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs finished [took 53.5645s] ----------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------- -06/11/23 10:25:08| INFO dataset rcv1_CCAT_1prevs -06/11/23 10:25:12| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started -06/11/23 10:26:02| INFO ref finished [took 43.6300s] -06/11/23 10:26:05| INFO atc_mc finished [took 46.2297s] -06/11/23 10:26:46| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00906) [took 88.7593s] -06/11/23 10:27:29| INFO mul_pacc_gs finished [took 132.4595s] -06/11/23 10:31:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00900) [took 379.8213s] -06/11/23 10:34:52| INFO bin_pacc_gs finished [took 576.1136s] ----------------------------------------------------------------------------------------------------- -06/11/23 10:55:40| INFO dataset rcv1_CCAT_1prevs -06/11/23 10:55:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started -06/11/23 10:56:36| INFO ref finished [took 44.5230s] -06/11/23 10:56:40| INFO atc_mc finished [took 47.6566s] -06/11/23 10:57:20| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00927) [took 90.0830s] -06/11/23 10:58:04| INFO mul_pacc_gs finished [took 134.5283s] -06/11/23 11:02:12| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00974) [took 383.6784s] -06/11/23 11:05:24| INFO bin_pacc_gs finished [took 574.8730s] -06/11/23 11:10:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00775) [took 897.6622s] -06/11/23 11:11:27| INFO mul_sld_gs finished [took 940.1205s] -06/11/23 11:18:11| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00835) [took 1344.8544s] -06/11/23 11:21:09| INFO bin_sld_gs finished [took 1523.4358s] -06/11/23 11:21:09| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs finished [took 1525.2268s] ----------------------------------------------------------------------------------------------------- -06/11/23 11:21:26| INFO dataset rcv1_CCAT_1prevs -06/11/23 11:21:30| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started -06/11/23 11:22:18| INFO ref finished [took 42.1948s] -06/11/23 11:22:23| INFO atc_mc finished [took 45.7857s] -06/11/23 11:23:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00987) [took 87.4022s] -06/11/23 11:23:45| INFO mul_pacc_gs finished [took 130.6127s] -06/11/23 11:27:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00925) [took 374.1460s] -06/11/23 11:29:46| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00775) [took 493.2309s] -06/11/23 11:30:30| INFO mul_sld_gs finished [took 537.7362s] -06/11/23 11:30:56| INFO bin_pacc_gs finished [took 562.8681s] -06/11/23 11:35:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00835) [took 840.9875s] -06/11/23 11:38:31| INFO bin_sld_gs finished [took 1019.8362s] -06/11/23 11:38:31| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs finished [took 1021.3535s] ----------------------------------------------------------------------------------------------------- -06/11/23 11:53:50| INFO dataset rcv1_CCAT_9prevs -06/11/23 11:53:56| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -06/11/23 11:56:45| INFO doc_feat finished [took 83.7957s] -06/11/23 11:56:58| INFO mulne_pacc finished [took 146.6577s] -06/11/23 11:57:03| INFO ref finished [took 120.2665s] -06/11/23 11:57:05| INFO mul_pacc finished [took 169.5909s] -06/11/23 11:57:07| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -06/11/23 11:57:08| INFO kfcv finished [took 130.0948s] -06/11/23 11:57:13| INFO mulmc_pacc finished [took 173.0431s] -06/11/23 11:57:16| INFO atc_mc finished [took 125.2363s] -06/11/23 11:57:17| INFO mul_sld finished [took 199.1179s] -06/11/23 11:57:18| INFO mul_cc finished [took 148.2203s] -06/11/23 11:57:20| INFO atc_ne finished [took 121.9570s] -06/11/23 11:57:39| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00899) [took 176.8809s] -06/11/23 11:58:48| INFO mul_pacc_gs finished [took 245.7641s] -06/11/23 12:00:56| INFO bin_pacc finished [took 409.8967s] -06/11/23 12:01:03| INFO bin_sld finished [took 426.0031s] -06/11/23 12:01:09| INFO binmc_pacc finished [took 412.9057s] -06/11/23 12:01:13| INFO bin_cc finished [took 389.4719s] -06/11/23 12:01:14| INFO binne_pacc finished [took 411.1276s] -06/11/23 12:02:20| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 12:03:18| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00719) [took 523.4665s] -06/11/23 12:04:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00644) [took 643.3372s] -06/11/23 12:05:27| INFO mul_sld_gs finished [took 686.5912s] -06/11/23 12:06:25| INFO bin_pacc_gs finished [took 710.5248s] -06/11/23 12:08:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 892.3454s] -06/11/23 12:11:50| INFO bin_sld_gs finished [took 1070.2847s] -06/11/23 12:11:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 1073.7689s] -06/11/23 12:11:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -06/11/23 12:14:15| INFO doc_feat finished [took 80.2198s] -06/11/23 12:14:27| INFO ref finished [took 106.1446s] -06/11/23 12:14:36| INFO mul_pacc finished [took 155.8715s] -06/11/23 12:14:37| INFO mul_sld finished [took 162.7857s] -06/11/23 12:14:47| INFO kfcv finished [took 132.1178s] -06/11/23 12:14:55| INFO atc_mc finished [took 127.9109s] -06/11/23 12:14:57| INFO atc_ne finished [took 121.6128s] -06/11/23 12:14:58| INFO mulmc_pacc finished [took 173.9023s] -06/11/23 12:14:58| INFO mulne_pacc finished [took 167.2920s] -06/11/23 12:14:59| INFO mul_cc finished [took 147.7428s] -06/11/23 12:15:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00931) [took 186.6087s] -06/11/23 12:16:44| INFO mul_pacc_gs finished [took 261.9352s] -06/11/23 12:18:49| INFO binmc_pacc finished [took 407.0394s] -06/11/23 12:18:50| INFO bin_pacc finished [took 410.4620s] -06/11/23 12:18:55| INFO bin_sld finished [took 422.8949s] -06/11/23 12:19:01| INFO binne_pacc finished [took 410.3575s] -06/11/23 12:19:03| INFO bin_cc finished [took 396.9482s] -06/11/23 12:20:14| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 12:20:41| INFO bin_sld_gsq finished [took 524.4318s] -06/11/23 12:21:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00784) [took 546.2730s] -06/11/23 12:22:36| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01221) [took 640.2364s] -06/11/23 12:23:20| INFO mul_sld_gs finished [took 683.6191s] -06/11/23 12:24:29| INFO bin_pacc_gs finished [took 732.6258s] -06/11/23 12:27:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00768) [took 948.8111s] -06/11/23 12:30:43| INFO bin_sld_gs finished [took 1128.0644s] -06/11/23 12:30:43| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 1132.7995s] -06/11/23 12:30:43| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -06/11/23 12:33:12| INFO mul_sld finished [took 146.3214s] -06/11/23 12:33:24| INFO mul_cc finished [took 118.2992s] -06/11/23 12:33:26| INFO doc_feat finished [took 96.3414s] -06/11/23 12:33:36| INFO atc_ne finished [took 108.2657s] -06/11/23 12:33:37| INFO mulne_pacc finished [took 155.4759s] -06/11/23 12:33:39| INFO atc_mc finished [took 119.0950s] -06/11/23 12:33:39| INFO mul_pacc finished [took 166.1039s] -06/11/23 12:33:40| INFO ref finished [took 122.3921s] -06/11/23 12:33:40| INFO mulmc_pacc finished [took 164.5722s] -06/11/23 12:33:43| INFO kfcv finished [took 131.6124s] -06/11/23 12:34:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00857) [took 188.0301s] -06/11/23 12:35:45| INFO mul_pacc_gs finished [took 269.4655s] -06/11/23 12:37:47| INFO binne_pacc finished [took 409.7038s] -06/11/23 12:37:47| INFO bin_sld finished [took 421.4590s] -06/11/23 12:37:55| INFO bin_pacc finished [took 423.8805s] -06/11/23 12:37:57| INFO binmc_pacc finished [took 422.8180s] -06/11/23 12:38:01| INFO bin_cc finished [took 400.2199s] -06/11/23 12:39:14| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 12:39:41| INFO bin_sld_gsq finished [took 531.7360s] -06/11/23 12:40:17| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00762) [took 549.6726s] -06/11/23 12:41:35| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00872) [took 646.0507s] -06/11/23 12:42:19| INFO mul_sld_gs finished [took 690.7431s] -06/11/23 12:43:25| INFO bin_pacc_gs finished [took 737.2287s] -06/11/23 12:47:07| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00782) [took 979.3211s] -06/11/23 12:50:07| INFO bin_sld_gs finished [took 1159.4207s] -06/11/23 12:50:07| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1163.9370s] -06/11/23 12:50:07| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -06/11/23 12:52:34| INFO doc_feat finished [took 79.3857s] -06/11/23 12:52:39| INFO mul_pacc finished [took 143.2358s] -06/11/23 12:52:49| INFO mul_sld finished [took 159.6160s] -06/11/23 12:52:58| INFO kfcv finished [took 121.1756s] -06/11/23 12:53:07| INFO mulmc_pacc finished [took 167.6154s] -06/11/23 12:53:09| INFO atc_ne finished [took 115.9704s] -06/11/23 12:53:11| INFO ref finished [took 127.9906s] -06/11/23 12:53:17| INFO atc_mc finished [took 129.9605s] -06/11/23 12:53:19| INFO mulne_pacc finished [took 166.1444s] -06/11/23 12:53:21| INFO mul_cc finished [took 152.0451s] -06/11/23 12:53:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00832) [took 183.1015s] -06/11/23 12:55:03| INFO mul_pacc_gs finished [took 261.6088s] -06/11/23 12:57:20| INFO binmc_pacc finished [took 422.6680s] -06/11/23 12:57:23| INFO bin_sld finished [took 434.1109s] -06/11/23 12:57:26| INFO bin_pacc finished [took 431.1893s] -06/11/23 12:57:28| INFO binne_pacc finished [took 427.7980s] -06/11/23 12:57:29| INFO bin_cc finished [took 402.6463s] -06/11/23 12:58:43| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 12:59:20| INFO bin_sld_gsq finished [took 546.8013s] -06/11/23 12:59:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00858) [took 550.8127s] -06/11/23 13:01:04| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01037) [took 652.8483s] -06/11/23 13:01:47| INFO mul_sld_gs finished [took 695.7927s] -06/11/23 13:02:56| INFO bin_pacc_gs finished [took 739.4380s] -06/11/23 13:06:49| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01112) [took 999.0699s] -06/11/23 13:09:50| INFO bin_sld_gs finished [took 1179.8181s] -06/11/23 13:09:50| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1183.4124s] -06/11/23 13:09:50| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -06/11/23 13:12:34| INFO doc_feat finished [took 88.8963s] -06/11/23 13:12:43| INFO mul_sld finished [took 169.3932s] -06/11/23 13:12:47| INFO mul_pacc finished [took 166.5633s] -06/11/23 13:12:50| INFO kfcv finished [took 134.2527s] -06/11/23 13:12:58| INFO ref finished [took 128.7367s] -06/11/23 13:12:59| INFO mulne_pacc finished [took 161.0902s] -06/11/23 13:13:00| INFO mulmc_pacc finished [took 176.8006s] -06/11/23 13:13:01| INFO atc_mc finished [took 129.5173s] -06/11/23 13:13:06| INFO atc_ne finished [took 122.8886s] -06/11/23 13:13:16| INFO mul_cc finished [took 152.5218s] -06/11/23 13:13:34| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00774) [took 183.0928s] -06/11/23 13:14:57| INFO mul_pacc_gs finished [took 266.4369s] -06/11/23 13:17:06| INFO bin_pacc finished [took 427.3693s] -06/11/23 13:17:08| INFO binmc_pacc finished [took 426.6359s] -06/11/23 13:17:14| INFO bin_sld finished [took 441.7834s] -06/11/23 13:17:20| INFO binne_pacc finished [took 435.6569s] -06/11/23 13:17:22| INFO bin_cc finished [took 412.7263s] -06/11/23 13:18:20| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 13:19:06| INFO bin_sld_gsq finished [took 549.0379s] -06/11/23 13:19:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00709) [took 545.0306s] -06/11/23 13:20:41| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00572) [took 645.5669s] -06/11/23 13:21:25| INFO mul_sld_gs finished [took 689.4814s] -06/11/23 13:22:32| INFO bin_pacc_gs finished [took 730.0602s] -06/11/23 13:26:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00653) [took 985.8522s] -06/11/23 13:29:21| INFO bin_sld_gs finished [took 1166.4200s] -06/11/23 13:29:21| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 1170.8744s] -06/11/23 13:29:21| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -06/11/23 13:31:52| INFO doc_feat finished [took 80.3058s] -06/11/23 13:32:00| INFO mul_sld finished [took 153.1261s] -06/11/23 13:32:00| INFO mul_pacc finished [took 148.0156s] -06/11/23 13:32:13| INFO kfcv finished [took 122.2270s] -06/11/23 13:32:13| INFO mulne_pacc finished [took 145.0130s] -06/11/23 13:32:22| INFO ref finished [took 122.3525s] -06/11/23 13:32:23| INFO atc_mc finished [took 120.2587s] -06/11/23 13:32:23| INFO atc_ne finished [took 113.5667s] -06/11/23 13:32:23| INFO mulmc_pacc finished [took 167.7106s] -06/11/23 13:32:36| INFO mul_cc finished [took 143.6517s] -06/11/23 13:33:04| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00802) [took 182.2049s] -06/11/23 13:34:23| INFO mul_pacc_gs finished [took 261.0038s] -06/11/23 13:36:19| INFO binmc_pacc finished [took 405.9199s] -06/11/23 13:36:31| INFO bin_sld finished [took 426.3780s] -06/11/23 13:36:32| INFO bin_pacc finished [took 420.2833s] -06/11/23 13:36:34| INFO binne_pacc finished [took 417.2048s] -06/11/23 13:36:42| INFO bin_cc finished [took 394.3524s] -06/11/23 13:37:45| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 13:38:18| INFO bin_sld_gsq finished [took 528.6956s] -06/11/23 13:38:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00707) [took 544.2769s] -06/11/23 13:39:56| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00637) [took 628.0656s] -06/11/23 13:40:41| INFO mul_sld_gs finished [took 673.3968s] -06/11/23 13:41:58| INFO bin_pacc_gs finished [took 730.5371s] -06/11/23 13:45:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00544) [took 960.1061s] -06/11/23 13:48:28| INFO bin_sld_gs finished [took 1140.6073s] -06/11/23 13:48:28| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1146.6395s] -06/11/23 13:48:28| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -06/11/23 13:50:49| INFO mul_pacc finished [took 130.9408s] -06/11/23 13:51:02| INFO doc_feat finished [took 86.2411s] -06/11/23 13:51:07| INFO mul_sld finished [took 155.4513s] -06/11/23 13:51:15| INFO atc_ne finished [took 101.0761s] -06/11/23 13:51:20| INFO ref finished [took 121.3689s] -06/11/23 13:51:20| INFO atc_mc finished [took 106.6415s] -06/11/23 13:51:22| INFO mulmc_pacc finished [took 160.4221s] -06/11/23 13:51:22| INFO mulne_pacc finished [took 150.1203s] -06/11/23 13:51:25| INFO kfcv finished [took 127.5280s] -06/11/23 13:51:35| INFO mul_cc finished [took 145.4437s] -06/11/23 13:52:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00886) [took 182.0543s] -06/11/23 13:53:23| INFO mul_pacc_gs finished [took 262.2496s] -06/11/23 13:55:28| INFO bin_sld finished [took 417.7315s] -06/11/23 13:55:30| INFO binmc_pacc finished [took 410.0114s] -06/11/23 13:55:30| INFO bin_pacc finished [took 413.5912s] -06/11/23 13:55:35| INFO binne_pacc finished [took 411.8241s] -06/11/23 13:55:42| INFO bin_cc finished [took 396.5011s] -06/11/23 13:56:50| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 13:57:22| INFO bin_sld_gsq finished [took 527.2507s] -06/11/23 13:58:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00910) [took 547.7641s] -06/11/23 13:59:09| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00765) [took 634.8469s] -06/11/23 13:59:54| INFO mul_sld_gs finished [took 680.2027s] -06/11/23 14:01:07| INFO bin_pacc_gs finished [took 731.8655s] -06/11/23 14:04:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01235) [took 960.6861s] -06/11/23 14:07:34| INFO bin_sld_gs finished [took 1141.7199s] -06/11/23 14:07:34| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 1146.2680s] -06/11/23 14:07:34| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -06/11/23 14:10:08| INFO mulmc_pacc finished [took 141.2266s] -06/11/23 14:10:19| INFO atc_ne finished [took 101.4512s] -06/11/23 14:10:20| INFO mul_sld finished [took 162.5808s] -06/11/23 14:10:23| INFO mul_pacc finished [took 158.9068s] -06/11/23 14:10:30| INFO kfcv finished [took 123.4790s] -06/11/23 14:10:33| INFO mulne_pacc finished [took 158.4983s] -06/11/23 14:10:33| INFO doc_feat finished [took 111.7987s] -06/11/23 14:10:35| INFO ref finished [took 124.4184s] -06/11/23 14:10:40| INFO atc_mc finished [took 126.3543s] -06/11/23 14:10:40| INFO mul_cc finished [took 139.5958s] -06/11/23 14:11:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.01309) [took 184.0645s] -06/11/23 14:12:44| INFO mul_pacc_gs finished [took 272.5404s] -06/11/23 14:14:32| INFO binmc_pacc finished [took 406.3609s] -06/11/23 14:14:38| INFO bin_pacc finished [took 414.8972s] -06/11/23 14:14:43| INFO binne_pacc finished [took 414.4123s] -06/11/23 14:14:51| INFO bin_cc finished [took 395.5254s] -06/11/23 14:14:55| INFO bin_sld finished [took 437.7681s] -06/11/23 14:15:58| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 14:16:33| INFO bin_sld_gsq finished [took 532.4040s] -06/11/23 14:16:57| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00812) [took 536.4746s] -06/11/23 14:18:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01306) [took 636.4067s] -06/11/23 14:19:00| INFO mul_sld_gs finished [took 680.2467s] -06/11/23 14:20:01| INFO bin_pacc_gs finished [took 720.9205s] -06/11/23 14:23:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00894) [took 954.4487s] -06/11/23 14:26:41| INFO bin_sld_gs finished [took 1142.3328s] -06/11/23 14:26:41| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 1146.7713s] -06/11/23 14:26:41| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -06/11/23 14:29:04| INFO mul_pacc finished [took 133.1736s] -06/11/23 14:29:07| INFO ref finished [took 87.1594s] -06/11/23 14:29:16| INFO doc_feat finished [took 83.8190s] -06/11/23 14:29:21| INFO mulmc_pacc finished [took 147.5202s] -06/11/23 14:29:22| INFO atc_mc finished [took 99.1039s] -06/11/23 14:29:23| INFO kfcv finished [took 109.5348s] -06/11/23 14:29:27| INFO mulne_pacc finished [took 148.1672s] -06/11/23 14:29:33| INFO atc_ne finished [took 101.4673s] -06/11/23 14:29:36| INFO mul_cc finished [took 126.0447s] -06/11/23 14:29:42| INFO mul_sld finished [took 177.5880s] -06/11/23 14:30:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01497) [took 175.1432s] -06/11/23 14:31:33| INFO mul_pacc_gs finished [took 252.3399s] -06/11/23 14:33:34| INFO binmc_pacc finished [took 401.7891s] -06/11/23 14:33:35| INFO binne_pacc finished [took 400.4138s] -06/11/23 14:33:36| INFO bin_pacc finished [took 406.7598s] -06/11/23 14:33:48| INFO bin_cc finished [took 378.7595s] -06/11/23 14:33:48| INFO bin_sld finished [took 423.7366s] -06/11/23 14:34:47| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 14:35:26| INFO bin_sld_gsq finished [took 518.8996s] -06/11/23 14:36:13| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00880) [took 539.3992s] -06/11/23 14:37:09| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00808) [took 622.0419s] -06/11/23 14:37:53| INFO mul_sld_gs finished [took 666.0058s] -06/11/23 14:39:16| INFO bin_pacc_gs finished [took 722.5231s] -06/11/23 14:42:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00604) [took 929.3464s] -06/11/23 14:45:14| INFO bin_sld_gs finished [took 1108.7356s] -06/11/23 14:45:14| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 1113.2364s] -06/11/23 14:48:04| INFO dataset imdb_9prevs -06/11/23 14:48:14| INFO Dataset sample 0.10 of dataset imdb_9prevs started -06/11/23 14:48:17| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 14:48:18| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 14:48:31| INFO doc_feat finished [took 11.5257s] -06/11/23 14:48:36| INFO ref finished [took 18.5620s] -06/11/23 14:48:42| INFO kfcv finished [took 24.7227s] -06/11/23 14:48:44| INFO atc_ne finished [took 25.5676s] -06/11/23 14:48:46| INFO atc_mc finished [took 27.4910s] -06/11/23 14:48:50| INFO mulne_pacc finished [took 34.0415s] -06/11/23 14:48:57| INFO mulmc_pacc finished [took 41.7500s] -06/11/23 14:48:58| INFO mul_pacc finished [took 43.0162s] -06/11/23 14:48:58| INFO mul_cc finished [took 40.3279s] -06/11/23 14:49:16| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -06/11/23 14:49:26| INFO mul_sld finished [took 71.4588s] -06/11/23 14:50:10| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -06/11/23 14:52:12| INFO binne_pacc finished [took 236.2174s] -06/11/23 14:52:16| INFO binmc_pacc finished [took 240.4686s] -06/11/23 14:52:19| INFO bin_cc finished [took 241.9141s] -06/11/23 14:52:20| INFO bin_pacc finished [took 244.5632s] -06/11/23 14:52:23| INFO bin_sld finished [took 249.0477s] -06/11/23 14:53:48| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 14:55:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.03127) [took 443.6010s] -06/11/23 14:55:51| INFO mul_sld_gs finished [took 455.9932s] -06/11/23 14:55:51| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 456.7746s] -06/11/23 14:55:51| INFO Dataset sample 0.20 of dataset imdb_9prevs started -06/11/23 14:56:07| INFO doc_feat finished [took 11.2758s] -06/11/23 14:56:18| INFO atc_mc finished [took 22.2986s] -06/11/23 14:56:22| INFO ref finished [took 26.3482s] -06/11/23 14:56:25| INFO kfcv finished [took 30.4761s] -06/11/23 14:56:29| INFO mul_pacc finished [took 36.5892s] -06/11/23 14:56:29| INFO mulmc_pacc finished [took 36.7773s] -06/11/23 14:56:38| INFO atc_ne finished [took 41.7824s] -06/11/23 14:56:41| INFO mulne_pacc finished [took 47.8318s] -06/11/23 14:56:41| INFO mul_cc finished [took 46.7221s] -06/11/23 14:56:55| INFO mul_sld finished [took 63.3547s] -06/11/23 14:57:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.01017) [took 119.9166s] -06/11/23 14:58:15| INFO mul_pacc_gs finished [took 141.4446s] -06/11/23 15:00:38| INFO binne_pacc finished [took 285.5562s] -06/11/23 15:00:48| INFO bin_cc finished [took 293.8727s] -06/11/23 15:00:49| INFO binmc_pacc finished [took 296.7176s] -06/11/23 15:00:49| INFO bin_pacc finished [took 297.1868s] -06/11/23 15:01:03| INFO bin_sld finished [took 312.0358s] -06/11/23 15:02:29| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 15:02:34| INFO bin_sld_gsq finished [took 402.0748s] -06/11/23 15:03:56| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00980) [took 482.9237s] -06/11/23 15:05:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01194) [took 548.0443s] -06/11/23 15:05:14| INFO mul_sld_gs finished [took 562.2966s] -06/11/23 15:06:30| INFO bin_pacc_gs finished [took 636.7956s] -06/11/23 15:10:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00924) [took 884.9748s] -06/11/23 15:13:11| INFO bin_sld_gs finished [took 1039.3282s] -06/11/23 15:13:11| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 1040.0772s] -06/11/23 15:13:11| INFO Dataset sample 0.30 of dataset imdb_9prevs started -06/11/23 15:13:39| INFO doc_feat finished [took 22.8145s] -06/11/23 15:13:41| INFO atc_ne finished [took 24.3471s] -06/11/23 15:13:45| INFO ref finished [took 28.7559s] -06/11/23 15:13:52| INFO mulne_pacc finished [took 38.1365s] -06/11/23 15:13:53| INFO kfcv finished [took 37.4026s] -06/11/23 15:13:56| INFO atc_mc finished [took 39.4198s] -06/11/23 15:13:59| INFO mul_pacc finished [took 45.9542s] -06/11/23 15:13:59| INFO mul_cc finished [took 43.9076s] -06/11/23 15:13:59| INFO mulmc_pacc finished [took 45.9395s] -06/11/23 15:14:11| INFO mul_sld finished [took 59.8835s] -06/11/23 15:15:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01028) [took 128.2866s] -06/11/23 15:15:44| INFO mul_pacc_gs finished [took 149.5820s] -06/11/23 15:18:03| INFO binne_pacc finished [took 289.3504s] -06/11/23 15:18:07| INFO bin_pacc finished [took 294.7115s] -06/11/23 15:18:14| INFO bin_cc finished [took 298.6839s] -06/11/23 15:18:14| INFO binmc_pacc finished [took 300.9499s] -06/11/23 15:18:14| INFO bin_sld finished [took 302.9035s] -06/11/23 15:19:46| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 15:20:05| INFO bin_sld_gsq finished [took 413.1151s] -06/11/23 15:21:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00916) [took 488.7327s] -06/11/23 15:22:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00953) [took 541.2865s] -06/11/23 15:22:28| INFO mul_sld_gs finished [took 556.0867s] -06/11/23 15:23:57| INFO bin_pacc_gs finished [took 643.0717s] -06/11/23 15:27:32| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01064) [took 860.3135s] -06/11/23 15:30:05| INFO bin_sld_gs finished [took 1013.1878s] -06/11/23 15:30:05| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 1014.3141s] -06/11/23 15:30:05| INFO Dataset sample 0.40 of dataset imdb_9prevs started -06/11/23 15:30:24| INFO doc_feat finished [took 13.8500s] -06/11/23 15:30:32| INFO ref finished [took 22.3531s] -06/11/23 15:30:41| INFO mul_pacc finished [took 34.1860s] -06/11/23 15:30:45| INFO atc_ne finished [took 34.8111s] -06/11/23 15:30:46| INFO kfcv finished [took 36.4055s] -06/11/23 15:30:49| INFO atc_mc finished [took 38.7978s] -06/11/23 15:30:49| INFO mulmc_pacc finished [took 42.4552s] -06/11/23 15:30:51| INFO mul_cc finished [took 42.6899s] -06/11/23 15:30:53| INFO mulne_pacc finished [took 45.2694s] -06/11/23 15:30:57| INFO mul_sld finished [took 51.2705s] -06/11/23 15:32:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01220) [took 124.5801s] -06/11/23 15:32:34| INFO mul_pacc_gs finished [took 145.3368s] -06/11/23 15:34:56| INFO binmc_pacc finished [took 289.1451s] -06/11/23 15:35:04| INFO bin_sld finished [took 298.3514s] -06/11/23 15:35:04| INFO binne_pacc finished [took 296.5538s] -06/11/23 15:35:05| INFO bin_pacc finished [took 298.5077s] -06/11/23 15:35:09| INFO bin_cc finished [took 300.1332s] -06/11/23 15:36:41| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 15:37:08| INFO bin_sld_gsq finished [took 421.3938s] -06/11/23 15:38:19| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01137) [took 490.9644s] -06/11/23 15:38:58| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01029) [took 531.8225s] -06/11/23 15:39:12| INFO mul_sld_gs finished [took 546.4524s] -06/11/23 15:40:53| INFO bin_pacc_gs finished [took 645.0957s] -06/11/23 15:44:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01176) [took 882.0550s] -06/11/23 15:47:19| INFO bin_sld_gs finished [took 1033.2802s] -06/11/23 15:47:19| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 1034.1241s] -06/11/23 15:47:19| INFO Dataset sample 0.50 of dataset imdb_9prevs started -06/11/23 15:47:36| INFO doc_feat finished [took 11.6005s] -06/11/23 15:47:40| INFO ref finished [took 16.3058s] -06/11/23 15:47:50| INFO atc_mc finished [took 25.8745s] -06/11/23 15:47:52| INFO kfcv finished [took 29.0931s] -06/11/23 15:47:53| INFO atc_ne finished [took 28.8903s] -06/11/23 15:47:53| INFO mul_pacc finished [took 32.5473s] -06/11/23 15:48:00| INFO mul_cc finished [took 37.3478s] -06/11/23 15:48:01| INFO mulne_pacc finished [took 39.9745s] -06/11/23 15:48:02| INFO mulmc_pacc finished [took 40.5057s] -06/11/23 15:48:10| INFO mul_sld finished [took 50.1825s] -06/11/23 15:49:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01107) [took 125.0329s] -06/11/23 15:49:49| INFO mul_pacc_gs finished [took 146.7316s] -06/11/23 15:52:15| INFO bin_cc finished [took 292.6719s] -06/11/23 15:52:15| INFO binne_pacc finished [took 293.9844s] -06/11/23 15:52:17| INFO bin_pacc finished [took 296.2830s] -06/11/23 15:52:21| INFO binmc_pacc finished [took 299.4873s] -06/11/23 15:52:23| INFO bin_sld finished [took 303.4889s] -06/11/23 15:53:57| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 15:54:18| INFO bin_sld_gsq finished [took 418.0959s] -06/11/23 15:55:32| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01038) [took 489.7797s] -06/11/23 15:56:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00885) [took 536.7408s] -06/11/23 15:56:33| INFO mul_sld_gs finished [took 552.5393s] -06/11/23 15:58:05| INFO bin_pacc_gs finished [took 643.1581s] -06/11/23 16:01:42| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00940) [took 862.6012s] -06/11/23 16:04:15| INFO bin_sld_gs finished [took 1015.3606s] -06/11/23 16:04:15| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 1016.0642s] -06/11/23 16:04:15| INFO Dataset sample 0.60 of dataset imdb_9prevs started -06/11/23 16:04:40| INFO doc_feat finished [took 19.9628s] -06/11/23 16:04:41| INFO kfcv finished [took 21.8848s] -06/11/23 16:04:46| INFO ref finished [took 26.2613s] -06/11/23 16:04:56| INFO mulmc_pacc finished [took 38.6399s] -06/11/23 16:04:56| INFO atc_ne finished [took 35.7501s] -06/11/23 16:04:57| INFO atc_mc finished [took 37.3907s] -06/11/23 16:05:01| INFO mul_cc finished [took 41.6420s] -06/11/23 16:05:01| INFO mul_pacc finished [took 44.6898s] -06/11/23 16:05:02| INFO mulne_pacc finished [took 44.7679s] -06/11/23 16:05:12| INFO mul_sld finished [took 56.0834s] -06/11/23 16:06:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01082) [took 125.2569s] -06/11/23 16:06:44| INFO mul_pacc_gs finished [took 146.2318s] -06/11/23 16:09:05| INFO binne_pacc finished [took 288.1949s] -06/11/23 16:09:10| INFO bin_pacc finished [took 293.3207s] -06/11/23 16:09:12| INFO bin_sld finished [took 296.1022s] -06/11/23 16:09:13| INFO binmc_pacc finished [took 296.4000s] -06/11/23 16:09:18| INFO bin_cc finished [took 299.1982s] -06/11/23 16:10:50| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 16:11:22| INFO bin_sld_gsq finished [took 425.6641s] -06/11/23 16:12:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00976) [took 492.8847s] -06/11/23 16:13:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00984) [took 536.8669s] -06/11/23 16:13:28| INFO mul_sld_gs finished [took 551.6187s] -06/11/23 16:15:03| INFO bin_pacc_gs finished [took 645.6602s] -06/11/23 16:19:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01142) [took 907.7074s] -06/11/23 16:21:57| INFO bin_sld_gs finished [took 1060.9759s] -06/11/23 16:21:57| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 1061.7730s] -06/11/23 16:21:57| INFO Dataset sample 0.70 of dataset imdb_9prevs started -06/11/23 16:22:23| INFO doc_feat finished [took 20.2428s] -06/11/23 16:22:34| INFO kfcv finished [took 32.1532s] -06/11/23 16:22:36| INFO ref finished [took 34.3738s] -06/11/23 16:22:38| INFO mul_sld finished [took 40.1101s] -06/11/23 16:22:40| INFO mul_cc finished [took 38.6722s] -06/11/23 16:22:41| INFO atc_mc finished [took 38.9379s] -06/11/23 16:22:43| INFO atc_ne finished [took 40.3132s] -06/11/23 16:22:43| INFO mulne_pacc finished [took 43.7833s] -06/11/23 16:22:44| INFO mulmc_pacc finished [took 44.4084s] -06/11/23 16:22:46| INFO mul_pacc finished [took 47.7998s] -06/11/23 16:24:08| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01575) [took 127.2393s] -06/11/23 16:24:31| INFO mul_pacc_gs finished [took 150.2100s] -06/11/23 16:26:49| INFO bin_cc finished [took 288.6128s] -06/11/23 16:26:51| INFO bin_pacc finished [took 292.1757s] -06/11/23 16:26:52| INFO binne_pacc finished [took 293.0194s] -06/11/23 16:27:01| INFO binmc_pacc finished [took 302.5703s] -06/11/23 16:27:01| INFO bin_sld finished [took 303.9303s] -06/11/23 16:28:32| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 16:28:53| INFO bin_sld_gsq finished [took 414.4520s] -06/11/23 16:30:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01641) [took 494.7681s] -06/11/23 16:31:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01102) [took 542.3282s] -06/11/23 16:31:15| INFO mul_sld_gs finished [took 557.2859s] -06/11/23 16:32:49| INFO bin_pacc_gs finished [took 648.9428s] -06/11/23 16:36:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.02196) [took 864.7237s] -06/11/23 16:38:54| INFO bin_sld_gs finished [took 1015.9618s] -06/11/23 16:38:54| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 1016.7743s] -06/11/23 16:38:54| INFO Dataset sample 0.80 of dataset imdb_9prevs started -06/11/23 16:39:19| INFO doc_feat finished [took 19.9639s] -06/11/23 16:39:22| INFO atc_mc finished [took 22.9650s] -06/11/23 16:39:26| INFO kfcv finished [took 27.9671s] -06/11/23 16:39:30| INFO mul_pacc finished [took 34.3899s] -06/11/23 16:39:31| INFO ref finished [took 32.4692s] -06/11/23 16:39:33| INFO mulne_pacc finished [took 37.2045s] -06/11/23 16:39:39| INFO atc_ne finished [took 39.7686s] -06/11/23 16:39:41| INFO mul_cc finished [took 42.9411s] -06/11/23 16:39:41| INFO mulmc_pacc finished [took 44.9724s] -06/11/23 16:39:46| INFO mul_sld finished [took 51.4269s] -06/11/23 16:40:59| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01013) [took 122.2196s] -06/11/23 16:41:24| INFO mul_pacc_gs finished [took 146.7076s] -06/11/23 16:43:40| INFO binne_pacc finished [took 284.1154s] -06/11/23 16:43:52| INFO bin_pacc finished [took 296.8885s] -06/11/23 16:43:54| INFO bin_cc finished [took 297.1714s] -06/11/23 16:43:56| INFO binmc_pacc finished [took 300.6806s] -06/11/23 16:43:57| INFO bin_sld finished [took 302.6966s] -06/11/23 16:45:26| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' -06/11/23 16:45:41| INFO bin_sld_gsq finished [took 405.8247s] -06/11/23 16:47:00| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00949) [took 483.3129s] -06/11/23 16:47:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00785) [took 539.6504s] -06/11/23 16:48:09| INFO mul_sld_gs finished [took 553.8401s] -06/11/23 16:49:34| INFO bin_pacc_gs finished [took 637.2772s] -06/11/23 16:53:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01449) [took 875.8870s] -06/11/23 16:56:08| INFO bin_sld_gs finished [took 1033.4325s] -06/11/23 16:56:08| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 1034.1983s] -06/11/23 16:56:08| INFO Dataset sample 0.90 of dataset imdb_9prevs started -06/11/23 16:56:09| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:09| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method mulmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method mulne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:10| WARNING Method binmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:11| WARNING Method binne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:11| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 16:56:22| INFO doc_feat finished [took 10.1613s] -06/11/23 16:56:25| INFO ref finished [took 13.7569s] -06/11/23 16:56:27| INFO kfcv finished [took 15.6337s] -06/11/23 16:56:29| INFO atc_mc finished [took 18.0104s] -06/11/23 16:56:30| INFO atc_ne finished [took 18.0260s] -06/11/23 16:56:31| INFO mul_cc finished [took 20.6201s] -06/11/23 16:56:40| INFO mul_sld finished [took 31.2942s] -06/11/23 16:56:47| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 16:58:55| INFO bin_cc finished [took 164.5182s] -06/11/23 16:58:59| INFO bin_sld finished [took 170.5046s] -06/11/23 17:02:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.04178) [took 368.6067s] -06/11/23 17:02:29| INFO mul_sld_gs finished [took 380.7801s] -06/11/23 17:02:29| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 381.5305s] ----------------------------------------------------------------------------------------------------- -06/11/23 18:04:06| INFO dataset rcv1_GCAT_9prevs -06/11/23 18:04:12| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs started -06/11/23 18:04:19| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:21| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:22| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:24| WARNING Method binmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:24| WARNING Method mulmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:26| WARNING Method binne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:27| WARNING Method mulne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:28| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:04:29| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:05:10| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -06/11/23 18:05:41| INFO ref finished [took 65.3048s] -06/11/23 18:05:43| INFO kfcv finished [took 66.9585s] -06/11/23 18:05:45| INFO doc_feat finished [took 56.7504s] -06/11/23 18:05:49| INFO mul_cc finished [took 77.6035s] -06/11/23 18:05:49| INFO atc_mc finished [took 66.4650s] -06/11/23 18:05:52| INFO atc_ne finished [took 65.1035s] -06/11/23 18:05:52| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -06/11/23 18:05:56| INFO mul_sld finished [took 101.3427s] -06/11/23 18:08:12| INFO bin_sld finished [took 238.6323s] -06/11/23 18:08:21| INFO bin_cc finished [took 230.3034s] -06/11/23 18:10:59| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00897) [took 402.3046s] -06/11/23 18:11:39| INFO mul_sld_gs finished [took 441.7473s] -06/11/23 18:11:39| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs finished [took 446.6543s] -06/11/23 18:11:39| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs started -06/11/23 18:14:11| INFO mulmc_pacc finished [took 140.5240s] -06/11/23 18:14:13| INFO kfcv finished [took 108.3325s] -06/11/23 18:14:16| INFO doc_feat finished [took 91.1407s] -06/11/23 18:14:21| INFO atc_ne finished [took 96.9645s] -06/11/23 18:14:22| INFO mul_pacc finished [took 154.9757s] -06/11/23 18:14:36| INFO ref finished [took 118.1583s] -06/11/23 18:14:37| INFO atc_mc finished [took 118.5016s] -06/11/23 18:14:41| INFO mulne_pacc finished [took 157.8831s] -06/11/23 18:14:49| INFO mul_cc finished [took 144.8053s] -06/11/23 18:14:50| INFO mul_sld finished [took 188.8450s] -06/11/23 18:15:19| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01452) [took 184.0929s] -06/11/23 18:16:33| INFO mul_pacc_gs finished [took 258.2234s] -06/11/23 18:18:46| INFO binmc_pacc finished [took 417.1372s] -06/11/23 18:18:48| INFO bin_pacc finished [took 422.0619s] -06/11/23 18:18:52| INFO bin_sld finished [took 431.4426s] -06/11/23 18:18:56| INFO binne_pacc finished [took 421.5812s] -06/11/23 18:19:02| INFO bin_cc finished [took 402.4673s] -06/11/23 18:19:32| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 18:20:26| INFO bin_sld_gsq finished [took 522.0734s] -06/11/23 18:21:11| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01038) [took 540.4022s] -06/11/23 18:21:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00821) [took 600.6611s] -06/11/23 18:22:25| INFO mul_sld_gs finished [took 642.1063s] -06/11/23 18:24:14| INFO bin_pacc_gs finished [took 723.2605s] -06/11/23 18:26:54| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00816) [took 911.5066s] -06/11/23 18:29:56| INFO bin_sld_gs finished [took 1093.4674s] -06/11/23 18:29:56| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs finished [took 1096.7184s] -06/11/23 18:29:56| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs started -06/11/23 18:32:21| INFO ref finished [took 89.3355s] -06/11/23 18:32:33| INFO doc_feat finished [took 91.9119s] -06/11/23 18:32:35| INFO mulmc_pacc finished [took 147.2084s] -06/11/23 18:32:38| INFO mulne_pacc finished [took 137.0643s] -06/11/23 18:32:54| INFO atc_mc finished [took 117.6847s] -06/11/23 18:32:56| INFO kfcv finished [took 129.8598s] -06/11/23 18:33:00| INFO mul_pacc finished [took 174.5769s] -06/11/23 18:33:00| INFO mul_sld finished [took 181.1734s] -06/11/23 18:33:03| INFO atc_ne finished [took 123.9984s] -06/11/23 18:33:09| INFO mul_cc finished [took 148.8635s] -06/11/23 18:33:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00629) [took 177.7598s] -06/11/23 18:34:51| INFO mul_pacc_gs finished [took 256.4186s] -06/11/23 18:37:10| INFO bin_pacc finished [took 425.7912s] -06/11/23 18:37:12| INFO binmc_pacc finished [took 425.7599s] -06/11/23 18:37:14| INFO binne_pacc finished [took 424.0101s] -06/11/23 18:37:18| INFO bin_sld finished [took 440.4389s] -06/11/23 18:37:22| INFO bin_cc finished [took 407.2413s] -06/11/23 18:37:52| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 18:38:51| INFO bin_sld_gsq finished [took 529.6242s] -06/11/23 18:39:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00489) [took 541.8062s] -06/11/23 18:40:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00615) [took 601.7630s] -06/11/23 18:40:45| INFO mul_sld_gs finished [took 644.5111s] -06/11/23 18:42:37| INFO bin_pacc_gs finished [took 729.3942s] -06/11/23 18:45:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00490) [took 936.3088s] -06/11/23 18:48:37| INFO bin_sld_gs finished [took 1117.0610s] -06/11/23 18:48:37| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs finished [took 1120.9681s] -06/11/23 18:48:37| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs started -06/11/23 18:51:02| INFO doc_feat finished [took 79.6380s] -06/11/23 18:51:20| INFO mulne_pacc finished [took 144.5625s] -06/11/23 18:51:30| INFO mul_sld finished [took 171.1473s] -06/11/23 18:51:35| INFO mulmc_pacc finished [took 166.1468s] -06/11/23 18:51:39| INFO mul_pacc finished [took 172.9449s] -06/11/23 18:51:43| INFO ref finished [took 132.2492s] -06/11/23 18:51:45| INFO kfcv finished [took 137.9538s] -06/11/23 18:51:52| INFO atc_mc finished [took 137.7185s] -06/11/23 18:51:54| INFO atc_ne finished [took 134.1066s] -06/11/23 18:51:59| INFO mul_cc finished [took 159.0670s] -06/11/23 18:52:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01049) [took 180.3366s] -06/11/23 18:53:38| INFO mul_pacc_gs finished [took 266.6075s] -06/11/23 18:56:00| INFO bin_sld finished [took 441.9022s] -06/11/23 18:56:02| INFO binne_pacc finished [took 431.1354s] -06/11/23 18:56:02| INFO binmc_pacc finished [took 434.5268s] -06/11/23 18:56:04| INFO bin_pacc finished [took 438.8400s] -06/11/23 18:56:07| INFO bin_cc finished [took 412.8827s] -06/11/23 18:56:38| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 18:57:38| INFO bin_sld_gsq finished [took 534.9970s] -06/11/23 18:58:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00762) [took 549.5790s] -06/11/23 18:58:58| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00692) [took 616.7506s] -06/11/23 18:59:43| INFO mul_sld_gs finished [took 661.6976s] -06/11/23 19:01:20| INFO bin_pacc_gs finished [took 735.1934s] -06/11/23 19:04:29| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00601) [took 948.3129s] -06/11/23 19:07:30| INFO bin_sld_gs finished [took 1129.1432s] -06/11/23 19:07:30| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs finished [took 1133.2853s] -06/11/23 19:07:30| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs started -06/11/23 19:10:11| INFO doc_feat finished [took 90.8028s] -06/11/23 19:10:12| INFO mul_sld finished [took 159.3725s] -06/11/23 19:10:17| INFO atc_mc finished [took 108.3872s] -06/11/23 19:10:20| INFO mulmc_pacc finished [took 158.7937s] -06/11/23 19:10:27| INFO kfcv finished [took 125.4384s] -06/11/23 19:10:32| INFO mul_cc finished [took 134.1449s] -06/11/23 19:10:33| INFO atc_ne finished [took 115.0137s] -06/11/23 19:10:33| INFO mul_pacc finished [took 173.4398s] -06/11/23 19:10:35| INFO ref finished [took 127.6900s] -06/11/23 19:10:35| INFO mulne_pacc finished [took 158.1989s] -06/11/23 19:11:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00893) [took 181.2645s] -06/11/23 19:12:28| INFO mul_pacc_gs finished [took 263.1619s] -06/11/23 19:14:45| INFO bin_sld finished [took 432.8401s] -06/11/23 19:14:48| INFO bin_pacc finished [took 430.1210s] -06/11/23 19:14:54| INFO binmc_pacc finished [took 433.8715s] -06/11/23 19:14:58| INFO bin_cc finished [took 405.7688s] -06/11/23 19:14:59| INFO binne_pacc finished [took 435.7315s] -06/11/23 19:15:29| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 19:16:36| INFO bin_sld_gsq finished [took 539.4078s] -06/11/23 19:17:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00831) [took 545.8362s] -06/11/23 19:17:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00797) [took 609.7895s] -06/11/23 19:18:32| INFO mul_sld_gs finished [took 657.1765s] -06/11/23 19:20:08| INFO bin_pacc_gs finished [took 728.9184s] -06/11/23 19:23:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00738) [took 966.8750s] -06/11/23 19:26:42| INFO bin_sld_gs finished [took 1148.2428s] -06/11/23 19:26:42| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs finished [took 1152.4463s] -06/11/23 19:26:43| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs started -06/11/23 19:29:16| INFO mul_pacc finished [took 142.3375s] -06/11/23 19:29:26| INFO doc_feat finished [took 92.0123s] -06/11/23 19:29:29| INFO atc_ne finished [took 96.1697s] -06/11/23 19:29:32| INFO mul_sld finished [took 164.7852s] -06/11/23 19:29:33| INFO ref finished [took 114.3664s] -06/11/23 19:29:36| INFO kfcv finished [took 118.4300s] -06/11/23 19:29:37| INFO atc_mc finished [took 111.6950s] -06/11/23 19:29:37| INFO mul_cc finished [took 127.8860s] -06/11/23 19:29:39| INFO mulmc_pacc finished [took 162.5217s] -06/11/23 19:29:46| INFO mulne_pacc finished [took 159.7535s] -06/11/23 19:30:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00832) [took 183.2267s] -06/11/23 19:31:42| INFO mul_pacc_gs finished [took 263.2959s] -06/11/23 19:33:48| INFO bin_pacc finished [took 415.7355s] -06/11/23 19:33:49| INFO binne_pacc finished [took 411.7032s] -06/11/23 19:33:49| INFO bin_sld finished [took 423.6935s] -06/11/23 19:33:56| INFO binmc_pacc finished [took 422.0731s] -06/11/23 19:34:02| INFO bin_cc finished [took 394.8074s] -06/11/23 19:34:45| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 19:35:33| INFO bin_sld_gsq finished [took 523.6794s] -06/11/23 19:36:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00651) [took 539.0149s] -06/11/23 19:36:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00646) [took 605.4721s] -06/11/23 19:37:37| INFO mul_sld_gs finished [took 647.9998s] -06/11/23 19:39:13| INFO bin_pacc_gs finished [took 722.3065s] -06/11/23 19:42:14| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00635) [took 926.2548s] -06/11/23 19:45:13| INFO bin_sld_gs finished [took 1105.8303s] -06/11/23 19:45:13| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs finished [took 1110.5345s] -06/11/23 19:45:13| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs started -06/11/23 19:47:55| INFO mul_pacc finished [took 151.2717s] -06/11/23 19:48:01| INFO mul_sld finished [took 164.7303s] -06/11/23 19:48:02| INFO doc_feat finished [took 95.7218s] -06/11/23 19:48:20| INFO kfcv finished [took 132.6414s] -06/11/23 19:48:26| INFO ref finished [took 136.4855s] -06/11/23 19:48:27| INFO mulmc_pacc finished [took 180.2510s] -06/11/23 19:48:30| INFO mulne_pacc finished [took 173.6996s] -06/11/23 19:48:33| INFO atc_mc finished [took 135.5939s] -06/11/23 19:48:34| INFO atc_ne finished [took 129.3719s] -06/11/23 19:48:35| INFO mul_cc finished [took 153.9587s] -06/11/23 19:48:47| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00782) [took 177.0918s] -06/11/23 19:50:13| INFO mul_pacc_gs finished [took 262.7047s] -06/11/23 19:52:34| INFO bin_pacc finished [took 431.9695s] -06/11/23 19:52:36| INFO binmc_pacc finished [took 430.1566s] -06/11/23 19:52:43| INFO bin_sld finished [took 446.4452s] -06/11/23 19:52:44| INFO bin_cc finished [took 407.8850s] -06/11/23 19:52:47| INFO binne_pacc finished [took 438.4423s] -06/11/23 19:53:19| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' -06/11/23 19:54:09| INFO bin_sld_gsq finished [took 528.9254s] -06/11/23 19:54:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00669) [took 545.8164s] -06/11/23 19:55:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00814) [took 619.0258s] -06/11/23 19:56:21| INFO mul_sld_gs finished [took 661.4303s] -06/11/23 19:57:51| INFO bin_pacc_gs finished [took 733.3970s] -06/11/23 20:00:54| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00795) [took 935.9973s] -06/11/23 20:03:55| INFO bin_sld_gs finished [took 1117.3981s] -06/11/23 20:03:55| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs finished [took 1121.8060s] -06/11/23 20:03:55| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs started -06/11/23 20:06:27| INFO mul_sld finished [took 147.0727s] -06/11/23 20:06:30| INFO doc_feat finished [took 81.1117s] -06/11/23 20:06:48| INFO mul_pacc finished [took 162.2312s] -06/11/23 20:07:04| INFO kfcv finished [took 133.4389s] -06/11/23 20:07:04| INFO ref finished [took 132.6728s] -06/11/23 20:07:05| INFO mulne_pacc finished [took 171.0782s] -06/11/23 20:07:08| INFO mulmc_pacc finished [took 179.6909s] -06/11/23 20:07:10| INFO atc_mc finished [took 130.5941s] -06/11/23 20:07:14| INFO mul_cc finished [took 150.3795s] -06/11/23 20:07:15| INFO atc_ne finished [took 131.0309s] -06/11/23 20:07:29| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00813) [took 177.7289s] -06/11/23 20:08:49| INFO mul_pacc_gs finished [took 257.9675s] -06/11/23 20:10:44| INFO bin_pacc finished [took 399.4800s] -06/11/23 21:01:51| INFO bin_pacc_gs finished [took 3446.1854s] -06/11/23 21:03:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00684) [took 3563.0699s] -06/11/23 21:04:07| INFO mul_sld_gs finished [took 3606.0194s] -06/11/23 21:08:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00519) [took 3863.9570s] -06/11/23 21:11:26| INFO bin_sld_gs finished [took 4046.4500s] -06/11/23 21:11:26| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs finished [took 4051.2016s] -06/11/23 21:11:26| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs started -06/11/23 21:11:31| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:32| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:34| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:35| WARNING Method mul_sld_gsq failed. Exception: a must be greater than 0 unless no samples are taken -06/11/23 21:11:36| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:38| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 21:11:38| WARNING Method binmc_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:40| WARNING Method mulmc_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 21:11:40| WARNING Method binne_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:42| WARNING Method mulne_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 21:11:42| WARNING Method bin_pacc_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:11:44| WARNING Method mul_pacc_gs failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 21:11:44| WARNING Method bin_cc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 21:12:27| INFO mul_sld finished [took 56.8958s] -06/11/23 21:12:32| INFO ref finished [took 44.3311s] -06/11/23 21:12:32| INFO doc_feat finished [took 41.1551s] -06/11/23 21:12:33| INFO kfcv finished [took 46.1873s] -06/11/23 21:12:36| INFO atc_mc finished [took 47.9541s] -06/11/23 21:12:37| INFO mul_cc finished [took 51.8838s] -06/11/23 21:12:37| INFO atc_ne finished [took 47.4962s] -06/11/23 21:16:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00843) [took 312.8612s] -06/11/23 21:17:24| INFO mul_sld_gs finished [took 351.5693s] -06/11/23 21:17:24| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs finished [took 357.7321s] -06/11/23 21:20:10| INFO dataset rcv1_MCAT_9prevs -06/11/23 21:20:18| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs started -06/11/23 21:23:12| INFO doc_feat finished [took 81.2849s] -06/11/23 21:23:21| INFO mul_pacc finished [took 168.3242s] -06/11/23 21:23:30| INFO mulmc_pacc finished [took 173.2730s] -06/11/23 21:23:35| INFO atc_mc finished [took 115.3803s] -06/11/23 21:23:41| INFO ref finished [took 125.5611s] -06/11/23 21:23:41| INFO kfcv finished [took 136.3040s] -06/11/23 21:23:51| INFO mulne_pacc finished [took 185.3346s] -06/11/23 21:23:58| INFO atc_ne finished [took 129.7752s] -06/11/23 21:23:59| INFO mul_cc finished [took 164.3501s] -06/11/23 21:24:26| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.02036) [took 203.9839s] -06/11/23 21:24:32| INFO mul_sld finished [took 249.6979s] -06/11/23 21:25:50| INFO mul_pacc_gs finished [took 287.7634s] -06/11/23 21:28:08| INFO binne_pacc finished [took 443.1314s] -06/11/23 21:28:11| INFO bin_cc finished [took 427.7416s] -06/11/23 21:28:26| INFO bin_pacc finished [took 475.7859s] -06/11/23 21:28:28| INFO binmc_pacc finished [took 472.2702s] -06/11/23 21:28:33| INFO bin_sld finished [took 492.0457s] -06/11/23 21:29:19| INFO mul_sld_gsq finished [took 529.8190s] -06/11/23 21:29:26| INFO bin_sld_gsq finished [took 539.2552s] -06/11/23 21:30:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00591) [took 573.3773s] -06/11/23 21:31:27| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00754) [took 661.4704s] -06/11/23 21:32:10| INFO mul_sld_gs finished [took 704.6441s] -06/11/23 21:33:40| INFO bin_pacc_gs finished [took 763.3541s] -06/11/23 21:36:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00760) [took 965.4559s] -06/11/23 21:39:31| INFO bin_sld_gs finished [took 1146.5622s] -06/11/23 21:39:31| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs finished [took 1152.1700s] -06/11/23 21:39:31| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs started -06/11/23 21:42:13| INFO doc_feat finished [took 88.9970s] -06/11/23 21:42:24| INFO mul_pacc finished [took 161.9999s] -06/11/23 21:42:31| INFO mulmc_pacc finished [took 159.0109s] -06/11/23 21:42:34| INFO mul_sld finished [took 179.5397s] -06/11/23 21:42:42| INFO kfcv finished [took 138.3784s] -06/11/23 21:42:42| INFO atc_mc finished [took 127.5150s] -06/11/23 21:42:45| INFO ref finished [took 133.9163s] -06/11/23 21:42:48| INFO mulne_pacc finished [took 170.3191s] -06/11/23 21:42:53| INFO mul_cc finished [took 153.9952s] -06/11/23 21:42:57| INFO atc_ne finished [took 133.9857s] -06/11/23 21:43:07| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01406) [took 179.2817s] -06/11/23 21:44:27| INFO mul_pacc_gs finished [took 259.4430s] -06/11/23 21:46:57| INFO bin_pacc finished [took 435.9586s] -06/11/23 21:47:02| INFO binmc_pacc finished [took 436.7170s] -06/11/23 21:47:02| INFO bin_sld finished [took 448.6901s] -06/11/23 21:47:03| INFO binne_pacc finished [took 430.3933s] -06/11/23 21:47:06| INFO bin_cc finished [took 413.3717s] -06/11/23 21:47:44| INFO mul_sld_gsq finished [took 485.8565s] -06/11/23 21:48:40| INFO bin_sld_gsq finished [took 542.6385s] -06/11/23 21:49:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01029) [took 558.1111s] -06/11/23 21:49:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00862) [took 617.7204s] -06/11/23 21:50:38| INFO mul_sld_gs finished [took 661.3619s] -06/11/23 21:52:31| INFO bin_pacc_gs finished [took 747.8605s] -06/11/23 21:55:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00631) [took 947.6305s] -06/11/23 21:58:25| INFO bin_sld_gs finished [took 1128.9705s] -06/11/23 21:58:25| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs finished [took 1133.8038s] -06/11/23 21:58:25| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs started -06/11/23 22:01:11| INFO doc_feat finished [took 91.6284s] -06/11/23 22:01:13| INFO mul_pacc finished [took 157.8979s] -06/11/23 22:01:21| INFO ref finished [took 117.5064s] -06/11/23 22:01:29| INFO mulmc_pacc finished [took 171.3367s] -06/11/23 22:01:34| INFO kfcv finished [took 138.8623s] -06/11/23 22:01:44| INFO atc_ne finished [took 127.4515s] -06/11/23 22:01:45| INFO mulne_pacc finished [took 175.7659s] -06/11/23 22:01:45| INFO atc_mc finished [took 134.5717s] -06/11/23 22:01:47| INFO mul_sld finished [took 198.7132s] -06/11/23 22:01:53| INFO mul_cc finished [took 156.7010s] -06/11/23 22:01:56| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00920) [took 169.8061s] -06/11/23 22:03:15| INFO mul_pacc_gs finished [took 248.7885s] -06/11/23 22:05:47| INFO bin_pacc finished [took 433.9454s] -06/11/23 22:05:52| INFO binmc_pacc finished [took 435.7566s] -06/11/23 22:05:55| INFO binne_pacc finished [took 432.5216s] -06/11/23 22:06:02| INFO bin_sld finished [took 455.4425s] -06/11/23 22:06:03| INFO bin_cc finished [took 409.9712s] -06/11/23 22:06:43| INFO mul_sld_gsq finished [took 490.7571s] -06/11/23 22:07:34| INFO bin_sld_gsq finished [took 542.4371s] -06/11/23 22:08:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00585) [took 557.7911s] -06/11/23 22:08:29| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 598.8338s] -06/11/23 22:09:12| INFO mul_sld_gs finished [took 641.4050s] -06/11/23 22:11:15| INFO bin_pacc_gs finished [took 742.8423s] -06/11/23 22:14:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00443) [took 940.7448s] -06/11/23 22:17:12| INFO bin_sld_gs finished [took 1122.5660s] -06/11/23 22:17:12| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs finished [took 1126.9865s] -06/11/23 22:17:12| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs started -06/11/23 22:19:32| INFO ref finished [took 85.6333s] -06/11/23 22:19:43| INFO mulmc_pacc finished [took 138.3833s] -06/11/23 22:19:44| INFO doc_feat finished [took 84.2844s] -06/11/23 22:19:54| INFO atc_ne finished [took 99.6744s] -06/11/23 22:19:57| INFO kfcv finished [took 114.5018s] -06/11/23 22:19:59| INFO mul_cc finished [took 123.4161s] -06/11/23 22:20:05| INFO mul_pacc finished [took 163.4607s] -06/11/23 22:20:09| INFO mul_sld finished [took 173.7721s] -06/11/23 22:20:16| INFO mulne_pacc finished [took 162.8502s] -06/11/23 22:20:16| INFO atc_mc finished [took 124.1504s] -06/11/23 22:20:36| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00596) [took 169.9575s] -06/11/23 22:21:55| INFO mul_pacc_gs finished [took 248.8139s] -06/11/23 22:24:04| INFO binmc_pacc finished [took 400.7570s] -06/11/23 22:24:12| INFO bin_pacc finished [took 411.2454s] -06/11/23 22:24:21| INFO binne_pacc finished [took 414.5496s] -06/11/23 22:24:21| INFO bin_cc finished [took 389.2880s] -06/11/23 22:24:25| INFO bin_sld finished [took 431.6256s] -06/11/23 22:25:13| INFO mul_sld_gsq finished [took 474.0621s] -06/11/23 22:25:54| INFO bin_sld_gsq finished [took 515.9435s] -06/11/23 22:26:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00626) [took 539.2315s] -06/11/23 22:27:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00612) [took 594.2222s] -06/11/23 22:27:54| INFO mul_sld_gs finished [took 637.0683s] -06/11/23 22:29:48| INFO bin_pacc_gs finished [took 725.8952s] -06/11/23 22:33:01| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00682) [took 945.7122s] -06/11/23 22:36:02| INFO bin_sld_gs finished [took 1126.7133s] -06/11/23 22:36:02| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs finished [took 1130.5566s] -06/11/23 22:36:02| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs started -06/11/23 22:38:42| INFO doc_feat finished [took 76.3995s] -06/11/23 22:39:04| INFO mul_pacc finished [took 170.8108s] -06/11/23 22:39:11| INFO mulmc_pacc finished [took 169.1537s] -06/11/23 22:39:22| INFO ref finished [took 134.5551s] -06/11/23 22:39:23| INFO kfcv finished [took 144.5049s] -06/11/23 22:39:28| INFO mul_sld finished [took 201.7112s] -06/11/23 22:39:31| INFO atc_ne finished [took 127.5871s] -06/11/23 22:39:32| INFO atc_mc finished [took 141.1615s] -06/11/23 22:39:33| INFO mulne_pacc finished [took 181.7155s] -06/11/23 22:39:34| INFO mul_cc finished [took 156.9505s] -06/11/23 22:39:37| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00848) [took 178.6463s] -06/11/23 22:41:02| INFO mul_pacc_gs finished [took 264.1109s] -06/11/23 22:43:34| INFO binmc_pacc finished [took 438.9742s] -06/11/23 22:43:34| INFO bin_pacc finished [took 442.7005s] -06/11/23 22:43:43| INFO bin_sld finished [took 458.4940s] -06/11/23 22:43:44| INFO binne_pacc finished [took 443.0455s] -06/11/23 22:43:55| INFO bin_cc finished [took 423.6361s] -06/11/23 22:44:45| INFO mul_sld_gsq finished [took 514.4586s] -06/11/23 22:45:32| INFO bin_sld_gsq finished [took 562.7681s] -06/11/23 22:46:12| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00790) [took 574.1156s] -06/11/23 22:46:42| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00824) [took 633.5645s] -06/11/23 22:47:24| INFO mul_sld_gs finished [took 676.3552s] -06/11/23 22:49:27| INFO bin_pacc_gs finished [took 768.3574s] -06/11/23 22:52:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00861) [took 979.5729s] -06/11/23 22:55:32| INFO bin_sld_gs finished [took 1164.7331s] -06/11/23 22:55:32| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs finished [took 1169.2748s] -06/11/23 22:55:32| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs started -06/11/23 22:58:47| INFO doc_feat finished [took 112.6375s] -06/11/23 22:59:00| INFO kfcv finished [took 150.2412s] -06/11/23 22:59:00| INFO mul_pacc finished [took 197.0521s] -06/11/23 22:59:06| INFO mul_sld finished [took 209.9482s] -06/11/23 22:59:07| INFO mulmc_pacc finished [took 198.8911s] -06/11/23 22:59:07| INFO ref finished [took 148.7702s] -06/11/23 22:59:16| INFO atc_ne finished [took 143.7730s] -06/11/23 22:59:18| INFO atc_mc finished [took 151.2783s] -06/11/23 22:59:19| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01122) [took 190.1694s] -06/11/23 22:59:26| INFO mul_cc finished [took 179.0100s] -06/11/23 22:59:33| INFO mulne_pacc finished [took 211.9002s] -06/11/23 23:00:52| INFO mul_pacc_gs finished [took 283.5718s] -06/11/23 23:03:21| INFO bin_sld finished [took 466.9682s] -06/11/23 23:03:24| INFO binmc_pacc finished [took 456.9500s] -06/11/23 23:03:25| INFO bin_pacc finished [took 464.1421s] -06/11/23 23:03:39| INFO bin_cc finished [took 445.8302s] -06/11/23 23:03:40| INFO binne_pacc finished [took 466.3427s] -06/11/23 23:04:10| INFO mul_sld_gsq finished [took 509.0584s] -06/11/23 23:04:56| INFO bin_sld_gsq finished [took 556.7578s] -06/11/23 23:05:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01033) [took 581.0899s] -06/11/23 23:06:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00791) [took 636.6955s] -06/11/23 23:07:00| INFO mul_sld_gs finished [took 682.1829s] -06/11/23 23:08:59| INFO bin_pacc_gs finished [took 772.0584s] -06/11/23 23:11:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00722) [took 966.7367s] -06/11/23 23:14:47| INFO bin_sld_gs finished [took 1150.2138s] -06/11/23 23:14:47| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs finished [took 1155.4582s] -06/11/23 23:14:47| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs started -06/11/23 23:17:29| INFO mulmc_pacc finished [took 140.6592s] -06/11/23 23:17:37| INFO doc_feat finished [took 93.5374s] -06/11/23 23:17:58| INFO mul_pacc finished [took 176.1101s] -06/11/23 23:18:06| INFO mulne_pacc finished [took 168.2578s] -06/11/23 23:18:06| INFO ref finished [took 138.9670s] -06/11/23 23:18:13| INFO atc_ne finished [took 133.8368s] -06/11/23 23:18:13| INFO mul_cc finished [took 156.3809s] -06/11/23 23:18:14| INFO atc_mc finished [took 140.7865s] -06/11/23 23:18:15| INFO kfcv finished [took 150.8563s] -06/11/23 23:18:28| INFO mul_sld finished [took 213.5502s] -06/11/23 23:18:37| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00696) [took 184.7202s] -06/11/23 23:20:04| INFO mul_pacc_gs finished [took 271.8620s] -06/11/23 23:22:38| INFO binne_pacc finished [took 444.7274s] -06/11/23 23:22:39| INFO binmc_pacc finished [took 454.8866s] -06/11/23 23:22:39| INFO bin_pacc finished [took 458.6381s] -06/11/23 23:22:47| INFO bin_cc finished [took 432.1075s] -06/11/23 23:22:54| INFO bin_sld finished [took 480.5003s] -06/11/23 23:23:33| INFO mul_sld_gsq finished [took 514.0066s] -06/11/23 23:24:13| INFO bin_sld_gsq finished [took 554.7885s] -06/11/23 23:24:57| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00603) [took 574.6463s] -06/11/23 23:25:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00528) [took 609.3079s] -06/11/23 23:25:51| INFO mul_sld_gs finished [took 654.2885s] -06/11/23 23:28:10| INFO bin_pacc_gs finished [took 767.8253s] -06/11/23 23:30:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00733) [took 947.2105s] -06/11/23 23:33:48| INFO bin_sld_gs finished [took 1132.1309s] -06/11/23 23:33:48| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs finished [took 1140.6743s] -06/11/23 23:33:48| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs started -06/11/23 23:36:55| INFO doc_feat finished [took 101.6311s] -06/11/23 23:37:16| INFO atc_ne finished [took 124.5854s] -06/11/23 23:37:39| INFO mulne_pacc finished [took 198.5060s] -06/11/23 23:37:42| INFO mulmc_pacc finished [took 210.8408s] -06/11/23 23:37:43| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01019) [took 194.4422s] -06/11/23 23:37:44| INFO mul_pacc finished [took 224.0155s] -06/11/23 23:37:44| INFO kfcv finished [took 178.0222s] -06/11/23 23:37:47| INFO ref finished [took 176.1278s] -06/11/23 23:37:55| INFO atc_mc finished [took 173.0154s] -06/11/23 23:37:58| INFO mul_cc finished [took 198.1420s] -06/11/23 23:38:10| INFO mul_sld finished [took 258.4898s] -06/11/23 23:39:25| INFO mul_pacc_gs finished [took 297.0552s] -06/11/23 23:41:55| INFO binmc_pacc finished [took 471.8397s] -06/11/23 23:42:06| INFO binne_pacc finished [took 470.6917s] -06/11/23 23:42:08| INFO bin_pacc finished [took 490.2025s] -06/11/23 23:42:11| INFO bin_sld finished [took 500.3974s] -06/11/23 23:42:17| INFO bin_cc finished [took 463.2719s] -06/11/23 23:42:33| INFO mul_sld_gsq finished [took 515.9211s] -06/11/23 23:43:19| INFO bin_sld_gsq finished [took 563.2792s] -06/11/23 23:44:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01110) [took 580.7011s] -06/11/23 23:44:33| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00755) [took 638.9055s] -06/11/23 23:45:18| INFO mul_sld_gs finished [took 683.7473s] -06/11/23 23:47:14| INFO bin_pacc_gs finished [took 769.5136s] -06/11/23 23:50:19| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00653) [took 986.1331s] -06/11/23 23:53:23| INFO bin_sld_gs finished [took 1170.3407s] -06/11/23 23:53:23| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs finished [took 1175.4004s] -06/11/23 23:53:23| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs started -06/11/23 23:53:29| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:31| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:32| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:34| WARNING Method mul_sld_gsq failed. Exception: a must be greater than 0 unless no samples are taken -06/11/23 23:53:34| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:36| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 23:53:37| WARNING Method binmc_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:38| WARNING Method binne_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:39| WARNING Method mulmc_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 23:53:41| WARNING Method mulne_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 23:53:41| WARNING Method bin_pacc_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:53:43| WARNING Method mul_pacc_gs failed. Exception: could not broadcast input array from shape (3,) into shape (4,) -06/11/23 23:53:44| WARNING Method bin_cc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -06/11/23 23:54:33| INFO ref finished [took 46.5615s] -06/11/23 23:54:34| INFO doc_feat finished [took 43.4254s] -06/11/23 23:54:34| INFO kfcv finished [took 48.7260s] -06/11/23 23:54:34| INFO mul_sld finished [took 64.5496s] -06/11/23 23:54:38| INFO atc_mc finished [took 49.9172s] -06/11/23 23:54:39| INFO atc_ne finished [took 49.8635s] -06/11/23 23:54:39| INFO mul_cc finished [took 54.7417s] -06/11/23 23:58:27| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01247) [took 295.7388s] -06/11/23 23:59:08| INFO mul_sld_gs finished [took 336.2009s] -06/11/23 23:59:08| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs finished [took 344.1389s] ----------------------------------------------------------------------------------------------------- -07/11/23 01:05:25| INFO dataset rcv1_CCAT_9prevs -07/11/23 01:05:30| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started -07/11/23 01:06:23| INFO ref finished [took 48.3560s] -07/11/23 01:06:29| INFO atc_mc finished [took 52.9929s] -07/11/23 01:06:30| INFO atc_ne finished [took 53.3908s] -07/11/23 01:07:06| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -07/11/23 01:11:38| INFO mul_sld_gsq finished [took 364.0698s] -07/11/23 01:13:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00644) [took 499.4945s] -07/11/23 01:14:34| INFO mul_sld_gs finished [took 542.6047s] -07/11/23 01:18:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 750.8663s] -07/11/23 01:21:01| INFO bin_sld_gs finished [took 930.1356s] -07/11/23 01:21:01| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 931.4321s] -07/11/23 01:21:01| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started -07/11/23 01:22:02| INFO ref finished [took 55.2212s] -07/11/23 01:22:07| INFO atc_mc finished [took 59.3890s] -07/11/23 01:22:09| INFO atc_ne finished [took 59.7388s] -07/11/23 01:27:21| INFO mul_sld_gsq finished [took 375.2352s] -07/11/23 01:27:24| INFO bin_sld_gsq finished [took 379.6159s] -07/11/23 01:29:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01221) [took 502.0302s] -07/11/23 01:30:08| INFO mul_sld_gs finished [took 545.0285s] -07/11/23 01:34:25| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00768) [took 802.3620s] -07/11/23 01:37:25| INFO bin_sld_gs finished [took 982.3260s] -07/11/23 01:37:25| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 983.7236s] -07/11/23 01:37:25| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started -07/11/23 01:38:20| INFO ref finished [took 49.9803s] -07/11/23 01:38:25| INFO atc_mc finished [took 53.3765s] -07/11/23 01:38:26| INFO atc_ne finished [took 53.8925s] -07/11/23 01:43:41| INFO mul_sld_gsq finished [took 372.2608s] -07/11/23 01:43:45| INFO bin_sld_gsq finished [took 377.3380s] -07/11/23 01:45:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00872) [took 497.5768s] -07/11/23 01:46:28| INFO mul_sld_gs finished [took 540.8267s] -07/11/23 01:51:09| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00782) [took 822.2849s] -07/11/23 01:54:10| INFO bin_sld_gs finished [took 1003.7804s] -07/11/23 01:54:10| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1005.2506s] -07/11/23 01:54:10| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started -07/11/23 01:55:05| INFO ref finished [took 49.8884s] -07/11/23 01:55:09| INFO atc_mc finished [took 53.3594s] -07/11/23 01:55:10| INFO atc_ne finished [took 53.5162s] -07/11/23 02:00:25| INFO mul_sld_gsq finished [took 371.4460s] -07/11/23 02:00:41| INFO bin_sld_gsq finished [took 387.6183s] -07/11/23 02:02:30| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01037) [took 498.0096s] -07/11/23 02:03:11| INFO mul_sld_gs finished [took 539.1531s] -07/11/23 02:07:53| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01112) [took 821.8730s] -07/11/23 02:10:52| INFO bin_sld_gs finished [took 1001.0803s] -07/11/23 02:10:52| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1002.3085s] -07/11/23 02:10:52| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started -07/11/23 02:11:44| INFO ref finished [took 47.2218s] -07/11/23 02:11:48| INFO atc_mc finished [took 49.6349s] -07/11/23 02:11:50| INFO atc_ne finished [took 50.9082s] -07/11/23 02:16:51| INFO mul_sld_gsq finished [took 354.3706s] -07/11/23 02:17:11| INFO bin_sld_gsq finished [took 376.0124s] -07/11/23 02:18:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00572) [took 476.0587s] -07/11/23 02:19:33| INFO mul_sld_gs finished [took 518.5692s] -07/11/23 02:24:17| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00653) [took 803.4978s] -07/11/23 02:27:16| INFO bin_sld_gs finished [took 982.4395s] -07/11/23 02:27:16| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 983.7838s] -07/11/23 02:27:16| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started -07/11/23 02:28:08| INFO ref finished [took 46.6191s] -07/11/23 02:28:13| INFO atc_mc finished [took 50.3543s] -07/11/23 02:28:15| INFO atc_ne finished [took 51.6601s] -07/11/23 02:33:15| INFO mul_sld_gsq finished [took 354.6014s] -07/11/23 02:33:34| INFO bin_sld_gsq finished [took 374.7872s] -07/11/23 02:35:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00637) [took 475.9302s] -07/11/23 02:35:57| INFO mul_sld_gs finished [took 518.5425s] -07/11/23 02:40:20| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00544) [took 782.7268s] -07/11/23 02:43:18| INFO bin_sld_gs finished [took 960.6334s] -07/11/23 02:43:18| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 961.9030s] -07/11/23 02:43:18| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started -07/11/23 02:44:10| INFO ref finished [took 47.1234s] -07/11/23 02:44:14| INFO atc_mc finished [took 49.9871s] -07/11/23 02:44:16| INFO atc_ne finished [took 50.9160s] -07/11/23 02:49:19| INFO mul_sld_gsq finished [took 357.0613s] -07/11/23 02:49:30| INFO bin_sld_gsq finished [took 368.8000s] -07/11/23 02:51:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00765) [took 475.7332s] -07/11/23 02:51:59| INFO mul_sld_gs finished [took 518.6671s] -07/11/23 02:56:28| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01235) [took 788.7117s] -07/11/23 02:59:28| INFO bin_sld_gs finished [took 968.7653s] -07/11/23 02:59:28| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 970.1516s] -07/11/23 02:59:28| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started -07/11/23 03:00:20| INFO ref finished [took 46.9898s] -07/11/23 03:00:24| INFO atc_mc finished [took 49.8768s] -07/11/23 03:00:25| INFO atc_ne finished [took 49.6324s] -07/11/23 03:05:23| INFO mul_sld_gsq finished [took 350.7932s] -07/11/23 03:05:32| INFO bin_sld_gsq finished [took 360.8665s] -07/11/23 03:07:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01306) [took 474.6581s] -07/11/23 03:08:07| INFO mul_sld_gs finished [took 516.4890s] -07/11/23 03:12:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00894) [took 774.9140s] -07/11/23 03:15:29| INFO bin_sld_gs finished [took 959.3579s] -07/11/23 03:15:29| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 960.6992s] -07/11/23 03:15:29| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started -07/11/23 03:16:21| INFO ref finished [took 47.3281s] -07/11/23 03:16:25| INFO atc_mc finished [took 49.8016s] -07/11/23 03:16:28| INFO atc_ne finished [took 51.2288s] -07/11/23 03:21:16| INFO mul_sld_gsq finished [took 343.2861s] -07/11/23 03:21:22| INFO bin_sld_gsq finished [took 349.6065s] -07/11/23 03:23:20| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00808) [took 468.7910s] -07/11/23 03:24:01| INFO mul_sld_gs finished [took 509.9001s] -07/11/23 03:28:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00604) [took 752.8185s] -07/11/23 03:31:01| INFO bin_sld_gs finished [took 930.3934s] -07/11/23 03:31:01| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 931.7055s] -07/11/23 03:31:29| INFO dataset imdb_9prevs -07/11/23 03:31:37| INFO Dataset sample 0.10 of dataset imdb_9prevs started -07/11/23 03:31:49| INFO ref finished [took 11.4117s] -07/11/23 03:31:53| INFO atc_mc finished [took 14.8218s] -07/11/23 03:31:53| INFO atc_ne finished [took 14.8359s] -07/11/23 03:32:11| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -07/11/23 03:32:56| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 -07/11/23 03:36:32| INFO mul_sld_gsq finished [took 294.6812s] -07/11/23 03:38:05| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.03127) [took 387.7698s] -07/11/23 03:38:18| INFO mul_sld_gs finished [took 400.7660s] -07/11/23 03:38:18| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 401.3208s] -07/11/23 03:38:18| INFO Dataset sample 0.20 of dataset imdb_9prevs started -07/11/23 03:38:30| INFO ref finished [took 11.1665s] -07/11/23 03:38:34| INFO atc_mc finished [took 14.4483s] -07/11/23 03:38:34| INFO atc_ne finished [took 14.8634s] -07/11/23 03:43:16| INFO bin_sld_gsq finished [took 296.8786s] -07/11/23 03:43:32| INFO mul_sld_gsq finished [took 312.4588s] -07/11/23 03:45:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01194) [took 445.1331s] -07/11/23 03:45:58| INFO mul_sld_gs finished [took 459.5855s] -07/11/23 03:51:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00924) [took 766.1528s] -07/11/23 03:53:40| INFO bin_sld_gs finished [took 921.5996s] -07/11/23 03:53:40| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 922.0949s] -07/11/23 03:53:40| INFO Dataset sample 0.30 of dataset imdb_9prevs started -07/11/23 03:53:53| INFO ref finished [took 11.5825s] -07/11/23 03:53:57| INFO atc_mc finished [took 14.8590s] -07/11/23 03:53:57| INFO atc_ne finished [took 15.3090s] -07/11/23 03:58:53| INFO mul_sld_gsq finished [took 311.9891s] -07/11/23 03:58:54| INFO bin_sld_gsq finished [took 313.1182s] -07/11/23 04:01:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00953) [took 441.3198s] -07/11/23 04:01:18| INFO mul_sld_gs finished [took 456.2347s] -07/11/23 04:06:06| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01064) [took 745.0596s] -07/11/23 04:08:40| INFO bin_sld_gs finished [took 898.9046s] -07/11/23 04:08:40| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 899.6778s] -07/11/23 04:08:40| INFO Dataset sample 0.40 of dataset imdb_9prevs started -07/11/23 04:08:52| INFO ref finished [took 11.0605s] -07/11/23 04:08:56| INFO atc_mc finished [took 14.9590s] -07/11/23 04:08:56| INFO atc_ne finished [took 14.8804s] -07/11/23 04:13:54| INFO mul_sld_gsq finished [took 313.3797s] -07/11/23 04:13:56| INFO bin_sld_gsq finished [took 315.5862s] -07/11/23 04:15:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01029) [took 432.9025s] -07/11/23 04:16:08| INFO mul_sld_gs finished [took 447.1098s] -07/11/23 04:21:25| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01176) [took 764.2230s] -07/11/23 04:23:56| INFO bin_sld_gs finished [took 915.4905s] -07/11/23 04:23:56| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 916.1187s] -07/11/23 04:23:56| INFO Dataset sample 0.50 of dataset imdb_9prevs started -07/11/23 04:24:08| INFO ref finished [took 10.9214s] -07/11/23 04:24:12| INFO atc_mc finished [took 14.9236s] -07/11/23 04:24:12| INFO atc_ne finished [took 14.9240s] -07/11/23 04:29:11| INFO bin_sld_gsq finished [took 314.3071s] -07/11/23 04:29:19| INFO mul_sld_gsq finished [took 322.1027s] -07/11/23 04:31:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00885) [took 448.0202s] -07/11/23 04:31:40| INFO mul_sld_gs finished [took 463.2243s] -07/11/23 04:36:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00940) [took 746.2797s] -07/11/23 04:38:55| INFO bin_sld_gs finished [took 898.7899s] -07/11/23 04:38:55| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 899.2924s] -07/11/23 04:38:55| INFO Dataset sample 0.60 of dataset imdb_9prevs started -07/11/23 04:39:08| INFO ref finished [took 11.9811s] -07/11/23 04:39:12| INFO atc_mc finished [took 15.7159s] -07/11/23 04:39:12| INFO atc_ne finished [took 15.9512s] -07/11/23 04:44:19| INFO bin_sld_gsq finished [took 323.1420s] -07/11/23 04:44:21| INFO mul_sld_gsq finished [took 325.2299s] -07/11/23 04:46:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00984) [took 445.8872s] -07/11/23 04:46:37| INFO mul_sld_gs finished [took 460.6339s] -07/11/23 04:52:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01142) [took 786.7500s] -07/11/23 04:54:36| INFO bin_sld_gs finished [took 940.1627s] -07/11/23 04:54:36| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 940.6023s] -07/11/23 04:54:36| INFO Dataset sample 0.70 of dataset imdb_9prevs started -07/11/23 04:54:48| INFO ref finished [took 11.1744s] -07/11/23 04:54:52| INFO atc_mc finished [took 14.7518s] -07/11/23 04:54:52| INFO atc_ne finished [took 14.8147s] -07/11/23 04:59:45| INFO bin_sld_gsq finished [took 308.3645s] -07/11/23 05:00:07| INFO mul_sld_gsq finished [took 330.3332s] -07/11/23 05:02:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01102) [took 456.8448s] -07/11/23 05:02:28| INFO mul_sld_gs finished [took 471.4675s] -07/11/23 05:06:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.02196) [took 731.2847s] -07/11/23 05:09:19| INFO bin_sld_gs finished [took 882.2200s] -07/11/23 05:09:19| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 882.8165s] -07/11/23 05:09:19| INFO Dataset sample 0.80 of dataset imdb_9prevs started -07/11/23 05:09:31| INFO ref finished [took 11.0645s] -07/11/23 05:09:35| INFO atc_mc finished [took 14.7375s] -07/11/23 05:09:35| INFO atc_ne finished [took 14.7704s] -07/11/23 05:14:22| INFO bin_sld_gsq finished [took 302.1848s] -07/11/23 05:14:33| INFO mul_sld_gsq finished [took 313.5459s] -07/11/23 05:16:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00785) [took 438.9863s] -07/11/23 05:16:52| INFO mul_sld_gs finished [took 452.7273s] -07/11/23 05:21:59| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01449) [took 759.8355s] -07/11/23 05:24:38| INFO bin_sld_gs finished [took 918.7338s] -07/11/23 05:24:38| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 919.2981s] -07/11/23 05:24:38| INFO Dataset sample 0.90 of dataset imdb_9prevs started -07/11/23 05:24:39| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -07/11/23 05:24:39| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. -07/11/23 05:24:48| INFO ref finished [took 9.1378s] -07/11/23 05:24:51| INFO atc_mc finished [took 12.1603s] -07/11/23 05:24:52| INFO atc_ne finished [took 12.3482s] -07/11/23 05:25:08| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 -07/11/23 05:30:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.04178) [took 353.7904s] -07/11/23 05:30:45| INFO mul_sld_gs finished [took 365.9283s] -07/11/23 05:30:45| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 366.4930s] ----------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +30/10/23 14:14:05| INFO: dataset imdb +---------------------------------------------------------------------------------------------------- +30/10/23 14:14:24| INFO: dataset imdb +30/10/23 14:14:31| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:14:35| WARNING: Method ref failed. Exception: 'dict' object has no attribute 'Keys' +30/10/23 14:14:35| WARNING: Method atc_mc failed. Exception: 'dict' object has no attribute 'Keys' +30/10/23 14:14:35| WARNING: Method atc_ne failed. Exception: 'dict' object has no attribute 'Keys' +30/10/23 14:14:42| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:14:52| WARNING: Method mul_sld failed. Exception: 'dict' object has no attribute 'Keys' +30/10/23 14:14:52| INFO: Dataset sample 0.90 of dataset imdb finished [took 21.1198s] +30/10/23 14:14:52| WARNING: Dataset sample 0.90 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 14:14:52| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable +30/10/23 14:14:52| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 14:16:15| INFO: dataset imdb +30/10/23 14:16:22| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:16:34| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:16:36| INFO: ref finished [took 11.6636s] +30/10/23 14:16:39| INFO: atc_mc finished [took 14.8672s] +30/10/23 14:16:39| INFO: atc_ne finished [took 14.8614s] +30/10/23 14:16:49| INFO: mul_sld finished [took 24.6212s] +30/10/23 14:16:49| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.7805s] +30/10/23 14:16:49| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 14:17:02| INFO: ref finished [took 13.0129s] +30/10/23 14:17:06| INFO: atc_mc finished [took 16.0277s] +30/10/23 14:17:06| INFO: atc_ne finished [took 16.1381s] +30/10/23 14:17:17| INFO: mul_sld finished [took 28.1917s] +30/10/23 14:17:23| INFO: mul_sld_bcts finished [took 33.5628s] +30/10/23 14:17:23| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.3680s] +30/10/23 14:17:23| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 14:17:36| INFO: ref finished [took 12.5930s] +30/10/23 14:17:40| INFO: atc_mc finished [took 16.1461s] +30/10/23 14:17:40| INFO: atc_ne finished [took 16.1788s] +30/10/23 14:17:52| INFO: mul_sld finished [took 28.5367s] +30/10/23 14:18:00| INFO: mul_sld_bcts finished [took 36.0452s] +30/10/23 14:18:00| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.7488s] +30/10/23 14:18:00| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 14:18:13| INFO: ref finished [took 12.3910s] +30/10/23 14:18:17| INFO: atc_mc finished [took 15.8804s] +30/10/23 14:18:17| INFO: atc_ne finished [took 15.7115s] +30/10/23 14:18:32| INFO: mul_sld_bcts finished [took 31.9997s] +30/10/23 14:18:34| INFO: mul_sld finished [took 33.3735s] +30/10/23 14:18:34| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.9557s] +30/10/23 14:18:34| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 14:18:44| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:18:46| INFO: ref finished [took 11.7451s] +30/10/23 14:18:50| INFO: atc_mc finished [took 15.2294s] +30/10/23 14:18:50| INFO: atc_ne finished [took 15.1239s] +30/10/23 14:18:55| INFO: mul_sld finished [took 21.3092s] +30/10/23 14:18:55| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.7186s] +30/10/23 14:18:55| ERROR: Configuration imdb_1prevs failed. Exception: 'mul_sld_bcts' +30/10/23 14:18:55| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 14:32:36| INFO: dataset imdb +30/10/23 14:32:43| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:32:56| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:32:58| INFO: ref finished [took 12.0197s] +30/10/23 14:33:01| INFO: atc_mc finished [took 15.0884s] +30/10/23 14:33:01| INFO: atc_ne finished [took 15.0503s] +30/10/23 14:33:10| INFO: mul_sld finished [took 24.4470s] +30/10/23 14:33:10| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.6099s] +30/10/23 14:33:10| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 14:33:23| INFO: ref finished [took 12.1812s] +30/10/23 14:33:27| INFO: atc_mc finished [took 15.5589s] +30/10/23 14:33:27| INFO: atc_ne finished [took 15.5283s] +30/10/23 14:33:38| INFO: mul_sld finished [took 27.1282s] +30/10/23 14:33:44| INFO: mul_sld_bcts finished [took 33.1098s] +30/10/23 14:33:44| INFO: Dataset sample 0.80 of dataset imdb finished [took 33.9196s] +30/10/23 14:33:44| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 14:33:57| INFO: ref finished [took 12.5959s] +30/10/23 14:34:01| INFO: atc_mc finished [took 15.9389s] +30/10/23 14:34:01| INFO: atc_ne finished [took 16.0795s] +30/10/23 14:34:13| INFO: mul_sld finished [took 28.1568s] +30/10/23 14:34:20| INFO: mul_sld_bcts finished [took 35.7147s] +30/10/23 14:34:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.3828s] +30/10/23 14:34:20| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 14:34:33| INFO: ref finished [took 12.2399s] +30/10/23 14:34:37| INFO: atc_mc finished [took 15.4570s] +30/10/23 14:34:37| INFO: atc_ne finished [took 15.5302s] +30/10/23 14:34:52| INFO: mul_sld_bcts finished [took 31.1972s] +30/10/23 14:34:54| INFO: mul_sld finished [took 32.9409s] +30/10/23 14:34:54| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.5034s] +30/10/23 14:34:54| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 14:35:04| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:35:06| INFO: ref finished [took 11.6742s] +30/10/23 14:35:09| INFO: atc_mc finished [took 14.8324s] +30/10/23 14:35:10| INFO: atc_ne finished [took 14.8661s] +30/10/23 14:35:15| INFO: mul_sld finished [took 21.1356s] +30/10/23 14:35:15| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.5814s] +30/10/23 14:35:15| ERROR: Configuration imdb_1prevs failed. Exception: ('acc', None) +30/10/23 14:35:15| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 14:37:47| INFO: dataset imdb +30/10/23 14:37:54| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:38:07| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:38:09| INFO: ref finished [took 12.0443s] +30/10/23 14:38:12| INFO: atc_mc finished [took 14.8929s] +30/10/23 14:38:12| INFO: atc_ne finished [took 15.0431s] +30/10/23 14:38:21| INFO: mul_sld finished [took 24.7987s] +30/10/23 14:38:21| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.0182s] +30/10/23 14:38:21| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 14:38:35| INFO: ref finished [took 12.4504s] +30/10/23 14:38:38| INFO: atc_mc finished [took 16.1560s] +30/10/23 14:38:39| INFO: atc_ne finished [took 16.1785s] +30/10/23 14:38:49| INFO: mul_sld finished [took 27.0617s] +30/10/23 14:38:55| INFO: mul_sld_bcts finished [took 32.7384s] +30/10/23 14:38:55| INFO: Dataset sample 0.80 of dataset imdb finished [took 33.5347s] +30/10/23 14:38:55| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 14:39:08| INFO: ref finished [took 12.4381s] +30/10/23 14:39:11| INFO: atc_mc finished [took 15.6709s] +30/10/23 14:39:11| INFO: atc_ne finished [took 15.7319s] +30/10/23 14:39:23| INFO: mul_sld finished [took 27.9301s] +30/10/23 14:39:31| INFO: mul_sld_bcts finished [took 35.5094s] +30/10/23 14:39:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.1333s] +30/10/23 14:39:31| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 14:39:44| INFO: ref finished [took 12.0382s] +30/10/23 14:39:47| INFO: atc_mc finished [took 15.0164s] +30/10/23 14:39:47| INFO: atc_ne finished [took 15.1080s] +30/10/23 14:40:02| INFO: mul_sld_bcts finished [took 30.9659s] +30/10/23 14:40:04| INFO: mul_sld finished [took 32.9418s] +30/10/23 14:40:04| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.4681s] +30/10/23 14:40:04| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 14:40:14| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:40:17| INFO: ref finished [took 11.8501s] +30/10/23 14:40:20| INFO: atc_mc finished [took 14.8473s] +30/10/23 14:40:21| INFO: atc_ne finished [took 15.2000s] +30/10/23 14:40:26| INFO: mul_sld finished [took 21.4799s] +30/10/23 14:40:26| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.9220s] +30/10/23 14:40:26| ERROR: Configuration imdb_1prevs failed. Exception: NDFrame.droplevel() got an unexpected keyword argument 'index' +30/10/23 14:40:26| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 14:42:13| INFO: dataset imdb +30/10/23 14:42:20| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:42:33| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:42:34| INFO: ref finished [took 12.1951s] +30/10/23 14:42:38| INFO: atc_ne finished [took 15.3431s] +30/10/23 14:42:38| INFO: atc_mc finished [took 15.4508s] +30/10/23 14:42:47| INFO: mul_sld finished [took 25.0246s] +30/10/23 14:42:47| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.1381s] +30/10/23 14:42:47| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 14:43:00| INFO: ref finished [took 12.3269s] +30/10/23 14:43:04| INFO: atc_ne finished [took 15.9216s] +30/10/23 14:43:04| INFO: atc_mc finished [took 16.1140s] +30/10/23 14:43:16| INFO: mul_sld finished [took 28.0575s] +30/10/23 14:43:22| INFO: mul_sld_bcts finished [took 33.9201s] +30/10/23 14:43:22| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.7703s] +30/10/23 14:43:22| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 14:43:35| INFO: ref finished [took 12.6508s] +30/10/23 14:43:39| INFO: atc_mc finished [took 16.0527s] +30/10/23 14:43:39| INFO: atc_ne finished [took 16.0515s] +30/10/23 14:43:50| INFO: mul_sld finished [took 28.1061s] +30/10/23 14:43:57| INFO: mul_sld_bcts finished [took 34.9278s] +30/10/23 14:43:57| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.6587s] +30/10/23 14:43:57| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 14:44:10| INFO: ref finished [took 12.0801s] +30/10/23 14:44:14| INFO: atc_mc finished [took 15.4685s] +30/10/23 14:44:14| INFO: atc_ne finished [took 15.4165s] +30/10/23 14:44:29| INFO: mul_sld_bcts finished [took 31.5628s] +30/10/23 14:44:31| INFO: mul_sld finished [took 33.3113s] +30/10/23 14:44:31| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.8828s] +30/10/23 14:44:31| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 14:44:41| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:44:44| INFO: ref finished [took 11.6822s] +30/10/23 14:44:47| INFO: atc_mc finished [took 14.8091s] +30/10/23 14:44:47| INFO: atc_ne finished [took 14.7900s] +30/10/23 14:44:53| INFO: mul_sld finished [took 21.0390s] +30/10/23 14:44:53| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.4515s] +30/10/23 14:44:53| ERROR: Configuration imdb_1prevs failed. Exception: 'function' object has no attribute 'index' +30/10/23 14:44:53| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 14:46:34| INFO: dataset imdb +30/10/23 14:46:41| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 14:46:54| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:46:56| INFO: ref finished [took 12.5001s] +30/10/23 14:46:59| INFO: atc_mc finished [took 15.5415s] +30/10/23 14:46:59| INFO: atc_ne finished [took 15.6358s] +30/10/23 14:47:08| INFO: mul_sld finished [took 24.5102s] +30/10/23 14:47:08| INFO: Dataset sample 0.90 of dataset imdb finished [took 26.5553s] +30/10/23 14:47:08| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 14:47:21| INFO: ref finished [took 12.0997s] +30/10/23 14:47:24| INFO: atc_mc finished [took 15.4285s] +30/10/23 14:47:24| INFO: atc_ne finished [took 15.5599s] +30/10/23 14:47:36| INFO: mul_sld finished [took 27.6146s] +30/10/23 14:47:43| INFO: mul_sld_bcts finished [took 34.2610s] +30/10/23 14:47:43| INFO: Dataset sample 0.80 of dataset imdb finished [took 35.0096s] +30/10/23 14:47:43| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 14:47:56| INFO: ref finished [took 12.1238s] +30/10/23 14:48:00| INFO: atc_mc finished [took 15.6990s] +30/10/23 14:48:00| INFO: atc_ne finished [took 15.8708s] +30/10/23 14:48:11| INFO: mul_sld finished [took 28.0048s] +30/10/23 14:48:20| INFO: mul_sld_bcts finished [took 36.1524s] +30/10/23 14:48:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 36.8480s] +30/10/23 14:48:20| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 14:48:32| INFO: ref finished [took 11.3690s] +30/10/23 14:48:35| INFO: atc_mc finished [took 14.3092s] +30/10/23 14:48:35| INFO: atc_ne finished [took 14.4043s] +30/10/23 14:48:51| INFO: mul_sld_bcts finished [took 30.2595s] +30/10/23 14:48:52| INFO: mul_sld finished [took 31.4270s] +30/10/23 14:48:52| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.9598s] +30/10/23 14:48:52| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 14:49:02| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 14:49:04| INFO: ref finished [took 12.1449s] +30/10/23 14:49:08| INFO: atc_mc finished [took 15.0332s] +30/10/23 14:49:08| INFO: atc_ne finished [took 15.3463s] +30/10/23 14:49:13| INFO: mul_sld finished [took 21.2802s] +30/10/23 14:49:13| INFO: Dataset sample 0.10 of dataset imdb finished [took 21.7079s] +30/10/23 14:49:14| ERROR: Configuration imdb_1prevs failed. Exception: unsupported operand type(s) for -: 'list' and 'list' +30/10/23 14:49:14| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 15:10:08| INFO: dataset imdb +30/10/23 15:10:14| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 15:10:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 15:10:27| INFO: ref finished [took 10.8100s] +30/10/23 15:10:30| INFO: atc_mc finished [took 13.5996s] +30/10/23 15:10:30| INFO: atc_ne finished [took 13.6110s] +30/10/23 15:10:39| INFO: mul_sld finished [took 22.7361s] +30/10/23 15:10:39| INFO: Dataset sample 0.90 of dataset imdb finished [took 24.8056s] +30/10/23 15:10:39| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 15:10:51| INFO: ref finished [took 10.9293s] +30/10/23 15:10:54| INFO: atc_mc finished [took 13.8377s] +30/10/23 15:10:54| INFO: atc_ne finished [took 13.9983s] +30/10/23 15:11:05| INFO: mul_sld finished [took 25.1977s] +30/10/23 15:11:11| INFO: mul_sld_bcts finished [took 31.1124s] +30/10/23 15:11:11| INFO: Dataset sample 0.80 of dataset imdb finished [took 31.8294s] +30/10/23 15:11:11| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 15:11:23| INFO: ref finished [took 11.0056s] +30/10/23 15:11:26| INFO: atc_mc finished [took 14.3946s] +30/10/23 15:11:27| INFO: atc_ne finished [took 14.6355s] +30/10/23 15:11:38| INFO: mul_sld finished [took 26.2697s] +30/10/23 15:11:45| INFO: mul_sld_bcts finished [took 33.8992s] +30/10/23 15:11:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 34.4963s] +30/10/23 15:11:45| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 15:11:57| INFO: ref finished [took 10.9836s] +30/10/23 15:12:00| INFO: atc_mc finished [took 13.8378s] +30/10/23 15:12:00| INFO: atc_ne finished [took 13.8318s] +30/10/23 15:12:16| INFO: mul_sld_bcts finished [took 29.9813s] +30/10/23 15:12:17| INFO: mul_sld finished [took 30.7175s] +30/10/23 15:12:17| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.2508s] +30/10/23 15:12:17| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 15:12:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 15:12:28| INFO: ref finished [took 10.4376s] +30/10/23 15:12:31| INFO: atc_ne finished [took 13.3510s] +30/10/23 15:12:31| INFO: atc_mc finished [took 13.5172s] +30/10/23 15:12:37| INFO: mul_sld finished [took 19.7440s] +30/10/23 15:12:37| INFO: Dataset sample 0.10 of dataset imdb finished [took 20.1519s] +30/10/23 15:12:37| ERROR: Configuration imdb_1prevs failed. Exception: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' +30/10/23 15:12:37| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 17:12:41| INFO: dataset imdb +30/10/23 17:12:48| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 17:13:01| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 17:13:03| INFO: ref finished [took 12.6699s] +30/10/23 17:13:07| INFO: atc_ne finished [took 15.6073s] +30/10/23 17:13:07| INFO: atc_mc finished [took 15.6695s] +30/10/23 17:13:15| INFO: mul_sld finished [took 24.8617s] +30/10/23 17:13:15| INFO: Dataset sample 0.90 of dataset imdb finished [took 27.6018s] +30/10/23 17:13:15| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 17:13:29| INFO: ref finished [took 12.6205s] +30/10/23 17:13:33| INFO: atc_mc finished [took 16.2005s] +30/10/23 17:13:33| INFO: atc_ne finished [took 16.2091s] +30/10/23 17:13:43| INFO: mul_sld finished [took 27.1113s] +30/10/23 17:13:49| INFO: mul_sld_bcts finished [took 33.3939s] +30/10/23 17:13:49| INFO: Dataset sample 0.80 of dataset imdb finished [took 34.1222s] +30/10/23 17:13:49| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 17:14:04| INFO: ref finished [took 13.2345s] +30/10/23 17:14:07| INFO: atc_mc finished [took 16.5475s] +30/10/23 17:14:07| INFO: atc_ne finished [took 16.6557s] +30/10/23 17:14:19| INFO: mul_sld finished [took 28.8817s] +30/10/23 17:14:27| INFO: mul_sld_bcts finished [took 36.5726s] +30/10/23 17:14:27| INFO: Dataset sample 0.50 of dataset imdb finished [took 37.2057s] +30/10/23 17:14:27| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 17:14:39| INFO: ref finished [took 11.7051s] +30/10/23 17:14:42| INFO: atc_mc finished [took 14.8335s] +30/10/23 17:14:43| INFO: atc_ne finished [took 15.0826s] +30/10/23 17:14:59| INFO: mul_sld_bcts finished [took 31.7685s] +30/10/23 17:15:00| INFO: mul_sld finished [took 33.2861s] +30/10/23 17:15:00| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.8225s] +30/10/23 17:15:00| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 17:15:11| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 17:15:13| INFO: ref finished [took 12.0927s] +30/10/23 17:15:17| INFO: atc_mc finished [took 15.4201s] +30/10/23 17:15:17| INFO: atc_ne finished [took 15.5212s] +30/10/23 17:15:23| INFO: mul_sld finished [took 21.7236s] +30/10/23 17:15:23| INFO: Dataset sample 0.10 of dataset imdb finished [took 22.2065s] +30/10/23 17:15:23| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) +30/10/23 17:15:23| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 17:16:39| INFO: dataset imdb +30/10/23 17:16:46| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 17:16:58| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 17:17:00| INFO: ref finished [took 11.7575s] +30/10/23 17:17:03| INFO: atc_ne finished [took 14.7709s] +30/10/23 17:17:03| INFO: atc_mc finished [took 14.8925s] +30/10/23 17:17:12| INFO: mul_sld finished [took 23.7037s] +30/10/23 17:17:12| INFO: Dataset sample 0.90 of dataset imdb finished [took 25.8491s] +30/10/23 17:17:12| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 17:17:25| INFO: ref finished [took 12.2081s] +30/10/23 17:17:28| INFO: atc_ne finished [took 15.3145s] +30/10/23 17:17:28| INFO: atc_mc finished [took 15.5166s] +30/10/23 17:17:39| INFO: mul_sld finished [took 26.7520s] +30/10/23 17:17:45| INFO: mul_sld_bcts finished [took 32.0850s] +30/10/23 17:17:45| INFO: Dataset sample 0.80 of dataset imdb finished [took 32.8702s] +30/10/23 17:17:45| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 17:17:57| INFO: ref finished [took 11.9494s] +30/10/23 17:18:01| INFO: atc_mc finished [took 15.3034s] +30/10/23 17:18:01| INFO: atc_ne finished [took 15.3254s] +30/10/23 17:18:12| INFO: mul_sld finished [took 27.2902s] +30/10/23 17:18:20| INFO: mul_sld_bcts finished [took 34.4237s] +30/10/23 17:18:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.1216s] +30/10/23 17:18:20| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 17:18:32| INFO: ref finished [took 11.7945s] +30/10/23 17:18:35| INFO: atc_mc finished [took 14.9218s] +30/10/23 17:18:36| INFO: atc_ne finished [took 14.9745s] +30/10/23 17:18:51| INFO: mul_sld_bcts finished [took 30.7287s] +30/10/23 17:18:53| INFO: mul_sld finished [took 32.5641s] +30/10/23 17:18:53| INFO: Dataset sample 0.20 of dataset imdb finished [took 33.0982s] +30/10/23 17:18:53| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 17:19:02| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 17:19:05| INFO: ref finished [took 11.4568s] +30/10/23 17:19:08| INFO: atc_mc finished [took 14.4778s] +30/10/23 17:19:08| INFO: atc_ne finished [took 14.5099s] +30/10/23 17:19:14| INFO: mul_sld finished [took 20.5183s] +30/10/23 17:19:14| INFO: Dataset sample 0.10 of dataset imdb finished [took 20.9251s] +30/10/23 17:19:14| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) +30/10/23 17:19:14| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +30/10/23 19:57:49| INFO: dataset imdb +30/10/23 19:58:00| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 19:58:11| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 19:58:22| INFO: ref finished [took 20.9010s] +30/10/23 19:58:29| INFO: atc_ne finished [took 27.8453s] +30/10/23 19:58:29| INFO: atc_mc finished [took 28.1079s] +30/10/23 19:58:37| INFO: mul_sld finished [took 36.1699s] +30/10/23 19:58:37| INFO: Dataset sample 0.90 of dataset imdb finished [took 36.7140s] +30/10/23 19:58:37| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 19:59:01| INFO: ref finished [took 23.2803s] +30/10/23 19:59:09| INFO: atc_ne finished [took 31.1099s] +30/10/23 19:59:09| INFO: atc_mc finished [took 31.5916s] +30/10/23 19:59:19| INFO: mul_sld finished [took 41.5113s] +30/10/23 19:59:24| INFO: mul_sld_bcts finished [took 46.6603s] +30/10/23 19:59:24| INFO: Dataset sample 0.80 of dataset imdb finished [took 47.4989s] +30/10/23 19:59:24| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 19:59:49| INFO: ref finished [took 23.6312s] +30/10/23 19:59:57| INFO: atc_ne finished [took 31.5195s] +30/10/23 19:59:57| INFO: atc_mc finished [took 31.8197s] +30/10/23 20:00:08| INFO: mul_sld finished [took 42.8675s] +30/10/23 20:00:15| INFO: mul_sld_bcts finished [took 50.5527s] +30/10/23 20:00:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 51.3659s] +30/10/23 20:00:16| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 20:00:41| INFO: ref finished [took 24.2178s] +30/10/23 20:00:48| INFO: atc_mc finished [took 31.9886s] +30/10/23 20:00:49| INFO: atc_ne finished [took 32.1537s] +30/10/23 20:01:03| INFO: mul_sld_bcts finished [took 46.2477s] +30/10/23 20:01:07| INFO: mul_sld finished [took 50.8912s] +30/10/23 20:01:07| INFO: Dataset sample 0.20 of dataset imdb finished [took 51.4589s] +30/10/23 20:01:07| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 20:01:18| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 20:01:30| INFO: ref finished [took 22.6404s] +30/10/23 20:01:38| INFO: atc_mc finished [took 29.8371s] +30/10/23 20:01:38| INFO: atc_ne finished [took 30.2098s] +30/10/23 20:01:41| INFO: mul_sld finished [took 33.6271s] +30/10/23 20:01:41| INFO: Dataset sample 0.10 of dataset imdb finished [took 34.1993s] +30/10/23 20:01:42| ERROR: Configuration imdb_1prevs failed. Exception: operands could not be broadcast together with shapes (0,) (0,21) +30/10/23 20:01:42| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 20:05:04| INFO: dataset imdb +30/10/23 20:05:14| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 20:05:26| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 20:05:38| INFO: ref finished [took 22.7241s] +30/10/23 20:05:45| INFO: atc_mc finished [took 29.9191s] +30/10/23 20:05:45| INFO: atc_ne finished [took 29.8405s] +30/10/23 20:05:52| INFO: mul_sld finished [took 37.4045s] +30/10/23 20:05:52| INFO: Dataset sample 0.90 of dataset imdb finished [took 37.9554s] +30/10/23 20:05:52| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 20:06:17| INFO: ref finished [took 23.2465s] +30/10/23 20:06:25| INFO: atc_ne finished [took 31.0138s] +30/10/23 20:06:25| INFO: atc_mc finished [took 31.1341s] +30/10/23 20:06:34| INFO: mul_sld finished [took 40.8777s] +30/10/23 20:06:40| INFO: mul_sld_bcts finished [took 46.7083s] +30/10/23 20:06:40| INFO: Dataset sample 0.80 of dataset imdb finished [took 47.5062s] +30/10/23 20:06:40| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:07:05| INFO: ref finished [took 24.3375s] +30/10/23 20:07:15| INFO: atc_mc finished [took 33.8014s] +30/10/23 20:07:15| INFO: atc_ne finished [took 33.7355s] +30/10/23 20:07:25| INFO: mul_sld finished [took 44.2891s] +30/10/23 20:07:32| INFO: mul_sld_bcts finished [took 51.2404s] +30/10/23 20:07:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 51.9917s] +30/10/23 20:07:32| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 20:07:55| INFO: ref finished [took 21.6828s] +30/10/23 20:08:01| INFO: atc_mc finished [took 28.2369s] +30/10/23 20:08:01| INFO: atc_ne finished [took 28.4328s] +30/10/23 20:08:15| INFO: mul_sld_bcts finished [took 41.9176s] +30/10/23 20:08:18| INFO: mul_sld finished [took 45.4999s] +30/10/23 20:08:18| INFO: Dataset sample 0.20 of dataset imdb finished [took 46.0301s] +30/10/23 20:08:18| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 20:08:28| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 20:08:38| INFO: ref finished [took 19.4082s] +30/10/23 20:08:45| INFO: atc_mc finished [took 26.2343s] +30/10/23 20:08:45| INFO: atc_ne finished [took 26.2322s] +30/10/23 20:08:48| INFO: mul_sld finished [took 29.8392s] +30/10/23 20:08:48| INFO: Dataset sample 0.10 of dataset imdb finished [took 30.3563s] +---------------------------------------------------------------------------------------------------- +30/10/23 20:29:28| INFO: dataset imdb +30/10/23 20:29:38| INFO: Dataset sample 0.50 of dataset imdb started + 30/10/23 20:29:59| INFO: ref finished [took 19.1581s] + 30/10/23 20:30:06| INFO: atc_mc finished [took 26.3398s] + 30/10/23 20:30:07| INFO: atc_ne finished [took 26.4359s] +30/10/23 20:30:07| INFO: Dataset sample 0.50 of dataset imdb finished [took 28.7984s] +---------------------------------------------------------------------------------------------------- +30/10/23 20:31:50| INFO: dataset imdb +30/10/23 20:32:00| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:33:59|INFO: ref finished [took 118.1306s] +---------------------------------------------------------------------------------------------------- +30/10/23 20:36:06| INFO: dataset imdb +30/10/23 20:36:17| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:38:52|WARNING: Method ref failed. Exception: "['acc_score' 'f1_score' 'ref'] not in index" +30/10/23 20:41:28|WARNING: Method atc_mc failed. Exception: "['acc' 'acc_score' 'atc_mc' 'f1' 'f1_score'] not in index" +30/10/23 20:41:32|WARNING: Method atc_ne failed. Exception: "['acc' 'acc_score' 'atc_ne' 'f1' 'f1_score'] not in index" +30/10/23 20:41:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 315.4626s] +30/10/23 20:41:32| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:41:32| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable +30/10/23 20:41:32| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 20:41:43| INFO: dataset imdb +30/10/23 20:41:54| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:42:26| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) +30/10/23 20:43:01| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:43:08| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:43:08| INFO: Dataset sample 0.50 of dataset imdb finished [took 73.6011s] +30/10/23 20:43:08| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:43:08| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable +30/10/23 20:43:08| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 20:44:25| INFO: dataset imdb +30/10/23 20:44:35| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:44:37| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) +30/10/23 20:44:37| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:44:38| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:44:38| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6758s] +30/10/23 20:44:38| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:44:38| ERROR: Configuration imdb_1prevs failed. Exception: 'module' object is not callable +30/10/23 20:44:38| ERROR: estimate comparison failed. Exceprion: 'module' object is not callable +---------------------------------------------------------------------------------------------------- +30/10/23 20:47:08| INFO: dataset imdb +30/10/23 20:47:18| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:47:20| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) +30/10/23 20:47:21| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:47:21| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:47:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6147s] +30/10/23 20:47:21| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:47:21| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 20:50:07| INFO: dataset imdb +30/10/23 20:50:17| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:50:19| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) +30/10/23 20:50:20| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:50:20| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:50:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5897s] +30/10/23 20:50:20| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:50:20| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 20:51:29| INFO: dataset imdb +30/10/23 20:51:39| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:51:42| WARNING: Method ref failed. Exception: Shape of passed values is (2, 1), indices imply (1, 2) +30/10/23 20:51:42| WARNING: Method atc_mc failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:51:42| WARNING: Method atc_ne failed. Exception: Shape of passed values is (4, 1), indices imply (1, 4) +30/10/23 20:51:42| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5821s] +30/10/23 20:51:42| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:51:42| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 20:56:28| INFO: dataset imdb +30/10/23 20:56:38| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:56:40| WARNING: Method ref failed. Exception: cannot reshape array of size 1 into shape (1,2) +30/10/23 20:56:40| WARNING: Method atc_mc failed. Exception: cannot reshape array of size 1 into shape (1,4) +30/10/23 20:56:40| WARNING: Method atc_ne failed. Exception: cannot reshape array of size 1 into shape (1,4) +30/10/23 20:56:40| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.6150s] +30/10/23 20:56:40| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:56:40| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 20:57:13| INFO: dataset imdb +30/10/23 20:57:23| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 20:59:40| WARNING: Method ref failed. Exception: cannot reshape array of size 1 into shape (1,2) +30/10/23 20:59:51| WARNING: Method atc_mc failed. Exception: cannot reshape array of size 1 into shape (1,4) +30/10/23 20:59:52| WARNING: Method atc_ne failed. Exception: cannot reshape array of size 1 into shape (1,4) +30/10/23 20:59:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 149.2395s] +30/10/23 20:59:52| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 20:59:52| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 21:00:04| INFO: dataset imdb +30/10/23 21:00:14| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:01:33| INFO: ref finished [took 78.2917s] +30/10/23 21:01:42| INFO: atc_mc finished [took 86.9003s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:01:59| INFO: dataset imdb +30/10/23 21:02:09| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:04:09| INFO: dataset imdb +30/10/23 21:04:19| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:06:25| INFO: dataset imdb +30/10/23 21:06:35| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:07:33| INFO: dataset imdb +30/10/23 21:07:43| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:07:45| WARNING: Method ref failed. Exception: setting an array element with a sequence. +30/10/23 21:07:45| WARNING: Method atc_mc failed. Exception: setting an array element with a sequence. +30/10/23 21:07:45| WARNING: Method atc_ne failed. Exception: setting an array element with a sequence. +30/10/23 21:07:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 2.5382s] +30/10/23 21:07:45| WARNING: Dataset sample 0.50 of dataset imdb failed. Exception: No objects to concatenate +30/10/23 21:07:45| ERROR: Configuration imdb_1prevs failed. Exception: No objects to concatenate +---------------------------------------------------------------------------------------------------- +30/10/23 21:09:07| INFO: dataset imdb +30/10/23 21:09:16| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:10:48| INFO: dataset imdb +30/10/23 21:10:58| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:18:53| INFO: dataset imdb +30/10/23 21:19:03| INFO: Dataset sample 0.50 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 21:22:03| INFO: dataset imdb +30/10/23 21:22:12| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:22:31| INFO: ref finished [took 17.0861s] +30/10/23 21:22:37| INFO: atc_mc finished [took 23.6279s] +30/10/23 21:22:38| INFO: atc_ne finished [took 23.7395s] +30/10/23 21:22:38| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.2007s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:29:55| INFO: dataset imdb +30/10/23 21:30:05| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:30:23| INFO: ref finished [took 16.7801s] +30/10/23 21:30:30| INFO: atc_mc finished [took 23.5645s] +30/10/23 21:30:30| INFO: atc_ne finished [took 23.5639s] +30/10/23 21:30:30| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.0459s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:33:45| INFO: dataset imdb +30/10/23 21:33:55| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:34:13| INFO: ref finished [took 17.0169s] +30/10/23 21:34:20| INFO: atc_mc finished [took 23.4725s] +30/10/23 21:34:20| INFO: atc_ne finished [took 23.5928s] +30/10/23 21:34:20| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9542s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:37:32| INFO: dataset imdb +30/10/23 21:37:39| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:37:49| INFO: ref finished [took 8.9050s] +30/10/23 21:37:52| INFO: atc_mc finished [took 11.7412s] +30/10/23 21:37:52| INFO: atc_ne finished [took 11.7256s] +30/10/23 21:37:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.9758s] +30/10/23 21:37:53| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices +---------------------------------------------------------------------------------------------------- +30/10/23 21:39:14| INFO: dataset imdb +30/10/23 21:39:21| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:39:31| INFO: ref finished [took 8.5615s] +30/10/23 21:39:34| INFO: atc_mc finished [took 11.4156s] +30/10/23 21:39:34| INFO: atc_ne finished [took 11.4156s] +30/10/23 21:39:34| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.7024s] +30/10/23 21:39:35| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices +---------------------------------------------------------------------------------------------------- +30/10/23 21:40:51| INFO: dataset imdb +30/10/23 21:41:01| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:41:19| INFO: ref finished [took 16.7164s] +30/10/23 21:41:26| INFO: atc_mc finished [took 23.3181s] +30/10/23 21:41:26| INFO: atc_ne finished [took 23.4811s] +30/10/23 21:41:26| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9698s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:43:25| INFO: dataset imdb +30/10/23 21:43:35| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:43:53| INFO: ref finished [took 16.9333s] +30/10/23 21:44:00| INFO: atc_mc finished [took 23.4183s] +30/10/23 21:44:00| INFO: atc_ne finished [took 23.4274s] +30/10/23 21:44:00| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.9308s] +30/10/23 21:44:19| ERROR: Configuration imdb_1prevs failed. Exception: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices +---------------------------------------------------------------------------------------------------- +30/10/23 21:45:16| INFO: dataset imdb +30/10/23 21:45:26| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:45:44| INFO: ref finished [took 17.6768s] +30/10/23 21:45:51| INFO: atc_mc finished [took 24.3756s] +30/10/23 21:45:52| INFO: atc_ne finished [took 24.5307s] +30/10/23 21:45:52| INFO: Dataset sample 0.50 of dataset imdb finished [took 25.8971s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:48:20| INFO: dataset imdb +30/10/23 21:48:27| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:48:36| INFO: ref finished [took 8.6456s] +30/10/23 21:48:39| INFO: atc_mc finished [took 11.2686s] +30/10/23 21:48:39| INFO: atc_ne finished [took 11.3112s] +30/10/23 21:48:39| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.5747s] +30/10/23 21:48:40| ERROR: Configuration imdb_1prevs failed. Exception: NDFrame.droplevel() got an unexpected keyword argument 'index' +---------------------------------------------------------------------------------------------------- +30/10/23 21:49:49| INFO: dataset imdb +30/10/23 21:49:55| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:50:05| INFO: ref finished [took 8.6556s] +30/10/23 21:50:08| INFO: atc_mc finished [took 11.6953s] +30/10/23 21:50:08| INFO: atc_ne finished [took 11.6000s] +30/10/23 21:50:08| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8578s] +30/10/23 21:50:09| ERROR: Configuration imdb_1prevs failed. Exception: 'NoneType' object has no attribute 'groupby' +---------------------------------------------------------------------------------------------------- +30/10/23 21:50:57| INFO: dataset imdb +30/10/23 21:51:07| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:51:25| INFO: ref finished [took 17.0426s] +30/10/23 21:51:31| INFO: atc_mc finished [took 23.5734s] +30/10/23 21:51:31| INFO: atc_ne finished [took 23.5276s] +30/10/23 21:51:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 24.8200s] +---------------------------------------------------------------------------------------------------- +30/10/23 21:55:21| INFO: dataset imdb +30/10/23 21:55:27| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:55:37| INFO: ref finished [took 8.8453s] +30/10/23 21:55:40| INFO: atc_mc finished [took 11.5585s] +30/10/23 21:55:40| INFO: atc_ne finished [took 11.5871s] +30/10/23 21:55:40| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8416s] +30/10/23 21:55:41| ERROR: Configuration imdb_1prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' +---------------------------------------------------------------------------------------------------- +30/10/23 21:57:00| INFO: dataset imdb +30/10/23 21:57:06| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:57:16| INFO: ref finished [took 8.5540s] +30/10/23 21:57:19| INFO: atc_mc finished [took 11.4482s] +30/10/23 21:57:19| INFO: atc_ne finished [took 11.5399s] +30/10/23 21:57:19| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.7681s] +30/10/23 21:57:20| ERROR: Configuration imdb_1prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' +---------------------------------------------------------------------------------------------------- +30/10/23 21:57:38| INFO: dataset imdb +30/10/23 21:57:45| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 21:57:55| INFO: ref finished [took 8.7982s] +30/10/23 21:57:58| INFO: atc_mc finished [took 11.4787s] +30/10/23 21:57:58| INFO: atc_ne finished [took 11.5419s] +30/10/23 21:57:58| INFO: Dataset sample 0.50 of dataset imdb finished [took 12.8803s] +---------------------------------------------------------------------------------------------------- +30/10/23 22:00:05| INFO: dataset imdb +30/10/23 22:00:12| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 22:00:21| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:00:22| INFO: ref finished [took 10.0983s] +30/10/23 22:00:25| INFO: atc_mc finished [took 13.0928s] +30/10/23 22:00:26| INFO: atc_ne finished [took 13.1088s] +30/10/23 22:00:34| INFO: mul_sld finished [took 22.3228s] +30/10/23 22:00:34| INFO: Dataset sample 0.90 of dataset imdb finished [took 22.7020s] +30/10/23 22:00:34| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 22:00:46| INFO: ref finished [took 10.5937s] +30/10/23 22:00:49| INFO: atc_mc finished [took 13.5008s] +30/10/23 22:00:49| INFO: atc_ne finished [took 13.7521s] +30/10/23 22:01:00| INFO: mul_sld finished [took 25.0319s] +30/10/23 22:01:06| INFO: mul_sld_bcts finished [took 31.0525s] +30/10/23 22:01:06| INFO: Dataset sample 0.80 of dataset imdb finished [took 31.7700s] +30/10/23 22:01:06| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 22:01:17| INFO: ref finished [took 10.6316s] +30/10/23 22:01:21| INFO: atc_ne finished [took 14.1054s] +30/10/23 22:01:21| INFO: atc_mc finished [took 14.4357s] +30/10/23 22:01:33| INFO: mul_sld finished [took 26.6800s] +30/10/23 22:01:41| INFO: mul_sld_bcts finished [took 34.4745s] +30/10/23 22:01:41| INFO: Dataset sample 0.50 of dataset imdb finished [took 35.1450s] +30/10/23 22:01:41| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 22:01:53| INFO: ref finished [took 10.7413s] +30/10/23 22:01:56| INFO: atc_ne finished [took 13.5169s] +30/10/23 22:01:56| INFO: atc_mc finished [took 13.5849s] +30/10/23 22:02:11| INFO: mul_sld_bcts finished [took 29.3981s] +30/10/23 22:02:12| INFO: mul_sld finished [took 30.6705s] +30/10/23 22:02:12| INFO: Dataset sample 0.20 of dataset imdb finished [took 31.2089s] +30/10/23 22:02:12| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 22:02:22| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:02:24| INFO: ref finished [took 10.3435s] +30/10/23 22:02:26| INFO: atc_mc finished [took 13.0763s] +30/10/23 22:02:27| INFO: atc_ne finished [took 13.2013s] +30/10/23 22:02:32| INFO: mul_sld finished [took 19.2237s] +30/10/23 22:02:32| INFO: Dataset sample 0.10 of dataset imdb finished [took 19.7097s] +---------------------------------------------------------------------------------------------------- +30/10/23 22:07:59| INFO: dataset imdb +30/10/23 22:08:07| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 22:08:10| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:08:11| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:08:11| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:08:11| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 22:08:18| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:08:20| INFO: ref finished [took 11.3765s] +30/10/23 22:08:23| INFO: atc_mc finished [took 14.2141s] +30/10/23 22:08:23| INFO: atc_ne finished [took 14.0568s] +30/10/23 22:08:31| INFO: mul_sld finished [took 23.9496s] +30/10/23 22:08:31| INFO: Dataset sample 0.90 of dataset imdb finished [took 24.5121s] +30/10/23 22:08:31| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 22:08:48| INFO: ref finished [took 14.8939s] +30/10/23 22:08:52| INFO: atc_mc finished [took 18.5014s] +30/10/23 22:08:52| INFO: atc_ne finished [took 18.4609s] +30/10/23 22:09:05| INFO: mul_sld finished [took 32.9898s] +30/10/23 22:09:12| INFO: mul_sld_bcts finished [took 39.3492s] +30/10/23 22:11:48| INFO: bin_sld_bcts finished [took 195.8293s] +30/10/23 22:11:49| INFO: bin_sld finished [took 196.6861s] +30/10/23 22:12:44| INFO: mul_sld_gs finished [took 250.9835s] +30/10/23 22:16:16| INFO: bin_sld_gs finished [took 462.9748s] +30/10/23 22:16:16| INFO: Dataset sample 0.80 of dataset imdb finished [took 464.4318s] +30/10/23 22:16:16| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 22:16:33| INFO: ref finished [took 15.2921s] +30/10/23 22:16:37| INFO: atc_mc finished [took 18.9592s] +30/10/23 22:16:37| INFO: atc_ne finished [took 19.1317s] +30/10/23 22:16:50| INFO: mul_sld finished [took 33.4304s] +30/10/23 22:16:59| INFO: mul_sld_bcts finished [took 42.3496s] +30/10/23 22:19:33| INFO: bin_sld finished [took 196.1571s] +30/10/23 22:19:36| INFO: bin_sld_bcts finished [took 199.7857s] +30/10/23 22:20:39| INFO: mul_sld_gs finished [took 261.6674s] +30/10/23 22:23:46| INFO: bin_sld_gs finished [took 449.3788s] +30/10/23 22:23:46| INFO: Dataset sample 0.50 of dataset imdb finished [took 450.7045s] +30/10/23 22:23:46| INFO: Dataset sample 0.20 of dataset imdb started +30/10/23 22:24:05| INFO: ref finished [took 16.4122s] +30/10/23 22:24:09| INFO: atc_mc finished [took 20.4920s] +30/10/23 22:24:09| INFO: atc_ne finished [took 20.3723s] +30/10/23 22:24:28| INFO: mul_sld_bcts finished [took 40.3400s] +30/10/23 22:24:30| INFO: mul_sld finished [took 43.2311s] +30/10/23 22:27:16| INFO: bin_sld_bcts finished [took 208.6113s] +30/10/23 22:27:21| INFO: bin_sld finished [took 214.1596s] +30/10/23 22:28:17| INFO: mul_sld_gs finished [took 269.1075s] +30/10/23 22:34:19| INFO: bin_sld_gs finished [took 630.9727s] +30/10/23 22:34:19| INFO: Dataset sample 0.20 of dataset imdb finished [took 632.2728s] +30/10/23 22:34:19| INFO: Dataset sample 0.10 of dataset imdb started +30/10/23 22:34:23| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 22:34:23| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +30/10/23 22:34:26| WARNING: Method bin_sld_bcts failed. Exception: Cannot have number of splits n_splits=5 greater than the number of samples: n_samples=4. +30/10/23 22:34:31| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:34:34| INFO: ref finished [took 13.7988s] +30/10/23 22:34:37| INFO: atc_mc finished [took 16.7490s] +30/10/23 22:34:38| INFO: atc_ne finished [took 16.7307s] +30/10/23 22:34:43| INFO: mul_sld finished [took 23.6079s] +30/10/23 22:36:42| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +30/10/23 22:36:42| INFO: Dataset sample 0.10 of dataset imdb finished [took 143.1097s] +---------------------------------------------------------------------------------------------------- +30/10/23 22:49:25| INFO: dataset imdb +30/10/23 22:49:37| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 22:49:42| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 22:49:43| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:49:43| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:49:43| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:49:51| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:50:02| INFO: ref finished [took 22.5398s] +30/10/23 22:50:09| INFO: atc_mc finished [took 29.3095s] +30/10/23 22:50:09| INFO: atc_ne finished [took 29.2984s] +30/10/23 22:50:16| INFO: mul_sld finished [took 37.6287s] +30/10/23 22:50:16| INFO: Dataset sample 0.90 of dataset imdb finished [took 38.3452s] +---------------------------------------------------------------------------------------------------- +30/10/23 22:53:57| INFO: dataset imdb +30/10/23 22:54:09| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 22:54:13| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 22:54:14| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:54:15| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:54:15| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 22:54:22| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 22:54:33| INFO: ref finished [took 22.4225s] +30/10/23 22:54:40| INFO: atc_ne finished [took 29.0085s] +30/10/23 22:54:41| INFO: atc_mc finished [took 29.6620s] +30/10/23 22:54:48| INFO: mul_sld finished [took 37.9580s] +30/10/23 22:54:48| INFO: Dataset sample 0.90 of dataset imdb finished [took 38.6632s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:02:33| INFO: dataset imdb +30/10/23 23:02:45| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 23:02:50| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 23:02:51| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:02:52| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:02:52| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:02:59| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 23:03:10| INFO: ref finished [took 23.4021s] +30/10/23 23:03:17| INFO: atc_mc finished [took 30.1849s] +30/10/23 23:03:18| INFO: atc_ne finished [took 30.4116s] +30/10/23 23:03:25| INFO: mul_sld finished [took 38.6513s] +30/10/23 23:03:25| INFO: Dataset sample 0.90 of dataset imdb finished [took 39.3497s] +30/10/23 23:07:32| INFO: Dataset sample 0.80 of dataset imdb started +---------------------------------------------------------------------------------------------------- +30/10/23 23:08:15| INFO: dataset imdb +30/10/23 23:08:26| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:08:51| INFO: ref finished [took 23.6855s] +30/10/23 23:08:59| INFO: atc_mc finished [took 31.1520s] +30/10/23 23:08:59| INFO: atc_ne finished [took 31.1659s] +30/10/23 23:09:10| INFO: mul_sld finished [took 42.2066s] +30/10/23 23:09:21| INFO: mul_sld_bcts finished [took 52.9631s] +30/10/23 23:09:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.5286s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:14:11| INFO: dataset imdb +30/10/23 23:14:22| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:14:47| INFO: ref finished [took 22.8152s] +30/10/23 23:14:55| INFO: atc_mc finished [took 31.2100s] +30/10/23 23:14:55| INFO: atc_ne finished [took 31.2325s] +30/10/23 23:15:06| INFO: mul_sld finished [took 42.5389s] +30/10/23 23:15:16| INFO: mul_sld_bcts finished [took 52.7119s] +30/10/23 23:15:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.2106s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:16:16| INFO: dataset imdb +30/10/23 23:16:27| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:16:51| INFO: ref finished [took 22.6482s] +30/10/23 23:17:00| INFO: atc_ne finished [took 30.5701s] +30/10/23 23:17:00| INFO: atc_mc finished [took 30.9988s] +30/10/23 23:17:10| INFO: mul_sld finished [took 41.9572s] +30/10/23 23:17:21| INFO: mul_sld_bcts finished [took 52.6091s] +30/10/23 23:17:21| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.1182s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:20:27| INFO: dataset imdb +30/10/23 23:20:38| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:21:02| INFO: ref finished [took 22.7779s] +30/10/23 23:21:10| INFO: atc_mc finished [took 30.4191s] +30/10/23 23:21:10| INFO: atc_ne finished [took 30.8097s] +30/10/23 23:21:20| INFO: mul_sld finished [took 41.5927s] +30/10/23 23:21:32| INFO: mul_sld_bcts finished [took 52.6374s] +30/10/23 23:21:32| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.1125s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:24:11| INFO: dataset imdb +30/10/23 23:24:22| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:24:46| INFO: ref finished [took 23.2007s] +30/10/23 23:24:54| INFO: atc_ne finished [took 30.9437s] +30/10/23 23:24:55| INFO: atc_mc finished [took 31.6008s] +30/10/23 23:25:05| INFO: mul_sld finished [took 42.0673s] +30/10/23 23:25:16| INFO: mul_sld_bcts finished [took 52.6228s] +30/10/23 23:25:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.0611s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:33:01| INFO: dataset imdb +30/10/23 23:33:11| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:33:36| INFO: ref finished [took 22.9215s] +30/10/23 23:33:44| INFO: atc_mc finished [took 30.5897s] +30/10/23 23:33:44| INFO: atc_ne finished [took 30.4788s] +30/10/23 23:33:55| INFO: mul_sld finished [took 42.0598s] +30/10/23 23:34:05| INFO: mul_sld_bcts finished [took 52.1772s] +30/10/23 23:34:05| INFO: Dataset sample 0.50 of dataset imdb finished [took 53.6878s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:38:11| INFO: dataset imdb +30/10/23 23:38:22| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:38:47| INFO: ref finished [took 22.8046s] +30/10/23 23:38:56| INFO: atc_mc finished [took 31.5660s] +30/10/23 23:38:56| INFO: atc_ne finished [took 31.5269s] +30/10/23 23:39:06| INFO: mul_sld finished [took 42.2553s] +30/10/23 23:39:16| INFO: mul_sld_bcts finished [took 52.2602s] +30/10/23 23:39:16| INFO: Dataset sample 0.50 of dataset imdb finished [took 53.7890s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:46:40| INFO: dataset imdb +30/10/23 23:46:51| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:47:16| INFO: ref finished [took 22.8069s] +30/10/23 23:47:24| INFO: atc_mc finished [took 30.7916s] +30/10/23 23:47:24| INFO: atc_ne finished [took 30.8668s] +30/10/23 23:47:35| INFO: mul_sld finished [took 42.2809s] +30/10/23 23:47:45| INFO: mul_sld_bcts finished [took 52.5498s] +30/10/23 23:47:45| INFO: Dataset sample 0.50 of dataset imdb finished [took 54.0424s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:50:43| INFO: dataset imdb +30/10/23 23:50:50| INFO: Dataset sample 0.50 of dataset imdb started +30/10/23 23:51:04| INFO: ref finished [took 12.0863s] +30/10/23 23:51:07| INFO: atc_ne finished [took 15.0218s] +30/10/23 23:51:08| INFO: atc_mc finished [took 15.7900s] +30/10/23 23:51:20| INFO: mul_sld finished [took 28.7221s] +30/10/23 23:51:31| INFO: mul_sld_bcts finished [took 39.4698s] +30/10/23 23:51:31| INFO: Dataset sample 0.50 of dataset imdb finished [took 40.8506s] +---------------------------------------------------------------------------------------------------- +30/10/23 23:52:29| INFO: dataset imdb +30/10/23 23:52:37| INFO: Dataset sample 0.90 of dataset imdb started +30/10/23 23:52:40| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:52:41| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:52:41| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +30/10/23 23:52:41| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +30/10/23 23:52:48| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +30/10/23 23:52:50| INFO: ref finished [took 12.4800s] +30/10/23 23:52:53| INFO: atc_mc finished [took 15.1770s] +30/10/23 23:52:54| INFO: atc_ne finished [took 15.2184s] +30/10/23 23:53:02| INFO: mul_sld finished [took 24.9402s] +30/10/23 23:53:02| INFO: Dataset sample 0.90 of dataset imdb finished [took 25.4588s] +30/10/23 23:53:02| INFO: Dataset sample 0.80 of dataset imdb started +30/10/23 23:53:20| INFO: ref finished [took 16.3699s] +30/10/23 23:53:25| INFO: atc_ne finished [took 20.5069s] +30/10/23 23:53:25| INFO: atc_mc finished [took 20.7398s] +30/10/23 23:53:38| INFO: mul_sld finished [took 35.3572s] +30/10/23 23:53:45| INFO: mul_sld_bcts finished [took 41.8712s] +30/10/23 23:56:35| INFO: bin_sld finished [took 212.1758s] +30/10/23 23:56:36| INFO: bin_sld_bcts finished [took 213.3641s] +30/10/23 23:57:38| INFO: mul_sld_gs finished [took 274.6360s] +31/10/23 00:01:13| INFO: bin_sld_gs finished [took 490.0221s] +31/10/23 00:01:13| INFO: Dataset sample 0.80 of dataset imdb finished [took 491.4099s] +31/10/23 00:01:13| INFO: Dataset sample 0.50 of dataset imdb started +31/10/23 00:01:32| INFO: ref finished [took 17.1003s] +31/10/23 00:01:37| INFO: atc_ne finished [took 21.2159s] +31/10/23 00:01:37| INFO: atc_mc finished [took 21.6794s] +31/10/23 00:01:51| INFO: mul_sld finished [took 37.3507s] +31/10/23 00:02:01| INFO: mul_sld_bcts finished [took 46.7227s] +31/10/23 00:04:46| INFO: bin_sld finished [took 211.9902s] +31/10/23 00:04:48| INFO: bin_sld_bcts finished [took 213.3398s] +31/10/23 00:05:55| INFO: mul_sld_gs finished [took 279.4401s] +31/10/23 00:08:56| INFO: bin_sld_gs finished [took 461.6571s] +31/10/23 00:08:56| INFO: Dataset sample 0.50 of dataset imdb finished [took 462.8616s] +31/10/23 00:08:56| INFO: Dataset sample 0.20 of dataset imdb started +31/10/23 00:09:15| INFO: ref finished [took 17.3643s] +31/10/23 00:09:20| INFO: atc_mc finished [took 21.0373s] +31/10/23 00:09:20| INFO: atc_ne finished [took 21.2599s] +31/10/23 00:09:38| INFO: mul_sld_bcts finished [took 41.0473s] +31/10/23 00:09:41| INFO: mul_sld finished [took 43.7800s] +31/10/23 00:12:30| INFO: bin_sld_bcts finished [took 212.8639s] +31/10/23 00:12:32| INFO: bin_sld finished [took 215.5704s] +31/10/23 00:13:29| INFO: mul_sld_gs finished [took 270.7454s] +31/10/23 00:19:19| INFO: bin_sld_gs finished [took 621.7089s] +31/10/23 00:19:19| INFO: Dataset sample 0.20 of dataset imdb finished [took 623.1501s] +31/10/23 00:19:19| INFO: Dataset sample 0.10 of dataset imdb started +31/10/23 00:19:24| WARNING: Method mul_sld_gs failed. Exception: a must be greater than 0 unless no samples are taken +31/10/23 00:19:24| WARNING: Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +31/10/23 00:19:26| WARNING: Method bin_sld_bcts failed. Exception: Cannot have number of splits n_splits=5 greater than the number of samples: n_samples=4. +31/10/23 00:19:31| WARNING: Method mul_sld_bcts failed. Exception: index 3 is out of bounds for axis 0 with size 3 +31/10/23 00:19:35| INFO: ref finished [took 13.7926s] +31/10/23 00:19:38| INFO: atc_mc finished [took 16.8128s] +31/10/23 00:19:39| INFO: atc_ne finished [took 16.9032s] +31/10/23 00:19:44| INFO: mul_sld finished [took 23.7188s] +31/10/23 00:21:43| WARNING: Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +31/10/23 00:21:43| INFO: Dataset sample 0.10 of dataset imdb finished [took 143.1001s] +---------------------------------------------------------------------------------------------------- +31/10/23 01:36:56| INFO: dataset imdb +31/10/23 01:37:04| INFO: Dataset sample 0.90 of dataset imdb started +31/10/23 01:37:13| WARNING: Method bin_sld_bcts failed. Exception: fun: nan + hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> + jac: array([ 1.06687127, -0.00373246, 0.00373246]) + message: 'ABNORMAL_TERMINATION_IN_LNSRCH' + nfev: 53 + nit: 34 + njev: 53 + status: 2 + success: False + x: array([ 0.11536329, -12.93833991, 12.93833991]) +31/10/23 01:37:24| INFO: ref finished [took 15.9844s] +31/10/23 01:37:28| INFO: atc_mc finished [took 19.7000s] +31/10/23 01:37:28| INFO: atc_ne finished [took 19.4612s] +31/10/23 01:37:39| INFO: mul_sld finished [took 33.2999s] +31/10/23 01:37:49| INFO: mul_sld_bcts finished [took 43.1402s] +31/10/23 01:40:23| INFO: bin_sld finished [took 197.7518s] +31/10/23 01:41:23| INFO: mul_sld_gs finished [took 256.4496s] +31/10/23 01:42:49| INFO: bin_sld_gs finished [took 342.7515s] +31/10/23 01:42:49| INFO: Dataset sample 0.90 of dataset imdb finished [took 344.8637s] +31/10/23 01:42:49| INFO: Dataset sample 0.50 of dataset imdb started +31/10/23 01:43:10| INFO: ref finished [took 17.6503s] +31/10/23 01:43:15| INFO: atc_mc finished [took 21.9510s] +31/10/23 01:43:15| INFO: atc_ne finished [took 21.5680s] +31/10/23 01:43:29| INFO: mul_sld finished [took 38.2515s] +31/10/23 01:43:41| INFO: mul_sld_bcts finished [took 48.9560s] +31/10/23 01:46:29| INFO: bin_sld_bcts finished [took 217.8464s] +31/10/23 01:46:29| INFO: bin_sld finished [took 218.6211s] +31/10/23 01:47:51| INFO: mul_sld_gs finished [took 298.6694s] +31/10/23 01:50:25| INFO: bin_sld_gs finished [took 452.8300s] +31/10/23 01:50:25| INFO: Dataset sample 0.50 of dataset imdb finished [took 455.3596s] +31/10/23 01:50:28| ERROR: Configuration imdb_2prevs failed. Exception: could not broadcast input array from shape (2100,7) into shape (2100,) +---------------------------------------------------------------------------------------------------- +31/10/23 02:13:21| INFO: dataset imdb +31/10/23 02:13:29| INFO: Dataset sample 0.90 of dataset imdb started +31/10/23 02:13:37| WARNING: Method bin_sld_bcts failed. Exception: fun: nan + hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> + jac: array([ 1.06687127, -0.00373246, 0.00373246]) + message: 'ABNORMAL_TERMINATION_IN_LNSRCH' + nfev: 53 + nit: 34 + njev: 53 + status: 2 + success: False + x: array([ 0.11536329, -12.93833991, 12.93833991]) +31/10/23 02:13:48| INFO: ref finished [took 16.5509s] +31/10/23 02:13:52| INFO: atc_mc finished [took 20.3138s] +31/10/23 02:13:52| INFO: atc_ne finished [took 20.1191s] +31/10/23 02:14:02| INFO: mul_sld finished [took 32.5158s] +31/10/23 02:14:12| INFO: mul_sld_bcts finished [took 42.0654s] +31/10/23 02:16:44| INFO: bin_sld finished [took 193.9189s] +31/10/23 02:17:44| INFO: mul_sld_gs finished [took 252.9066s] +31/10/23 02:19:11| INFO: bin_sld_gs finished [took 339.9813s] +31/10/23 02:19:11| INFO: Dataset sample 0.90 of dataset imdb finished [took 341.6967s] +31/10/23 02:19:11| INFO: Dataset sample 0.50 of dataset imdb started +31/10/23 02:19:30| INFO: ref finished [took 16.1334s] +31/10/23 02:19:35| INFO: atc_mc finished [took 20.5691s] +31/10/23 02:19:35| INFO: atc_ne finished [took 20.0126s] +31/10/23 02:19:49| INFO: mul_sld finished [took 36.4597s] +31/10/23 02:20:02| INFO: mul_sld_bcts finished [took 48.7131s] +31/10/23 02:22:38| INFO: bin_sld finished [took 205.8577s] +31/10/23 02:22:41| INFO: bin_sld_bcts finished [took 208.1999s] +31/10/23 02:23:58| INFO: mul_sld_gs finished [took 284.9247s] +31/10/23 02:26:26| INFO: bin_sld_gs finished [took 432.5665s] +31/10/23 02:26:26| INFO: Dataset sample 0.50 of dataset imdb finished [took 435.0679s] +---------------------------------------------------------------------------------------------------- +31/10/23 03:05:44| INFO: dataset rcv1_CCAT +31/10/23 03:05:49| INFO: Dataset sample 0.90 of dataset rcv1_CCAT started +31/10/23 03:06:59| INFO: kfcv finished [took 59.0143s] +31/10/23 03:06:59| INFO: ref finished [took 56.9074s] +31/10/23 03:07:03| INFO: doc_feat finished [took 49.0683s] +31/10/23 03:07:05| INFO: atc_mc finished [took 59.3988s] +31/10/23 03:07:07| INFO: atc_ne finished [took 58.0283s] +31/10/23 03:07:09| INFO: mul_sld finished [took 76.8284s] +31/10/23 03:07:19| INFO: mul_sld_bcts finished [took 84.0129s] +31/10/23 03:09:51| INFO: bin_sld_bcts finished [took 237.9395s] +31/10/23 03:09:53| INFO: bin_sld finished [took 242.1415s] +31/10/23 03:10:13| INFO: mul_sld_gs finished [took 255.3743s] +31/10/23 03:13:59| INFO: bin_sld_gs finished [took 483.0217s] +31/10/23 03:13:59| INFO: Dataset sample 0.90 of dataset rcv1_CCAT finished [took 489.9328s] +31/10/23 03:13:59| INFO: Dataset sample 0.80 of dataset rcv1_CCAT started +31/10/23 03:15:04| INFO: ref finished [took 53.0703s] +31/10/23 03:15:05| INFO: kfcv finished [took 55.9779s] +31/10/23 03:15:05| INFO: doc_feat finished [took 48.1315s] +31/10/23 03:15:10| INFO: atc_mc finished [took 56.8062s] +31/10/23 03:15:11| INFO: atc_ne finished [took 55.9933s] +31/10/23 03:15:20| INFO: mul_sld finished [took 77.2840s] +31/10/23 03:15:25| INFO: mul_sld_bcts finished [took 80.0502s] +31/10/23 03:17:55| INFO: bin_sld finished [took 233.0173s] +31/10/23 03:17:55| INFO: bin_sld_bcts finished [took 231.2358s] +31/10/23 03:18:59| INFO: mul_sld_gs finished [took 291.7573s] +31/10/23 03:21:50| INFO: bin_sld_gs finished [took 463.8743s] +31/10/23 03:21:50| INFO: Dataset sample 0.80 of dataset rcv1_CCAT finished [took 470.4706s] +31/10/23 03:21:50| INFO: Dataset sample 0.70 of dataset rcv1_CCAT started +31/10/23 03:22:52| INFO: doc_feat finished [took 46.9563s] +31/10/23 03:22:53| INFO: ref finished [took 52.8185s] +31/10/23 03:22:54| INFO: kfcv finished [took 55.3202s] +31/10/23 03:22:57| INFO: atc_mc finished [took 55.1482s] +31/10/23 03:22:58| INFO: atc_ne finished [took 54.7420s] +31/10/23 03:23:09| INFO: mul_sld finished [took 76.8111s] +31/10/23 03:23:14| INFO: mul_sld_bcts finished [took 80.0460s] +31/10/23 03:25:43| INFO: bin_sld finished [took 231.7146s] +31/10/23 03:25:44| INFO: bin_sld_bcts finished [took 230.9954s] +31/10/23 03:26:53| INFO: mul_sld_gs finished [took 296.5824s] +31/10/23 03:29:12| INFO: bin_sld_gs finished [took 437.2666s] +31/10/23 03:29:12| INFO: Dataset sample 0.70 of dataset rcv1_CCAT finished [took 442.6584s] +31/10/23 03:29:12| INFO: Dataset sample 0.60 of dataset rcv1_CCAT started +31/10/23 03:30:14| INFO: doc_feat finished [took 47.2841s] +31/10/23 03:30:15| INFO: ref finished [took 52.5819s] +31/10/23 03:30:16| INFO: kfcv finished [took 54.9735s] +31/10/23 03:30:19| INFO: atc_mc finished [took 55.5994s] +31/10/23 03:30:20| INFO: atc_ne finished [took 55.0062s] +31/10/23 03:30:30| INFO: mul_sld finished [took 75.3263s] +31/10/23 03:30:37| INFO: mul_sld_bcts finished [took 80.4052s] +31/10/23 03:33:04| INFO: bin_sld finished [took 229.9416s] +31/10/23 03:33:05| INFO: bin_sld_bcts finished [took 229.0971s] +31/10/23 03:34:12| INFO: mul_sld_gs finished [took 292.9916s] +31/10/23 03:37:15| INFO: bin_sld_gs finished [took 477.2157s] +31/10/23 03:37:15| INFO: Dataset sample 0.60 of dataset rcv1_CCAT finished [took 482.5150s] +31/10/23 03:37:15| INFO: Dataset sample 0.50 of dataset rcv1_CCAT started +31/10/23 03:38:17| INFO: doc_feat finished [took 47.6798s] +31/10/23 03:38:17| INFO: ref finished [took 52.2283s] +31/10/23 03:38:18| INFO: kfcv finished [took 54.3535s] +31/10/23 03:38:22| INFO: atc_mc finished [took 55.4316s] +31/10/23 03:38:23| INFO: atc_ne finished [took 55.3697s] +31/10/23 03:38:32| INFO: mul_sld finished [took 74.3762s] +31/10/23 03:38:39| INFO: mul_sld_bcts finished [took 79.7216s] +31/10/23 03:41:05| INFO: bin_sld finished [took 228.4963s] +31/10/23 03:41:08| INFO: bin_sld_bcts finished [took 230.0901s] +31/10/23 03:42:09| INFO: mul_sld_gs finished [took 287.8477s] +31/10/23 03:45:08| INFO: bin_sld_gs finished [took 467.2633s] +31/10/23 03:45:08| INFO: Dataset sample 0.50 of dataset rcv1_CCAT finished [took 472.6090s] +31/10/23 03:45:08| INFO: Dataset sample 0.40 of dataset rcv1_CCAT started +31/10/23 03:46:08| INFO: doc_feat finished [took 47.0674s] +31/10/23 03:46:09| INFO: ref finished [took 51.5844s] +31/10/23 03:46:09| INFO: kfcv finished [took 53.6481s] +31/10/23 03:46:14| INFO: atc_mc finished [took 55.3679s] +31/10/23 03:46:14| INFO: atc_ne finished [took 54.6174s] +31/10/23 03:46:21| INFO: mul_sld finished [took 71.5925s] +31/10/23 03:46:29| INFO: mul_sld_bcts finished [took 77.5938s] +31/10/23 03:48:55| INFO: bin_sld finished [took 226.3217s] +31/10/23 03:48:57| INFO: bin_sld_bcts finished [took 226.5561s] +31/10/23 03:50:04| INFO: mul_sld_gs finished [took 289.8958s] +31/10/23 03:53:13| INFO: bin_sld_gs finished [took 479.9650s] +31/10/23 03:53:13| INFO: Dataset sample 0.40 of dataset rcv1_CCAT finished [took 485.0438s] +31/10/23 03:53:13| INFO: Dataset sample 0.30 of dataset rcv1_CCAT started +31/10/23 03:54:15| INFO: doc_feat finished [took 47.1959s] +31/10/23 03:54:16| INFO: ref finished [took 52.7452s] +31/10/23 03:54:17| INFO: kfcv finished [took 55.3715s] +31/10/23 03:54:20| INFO: atc_mc finished [took 55.5749s] +31/10/23 03:54:21| INFO: atc_ne finished [took 54.8719s] +31/10/23 03:54:29| INFO: mul_sld finished [took 74.1932s] +31/10/23 03:54:37| INFO: mul_sld_bcts finished [took 80.1150s] +31/10/23 03:57:01| INFO: bin_sld finished [took 227.2338s] +31/10/23 03:57:06| INFO: bin_sld_bcts finished [took 229.7342s] +31/10/23 03:58:13| INFO: mul_sld_gs finished [took 293.4750s] +31/10/23 04:00:50| INFO: bin_sld_gs finished [took 451.3322s] +31/10/23 04:00:50| INFO: Dataset sample 0.30 of dataset rcv1_CCAT finished [took 456.9583s] +31/10/23 04:00:50| INFO: Dataset sample 0.20 of dataset rcv1_CCAT started +31/10/23 04:01:54| INFO: doc_feat finished [took 48.9670s] +31/10/23 04:01:54| INFO: ref finished [took 54.3281s] +31/10/23 04:01:55| INFO: kfcv finished [took 57.0798s] +31/10/23 04:01:59| INFO: atc_mc finished [took 57.5663s] +31/10/23 04:02:00| INFO: atc_ne finished [took 57.1756s] +31/10/23 04:02:07| INFO: mul_sld finished [took 74.8552s] +31/10/23 04:02:14| INFO: mul_sld_bcts finished [took 79.8097s] +31/10/23 04:04:43| INFO: bin_sld finished [took 231.6926s] +31/10/23 04:04:43| INFO: bin_sld_bcts finished [took 229.9267s] +31/10/23 04:05:23| INFO: mul_sld_gs finished [took 266.8226s] +31/10/23 04:08:36| INFO: bin_sld_gs finished [took 460.9384s] +31/10/23 04:08:36| INFO: Dataset sample 0.20 of dataset rcv1_CCAT finished [took 466.5653s] +31/10/23 04:08:36| INFO: Dataset sample 0.10 of dataset rcv1_CCAT started +31/10/23 04:08:46| WARNING: Method mul_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +31/10/23 04:08:46| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +31/10/23 04:08:55| WARNING: Method mul_sld_bcts failed. Exception: fun: nan + hess_inv: <5x5 LbfgsInvHessProduct with dtype=float64> + jac: array([nan, nan, nan, nan, nan]) + message: 'ABNORMAL_TERMINATION_IN_LNSRCH' + nfev: 21 + nit: 0 + njev: 21 + status: 2 + success: False + x: array([1., 0., 0., 0., 0.]) +31/10/23 04:09:33| INFO: doc_feat finished [took 42.6167s] +31/10/23 04:09:33| INFO: ref finished [took 46.6961s] +31/10/23 04:09:33| INFO: kfcv finished [took 48.7570s] +31/10/23 04:09:37| INFO: atc_mc finished [took 49.6198s] +31/10/23 04:09:38| INFO: atc_ne finished [took 49.1195s] +31/10/23 04:09:42| INFO: mul_sld finished [took 63.1364s] +31/10/23 04:11:02| WARNING: Method bin_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +31/10/23 04:12:05| INFO: bin_sld finished [took 207.4063s] +31/10/23 04:12:05| INFO: Dataset sample 0.10 of dataset rcv1_CCAT finished [took 208.7423s] +31/10/23 04:12:16| ERROR: Configuration rcv1_CCAT_9prevs failed. Exception: 'DatasetReport' object has no attribute 'fit_scores' +---------------------------------------------------------------------------------------------------- +31/10/23 11:30:20| INFO: dataset rcv1_CCAT +31/10/23 11:30:26| INFO: Dataset sample 0.90 of dataset rcv1_CCAT started +31/10/23 11:31:32| INFO: doc_feat finished [took 48.1486s] +31/10/23 11:31:32| INFO: ref finished [took 53.9235s] +31/10/23 11:31:33| INFO: kfcv finished [took 56.4175s] +31/10/23 11:31:37| INFO: atc_mc finished [took 57.2963s] +31/10/23 11:31:39| INFO: atc_ne finished [took 56.1470s] +31/10/23 11:31:43| INFO: mul_sld finished [took 74.0703s] +31/10/23 11:31:50| INFO: mul_sld_bcts finished [took 78.8253s] +31/10/23 11:34:16| INFO: bin_sld_bcts finished [took 225.7409s] +31/10/23 11:34:18| INFO: bin_sld finished [took 229.9705s] +31/10/23 11:34:42| INFO: mul_sld_gs finished [took 247.4756s] +31/10/23 11:38:30| INFO: bin_sld_gs finished [took 477.2173s] +31/10/23 11:38:30| INFO: Dataset sample 0.90 of dataset rcv1_CCAT finished [took 483.7632s] +31/10/23 11:38:30| INFO: Dataset sample 0.80 of dataset rcv1_CCAT started +31/10/23 11:39:32| INFO: doc_feat finished [took 47.0343s] +31/10/23 11:39:33| INFO: ref finished [took 52.5674s] +31/10/23 11:39:33| INFO: kfcv finished [took 54.5521s] +31/10/23 11:39:38| INFO: atc_mc finished [took 55.5394s] +31/10/23 11:39:38| INFO: atc_ne finished [took 54.9616s] +31/10/23 11:39:48| INFO: mul_sld finished [took 75.9068s] +31/10/23 11:39:53| INFO: mul_sld_bcts finished [took 78.1581s] +31/10/23 11:42:22| INFO: bin_sld finished [took 230.5536s] +31/10/23 11:42:23| INFO: bin_sld_bcts finished [took 229.2262s] +31/10/23 11:43:29| INFO: mul_sld_gs finished [took 291.7013s] +31/10/23 11:46:14| INFO: bin_sld_gs finished [took 457.9059s] +31/10/23 11:46:14| INFO: Dataset sample 0.80 of dataset rcv1_CCAT finished [took 463.5174s] +31/10/23 11:46:14| INFO: Dataset sample 0.70 of dataset rcv1_CCAT started +31/10/23 11:47:17| INFO: doc_feat finished [took 46.4490s] +31/10/23 11:47:17| INFO: ref finished [took 52.6852s] +31/10/23 11:47:17| INFO: kfcv finished [took 55.0158s] +31/10/23 11:47:21| INFO: atc_mc finished [took 55.4861s] +31/10/23 11:47:22| INFO: atc_ne finished [took 54.9236s] +31/10/23 11:47:32| INFO: mul_sld finished [took 75.5717s] +31/10/23 11:47:38| INFO: mul_sld_bcts finished [took 80.0893s] +31/10/23 11:50:02| INFO: bin_sld finished [took 226.8402s] +31/10/23 11:50:05| INFO: bin_sld_bcts finished [took 227.7311s] +31/10/23 11:51:15| INFO: mul_sld_gs finished [took 294.0087s] +31/10/23 11:53:36| INFO: bin_sld_gs finished [took 436.3031s] +31/10/23 11:53:36| INFO: Dataset sample 0.70 of dataset rcv1_CCAT finished [took 441.8200s] +31/10/23 11:53:36| INFO: Dataset sample 0.60 of dataset rcv1_CCAT started +31/10/23 11:54:37| INFO: doc_feat finished [took 47.1550s] +31/10/23 11:54:38| INFO: ref finished [took 52.2980s] +31/10/23 11:54:39| INFO: kfcv finished [took 54.5489s] +31/10/23 11:54:42| INFO: atc_mc finished [took 55.2076s] +31/10/23 11:54:43| INFO: atc_ne finished [took 54.6137s] +31/10/23 11:54:53| INFO: mul_sld finished [took 74.8407s] +31/10/23 11:54:59| INFO: mul_sld_bcts finished [took 79.4977s] +31/10/23 11:57:24| INFO: bin_sld finished [took 226.9209s] +31/10/23 11:57:26| INFO: bin_sld_bcts finished [took 227.3112s] +31/10/23 11:58:29| INFO: mul_sld_gs finished [took 286.1947s] +31/10/23 12:01:41| INFO: bin_sld_gs finished [took 479.8610s] +31/10/23 12:01:41| INFO: Dataset sample 0.60 of dataset rcv1_CCAT finished [took 485.3472s] +31/10/23 12:01:41| INFO: Dataset sample 0.50 of dataset rcv1_CCAT started +31/10/23 12:02:42| INFO: doc_feat finished [took 46.7340s] +31/10/23 12:02:42| INFO: ref finished [took 51.5482s] +31/10/23 12:02:43| INFO: kfcv finished [took 53.9559s] +31/10/23 12:02:47| INFO: atc_mc finished [took 54.7558s] +31/10/23 12:02:48| INFO: atc_ne finished [took 54.5216s] +31/10/23 12:02:57| INFO: mul_sld finished [took 73.4013s] +31/10/23 12:03:04| INFO: mul_sld_bcts finished [took 78.9197s] +31/10/23 12:05:30| INFO: bin_sld finished [took 227.3887s] +31/10/23 12:05:31| INFO: bin_sld_bcts finished [took 226.6540s] +31/10/23 12:06:37| INFO: mul_sld_gs finished [took 289.2631s] +31/10/23 12:09:30| INFO: bin_sld_gs finished [took 463.2754s] +31/10/23 12:09:30| INFO: Dataset sample 0.50 of dataset rcv1_CCAT finished [took 468.6356s] +31/10/23 12:09:30| INFO: Dataset sample 0.40 of dataset rcv1_CCAT started +31/10/23 12:10:30| INFO: doc_feat finished [took 47.0178s] +31/10/23 12:10:31| INFO: ref finished [took 51.8808s] +31/10/23 12:10:31| INFO: kfcv finished [took 53.6165s] +31/10/23 12:10:36| INFO: atc_mc finished [took 55.8052s] +31/10/23 12:10:36| INFO: atc_ne finished [took 55.0541s] +31/10/23 12:10:44| INFO: mul_sld finished [took 72.1431s] +31/10/23 12:10:52| INFO: mul_sld_bcts finished [took 78.0435s] +31/10/23 12:13:18| INFO: bin_sld finished [took 227.6364s] +31/10/23 12:13:20| INFO: bin_sld_bcts finished [took 227.4485s] +31/10/23 12:14:23| INFO: mul_sld_gs finished [took 287.2824s] +31/10/23 12:17:20| INFO: bin_sld_gs finished [took 465.4084s] +31/10/23 12:17:20| INFO: Dataset sample 0.40 of dataset rcv1_CCAT finished [took 470.5362s] +31/10/23 12:17:20| INFO: Dataset sample 0.30 of dataset rcv1_CCAT started +31/10/23 12:18:22| INFO: doc_feat finished [took 46.7203s] +31/10/23 12:18:24| INFO: ref finished [took 52.8941s] +31/10/23 12:18:24| INFO: kfcv finished [took 55.1602s] +31/10/23 12:18:27| INFO: atc_mc finished [took 55.1296s] +31/10/23 12:18:29| INFO: atc_ne finished [took 54.8176s] +31/10/23 12:18:36| INFO: mul_sld finished [took 73.6368s] +31/10/23 12:18:44| INFO: mul_sld_bcts finished [took 79.2444s] +31/10/23 12:21:09| INFO: bin_sld finished [took 227.4633s] +31/10/23 12:21:11| INFO: bin_sld_bcts finished [took 227.8848s] +31/10/23 12:22:18| INFO: mul_sld_gs finished [took 290.8750s] +31/10/23 12:25:01| INFO: bin_sld_gs finished [took 455.0492s] +31/10/23 12:25:01| INFO: Dataset sample 0.30 of dataset rcv1_CCAT finished [took 460.7077s] +31/10/23 12:25:01| INFO: Dataset sample 0.20 of dataset rcv1_CCAT started +31/10/23 12:26:04| INFO: doc_feat finished [took 48.7419s] +31/10/23 12:26:05| INFO: ref finished [took 53.9956s] +31/10/23 12:26:06| INFO: kfcv finished [took 56.7159s] +31/10/23 12:26:10| INFO: atc_mc finished [took 57.0141s] +31/10/23 12:26:11| INFO: atc_ne finished [took 56.6235s] +31/10/23 12:26:18| INFO: mul_sld finished [took 74.9361s] +31/10/23 12:26:24| INFO: mul_sld_bcts finished [took 78.6411s] +31/10/23 12:28:51| INFO: bin_sld finished [took 228.5964s] +31/10/23 12:28:51| INFO: bin_sld_bcts finished [took 226.9077s] +31/10/23 12:29:34| INFO: mul_sld_gs finished [took 265.9319s] +31/10/23 12:32:39| INFO: bin_sld_gs finished [took 452.9439s] +31/10/23 12:32:39| INFO: Dataset sample 0.20 of dataset rcv1_CCAT finished [took 458.4924s] +31/10/23 12:32:39| INFO: Dataset sample 0.10 of dataset rcv1_CCAT started +31/10/23 12:32:49| WARNING: Method mul_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +31/10/23 12:32:49| WARNING: Method bin_sld_bcts failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +31/10/23 12:32:57| WARNING: Method mul_sld_bcts failed. Exception: fun: nan + hess_inv: <5x5 LbfgsInvHessProduct with dtype=float64> + jac: array([nan, nan, nan, nan, nan]) + message: 'ABNORMAL_TERMINATION_IN_LNSRCH' + nfev: 21 + nit: 0 + njev: 21 + status: 2 + success: False + x: array([1., 0., 0., 0., 0.]) +31/10/23 12:33:33| INFO: doc_feat finished [took 40.8855s] +31/10/23 12:33:34| INFO: ref finished [took 44.7933s] +31/10/23 12:33:34| INFO: kfcv finished [took 47.0146s] +31/10/23 12:33:38| INFO: atc_mc finished [took 47.7008s] +31/10/23 12:33:39| INFO: atc_ne finished [took 47.4664s] +31/10/23 12:33:42| INFO: mul_sld finished [took 60.5341s] +31/10/23 12:35:06| WARNING: Method bin_sld_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +31/10/23 12:36:11| INFO: bin_sld finished [took 210.5128s] +31/10/23 12:36:11| INFO: Dataset sample 0.10 of dataset rcv1_CCAT finished [took 211.7476s] +---------------------------------------------------------------------------------------------------- +31/10/23 13:07:34| INFO: dataset imdb_2prevs +31/10/23 13:07:41| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 13:07:55| INFO: ref finished [took 12.2932s] +31/10/23 13:07:58| INFO: atc_mc finished [took 15.8781s] +31/10/23 13:07:58| INFO: atc_ne finished [took 15.8256s] +31/10/23 13:08:08| INFO: mul_sld finished [took 25.6841s] +31/10/23 13:08:18| INFO: mul_sld_bcts finished [took 35.3498s] +31/10/23 13:08:18| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 36.3540s] +31/10/23 13:08:18| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 13:08:32| INFO: ref finished [took 12.8011s] +31/10/23 13:08:36| INFO: atc_mc finished [took 16.7266s] +31/10/23 13:08:36| INFO: atc_ne finished [took 16.9577s] +31/10/23 13:08:49| INFO: mul_sld finished [took 30.1948s] +31/10/23 13:09:00| INFO: mul_sld_bcts finished [took 41.0998s] +31/10/23 13:09:00| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 42.6008s] +31/10/23 13:09:00| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' +---------------------------------------------------------------------------------------------------- +31/10/23 13:10:27| INFO: dataset imdb_2prevs +31/10/23 13:10:34| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 13:10:47| INFO: ref finished [took 11.6569s] +31/10/23 13:10:51| INFO: atc_mc finished [took 15.6380s] +31/10/23 13:10:51| INFO: atc_ne finished [took 15.5430s] +31/10/23 13:11:00| INFO: mul_sld finished [took 24.9236s] +31/10/23 13:11:10| INFO: mul_sld_bcts finished [took 34.5252s] +31/10/23 13:11:10| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 35.4874s] +31/10/23 13:11:10| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 13:11:23| INFO: ref finished [took 11.5602s] +31/10/23 13:11:26| INFO: atc_ne finished [took 14.3888s] +31/10/23 13:11:26| INFO: atc_mc finished [took 14.5643s] +31/10/23 13:11:39| INFO: mul_sld finished [took 27.8023s] +31/10/23 13:11:51| INFO: mul_sld_bcts finished [took 39.2892s] +31/10/23 13:11:51| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.6914s] +31/10/23 13:11:51| DEBUG: ['COMP_ESTIMATORS', 'DATASET_DIR_UPDATE', 'DATASET_NAME', 'DATASET_N_PREVS', 'DATASET_PREVS', 'DATASET_TARGET', 'METRICS', 'OUT_DIR', 'OUT_DIR_NAME', 'PLOT_DIR_NAME', 'PLOT_ESTIMATORS', 'PLOT_OUT_DIR', 'PLOT_STDEV', 'PROTOCOL_N_PREVS', 'PROTOCOL_REPEATS', 'SAMPLE_SIZE', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slotnames__', '__str__', '__subclasshook__', '__weakref__', '_current_conf', '_default', '_environ__getdict', '_environ__setdict', '_instance', '_keys', 'confs', 'exec', 'get_confs', 'get_plot_confs', 'load_conf', 'plot_confs'] +31/10/23 13:11:51| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' +---------------------------------------------------------------------------------------------------- +31/10/23 13:12:56| INFO: dataset imdb_2prevs +31/10/23 13:13:03| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 13:13:15| INFO: ref finished [took 11.2578s] +31/10/23 13:13:19| INFO: atc_mc finished [took 14.7895s] +31/10/23 13:13:19| INFO: atc_ne finished [took 14.8637s] +31/10/23 13:13:28| INFO: mul_sld finished [took 24.2622s] +31/10/23 13:13:38| INFO: mul_sld_bcts finished [took 34.0592s] +31/10/23 13:13:38| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.9907s] +31/10/23 13:13:38| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 13:13:52| INFO: ref finished [took 12.2334s] +31/10/23 13:13:56| INFO: atc_ne finished [took 16.1148s] +31/10/23 13:13:56| INFO: atc_mc finished [took 16.4135s] +31/10/23 13:14:10| INFO: mul_sld finished [took 30.7003s] +31/10/23 13:14:21| INFO: mul_sld_bcts finished [took 41.5915s] +31/10/23 13:14:21| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 43.0339s] +31/10/23 13:14:21| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '__getdict' +---------------------------------------------------------------------------------------------------- +31/10/23 14:05:25| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '_current_conf' +---------------------------------------------------------------------------------------------------- +31/10/23 14:06:00| INFO: dataset imdb_2prevs +31/10/23 14:06:07| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:06:19| INFO: ref finished [took 10.8776s] +31/10/23 14:06:23| INFO: atc_ne finished [took 14.0744s] +31/10/23 14:06:23| INFO: atc_mc finished [took 14.4000s] +31/10/23 14:06:33| INFO: mul_sld finished [took 24.5149s] +31/10/23 14:06:42| INFO: mul_sld_bcts finished [took 33.9116s] +31/10/23 14:06:42| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.8416s] +31/10/23 14:06:42| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:06:55| INFO: ref finished [took 11.2125s] +31/10/23 14:06:59| INFO: atc_ne finished [took 14.9235s] +31/10/23 14:06:59| INFO: atc_mc finished [took 15.1623s] +31/10/23 14:07:12| INFO: mul_sld finished [took 28.3463s] +31/10/23 14:07:23| INFO: mul_sld_bcts finished [took 39.0377s] +31/10/23 14:07:23| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.4312s] +31/10/23 14:07:23| ERROR: estimate comparison failed. Exceprion: 'environ' object has no attribute '__getdict' +---------------------------------------------------------------------------------------------------- +31/10/23 14:09:14| INFO: dataset imdb_2prevs +31/10/23 14:09:22| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:09:33| INFO: ref finished [took 10.6213s] +31/10/23 14:09:37| INFO: atc_mc finished [took 14.2004s] +31/10/23 14:09:37| INFO: atc_ne finished [took 14.2574s] +31/10/23 14:09:46| INFO: mul_sld finished [took 23.8084s] +31/10/23 14:09:56| INFO: mul_sld_bcts finished [took 33.4634s] +31/10/23 14:09:56| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.4023s] +31/10/23 14:09:56| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:10:09| INFO: ref finished [took 11.0452s] +31/10/23 14:10:12| INFO: atc_mc finished [took 14.0363s] +31/10/23 14:10:12| INFO: atc_ne finished [took 14.2492s] +31/10/23 14:10:25| INFO: mul_sld finished [took 27.1464s] +31/10/23 14:10:35| INFO: mul_sld_bcts finished [took 37.7957s] +31/10/23 14:10:35| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 39.1750s] +31/10/23 14:10:35| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' +---------------------------------------------------------------------------------------------------- +31/10/23 14:14:00| INFO: dataset imdb_2prevs +31/10/23 14:14:07| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:14:19| INFO: ref finished [took 11.0176s] +31/10/23 14:14:22| INFO: atc_mc finished [took 14.0422s] +31/10/23 14:14:23| INFO: atc_ne finished [took 14.2169s] +31/10/23 14:14:31| INFO: mul_sld finished [took 23.7014s] +31/10/23 14:14:42| INFO: mul_sld_bcts finished [took 33.7536s] +31/10/23 14:14:42| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.7003s] +31/10/23 14:14:42| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:14:54| INFO: ref finished [took 11.0187s] +31/10/23 14:14:57| INFO: atc_mc finished [took 14.0175s] +31/10/23 14:14:58| INFO: atc_ne finished [took 14.4154s] +31/10/23 14:15:10| INFO: mul_sld finished [took 27.5946s] +31/10/23 14:15:21| INFO: mul_sld_bcts finished [took 38.0464s] +31/10/23 14:15:21| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 39.4594s] +31/10/23 14:15:21| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts', 'ref'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': None, 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': None, 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 14:15:21| ERROR: estimate comparison failed. Exceprion: unsupported operand type(s) for /: 'NoneType' and 'str' +---------------------------------------------------------------------------------------------------- +31/10/23 14:30:59| INFO: dataset imdb_2prevs +31/10/23 14:31:10| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:31:33| INFO: ref finished [took 22.2885s] +31/10/23 14:31:41| INFO: atc_mc finished [took 29.8328s] +31/10/23 14:31:41| INFO: atc_ne finished [took 30.1421s] +31/10/23 14:31:46| INFO: mul_sld finished [took 35.8373s] +31/10/23 14:31:57| INFO: mul_sld_bcts finished [took 46.5130s] +31/10/23 14:31:57| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 47.5379s] +31/10/23 14:31:57| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:32:24| INFO: ref finished [took 24.3912s] +31/10/23 14:32:31| INFO: atc_mc finished [took 31.2488s] +31/10/23 14:32:31| INFO: atc_ne finished [took 31.5120s] +31/10/23 14:32:43| INFO: mul_sld finished [took 44.6372s] +31/10/23 14:32:54| INFO: mul_sld_bcts finished [took 54.5749s] +31/10/23 14:32:54| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 56.2358s] +31/10/23 14:32:54| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_ne', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 14:32:58| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_ne', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} +---------------------------------------------------------------------------------------------------- +31/10/23 14:37:38| INFO: dataset imdb_2prevs +31/10/23 14:37:45| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:37:58| INFO: ref finished [took 11.4397s] +31/10/23 14:38:01| INFO: atc_mc finished [took 14.6411s] +31/10/23 14:38:01| INFO: atc_ne finished [took 14.8218s] +31/10/23 14:38:11| INFO: mul_sld finished [took 24.4862s] +31/10/23 14:38:20| INFO: mul_sld_bcts finished [took 33.9900s] +31/10/23 14:38:20| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 34.9816s] +31/10/23 14:38:20| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:38:33| INFO: ref finished [took 11.3787s] +31/10/23 14:38:36| INFO: atc_mc finished [took 14.4366s] +31/10/23 14:38:36| INFO: atc_ne finished [took 14.3365s] +31/10/23 14:38:50| INFO: mul_sld finished [took 28.1585s] +31/10/23 14:39:00| INFO: mul_sld_bcts finished [took 38.5508s] +31/10/23 14:39:00| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.0193s] +31/10/23 14:39:00| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld', 'ref', 'mul_sld_bcts', 'atc_mc'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 14:39:03| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld', 'ref', 'mul_sld_bcts', 'atc_mc'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} +---------------------------------------------------------------------------------------------------- +31/10/23 14:44:15| INFO: dataset imdb_2prevs +31/10/23 14:44:22| INFO: Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:44:35| INFO: ref finished [took 11.5902s] +31/10/23 14:44:38| INFO: atc_mc finished [took 14.7915s] +31/10/23 14:44:39| INFO: atc_ne finished [took 14.8611s] +31/10/23 14:44:48| INFO: mul_sld finished [took 24.7670s] +31/10/23 14:44:58| INFO: mul_sld_bcts finished [took 34.3171s] +31/10/23 14:44:58| INFO: Dataset sample 0.10 of dataset imdb_2prevs finished [took 35.3088s] +31/10/23 14:44:58| INFO: Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:45:11| INFO: ref finished [took 11.8887s] +31/10/23 14:45:15| INFO: atc_mc finished [took 15.2624s] +31/10/23 14:45:15| INFO: atc_ne finished [took 15.2085s] +31/10/23 14:45:28| INFO: mul_sld finished [took 28.5408s] +31/10/23 14:45:38| INFO: mul_sld_bcts finished [took 38.9976s] +31/10/23 14:45:38| INFO: Dataset sample 0.50 of dataset imdb_2prevs finished [took 40.5854s] +31/10/23 14:45:38| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 14:45:41| DEBUG: {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['atc_ne', 'mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} +---------------------------------------------------------------------------------------------------- +31/10/23 14:55:18| INFO dataset imdb_2prevs +31/10/23 14:55:26| INFO Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 14:55:37| INFO ref finished [took 10.1990s] +31/10/23 14:55:40| INFO atc_mc finished [took 13.4778s] +31/10/23 14:55:41| INFO atc_ne finished [took 13.5559s] +31/10/23 14:55:50| INFO mul_sld finished [took 23.0450s] +31/10/23 14:55:59| INFO mul_sld_bcts finished [took 32.4582s] +31/10/23 14:55:59| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.3679s] +31/10/23 14:55:59| INFO Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 14:56:11| INFO ref finished [took 10.3415s] +31/10/23 14:56:14| INFO atc_mc finished [took 13.4638s] +31/10/23 14:56:14| INFO atc_ne finished [took 13.4791s] +31/10/23 14:56:27| INFO mul_sld finished [took 26.3298s] +31/10/23 14:56:38| INFO mul_sld_bcts finished [took 37.2449s] +31/10/23 14:56:38| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.6430s] +31/10/23 14:56:38| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld', 'atc_ne'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 14:56:41| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['mul_sld_bcts', 'atc_mc', 'ref', 'mul_sld', 'atc_ne'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} +---------------------------------------------------------------------------------------------------- +31/10/23 15:00:19| INFO dataset imdb_2prevs +31/10/23 15:00:26| INFO Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 15:00:38| INFO ref finished [took 10.7099s] +31/10/23 15:00:41| INFO atc_ne finished [took 13.7392s] +31/10/23 15:00:41| INFO atc_mc finished [took 13.9108s] +31/10/23 15:00:50| INFO mul_sld finished [took 23.3628s] +31/10/23 15:01:00| INFO mul_sld_bcts finished [took 33.0440s] +31/10/23 15:01:00| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.9805s] +31/10/23 15:01:00| INFO Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 15:01:12| INFO ref finished [took 10.7020s] +31/10/23 15:01:15| INFO atc_mc finished [took 13.9521s] +31/10/23 15:01:15| INFO atc_ne finished [took 13.8623s] +31/10/23 15:01:28| INFO mul_sld finished [took 26.8476s] +31/10/23 15:01:39| INFO mul_sld_bcts finished [took 37.4291s] +31/10/23 15:01:39| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.8721s] +31/10/23 15:01:39| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['ref', 'atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': True} +31/10/23 15:01:42| DEBUG {'DATASET_NAME': 'imdb', 'DATASET_TARGET': None, 'METRICS': ['acc'], 'COMP_ESTIMATORS': ['ref', 'atc_mc', 'mul_sld', 'atc_ne', 'mul_sld_bcts'], 'DATASET_N_PREVS': 5, 'DATASET_PREVS': [0.5, 0.1], 'OUT_DIR_NAME': 'output', 'OUT_DIR': WindowsPath('output/imdb_2prevs'), 'PLOT_DIR_NAME': 'plot', 'PLOT_OUT_DIR': WindowsPath('output/imdb_2prevs/plot'), 'DATASET_DIR_UPDATE': False, 'PROTOCOL_N_PREVS': 21, 'PROTOCOL_REPEATS': 100, 'SAMPLE_SIZE': 1000, 'PLOT_ESTIMATORS': ['mul_sld_bcts', 'mul_sld', 'ref', 'atc_mc', 'atc_ne'], 'PLOT_STDEV': False} +---------------------------------------------------------------------------------------------------- +31/10/23 15:02:43| INFO dataset imdb_2prevs +31/10/23 15:02:50| INFO Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 15:03:02| INFO ref finished [took 10.5528s] +31/10/23 15:03:05| INFO atc_mc finished [took 13.7838s] +31/10/23 15:03:05| INFO atc_ne finished [took 13.6736s] +31/10/23 15:03:14| INFO mul_sld finished [took 23.2705s] +31/10/23 15:03:24| INFO mul_sld_bcts finished [took 32.8493s] +31/10/23 15:03:24| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 33.7917s] +31/10/23 15:03:24| INFO Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 15:03:36| INFO ref finished [took 10.4338s] +31/10/23 15:03:39| INFO atc_mc finished [took 13.5140s] +31/10/23 15:03:39| INFO atc_ne finished [took 13.5920s] +31/10/23 15:03:52| INFO mul_sld finished [took 26.7677s] +31/10/23 15:04:03| INFO mul_sld_bcts finished [took 37.2882s] +31/10/23 15:04:03| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 38.7014s] +---------------------------------------------------------------------------------------------------- +31/10/23 17:01:56| INFO dataset imdb_2prevs +31/10/23 17:02:03| INFO Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 17:02:16| INFO ref finished [took 11.7260s] +31/10/23 17:02:19| INFO atc_mc finished [took 14.9332s] +31/10/23 17:02:20| INFO atc_ne finished [took 14.9267s] +31/10/23 17:02:29| INFO mul_sld finished [took 25.0825s] +31/10/23 17:02:39| INFO mul_sld_bcts finished [took 35.1456s] +31/10/23 17:02:39| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 36.1167s] +31/10/23 17:02:39| INFO Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 17:02:53| INFO ref finished [took 12.1990s] +31/10/23 17:02:57| INFO atc_mc finished [took 15.9130s] +31/10/23 17:02:57| INFO atc_ne finished [took 15.8122s] +31/10/23 17:03:10| INFO mul_sld finished [took 29.0681s] +31/10/23 17:03:21| INFO mul_sld_bcts finished [took 40.0346s] +31/10/23 17:03:21| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 41.6137s] +---------------------------------------------------------------------------------------------------- +31/10/23 17:04:35| INFO dataset imdb_2prevs +31/10/23 17:04:42| INFO Dataset sample 0.10 of dataset imdb_2prevs started +31/10/23 17:04:56| INFO ref finished [took 11.5027s] +31/10/23 17:05:00| INFO atc_mc finished [took 15.1600s] +31/10/23 17:05:00| INFO atc_ne finished [took 15.0072s] +31/10/23 17:05:09| INFO mul_sld finished [took 24.7931s] +31/10/23 17:05:19| INFO mul_sld_bcts finished [took 34.6305s] +31/10/23 17:05:19| INFO Dataset sample 0.10 of dataset imdb_2prevs finished [took 37.1778s] +31/10/23 17:05:19| INFO Dataset sample 0.50 of dataset imdb_2prevs started +31/10/23 17:05:33| INFO ref finished [took 12.2649s] +31/10/23 17:05:36| INFO atc_mc finished [took 15.5987s] +31/10/23 17:05:37| INFO atc_ne finished [took 15.8214s] +31/10/23 17:05:50| INFO mul_sld finished [took 29.3523s] +31/10/23 17:06:00| INFO mul_sld_bcts finished [took 39.8376s] +31/10/23 17:06:00| INFO Dataset sample 0.50 of dataset imdb_2prevs finished [took 41.2888s] +---------------------------------------------------------------------------------------------------- +31/10/23 20:19:37| INFO dataset imdb_1prevs +31/10/23 20:19:48| INFO Dataset sample 0.50 of dataset imdb_1prevs started +31/10/23 20:20:07| INFO ref finished [took 17.4125s] +---------------------------------------------------------------------------------------------------- +31/10/23 20:20:50| INFO dataset imdb_1prevs +31/10/23 20:21:01| INFO Dataset sample 0.50 of dataset imdb_1prevs started +31/10/23 20:21:19| INFO ref finished [took 17.0717s] +---------------------------------------------------------------------------------------------------- +31/10/23 20:22:05| INFO dataset imdb_1prevs +31/10/23 20:22:15| INFO Dataset sample 0.50 of dataset imdb_1prevs started +31/10/23 20:22:35| INFO ref finished [took 18.4752s] +---------------------------------------------------------------------------------------------------- +31/10/23 20:23:38| INFO dataset imdb_1prevs +31/10/23 20:23:48| INFO Dataset sample 0.50 of dataset imdb_1prevs started +31/10/23 20:24:08| INFO ref finished [took 18.3216s] +---------------------------------------------------------------------------------------------------- +01/11/23 13:07:19| INFO dataset imdb_1prevs +01/11/23 13:07:27| INFO Dataset sample 0.50 of dataset imdb_1prevs started +01/11/23 13:07:27| ERROR Evaluation over imdb_1prevs failed. Exception: 'Invalid estimator: estimator mul_sld_gs does not exist' +01/11/23 13:07:27| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value +---------------------------------------------------------------------------------------------------- +03/11/23 20:54:19| INFO dataset rcv1_CCAT_9prevs +03/11/23 20:54:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +03/11/23 20:54:28| WARNING Method mul_sld_gs failed. Exception: Invalid parameter 'quantifier' for estimator EMQ(classifier=LogisticRegression()). Valid parameters are: ['classifier', 'exact_train_prev', 'recalib']. +03/11/23 20:54:29| WARNING Method mul_sld failed. Exception: evaluation_report() got an unexpected keyword argument 'protocor' +03/11/23 20:54:30| WARNING Method bin_sld_gs failed. Exception: Invalid parameter 'quantifier' for estimator EMQ(classifier=LogisticRegression()). Valid parameters are: ['classifier', 'exact_train_prev', 'recalib']. +03/11/23 20:55:09| INFO ref finished [took 38.5179s] +---------------------------------------------------------------------------------------------------- +03/11/23 21:28:36| INFO dataset rcv1_CCAT_9prevs +03/11/23 21:28:41| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +03/11/23 21:28:45| WARNING Method mul_sld failed. Exception: evaluation_report() got an unexpected keyword argument 'protocor' +---------------------------------------------------------------------------------------------------- +03/11/23 21:31:03| INFO dataset rcv1_CCAT_9prevs +03/11/23 21:31:08| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +03/11/23 21:31:59| INFO ref finished [took 45.6616s] +03/11/23 21:32:03| INFO atc_mc finished [took 48.4360s] +03/11/23 21:32:07| INFO atc_ne finished [took 51.0515s] +03/11/23 21:32:23| INFO mul_sld finished [took 72.9229s] +03/11/23 21:34:43| INFO bin_sld finished [took 213.9538s] +03/11/23 21:36:27| INFO mul_sld_gs finished [took 314.9357s] +03/11/23 21:40:50| INFO bin_sld_gs finished [took 579.2530s] +03/11/23 21:40:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 582.5876s] +03/11/23 21:40:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +03/11/23 21:41:39| INFO ref finished [took 43.7409s] +03/11/23 21:41:43| INFO atc_mc finished [took 46.4580s] +03/11/23 21:41:44| INFO atc_ne finished [took 46.4267s] +03/11/23 21:41:54| INFO mul_sld finished [took 61.3005s] +03/11/23 21:44:18| INFO bin_sld finished [took 206.3680s] +03/11/23 21:45:59| INFO mul_sld_gs finished [took 304.4726s] +03/11/23 21:50:33| INFO bin_sld_gs finished [took 579.3455s] +03/11/23 21:50:33| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 582.4808s] +03/11/23 21:50:33| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +03/11/23 21:51:22| INFO ref finished [took 43.6853s] +03/11/23 21:51:26| INFO atc_mc finished [took 47.1366s] +03/11/23 21:51:30| INFO atc_ne finished [took 49.4868s] +03/11/23 21:51:34| INFO mul_sld finished [took 59.0964s] +03/11/23 21:53:59| INFO bin_sld finished [took 205.0248s] +03/11/23 21:55:50| INFO mul_sld_gs finished [took 312.5630s] +03/11/23 22:00:27| INFO bin_sld_gs finished [took 591.1460s] +03/11/23 22:00:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 594.3163s] +03/11/23 22:00:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +03/11/23 22:01:15| INFO ref finished [took 43.3806s] +03/11/23 22:01:19| INFO atc_mc finished [took 46.6674s] +03/11/23 22:01:21| INFO atc_ne finished [took 47.1220s] +03/11/23 22:01:28| INFO mul_sld finished [took 58.6799s] +03/11/23 22:03:53| INFO bin_sld finished [took 204.7659s] +03/11/23 22:05:39| INFO mul_sld_gs finished [took 307.8811s] +03/11/23 22:10:32| INFO bin_sld_gs finished [took 601.9995s] +03/11/23 22:10:32| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 604.8406s] +03/11/23 22:10:32| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +03/11/23 22:11:20| INFO ref finished [took 42.8256s] +03/11/23 22:11:25| INFO atc_mc finished [took 46.9203s] +03/11/23 22:11:28| INFO atc_ne finished [took 49.3042s] +03/11/23 22:11:34| INFO mul_sld finished [took 60.2744s] +03/11/23 22:13:59| INFO bin_sld finished [took 205.7078s] +03/11/23 22:15:45| INFO mul_sld_gs finished [took 309.0888s] +03/11/23 22:20:32| INFO bin_sld_gs finished [took 596.5102s] +03/11/23 22:20:32| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 599.5067s] +03/11/23 22:20:32| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +03/11/23 22:21:20| INFO ref finished [took 43.1698s] +03/11/23 22:21:24| INFO atc_mc finished [took 46.5768s] +03/11/23 22:21:25| INFO atc_ne finished [took 46.3408s] +03/11/23 22:21:34| INFO mul_sld finished [took 60.8070s] +03/11/23 22:23:58| INFO bin_sld finished [took 205.3362s] +03/11/23 22:25:44| INFO mul_sld_gs finished [took 308.1859s] +03/11/23 22:30:44| INFO bin_sld_gs finished [took 609.5468s] +03/11/23 22:30:44| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 612.5803s] +03/11/23 22:30:44| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +03/11/23 22:31:32| INFO ref finished [took 43.2949s] +03/11/23 22:31:37| INFO atc_mc finished [took 46.3686s] +03/11/23 22:31:40| INFO atc_ne finished [took 49.2242s] +03/11/23 22:31:47| INFO mul_sld finished [took 60.9437s] +03/11/23 22:34:11| INFO bin_sld finished [took 205.9299s] +03/11/23 22:35:56| INFO mul_sld_gs finished [took 308.2738s] +03/11/23 22:40:36| INFO bin_sld_gs finished [took 588.7918s] +03/11/23 22:40:36| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 591.8830s] +03/11/23 22:40:36| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +03/11/23 22:41:24| INFO ref finished [took 43.3321s] +03/11/23 22:41:29| INFO atc_mc finished [took 46.8041s] +03/11/23 22:41:29| INFO atc_ne finished [took 46.5810s] +03/11/23 22:41:38| INFO mul_sld finished [took 60.2962s] +03/11/23 22:44:07| INFO bin_sld finished [took 209.6435s] +03/11/23 22:45:44| INFO mul_sld_gs finished [took 304.4809s] +03/11/23 22:50:39| INFO bin_sld_gs finished [took 599.5588s] +03/11/23 22:50:39| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 602.5720s] +03/11/23 22:50:39| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +03/11/23 22:51:26| INFO ref finished [took 42.4313s] +03/11/23 22:51:30| INFO atc_mc finished [took 45.5261s] +03/11/23 22:51:34| INFO atc_ne finished [took 48.4488s] +03/11/23 22:51:47| INFO mul_sld finished [took 66.4801s] +03/11/23 22:54:08| INFO bin_sld finished [took 208.4272s] +03/11/23 22:55:49| INFO mul_sld_gs finished [took 306.4505s] +03/11/23 23:00:15| INFO bin_sld_gs finished [took 573.7761s] +03/11/23 23:00:15| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 576.7586s] +---------------------------------------------------------------------------------------------------- +03/11/23 23:33:15| INFO dataset imdb_1prevs +03/11/23 23:33:22| INFO Dataset sample 0.50 of dataset imdb_1prevs started +03/11/23 23:33:22| ERROR Evaluation over imdb_1prevs failed. Exception: 'function' object is not iterable +03/11/23 23:33:22| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value +---------------------------------------------------------------------------------------------------- +03/11/23 23:34:15| INFO dataset imdb_1prevs +03/11/23 23:34:23| INFO Dataset sample 0.50 of dataset imdb_1prevs started +03/11/23 23:34:35| INFO atc_mc finished [took 11.5081s] +03/11/23 23:34:45| INFO ref finished [took 8.7754s] +03/11/23 23:34:47| INFO mul_sld finished [took 22.9651s] +03/11/23 23:34:47| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 23.9721s] +03/11/23 23:34:47| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: 'ref' +---------------------------------------------------------------------------------------------------- +03/11/23 23:36:10| INFO dataset imdb_1prevs +03/11/23 23:36:30| INFO Dataset sample 0.50 of dataset imdb_1prevs started +03/11/23 23:38:02| INFO atc_mc finished [took 56.2957s] +03/11/23 23:38:03| INFO mul_sld finished [took 57.6237s] +03/11/23 23:38:40| INFO ref finished [took 37.7811s] +03/11/23 23:38:40| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 130.9031s] +03/11/23 23:38:42| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: 'ref' +---------------------------------------------------------------------------------------------------- +03/11/23 23:39:32| INFO dataset imdb_1prevs +03/11/23 23:39:42| INFO Dataset sample 0.50 of dataset imdb_1prevs started +03/11/23 23:40:08| INFO atc_mc finished [took 24.7110s] +03/11/23 23:40:23| INFO mul_sld finished [took 40.2345s] +03/11/23 23:40:26| INFO ref finished [took 17.8417s] +03/11/23 23:40:26| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 44.8087s] +---------------------------------------------------------------------------------------------------- +03/11/23 23:41:18| INFO dataset imdb_1prevs +03/11/23 23:41:28| INFO Dataset sample 0.50 of dataset imdb_1prevs started +03/11/23 23:41:54| INFO atc_mc finished [took 24.0569s] +03/11/23 23:42:03| INFO mul_sld finished [took 33.3390s] +03/11/23 23:42:12| INFO ref finished [took 16.9551s] +03/11/23 23:42:12| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 43.2484s] +---------------------------------------------------------------------------------------------------- +04/11/23 00:03:17| ERROR Evaluation over imdb_1prevs failed. Exception: CompEstimatorName_.__init__() missing 1 required positional argument: 'ce' +04/11/23 00:03:17| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value +---------------------------------------------------------------------------------------------------- +04/11/23 00:03:50| ERROR Evaluation over imdb_1prevs failed. Exception: 'CompEstimator' object has no attribute '_CompEstimatorName___get' +04/11/23 00:03:50| ERROR Failed while saving configuration imdb_debug of imdb_1prevs. Exception: cannot access local variable 'dr' where it is not associated with a value +---------------------------------------------------------------------------------------------------- +04/11/23 00:04:42| INFO dataset imdb_1prevs +04/11/23 00:04:53| INFO Dataset sample 0.50 of dataset imdb_1prevs started +04/11/23 00:05:13| INFO ref finished [took 19.2363s] +04/11/23 00:05:20| INFO atc_mc finished [took 26.4278s] +04/11/23 00:05:29| INFO mul_sld finished [took 35.3110s] +04/11/23 00:05:29| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 36.4422s] +---------------------------------------------------------------------------------------------------- +04/11/23 00:19:43| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:19:49| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:19:53| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:19:55| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:19:57| WARNING Method bin_pacc failed. Exception: PACC.__init__() got an unexpected keyword argument 'recalib' +04/11/23 00:19:59| WARNING Method mul_pacc failed. Exception: PACC.__init__() got an unexpected keyword argument 'recalib' +04/11/23 00:20:00| WARNING Method bin_pacc_gs failed. Exception: Invalid parameter 'recalib' for estimator PACC(classifier=LogisticRegression(), n_jobs=1). Valid parameters are: ['classifier', 'n_jobs', 'val_split']. +04/11/23 00:20:01| WARNING Method mul_pacc_gs failed. Exception: Invalid parameter 'recalib' for estimator PACC(classifier=LogisticRegression(), n_jobs=1). Valid parameters are: ['classifier', 'n_jobs', 'val_split']. +---------------------------------------------------------------------------------------------------- +04/11/23 00:22:45| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:22:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:22:54| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:22:55| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +---------------------------------------------------------------------------------------------------- +04/11/23 00:28:11| INFO dataset rcv1_CCAT_9prevs +---------------------------------------------------------------------------------------------------- +04/11/23 00:29:39| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:29:45| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:29:49| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:29:51| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:30:39| WARNING Method mul_pacc_gs failed. Exception: evaluation_report() got an unexpected keyword argument 'method_name' +04/11/23 00:31:00| INFO ref finished [took 60.5788s] +04/11/23 00:31:09| INFO atc_mc finished [took 64.6156s] +04/11/23 00:31:09| INFO mul_pacc finished [took 75.1821s] +04/11/23 00:31:12| INFO atc_ne finished [took 62.8665s] +04/11/23 00:31:24| INFO mul_sld finished [took 96.8624s] +---------------------------------------------------------------------------------------------------- +04/11/23 00:33:26| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:33:31| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:33:35| WARNING Method bin_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +04/11/23 00:33:37| WARNING Method mul_sld_qgs failed. Exception: X has 47236 features, but LogisticRegression is expecting 47238 features as input. +---------------------------------------------------------------------------------------------------- +04/11/23 00:38:42| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:38:48| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:38:51| WARNING Method bin_sld_qgs failed. Exception: ExtendedCollection.extend_collection() missing 1 required positional argument: 'pred_proba' +04/11/23 00:38:52| WARNING Method mul_sld_qgs failed. Exception: ExtendedCollection.extend_collection() missing 1 required positional argument: 'pred_proba' +04/11/23 00:39:41| WARNING Method mul_pacc_gs failed. Exception: evaluation_report() got an unexpected keyword argument 'method_name' +---------------------------------------------------------------------------------------------------- +04/11/23 00:46:33| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:46:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:46:40| WARNING Method bin_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:41| WARNING Method mul_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:42| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +04/11/23 00:46:43| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +04/11/23 00:46:44| WARNING Method bin_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:45| WARNING Method mul_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:46| WARNING Method bin_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:47| WARNING Method mul_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:47| WARNING Method bin_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:46:48| WARNING Method mul_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:27| INFO ref finished [took 37.5294s] +04/11/23 00:47:31| INFO atc_mc finished [took 40.5777s] +04/11/23 00:47:32| INFO atc_ne finished [took 40.7565s] +04/11/23 00:47:32| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 52.8106s] +04/11/23 00:47:32| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 00:47:33| WARNING Method bin_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:34| WARNING Method mul_sld failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:35| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +04/11/23 00:47:36| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +04/11/23 00:47:37| WARNING Method bin_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:38| WARNING Method mul_sld_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:39| WARNING Method bin_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:39| WARNING Method mul_pacc failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:40| WARNING Method bin_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +04/11/23 00:47:41| WARNING Method mul_pacc_gs failed. Exception: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() +---------------------------------------------------------------------------------------------------- +04/11/23 00:48:05| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:48:10| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:48:13| WARNING Method bin_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +04/11/23 00:48:14| WARNING Method mul_sld_qgs failed. Exception: 'LogisticRegression' object has no attribute 'pred_proba' +---------------------------------------------------------------------------------------------------- +04/11/23 00:49:18| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:49:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:49:27| WARNING Method bin_sld_qgs failed. Exception: GridSearchQ.__init__() missing 1 required positional argument: 'model' +04/11/23 00:49:28| WARNING Method mul_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. +---------------------------------------------------------------------------------------------------- +04/11/23 00:51:27| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:51:32| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:51:36| WARNING Method bin_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. +04/11/23 00:51:37| WARNING Method mul_sld_qgs failed. Exception: X has 47238 features, but LogisticRegression is expecting 47236 features as input. +---------------------------------------------------------------------------------------------------- +04/11/23 00:54:47| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:54:53| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 00:54:57| WARNING Method bin_sld_qgs failed. Exception: a must be greater than 0 unless no samples are taken +04/11/23 00:54:58| WARNING Method mul_sld_qgs failed. Exception: a must be greater than 0 unless no samples are taken +---------------------------------------------------------------------------------------------------- +04/11/23 00:58:47| INFO dataset rcv1_CCAT_9prevs +04/11/23 00:58:52| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 01:00:04| INFO ref finished [took 61.6328s] +04/11/23 01:00:11| INFO atc_mc finished [took 65.4916s] +04/11/23 01:00:13| INFO atc_ne finished [took 63.2288s] +04/11/23 01:00:14| INFO mul_pacc finished [took 75.5101s] +04/11/23 01:00:30| INFO mul_sld finished [took 96.6656s] +04/11/23 01:00:41| INFO mul_pacc_gs finished [took 99.7211s] +04/11/23 01:03:02| INFO bin_pacc finished [took 244.6260s] +04/11/23 01:03:07| INFO bin_sld finished [took 254.3478s] +04/11/23 01:04:51| INFO mul_sld_gs finished [took 354.7477s] +04/11/23 01:05:02| INFO bin_pacc_gs finished [took 362.1808s] +04/11/23 01:09:24| INFO bin_sld_gs finished [took 628.6714s] +04/11/23 01:09:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 631.8421s] +04/11/23 01:09:24| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 01:10:39| INFO ref finished [took 63.5158s] +04/11/23 01:10:44| INFO atc_mc finished [took 66.4279s] +04/11/23 01:10:46| INFO mul_pacc finished [took 75.3281s] +04/11/23 01:10:47| INFO atc_ne finished [took 67.5374s] +04/11/23 01:10:52| INFO mul_sld finished [took 86.6592s] +04/11/23 01:11:19| INFO mul_pacc_gs finished [took 104.6374s] +04/11/23 01:13:58| INFO bin_sld finished [took 273.4932s] +04/11/23 01:14:01| INFO bin_pacc finished [took 271.3481s] +04/11/23 01:15:42| INFO mul_sld_gs finished [took 374.2416s] +04/11/23 01:16:01| INFO bin_pacc_gs finished [took 388.0839s] +04/11/23 01:20:29| INFO bin_sld_gs finished [took 661.9729s] +04/11/23 01:20:29| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 665.2874s] +04/11/23 01:20:29| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +04/11/23 01:21:46| INFO ref finished [took 63.8544s] +04/11/23 01:21:50| INFO atc_mc finished [took 66.6917s] +04/11/23 01:21:52| INFO atc_ne finished [took 65.0860s] +04/11/23 01:21:53| INFO mul_pacc finished [took 77.2630s] +04/11/23 01:21:55| INFO mul_sld finished [took 83.3146s] +04/11/23 01:22:23| INFO mul_pacc_gs finished [took 102.3761s] +04/11/23 01:24:47| INFO bin_pacc finished [took 252.0964s] +04/11/23 01:24:49| INFO bin_sld finished [took 258.6998s] +04/11/23 01:26:37| INFO mul_sld_gs finished [took 363.7500s] +04/11/23 01:26:49| INFO bin_pacc_gs finished [took 370.5817s] +04/11/23 01:31:27| INFO bin_sld_gs finished [took 654.3921s] +04/11/23 01:31:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 658.0041s] +04/11/23 01:31:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +04/11/23 01:32:33| INFO ref finished [took 55.7749s] +04/11/23 01:32:38| INFO atc_mc finished [took 59.4190s] +04/11/23 01:32:40| INFO atc_ne finished [took 59.5155s] +04/11/23 01:32:42| INFO mul_pacc finished [took 68.8994s] +04/11/23 01:32:44| INFO mul_sld finished [took 74.6470s] +04/11/23 01:33:09| INFO mul_pacc_gs finished [took 92.6473s] +04/11/23 01:35:32| INFO bin_pacc finished [took 239.7541s] +04/11/23 01:35:34| INFO bin_sld finished [took 245.7504s] +04/11/23 01:37:19| INFO mul_sld_gs finished [took 348.1188s] +04/11/23 01:37:30| INFO bin_pacc_gs finished [took 355.4729s] +04/11/23 01:42:07| INFO bin_sld_gs finished [took 636.8598s] +04/11/23 01:42:07| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 639.9201s] +04/11/23 01:42:07| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +04/11/23 01:43:14| INFO ref finished [took 56.1531s] +04/11/23 01:43:19| INFO atc_mc finished [took 59.7473s] +04/11/23 01:43:20| INFO atc_ne finished [took 59.0606s] +04/11/23 01:43:23| INFO mul_pacc finished [took 69.4266s] +04/11/23 01:43:25| INFO mul_sld finished [took 76.3328s] +04/11/23 01:43:49| INFO mul_pacc_gs finished [took 92.3926s] +04/11/23 01:46:05| INFO bin_pacc finished [took 233.1877s] +04/11/23 01:46:08| INFO bin_sld finished [took 239.8757s] +04/11/23 01:47:51| INFO mul_sld_gs finished [took 339.5911s] +04/11/23 01:48:00| INFO bin_pacc_gs finished [took 345.7788s] +04/11/23 01:52:44| INFO bin_sld_gs finished [took 633.8407s] +04/11/23 01:52:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 637.0648s] +04/11/23 01:52:44| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +04/11/23 01:53:52| INFO ref finished [took 57.4958s] +04/11/23 01:53:57| INFO atc_mc finished [took 60.9998s] +04/11/23 01:53:58| INFO atc_ne finished [took 60.4847s] +04/11/23 01:54:01| INFO mul_pacc finished [took 70.5216s] +04/11/23 01:54:04| INFO mul_sld finished [took 78.2910s] +04/11/23 01:54:27| INFO mul_pacc_gs finished [took 94.4726s] +04/11/23 01:56:48| INFO bin_pacc finished [took 238.5969s] +04/11/23 01:56:50| INFO bin_sld finished [took 244.5679s] +04/11/23 01:58:31| INFO mul_sld_gs finished [took 342.4843s] +04/11/23 01:58:44| INFO bin_pacc_gs finished [took 352.8264s] +04/11/23 02:03:32| INFO bin_sld_gs finished [took 644.7046s] +04/11/23 02:03:32| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 647.8055s] +04/11/23 02:03:32| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +04/11/23 02:04:37| INFO ref finished [took 55.4488s] +04/11/23 02:04:42| INFO atc_mc finished [took 59.2634s] +04/11/23 02:04:44| INFO atc_ne finished [took 59.1371s] +04/11/23 02:04:46| INFO mul_pacc finished [took 68.0960s] +04/11/23 02:04:50| INFO mul_sld finished [took 76.4282s] +04/11/23 02:05:12| INFO mul_pacc_gs finished [took 91.7735s] +04/11/23 02:07:30| INFO bin_pacc finished [took 232.7650s] +04/11/23 02:07:36| INFO bin_sld finished [took 242.4077s] +04/11/23 02:09:14| INFO mul_sld_gs finished [took 338.1418s] +04/11/23 02:09:26| INFO bin_pacc_gs finished [took 347.2033s] +04/11/23 02:13:59| INFO bin_sld_gs finished [took 624.6098s] +04/11/23 02:13:59| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 627.7979s] +04/11/23 02:13:59| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +04/11/23 02:15:05| INFO ref finished [took 55.1962s] +04/11/23 02:15:10| INFO atc_mc finished [took 59.0907s] +04/11/23 02:15:11| INFO atc_ne finished [took 59.1531s] +04/11/23 02:15:13| INFO mul_pacc finished [took 67.6705s] +04/11/23 02:15:17| INFO mul_sld finished [took 75.4559s] +04/11/23 02:15:41| INFO mul_pacc_gs finished [took 92.4901s] +04/11/23 02:17:59| INFO bin_pacc finished [took 233.8600s] +04/11/23 02:18:04| INFO bin_sld finished [took 243.2382s] +04/11/23 02:19:40| INFO mul_sld_gs finished [took 336.0961s] +04/11/23 02:19:51| INFO bin_pacc_gs finished [took 344.4075s] +04/11/23 02:24:30| INFO bin_sld_gs finished [took 627.6209s] +04/11/23 02:24:30| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 630.8251s] +04/11/23 02:24:30| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +04/11/23 02:25:35| INFO ref finished [took 54.8513s] +04/11/23 02:25:40| INFO atc_mc finished [took 58.8528s] +04/11/23 02:25:41| INFO atc_ne finished [took 58.6035s] +04/11/23 02:25:43| INFO mul_pacc finished [took 66.9030s] +04/11/23 02:25:57| INFO mul_sld finished [took 84.2072s] +04/11/23 02:26:10| INFO mul_pacc_gs finished [took 91.0973s] +04/11/23 02:28:31| INFO bin_pacc finished [took 235.7331s] +04/11/23 02:28:35| INFO bin_sld finished [took 243.6260s] +04/11/23 02:30:09| INFO mul_sld_gs finished [took 334.4842s] +04/11/23 02:30:22| INFO bin_pacc_gs finished [took 344.6874s] +04/11/23 02:34:46| INFO bin_sld_gs finished [took 612.1219s] +04/11/23 02:34:46| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 615.2004s] +---------------------------------------------------------------------------------------------------- +04/11/23 02:57:35| INFO dataset rcv1_CCAT_9prevs +04/11/23 02:57:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 02:57:47| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 02:58:59| INFO ref finished [took 64.5948s] +04/11/23 02:59:06| INFO atc_mc finished [took 69.5808s] +04/11/23 02:59:12| INFO mul_pacc finished [took 82.8518s] +04/11/23 02:59:13| INFO atc_ne finished [took 72.1303s] +04/11/23 02:59:26| INFO mul_sld finished [took 103.4201s] +04/11/23 02:59:30| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +04/11/23 02:59:41| INFO mul_pacc_gs finished [took 109.4672s] +04/11/23 03:01:59| INFO bin_pacc finished [took 251.3945s] +04/11/23 03:02:02| INFO bin_sld finished [took 260.0226s] +04/11/23 03:03:35| INFO mul_sld_gs finished [took 350.1705s] +04/11/23 03:03:48| INFO bin_pacc_gs finished [took 357.9668s] +04/11/23 03:07:59| INFO bin_sld_gs finished [took 615.8087s] +04/11/23 03:07:59| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 620.4985s] +04/11/23 03:07:59| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 03:08:06| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 03:09:17| INFO ref finished [took 64.4692s] +04/11/23 03:09:25| INFO atc_mc finished [took 71.3766s] +04/11/23 03:09:27| INFO atc_ne finished [took 71.0947s] +04/11/23 03:09:28| INFO mul_pacc finished [took 80.0201s] +04/11/23 03:09:31| INFO mul_sld finished [took 89.4295s] +04/11/23 03:09:55| INFO mul_pacc_gs finished [took 104.7292s] +04/11/23 03:12:25| INFO bin_sld finished [took 263.6824s] +04/11/23 03:12:25| INFO bin_pacc finished [took 258.6502s] +04/11/23 03:14:01| INFO mul_sld_gs finished [took 357.3344s] +04/11/23 03:14:14| INFO bin_sld_gsq finished [took 369.1636s] +04/11/23 03:14:22| INFO bin_pacc_gs finished [took 372.8646s] +04/11/23 03:18:40| INFO bin_sld_gs finished [took 636.9190s] +04/11/23 03:18:40| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 640.2322s] +04/11/23 03:18:40| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +04/11/23 03:18:46| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 03:19:58| INFO ref finished [took 65.9462s] +04/11/23 03:20:02| INFO atc_mc finished [took 68.5710s] +04/11/23 03:20:04| INFO atc_ne finished [took 68.9466s] +04/11/23 03:20:06| INFO mul_pacc finished [took 77.9039s] +04/11/23 03:20:06| INFO mul_sld finished [took 84.0917s] +04/11/23 03:20:37| INFO mul_pacc_gs finished [took 106.2536s] +04/11/23 03:23:04| INFO bin_pacc finished [took 257.4211s] +04/11/23 03:23:05| INFO bin_sld finished [took 264.3442s] +04/11/23 03:24:49| INFO mul_sld_gs finished [took 365.1691s] +04/11/23 03:25:01| INFO bin_pacc_gs finished [took 371.9184s] +04/11/23 03:25:02| INFO bin_sld_gsq finished [took 377.0442s] +04/11/23 03:29:37| INFO bin_sld_gs finished [took 654.0366s] +04/11/23 03:29:37| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 657.0840s] +04/11/23 03:29:37| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +04/11/23 03:29:42| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 03:30:51| INFO ref finished [took 62.7217s] +04/11/23 03:30:58| INFO atc_mc finished [took 67.8613s] +04/11/23 03:31:00| INFO atc_ne finished [took 68.5026s] +04/11/23 03:31:03| INFO mul_sld finished [took 83.8857s] +04/11/23 03:31:03| INFO mul_pacc finished [took 78.6340s] +04/11/23 03:31:30| INFO mul_pacc_gs finished [took 103.4683s] +04/11/23 03:34:00| INFO bin_sld finished [took 262.4457s] +04/11/23 03:34:02| INFO bin_pacc finished [took 258.2247s] +04/11/23 03:35:44| INFO mul_sld_gs finished [took 363.8135s] +04/11/23 03:35:58| INFO bin_pacc_gs finished [took 372.0485s] +04/11/23 03:36:05| INFO bin_sld_gsq finished [took 382.9585s] +04/11/23 03:40:39| INFO bin_sld_gs finished [took 659.6222s] +04/11/23 03:40:39| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 662.5763s] +04/11/23 03:40:39| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +04/11/23 03:40:45| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 03:41:56| INFO ref finished [took 64.5923s] +04/11/23 03:42:01| INFO atc_mc finished [took 68.0148s] +04/11/23 03:42:03| INFO atc_ne finished [took 68.3119s] +04/11/23 03:42:04| INFO mul_pacc finished [took 76.9397s] +04/11/23 03:42:07| INFO mul_sld finished [took 85.5363s] +04/11/23 03:42:34| INFO mul_pacc_gs finished [took 103.4448s] +04/11/23 03:45:01| INFO bin_sld finished [took 260.0814s] +04/11/23 03:45:03| INFO bin_pacc finished [took 256.9386s] +04/11/23 03:46:45| INFO mul_sld_gs finished [took 361.5910s] +04/11/23 03:47:01| INFO bin_pacc_gs finished [took 371.9657s] +04/11/23 03:47:13| INFO bin_sld_gsq finished [took 388.2498s] +04/11/23 03:51:40| INFO bin_sld_gs finished [took 657.4008s] +04/11/23 03:51:40| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 660.5115s] +04/11/23 03:51:40| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +04/11/23 03:51:46| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 03:52:54| INFO ref finished [took 61.9225s] +04/11/23 03:53:00| INFO atc_mc finished [took 66.3156s] +04/11/23 03:53:02| INFO atc_ne finished [took 66.5025s] +04/11/23 03:53:04| INFO mul_pacc finished [took 75.8808s] +04/11/23 03:53:06| INFO mul_sld finished [took 84.3204s] +04/11/23 03:53:33| INFO mul_pacc_gs finished [took 102.5763s] +04/11/23 03:56:04| INFO bin_sld finished [took 263.2781s] +04/11/23 03:56:04| INFO bin_pacc finished [took 257.7298s] +04/11/23 03:57:44| INFO mul_sld_gs finished [took 359.7910s] +04/11/23 03:58:00| INFO bin_pacc_gs finished [took 371.3848s] +04/11/23 03:58:11| INFO bin_sld_gsq finished [took 386.0904s] +04/11/23 04:02:50| INFO bin_sld_gs finished [took 667.6623s] +04/11/23 04:02:50| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 670.7255s] +04/11/23 04:02:50| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +04/11/23 04:02:57| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 04:04:05| INFO ref finished [took 62.3256s] +04/11/23 04:04:13| INFO atc_mc finished [took 68.9525s] +04/11/23 04:04:15| INFO atc_ne finished [took 68.8750s] +04/11/23 04:04:16| INFO mul_pacc finished [took 77.5049s] +04/11/23 04:04:19| INFO mul_sld finished [took 86.0694s] +04/11/23 04:04:45| INFO mul_pacc_gs finished [took 103.3513s] +04/11/23 04:07:15| INFO bin_pacc finished [took 257.6456s] +04/11/23 04:07:16| INFO bin_sld finished [took 263.9914s] +04/11/23 04:08:55| INFO mul_sld_gs finished [took 360.5634s] +04/11/23 04:09:12| INFO bin_pacc_gs finished [took 372.2665s] +04/11/23 04:09:18| INFO bin_sld_gsq finished [took 381.8311s] +04/11/23 04:13:39| INFO bin_sld_gs finished [took 645.3599s] +04/11/23 04:13:39| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 648.5328s] +04/11/23 04:13:39| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +04/11/23 04:13:45| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 04:14:51| INFO ref finished [took 59.8110s] +04/11/23 04:14:58| INFO atc_mc finished [took 65.2666s] +04/11/23 04:14:59| INFO atc_ne finished [took 64.5173s] +04/11/23 04:15:01| INFO mul_pacc finished [took 73.8332s] +04/11/23 04:15:04| INFO mul_sld finished [took 82.3509s] +04/11/23 04:15:29| INFO mul_pacc_gs finished [took 99.3541s] +04/11/23 04:18:00| INFO bin_pacc finished [took 254.3308s] +04/11/23 04:18:03| INFO bin_sld finished [took 262.3008s] +04/11/23 04:19:40| INFO mul_sld_gs finished [took 357.1229s] +04/11/23 04:19:57| INFO bin_pacc_gs finished [took 368.4516s] +04/11/23 04:20:03| INFO bin_sld_gsq finished [took 378.7658s] +04/11/23 04:24:37| INFO bin_sld_gs finished [took 655.1931s] +04/11/23 04:24:37| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 658.3505s] +04/11/23 04:24:37| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +04/11/23 04:24:43| WARNING Method mul_sld_gsq failed. Exception: MultiClassAccuracyEstimator.__init__() got an unexpected keyword argument 'param_grid' +04/11/23 04:25:49| INFO ref finished [took 59.4546s] +04/11/23 04:25:55| INFO atc_mc finished [took 63.5805s] +04/11/23 04:25:58| INFO atc_ne finished [took 63.2985s] +04/11/23 04:25:58| INFO mul_pacc finished [took 72.5198s] +04/11/23 04:26:11| INFO mul_sld finished [took 91.7136s] +04/11/23 04:26:27| INFO mul_pacc_gs finished [took 98.8722s] +04/11/23 04:28:57| INFO bin_pacc finished [took 252.8144s] +04/11/23 04:29:02| INFO bin_sld finished [took 263.8013s] +04/11/23 04:30:35| INFO mul_sld_gs finished [took 353.3693s] +04/11/23 04:30:51| INFO bin_sld_gsq finished [took 368.8564s] +04/11/23 04:30:54| INFO bin_pacc_gs finished [took 367.5592s] +04/11/23 04:35:11| INFO bin_sld_gs finished [took 630.6700s] +04/11/23 04:35:11| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 633.7494s] +---------------------------------------------------------------------------------------------------- +04/11/23 19:09:42| INFO dataset rcv1_CCAT_9prevs +04/11/23 19:09:47| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 19:10:28| INFO ref finished [took 36.0351s] +04/11/23 19:10:32| INFO atc_mc finished [took 38.9507s] +04/11/23 19:10:35| INFO mulmc_sld finished [took 43.7869s] +04/11/23 19:10:50| INFO mul_sld finished [took 60.8007s] +04/11/23 19:10:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 62.9600s] +04/11/23 19:10:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 19:11:29| INFO ref finished [took 36.3632s] +04/11/23 19:11:34| INFO atc_mc finished [took 39.5928s] +04/11/23 19:11:36| INFO mulmc_sld finished [took 44.2915s] +04/11/23 19:11:44| INFO mul_sld finished [took 52.6727s] +04/11/23 19:11:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 54.0362s] +04/11/23 19:11:44| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +04/11/23 19:12:24| INFO ref finished [took 36.4303s] +04/11/23 19:12:27| INFO atc_mc finished [took 39.2329s] +04/11/23 19:12:30| INFO mulmc_sld finished [took 43.6247s] +04/11/23 19:12:36| INFO mul_sld finished [took 50.2041s] +04/11/23 19:12:36| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 51.6412s] +04/11/23 19:12:36| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +04/11/23 19:13:16| INFO ref finished [took 36.7551s] +04/11/23 19:13:19| INFO atc_mc finished [took 39.2806s] +04/11/23 19:13:21| INFO mulmc_sld finished [took 43.6120s] +04/11/23 19:13:27| INFO mul_sld finished [took 50.4446s] +04/11/23 19:13:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 51.6672s] +04/11/23 19:13:27| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +04/11/23 19:14:07| INFO ref finished [took 35.8789s] +04/11/23 19:14:11| INFO atc_mc finished [took 39.2168s] +04/11/23 19:14:13| INFO mulmc_sld finished [took 43.4580s] +04/11/23 19:14:20| INFO mul_sld finished [took 51.2902s] +04/11/23 19:14:20| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 52.6303s] +04/11/23 19:14:20| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +04/11/23 19:15:00| INFO ref finished [took 36.3735s] +04/11/23 19:15:04| INFO atc_mc finished [took 39.7035s] +04/11/23 19:15:06| INFO mulmc_sld finished [took 43.6364s] +04/11/23 19:15:13| INFO mul_sld finished [took 52.0138s] +04/11/23 19:15:13| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 53.3303s] +04/11/23 19:15:13| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +04/11/23 19:15:54| INFO ref finished [took 37.3366s] +04/11/23 19:15:57| INFO atc_mc finished [took 39.8921s] +04/11/23 19:16:00| INFO mulmc_sld finished [took 44.5159s] +04/11/23 19:16:08| INFO mul_sld finished [took 53.0806s] +04/11/23 19:16:08| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 54.4117s] +04/11/23 19:16:08| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +04/11/23 19:16:47| INFO ref finished [took 35.7800s] +04/11/23 19:16:50| INFO atc_mc finished [took 38.4484s] +04/11/23 19:16:53| INFO mulmc_sld finished [took 42.7405s] +04/11/23 19:17:01| INFO mul_sld finished [took 51.5556s] +04/11/23 19:17:01| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 52.9684s] +04/11/23 19:17:01| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +04/11/23 19:17:39| INFO ref finished [took 35.0919s] +04/11/23 19:17:43| INFO atc_mc finished [took 38.1718s] +04/11/23 19:17:45| INFO mulmc_sld finished [took 42.4413s] +04/11/23 19:17:59| INFO mul_sld finished [took 57.0766s] +04/11/23 19:17:59| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 58.3668s] +---------------------------------------------------------------------------------------------------- +04/11/23 19:42:38| INFO dataset rcv1_CCAT_9prevs +04/11/23 19:42:43| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 19:43:27| INFO ref finished [took 38.7664s] +04/11/23 19:43:31| INFO atc_mc finished [took 42.4000s] +04/11/23 19:43:33| INFO mulmc_sld finished [took 47.0913s] +04/11/23 19:43:34| INFO binmc_sld finished [took 47.1675s] +04/11/23 19:43:49| INFO mul_sld finished [took 64.1382s] +04/11/23 19:46:00| INFO bin_sld finished [took 195.9822s] +04/11/23 19:46:00| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 197.2916s] +04/11/23 19:46:00| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 19:46:44| INFO ref finished [took 38.5976s] +04/11/23 19:46:48| INFO atc_mc finished [took 41.9465s] +04/11/23 19:46:49| INFO mulmc_sld finished [took 46.2205s] +04/11/23 19:46:51| INFO binmc_sld finished [took 46.7475s] +04/11/23 19:46:58| INFO mul_sld finished [took 56.3552s] +04/11/23 19:49:14| INFO bin_sld finished [took 193.2923s] +04/11/23 19:49:14| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 194.6251s] +04/11/23 19:49:14| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +04/11/23 19:49:58| INFO ref finished [took 38.3754s] +04/11/23 19:50:02| INFO atc_mc finished [took 41.0091s] +04/11/23 19:50:03| INFO mulmc_sld finished [took 45.6205s] +04/11/23 19:50:05| INFO binmc_sld finished [took 46.1852s] +04/11/23 19:50:10| INFO mul_sld finished [took 52.9704s] +04/11/23 19:52:27| INFO bin_sld finished [took 190.6101s] +04/11/23 19:52:27| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 192.0378s] +04/11/23 19:52:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +04/11/23 19:53:10| INFO ref finished [took 38.4467s] +04/11/23 19:53:13| INFO atc_mc finished [took 41.2602s] +04/11/23 19:53:15| INFO mulmc_sld finished [took 45.7496s] +04/11/23 19:53:16| INFO binmc_sld finished [took 45.5531s] +04/11/23 19:53:21| INFO mul_sld finished [took 52.5067s] +04/11/23 19:55:38| INFO bin_sld finished [took 190.7744s] +04/11/23 19:55:38| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 191.9715s] +04/11/23 19:55:39| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +04/11/23 19:56:21| INFO ref finished [took 37.9420s] +04/11/23 19:56:26| INFO atc_mc finished [took 41.2056s] +04/11/23 19:56:27| INFO mulmc_sld finished [took 45.7577s] +04/11/23 19:56:28| INFO binmc_sld finished [took 45.6411s] +04/11/23 19:56:34| INFO mul_sld finished [took 53.5219s] +04/11/23 19:58:51| INFO bin_sld finished [took 191.1772s] +04/11/23 19:58:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 192.4566s] +04/11/23 19:58:51| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +04/11/23 19:59:34| INFO ref finished [took 37.8604s] +04/11/23 19:59:38| INFO atc_mc finished [took 41.0334s] +04/11/23 19:59:39| INFO mulmc_sld finished [took 45.1999s] +04/11/23 19:59:40| INFO binmc_sld finished [took 45.4846s] +04/11/23 19:59:47| INFO mul_sld finished [took 54.3166s] +04/11/23 20:02:04| INFO bin_sld finished [took 191.4002s] +04/11/23 20:02:04| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 192.6275s] +04/11/23 20:02:04| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +04/11/23 20:02:48| INFO ref finished [took 38.8313s] +04/11/23 20:02:52| INFO atc_mc finished [took 42.1162s] +04/11/23 20:02:54| INFO mulmc_sld finished [took 47.0413s] +04/11/23 20:02:55| INFO binmc_sld finished [took 46.8891s] +04/11/23 20:03:02| INFO mul_sld finished [took 55.8821s] +04/11/23 20:05:19| INFO bin_sld finished [took 193.7571s] +04/11/23 20:05:19| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 195.2404s] +04/11/23 20:05:19| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +04/11/23 20:06:03| INFO ref finished [took 38.7982s] +04/11/23 20:06:06| INFO atc_mc finished [took 41.6213s] +04/11/23 20:06:08| INFO mulmc_sld finished [took 46.2646s] +04/11/23 20:06:09| INFO binmc_sld finished [took 46.2453s] +04/11/23 20:06:16| INFO mul_sld finished [took 54.8621s] +04/11/23 20:08:35| INFO bin_sld finished [took 194.5226s] +04/11/23 20:08:35| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 195.9251s] +04/11/23 20:08:35| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +04/11/23 20:09:18| INFO ref finished [took 38.3873s] +04/11/23 20:09:22| INFO atc_mc finished [took 41.2537s] +04/11/23 20:09:24| INFO mulmc_sld finished [took 46.2211s] +04/11/23 20:09:25| INFO binmc_sld finished [took 46.6421s] +04/11/23 20:09:38| INFO mul_sld finished [took 60.9539s] +04/11/23 20:11:51| INFO bin_sld finished [took 195.1888s] +04/11/23 20:11:51| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 196.4776s] +---------------------------------------------------------------------------------------------------- +04/11/23 20:56:32| INFO dataset rcv1_CCAT_9prevs +04/11/23 20:56:37| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +04/11/23 20:57:33| INFO ref finished [took 49.2697s] +04/11/23 20:57:38| INFO atc_mc finished [took 53.2068s] +04/11/23 20:57:39| INFO mulmc_sld finished [took 58.6224s] +04/11/23 20:58:59| INFO mulmc_sld_gs finished [took 136.0930s] +04/11/23 21:00:30| INFO binmc_sld finished [took 230.3290s] +04/11/23 21:02:12| INFO mul_sld_gs finished [took 333.4899s] +04/11/23 21:06:49| INFO bin_sld_gs finished [took 610.5751s] +04/11/23 21:06:54| INFO binmc_sld_gs finished [took 612.8900s] +04/11/23 21:06:55| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 617.6873s] +04/11/23 21:06:55| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +04/11/23 21:07:52| INFO ref finished [took 49.8077s] +04/11/23 21:07:56| INFO atc_mc finished [took 53.3303s] +04/11/23 21:07:57| INFO mulmc_sld finished [took 58.9345s] +04/11/23 21:09:17| INFO mulmc_sld_gs finished [took 136.5258s] +04/11/23 21:10:51| INFO binmc_sld finished [took 233.4049s] +04/11/23 21:12:35| INFO mul_sld_gs finished [took 338.2751s] +04/11/23 21:17:38| INFO bin_sld_gs finished [took 641.8524s] +04/11/23 21:18:19| INFO binmc_sld_gs finished [took 679.9471s] +04/11/23 21:18:19| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 684.7098s] +04/11/23 21:18:19| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +04/11/23 21:19:24| INFO ref finished [took 55.3767s] +04/11/23 21:19:28| INFO mulmc_sld finished [took 64.2789s] +04/11/23 21:19:29| INFO atc_mc finished [took 59.5610s] +04/11/23 21:20:57| INFO mulmc_sld_gs finished [took 150.1392s] +04/11/23 21:22:36| INFO binmc_sld finished [took 253.0960s] +04/11/23 21:24:16| INFO mul_sld_gs finished [took 354.6283s] +04/11/23 21:29:15| INFO bin_sld_gs finished [took 654.3325s] +04/11/23 21:29:50| INFO binmc_sld_gs finished [took 684.5074s] +04/11/23 21:29:50| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 690.4897s] +04/11/23 21:29:50| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +04/11/23 21:30:45| INFO ref finished [took 48.2647s] +04/11/23 21:30:51| INFO atc_mc finished [took 52.2724s] +04/11/23 21:30:51| INFO mulmc_sld finished [took 57.5142s] +04/11/23 21:32:07| INFO mulmc_sld_gs finished [took 131.4908s] +04/11/23 21:33:38| INFO binmc_sld finished [took 224.9620s] +04/11/23 21:35:22| INFO mul_sld_gs finished [took 329.9053s] +04/11/23 21:40:25| INFO bin_sld_gs finished [took 634.4342s] +04/11/23 21:41:08| INFO binmc_sld_gs finished [took 673.6071s] +04/11/23 21:41:08| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 678.4725s] +04/11/23 21:41:08| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +04/11/23 21:42:03| INFO ref finished [took 47.4381s] +04/11/23 21:42:08| INFO atc_mc finished [took 51.3566s] +04/11/23 21:42:09| INFO mulmc_sld finished [took 56.6180s] +04/11/23 21:43:23| INFO mulmc_sld_gs finished [took 128.6413s] +04/11/23 21:44:54| INFO binmc_sld finished [took 222.7951s] +04/11/23 21:46:39| INFO mul_sld_gs finished [took 328.8118s] +04/11/23 21:51:37| INFO bin_sld_gs finished [took 627.4937s] +04/11/23 21:52:17| INFO binmc_sld_gs finished [took 663.8116s] +04/11/23 21:52:17| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 668.8948s] +04/11/23 21:52:17| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +04/11/23 21:53:12| INFO ref finished [took 47.6269s] +04/11/23 21:53:16| INFO atc_mc finished [took 51.1109s] +04/11/23 21:53:17| INFO mulmc_sld finished [took 56.5728s] +04/11/23 21:54:31| INFO mulmc_sld_gs finished [took 128.0358s] +04/11/23 21:56:00| INFO binmc_sld finished [took 220.0811s] +04/11/23 21:57:46| INFO mul_sld_gs finished [took 327.0856s] +04/11/23 22:02:58| INFO bin_sld_gs finished [took 639.3432s] +04/11/23 22:03:48| INFO binmc_sld_gs finished [took 686.2326s] +04/11/23 22:03:48| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 690.9677s] +04/11/23 22:03:48| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +04/11/23 22:04:42| INFO ref finished [took 47.2804s] +04/11/23 22:04:48| INFO atc_mc finished [took 51.6888s] +04/11/23 22:04:48| INFO mulmc_sld finished [took 56.1465s] +04/11/23 22:06:06| INFO mulmc_sld_gs finished [took 132.4278s] +04/11/23 22:07:33| INFO binmc_sld finished [took 221.9299s] +04/11/23 22:09:19| INFO mul_sld_gs finished [took 329.1446s] +04/11/23 22:14:09| INFO bin_sld_gs finished [took 619.3584s] +04/11/23 22:14:32| INFO binmc_sld_gs finished [took 638.7326s] +04/11/23 22:14:32| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 643.6278s] +04/11/23 22:14:32| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +04/11/23 22:15:26| INFO ref finished [took 47.3139s] +04/11/23 22:15:30| INFO atc_mc finished [took 50.8602s] +04/11/23 22:15:32| INFO mulmc_sld finished [took 56.5107s] +04/11/23 22:16:47| INFO mulmc_sld_gs finished [took 129.5292s] +04/11/23 22:18:22| INFO binmc_sld finished [took 226.9238s] +04/11/23 22:20:02| INFO mul_sld_gs finished [took 327.7014s] +04/11/23 22:24:57| INFO bin_sld_gs finished [took 624.4254s] +04/11/23 22:25:13| INFO binmc_sld_gs finished [took 636.2675s] +04/11/23 22:25:13| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 641.0382s] +04/11/23 22:25:13| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +04/11/23 22:26:07| INFO ref finished [took 47.3224s] +04/11/23 22:26:12| INFO atc_mc finished [took 51.1828s] +04/11/23 22:26:13| INFO mulmc_sld finished [took 56.6133s] +04/11/23 22:27:30| INFO mulmc_sld_gs finished [took 131.3662s] +04/11/23 22:29:05| INFO binmc_sld finished [took 229.3002s] +04/11/23 22:30:38| INFO mul_sld_gs finished [took 323.5271s] +04/11/23 22:35:21| INFO bin_sld_gs finished [took 606.6430s] +04/11/23 22:35:30| INFO binmc_sld_gs finished [took 612.5966s] +04/11/23 22:35:30| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 617.3109s] +---------------------------------------------------------------------------------------------------- +04/11/23 22:49:37| ERROR Evaluation over rcv1_CCAT_3prevs failed. Exception: 'Invalid estimator: estimator binmc_sld_gs does not exist' +04/11/23 22:49:37| ERROR Failed while saving configuration rcv1_CCAT_debug of rcv1_CCAT_3prevs. Exception: cannot access local variable 'dr' where it is not associated with a value +---------------------------------------------------------------------------------------------------- +04/11/23 22:50:07| INFO dataset rcv1_CCAT_3prevs +04/11/23 22:50:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +---------------------------------------------------------------------------------------------------- +04/11/23 22:55:55| INFO dataset rcv1_CCAT_3prevs +04/11/23 22:55:59| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 22:56:48| INFO ref finished [took 44.4275s] +---------------------------------------------------------------------------------------------------- +04/11/23 22:56:59| INFO dataset rcv1_CCAT_3prevs +04/11/23 22:57:03| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 22:57:09| WARNING Method mul_sld_gs failed. Exception: '>=' not supported between instances of 'TypeError' and 'int' +04/11/23 22:57:17| WARNING Method bin_sld_gs failed. Exception: '>=' not supported between instances of 'TypeError' and 'int' +---------------------------------------------------------------------------------------------------- +04/11/23 22:58:04| INFO dataset rcv1_CCAT_3prevs +04/11/23 22:58:09| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 22:58:58| INFO ref finished [took 43.7541s] +04/11/23 22:59:05| INFO atc_mc finished [took 50.0628s] +---------------------------------------------------------------------------------------------------- +04/11/23 23:01:22| INFO dataset rcv1_CCAT_3prevs +04/11/23 23:01:27| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 23:02:16| INFO ref finished [took 43.9765s] +04/11/23 23:02:23| INFO atc_mc finished [took 50.5568s] +---------------------------------------------------------------------------------------------------- +04/11/23 23:09:33| INFO dataset rcv1_CCAT_3prevs +04/11/23 23:09:37| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 23:09:38| WARNING Method binmc_sld failed. Exception: classifier and pred_proba cannot be both None +04/11/23 23:09:39| WARNING Method mulmc_sld failed. Exception: classifier and pred_proba cannot be both None +04/11/23 23:09:40| WARNING Method bin_sld_gs failed. Exception: no combination of hyperparameters seem to work +04/11/23 23:09:41| WARNING Method mul_sld_gs failed. Exception: no combination of hyperparameters seem to work +---------------------------------------------------------------------------------------------------- +04/11/23 23:10:23| INFO dataset rcv1_CCAT_3prevs +04/11/23 23:10:28| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 23:11:15| INFO ref finished [took 42.4887s] +04/11/23 23:11:20| INFO atc_mc finished [took 45.6262s] +04/11/23 23:11:21| INFO mulmc_sld finished [took 50.9790s] +04/11/23 23:13:57| INFO binmc_sld finished [took 208.3159s] +---------------------------------------------------------------------------------------------------- +04/11/23 23:16:22| INFO dataset rcv1_CCAT_3prevs +04/11/23 23:16:26| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +04/11/23 23:17:12| INFO ref finished [took 40.5978s] +04/11/23 23:17:16| INFO atc_mc finished [took 43.6933s] +04/11/23 23:17:17| INFO mulmc_sld finished [took 49.0808s] +04/11/23 23:19:53| INFO binmc_sld finished [took 205.5731s] +04/11/23 23:22:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00672) [took 354.1411s] +04/11/23 23:23:05| INFO mul_sld_gs finished [took 394.8240s] +04/11/23 23:30:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00891) [took 852.1465s] +04/11/23 23:33:44| INFO bin_sld_gs finished [took 1035.2071s] +04/11/23 23:33:44| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs finished [took 1038.1845s] +04/11/23 23:33:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_3prevs started +04/11/23 23:34:33| INFO ref finished [took 43.6409s] +04/11/23 23:34:37| INFO atc_mc finished [took 46.7818s] +04/11/23 23:34:38| INFO mulmc_sld finished [took 51.3459s] +04/11/23 23:37:15| INFO binmc_sld finished [took 209.5746s] +04/11/23 23:39:48| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00553) [took 359.3210s] +04/11/23 23:40:28| INFO mul_sld_gs finished [took 399.5320s] +04/11/23 23:48:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': None} (score=0.01058) [took 855.1289s] +04/11/23 23:51:06| INFO bin_sld_gs finished [took 1038.6344s] +04/11/23 23:51:06| INFO Dataset sample 0.50 of dataset rcv1_CCAT_3prevs finished [took 1041.6478s] +04/11/23 23:51:06| INFO Dataset sample 0.60 of dataset rcv1_CCAT_3prevs started +04/11/23 23:51:51| INFO ref finished [took 40.0694s] +04/11/23 23:51:55| INFO atc_mc finished [took 42.4882s] +04/11/23 23:51:56| INFO mulmc_sld finished [took 47.7936s] +04/11/23 23:54:29| INFO binmc_sld finished [took 201.3777s] +04/11/23 23:57:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00429) [took 352.7820s] +04/11/23 23:57:43| INFO mul_sld_gs finished [took 392.5201s] +05/11/23 00:05:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00552) [took 851.9361s] +05/11/23 00:08:24| INFO bin_sld_gs finished [took 1034.7353s] +05/11/23 00:08:24| INFO Dataset sample 0.60 of dataset rcv1_CCAT_3prevs finished [took 1037.8033s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:11:07| INFO dataset rcv1_CCAT_3prevs +05/11/23 00:11:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_3prevs started +---------------------------------------------------------------------------------------------------- +05/11/23 00:28:39| INFO dataset imdb_3prevs +05/11/23 00:28:46| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 00:28:55| INFO ref finished [took 8.7347s] +05/11/23 00:28:58| INFO atc_mc finished [took 11.6376s] +05/11/23 00:28:59| INFO mulmc_sld finished [took 13.5476s] +05/11/23 00:28:59| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 13.9513s] +05/11/23 00:28:59| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 00:29:09| INFO ref finished [took 8.7049s] +05/11/23 00:29:12| INFO atc_mc finished [took 11.6170s] +05/11/23 00:29:14| INFO mulmc_sld finished [took 13.7416s] +05/11/23 00:29:14| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.2842s] +05/11/23 00:29:14| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 00:29:23| INFO ref finished [took 8.6894s] +05/11/23 00:29:26| INFO atc_mc finished [took 11.5275s] +05/11/23 00:29:28| INFO mulmc_sld finished [took 13.6977s] +05/11/23 00:29:28| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2742s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:34:12| INFO dataset imdb_3prevs +05/11/23 00:34:22| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 00:34:41| INFO ref finished [took 17.6558s] +05/11/23 00:34:46| INFO mulmc_sld finished [took 22.9646s] +05/11/23 00:34:48| INFO atc_mc finished [took 24.5871s] +05/11/23 00:34:48| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 25.3179s] +05/11/23 00:34:48| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 00:35:05| INFO ref finished [took 17.2188s] +05/11/23 00:35:10| INFO mulmc_sld finished [took 22.2420s] +05/11/23 00:35:12| INFO atc_mc finished [took 23.9752s] +05/11/23 00:35:12| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 24.7688s] +05/11/23 00:35:12| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 00:35:33| INFO ref finished [took 20.0731s] +05/11/23 00:35:38| INFO mulmc_sld finished [took 24.8736s] +05/11/23 00:35:40| INFO atc_mc finished [took 27.0318s] +05/11/23 00:35:40| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 27.8108s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:39:57| INFO dataset imdb_1prevs +05/11/23 00:40:07| INFO Dataset sample 0.50 of dataset imdb_1prevs started +05/11/23 00:40:26| INFO ref finished [took 17.4863s] +05/11/23 00:40:31| INFO mulmc_sld finished [took 22.5384s] +05/11/23 00:40:33| INFO atc_mc finished [took 24.2747s] +05/11/23 00:40:33| INFO Dataset sample 0.50 of dataset imdb_1prevs finished [took 25.6430s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:41:36| INFO dataset imdb_2prevs +05/11/23 00:41:46| INFO Dataset sample 0.20 of dataset imdb_2prevs started +05/11/23 00:42:05| INFO ref finished [took 17.6637s] +05/11/23 00:42:10| INFO mulmc_sld finished [took 22.6956s] +05/11/23 00:42:11| INFO atc_mc finished [took 24.3529s] +05/11/23 00:42:11| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 25.0708s] +05/11/23 00:42:11| INFO Dataset sample 0.80 of dataset imdb_2prevs started +05/11/23 00:42:29| INFO ref finished [took 17.2818s] +05/11/23 00:42:34| INFO mulmc_sld finished [took 22.4054s] +05/11/23 00:42:36| INFO atc_mc finished [took 23.9392s] +05/11/23 00:42:36| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.7193s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:45:54| INFO dataset imdb_2prevs +05/11/23 00:46:04| INFO Dataset sample 0.20 of dataset imdb_2prevs started +05/11/23 00:46:22| INFO ref finished [took 17.2217s] +05/11/23 00:46:27| INFO mulmc_sld finished [took 22.2712s] +05/11/23 00:46:28| INFO atc_mc finished [took 23.7770s] +05/11/23 00:46:28| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 24.5092s] +05/11/23 00:46:28| INFO Dataset sample 0.80 of dataset imdb_2prevs started +05/11/23 00:46:46| INFO ref finished [took 17.1303s] +05/11/23 00:46:51| INFO mulmc_sld finished [took 22.5084s] +05/11/23 00:46:53| INFO atc_mc finished [took 23.9160s] +05/11/23 00:46:53| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.6992s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:51:06| INFO dataset imdb_2prevs +05/11/23 00:51:16| INFO Dataset sample 0.20 of dataset imdb_2prevs started +05/11/23 00:51:33| INFO ref finished [took 17.0670s] +05/11/23 00:51:38| INFO mulmc_sld finished [took 22.3141s] +05/11/23 00:51:40| INFO atc_mc finished [took 23.7219s] +05/11/23 00:51:40| INFO Dataset sample 0.20 of dataset imdb_2prevs finished [took 24.4385s] +05/11/23 00:51:40| INFO Dataset sample 0.80 of dataset imdb_2prevs started +05/11/23 00:51:58| INFO ref finished [took 17.1894s] +05/11/23 00:52:03| INFO mulmc_sld finished [took 22.4247s] +05/11/23 00:52:04| INFO atc_mc finished [took 23.6032s] +05/11/23 00:52:04| INFO Dataset sample 0.80 of dataset imdb_2prevs finished [took 24.3674s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:53:32| INFO dataset imdb_3prevs +05/11/23 00:53:39| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 00:53:48| INFO ref finished [took 8.8062s] +05/11/23 00:53:51| INFO atc_mc finished [took 11.7173s] +05/11/23 00:53:53| INFO mulmc_sld finished [took 13.8761s] +05/11/23 00:53:53| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.3147s] +05/11/23 00:53:53| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 00:54:03| INFO ref finished [took 8.9071s] +05/11/23 00:54:06| INFO atc_mc finished [took 11.7005s] +05/11/23 00:54:08| INFO mulmc_sld finished [took 13.6266s] +05/11/23 00:54:08| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.1625s] +05/11/23 00:54:08| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 00:54:17| INFO ref finished [took 8.7680s] +05/11/23 00:54:20| INFO atc_mc finished [took 11.4957s] +05/11/23 00:54:22| INFO mulmc_sld finished [took 13.5719s] +05/11/23 00:54:22| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.1564s] +---------------------------------------------------------------------------------------------------- +05/11/23 00:57:53| INFO dataset imdb_3prevs +05/11/23 00:57:59| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 00:58:08| INFO ref finished [took 8.7497s] +05/11/23 00:58:12| INFO atc_mc finished [took 11.6903s] +05/11/23 00:58:13| INFO mulmc_sld finished [took 13.6731s] +05/11/23 00:58:13| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.1073s] +05/11/23 00:58:13| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 00:58:23| INFO ref finished [took 8.7718s] +05/11/23 00:58:26| INFO atc_mc finished [took 11.7653s] +05/11/23 00:58:28| INFO mulmc_sld finished [took 13.9184s] +05/11/23 00:58:28| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.4270s] +05/11/23 00:58:28| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 00:58:37| INFO ref finished [took 8.8129s] +05/11/23 00:58:40| INFO atc_mc finished [took 11.7267s] +05/11/23 00:58:42| INFO mulmc_sld finished [took 13.6726s] +05/11/23 00:58:42| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2387s] +---------------------------------------------------------------------------------------------------- +05/11/23 01:04:04| INFO dataset imdb_3prevs +05/11/23 01:04:10| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 01:04:20| INFO ref finished [took 8.7879s] +05/11/23 01:04:23| INFO atc_mc finished [took 11.8757s] +05/11/23 01:04:25| INFO mulmc_sld finished [took 13.8698s] +05/11/23 01:04:25| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 14.2927s] +05/11/23 01:04:25| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 01:04:34| INFO ref finished [took 8.9200s] +05/11/23 01:04:37| INFO atc_mc finished [took 11.9555s] +05/11/23 01:04:39| INFO mulmc_sld finished [took 13.9860s] +05/11/23 01:04:39| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 14.5339s] +05/11/23 01:04:39| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 01:04:49| INFO ref finished [took 8.8757s] +05/11/23 01:04:52| INFO atc_mc finished [took 11.8222s] +05/11/23 01:04:53| INFO mulmc_sld finished [took 13.7034s] +05/11/23 01:04:53| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 14.2710s] +---------------------------------------------------------------------------------------------------- +05/11/23 01:08:05| INFO dataset rcv1_CCAT_9prevs +05/11/23 01:08:09| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 01:08:55| INFO ref finished [took 40.9427s] +05/11/23 01:09:00| INFO atc_mc finished [took 44.2152s] +05/11/23 01:09:01| INFO mulmc_sld finished [took 49.6089s] +05/11/23 01:11:38| INFO bin_sld finished [took 207.5917s] +05/11/23 01:13:47| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00663) [took 333.7044s] +05/11/23 01:14:30| INFO mul_sld_gs finished [took 376.3503s] +05/11/23 01:20:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00619) [took 751.2730s] +05/11/23 01:23:45| INFO bin_sld_gs finished [took 932.2941s] +05/11/23 01:23:45| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 935.3228s] +05/11/23 01:23:45| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 01:24:30| INFO ref finished [took 39.9821s] +05/11/23 01:24:34| INFO atc_mc finished [took 43.3585s] +05/11/23 01:24:36| INFO mulmc_sld finished [took 48.6404s] +05/11/23 01:27:08| INFO bin_sld finished [took 202.3970s] +05/11/23 01:29:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00699) [took 328.6883s] +05/11/23 01:30:00| INFO mul_sld_gs finished [took 371.2150s] +05/11/23 01:36:40| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00780) [took 771.8150s] +05/11/23 01:39:44| INFO bin_sld_gs finished [took 956.5831s] +05/11/23 01:39:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 959.5214s] +05/11/23 01:39:44| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 01:40:38| INFO ref finished [took 46.9727s] +05/11/23 01:40:42| INFO atc_mc finished [took 49.6456s] +05/11/23 01:40:43| INFO mulmc_sld finished [took 55.1784s] +05/11/23 01:43:16| INFO bin_sld finished [took 209.5653s] +05/11/23 01:45:30| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00720) [took 340.0613s] +05/11/23 01:46:09| INFO mul_sld_gs finished [took 379.3695s] +05/11/23 01:53:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00810) [took 813.3118s] +05/11/23 01:56:25| INFO bin_sld_gs finished [took 996.4380s] +05/11/23 01:56:25| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1000.8297s] +05/11/23 01:56:25| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 01:57:11| INFO ref finished [took 40.2515s] +05/11/23 01:57:15| INFO atc_mc finished [took 43.5348s] +05/11/23 01:57:15| INFO mulmc_sld finished [took 48.1622s] +05/11/23 01:59:46| INFO bin_sld finished [took 200.0955s] +05/11/23 02:02:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00644) [took 331.2368s] +05/11/23 02:02:40| INFO mul_sld_gs finished [took 370.8879s] +05/11/23 02:10:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.01269) [took 813.7272s] +05/11/23 02:13:04| INFO bin_sld_gs finished [took 995.6098s] +05/11/23 02:13:04| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 998.5647s] +05/11/23 02:13:04| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 02:13:50| INFO ref finished [took 41.4181s] +05/11/23 02:13:55| INFO atc_mc finished [took 44.8414s] +05/11/23 02:13:55| INFO mulmc_sld finished [took 49.5767s] +05/11/23 02:16:27| INFO bin_sld finished [took 201.8696s] +05/11/23 02:18:33| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00636) [took 325.5401s] +05/11/23 02:19:17| INFO mul_sld_gs finished [took 368.9682s] +05/11/23 02:26:26| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00670) [took 799.7618s] +05/11/23 02:29:29| INFO bin_sld_gs finished [took 982.2921s] +05/11/23 02:29:29| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 985.2925s] +05/11/23 02:29:29| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 02:30:14| INFO ref finished [took 40.3341s] +05/11/23 02:30:18| INFO atc_mc finished [took 43.3032s] +05/11/23 02:30:19| INFO mulmc_sld finished [took 47.8507s] +05/11/23 02:32:51| INFO bin_sld finished [took 200.9647s] +05/11/23 02:34:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00596) [took 321.5172s] +05/11/23 02:35:33| INFO mul_sld_gs finished [took 360.5222s] +05/11/23 02:43:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00544) [took 829.7314s] +05/11/23 02:46:23| INFO bin_sld_gs finished [took 1011.3917s] +05/11/23 02:46:23| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1014.2514s] +05/11/23 02:46:23| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 02:47:09| INFO ref finished [took 40.4272s] +05/11/23 02:47:13| INFO atc_mc finished [took 43.8966s] +05/11/23 02:47:14| INFO mulmc_sld finished [took 48.4437s] +05/11/23 02:49:47| INFO bin_sld finished [took 202.6013s] +05/11/23 02:51:57| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00653) [took 329.4236s] +05/11/23 02:52:36| INFO mul_sld_gs finished [took 368.7426s] +05/11/23 02:59:51| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01370) [took 804.3215s] +05/11/23 03:02:54| INFO bin_sld_gs finished [took 987.5377s] +05/11/23 03:02:54| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 990.4607s] +05/11/23 03:02:54| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 03:03:40| INFO ref finished [took 41.5104s] +05/11/23 03:03:44| INFO atc_mc finished [took 44.1770s] +05/11/23 03:03:46| INFO mulmc_sld finished [took 49.7176s] +05/11/23 03:06:27| INFO bin_sld finished [took 211.4985s] +05/11/23 03:08:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00822) [took 334.7029s] +05/11/23 03:09:16| INFO mul_sld_gs finished [took 377.8219s] +05/11/23 03:16:22| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.00984) [took 805.4146s] +05/11/23 03:19:28| INFO bin_sld_gs finished [took 991.0520s] +05/11/23 03:19:28| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 994.1016s] +05/11/23 03:19:28| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 03:20:18| INFO ref finished [took 44.1663s] +05/11/23 03:20:22| INFO atc_mc finished [took 47.3231s] +05/11/23 03:20:23| INFO mulmc_sld finished [took 53.1243s] +05/11/23 03:23:10| INFO bin_sld finished [took 220.4921s] +05/11/23 03:25:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00811) [took 338.9143s] +05/11/23 03:25:56| INFO mul_sld_gs finished [took 383.9350s] +05/11/23 03:32:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': None} (score=0.00954) [took 792.6190s] +05/11/23 03:35:44| INFO bin_sld_gs finished [took 973.2397s] +05/11/23 03:35:44| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 976.4502s] +05/11/23 03:35:57| INFO dataset imbd_9prevs +05/11/23 03:35:57| ERROR Evaluation over imbd_9prevs failed. Exception: 'imbd' +---------------------------------------------------------------------------------------------------- +05/11/23 09:42:24| INFO dataset imdb_9prevs +05/11/23 09:42:30| INFO Dataset sample 0.10 of dataset imdb_9prevs started +05/11/23 09:42:44| INFO ref finished [took 10.6450s] +05/11/23 09:42:47| INFO atc_mc finished [took 13.9369s] +05/11/23 09:42:49| INFO mulmc_sld finished [took 16.6526s] +05/11/23 09:42:56| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +05/11/23 09:45:26| INFO bin_sld finished [took 173.8798s] +05/11/23 09:47:18| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.03792) [took 285.1339s] +05/11/23 09:47:33| INFO mul_sld_gs finished [took 300.3243s] +05/11/23 09:47:33| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 302.7180s] +05/11/23 09:47:33| INFO Dataset sample 0.20 of dataset imdb_9prevs started +05/11/23 09:47:46| INFO ref finished [took 12.0501s] +05/11/23 09:47:50| INFO atc_mc finished [took 15.0907s] +05/11/23 09:47:52| INFO mulmc_sld finished [took 17.8502s] +05/11/23 09:50:42| INFO bin_sld finished [took 188.2533s] +05/11/23 09:53:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.01067) [took 328.6088s] +05/11/23 09:53:19| INFO mul_sld_gs finished [took 345.1926s] +05/11/23 10:00:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00923) [took 798.1316s] +05/11/23 10:03:34| INFO bin_sld_gs finished [took 960.1696s] +05/11/23 10:03:34| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 960.9823s] +05/11/23 10:03:34| INFO Dataset sample 0.30 of dataset imdb_9prevs started +05/11/23 10:03:46| INFO ref finished [took 10.8585s] +05/11/23 10:03:49| INFO atc_mc finished [took 13.6836s] +05/11/23 10:03:51| INFO mulmc_sld finished [took 15.8085s] +05/11/23 10:06:39| INFO bin_sld finished [took 183.7435s] +05/11/23 10:09:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00707) [took 326.5308s] +05/11/23 10:09:15| INFO mul_sld_gs finished [took 339.3412s] +05/11/23 10:16:51| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01094) [took 796.0895s] +05/11/23 10:19:27| INFO bin_sld_gs finished [took 952.0793s] +05/11/23 10:19:27| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 953.0580s] +05/11/23 10:19:27| INFO Dataset sample 0.40 of dataset imdb_9prevs started +05/11/23 10:19:39| INFO ref finished [took 10.8707s] +05/11/23 10:19:42| INFO atc_mc finished [took 13.9089s] +05/11/23 10:19:44| INFO mulmc_sld finished [took 15.9994s] +05/11/23 10:22:17| INFO bin_sld finished [took 168.9998s] +05/11/23 10:24:35| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00999) [took 306.5297s] +05/11/23 10:24:47| INFO mul_sld_gs finished [took 318.6584s] +05/11/23 10:32:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.01176) [took 782.7189s] +05/11/23 10:35:07| INFO bin_sld_gs finished [took 939.3830s] +05/11/23 10:35:07| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 940.2365s] +05/11/23 10:35:07| INFO Dataset sample 0.50 of dataset imdb_9prevs started +05/11/23 10:35:19| INFO ref finished [took 10.1160s] +05/11/23 10:35:22| INFO atc_mc finished [took 13.6292s] +05/11/23 10:35:24| INFO mulmc_sld finished [took 15.7949s] +05/11/23 10:38:04| INFO bin_sld finished [took 176.0746s] +05/11/23 10:40:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01008) [took 319.8045s] +05/11/23 10:40:45| INFO mul_sld_gs finished [took 336.8792s] +05/11/23 10:48:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00942) [took 802.9266s] +05/11/23 10:51:09| INFO bin_sld_gs finished [took 961.0002s] +05/11/23 10:51:09| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 961.8311s] +05/11/23 10:51:09| INFO Dataset sample 0.60 of dataset imdb_9prevs started +05/11/23 10:51:21| INFO ref finished [took 10.7059s] +05/11/23 10:51:24| INFO atc_mc finished [took 13.8934s] +05/11/23 10:51:26| INFO mulmc_sld finished [took 16.0295s] +05/11/23 10:54:09| INFO bin_sld finished [took 179.1806s] +05/11/23 10:56:31| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00906) [took 320.5350s] +05/11/23 10:56:45| INFO mul_sld_gs finished [took 334.5485s] +05/11/23 11:04:55| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': None} (score=0.01228) [took 824.5702s] +05/11/23 11:07:33| INFO bin_sld_gs finished [took 983.3806s] +05/11/23 11:07:33| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 984.1981s] +05/11/23 11:07:34| INFO Dataset sample 0.70 of dataset imdb_9prevs started +05/11/23 11:07:45| INFO ref finished [took 10.7034s] +05/11/23 11:07:49| INFO atc_mc finished [took 14.0668s] +05/11/23 11:07:51| INFO mulmc_sld finished [took 16.2499s] +---------------------------------------------------------------------------------------------------- +05/11/23 11:09:19| INFO dataset rcv1_CCAT_9prevs +05/11/23 11:09:24| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +---------------------------------------------------------------------------------------------------- +05/11/23 11:10:40| INFO dataset rcv1_CCAT_9prevs +05/11/23 11:10:44| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 11:11:21| INFO ref finished [took 34.7944s] +05/11/23 11:11:25| INFO atc_mc finished [took 37.6168s] +05/11/23 11:11:36| INFO mulmc_sld finished [took 51.0883s] +05/11/23 11:11:36| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 52.3442s] +05/11/23 11:11:36| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 11:12:14| INFO ref finished [took 35.0033s] +05/11/23 11:12:17| INFO atc_mc finished [took 37.7761s] +05/11/23 11:12:24| INFO mulmc_sld finished [took 46.2195s] +05/11/23 11:12:24| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 47.5446s] +05/11/23 11:12:24| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 11:13:01| INFO ref finished [took 35.1077s] +05/11/23 11:13:05| INFO atc_mc finished [took 37.7889s] +05/11/23 11:13:12| INFO mulmc_sld finished [took 46.6515s] +05/11/23 11:13:12| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 48.0359s] +05/11/23 11:13:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 11:13:49| INFO ref finished [took 35.0214s] +05/11/23 11:13:53| INFO atc_mc finished [took 37.9480s] +05/11/23 11:14:00| INFO mulmc_sld finished [took 46.4140s] +05/11/23 11:14:00| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 47.5164s] +05/11/23 11:14:00| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 11:14:37| INFO ref finished [took 35.2699s] +05/11/23 11:14:41| INFO atc_mc finished [took 37.9490s] +05/11/23 11:14:49| INFO mulmc_sld finished [took 47.7005s] +05/11/23 11:14:49| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 49.0189s] +05/11/23 11:14:49| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 11:15:26| INFO ref finished [took 35.2350s] +05/11/23 11:15:30| INFO atc_mc finished [took 38.6364s] +05/11/23 11:15:39| INFO mulmc_sld finished [took 48.8860s] +05/11/23 11:15:39| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 50.1097s] +05/11/23 11:15:39| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 11:16:16| INFO ref finished [took 35.0322s] +05/11/23 11:16:20| INFO atc_mc finished [took 38.4809s] +05/11/23 11:16:29| INFO mulmc_sld finished [took 48.6466s] +05/11/23 11:16:29| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 50.0372s] +05/11/23 11:16:29| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 11:17:06| INFO ref finished [took 35.2988s] +05/11/23 11:17:10| INFO atc_mc finished [took 38.3390s] +05/11/23 11:17:18| INFO mulmc_sld finished [took 47.8829s] +05/11/23 11:17:18| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 49.2700s] +05/11/23 11:17:18| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 11:17:56| INFO ref finished [took 35.2614s] +05/11/23 11:17:59| INFO atc_mc finished [took 38.1131s] +05/11/23 11:18:08| INFO mulmc_sld finished [took 49.0925s] +05/11/23 11:18:09| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 50.4765s] +---------------------------------------------------------------------------------------------------- +05/11/23 11:26:35| INFO dataset rcv1_CCAT_9prevs +05/11/23 11:26:39| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 11:27:17| INFO ref finished [took 35.3305s] +05/11/23 11:27:21| INFO atc_mc finished [took 37.9469s] +05/11/23 11:27:28| INFO mulmc_sld finished [took 46.9769s] +05/11/23 11:27:28| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 48.3022s] +05/11/23 11:27:28| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 11:28:05| INFO ref finished [took 35.2459s] +05/11/23 11:28:09| INFO atc_mc finished [took 38.1660s] +05/11/23 11:28:15| INFO mulmc_sld finished [took 46.3832s] +05/11/23 11:28:15| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 47.7328s] +05/11/23 11:28:15| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 11:28:53| INFO ref finished [took 35.4919s] +05/11/23 11:28:57| INFO atc_mc finished [took 38.1023s] +05/11/23 11:29:03| INFO mulmc_sld finished [took 46.4657s] +05/11/23 11:29:03| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 47.8578s] +05/11/23 11:29:03| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 11:29:41| INFO ref finished [took 35.3209s] +05/11/23 11:29:45| INFO atc_mc finished [took 38.3693s] +05/11/23 11:29:51| INFO mulmc_sld finished [took 46.5707s] +05/11/23 11:29:51| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 47.7036s] +05/11/23 11:29:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 11:30:28| INFO ref finished [took 35.0276s] +05/11/23 11:30:32| INFO atc_mc finished [took 38.1508s] +05/11/23 11:30:40| INFO mulmc_sld finished [took 47.6215s] +05/11/23 11:30:40| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 48.9244s] +05/11/23 11:30:40| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 11:31:17| INFO ref finished [took 35.3308s] +05/11/23 11:31:21| INFO atc_mc finished [took 38.0629s] +05/11/23 11:31:29| INFO mulmc_sld finished [took 47.8783s] +05/11/23 11:31:29| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 49.1655s] +05/11/23 11:31:29| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 11:32:07| INFO ref finished [took 35.1485s] +05/11/23 11:32:10| INFO atc_mc finished [took 38.1974s] +05/11/23 11:32:18| INFO mulmc_sld finished [took 47.6056s] +05/11/23 11:32:18| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 48.9545s] +05/11/23 11:32:18| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 11:32:56| INFO ref finished [took 35.1879s] +05/11/23 11:32:59| INFO atc_mc finished [took 38.1684s] +05/11/23 11:33:07| INFO mulmc_sld finished [took 47.5635s] +05/11/23 11:33:07| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 48.9364s] +05/11/23 11:33:07| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 11:33:45| INFO ref finished [took 35.5855s] +05/11/23 11:33:48| INFO atc_mc finished [took 38.1206s] +05/11/23 11:33:54| INFO mulmc_sld finished [took 45.1957s] +05/11/23 11:33:54| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 46.5129s] +---------------------------------------------------------------------------------------------------- +05/11/23 12:02:53| INFO dataset rcv1_CCAT_9prevs +05/11/23 12:02:58| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 12:03:01| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 +05/11/23 12:03:37| INFO ref finished [took 34.9513s] +05/11/23 12:03:41| INFO atc_mc finished [took 37.7710s] +05/11/23 12:03:47| INFO mulmc_sld finished [took 47.2144s] +05/11/23 12:03:56| INFO mul_sld finished [took 56.8347s] +05/11/23 12:03:56| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 58.1052s] +05/11/23 12:03:56| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 12:03:59| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 +05/11/23 12:04:36| INFO ref finished [took 35.5921s] +05/11/23 12:04:40| INFO atc_mc finished [took 38.4587s] +05/11/23 12:04:46| INFO mulmc_sld finished [took 47.5756s] +05/11/23 12:04:47| INFO mul_sld finished [took 50.0690s] +05/11/23 12:04:47| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 51.3609s] +05/11/23 12:04:47| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 12:04:50| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 +05/11/23 12:05:28| INFO ref finished [took 36.0399s] +05/11/23 12:05:31| INFO atc_mc finished [took 38.9414s] +05/11/23 12:05:38| INFO mulmc_sld finished [took 48.4594s] +05/11/23 12:05:38| INFO mul_sld finished [took 49.4355s] +05/11/23 12:05:38| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 50.7799s] +05/11/23 12:05:38| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 12:05:41| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 +---------------------------------------------------------------------------------------------------- +05/11/23 12:06:13| INFO dataset rcv1_CCAT_9prevs +05/11/23 12:06:18| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 12:07:03| INFO ref finished [took 41.7793s] +05/11/23 12:07:10| INFO atc_mc finished [took 48.0537s] +05/11/23 12:07:38| WARNING Method mulne_sld failed. Exception: axis 1 is out of bounds for array of dimension 1 +---------------------------------------------------------------------------------------------------- +05/11/23 12:08:00| INFO dataset rcv1_CCAT_9prevs +05/11/23 12:08:04| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 12:08:47| INFO ref finished [took 37.5352s] +05/11/23 12:08:50| INFO atc_mc finished [took 40.2843s] +05/11/23 12:08:55| INFO mulne_sld finished [took 47.4558s] +05/11/23 12:08:56| INFO mulmc_sld finished [took 49.8247s] +05/11/23 12:09:05| INFO mul_sld finished [took 59.5033s] +05/11/23 12:09:05| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 60.7605s] +05/11/23 12:09:05| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 12:09:47| INFO ref finished [took 37.4891s] +05/11/23 12:09:52| INFO atc_mc finished [took 40.9763s] +05/11/23 12:09:58| INFO mulmc_sld finished [took 50.3687s] +05/11/23 12:09:59| INFO mulne_sld finished [took 50.8494s] +05/11/23 12:10:00| INFO mul_sld finished [took 53.7955s] +05/11/23 12:10:00| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 55.1095s] +05/11/23 12:10:00| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 12:10:44| INFO ref finished [took 39.3665s] +05/11/23 12:10:49| INFO atc_mc finished [took 43.1884s] +05/11/23 12:10:55| INFO mul_sld finished [took 53.2533s] +05/11/23 12:10:55| INFO mulmc_sld finished [took 52.6179s] +05/11/23 12:10:56| INFO mulne_sld finished [took 52.7117s] +05/11/23 12:10:56| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 56.0058s] +05/11/23 12:10:56| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 12:11:40| INFO ref finished [took 39.1357s] +05/11/23 12:11:44| INFO atc_mc finished [took 42.7168s] +05/11/23 12:11:50| INFO mul_sld finished [took 53.1250s] +05/11/23 12:11:51| INFO mulmc_sld finished [took 52.6875s] +05/11/23 12:11:51| INFO mulne_sld finished [took 51.9871s] +05/11/23 12:11:51| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 55.0715s] +05/11/23 12:11:51| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 12:12:34| INFO ref finished [took 38.0624s] +05/11/23 12:12:38| INFO atc_mc finished [took 40.9414s] +05/11/23 12:12:45| INFO mul_sld finished [took 52.0220s] +05/11/23 12:12:46| INFO mulmc_sld finished [took 52.0904s] +05/11/23 12:12:47| INFO mulne_sld finished [took 52.2011s] +05/11/23 12:12:47| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 55.6901s] +05/11/23 12:12:47| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 12:13:29| INFO ref finished [took 37.9734s] +05/11/23 12:13:34| INFO atc_mc finished [took 41.4316s] +05/11/23 12:13:41| INFO mulmc_sld finished [took 51.9276s] +05/11/23 12:13:42| INFO mul_sld finished [took 53.8232s] +05/11/23 12:13:43| INFO mulne_sld finished [took 52.4359s] +05/11/23 12:13:43| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 55.5737s] +05/11/23 12:13:43| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 12:14:25| INFO ref finished [took 38.1687s] +05/11/23 12:14:29| INFO atc_mc finished [took 40.6142s] +05/11/23 12:14:37| INFO mulmc_sld finished [took 52.4191s] +05/11/23 12:14:38| INFO mul_sld finished [took 53.7962s] +05/11/23 12:14:38| INFO mulne_sld finished [took 52.1544s] +05/11/23 12:14:38| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 55.4465s] +05/11/23 12:14:38| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 12:15:21| INFO ref finished [took 38.4494s] +05/11/23 12:15:25| INFO atc_mc finished [took 40.9944s] +05/11/23 12:15:32| INFO mulmc_sld finished [took 51.8551s] +05/11/23 12:15:33| INFO mul_sld finished [took 53.4409s] +05/11/23 12:15:33| INFO mulne_sld finished [took 51.7256s] +05/11/23 12:15:33| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 55.1132s] +05/11/23 12:15:33| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 12:16:16| INFO ref finished [took 38.2838s] +05/11/23 12:16:20| INFO atc_mc finished [took 41.3532s] +05/11/23 12:16:24| INFO mulmc_sld finished [took 49.1257s] +05/11/23 12:16:26| INFO mulne_sld finished [took 49.8205s] +05/11/23 12:16:34| INFO mul_sld finished [took 59.1323s] +05/11/23 12:16:34| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 60.4191s] +---------------------------------------------------------------------------------------------------- +05/11/23 12:23:45| INFO dataset rcv1_CCAT_9prevs +05/11/23 12:23:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 12:24:32| INFO ref finished [took 38.5638s] +05/11/23 12:24:36| INFO atc_mc finished [took 41.5043s] +05/11/23 12:27:13| INFO binmc_sld finished [took 201.7673s] +05/11/23 12:27:14| INFO bin_sld finished [took 203.3515s] +05/11/23 12:27:17| INFO binne_sld finished [took 204.1403s] +05/11/23 12:27:17| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 207.1212s] +05/11/23 12:27:17| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 12:27:59| INFO ref finished [took 37.8990s] +05/11/23 12:28:02| INFO atc_mc finished [took 40.1947s] +05/11/23 12:30:36| INFO bin_sld finished [took 197.7408s] +05/11/23 12:30:37| INFO binne_sld finished [took 197.0819s] +05/11/23 12:30:37| INFO binmc_sld finished [took 198.5545s] +05/11/23 12:30:37| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 200.7762s] +05/11/23 12:30:37| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 12:31:19| INFO ref finished [took 37.3749s] +05/11/23 12:31:23| INFO atc_mc finished [took 40.9120s] +05/11/23 12:33:54| INFO binmc_sld finished [took 194.5722s] +05/11/23 12:33:55| INFO bin_sld finished [took 195.8961s] +05/11/23 12:33:56| INFO binne_sld finished [took 194.9605s] +05/11/23 12:33:56| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 198.1296s] +05/11/23 12:33:56| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 12:34:37| INFO ref finished [took 37.5561s] +05/11/23 12:34:40| INFO atc_mc finished [took 40.3775s] +05/11/23 12:37:09| INFO bin_sld finished [took 192.3652s] +05/11/23 12:37:12| INFO binne_sld finished [took 193.4666s] +05/11/23 12:37:12| INFO binmc_sld finished [took 194.3499s] +05/11/23 12:37:12| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 196.3675s] +05/11/23 12:37:12| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 12:37:53| INFO ref finished [took 36.5043s] +05/11/23 12:37:57| INFO atc_mc finished [took 40.0038s] +05/11/23 12:40:26| INFO bin_sld finished [took 192.4518s] +05/11/23 12:40:26| INFO binne_sld finished [took 191.2727s] +05/11/23 12:40:26| INFO binmc_sld finished [took 192.3339s] +05/11/23 12:40:26| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 194.5333s] +05/11/23 12:40:27| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 12:41:07| INFO ref finished [took 36.6809s] +05/11/23 12:41:11| INFO atc_mc finished [took 39.9520s] +05/11/23 12:43:40| INFO bin_sld finished [took 192.1873s] +05/11/23 12:43:40| INFO binmc_sld finished [took 191.7820s] +05/11/23 12:43:41| INFO binne_sld finished [took 191.9164s] +05/11/23 12:43:41| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 194.8818s] +05/11/23 12:43:41| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 12:44:22| INFO ref finished [took 36.9564s] +05/11/23 12:44:26| INFO atc_mc finished [took 40.1293s] +05/11/23 12:46:55| INFO bin_sld finished [took 192.4960s] +05/11/23 12:46:56| INFO binmc_sld finished [took 192.8281s] +05/11/23 12:46:58| INFO binne_sld finished [took 193.1524s] +05/11/23 12:46:58| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 196.2697s] +05/11/23 12:46:58| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 12:47:39| INFO ref finished [took 37.2831s] +05/11/23 12:47:42| INFO atc_mc finished [took 39.7258s] +05/11/23 12:50:16| INFO binmc_sld finished [took 195.9783s] +05/11/23 12:50:16| INFO binne_sld finished [took 195.2592s] +05/11/23 12:50:16| INFO bin_sld finished [took 197.4676s] +05/11/23 12:50:16| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 198.8232s] +05/11/23 12:50:16| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 12:50:58| INFO ref finished [took 37.4054s] +05/11/23 12:51:02| INFO atc_mc finished [took 40.4573s] +05/11/23 12:53:36| INFO bin_sld finished [took 198.0953s] +05/11/23 12:53:36| INFO binmc_sld finished [took 197.8028s] +05/11/23 12:53:37| INFO binne_sld finished [took 197.3027s] +05/11/23 12:53:37| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 200.2560s] +---------------------------------------------------------------------------------------------------- +05/11/23 13:29:43| INFO dataset rcv1_CCAT_9prevs +05/11/23 13:29:47| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +05/11/23 13:30:43| INFO ref finished [took 47.3558s] +05/11/23 13:30:48| INFO atc_mc finished [took 50.8788s] +05/11/23 13:30:52| INFO mulne_sld finished [took 60.4851s] +05/11/23 13:30:53| INFO mulmc_sld finished [took 63.4717s] +05/11/23 13:33:31| INFO binmc_sld finished [took 222.0328s] +05/11/23 13:33:33| INFO binne_sld finished [took 223.0449s] +05/11/23 13:43:18| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': 'max_conf'} (score=0.00644) [took 803.9708s] +05/11/23 13:44:01| INFO mul_sld_gs finished [took 847.1261s] +05/11/23 13:49:04| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00589) [took 1151.2473s] +05/11/23 13:52:06| INFO bin_sld_gs finished [took 1333.4736s] +05/11/23 13:52:06| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 1338.9046s] +05/11/23 13:52:06| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +05/11/23 13:53:00| INFO ref finished [took 45.3095s] +05/11/23 13:53:04| INFO atc_mc finished [took 48.2659s] +05/11/23 13:53:08| INFO mulmc_sld finished [took 58.9237s] +05/11/23 13:53:11| INFO mulne_sld finished [took 59.5712s] +05/11/23 13:55:46| INFO binmc_sld finished [took 218.1315s] +05/11/23 13:55:51| INFO binne_sld finished [took 220.8543s] +05/11/23 14:05:34| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00699) [took 800.6256s] +05/11/23 14:06:16| INFO mul_sld_gs finished [took 842.1616s] +05/11/23 14:12:14| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00768) [took 1201.3712s] +05/11/23 14:15:15| INFO bin_sld_gs finished [took 1382.8113s] +05/11/23 14:15:15| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 1388.8622s] +05/11/23 14:15:15| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +05/11/23 14:16:11| INFO ref finished [took 46.8666s] +05/11/23 14:16:15| INFO atc_mc finished [took 49.6779s] +05/11/23 14:16:19| INFO mulmc_sld finished [took 61.0610s] +05/11/23 14:16:22| INFO mulne_sld finished [took 62.2089s] +05/11/23 14:19:02| INFO binmc_sld finished [took 225.5737s] +05/11/23 14:19:03| INFO binne_sld finished [took 223.9017s] +05/11/23 14:28:50| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00756) [took 806.7930s] +05/11/23 14:29:32| INFO mul_sld_gs finished [took 848.7630s] +05/11/23 14:36:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00781) [took 1240.9138s] +05/11/23 14:39:04| INFO bin_sld_gs finished [took 1422.5520s] +05/11/23 14:39:04| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1428.8824s] +05/11/23 14:39:04| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +05/11/23 14:39:58| INFO ref finished [took 45.7514s] +05/11/23 14:40:02| INFO atc_mc finished [took 48.3888s] +05/11/23 14:40:05| INFO mulmc_sld finished [took 59.0537s] +05/11/23 14:40:09| INFO mulne_sld finished [took 60.9189s] +05/11/23 14:42:42| INFO binne_sld finished [took 214.5464s] +05/11/23 14:42:44| INFO binmc_sld finished [took 218.8429s] +05/11/23 14:52:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00984) [took 792.5474s] +05/11/23 14:53:05| INFO mul_sld_gs finished [took 834.1824s] +05/11/23 14:59:56| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.01112) [took 1247.0092s] +05/11/23 15:02:57| INFO bin_sld_gs finished [took 1427.5051s] +05/11/23 15:02:57| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1432.9172s] +05/11/23 15:02:57| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +05/11/23 15:03:49| INFO ref finished [took 44.4148s] +05/11/23 15:03:54| INFO atc_mc finished [took 47.7566s] +05/11/23 15:04:00| INFO mulmc_sld finished [took 60.5480s] +05/11/23 15:04:03| INFO mulne_sld finished [took 61.2226s] +05/11/23 15:06:30| INFO binmc_sld finished [took 211.9647s] +05/11/23 15:06:32| INFO binne_sld finished [took 211.4312s] +05/11/23 15:16:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.00571) [took 776.6085s] +05/11/23 15:16:42| INFO mul_sld_gs finished [took 817.9358s] +05/11/23 15:23:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'vs', 'confidence': 'entropy'} (score=0.00653) [took 1221.6531s] +05/11/23 15:26:23| INFO bin_sld_gs finished [took 1400.9688s] +05/11/23 15:26:23| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 1406.4620s] +05/11/23 15:26:23| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +05/11/23 15:27:16| INFO ref finished [took 44.3988s] +05/11/23 15:27:21| INFO atc_mc finished [took 48.5589s] +05/11/23 15:27:27| INFO mulmc_sld finished [took 61.4269s] +05/11/23 15:27:29| INFO mulne_sld finished [took 61.8292s] +05/11/23 15:29:55| INFO binmc_sld finished [took 210.1585s] +05/11/23 15:29:59| INFO binne_sld finished [took 212.0930s] +05/11/23 15:39:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.00616) [took 771.6071s] +05/11/23 15:40:03| INFO mul_sld_gs finished [took 813.2905s] +05/11/23 15:47:04| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': None} (score=0.00544) [took 1234.9832s] +05/11/23 15:50:10| INFO bin_sld_gs finished [took 1421.7775s] +05/11/23 15:50:10| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1427.0062s] +05/11/23 15:50:10| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +05/11/23 15:51:11| INFO ref finished [took 49.7682s] +05/11/23 15:51:19| INFO atc_mc finished [took 54.2855s] +05/11/23 15:51:22| INFO mulmc_sld finished [took 68.7688s] +05/11/23 15:51:26| INFO mulne_sld finished [took 69.3711s] +05/11/23 15:54:07| INFO binmc_sld finished [took 234.7962s] +05/11/23 15:54:09| INFO binne_sld finished [took 234.6444s] +05/11/23 16:03:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'confidence': 'entropy'} (score=0.00765) [took 811.6704s] +05/11/23 16:04:34| INFO mul_sld_gs finished [took 854.8196s] +05/11/23 16:11:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': 'max_conf'} (score=0.01234) [took 1252.4784s] +05/11/23 16:14:10| INFO bin_sld_gs finished [took 1431.7446s] +05/11/23 16:14:10| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 1439.1145s] +05/11/23 16:14:10| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +05/11/23 16:15:02| INFO ref finished [took 44.0970s] +05/11/23 16:15:07| INFO atc_mc finished [took 48.2871s] +05/11/23 16:15:13| INFO mulmc_sld finished [took 61.0461s] +05/11/23 16:15:15| INFO mulne_sld finished [took 60.6375s] +05/11/23 16:17:46| INFO binmc_sld finished [took 215.1734s] +05/11/23 16:17:49| INFO binne_sld finished [took 215.7846s] +05/11/23 16:27:15| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'confidence': None} (score=0.00822) [took 778.5688s] +05/11/23 16:27:56| INFO mul_sld_gs finished [took 819.2615s] +05/11/23 16:34:16| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'entropy'} (score=0.00894) [took 1200.6639s] +05/11/23 16:37:21| INFO bin_sld_gs finished [took 1385.9035s] +05/11/23 16:37:21| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 1391.5055s] +05/11/23 16:37:21| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +05/11/23 16:38:13| INFO ref finished [took 44.7046s] +05/11/23 16:38:18| INFO atc_mc finished [took 48.7802s] +05/11/23 16:38:21| INFO mulmc_sld finished [took 57.4163s] +05/11/23 16:38:24| INFO mulne_sld finished [took 58.9847s] +05/11/23 16:40:59| INFO binmc_sld finished [took 216.7311s] +05/11/23 16:41:01| INFO binne_sld finished [took 216.5312s] +05/11/23 16:50:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'confidence': 'max_conf'} (score=0.00808) [took 758.6896s] +05/11/23 16:50:46| INFO mul_sld_gs finished [took 798.8038s] +05/11/23 16:56:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'confidence': 'entropy'} (score=0.00604) [took 1154.7043s] +05/11/23 16:59:39| INFO bin_sld_gs finished [took 1332.5521s] +05/11/23 16:59:39| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 1337.7947s] +---------------------------------------------------------------------------------------------------- +05/11/23 20:08:46| ERROR estimate comparison failed. Exceprion: 'environ' object has no attribute 'OUT_PATH' +---------------------------------------------------------------------------------------------------- +05/11/23 20:09:08| ERROR estimate comparison failed. Exceprion: 'environ' object has no attribute 'OUT_PATH' +---------------------------------------------------------------------------------------------------- +05/11/23 20:09:27| INFO dataset imdb_3prevs +05/11/23 20:09:34| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 20:09:44| INFO ref finished [took 8.9550s] +05/11/23 20:09:47| INFO atc_mc finished [took 11.8923s] +05/11/23 20:09:56| INFO mulmc_sld finished [took 21.3196s] +05/11/23 20:09:56| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 21.7709s] +05/11/23 20:09:56| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 20:10:05| INFO ref finished [took 8.6116s] +05/11/23 20:10:08| INFO atc_mc finished [took 11.6880s] +05/11/23 20:10:16| INFO mulmc_sld finished [took 19.7793s] +05/11/23 20:10:16| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.3246s] +05/11/23 20:10:16| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 20:10:26| INFO ref finished [took 8.6654s] +05/11/23 20:10:29| INFO atc_mc finished [took 11.6975s] +05/11/23 20:10:35| INFO mulmc_sld finished [took 18.1478s] +05/11/23 20:10:35| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.7200s] +---------------------------------------------------------------------------------------------------- +05/11/23 20:11:42| INFO dataset imdb_3prevs +05/11/23 20:11:49| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 20:11:58| INFO ref finished [took 8.7146s] +05/11/23 20:12:02| INFO atc_mc finished [took 11.9672s] +05/11/23 20:12:10| INFO mulmc_sld finished [took 20.7824s] +05/11/23 20:12:10| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 21.2293s] +05/11/23 20:12:10| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 20:12:19| INFO ref finished [took 8.5867s] +05/11/23 20:12:23| INFO atc_mc finished [took 11.6542s] +05/11/23 20:12:30| INFO mulmc_sld finished [took 19.6709s] +05/11/23 20:12:30| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.1802s] +05/11/23 20:12:30| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 20:12:40| INFO ref finished [took 8.7231s] +05/11/23 20:12:43| INFO atc_mc finished [took 11.8244s] +05/11/23 20:12:49| INFO mulmc_sld finished [took 18.0420s] +05/11/23 20:12:49| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.6102s] +---------------------------------------------------------------------------------------------------- +05/11/23 20:14:32| INFO dataset imdb_3prevs +05/11/23 20:14:39| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 20:14:48| INFO ref finished [took 8.6247s] +05/11/23 20:14:51| INFO atc_mc finished [took 11.6363s] +05/11/23 20:15:00| INFO mulmc_sld finished [took 20.4634s] +05/11/23 20:15:00| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 20.9026s] +05/11/23 20:15:00| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 20:15:09| INFO ref finished [took 8.5219s] +05/11/23 20:15:12| INFO atc_mc finished [took 11.6739s] +05/11/23 20:15:20| INFO mulmc_sld finished [took 19.8454s] +05/11/23 20:15:20| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.3705s] +05/11/23 20:15:20| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 20:15:29| INFO ref finished [took 8.5948s] +05/11/23 20:15:32| INFO atc_mc finished [took 11.7465s] +05/11/23 20:15:39| INFO mulmc_sld finished [took 17.9276s] +05/11/23 20:15:39| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.4893s] +---------------------------------------------------------------------------------------------------- +05/11/23 20:16:10| INFO dataset imdb_3prevs +05/11/23 20:16:17| INFO Dataset sample 0.20 of dataset imdb_3prevs started +05/11/23 20:16:26| INFO ref finished [took 8.3736s] +05/11/23 20:16:29| INFO atc_mc finished [took 11.3995s] +05/11/23 20:16:38| INFO mulmc_sld finished [took 20.4916s] +05/11/23 20:16:38| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 20.9187s] +05/11/23 20:16:38| INFO Dataset sample 0.50 of dataset imdb_3prevs started +05/11/23 20:16:47| INFO ref finished [took 8.4368s] +05/11/23 20:16:50| INFO atc_mc finished [took 11.4889s] +05/11/23 20:16:58| INFO mulmc_sld finished [took 19.6803s] +05/11/23 20:16:58| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 20.2091s] +05/11/23 20:16:58| INFO Dataset sample 0.80 of dataset imdb_3prevs started +05/11/23 20:17:08| INFO ref finished [took 8.9281s] +05/11/23 20:17:11| INFO atc_mc finished [took 11.9333s] +05/11/23 20:17:17| INFO mulmc_sld finished [took 18.2367s] +05/11/23 20:17:17| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 18.8309s] +---------------------------------------------------------------------------------------------------- +06/11/23 01:34:48| INFO dataset imdb_3prevs +06/11/23 01:34:59| INFO Dataset sample 0.20 of dataset imdb_3prevs started +06/11/23 01:35:18| INFO ref finished [took 18.0987s] +06/11/23 01:35:24| INFO atc_mc finished [took 24.9118s] +06/11/23 01:35:31| INFO mulmc_sld finished [took 32.0631s] +06/11/23 01:35:31| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 32.6119s] +06/11/23 01:35:31| INFO Dataset sample 0.50 of dataset imdb_3prevs started +06/11/23 01:35:51| INFO ref finished [took 18.7770s] +06/11/23 01:35:58| INFO atc_mc finished [took 25.5592s] +06/11/23 01:36:04| INFO mulmc_sld finished [took 31.9103s] +06/11/23 01:36:04| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 32.5205s] +06/11/23 01:36:04| INFO Dataset sample 0.80 of dataset imdb_3prevs started +06/11/23 01:36:23| INFO ref finished [took 18.5730s] +06/11/23 01:36:31| INFO atc_mc finished [took 25.8019s] +06/11/23 01:36:33| INFO mulmc_sld finished [took 28.9526s] +06/11/23 01:36:33| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 29.5292s] +---------------------------------------------------------------------------------------------------- +06/11/23 02:06:40| INFO dataset imdb_3prevs +06/11/23 02:06:47| INFO Dataset sample 0.20 of dataset imdb_3prevs started +06/11/23 02:06:56| INFO ref finished [took 9.0989s] +06/11/23 02:06:59| INFO atc_mc finished [took 12.2513s] +06/11/23 03:01:43| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'vs', 'quantifier__exact_train_prev': False, 'confidence': 'max_conf'} (score=0.00738) [took 3296.0714s] +06/11/23 03:01:56| INFO mul_sld_gs finished [took 3309.2417s] +06/11/23 03:01:56| INFO Dataset sample 0.20 of dataset imdb_3prevs finished [took 3309.7038s] +06/11/23 03:01:56| INFO Dataset sample 0.50 of dataset imdb_3prevs started +06/11/23 03:02:06| INFO ref finished [took 8.5518s] +06/11/23 03:02:09| INFO atc_mc finished [took 11.4390s] +06/11/23 03:54:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00899) [took 3146.2364s] +06/11/23 03:54:37| INFO mul_sld_gs finished [took 3159.8209s] +06/11/23 03:54:37| INFO Dataset sample 0.50 of dataset imdb_3prevs finished [took 3160.3546s] +06/11/23 03:54:37| INFO Dataset sample 0.80 of dataset imdb_3prevs started +06/11/23 03:54:46| INFO ref finished [took 8.2678s] +06/11/23 03:54:48| INFO atc_mc finished [took 11.0430s] +06/11/23 04:47:50| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': False, 'confidence': 'entropy'} (score=0.00770) [took 3193.1812s] +06/11/23 04:48:04| INFO mul_sld_gs finished [took 3206.9550s] +06/11/23 04:48:04| INFO Dataset sample 0.80 of dataset imdb_3prevs finished [took 3207.5040s] +---------------------------------------------------------------------------------------------------- +06/11/23 05:14:48| INFO dataset rcv1_CCAT_9prevs +06/11/23 05:14:53| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +06/11/23 05:15:55| INFO ref finished [took 48.0242s] +06/11/23 05:16:01| INFO atc_mc finished [took 51.7851s] +06/11/23 05:16:04| INFO mul_pacc finished [took 58.4704s] +06/11/23 05:16:04| INFO mulne_sld finished [took 62.7354s] +06/11/23 05:16:04| INFO mulmc_sld finished [took 66.2593s] +06/11/23 05:16:14| INFO mul_sld finished [took 78.2483s] +06/11/23 05:18:40| INFO bin_pacc finished [took 217.0012s] +06/11/23 05:18:43| INFO bin_sld finished [took 227.8835s] +06/11/23 05:18:43| INFO binne_sld finished [took 223.2764s] +06/11/23 05:18:44| INFO binmc_sld finished [took 226.7324s] +06/11/23 05:18:44| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 230.5906s] +06/11/23 05:18:44| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +06/11/23 05:19:44| INFO ref finished [took 49.5147s] +06/11/23 05:19:51| INFO atc_mc finished [took 54.8022s] +06/11/23 05:19:53| INFO mul_pacc finished [took 60.3260s] +06/11/23 05:19:55| INFO mulmc_sld finished [took 67.0280s] +06/11/23 05:19:56| INFO mul_sld finished [took 70.4092s] +06/11/23 05:19:58| INFO mulne_sld finished [took 67.3468s] +06/11/23 05:22:30| INFO bin_sld finished [took 224.7344s] +06/11/23 05:22:30| INFO bin_pacc finished [took 218.3044s] +06/11/23 05:22:30| INFO binmc_sld finished [took 223.3607s] +06/11/23 05:22:33| INFO binne_sld finished [took 223.6042s] +06/11/23 05:22:33| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 229.0745s] +06/11/23 05:22:33| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +06/11/23 05:23:32| INFO ref finished [took 48.1565s] +06/11/23 05:23:37| INFO atc_mc finished [took 52.1124s] +06/11/23 05:23:40| INFO mul_pacc finished [took 58.0112s] +06/11/23 05:23:40| INFO mul_sld finished [took 65.2727s] +06/11/23 05:23:42| INFO mulmc_sld finished [took 64.5943s] +06/11/23 05:23:43| INFO mulne_sld finished [took 63.9053s] +06/11/23 05:26:13| INFO bin_sld finished [took 218.6511s] +06/11/23 05:26:16| INFO bin_pacc finished [took 215.1485s] +06/11/23 05:26:17| INFO binne_sld finished [took 218.6855s] +06/11/23 05:26:17| INFO binmc_sld finished [took 221.2605s] +06/11/23 05:26:17| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 224.5608s] +06/11/23 05:26:17| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +06/11/23 05:27:15| INFO ref finished [took 48.2181s] +06/11/23 05:27:21| INFO atc_mc finished [took 52.3420s] +06/11/23 05:27:23| INFO mul_pacc finished [took 57.1950s] +06/11/23 05:27:24| INFO mul_sld finished [took 64.4722s] +06/11/23 05:27:26| INFO mulmc_sld finished [took 64.1870s] +06/11/23 05:27:27| INFO mulne_sld finished [took 63.7407s] +06/11/23 05:29:52| INFO bin_sld finished [took 213.1913s] +06/11/23 05:29:53| INFO bin_pacc finished [took 208.1322s] +06/11/23 05:29:53| INFO binmc_sld finished [took 212.6473s] +06/11/23 05:29:57| INFO binne_sld finished [took 214.5243s] +06/11/23 05:29:57| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 219.5765s] +06/11/23 05:29:57| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +06/11/23 05:30:55| INFO ref finished [took 47.7289s] +06/11/23 05:31:01| INFO atc_mc finished [took 52.1531s] +06/11/23 05:31:03| INFO mul_pacc finished [took 57.3804s] +06/11/23 05:31:06| INFO mul_sld finished [took 66.9237s] +06/11/23 05:31:06| INFO mulmc_sld finished [took 65.3230s] +06/11/23 05:31:09| INFO mulne_sld finished [took 65.6645s] +06/11/23 05:33:33| INFO bin_sld finished [took 214.3242s] +06/11/23 05:33:34| INFO bin_pacc finished [took 209.3862s] +06/11/23 05:33:35| INFO binmc_sld finished [took 214.4687s] +06/11/23 05:33:37| INFO binne_sld finished [took 214.7267s] +06/11/23 05:33:37| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 220.0212s] +06/11/23 05:33:37| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +06/11/23 05:34:35| INFO ref finished [took 48.0021s] +06/11/23 05:34:41| INFO atc_mc finished [took 52.2171s] +06/11/23 05:34:43| INFO mul_pacc finished [took 57.2348s] +06/11/23 05:34:46| INFO mul_sld finished [took 67.0899s] +06/11/23 05:34:47| INFO mulmc_sld finished [took 66.1078s] +06/11/23 05:34:49| INFO mulne_sld finished [took 66.0237s] +06/11/23 05:37:13| INFO bin_sld finished [took 214.9942s] +06/11/23 05:37:13| INFO binmc_sld finished [took 213.1574s] +06/11/23 05:37:14| INFO bin_pacc finished [took 209.1347s] +06/11/23 05:37:17| INFO binne_sld finished [took 214.9703s] +06/11/23 05:37:17| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 220.1235s] +06/11/23 05:37:17| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +06/11/23 05:38:15| INFO ref finished [took 47.8227s] +06/11/23 05:38:20| INFO atc_mc finished [took 51.9364s] +06/11/23 05:38:23| INFO mul_pacc finished [took 56.9053s] +06/11/23 05:38:27| INFO mul_sld finished [took 67.4535s] +06/11/23 05:38:27| INFO mulmc_sld finished [took 65.5956s] +06/11/23 05:38:30| INFO mulne_sld finished [took 66.0476s] +06/11/23 05:40:55| INFO bin_pacc finished [took 210.0633s] +06/11/23 05:40:56| INFO binmc_sld finished [took 215.3452s] +06/11/23 05:40:56| INFO bin_sld finished [took 217.8091s] +06/11/23 05:40:59| INFO binne_sld finished [took 216.8970s] +06/11/23 05:40:59| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 222.2971s] +06/11/23 05:40:59| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +06/11/23 05:41:57| INFO ref finished [took 47.6970s] +06/11/23 05:42:03| INFO atc_mc finished [took 52.0893s] +06/11/23 05:42:05| INFO mul_pacc finished [took 56.6428s] +06/11/23 05:42:09| INFO mul_sld finished [took 66.8810s] +06/11/23 05:42:09| INFO mulmc_sld finished [took 65.8427s] +06/11/23 05:42:11| INFO mulne_sld finished [took 64.8594s] +06/11/23 05:44:36| INFO bin_pacc finished [took 208.7884s] +06/11/23 05:44:38| INFO bin_sld finished [took 216.6052s] +06/11/23 05:44:38| INFO binmc_sld finished [took 215.5486s] +06/11/23 05:44:43| INFO binne_sld finished [took 217.9926s] +06/11/23 05:44:43| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 223.2270s] +06/11/23 05:44:43| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +06/11/23 05:45:40| INFO ref finished [took 48.0710s] +06/11/23 05:45:46| INFO atc_mc finished [took 52.0992s] +06/11/23 05:45:48| INFO mul_pacc finished [took 56.6568s] +06/11/23 05:45:49| INFO mulmc_sld finished [took 61.7314s] +06/11/23 05:45:52| INFO mulne_sld finished [took 62.7505s] +06/11/23 05:45:59| INFO mul_sld finished [took 73.7681s] +06/11/23 05:48:18| INFO bin_pacc finished [took 208.2267s] +06/11/23 05:48:23| INFO bin_sld finished [took 218.9333s] +06/11/23 05:48:24| INFO binmc_sld finished [took 218.0032s] +06/11/23 05:48:27| INFO binne_sld finished [took 219.2450s] +06/11/23 05:48:27| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 224.3446s] +06/11/23 05:49:49| INFO dataset imdb_9prevs +06/11/23 05:49:57| INFO Dataset sample 0.10 of dataset imdb_9prevs started +06/11/23 05:50:12| INFO ref finished [took 13.3064s] +06/11/23 05:50:17| INFO atc_mc finished [took 17.3508s] +06/11/23 05:50:19| INFO mul_pacc finished [took 20.0802s] +06/11/23 05:50:22| INFO mulne_sld finished [took 23.6723s] +06/11/23 05:50:24| INFO mulmc_sld finished [took 25.5159s] +06/11/23 05:50:39| INFO mul_sld finished [took 40.7099s] +06/11/23 05:52:55| INFO bin_pacc finished [took 176.3728s] +06/11/23 05:53:05| INFO binmc_sld finished [took 186.8240s] +06/11/23 05:53:06| INFO binne_sld finished [took 187.6585s] +06/11/23 05:53:07| INFO bin_sld finished [took 189.1728s] +06/11/23 05:53:07| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 189.6034s] +06/11/23 05:53:07| INFO Dataset sample 0.20 of dataset imdb_9prevs started +06/11/23 05:53:22| INFO ref finished [took 13.2778s] +06/11/23 05:53:26| INFO atc_mc finished [took 17.4491s] +06/11/23 05:53:28| INFO mul_pacc finished [took 19.9359s] +06/11/23 05:53:40| INFO mulmc_sld finished [took 31.6686s] +06/11/23 05:53:44| INFO mulne_sld finished [took 35.2085s] +06/11/23 05:53:44| INFO mul_sld finished [took 36.2502s] +06/11/23 05:56:05| INFO bin_pacc finished [took 177.0225s] +06/11/23 05:56:13| INFO binmc_sld finished [took 185.4811s] +06/11/23 05:56:15| INFO bin_sld finished [took 187.1039s] +06/11/23 05:56:16| INFO binne_sld finished [took 187.3163s] +06/11/23 05:56:16| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 188.4781s] +06/11/23 05:56:16| INFO Dataset sample 0.30 of dataset imdb_9prevs started +06/11/23 05:56:31| INFO ref finished [took 13.4513s] +06/11/23 05:56:36| INFO atc_mc finished [took 18.1025s] +06/11/23 05:56:38| INFO mul_pacc finished [took 20.3997s] +06/11/23 05:56:45| INFO mulmc_sld finished [took 28.4298s] +06/11/23 05:56:46| INFO mulne_sld finished [took 28.8678s] +06/11/23 05:56:46| INFO mul_sld finished [took 29.5573s] +06/11/23 05:59:11| INFO bin_pacc finished [took 174.0262s] +06/11/23 05:59:17| INFO binmc_sld finished [took 180.1998s] +06/11/23 05:59:18| INFO binne_sld finished [took 181.2200s] +06/11/23 05:59:19| INFO bin_sld finished [took 182.1672s] +06/11/23 05:59:19| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 183.0515s] +06/11/23 05:59:19| INFO Dataset sample 0.40 of dataset imdb_9prevs started +06/11/23 05:59:34| INFO ref finished [took 13.5163s] +06/11/23 05:59:39| INFO atc_mc finished [took 17.9856s] +06/11/23 05:59:41| INFO mul_pacc finished [took 20.7441s] +06/11/23 05:59:49| INFO mulmc_sld finished [took 29.2747s] +06/11/23 05:59:50| INFO mulne_sld finished [took 29.6624s] +06/11/23 05:59:50| INFO mul_sld finished [took 30.3432s] +06/11/23 06:02:17| INFO bin_pacc finished [took 176.7354s] +06/11/23 06:02:19| INFO binmc_sld finished [took 179.9981s] +06/11/23 06:02:21| INFO bin_sld finished [took 181.6844s] +06/11/23 06:02:22| INFO binne_sld finished [took 182.0846s] +06/11/23 06:02:22| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 183.2033s] +06/11/23 06:02:22| INFO Dataset sample 0.50 of dataset imdb_9prevs started +06/11/23 06:02:37| INFO ref finished [took 13.4688s] +06/11/23 06:02:42| INFO atc_mc finished [took 18.0218s] +06/11/23 06:02:44| INFO mul_pacc finished [took 20.5800s] +06/11/23 06:02:52| INFO mulmc_sld finished [took 29.0192s] +06/11/23 06:02:52| INFO mul_sld finished [took 29.4403s] +06/11/23 06:02:52| INFO mulne_sld finished [took 29.1611s] +06/11/23 06:05:19| INFO bin_pacc finished [took 175.5125s] +06/11/23 06:05:23| INFO binmc_sld finished [took 180.0427s] +06/11/23 06:05:25| INFO binne_sld finished [took 182.5814s] +06/11/23 06:05:26| INFO bin_sld finished [took 183.2892s] +06/11/23 06:05:26| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 183.8611s] +06/11/23 06:05:26| INFO Dataset sample 0.60 of dataset imdb_9prevs started +06/11/23 06:05:41| INFO ref finished [took 13.4643s] +06/11/23 06:05:45| INFO atc_mc finished [took 17.9768s] +06/11/23 06:05:48| INFO mul_pacc finished [took 20.7525s] +06/11/23 06:05:55| INFO mulmc_sld finished [took 28.8234s] +06/11/23 06:05:55| INFO mulne_sld finished [took 28.6537s] +06/11/23 06:05:56| INFO mul_sld finished [took 29.6167s] +06/11/23 06:08:24| INFO bin_pacc finished [took 176.5335s] +06/11/23 06:08:27| INFO binmc_sld finished [took 180.4803s] +06/11/23 06:08:28| INFO bin_sld finished [took 181.6676s] +06/11/23 06:08:29| INFO binne_sld finished [took 182.0534s] +06/11/23 06:08:29| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 183.0240s] +06/11/23 06:08:29| INFO Dataset sample 0.70 of dataset imdb_9prevs started +06/11/23 06:08:44| INFO ref finished [took 13.7566s] +06/11/23 06:08:49| INFO atc_mc finished [took 17.9495s] +06/11/23 06:08:51| INFO mul_pacc finished [took 20.5859s] +06/11/23 06:08:57| INFO mulmc_sld finished [took 27.4370s] +06/11/23 06:08:58| INFO mul_sld finished [took 28.3224s] +06/11/23 06:08:58| INFO mulne_sld finished [took 28.1390s] +06/11/23 06:11:26| INFO bin_pacc finished [took 175.7412s] +06/11/23 06:11:31| INFO binmc_sld finished [took 181.4310s] +06/11/23 06:11:32| INFO binne_sld finished [took 182.0095s] +06/11/23 06:11:33| INFO bin_sld finished [took 183.6520s] +06/11/23 06:11:33| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 184.2005s] +06/11/23 06:11:33| INFO Dataset sample 0.80 of dataset imdb_9prevs started +06/11/23 06:11:48| INFO ref finished [took 13.5418s] +06/11/23 06:11:53| INFO atc_mc finished [took 17.8150s] +06/11/23 06:11:55| INFO mul_pacc finished [took 20.4761s] +06/11/23 06:12:01| INFO mulmc_sld finished [took 27.2741s] +06/11/23 06:12:02| INFO mulne_sld finished [took 27.2693s] +06/11/23 06:12:02| INFO mul_sld finished [took 28.3364s] +06/11/23 06:14:30| INFO bin_pacc finished [took 175.7637s] +06/11/23 06:14:37| INFO binmc_sld finished [took 183.2422s] +06/11/23 06:14:38| INFO bin_sld finished [took 184.1064s] +06/11/23 06:14:39| INFO binne_sld finished [took 184.9073s] +06/11/23 06:14:39| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 186.2580s] +06/11/23 06:14:39| INFO Dataset sample 0.90 of dataset imdb_9prevs started +06/11/23 06:14:41| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 06:14:41| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 06:14:52| INFO ref finished [took 11.6315s] +06/11/23 06:14:56| INFO atc_mc finished [took 15.3068s] +06/11/23 06:15:01| INFO mulne_sld finished [took 21.1133s] +06/11/23 06:15:02| INFO mulmc_sld finished [took 22.2375s] +06/11/23 06:15:08| INFO mul_sld finished [took 27.8149s] +06/11/23 06:17:32| INFO binne_sld finished [took 171.8722s] +06/11/23 06:17:32| INFO bin_sld finished [took 172.4710s] +06/11/23 06:17:33| INFO binmc_sld finished [took 172.8193s] +06/11/23 06:17:33| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 173.4411s] +06/11/23 06:18:54| INFO dataset rcv1_GCAT_9prevs +06/11/23 06:18:59| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs started +06/11/23 06:19:11| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 06:19:11| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 06:19:54| INFO ref finished [took 42.0769s] +06/11/23 06:19:59| INFO atc_mc finished [took 45.5011s] +06/11/23 06:20:14| INFO mulne_sld finished [took 66.0516s] +06/11/23 06:20:15| INFO mul_sld finished [took 73.1171s] +06/11/23 06:20:17| INFO mulmc_sld finished [took 72.1930s] +06/11/23 06:22:23| INFO bin_sld finished [took 203.0368s] +06/11/23 06:22:27| INFO binmc_sld finished [took 203.2975s] +06/11/23 06:22:29| INFO binne_sld finished [took 202.7501s] +06/11/23 06:22:29| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs finished [took 210.2201s] +06/11/23 06:22:29| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs started +06/11/23 06:23:26| INFO ref finished [took 46.6022s] +06/11/23 06:23:31| INFO atc_mc finished [took 50.3293s] +06/11/23 06:23:33| INFO mul_pacc finished [took 54.9265s] +06/11/23 06:23:46| INFO mul_sld finished [took 74.9035s] +06/11/23 06:23:52| INFO mulne_sld finished [took 76.2697s] +06/11/23 06:23:54| INFO mulmc_sld finished [took 80.8754s] +06/11/23 06:26:06| INFO bin_pacc finished [took 209.7751s] +06/11/23 06:26:08| INFO bin_sld finished [took 217.8889s] +06/11/23 06:26:13| INFO binmc_sld finished [took 220.7753s] +06/11/23 06:26:14| INFO binne_sld finished [took 219.7510s] +06/11/23 06:26:14| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs finished [took 224.9268s] +06/11/23 06:26:14| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs started +06/11/23 06:27:10| INFO ref finished [took 46.4938s] +06/11/23 06:27:16| INFO atc_mc finished [took 50.5904s] +06/11/23 06:27:18| INFO mul_pacc finished [took 55.4949s] +06/11/23 06:27:26| INFO mulmc_sld finished [took 67.7140s] +06/11/23 06:27:26| INFO mul_sld finished [took 70.0891s] +06/11/23 06:27:28| INFO mulne_sld finished [took 68.1806s] +06/11/23 06:29:50| INFO bin_pacc finished [took 208.6091s] +06/11/23 06:29:51| INFO binmc_sld finished [took 213.7985s] +06/11/23 06:29:51| INFO bin_sld finished [took 215.8158s] +06/11/23 06:29:55| INFO binne_sld finished [took 215.5523s] +06/11/23 06:29:55| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs finished [took 220.4589s] +06/11/23 06:29:55| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs started +06/11/23 06:30:51| INFO ref finished [took 46.3752s] +06/11/23 06:30:56| INFO atc_mc finished [took 50.7062s] +06/11/23 06:30:58| INFO mul_pacc finished [took 55.2260s] +06/11/23 06:31:01| INFO mul_sld finished [took 64.2359s] +06/11/23 06:31:02| INFO mulmc_sld finished [took 63.5099s] +06/11/23 06:31:04| INFO mulne_sld finished [took 62.9188s] +06/11/23 06:33:29| INFO bin_sld finished [took 213.2716s] +06/11/23 06:33:30| INFO bin_pacc finished [took 208.6574s] +06/11/23 06:33:31| INFO binmc_sld finished [took 213.1856s] +06/11/23 06:33:33| INFO binne_sld finished [took 213.2771s] +06/11/23 06:33:33| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs finished [took 218.1742s] +06/11/23 06:33:33| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs started +06/11/23 06:34:29| INFO ref finished [took 46.6793s] +06/11/23 06:34:34| INFO atc_mc finished [took 50.9915s] +06/11/23 06:34:37| INFO mul_pacc finished [took 55.9725s] +06/11/23 06:34:38| INFO mul_sld finished [took 63.1317s] +06/11/23 06:34:40| INFO mulmc_sld finished [took 62.7473s] +06/11/23 06:34:41| INFO mulne_sld finished [took 62.1303s] +06/11/23 06:37:08| INFO bin_pacc finished [took 207.7854s] +06/11/23 06:37:08| INFO bin_sld finished [took 213.7945s] +06/11/23 06:37:08| INFO binmc_sld finished [took 212.6207s] +06/11/23 06:37:12| INFO binne_sld finished [took 213.8742s] +06/11/23 06:37:12| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs finished [took 218.7265s] +06/11/23 06:37:12| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs started +06/11/23 06:38:08| INFO ref finished [took 46.6057s] +06/11/23 06:38:14| INFO atc_mc finished [took 51.1055s] +06/11/23 06:38:15| INFO mul_pacc finished [took 55.5338s] +06/11/23 06:38:17| INFO mul_sld finished [took 63.2113s] +06/11/23 06:38:18| INFO mulmc_sld finished [took 62.2265s] +06/11/23 06:38:20| INFO mulne_sld finished [took 61.9918s] +06/11/23 06:40:46| INFO bin_pacc finished [took 207.5094s] +06/11/23 06:40:46| INFO bin_sld finished [took 213.6350s] +06/11/23 06:40:47| INFO binmc_sld finished [took 212.8363s] +06/11/23 06:40:49| INFO binne_sld finished [took 212.2587s] +06/11/23 06:40:49| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs finished [took 217.1976s] +06/11/23 06:40:49| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs started +06/11/23 06:41:44| INFO ref finished [took 45.9582s] +06/11/23 06:41:50| INFO atc_mc finished [took 50.0401s] +06/11/23 06:41:54| INFO mul_sld finished [took 62.6045s] +06/11/23 06:41:54| INFO mulmc_sld finished [took 61.4168s] +06/11/23 06:41:56| INFO mulne_sld finished [took 61.3708s] +06/11/23 06:42:00| INFO mul_pacc finished [took 62.6486s] +06/11/23 06:44:23| INFO bin_sld finished [took 212.5992s] +06/11/23 06:44:23| INFO bin_pacc finished [took 207.5241s] +06/11/23 06:44:24| INFO binmc_sld finished [took 212.2794s] +06/11/23 06:44:27| INFO binne_sld finished [took 212.8325s] +06/11/23 06:44:27| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs finished [took 217.7909s] +06/11/23 06:44:27| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs started +06/11/23 06:45:23| INFO ref finished [took 46.6997s] +06/11/23 06:45:28| INFO atc_mc finished [took 50.6417s] +06/11/23 06:45:30| INFO mul_sld finished [took 61.5352s] +06/11/23 06:45:31| INFO mul_pacc finished [took 55.9055s] +06/11/23 06:45:31| INFO mulmc_sld finished [took 60.6608s] +06/11/23 06:45:33| INFO mulne_sld finished [took 60.1616s] +06/11/23 06:48:01| INFO bin_pacc finished [took 207.7543s] +06/11/23 06:48:02| INFO bin_sld finished [took 213.7056s] +06/11/23 06:48:03| INFO binmc_sld finished [took 213.7901s] +06/11/23 06:48:04| INFO binne_sld finished [took 212.4421s] +06/11/23 06:48:04| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs finished [took 217.4465s] +06/11/23 06:48:04| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs started +06/11/23 06:48:06| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 06:48:07| WARNING Method binmc_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 06:48:09| WARNING Method binne_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 06:48:11| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 06:48:13| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 06:48:49| INFO ref finished [took 36.4085s] +06/11/23 06:48:53| INFO atc_mc finished [took 39.1380s] +06/11/23 06:48:54| INFO mulmc_sld finished [took 46.0254s] +06/11/23 06:48:55| INFO mulne_sld finished [took 45.1935s] +06/11/23 06:49:00| INFO mul_sld finished [took 53.9145s] +06/11/23 06:49:00| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs finished [took 55.9159s] +06/11/23 06:50:22| INFO dataset rcv1_MCAT_9prevs +06/11/23 06:50:27| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs started +06/11/23 06:51:29| INFO ref finished [took 47.8430s] +06/11/23 06:51:34| INFO atc_mc finished [took 51.2418s] +06/11/23 06:51:36| INFO mul_pacc finished [took 56.8770s] +06/11/23 06:52:00| INFO mulne_sld finished [took 85.8579s] +06/11/23 06:52:00| INFO mul_sld finished [took 90.6401s] +06/11/23 06:52:12| INFO mulmc_sld finished [took 100.3728s] +06/11/23 06:54:15| INFO bin_pacc finished [took 217.8843s] +06/11/23 06:54:15| INFO bin_sld finished [took 226.6925s] +06/11/23 06:54:17| INFO binne_sld finished [took 224.5785s] +06/11/23 06:54:17| INFO binmc_sld finished [took 226.9490s] +06/11/23 06:54:17| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs finished [took 229.9256s] +06/11/23 06:54:17| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs started +06/11/23 06:55:14| INFO ref finished [took 46.7323s] +06/11/23 06:55:20| INFO atc_mc finished [took 51.0126s] +06/11/23 06:55:22| INFO mul_pacc finished [took 55.8357s] +06/11/23 06:55:23| INFO mulmc_sld finished [took 62.0464s] +06/11/23 06:55:24| INFO mul_sld finished [took 64.8106s] +06/11/23 06:55:25| INFO mulne_sld finished [took 61.6750s] +06/11/23 06:57:56| INFO bin_pacc finished [took 210.8901s] +06/11/23 06:57:56| INFO bin_sld finished [took 217.3461s] +06/11/23 06:57:57| INFO binmc_sld finished [took 216.6599s] +06/11/23 06:58:00| INFO binne_sld finished [took 216.9668s] +06/11/23 06:58:00| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs finished [took 222.3450s] +06/11/23 06:58:00| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs started +06/11/23 06:58:57| INFO ref finished [took 47.5989s] +06/11/23 06:59:02| INFO atc_mc finished [took 51.5080s] +06/11/23 06:59:04| INFO mul_pacc finished [took 56.1671s] +06/11/23 06:59:09| INFO mulmc_sld finished [took 65.0229s] +06/11/23 06:59:10| INFO mul_sld finished [took 68.8366s] +06/11/23 06:59:11| INFO mulne_sld finished [took 65.2964s] +06/11/23 07:01:39| INFO bin_pacc finished [took 212.3570s] +06/11/23 07:01:40| INFO bin_sld finished [took 219.3886s] +06/11/23 07:01:42| INFO binmc_sld finished [took 219.1471s] +06/11/23 07:01:43| INFO binne_sld finished [took 218.3714s] +06/11/23 07:01:43| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs finished [took 223.2305s] +06/11/23 07:01:43| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs started +06/11/23 07:02:40| INFO ref finished [took 47.3513s] +06/11/23 07:02:45| INFO atc_mc finished [took 51.3858s] +06/11/23 07:02:47| INFO mul_pacc finished [took 56.3829s] +06/11/23 07:02:50| INFO mul_sld finished [took 64.9257s] +06/11/23 07:02:50| INFO mulmc_sld finished [took 63.6515s] +06/11/23 07:02:52| INFO mulne_sld finished [took 63.8008s] +06/11/23 07:05:22| INFO bin_pacc finished [took 211.8418s] +06/11/23 07:05:22| INFO binmc_sld finished [took 216.4950s] +06/11/23 07:05:22| INFO bin_sld finished [took 218.2730s] +06/11/23 07:05:25| INFO binne_sld finished [took 217.6016s] +06/11/23 07:05:25| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs finished [took 222.4416s] +06/11/23 07:05:25| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs started +06/11/23 07:06:22| INFO ref finished [took 47.3783s] +06/11/23 07:06:27| INFO atc_mc finished [took 51.1924s] +06/11/23 07:06:30| INFO mul_pacc finished [took 56.3115s] +06/11/23 07:06:34| INFO mul_sld finished [took 66.6559s] +06/11/23 07:06:34| INFO mulmc_sld finished [took 65.2448s] +06/11/23 07:06:37| INFO mulne_sld finished [took 65.6557s] +06/11/23 07:09:03| INFO binmc_sld finished [took 214.4549s] +06/11/23 07:09:03| INFO bin_sld finished [took 216.8097s] +06/11/23 07:09:04| INFO bin_pacc finished [took 211.9484s] +06/11/23 07:09:06| INFO binne_sld finished [took 215.5010s] +06/11/23 07:09:06| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs finished [took 220.4788s] +06/11/23 07:09:06| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs started +06/11/23 07:10:03| INFO ref finished [took 47.0882s] +06/11/23 07:10:08| INFO atc_mc finished [took 51.1826s] +06/11/23 07:10:10| INFO mul_pacc finished [took 55.8766s] +06/11/23 07:10:14| INFO mulmc_sld finished [took 64.7175s] +06/11/23 07:10:15| INFO mul_sld finished [took 67.2892s] +06/11/23 07:10:17| INFO mulne_sld finished [took 64.9305s] +06/11/23 07:12:40| INFO bin_pacc finished [took 207.6921s] +06/11/23 07:12:41| INFO binmc_sld finished [took 212.3821s] +06/11/23 07:12:41| INFO bin_sld finished [took 214.5241s] +06/11/23 07:12:43| INFO binne_sld finished [took 212.5115s] +06/11/23 07:12:43| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs finished [took 217.3597s] +06/11/23 07:12:43| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs started +06/11/23 07:13:39| INFO ref finished [took 46.5374s] +06/11/23 07:13:45| INFO atc_mc finished [took 51.0121s] +06/11/23 07:13:47| INFO mul_pacc finished [took 55.4950s] +06/11/23 07:13:52| INFO mulmc_sld finished [took 64.7651s] +06/11/23 07:13:52| INFO mul_sld finished [took 67.0632s] +06/11/23 07:13:54| INFO mulne_sld finished [took 65.2533s] +06/11/23 07:16:18| INFO bin_pacc finished [took 207.9541s] +06/11/23 07:16:19| INFO bin_sld finished [took 214.6495s] +06/11/23 07:16:19| INFO binmc_sld finished [took 213.2167s] +06/11/23 07:16:24| INFO binne_sld finished [took 215.5646s] +06/11/23 07:16:24| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs finished [took 220.4851s] +06/11/23 07:16:24| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs started +06/11/23 07:17:20| INFO ref finished [took 46.7655s] +06/11/23 07:17:25| INFO atc_mc finished [took 50.9430s] +06/11/23 07:17:27| INFO mul_pacc finished [took 55.4948s] +06/11/23 07:17:44| INFO mul_sld finished [took 78.5002s] +06/11/23 07:17:46| INFO mulmc_sld finished [took 78.7519s] +06/11/23 07:17:48| INFO mulne_sld finished [took 78.4293s] +06/11/23 07:19:59| INFO bin_pacc finished [took 208.5200s] +06/11/23 07:20:02| INFO bin_sld finished [took 216.9046s] +06/11/23 07:20:03| INFO binmc_sld finished [took 216.2736s] +06/11/23 07:20:03| INFO binne_sld finished [took 214.9573s] +06/11/23 07:20:03| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs finished [took 219.7592s] +06/11/23 07:20:03| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs started +06/11/23 07:20:05| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 07:20:06| WARNING Method binmc_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 07:20:08| WARNING Method binne_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 07:20:10| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 07:20:12| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 07:20:48| INFO ref finished [took 36.6985s] +06/11/23 07:20:52| INFO atc_mc finished [took 39.8292s] +06/11/23 07:20:55| INFO mulmc_sld finished [took 48.0943s] +06/11/23 07:20:56| INFO mul_sld finished [took 50.2138s] +06/11/23 07:20:57| INFO mulne_sld finished [took 47.9755s] +06/11/23 07:20:57| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs finished [took 53.5645s] +---------------------------------------------------------------------------------------------------- +---------------------------------------------------------------------------------------------------- +06/11/23 10:25:08| INFO dataset rcv1_CCAT_1prevs +06/11/23 10:25:12| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started +06/11/23 10:26:02| INFO ref finished [took 43.6300s] +06/11/23 10:26:05| INFO atc_mc finished [took 46.2297s] +06/11/23 10:26:46| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00906) [took 88.7593s] +06/11/23 10:27:29| INFO mul_pacc_gs finished [took 132.4595s] +06/11/23 10:31:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00900) [took 379.8213s] +06/11/23 10:34:52| INFO bin_pacc_gs finished [took 576.1136s] +---------------------------------------------------------------------------------------------------- +06/11/23 10:55:40| INFO dataset rcv1_CCAT_1prevs +06/11/23 10:55:44| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started +06/11/23 10:56:36| INFO ref finished [took 44.5230s] +06/11/23 10:56:40| INFO atc_mc finished [took 47.6566s] +06/11/23 10:57:20| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00927) [took 90.0830s] +06/11/23 10:58:04| INFO mul_pacc_gs finished [took 134.5283s] +06/11/23 11:02:12| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00974) [took 383.6784s] +06/11/23 11:05:24| INFO bin_pacc_gs finished [took 574.8730s] +06/11/23 11:10:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00775) [took 897.6622s] +06/11/23 11:11:27| INFO mul_sld_gs finished [took 940.1205s] +06/11/23 11:18:11| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00835) [took 1344.8544s] +06/11/23 11:21:09| INFO bin_sld_gs finished [took 1523.4358s] +06/11/23 11:21:09| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs finished [took 1525.2268s] +---------------------------------------------------------------------------------------------------- +06/11/23 11:21:26| INFO dataset rcv1_CCAT_1prevs +06/11/23 11:21:30| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs started +06/11/23 11:22:18| INFO ref finished [took 42.1948s] +06/11/23 11:22:23| INFO atc_mc finished [took 45.7857s] +06/11/23 11:23:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00987) [took 87.4022s] +06/11/23 11:23:45| INFO mul_pacc_gs finished [took 130.6127s] +06/11/23 11:27:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00925) [took 374.1460s] +06/11/23 11:29:46| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00775) [took 493.2309s] +06/11/23 11:30:30| INFO mul_sld_gs finished [took 537.7362s] +06/11/23 11:30:56| INFO bin_pacc_gs finished [took 562.8681s] +06/11/23 11:35:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00835) [took 840.9875s] +06/11/23 11:38:31| INFO bin_sld_gs finished [took 1019.8362s] +06/11/23 11:38:31| INFO Dataset sample 0.50 of dataset rcv1_CCAT_1prevs finished [took 1021.3535s] +---------------------------------------------------------------------------------------------------- +06/11/23 11:53:50| INFO dataset rcv1_CCAT_9prevs +06/11/23 11:53:56| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +06/11/23 11:56:45| INFO doc_feat finished [took 83.7957s] +06/11/23 11:56:58| INFO mulne_pacc finished [took 146.6577s] +06/11/23 11:57:03| INFO ref finished [took 120.2665s] +06/11/23 11:57:05| INFO mul_pacc finished [took 169.5909s] +06/11/23 11:57:07| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +06/11/23 11:57:08| INFO kfcv finished [took 130.0948s] +06/11/23 11:57:13| INFO mulmc_pacc finished [took 173.0431s] +06/11/23 11:57:16| INFO atc_mc finished [took 125.2363s] +06/11/23 11:57:17| INFO mul_sld finished [took 199.1179s] +06/11/23 11:57:18| INFO mul_cc finished [took 148.2203s] +06/11/23 11:57:20| INFO atc_ne finished [took 121.9570s] +06/11/23 11:57:39| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00899) [took 176.8809s] +06/11/23 11:58:48| INFO mul_pacc_gs finished [took 245.7641s] +06/11/23 12:00:56| INFO bin_pacc finished [took 409.8967s] +06/11/23 12:01:03| INFO bin_sld finished [took 426.0031s] +06/11/23 12:01:09| INFO binmc_pacc finished [took 412.9057s] +06/11/23 12:01:13| INFO bin_cc finished [took 389.4719s] +06/11/23 12:01:14| INFO binne_pacc finished [took 411.1276s] +06/11/23 12:02:20| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 12:03:18| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00719) [took 523.4665s] +06/11/23 12:04:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00644) [took 643.3372s] +06/11/23 12:05:27| INFO mul_sld_gs finished [took 686.5912s] +06/11/23 12:06:25| INFO bin_pacc_gs finished [took 710.5248s] +06/11/23 12:08:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 892.3454s] +06/11/23 12:11:50| INFO bin_sld_gs finished [took 1070.2847s] +06/11/23 12:11:50| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 1073.7689s] +06/11/23 12:11:50| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +06/11/23 12:14:15| INFO doc_feat finished [took 80.2198s] +06/11/23 12:14:27| INFO ref finished [took 106.1446s] +06/11/23 12:14:36| INFO mul_pacc finished [took 155.8715s] +06/11/23 12:14:37| INFO mul_sld finished [took 162.7857s] +06/11/23 12:14:47| INFO kfcv finished [took 132.1178s] +06/11/23 12:14:55| INFO atc_mc finished [took 127.9109s] +06/11/23 12:14:57| INFO atc_ne finished [took 121.6128s] +06/11/23 12:14:58| INFO mulmc_pacc finished [took 173.9023s] +06/11/23 12:14:58| INFO mulne_pacc finished [took 167.2920s] +06/11/23 12:14:59| INFO mul_cc finished [took 147.7428s] +06/11/23 12:15:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00931) [took 186.6087s] +06/11/23 12:16:44| INFO mul_pacc_gs finished [took 261.9352s] +06/11/23 12:18:49| INFO binmc_pacc finished [took 407.0394s] +06/11/23 12:18:50| INFO bin_pacc finished [took 410.4620s] +06/11/23 12:18:55| INFO bin_sld finished [took 422.8949s] +06/11/23 12:19:01| INFO binne_pacc finished [took 410.3575s] +06/11/23 12:19:03| INFO bin_cc finished [took 396.9482s] +06/11/23 12:20:14| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 12:20:41| INFO bin_sld_gsq finished [took 524.4318s] +06/11/23 12:21:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00784) [took 546.2730s] +06/11/23 12:22:36| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01221) [took 640.2364s] +06/11/23 12:23:20| INFO mul_sld_gs finished [took 683.6191s] +06/11/23 12:24:29| INFO bin_pacc_gs finished [took 732.6258s] +06/11/23 12:27:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00768) [took 948.8111s] +06/11/23 12:30:43| INFO bin_sld_gs finished [took 1128.0644s] +06/11/23 12:30:43| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 1132.7995s] +06/11/23 12:30:43| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +06/11/23 12:33:12| INFO mul_sld finished [took 146.3214s] +06/11/23 12:33:24| INFO mul_cc finished [took 118.2992s] +06/11/23 12:33:26| INFO doc_feat finished [took 96.3414s] +06/11/23 12:33:36| INFO atc_ne finished [took 108.2657s] +06/11/23 12:33:37| INFO mulne_pacc finished [took 155.4759s] +06/11/23 12:33:39| INFO atc_mc finished [took 119.0950s] +06/11/23 12:33:39| INFO mul_pacc finished [took 166.1039s] +06/11/23 12:33:40| INFO ref finished [took 122.3921s] +06/11/23 12:33:40| INFO mulmc_pacc finished [took 164.5722s] +06/11/23 12:33:43| INFO kfcv finished [took 131.6124s] +06/11/23 12:34:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00857) [took 188.0301s] +06/11/23 12:35:45| INFO mul_pacc_gs finished [took 269.4655s] +06/11/23 12:37:47| INFO binne_pacc finished [took 409.7038s] +06/11/23 12:37:47| INFO bin_sld finished [took 421.4590s] +06/11/23 12:37:55| INFO bin_pacc finished [took 423.8805s] +06/11/23 12:37:57| INFO binmc_pacc finished [took 422.8180s] +06/11/23 12:38:01| INFO bin_cc finished [took 400.2199s] +06/11/23 12:39:14| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 12:39:41| INFO bin_sld_gsq finished [took 531.7360s] +06/11/23 12:40:17| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00762) [took 549.6726s] +06/11/23 12:41:35| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00872) [took 646.0507s] +06/11/23 12:42:19| INFO mul_sld_gs finished [took 690.7431s] +06/11/23 12:43:25| INFO bin_pacc_gs finished [took 737.2287s] +06/11/23 12:47:07| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00782) [took 979.3211s] +06/11/23 12:50:07| INFO bin_sld_gs finished [took 1159.4207s] +06/11/23 12:50:07| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1163.9370s] +06/11/23 12:50:07| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +06/11/23 12:52:34| INFO doc_feat finished [took 79.3857s] +06/11/23 12:52:39| INFO mul_pacc finished [took 143.2358s] +06/11/23 12:52:49| INFO mul_sld finished [took 159.6160s] +06/11/23 12:52:58| INFO kfcv finished [took 121.1756s] +06/11/23 12:53:07| INFO mulmc_pacc finished [took 167.6154s] +06/11/23 12:53:09| INFO atc_ne finished [took 115.9704s] +06/11/23 12:53:11| INFO ref finished [took 127.9906s] +06/11/23 12:53:17| INFO atc_mc finished [took 129.9605s] +06/11/23 12:53:19| INFO mulne_pacc finished [took 166.1444s] +06/11/23 12:53:21| INFO mul_cc finished [took 152.0451s] +06/11/23 12:53:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00832) [took 183.1015s] +06/11/23 12:55:03| INFO mul_pacc_gs finished [took 261.6088s] +06/11/23 12:57:20| INFO binmc_pacc finished [took 422.6680s] +06/11/23 12:57:23| INFO bin_sld finished [took 434.1109s] +06/11/23 12:57:26| INFO bin_pacc finished [took 431.1893s] +06/11/23 12:57:28| INFO binne_pacc finished [took 427.7980s] +06/11/23 12:57:29| INFO bin_cc finished [took 402.6463s] +06/11/23 12:58:43| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 12:59:20| INFO bin_sld_gsq finished [took 546.8013s] +06/11/23 12:59:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00858) [took 550.8127s] +06/11/23 13:01:04| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01037) [took 652.8483s] +06/11/23 13:01:47| INFO mul_sld_gs finished [took 695.7927s] +06/11/23 13:02:56| INFO bin_pacc_gs finished [took 739.4380s] +06/11/23 13:06:49| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01112) [took 999.0699s] +06/11/23 13:09:50| INFO bin_sld_gs finished [took 1179.8181s] +06/11/23 13:09:50| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1183.4124s] +06/11/23 13:09:50| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +06/11/23 13:12:34| INFO doc_feat finished [took 88.8963s] +06/11/23 13:12:43| INFO mul_sld finished [took 169.3932s] +06/11/23 13:12:47| INFO mul_pacc finished [took 166.5633s] +06/11/23 13:12:50| INFO kfcv finished [took 134.2527s] +06/11/23 13:12:58| INFO ref finished [took 128.7367s] +06/11/23 13:12:59| INFO mulne_pacc finished [took 161.0902s] +06/11/23 13:13:00| INFO mulmc_pacc finished [took 176.8006s] +06/11/23 13:13:01| INFO atc_mc finished [took 129.5173s] +06/11/23 13:13:06| INFO atc_ne finished [took 122.8886s] +06/11/23 13:13:16| INFO mul_cc finished [took 152.5218s] +06/11/23 13:13:34| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00774) [took 183.0928s] +06/11/23 13:14:57| INFO mul_pacc_gs finished [took 266.4369s] +06/11/23 13:17:06| INFO bin_pacc finished [took 427.3693s] +06/11/23 13:17:08| INFO binmc_pacc finished [took 426.6359s] +06/11/23 13:17:14| INFO bin_sld finished [took 441.7834s] +06/11/23 13:17:20| INFO binne_pacc finished [took 435.6569s] +06/11/23 13:17:22| INFO bin_cc finished [took 412.7263s] +06/11/23 13:18:20| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 13:19:06| INFO bin_sld_gsq finished [took 549.0379s] +06/11/23 13:19:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00709) [took 545.0306s] +06/11/23 13:20:41| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00572) [took 645.5669s] +06/11/23 13:21:25| INFO mul_sld_gs finished [took 689.4814s] +06/11/23 13:22:32| INFO bin_pacc_gs finished [took 730.0602s] +06/11/23 13:26:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00653) [took 985.8522s] +06/11/23 13:29:21| INFO bin_sld_gs finished [took 1166.4200s] +06/11/23 13:29:21| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 1170.8744s] +06/11/23 13:29:21| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +06/11/23 13:31:52| INFO doc_feat finished [took 80.3058s] +06/11/23 13:32:00| INFO mul_sld finished [took 153.1261s] +06/11/23 13:32:00| INFO mul_pacc finished [took 148.0156s] +06/11/23 13:32:13| INFO kfcv finished [took 122.2270s] +06/11/23 13:32:13| INFO mulne_pacc finished [took 145.0130s] +06/11/23 13:32:22| INFO ref finished [took 122.3525s] +06/11/23 13:32:23| INFO atc_mc finished [took 120.2587s] +06/11/23 13:32:23| INFO atc_ne finished [took 113.5667s] +06/11/23 13:32:23| INFO mulmc_pacc finished [took 167.7106s] +06/11/23 13:32:36| INFO mul_cc finished [took 143.6517s] +06/11/23 13:33:04| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00802) [took 182.2049s] +06/11/23 13:34:23| INFO mul_pacc_gs finished [took 261.0038s] +06/11/23 13:36:19| INFO binmc_pacc finished [took 405.9199s] +06/11/23 13:36:31| INFO bin_sld finished [took 426.3780s] +06/11/23 13:36:32| INFO bin_pacc finished [took 420.2833s] +06/11/23 13:36:34| INFO binne_pacc finished [took 417.2048s] +06/11/23 13:36:42| INFO bin_cc finished [took 394.3524s] +06/11/23 13:37:45| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 13:38:18| INFO bin_sld_gsq finished [took 528.6956s] +06/11/23 13:38:52| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00707) [took 544.2769s] +06/11/23 13:39:56| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00637) [took 628.0656s] +06/11/23 13:40:41| INFO mul_sld_gs finished [took 673.3968s] +06/11/23 13:41:58| INFO bin_pacc_gs finished [took 730.5371s] +06/11/23 13:45:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00544) [took 960.1061s] +06/11/23 13:48:28| INFO bin_sld_gs finished [took 1140.6073s] +06/11/23 13:48:28| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 1146.6395s] +06/11/23 13:48:28| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +06/11/23 13:50:49| INFO mul_pacc finished [took 130.9408s] +06/11/23 13:51:02| INFO doc_feat finished [took 86.2411s] +06/11/23 13:51:07| INFO mul_sld finished [took 155.4513s] +06/11/23 13:51:15| INFO atc_ne finished [took 101.0761s] +06/11/23 13:51:20| INFO ref finished [took 121.3689s] +06/11/23 13:51:20| INFO atc_mc finished [took 106.6415s] +06/11/23 13:51:22| INFO mulmc_pacc finished [took 160.4221s] +06/11/23 13:51:22| INFO mulne_pacc finished [took 150.1203s] +06/11/23 13:51:25| INFO kfcv finished [took 127.5280s] +06/11/23 13:51:35| INFO mul_cc finished [took 145.4437s] +06/11/23 13:52:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00886) [took 182.0543s] +06/11/23 13:53:23| INFO mul_pacc_gs finished [took 262.2496s] +06/11/23 13:55:28| INFO bin_sld finished [took 417.7315s] +06/11/23 13:55:30| INFO binmc_pacc finished [took 410.0114s] +06/11/23 13:55:30| INFO bin_pacc finished [took 413.5912s] +06/11/23 13:55:35| INFO binne_pacc finished [took 411.8241s] +06/11/23 13:55:42| INFO bin_cc finished [took 396.5011s] +06/11/23 13:56:50| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 13:57:22| INFO bin_sld_gsq finished [took 527.2507s] +06/11/23 13:58:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00910) [took 547.7641s] +06/11/23 13:59:09| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00765) [took 634.8469s] +06/11/23 13:59:54| INFO mul_sld_gs finished [took 680.2027s] +06/11/23 14:01:07| INFO bin_pacc_gs finished [took 731.8655s] +06/11/23 14:04:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01235) [took 960.6861s] +06/11/23 14:07:34| INFO bin_sld_gs finished [took 1141.7199s] +06/11/23 14:07:34| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 1146.2680s] +06/11/23 14:07:34| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +06/11/23 14:10:08| INFO mulmc_pacc finished [took 141.2266s] +06/11/23 14:10:19| INFO atc_ne finished [took 101.4512s] +06/11/23 14:10:20| INFO mul_sld finished [took 162.5808s] +06/11/23 14:10:23| INFO mul_pacc finished [took 158.9068s] +06/11/23 14:10:30| INFO kfcv finished [took 123.4790s] +06/11/23 14:10:33| INFO mulne_pacc finished [took 158.4983s] +06/11/23 14:10:33| INFO doc_feat finished [took 111.7987s] +06/11/23 14:10:35| INFO ref finished [took 124.4184s] +06/11/23 14:10:40| INFO atc_mc finished [took 126.3543s] +06/11/23 14:10:40| INFO mul_cc finished [took 139.5958s] +06/11/23 14:11:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.01309) [took 184.0645s] +06/11/23 14:12:44| INFO mul_pacc_gs finished [took 272.5404s] +06/11/23 14:14:32| INFO binmc_pacc finished [took 406.3609s] +06/11/23 14:14:38| INFO bin_pacc finished [took 414.8972s] +06/11/23 14:14:43| INFO binne_pacc finished [took 414.4123s] +06/11/23 14:14:51| INFO bin_cc finished [took 395.5254s] +06/11/23 14:14:55| INFO bin_sld finished [took 437.7681s] +06/11/23 14:15:58| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 14:16:33| INFO bin_sld_gsq finished [took 532.4040s] +06/11/23 14:16:57| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00812) [took 536.4746s] +06/11/23 14:18:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01306) [took 636.4067s] +06/11/23 14:19:00| INFO mul_sld_gs finished [took 680.2467s] +06/11/23 14:20:01| INFO bin_pacc_gs finished [took 720.9205s] +06/11/23 14:23:33| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00894) [took 954.4487s] +06/11/23 14:26:41| INFO bin_sld_gs finished [took 1142.3328s] +06/11/23 14:26:41| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 1146.7713s] +06/11/23 14:26:41| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +06/11/23 14:29:04| INFO mul_pacc finished [took 133.1736s] +06/11/23 14:29:07| INFO ref finished [took 87.1594s] +06/11/23 14:29:16| INFO doc_feat finished [took 83.8190s] +06/11/23 14:29:21| INFO mulmc_pacc finished [took 147.5202s] +06/11/23 14:29:22| INFO atc_mc finished [took 99.1039s] +06/11/23 14:29:23| INFO kfcv finished [took 109.5348s] +06/11/23 14:29:27| INFO mulne_pacc finished [took 148.1672s] +06/11/23 14:29:33| INFO atc_ne finished [took 101.4673s] +06/11/23 14:29:36| INFO mul_cc finished [took 126.0447s] +06/11/23 14:29:42| INFO mul_sld finished [took 177.5880s] +06/11/23 14:30:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01497) [took 175.1432s] +06/11/23 14:31:33| INFO mul_pacc_gs finished [took 252.3399s] +06/11/23 14:33:34| INFO binmc_pacc finished [took 401.7891s] +06/11/23 14:33:35| INFO binne_pacc finished [took 400.4138s] +06/11/23 14:33:36| INFO bin_pacc finished [took 406.7598s] +06/11/23 14:33:48| INFO bin_cc finished [took 378.7595s] +06/11/23 14:33:48| INFO bin_sld finished [took 423.7366s] +06/11/23 14:34:47| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 14:35:26| INFO bin_sld_gsq finished [took 518.8996s] +06/11/23 14:36:13| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00880) [took 539.3992s] +06/11/23 14:37:09| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00808) [took 622.0419s] +06/11/23 14:37:53| INFO mul_sld_gs finished [took 666.0058s] +06/11/23 14:39:16| INFO bin_pacc_gs finished [took 722.5231s] +06/11/23 14:42:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00604) [took 929.3464s] +06/11/23 14:45:14| INFO bin_sld_gs finished [took 1108.7356s] +06/11/23 14:45:14| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 1113.2364s] +06/11/23 14:48:04| INFO dataset imdb_9prevs +06/11/23 14:48:14| INFO Dataset sample 0.10 of dataset imdb_9prevs started +06/11/23 14:48:17| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 14:48:18| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 14:48:31| INFO doc_feat finished [took 11.5257s] +06/11/23 14:48:36| INFO ref finished [took 18.5620s] +06/11/23 14:48:42| INFO kfcv finished [took 24.7227s] +06/11/23 14:48:44| INFO atc_ne finished [took 25.5676s] +06/11/23 14:48:46| INFO atc_mc finished [took 27.4910s] +06/11/23 14:48:50| INFO mulne_pacc finished [took 34.0415s] +06/11/23 14:48:57| INFO mulmc_pacc finished [took 41.7500s] +06/11/23 14:48:58| INFO mul_pacc finished [took 43.0162s] +06/11/23 14:48:58| INFO mul_cc finished [took 40.3279s] +06/11/23 14:49:16| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +06/11/23 14:49:26| INFO mul_sld finished [took 71.4588s] +06/11/23 14:50:10| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +06/11/23 14:52:12| INFO binne_pacc finished [took 236.2174s] +06/11/23 14:52:16| INFO binmc_pacc finished [took 240.4686s] +06/11/23 14:52:19| INFO bin_cc finished [took 241.9141s] +06/11/23 14:52:20| INFO bin_pacc finished [took 244.5632s] +06/11/23 14:52:23| INFO bin_sld finished [took 249.0477s] +06/11/23 14:53:48| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 14:55:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.03127) [took 443.6010s] +06/11/23 14:55:51| INFO mul_sld_gs finished [took 455.9932s] +06/11/23 14:55:51| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 456.7746s] +06/11/23 14:55:51| INFO Dataset sample 0.20 of dataset imdb_9prevs started +06/11/23 14:56:07| INFO doc_feat finished [took 11.2758s] +06/11/23 14:56:18| INFO atc_mc finished [took 22.2986s] +06/11/23 14:56:22| INFO ref finished [took 26.3482s] +06/11/23 14:56:25| INFO kfcv finished [took 30.4761s] +06/11/23 14:56:29| INFO mul_pacc finished [took 36.5892s] +06/11/23 14:56:29| INFO mulmc_pacc finished [took 36.7773s] +06/11/23 14:56:38| INFO atc_ne finished [took 41.7824s] +06/11/23 14:56:41| INFO mulne_pacc finished [took 47.8318s] +06/11/23 14:56:41| INFO mul_cc finished [took 46.7221s] +06/11/23 14:56:55| INFO mul_sld finished [took 63.3547s] +06/11/23 14:57:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.01017) [took 119.9166s] +06/11/23 14:58:15| INFO mul_pacc_gs finished [took 141.4446s] +06/11/23 15:00:38| INFO binne_pacc finished [took 285.5562s] +06/11/23 15:00:48| INFO bin_cc finished [took 293.8727s] +06/11/23 15:00:49| INFO binmc_pacc finished [took 296.7176s] +06/11/23 15:00:49| INFO bin_pacc finished [took 297.1868s] +06/11/23 15:01:03| INFO bin_sld finished [took 312.0358s] +06/11/23 15:02:29| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 15:02:34| INFO bin_sld_gsq finished [took 402.0748s] +06/11/23 15:03:56| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00980) [took 482.9237s] +06/11/23 15:05:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01194) [took 548.0443s] +06/11/23 15:05:14| INFO mul_sld_gs finished [took 562.2966s] +06/11/23 15:06:30| INFO bin_pacc_gs finished [took 636.7956s] +06/11/23 15:10:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00924) [took 884.9748s] +06/11/23 15:13:11| INFO bin_sld_gs finished [took 1039.3282s] +06/11/23 15:13:11| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 1040.0772s] +06/11/23 15:13:11| INFO Dataset sample 0.30 of dataset imdb_9prevs started +06/11/23 15:13:39| INFO doc_feat finished [took 22.8145s] +06/11/23 15:13:41| INFO atc_ne finished [took 24.3471s] +06/11/23 15:13:45| INFO ref finished [took 28.7559s] +06/11/23 15:13:52| INFO mulne_pacc finished [took 38.1365s] +06/11/23 15:13:53| INFO kfcv finished [took 37.4026s] +06/11/23 15:13:56| INFO atc_mc finished [took 39.4198s] +06/11/23 15:13:59| INFO mul_pacc finished [took 45.9542s] +06/11/23 15:13:59| INFO mul_cc finished [took 43.9076s] +06/11/23 15:13:59| INFO mulmc_pacc finished [took 45.9395s] +06/11/23 15:14:11| INFO mul_sld finished [took 59.8835s] +06/11/23 15:15:23| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01028) [took 128.2866s] +06/11/23 15:15:44| INFO mul_pacc_gs finished [took 149.5820s] +06/11/23 15:18:03| INFO binne_pacc finished [took 289.3504s] +06/11/23 15:18:07| INFO bin_pacc finished [took 294.7115s] +06/11/23 15:18:14| INFO bin_cc finished [took 298.6839s] +06/11/23 15:18:14| INFO binmc_pacc finished [took 300.9499s] +06/11/23 15:18:14| INFO bin_sld finished [took 302.9035s] +06/11/23 15:19:46| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 15:20:05| INFO bin_sld_gsq finished [took 413.1151s] +06/11/23 15:21:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.00916) [took 488.7327s] +06/11/23 15:22:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00953) [took 541.2865s] +06/11/23 15:22:28| INFO mul_sld_gs finished [took 556.0867s] +06/11/23 15:23:57| INFO bin_pacc_gs finished [took 643.0717s] +06/11/23 15:27:32| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01064) [took 860.3135s] +06/11/23 15:30:05| INFO bin_sld_gs finished [took 1013.1878s] +06/11/23 15:30:05| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 1014.3141s] +06/11/23 15:30:05| INFO Dataset sample 0.40 of dataset imdb_9prevs started +06/11/23 15:30:24| INFO doc_feat finished [took 13.8500s] +06/11/23 15:30:32| INFO ref finished [took 22.3531s] +06/11/23 15:30:41| INFO mul_pacc finished [took 34.1860s] +06/11/23 15:30:45| INFO atc_ne finished [took 34.8111s] +06/11/23 15:30:46| INFO kfcv finished [took 36.4055s] +06/11/23 15:30:49| INFO atc_mc finished [took 38.7978s] +06/11/23 15:30:49| INFO mulmc_pacc finished [took 42.4552s] +06/11/23 15:30:51| INFO mul_cc finished [took 42.6899s] +06/11/23 15:30:53| INFO mulne_pacc finished [took 45.2694s] +06/11/23 15:30:57| INFO mul_sld finished [took 51.2705s] +06/11/23 15:32:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01220) [took 124.5801s] +06/11/23 15:32:34| INFO mul_pacc_gs finished [took 145.3368s] +06/11/23 15:34:56| INFO binmc_pacc finished [took 289.1451s] +06/11/23 15:35:04| INFO bin_sld finished [took 298.3514s] +06/11/23 15:35:04| INFO binne_pacc finished [took 296.5538s] +06/11/23 15:35:05| INFO bin_pacc finished [took 298.5077s] +06/11/23 15:35:09| INFO bin_cc finished [took 300.1332s] +06/11/23 15:36:41| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 15:37:08| INFO bin_sld_gsq finished [took 421.3938s] +06/11/23 15:38:19| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01137) [took 490.9644s] +06/11/23 15:38:58| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01029) [took 531.8225s] +06/11/23 15:39:12| INFO mul_sld_gs finished [took 546.4524s] +06/11/23 15:40:53| INFO bin_pacc_gs finished [took 645.0957s] +06/11/23 15:44:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01176) [took 882.0550s] +06/11/23 15:47:19| INFO bin_sld_gs finished [took 1033.2802s] +06/11/23 15:47:19| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 1034.1241s] +06/11/23 15:47:19| INFO Dataset sample 0.50 of dataset imdb_9prevs started +06/11/23 15:47:36| INFO doc_feat finished [took 11.6005s] +06/11/23 15:47:40| INFO ref finished [took 16.3058s] +06/11/23 15:47:50| INFO atc_mc finished [took 25.8745s] +06/11/23 15:47:52| INFO kfcv finished [took 29.0931s] +06/11/23 15:47:53| INFO atc_ne finished [took 28.8903s] +06/11/23 15:47:53| INFO mul_pacc finished [took 32.5473s] +06/11/23 15:48:00| INFO mul_cc finished [took 37.3478s] +06/11/23 15:48:01| INFO mulne_pacc finished [took 39.9745s] +06/11/23 15:48:02| INFO mulmc_pacc finished [took 40.5057s] +06/11/23 15:48:10| INFO mul_sld finished [took 50.1825s] +06/11/23 15:49:28| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01107) [took 125.0329s] +06/11/23 15:49:49| INFO mul_pacc_gs finished [took 146.7316s] +06/11/23 15:52:15| INFO bin_cc finished [took 292.6719s] +06/11/23 15:52:15| INFO binne_pacc finished [took 293.9844s] +06/11/23 15:52:17| INFO bin_pacc finished [took 296.2830s] +06/11/23 15:52:21| INFO binmc_pacc finished [took 299.4873s] +06/11/23 15:52:23| INFO bin_sld finished [took 303.4889s] +06/11/23 15:53:57| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 15:54:18| INFO bin_sld_gsq finished [took 418.0959s] +06/11/23 15:55:32| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01038) [took 489.7797s] +06/11/23 15:56:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00885) [took 536.7408s] +06/11/23 15:56:33| INFO mul_sld_gs finished [took 552.5393s] +06/11/23 15:58:05| INFO bin_pacc_gs finished [took 643.1581s] +06/11/23 16:01:42| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00940) [took 862.6012s] +06/11/23 16:04:15| INFO bin_sld_gs finished [took 1015.3606s] +06/11/23 16:04:15| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 1016.0642s] +06/11/23 16:04:15| INFO Dataset sample 0.60 of dataset imdb_9prevs started +06/11/23 16:04:40| INFO doc_feat finished [took 19.9628s] +06/11/23 16:04:41| INFO kfcv finished [took 21.8848s] +06/11/23 16:04:46| INFO ref finished [took 26.2613s] +06/11/23 16:04:56| INFO mulmc_pacc finished [took 38.6399s] +06/11/23 16:04:56| INFO atc_ne finished [took 35.7501s] +06/11/23 16:04:57| INFO atc_mc finished [took 37.3907s] +06/11/23 16:05:01| INFO mul_cc finished [took 41.6420s] +06/11/23 16:05:01| INFO mul_pacc finished [took 44.6898s] +06/11/23 16:05:02| INFO mulne_pacc finished [took 44.7679s] +06/11/23 16:05:12| INFO mul_sld finished [took 56.0834s] +06/11/23 16:06:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01082) [took 125.2569s] +06/11/23 16:06:44| INFO mul_pacc_gs finished [took 146.2318s] +06/11/23 16:09:05| INFO binne_pacc finished [took 288.1949s] +06/11/23 16:09:10| INFO bin_pacc finished [took 293.3207s] +06/11/23 16:09:12| INFO bin_sld finished [took 296.1022s] +06/11/23 16:09:13| INFO binmc_pacc finished [took 296.4000s] +06/11/23 16:09:18| INFO bin_cc finished [took 299.1982s] +06/11/23 16:10:50| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 16:11:22| INFO bin_sld_gsq finished [took 425.6641s] +06/11/23 16:12:31| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00976) [took 492.8847s] +06/11/23 16:13:13| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00984) [took 536.8669s] +06/11/23 16:13:28| INFO mul_sld_gs finished [took 551.6187s] +06/11/23 16:15:03| INFO bin_pacc_gs finished [took 645.6602s] +06/11/23 16:19:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01142) [took 907.7074s] +06/11/23 16:21:57| INFO bin_sld_gs finished [took 1060.9759s] +06/11/23 16:21:57| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 1061.7730s] +06/11/23 16:21:57| INFO Dataset sample 0.70 of dataset imdb_9prevs started +06/11/23 16:22:23| INFO doc_feat finished [took 20.2428s] +06/11/23 16:22:34| INFO kfcv finished [took 32.1532s] +06/11/23 16:22:36| INFO ref finished [took 34.3738s] +06/11/23 16:22:38| INFO mul_sld finished [took 40.1101s] +06/11/23 16:22:40| INFO mul_cc finished [took 38.6722s] +06/11/23 16:22:41| INFO atc_mc finished [took 38.9379s] +06/11/23 16:22:43| INFO atc_ne finished [took 40.3132s] +06/11/23 16:22:43| INFO mulne_pacc finished [took 43.7833s] +06/11/23 16:22:44| INFO mulmc_pacc finished [took 44.4084s] +06/11/23 16:22:46| INFO mul_pacc finished [took 47.7998s] +06/11/23 16:24:08| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01575) [took 127.2393s] +06/11/23 16:24:31| INFO mul_pacc_gs finished [took 150.2100s] +06/11/23 16:26:49| INFO bin_cc finished [took 288.6128s] +06/11/23 16:26:51| INFO bin_pacc finished [took 292.1757s] +06/11/23 16:26:52| INFO binne_pacc finished [took 293.0194s] +06/11/23 16:27:01| INFO binmc_pacc finished [took 302.5703s] +06/11/23 16:27:01| INFO bin_sld finished [took 303.9303s] +06/11/23 16:28:32| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 16:28:53| INFO bin_sld_gsq finished [took 414.4520s] +06/11/23 16:30:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01641) [took 494.7681s] +06/11/23 16:31:00| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01102) [took 542.3282s] +06/11/23 16:31:15| INFO mul_sld_gs finished [took 557.2859s] +06/11/23 16:32:49| INFO bin_pacc_gs finished [took 648.9428s] +06/11/23 16:36:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.02196) [took 864.7237s] +06/11/23 16:38:54| INFO bin_sld_gs finished [took 1015.9618s] +06/11/23 16:38:54| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 1016.7743s] +06/11/23 16:38:54| INFO Dataset sample 0.80 of dataset imdb_9prevs started +06/11/23 16:39:19| INFO doc_feat finished [took 19.9639s] +06/11/23 16:39:22| INFO atc_mc finished [took 22.9650s] +06/11/23 16:39:26| INFO kfcv finished [took 27.9671s] +06/11/23 16:39:30| INFO mul_pacc finished [took 34.3899s] +06/11/23 16:39:31| INFO ref finished [took 32.4692s] +06/11/23 16:39:33| INFO mulne_pacc finished [took 37.2045s] +06/11/23 16:39:39| INFO atc_ne finished [took 39.7686s] +06/11/23 16:39:41| INFO mul_cc finished [took 42.9411s] +06/11/23 16:39:41| INFO mulmc_pacc finished [took 44.9724s] +06/11/23 16:39:46| INFO mul_sld finished [took 51.4269s] +06/11/23 16:40:59| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01013) [took 122.2196s] +06/11/23 16:41:24| INFO mul_pacc_gs finished [took 146.7076s] +06/11/23 16:43:40| INFO binne_pacc finished [took 284.1154s] +06/11/23 16:43:52| INFO bin_pacc finished [took 296.8885s] +06/11/23 16:43:54| INFO bin_cc finished [took 297.1714s] +06/11/23 16:43:56| INFO binmc_pacc finished [took 300.6806s] +06/11/23 16:43:57| INFO bin_sld finished [took 302.6966s] +06/11/23 16:45:26| WARNING Method mul_sld_gsq failed. Exception: 'GridSearchQ' object has no attribute 'classes_' +06/11/23 16:45:41| INFO bin_sld_gsq finished [took 405.8247s] +06/11/23 16:47:00| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00949) [took 483.3129s] +06/11/23 16:47:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00785) [took 539.6504s] +06/11/23 16:48:09| INFO mul_sld_gs finished [took 553.8401s] +06/11/23 16:49:34| INFO bin_pacc_gs finished [took 637.2772s] +06/11/23 16:53:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01449) [took 875.8870s] +06/11/23 16:56:08| INFO bin_sld_gs finished [took 1033.4325s] +06/11/23 16:56:08| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 1034.1983s] +06/11/23 16:56:08| INFO Dataset sample 0.90 of dataset imdb_9prevs started +06/11/23 16:56:09| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:09| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method mulmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method mulne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:10| WARNING Method binmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:11| WARNING Method binne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:11| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 16:56:22| INFO doc_feat finished [took 10.1613s] +06/11/23 16:56:25| INFO ref finished [took 13.7569s] +06/11/23 16:56:27| INFO kfcv finished [took 15.6337s] +06/11/23 16:56:29| INFO atc_mc finished [took 18.0104s] +06/11/23 16:56:30| INFO atc_ne finished [took 18.0260s] +06/11/23 16:56:31| INFO mul_cc finished [took 20.6201s] +06/11/23 16:56:40| INFO mul_sld finished [took 31.2942s] +06/11/23 16:56:47| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 16:58:55| INFO bin_cc finished [took 164.5182s] +06/11/23 16:58:59| INFO bin_sld finished [took 170.5046s] +06/11/23 17:02:17| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.04178) [took 368.6067s] +06/11/23 17:02:29| INFO mul_sld_gs finished [took 380.7801s] +06/11/23 17:02:29| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 381.5305s] +---------------------------------------------------------------------------------------------------- +06/11/23 18:04:06| INFO dataset rcv1_GCAT_9prevs +06/11/23 18:04:12| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs started +06/11/23 18:04:19| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:21| WARNING Method bin_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:22| WARNING Method mul_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:24| WARNING Method binmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:24| WARNING Method mulmc_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:26| WARNING Method binne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:27| WARNING Method mulne_pacc failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:28| WARNING Method bin_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:04:29| WARNING Method mul_pacc_gs failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:05:10| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +06/11/23 18:05:41| INFO ref finished [took 65.3048s] +06/11/23 18:05:43| INFO kfcv finished [took 66.9585s] +06/11/23 18:05:45| INFO doc_feat finished [took 56.7504s] +06/11/23 18:05:49| INFO mul_cc finished [took 77.6035s] +06/11/23 18:05:49| INFO atc_mc finished [took 66.4650s] +06/11/23 18:05:52| INFO atc_ne finished [took 65.1035s] +06/11/23 18:05:52| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +06/11/23 18:05:56| INFO mul_sld finished [took 101.3427s] +06/11/23 18:08:12| INFO bin_sld finished [took 238.6323s] +06/11/23 18:08:21| INFO bin_cc finished [took 230.3034s] +06/11/23 18:10:59| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00897) [took 402.3046s] +06/11/23 18:11:39| INFO mul_sld_gs finished [took 441.7473s] +06/11/23 18:11:39| INFO Dataset sample 0.10 of dataset rcv1_GCAT_9prevs finished [took 446.6543s] +06/11/23 18:11:39| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs started +06/11/23 18:14:11| INFO mulmc_pacc finished [took 140.5240s] +06/11/23 18:14:13| INFO kfcv finished [took 108.3325s] +06/11/23 18:14:16| INFO doc_feat finished [took 91.1407s] +06/11/23 18:14:21| INFO atc_ne finished [took 96.9645s] +06/11/23 18:14:22| INFO mul_pacc finished [took 154.9757s] +06/11/23 18:14:36| INFO ref finished [took 118.1583s] +06/11/23 18:14:37| INFO atc_mc finished [took 118.5016s] +06/11/23 18:14:41| INFO mulne_pacc finished [took 157.8831s] +06/11/23 18:14:49| INFO mul_cc finished [took 144.8053s] +06/11/23 18:14:50| INFO mul_sld finished [took 188.8450s] +06/11/23 18:15:19| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01452) [took 184.0929s] +06/11/23 18:16:33| INFO mul_pacc_gs finished [took 258.2234s] +06/11/23 18:18:46| INFO binmc_pacc finished [took 417.1372s] +06/11/23 18:18:48| INFO bin_pacc finished [took 422.0619s] +06/11/23 18:18:52| INFO bin_sld finished [took 431.4426s] +06/11/23 18:18:56| INFO binne_pacc finished [took 421.5812s] +06/11/23 18:19:02| INFO bin_cc finished [took 402.4673s] +06/11/23 18:19:32| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 18:20:26| INFO bin_sld_gsq finished [took 522.0734s] +06/11/23 18:21:11| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01038) [took 540.4022s] +06/11/23 18:21:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00821) [took 600.6611s] +06/11/23 18:22:25| INFO mul_sld_gs finished [took 642.1063s] +06/11/23 18:24:14| INFO bin_pacc_gs finished [took 723.2605s] +06/11/23 18:26:54| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00816) [took 911.5066s] +06/11/23 18:29:56| INFO bin_sld_gs finished [took 1093.4674s] +06/11/23 18:29:56| INFO Dataset sample 0.20 of dataset rcv1_GCAT_9prevs finished [took 1096.7184s] +06/11/23 18:29:56| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs started +06/11/23 18:32:21| INFO ref finished [took 89.3355s] +06/11/23 18:32:33| INFO doc_feat finished [took 91.9119s] +06/11/23 18:32:35| INFO mulmc_pacc finished [took 147.2084s] +06/11/23 18:32:38| INFO mulne_pacc finished [took 137.0643s] +06/11/23 18:32:54| INFO atc_mc finished [took 117.6847s] +06/11/23 18:32:56| INFO kfcv finished [took 129.8598s] +06/11/23 18:33:00| INFO mul_pacc finished [took 174.5769s] +06/11/23 18:33:00| INFO mul_sld finished [took 181.1734s] +06/11/23 18:33:03| INFO atc_ne finished [took 123.9984s] +06/11/23 18:33:09| INFO mul_cc finished [took 148.8635s] +06/11/23 18:33:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00629) [took 177.7598s] +06/11/23 18:34:51| INFO mul_pacc_gs finished [took 256.4186s] +06/11/23 18:37:10| INFO bin_pacc finished [took 425.7912s] +06/11/23 18:37:12| INFO binmc_pacc finished [took 425.7599s] +06/11/23 18:37:14| INFO binne_pacc finished [took 424.0101s] +06/11/23 18:37:18| INFO bin_sld finished [took 440.4389s] +06/11/23 18:37:22| INFO bin_cc finished [took 407.2413s] +06/11/23 18:37:52| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 18:38:51| INFO bin_sld_gsq finished [took 529.6242s] +06/11/23 18:39:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00489) [took 541.8062s] +06/11/23 18:40:02| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00615) [took 601.7630s] +06/11/23 18:40:45| INFO mul_sld_gs finished [took 644.5111s] +06/11/23 18:42:37| INFO bin_pacc_gs finished [took 729.3942s] +06/11/23 18:45:36| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00490) [took 936.3088s] +06/11/23 18:48:37| INFO bin_sld_gs finished [took 1117.0610s] +06/11/23 18:48:37| INFO Dataset sample 0.30 of dataset rcv1_GCAT_9prevs finished [took 1120.9681s] +06/11/23 18:48:37| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs started +06/11/23 18:51:02| INFO doc_feat finished [took 79.6380s] +06/11/23 18:51:20| INFO mulne_pacc finished [took 144.5625s] +06/11/23 18:51:30| INFO mul_sld finished [took 171.1473s] +06/11/23 18:51:35| INFO mulmc_pacc finished [took 166.1468s] +06/11/23 18:51:39| INFO mul_pacc finished [took 172.9449s] +06/11/23 18:51:43| INFO ref finished [took 132.2492s] +06/11/23 18:51:45| INFO kfcv finished [took 137.9538s] +06/11/23 18:51:52| INFO atc_mc finished [took 137.7185s] +06/11/23 18:51:54| INFO atc_ne finished [took 134.1066s] +06/11/23 18:51:59| INFO mul_cc finished [took 159.0670s] +06/11/23 18:52:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01049) [took 180.3366s] +06/11/23 18:53:38| INFO mul_pacc_gs finished [took 266.6075s] +06/11/23 18:56:00| INFO bin_sld finished [took 441.9022s] +06/11/23 18:56:02| INFO binne_pacc finished [took 431.1354s] +06/11/23 18:56:02| INFO binmc_pacc finished [took 434.5268s] +06/11/23 18:56:04| INFO bin_pacc finished [took 438.8400s] +06/11/23 18:56:07| INFO bin_cc finished [took 412.8827s] +06/11/23 18:56:38| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 18:57:38| INFO bin_sld_gsq finished [took 534.9970s] +06/11/23 18:58:15| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00762) [took 549.5790s] +06/11/23 18:58:58| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00692) [took 616.7506s] +06/11/23 18:59:43| INFO mul_sld_gs finished [took 661.6976s] +06/11/23 19:01:20| INFO bin_pacc_gs finished [took 735.1934s] +06/11/23 19:04:29| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00601) [took 948.3129s] +06/11/23 19:07:30| INFO bin_sld_gs finished [took 1129.1432s] +06/11/23 19:07:30| INFO Dataset sample 0.40 of dataset rcv1_GCAT_9prevs finished [took 1133.2853s] +06/11/23 19:07:30| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs started +06/11/23 19:10:11| INFO doc_feat finished [took 90.8028s] +06/11/23 19:10:12| INFO mul_sld finished [took 159.3725s] +06/11/23 19:10:17| INFO atc_mc finished [took 108.3872s] +06/11/23 19:10:20| INFO mulmc_pacc finished [took 158.7937s] +06/11/23 19:10:27| INFO kfcv finished [took 125.4384s] +06/11/23 19:10:32| INFO mul_cc finished [took 134.1449s] +06/11/23 19:10:33| INFO atc_ne finished [took 115.0137s] +06/11/23 19:10:33| INFO mul_pacc finished [took 173.4398s] +06/11/23 19:10:35| INFO ref finished [took 127.6900s] +06/11/23 19:10:35| INFO mulne_pacc finished [took 158.1989s] +06/11/23 19:11:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00893) [took 181.2645s] +06/11/23 19:12:28| INFO mul_pacc_gs finished [took 263.1619s] +06/11/23 19:14:45| INFO bin_sld finished [took 432.8401s] +06/11/23 19:14:48| INFO bin_pacc finished [took 430.1210s] +06/11/23 19:14:54| INFO binmc_pacc finished [took 433.8715s] +06/11/23 19:14:58| INFO bin_cc finished [took 405.7688s] +06/11/23 19:14:59| INFO binne_pacc finished [took 435.7315s] +06/11/23 19:15:29| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 19:16:36| INFO bin_sld_gsq finished [took 539.4078s] +06/11/23 19:17:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00831) [took 545.8362s] +06/11/23 19:17:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.001, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00797) [took 609.7895s] +06/11/23 19:18:32| INFO mul_sld_gs finished [took 657.1765s] +06/11/23 19:20:08| INFO bin_pacc_gs finished [took 728.9184s] +06/11/23 19:23:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00738) [took 966.8750s] +06/11/23 19:26:42| INFO bin_sld_gs finished [took 1148.2428s] +06/11/23 19:26:42| INFO Dataset sample 0.50 of dataset rcv1_GCAT_9prevs finished [took 1152.4463s] +06/11/23 19:26:43| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs started +06/11/23 19:29:16| INFO mul_pacc finished [took 142.3375s] +06/11/23 19:29:26| INFO doc_feat finished [took 92.0123s] +06/11/23 19:29:29| INFO atc_ne finished [took 96.1697s] +06/11/23 19:29:32| INFO mul_sld finished [took 164.7852s] +06/11/23 19:29:33| INFO ref finished [took 114.3664s] +06/11/23 19:29:36| INFO kfcv finished [took 118.4300s] +06/11/23 19:29:37| INFO atc_mc finished [took 111.6950s] +06/11/23 19:29:37| INFO mul_cc finished [took 127.8860s] +06/11/23 19:29:39| INFO mulmc_pacc finished [took 162.5217s] +06/11/23 19:29:46| INFO mulne_pacc finished [took 159.7535s] +06/11/23 19:30:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00832) [took 183.2267s] +06/11/23 19:31:42| INFO mul_pacc_gs finished [took 263.2959s] +06/11/23 19:33:48| INFO bin_pacc finished [took 415.7355s] +06/11/23 19:33:49| INFO binne_pacc finished [took 411.7032s] +06/11/23 19:33:49| INFO bin_sld finished [took 423.6935s] +06/11/23 19:33:56| INFO binmc_pacc finished [took 422.0731s] +06/11/23 19:34:02| INFO bin_cc finished [took 394.8074s] +06/11/23 19:34:45| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 19:35:33| INFO bin_sld_gsq finished [took 523.6794s] +06/11/23 19:36:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': 'max_conf'} (score=0.00651) [took 539.0149s] +06/11/23 19:36:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00646) [took 605.4721s] +06/11/23 19:37:37| INFO mul_sld_gs finished [took 647.9998s] +06/11/23 19:39:13| INFO bin_pacc_gs finished [took 722.3065s] +06/11/23 19:42:14| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00635) [took 926.2548s] +06/11/23 19:45:13| INFO bin_sld_gs finished [took 1105.8303s] +06/11/23 19:45:13| INFO Dataset sample 0.60 of dataset rcv1_GCAT_9prevs finished [took 1110.5345s] +06/11/23 19:45:13| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs started +06/11/23 19:47:55| INFO mul_pacc finished [took 151.2717s] +06/11/23 19:48:01| INFO mul_sld finished [took 164.7303s] +06/11/23 19:48:02| INFO doc_feat finished [took 95.7218s] +06/11/23 19:48:20| INFO kfcv finished [took 132.6414s] +06/11/23 19:48:26| INFO ref finished [took 136.4855s] +06/11/23 19:48:27| INFO mulmc_pacc finished [took 180.2510s] +06/11/23 19:48:30| INFO mulne_pacc finished [took 173.6996s] +06/11/23 19:48:33| INFO atc_mc finished [took 135.5939s] +06/11/23 19:48:34| INFO atc_ne finished [took 129.3719s] +06/11/23 19:48:35| INFO mul_cc finished [took 153.9587s] +06/11/23 19:48:47| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00782) [took 177.0918s] +06/11/23 19:50:13| INFO mul_pacc_gs finished [took 262.7047s] +06/11/23 19:52:34| INFO bin_pacc finished [took 431.9695s] +06/11/23 19:52:36| INFO binmc_pacc finished [took 430.1566s] +06/11/23 19:52:43| INFO bin_sld finished [took 446.4452s] +06/11/23 19:52:44| INFO bin_cc finished [took 407.8850s] +06/11/23 19:52:47| INFO binne_pacc finished [took 438.4423s] +06/11/23 19:53:19| WARNING Method mul_sld_gsq failed. Exception: 'function' object has no attribute 'classes_' +06/11/23 19:54:09| INFO bin_sld_gsq finished [took 528.9254s] +06/11/23 19:54:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00669) [took 545.8164s] +06/11/23 19:55:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00814) [took 619.0258s] +06/11/23 19:56:21| INFO mul_sld_gs finished [took 661.4303s] +06/11/23 19:57:51| INFO bin_pacc_gs finished [took 733.3970s] +06/11/23 20:00:54| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00795) [took 935.9973s] +06/11/23 20:03:55| INFO bin_sld_gs finished [took 1117.3981s] +06/11/23 20:03:55| INFO Dataset sample 0.70 of dataset rcv1_GCAT_9prevs finished [took 1121.8060s] +06/11/23 20:03:55| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs started +06/11/23 20:06:27| INFO mul_sld finished [took 147.0727s] +06/11/23 20:06:30| INFO doc_feat finished [took 81.1117s] +06/11/23 20:06:48| INFO mul_pacc finished [took 162.2312s] +06/11/23 20:07:04| INFO kfcv finished [took 133.4389s] +06/11/23 20:07:04| INFO ref finished [took 132.6728s] +06/11/23 20:07:05| INFO mulne_pacc finished [took 171.0782s] +06/11/23 20:07:08| INFO mulmc_pacc finished [took 179.6909s] +06/11/23 20:07:10| INFO atc_mc finished [took 130.5941s] +06/11/23 20:07:14| INFO mul_cc finished [took 150.3795s] +06/11/23 20:07:15| INFO atc_ne finished [took 131.0309s] +06/11/23 20:07:29| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00813) [took 177.7289s] +06/11/23 20:08:49| INFO mul_pacc_gs finished [took 257.9675s] +06/11/23 20:10:44| INFO bin_pacc finished [took 399.4800s] +06/11/23 21:01:51| INFO bin_pacc_gs finished [took 3446.1854s] +06/11/23 21:03:24| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00684) [took 3563.0699s] +06/11/23 21:04:07| INFO mul_sld_gs finished [took 3606.0194s] +06/11/23 21:08:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00519) [took 3863.9570s] +06/11/23 21:11:26| INFO bin_sld_gs finished [took 4046.4500s] +06/11/23 21:11:26| INFO Dataset sample 0.80 of dataset rcv1_GCAT_9prevs finished [took 4051.2016s] +06/11/23 21:11:26| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs started +06/11/23 21:11:31| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:32| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:34| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:35| WARNING Method mul_sld_gsq failed. Exception: a must be greater than 0 unless no samples are taken +06/11/23 21:11:36| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:38| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 21:11:38| WARNING Method binmc_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:40| WARNING Method mulmc_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 21:11:40| WARNING Method binne_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:42| WARNING Method mulne_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 21:11:42| WARNING Method bin_pacc_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:11:44| WARNING Method mul_pacc_gs failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 21:11:44| WARNING Method bin_cc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 21:12:27| INFO mul_sld finished [took 56.8958s] +06/11/23 21:12:32| INFO ref finished [took 44.3311s] +06/11/23 21:12:32| INFO doc_feat finished [took 41.1551s] +06/11/23 21:12:33| INFO kfcv finished [took 46.1873s] +06/11/23 21:12:36| INFO atc_mc finished [took 47.9541s] +06/11/23 21:12:37| INFO mul_cc finished [took 51.8838s] +06/11/23 21:12:37| INFO atc_ne finished [took 47.4962s] +06/11/23 21:16:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00843) [took 312.8612s] +06/11/23 21:17:24| INFO mul_sld_gs finished [took 351.5693s] +06/11/23 21:17:24| INFO Dataset sample 0.90 of dataset rcv1_GCAT_9prevs finished [took 357.7321s] +06/11/23 21:20:10| INFO dataset rcv1_MCAT_9prevs +06/11/23 21:20:18| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs started +06/11/23 21:23:12| INFO doc_feat finished [took 81.2849s] +06/11/23 21:23:21| INFO mul_pacc finished [took 168.3242s] +06/11/23 21:23:30| INFO mulmc_pacc finished [took 173.2730s] +06/11/23 21:23:35| INFO atc_mc finished [took 115.3803s] +06/11/23 21:23:41| INFO ref finished [took 125.5611s] +06/11/23 21:23:41| INFO kfcv finished [took 136.3040s] +06/11/23 21:23:51| INFO mulne_pacc finished [took 185.3346s] +06/11/23 21:23:58| INFO atc_ne finished [took 129.7752s] +06/11/23 21:23:59| INFO mul_cc finished [took 164.3501s] +06/11/23 21:24:26| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'confidence': 'entropy'} (score=0.02036) [took 203.9839s] +06/11/23 21:24:32| INFO mul_sld finished [took 249.6979s] +06/11/23 21:25:50| INFO mul_pacc_gs finished [took 287.7634s] +06/11/23 21:28:08| INFO binne_pacc finished [took 443.1314s] +06/11/23 21:28:11| INFO bin_cc finished [took 427.7416s] +06/11/23 21:28:26| INFO bin_pacc finished [took 475.7859s] +06/11/23 21:28:28| INFO binmc_pacc finished [took 472.2702s] +06/11/23 21:28:33| INFO bin_sld finished [took 492.0457s] +06/11/23 21:29:19| INFO mul_sld_gsq finished [took 529.8190s] +06/11/23 21:29:26| INFO bin_sld_gsq finished [took 539.2552s] +06/11/23 21:30:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00591) [took 573.3773s] +06/11/23 21:31:27| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00754) [took 661.4704s] +06/11/23 21:32:10| INFO mul_sld_gs finished [took 704.6441s] +06/11/23 21:33:40| INFO bin_pacc_gs finished [took 763.3541s] +06/11/23 21:36:30| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00760) [took 965.4559s] +06/11/23 21:39:31| INFO bin_sld_gs finished [took 1146.5622s] +06/11/23 21:39:31| INFO Dataset sample 0.10 of dataset rcv1_MCAT_9prevs finished [took 1152.1700s] +06/11/23 21:39:31| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs started +06/11/23 21:42:13| INFO doc_feat finished [took 88.9970s] +06/11/23 21:42:24| INFO mul_pacc finished [took 161.9999s] +06/11/23 21:42:31| INFO mulmc_pacc finished [took 159.0109s] +06/11/23 21:42:34| INFO mul_sld finished [took 179.5397s] +06/11/23 21:42:42| INFO kfcv finished [took 138.3784s] +06/11/23 21:42:42| INFO atc_mc finished [took 127.5150s] +06/11/23 21:42:45| INFO ref finished [took 133.9163s] +06/11/23 21:42:48| INFO mulne_pacc finished [took 170.3191s] +06/11/23 21:42:53| INFO mul_cc finished [took 153.9952s] +06/11/23 21:42:57| INFO atc_ne finished [took 133.9857s] +06/11/23 21:43:07| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.01406) [took 179.2817s] +06/11/23 21:44:27| INFO mul_pacc_gs finished [took 259.4430s] +06/11/23 21:46:57| INFO bin_pacc finished [took 435.9586s] +06/11/23 21:47:02| INFO binmc_pacc finished [took 436.7170s] +06/11/23 21:47:02| INFO bin_sld finished [took 448.6901s] +06/11/23 21:47:03| INFO binne_pacc finished [took 430.3933s] +06/11/23 21:47:06| INFO bin_cc finished [took 413.3717s] +06/11/23 21:47:44| INFO mul_sld_gsq finished [took 485.8565s] +06/11/23 21:48:40| INFO bin_sld_gsq finished [took 542.6385s] +06/11/23 21:49:21| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01029) [took 558.1111s] +06/11/23 21:49:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00862) [took 617.7204s] +06/11/23 21:50:38| INFO mul_sld_gs finished [took 661.3619s] +06/11/23 21:52:31| INFO bin_pacc_gs finished [took 747.8605s] +06/11/23 21:55:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00631) [took 947.6305s] +06/11/23 21:58:25| INFO bin_sld_gs finished [took 1128.9705s] +06/11/23 21:58:25| INFO Dataset sample 0.20 of dataset rcv1_MCAT_9prevs finished [took 1133.8038s] +06/11/23 21:58:25| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs started +06/11/23 22:01:11| INFO doc_feat finished [took 91.6284s] +06/11/23 22:01:13| INFO mul_pacc finished [took 157.8979s] +06/11/23 22:01:21| INFO ref finished [took 117.5064s] +06/11/23 22:01:29| INFO mulmc_pacc finished [took 171.3367s] +06/11/23 22:01:34| INFO kfcv finished [took 138.8623s] +06/11/23 22:01:44| INFO atc_ne finished [took 127.4515s] +06/11/23 22:01:45| INFO mulne_pacc finished [took 175.7659s] +06/11/23 22:01:45| INFO atc_mc finished [took 134.5717s] +06/11/23 22:01:47| INFO mul_sld finished [took 198.7132s] +06/11/23 22:01:53| INFO mul_cc finished [took 156.7010s] +06/11/23 22:01:56| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00920) [took 169.8061s] +06/11/23 22:03:15| INFO mul_pacc_gs finished [took 248.7885s] +06/11/23 22:05:47| INFO bin_pacc finished [took 433.9454s] +06/11/23 22:05:52| INFO binmc_pacc finished [took 435.7566s] +06/11/23 22:05:55| INFO binne_pacc finished [took 432.5216s] +06/11/23 22:06:02| INFO bin_sld finished [took 455.4425s] +06/11/23 22:06:03| INFO bin_cc finished [took 409.9712s] +06/11/23 22:06:43| INFO mul_sld_gsq finished [took 490.7571s] +06/11/23 22:07:34| INFO bin_sld_gsq finished [took 542.4371s] +06/11/23 22:08:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': None} (score=0.00585) [took 557.7911s] +06/11/23 22:08:29| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 598.8338s] +06/11/23 22:09:12| INFO mul_sld_gs finished [took 641.4050s] +06/11/23 22:11:15| INFO bin_pacc_gs finished [took 742.8423s] +06/11/23 22:14:10| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00443) [took 940.7448s] +06/11/23 22:17:12| INFO bin_sld_gs finished [took 1122.5660s] +06/11/23 22:17:12| INFO Dataset sample 0.30 of dataset rcv1_MCAT_9prevs finished [took 1126.9865s] +06/11/23 22:17:12| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs started +06/11/23 22:19:32| INFO ref finished [took 85.6333s] +06/11/23 22:19:43| INFO mulmc_pacc finished [took 138.3833s] +06/11/23 22:19:44| INFO doc_feat finished [took 84.2844s] +06/11/23 22:19:54| INFO atc_ne finished [took 99.6744s] +06/11/23 22:19:57| INFO kfcv finished [took 114.5018s] +06/11/23 22:19:59| INFO mul_cc finished [took 123.4161s] +06/11/23 22:20:05| INFO mul_pacc finished [took 163.4607s] +06/11/23 22:20:09| INFO mul_sld finished [took 173.7721s] +06/11/23 22:20:16| INFO mulne_pacc finished [took 162.8502s] +06/11/23 22:20:16| INFO atc_mc finished [took 124.1504s] +06/11/23 22:20:36| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00596) [took 169.9575s] +06/11/23 22:21:55| INFO mul_pacc_gs finished [took 248.8139s] +06/11/23 22:24:04| INFO binmc_pacc finished [took 400.7570s] +06/11/23 22:24:12| INFO bin_pacc finished [took 411.2454s] +06/11/23 22:24:21| INFO binne_pacc finished [took 414.5496s] +06/11/23 22:24:21| INFO bin_cc finished [took 389.2880s] +06/11/23 22:24:25| INFO bin_sld finished [took 431.6256s] +06/11/23 22:25:13| INFO mul_sld_gsq finished [took 474.0621s] +06/11/23 22:25:54| INFO bin_sld_gsq finished [took 515.9435s] +06/11/23 22:26:41| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00626) [took 539.2315s] +06/11/23 22:27:11| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00612) [took 594.2222s] +06/11/23 22:27:54| INFO mul_sld_gs finished [took 637.0683s] +06/11/23 22:29:48| INFO bin_pacc_gs finished [took 725.8952s] +06/11/23 22:33:01| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00682) [took 945.7122s] +06/11/23 22:36:02| INFO bin_sld_gs finished [took 1126.7133s] +06/11/23 22:36:02| INFO Dataset sample 0.40 of dataset rcv1_MCAT_9prevs finished [took 1130.5566s] +06/11/23 22:36:02| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs started +06/11/23 22:38:42| INFO doc_feat finished [took 76.3995s] +06/11/23 22:39:04| INFO mul_pacc finished [took 170.8108s] +06/11/23 22:39:11| INFO mulmc_pacc finished [took 169.1537s] +06/11/23 22:39:22| INFO ref finished [took 134.5551s] +06/11/23 22:39:23| INFO kfcv finished [took 144.5049s] +06/11/23 22:39:28| INFO mul_sld finished [took 201.7112s] +06/11/23 22:39:31| INFO atc_ne finished [took 127.5871s] +06/11/23 22:39:32| INFO atc_mc finished [took 141.1615s] +06/11/23 22:39:33| INFO mulne_pacc finished [took 181.7155s] +06/11/23 22:39:34| INFO mul_cc finished [took 156.9505s] +06/11/23 22:39:37| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.00848) [took 178.6463s] +06/11/23 22:41:02| INFO mul_pacc_gs finished [took 264.1109s] +06/11/23 22:43:34| INFO binmc_pacc finished [took 438.9742s] +06/11/23 22:43:34| INFO bin_pacc finished [took 442.7005s] +06/11/23 22:43:43| INFO bin_sld finished [took 458.4940s] +06/11/23 22:43:44| INFO binne_pacc finished [took 443.0455s] +06/11/23 22:43:55| INFO bin_cc finished [took 423.6361s] +06/11/23 22:44:45| INFO mul_sld_gsq finished [took 514.4586s] +06/11/23 22:45:32| INFO bin_sld_gsq finished [took 562.7681s] +06/11/23 22:46:12| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00790) [took 574.1156s] +06/11/23 22:46:42| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00824) [took 633.5645s] +06/11/23 22:47:24| INFO mul_sld_gs finished [took 676.3552s] +06/11/23 22:49:27| INFO bin_pacc_gs finished [took 768.3574s] +06/11/23 22:52:27| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00861) [took 979.5729s] +06/11/23 22:55:32| INFO bin_sld_gs finished [took 1164.7331s] +06/11/23 22:55:32| INFO Dataset sample 0.50 of dataset rcv1_MCAT_9prevs finished [took 1169.2748s] +06/11/23 22:55:32| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs started +06/11/23 22:58:47| INFO doc_feat finished [took 112.6375s] +06/11/23 22:59:00| INFO kfcv finished [took 150.2412s] +06/11/23 22:59:00| INFO mul_pacc finished [took 197.0521s] +06/11/23 22:59:06| INFO mul_sld finished [took 209.9482s] +06/11/23 22:59:07| INFO mulmc_pacc finished [took 198.8911s] +06/11/23 22:59:07| INFO ref finished [took 148.7702s] +06/11/23 22:59:16| INFO atc_ne finished [took 143.7730s] +06/11/23 22:59:18| INFO atc_mc finished [took 151.2783s] +06/11/23 22:59:19| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01122) [took 190.1694s] +06/11/23 22:59:26| INFO mul_cc finished [took 179.0100s] +06/11/23 22:59:33| INFO mulne_pacc finished [took 211.9002s] +06/11/23 23:00:52| INFO mul_pacc_gs finished [took 283.5718s] +06/11/23 23:03:21| INFO bin_sld finished [took 466.9682s] +06/11/23 23:03:24| INFO binmc_pacc finished [took 456.9500s] +06/11/23 23:03:25| INFO bin_pacc finished [took 464.1421s] +06/11/23 23:03:39| INFO bin_cc finished [took 445.8302s] +06/11/23 23:03:40| INFO binne_pacc finished [took 466.3427s] +06/11/23 23:04:10| INFO mul_sld_gsq finished [took 509.0584s] +06/11/23 23:04:56| INFO bin_sld_gsq finished [took 556.7578s] +06/11/23 23:05:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'confidence': None} (score=0.01033) [took 581.0899s] +06/11/23 23:06:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00791) [took 636.6955s] +06/11/23 23:07:00| INFO mul_sld_gs finished [took 682.1829s] +06/11/23 23:08:59| INFO bin_pacc_gs finished [took 772.0584s] +06/11/23 23:11:44| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00722) [took 966.7367s] +06/11/23 23:14:47| INFO bin_sld_gs finished [took 1150.2138s] +06/11/23 23:14:47| INFO Dataset sample 0.60 of dataset rcv1_MCAT_9prevs finished [took 1155.4582s] +06/11/23 23:14:47| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs started +06/11/23 23:17:29| INFO mulmc_pacc finished [took 140.6592s] +06/11/23 23:17:37| INFO doc_feat finished [took 93.5374s] +06/11/23 23:17:58| INFO mul_pacc finished [took 176.1101s] +06/11/23 23:18:06| INFO mulne_pacc finished [took 168.2578s] +06/11/23 23:18:06| INFO ref finished [took 138.9670s] +06/11/23 23:18:13| INFO atc_ne finished [took 133.8368s] +06/11/23 23:18:13| INFO mul_cc finished [took 156.3809s] +06/11/23 23:18:14| INFO atc_mc finished [took 140.7865s] +06/11/23 23:18:15| INFO kfcv finished [took 150.8563s] +06/11/23 23:18:28| INFO mul_sld finished [took 213.5502s] +06/11/23 23:18:37| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.00696) [took 184.7202s] +06/11/23 23:20:04| INFO mul_pacc_gs finished [took 271.8620s] +06/11/23 23:22:38| INFO binne_pacc finished [took 444.7274s] +06/11/23 23:22:39| INFO binmc_pacc finished [took 454.8866s] +06/11/23 23:22:39| INFO bin_pacc finished [took 458.6381s] +06/11/23 23:22:47| INFO bin_cc finished [took 432.1075s] +06/11/23 23:22:54| INFO bin_sld finished [took 480.5003s] +06/11/23 23:23:33| INFO mul_sld_gsq finished [took 514.0066s] +06/11/23 23:24:13| INFO bin_sld_gsq finished [took 554.7885s] +06/11/23 23:24:57| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.00603) [took 574.6463s] +06/11/23 23:25:06| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00528) [took 609.3079s] +06/11/23 23:25:51| INFO mul_sld_gs finished [took 654.2885s] +06/11/23 23:28:10| INFO bin_pacc_gs finished [took 767.8253s] +06/11/23 23:30:43| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00733) [took 947.2105s] +06/11/23 23:33:48| INFO bin_sld_gs finished [took 1132.1309s] +06/11/23 23:33:48| INFO Dataset sample 0.70 of dataset rcv1_MCAT_9prevs finished [took 1140.6743s] +06/11/23 23:33:48| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs started +06/11/23 23:36:55| INFO doc_feat finished [took 101.6311s] +06/11/23 23:37:16| INFO atc_ne finished [took 124.5854s] +06/11/23 23:37:39| INFO mulne_pacc finished [took 198.5060s] +06/11/23 23:37:42| INFO mulmc_pacc finished [took 210.8408s] +06/11/23 23:37:43| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'max_conf'} (score=0.01019) [took 194.4422s] +06/11/23 23:37:44| INFO mul_pacc finished [took 224.0155s] +06/11/23 23:37:44| INFO kfcv finished [took 178.0222s] +06/11/23 23:37:47| INFO ref finished [took 176.1278s] +06/11/23 23:37:55| INFO atc_mc finished [took 173.0154s] +06/11/23 23:37:58| INFO mul_cc finished [took 198.1420s] +06/11/23 23:38:10| INFO mul_sld finished [took 258.4898s] +06/11/23 23:39:25| INFO mul_pacc_gs finished [took 297.0552s] +06/11/23 23:41:55| INFO binmc_pacc finished [took 471.8397s] +06/11/23 23:42:06| INFO binne_pacc finished [took 470.6917s] +06/11/23 23:42:08| INFO bin_pacc finished [took 490.2025s] +06/11/23 23:42:11| INFO bin_sld finished [took 500.3974s] +06/11/23 23:42:17| INFO bin_cc finished [took 463.2719s] +06/11/23 23:42:33| INFO mul_sld_gsq finished [took 515.9211s] +06/11/23 23:43:19| INFO bin_sld_gsq finished [took 563.2792s] +06/11/23 23:44:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'confidence': 'entropy'} (score=0.01110) [took 580.7011s] +06/11/23 23:44:33| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00755) [took 638.9055s] +06/11/23 23:45:18| INFO mul_sld_gs finished [took 683.7473s] +06/11/23 23:47:14| INFO bin_pacc_gs finished [took 769.5136s] +06/11/23 23:50:19| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00653) [took 986.1331s] +06/11/23 23:53:23| INFO bin_sld_gs finished [took 1170.3407s] +06/11/23 23:53:23| INFO Dataset sample 0.80 of dataset rcv1_MCAT_9prevs finished [took 1175.4004s] +06/11/23 23:53:23| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs started +06/11/23 23:53:29| WARNING Method bin_sld failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:31| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:32| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:34| WARNING Method mul_sld_gsq failed. Exception: a must be greater than 0 unless no samples are taken +06/11/23 23:53:34| WARNING Method bin_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:36| WARNING Method mul_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 23:53:37| WARNING Method binmc_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:38| WARNING Method binne_pacc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:39| WARNING Method mulmc_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 23:53:41| WARNING Method mulne_pacc failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 23:53:41| WARNING Method bin_pacc_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:53:43| WARNING Method mul_pacc_gs failed. Exception: could not broadcast input array from shape (3,) into shape (4,) +06/11/23 23:53:44| WARNING Method bin_cc failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +06/11/23 23:54:33| INFO ref finished [took 46.5615s] +06/11/23 23:54:34| INFO doc_feat finished [took 43.4254s] +06/11/23 23:54:34| INFO kfcv finished [took 48.7260s] +06/11/23 23:54:34| INFO mul_sld finished [took 64.5496s] +06/11/23 23:54:38| INFO atc_mc finished [took 49.9172s] +06/11/23 23:54:39| INFO atc_ne finished [took 49.8635s] +06/11/23 23:54:39| INFO mul_cc finished [took 54.7417s] +06/11/23 23:58:27| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01247) [took 295.7388s] +06/11/23 23:59:08| INFO mul_sld_gs finished [took 336.2009s] +06/11/23 23:59:08| INFO Dataset sample 0.90 of dataset rcv1_MCAT_9prevs finished [took 344.1389s] +---------------------------------------------------------------------------------------------------- +07/11/23 01:05:25| INFO dataset rcv1_CCAT_9prevs +07/11/23 01:05:30| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs started +07/11/23 01:06:23| INFO ref finished [took 48.3560s] +07/11/23 01:06:29| INFO atc_mc finished [took 52.9929s] +07/11/23 01:06:30| INFO atc_ne finished [took 53.3908s] +07/11/23 01:07:06| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +07/11/23 01:11:38| INFO mul_sld_gsq finished [took 364.0698s] +07/11/23 01:13:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00644) [took 499.4945s] +07/11/23 01:14:34| INFO mul_sld_gs finished [took 542.6047s] +07/11/23 01:18:02| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00589) [took 750.8663s] +07/11/23 01:21:01| INFO bin_sld_gs finished [took 930.1356s] +07/11/23 01:21:01| INFO Dataset sample 0.10 of dataset rcv1_CCAT_9prevs finished [took 931.4321s] +07/11/23 01:21:01| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs started +07/11/23 01:22:02| INFO ref finished [took 55.2212s] +07/11/23 01:22:07| INFO atc_mc finished [took 59.3890s] +07/11/23 01:22:09| INFO atc_ne finished [took 59.7388s] +07/11/23 01:27:21| INFO mul_sld_gsq finished [took 375.2352s] +07/11/23 01:27:24| INFO bin_sld_gsq finished [took 379.6159s] +07/11/23 01:29:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01221) [took 502.0302s] +07/11/23 01:30:08| INFO mul_sld_gs finished [took 545.0285s] +07/11/23 01:34:25| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00768) [took 802.3620s] +07/11/23 01:37:25| INFO bin_sld_gs finished [took 982.3260s] +07/11/23 01:37:25| INFO Dataset sample 0.20 of dataset rcv1_CCAT_9prevs finished [took 983.7236s] +07/11/23 01:37:25| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs started +07/11/23 01:38:20| INFO ref finished [took 49.9803s] +07/11/23 01:38:25| INFO atc_mc finished [took 53.3765s] +07/11/23 01:38:26| INFO atc_ne finished [took 53.8925s] +07/11/23 01:43:41| INFO mul_sld_gsq finished [took 372.2608s] +07/11/23 01:43:45| INFO bin_sld_gsq finished [took 377.3380s] +07/11/23 01:45:45| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00872) [took 497.5768s] +07/11/23 01:46:28| INFO mul_sld_gs finished [took 540.8267s] +07/11/23 01:51:09| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00782) [took 822.2849s] +07/11/23 01:54:10| INFO bin_sld_gs finished [took 1003.7804s] +07/11/23 01:54:10| INFO Dataset sample 0.30 of dataset rcv1_CCAT_9prevs finished [took 1005.2506s] +07/11/23 01:54:10| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs started +07/11/23 01:55:05| INFO ref finished [took 49.8884s] +07/11/23 01:55:09| INFO atc_mc finished [took 53.3594s] +07/11/23 01:55:10| INFO atc_ne finished [took 53.5162s] +07/11/23 02:00:25| INFO mul_sld_gsq finished [took 371.4460s] +07/11/23 02:00:41| INFO bin_sld_gsq finished [took 387.6183s] +07/11/23 02:02:30| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01037) [took 498.0096s] +07/11/23 02:03:11| INFO mul_sld_gs finished [took 539.1531s] +07/11/23 02:07:53| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01112) [took 821.8730s] +07/11/23 02:10:52| INFO bin_sld_gs finished [took 1001.0803s] +07/11/23 02:10:52| INFO Dataset sample 0.40 of dataset rcv1_CCAT_9prevs finished [took 1002.3085s] +07/11/23 02:10:52| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs started +07/11/23 02:11:44| INFO ref finished [took 47.2218s] +07/11/23 02:11:48| INFO atc_mc finished [took 49.6349s] +07/11/23 02:11:50| INFO atc_ne finished [took 50.9082s] +07/11/23 02:16:51| INFO mul_sld_gsq finished [took 354.3706s] +07/11/23 02:17:11| INFO bin_sld_gsq finished [took 376.0124s] +07/11/23 02:18:51| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00572) [took 476.0587s] +07/11/23 02:19:33| INFO mul_sld_gs finished [took 518.5692s] +07/11/23 02:24:17| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00653) [took 803.4978s] +07/11/23 02:27:16| INFO bin_sld_gs finished [took 982.4395s] +07/11/23 02:27:16| INFO Dataset sample 0.50 of dataset rcv1_CCAT_9prevs finished [took 983.7838s] +07/11/23 02:27:16| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs started +07/11/23 02:28:08| INFO ref finished [took 46.6191s] +07/11/23 02:28:13| INFO atc_mc finished [took 50.3543s] +07/11/23 02:28:15| INFO atc_ne finished [took 51.6601s] +07/11/23 02:33:15| INFO mul_sld_gsq finished [took 354.6014s] +07/11/23 02:33:34| INFO bin_sld_gsq finished [took 374.7872s] +07/11/23 02:35:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00637) [took 475.9302s] +07/11/23 02:35:57| INFO mul_sld_gs finished [took 518.5425s] +07/11/23 02:40:20| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00544) [took 782.7268s] +07/11/23 02:43:18| INFO bin_sld_gs finished [took 960.6334s] +07/11/23 02:43:18| INFO Dataset sample 0.60 of dataset rcv1_CCAT_9prevs finished [took 961.9030s] +07/11/23 02:43:18| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs started +07/11/23 02:44:10| INFO ref finished [took 47.1234s] +07/11/23 02:44:14| INFO atc_mc finished [took 49.9871s] +07/11/23 02:44:16| INFO atc_ne finished [took 50.9160s] +07/11/23 02:49:19| INFO mul_sld_gsq finished [took 357.0613s] +07/11/23 02:49:30| INFO bin_sld_gsq finished [took 368.8000s] +07/11/23 02:51:16| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00765) [took 475.7332s] +07/11/23 02:51:59| INFO mul_sld_gs finished [took 518.6671s] +07/11/23 02:56:28| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.1, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01235) [took 788.7117s] +07/11/23 02:59:28| INFO bin_sld_gs finished [took 968.7653s] +07/11/23 02:59:28| INFO Dataset sample 0.70 of dataset rcv1_CCAT_9prevs finished [took 970.1516s] +07/11/23 02:59:28| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs started +07/11/23 03:00:20| INFO ref finished [took 46.9898s] +07/11/23 03:00:24| INFO atc_mc finished [took 49.8768s] +07/11/23 03:00:25| INFO atc_ne finished [took 49.6324s] +07/11/23 03:05:23| INFO mul_sld_gsq finished [took 350.7932s] +07/11/23 03:05:32| INFO bin_sld_gsq finished [took 360.8665s] +07/11/23 03:07:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01306) [took 474.6581s] +07/11/23 03:08:07| INFO mul_sld_gs finished [took 516.4890s] +07/11/23 03:12:24| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00894) [took 774.9140s] +07/11/23 03:15:29| INFO bin_sld_gs finished [took 959.3579s] +07/11/23 03:15:29| INFO Dataset sample 0.80 of dataset rcv1_CCAT_9prevs finished [took 960.6992s] +07/11/23 03:15:29| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs started +07/11/23 03:16:21| INFO ref finished [took 47.3281s] +07/11/23 03:16:25| INFO atc_mc finished [took 49.8016s] +07/11/23 03:16:28| INFO atc_ne finished [took 51.2288s] +07/11/23 03:21:16| INFO mul_sld_gsq finished [took 343.2861s] +07/11/23 03:21:22| INFO bin_sld_gsq finished [took 349.6065s] +07/11/23 03:23:20| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00808) [took 468.7910s] +07/11/23 03:24:01| INFO mul_sld_gs finished [took 509.9001s] +07/11/23 03:28:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00604) [took 752.8185s] +07/11/23 03:31:01| INFO bin_sld_gs finished [took 930.3934s] +07/11/23 03:31:01| INFO Dataset sample 0.90 of dataset rcv1_CCAT_9prevs finished [took 931.7055s] +07/11/23 03:31:29| INFO dataset imdb_9prevs +07/11/23 03:31:37| INFO Dataset sample 0.10 of dataset imdb_9prevs started +07/11/23 03:31:49| INFO ref finished [took 11.4117s] +07/11/23 03:31:53| INFO atc_mc finished [took 14.8218s] +07/11/23 03:31:53| INFO atc_ne finished [took 14.8359s] +07/11/23 03:32:11| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +07/11/23 03:32:56| WARNING Method bin_sld_gsq failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 +07/11/23 03:36:32| INFO mul_sld_gsq finished [took 294.6812s] +07/11/23 03:38:05| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.03127) [took 387.7698s] +07/11/23 03:38:18| INFO mul_sld_gs finished [took 400.7660s] +07/11/23 03:38:18| INFO Dataset sample 0.10 of dataset imdb_9prevs finished [took 401.3208s] +07/11/23 03:38:18| INFO Dataset sample 0.20 of dataset imdb_9prevs started +07/11/23 03:38:30| INFO ref finished [took 11.1665s] +07/11/23 03:38:34| INFO atc_mc finished [took 14.4483s] +07/11/23 03:38:34| INFO atc_ne finished [took 14.8634s] +07/11/23 03:43:16| INFO bin_sld_gsq finished [took 296.8786s] +07/11/23 03:43:32| INFO mul_sld_gsq finished [took 312.4588s] +07/11/23 03:45:44| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01194) [took 445.1331s] +07/11/23 03:45:58| INFO mul_sld_gs finished [took 459.5855s] +07/11/23 03:51:05| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00924) [took 766.1528s] +07/11/23 03:53:40| INFO bin_sld_gs finished [took 921.5996s] +07/11/23 03:53:40| INFO Dataset sample 0.20 of dataset imdb_9prevs finished [took 922.0949s] +07/11/23 03:53:40| INFO Dataset sample 0.30 of dataset imdb_9prevs started +07/11/23 03:53:53| INFO ref finished [took 11.5825s] +07/11/23 03:53:57| INFO atc_mc finished [took 14.8590s] +07/11/23 03:53:57| INFO atc_ne finished [took 15.3090s] +07/11/23 03:58:53| INFO mul_sld_gsq finished [took 311.9891s] +07/11/23 03:58:54| INFO bin_sld_gsq finished [took 313.1182s] +07/11/23 04:01:03| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.00953) [took 441.3198s] +07/11/23 04:01:18| INFO mul_sld_gs finished [took 456.2347s] +07/11/23 04:06:06| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01064) [took 745.0596s] +07/11/23 04:08:40| INFO bin_sld_gs finished [took 898.9046s] +07/11/23 04:08:40| INFO Dataset sample 0.30 of dataset imdb_9prevs finished [took 899.6778s] +07/11/23 04:08:40| INFO Dataset sample 0.40 of dataset imdb_9prevs started +07/11/23 04:08:52| INFO ref finished [took 11.0605s] +07/11/23 04:08:56| INFO atc_mc finished [took 14.9590s] +07/11/23 04:08:56| INFO atc_ne finished [took 14.8804s] +07/11/23 04:13:54| INFO mul_sld_gsq finished [took 313.3797s] +07/11/23 04:13:56| INFO bin_sld_gsq finished [took 315.5862s] +07/11/23 04:15:54| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01029) [took 432.9025s] +07/11/23 04:16:08| INFO mul_sld_gs finished [took 447.1098s] +07/11/23 04:21:25| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01176) [took 764.2230s] +07/11/23 04:23:56| INFO bin_sld_gs finished [took 915.4905s] +07/11/23 04:23:56| INFO Dataset sample 0.40 of dataset imdb_9prevs finished [took 916.1187s] +07/11/23 04:23:56| INFO Dataset sample 0.50 of dataset imdb_9prevs started +07/11/23 04:24:08| INFO ref finished [took 10.9214s] +07/11/23 04:24:12| INFO atc_mc finished [took 14.9236s] +07/11/23 04:24:12| INFO atc_ne finished [took 14.9240s] +07/11/23 04:29:11| INFO bin_sld_gsq finished [took 314.3071s] +07/11/23 04:29:19| INFO mul_sld_gsq finished [took 322.1027s] +07/11/23 04:31:25| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.00885) [took 448.0202s] +07/11/23 04:31:40| INFO mul_sld_gs finished [took 463.2243s] +07/11/23 04:36:23| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 10.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00940) [took 746.2797s] +07/11/23 04:38:55| INFO bin_sld_gs finished [took 898.7899s] +07/11/23 04:38:55| INFO Dataset sample 0.50 of dataset imdb_9prevs finished [took 899.2924s] +07/11/23 04:38:55| INFO Dataset sample 0.60 of dataset imdb_9prevs started +07/11/23 04:39:08| INFO ref finished [took 11.9811s] +07/11/23 04:39:12| INFO atc_mc finished [took 15.7159s] +07/11/23 04:39:12| INFO atc_ne finished [took 15.9512s] +07/11/23 04:44:19| INFO bin_sld_gsq finished [took 323.1420s] +07/11/23 04:44:21| INFO mul_sld_gsq finished [took 325.2299s] +07/11/23 04:46:22| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00984) [took 445.8872s] +07/11/23 04:46:37| INFO mul_sld_gs finished [took 460.6339s] +07/11/23 04:52:03| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.01142) [took 786.7500s] +07/11/23 04:54:36| INFO bin_sld_gs finished [took 940.1627s] +07/11/23 04:54:36| INFO Dataset sample 0.60 of dataset imdb_9prevs finished [took 940.6023s] +07/11/23 04:54:36| INFO Dataset sample 0.70 of dataset imdb_9prevs started +07/11/23 04:54:48| INFO ref finished [took 11.1744s] +07/11/23 04:54:52| INFO atc_mc finished [took 14.7518s] +07/11/23 04:54:52| INFO atc_ne finished [took 14.8147s] +07/11/23 04:59:45| INFO bin_sld_gsq finished [took 308.3645s] +07/11/23 05:00:07| INFO mul_sld_gsq finished [took 330.3332s] +07/11/23 05:02:14| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 1000.0, 'quantifier__classifier__class_weight': None, 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'max_conf'} (score=0.01102) [took 456.8448s] +07/11/23 05:02:28| INFO mul_sld_gs finished [took 471.4675s] +07/11/23 05:06:48| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.02196) [took 731.2847s] +07/11/23 05:09:19| INFO bin_sld_gs finished [took 882.2200s] +07/11/23 05:09:19| INFO Dataset sample 0.70 of dataset imdb_9prevs finished [took 882.8165s] +07/11/23 05:09:19| INFO Dataset sample 0.80 of dataset imdb_9prevs started +07/11/23 05:09:31| INFO ref finished [took 11.0645s] +07/11/23 05:09:35| INFO atc_mc finished [took 14.7375s] +07/11/23 05:09:35| INFO atc_ne finished [took 14.7704s] +07/11/23 05:14:22| INFO bin_sld_gsq finished [took 302.1848s] +07/11/23 05:14:33| INFO mul_sld_gsq finished [took 313.5459s] +07/11/23 05:16:38| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.00785) [took 438.9863s] +07/11/23 05:16:52| INFO mul_sld_gs finished [took 452.7273s] +07/11/23 05:21:59| DEBUG [BinaryQuantifierAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 0.01, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': 'bcts', 'quantifier__exact_train_prev': True, 'confidence': None} (score=0.01449) [took 759.8355s] +07/11/23 05:24:38| INFO bin_sld_gs finished [took 918.7338s] +07/11/23 05:24:38| INFO Dataset sample 0.80 of dataset imdb_9prevs finished [took 919.2981s] +07/11/23 05:24:38| INFO Dataset sample 0.90 of dataset imdb_9prevs started +07/11/23 05:24:39| WARNING Method mul_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +07/11/23 05:24:39| WARNING Method bin_sld_gsq failed. Exception: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. +07/11/23 05:24:48| INFO ref finished [took 9.1378s] +07/11/23 05:24:51| INFO atc_mc finished [took 12.1603s] +07/11/23 05:24:52| INFO atc_ne finished [took 12.3482s] +07/11/23 05:25:08| WARNING Method bin_sld_gs failed. Exception: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0 +07/11/23 05:30:32| DEBUG [MultiClassAccuracyEstimator] optimization finished: best params {'quantifier__classifier__C': 100.0, 'quantifier__classifier__class_weight': 'balanced', 'quantifier__recalib': None, 'quantifier__exact_train_prev': True, 'confidence': 'entropy'} (score=0.04178) [took 353.7904s] +07/11/23 05:30:45| INFO mul_sld_gs finished [took 365.9283s] +07/11/23 05:30:45| INFO Dataset sample 0.90 of dataset imdb_9prevs finished [took 366.4930s] +---------------------------------------------------------------------------------------------------- diff --git a/quacc/data.py b/quacc/data.py index 7eb1809..3103d74 100644 --- a/quacc/data.py +++ b/quacc/data.py @@ -1,150 +1,150 @@ -import math -from typing import List, Optional - -import numpy as np -import scipy.sparse as sp -from quapy.data import LabelledCollection - - -# Extended classes -# -# 0 ~ True 0 -# 1 ~ False 1 -# 2 ~ False 0 -# 3 ~ True 1 -# _____________________ -# | | | -# | True 0 | False 1 | -# |__________|__________| -# | | | -# | False 0 | True 1 | -# |__________|__________| -# -class ExClassManager: - @staticmethod - def get_ex(n_classes: int, true_class: int, pred_class: int) -> int: - return true_class * n_classes + pred_class - - @staticmethod - def get_pred(n_classes: int, ex_class: int) -> int: - return ex_class % n_classes - - @staticmethod - def get_true(n_classes: int, ex_class: int) -> int: - return ex_class // n_classes - - -class ExtendedCollection(LabelledCollection): - def __init__( - self, - instances: np.ndarray | sp.csr_matrix, - labels: np.ndarray, - classes: Optional[List] = None, - ): - super().__init__(instances, labels, classes=classes) - - def split_by_pred(self): - _ncl = int(math.sqrt(self.n_classes)) - _indexes = ExtendedCollection._split_index_by_pred(_ncl, self.instances) - if isinstance(self.instances, np.ndarray): - _instances = [ - self.instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int) - for ind in _indexes - ] - elif isinstance(self.instances, sp.csr_matrix): - _instances = [ - self.instances[ind] - if ind.shape[0] > 0 - else sp.csr_matrix(np.empty((0, 0), dtype=int)) - for ind in _indexes - ] - _labels = [ - np.asarray( - [ - ExClassManager.get_true(_ncl, lbl) - for lbl in (self.labels[ind] if len(ind) > 0 else []) - ], - dtype=int, - ) - for ind in _indexes - ] - return [ - ExtendedCollection(inst, lbl, classes=range(0, _ncl)) - for (inst, lbl) in zip(_instances, _labels) - ] - - @classmethod - def split_inst_by_pred( - cls, n_classes: int, instances: np.ndarray | sp.csr_matrix - ) -> (List[np.ndarray | sp.csr_matrix], List[float]): - _indexes = cls._split_index_by_pred(n_classes, instances) - if isinstance(instances, np.ndarray): - _instances = [ - instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int) - for ind in _indexes - ] - elif isinstance(instances, sp.csr_matrix): - _instances = [ - instances[ind] - if ind.shape[0] > 0 - else sp.csr_matrix(np.empty((0, 0), dtype=int)) - for ind in _indexes - ] - norms = [inst.shape[0] / instances.shape[0] for inst in _instances] - return _instances, norms - - @classmethod - def _split_index_by_pred( - cls, n_classes: int, instances: np.ndarray | sp.csr_matrix - ) -> List[np.ndarray]: - if isinstance(instances, np.ndarray): - _pred_label = [np.argmax(inst[-n_classes:], axis=0) for inst in instances] - elif isinstance(instances, sp.csr_matrix): - _pred_label = [ - np.argmax(inst[:, -n_classes:].toarray().flatten(), axis=0) - for inst in instances - ] - else: - raise ValueError("Unsupported matrix format") - - return [ - np.asarray([j for (j, x) in enumerate(_pred_label) if x == i], dtype=int) - for i in range(0, n_classes) - ] - - @classmethod - def extend_instances( - cls, instances: np.ndarray | sp.csr_matrix, pred_proba: np.ndarray - ) -> np.ndarray | sp.csr_matrix: - if isinstance(instances, sp.csr_matrix): - _pred_proba = sp.csr_matrix(pred_proba) - n_x = sp.hstack([instances, _pred_proba]) - elif isinstance(instances, np.ndarray): - n_x = np.concatenate((instances, pred_proba), axis=1) - else: - raise ValueError("Unsupported matrix format") - - return n_x - - @classmethod - def extend_collection( - cls, - base: LabelledCollection, - pred_proba: np.ndarray, - ): - n_classes = base.n_classes - - # n_X = [ X | predicted probs. ] - n_x = cls.extend_instances(base.X, pred_proba) - - # n_y = (exptected y, predicted y) - pred_proba = pred_proba[:, -n_classes:] - preds = np.argmax(pred_proba, axis=-1) - n_y = np.asarray( - [ - ExClassManager.get_ex(n_classes, true_class, pred_class) - for (true_class, pred_class) in zip(base.y, preds) - ] - ) - - return ExtendedCollection(n_x, n_y, classes=[*range(0, n_classes * n_classes)]) +import math +from typing import List, Optional + +import numpy as np +import scipy.sparse as sp +from quapy.data import LabelledCollection + + +# Extended classes +# +# 0 ~ True 0 +# 1 ~ False 1 +# 2 ~ False 0 +# 3 ~ True 1 +# _____________________ +# | | | +# | True 0 | False 1 | +# |__________|__________| +# | | | +# | False 0 | True 1 | +# |__________|__________| +# +class ExClassManager: + @staticmethod + def get_ex(n_classes: int, true_class: int, pred_class: int) -> int: + return true_class * n_classes + pred_class + + @staticmethod + def get_pred(n_classes: int, ex_class: int) -> int: + return ex_class % n_classes + + @staticmethod + def get_true(n_classes: int, ex_class: int) -> int: + return ex_class // n_classes + + +class ExtendedCollection(LabelledCollection): + def __init__( + self, + instances: np.ndarray | sp.csr_matrix, + labels: np.ndarray, + classes: Optional[List] = None, + ): + super().__init__(instances, labels, classes=classes) + + def split_by_pred(self): + _ncl = int(math.sqrt(self.n_classes)) + _indexes = ExtendedCollection._split_index_by_pred(_ncl, self.instances) + if isinstance(self.instances, np.ndarray): + _instances = [ + self.instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int) + for ind in _indexes + ] + elif isinstance(self.instances, sp.csr_matrix): + _instances = [ + self.instances[ind] + if ind.shape[0] > 0 + else sp.csr_matrix(np.empty((0, 0), dtype=int)) + for ind in _indexes + ] + _labels = [ + np.asarray( + [ + ExClassManager.get_true(_ncl, lbl) + for lbl in (self.labels[ind] if len(ind) > 0 else []) + ], + dtype=int, + ) + for ind in _indexes + ] + return [ + ExtendedCollection(inst, lbl, classes=range(0, _ncl)) + for (inst, lbl) in zip(_instances, _labels) + ] + + @classmethod + def split_inst_by_pred( + cls, n_classes: int, instances: np.ndarray | sp.csr_matrix + ) -> (List[np.ndarray | sp.csr_matrix], List[float]): + _indexes = cls._split_index_by_pred(n_classes, instances) + if isinstance(instances, np.ndarray): + _instances = [ + instances[ind] if ind.shape[0] > 0 else np.asarray([], dtype=int) + for ind in _indexes + ] + elif isinstance(instances, sp.csr_matrix): + _instances = [ + instances[ind] + if ind.shape[0] > 0 + else sp.csr_matrix(np.empty((0, 0), dtype=int)) + for ind in _indexes + ] + norms = [inst.shape[0] / instances.shape[0] for inst in _instances] + return _instances, norms + + @classmethod + def _split_index_by_pred( + cls, n_classes: int, instances: np.ndarray | sp.csr_matrix + ) -> List[np.ndarray]: + if isinstance(instances, np.ndarray): + _pred_label = [np.argmax(inst[-n_classes:], axis=0) for inst in instances] + elif isinstance(instances, sp.csr_matrix): + _pred_label = [ + np.argmax(inst[:, -n_classes:].toarray().flatten(), axis=0) + for inst in instances + ] + else: + raise ValueError("Unsupported matrix format") + + return [ + np.asarray([j for (j, x) in enumerate(_pred_label) if x == i], dtype=int) + for i in range(0, n_classes) + ] + + @classmethod + def extend_instances( + cls, instances: np.ndarray | sp.csr_matrix, pred_proba: np.ndarray + ) -> np.ndarray | sp.csr_matrix: + if isinstance(instances, sp.csr_matrix): + _pred_proba = sp.csr_matrix(pred_proba) + n_x = sp.hstack([instances, _pred_proba]) + elif isinstance(instances, np.ndarray): + n_x = np.concatenate((instances, pred_proba), axis=1) + else: + raise ValueError("Unsupported matrix format") + + return n_x + + @classmethod + def extend_collection( + cls, + base: LabelledCollection, + pred_proba: np.ndarray, + ): + n_classes = base.n_classes + + # n_X = [ X | predicted probs. ] + n_x = cls.extend_instances(base.X, pred_proba) + + # n_y = (exptected y, predicted y) + pred_proba = pred_proba[:, -n_classes:] + preds = np.argmax(pred_proba, axis=-1) + n_y = np.asarray( + [ + ExClassManager.get_ex(n_classes, true_class, pred_class) + for (true_class, pred_class) in zip(base.y, preds) + ] + ) + + return ExtendedCollection(n_x, n_y, classes=[*range(0, n_classes * n_classes)]) diff --git a/quacc/dataset.py b/quacc/dataset.py index aff0fbb..fe91e92 100644 --- a/quacc/dataset.py +++ b/quacc/dataset.py @@ -1,171 +1,171 @@ -import math -from typing import List - -import numpy as np -import quapy as qp -from quapy.data.base import LabelledCollection -from sklearn.conftest import fetch_rcv1 - -TRAIN_VAL_PROP = 0.5 - - -class DatasetSample: - def __init__( - self, - train: LabelledCollection, - validation: LabelledCollection, - test: LabelledCollection, - ): - self.train = train - self.validation = validation - self.test = test - - @property - def train_prev(self): - return self.train.prevalence() - - @property - def validation_prev(self): - return self.validation.prevalence() - - @property - def prevs(self): - return {"train": self.train_prev, "validation": self.validation_prev} - - -class Dataset: - def __init__(self, name, n_prevalences=9, prevs=None, target=None): - self._name = name - self._target = target - - self.prevs = None - self.n_prevs = n_prevalences - if prevs is not None: - prevs = np.unique([p for p in prevs if p > 0.0 and p < 1.0]) - if prevs.shape[0] > 0: - self.prevs = np.sort(prevs) - self.n_prevs = self.prevs.shape[0] - - def __spambase(self): - return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test - - # provare min_df=5 - def __imdb(self): - return qp.datasets.fetch_reviews("imdb", tfidf=True, min_df=3).train_test - - def __rcv1(self): - n_train = 23149 - available_targets = ["CCAT", "GCAT", "MCAT"] - - if self._target is None or self._target not in available_targets: - raise ValueError(f"Invalid target {self._target}") - - dataset = fetch_rcv1() - target_index = np.where(dataset.target_names == self._target)[0] - all_train_d = dataset.data[:n_train, :] - test_d = dataset.data[n_train:, :] - labels = dataset.target[:, target_index].toarray().flatten() - all_train_l, test_l = labels[:n_train], labels[n_train:] - all_train = LabelledCollection(all_train_d, all_train_l, classes=[0, 1]) - test = LabelledCollection(test_d, test_l, classes=[0, 1]) - - return all_train, test - - def get_raw(self) -> DatasetSample: - all_train, test = { - "spambase": self.__spambase, - "imdb": self.__imdb, - "rcv1": self.__rcv1, - }[self._name]() - - train, val = all_train.split_stratified( - train_prop=TRAIN_VAL_PROP, random_state=0 - ) - - return DatasetSample(train, val, test) - - def get(self) -> List[DatasetSample]: - (all_train, test) = { - "spambase": self.__spambase, - "imdb": self.__imdb, - "rcv1": self.__rcv1, - }[self._name]() - - # resample all_train set to have (0.5, 0.5) prevalence - at_positives = np.sum(all_train.y) - all_train = all_train.sampling( - min(at_positives, len(all_train) - at_positives) * 2, 0.5, random_state=0 - ) - - # sample prevalences - if self.prevs is not None: - prevs = self.prevs - else: - prevs = np.linspace(0.0, 1.0, num=self.n_prevs + 1, endpoint=False)[1:] - - at_size = min(math.floor(len(all_train) * 0.5 / p) for p in prevs) - datasets = [] - for p in 1.0 - prevs: - all_train_sampled = all_train.sampling(at_size, p, random_state=0) - train, validation = all_train_sampled.split_stratified( - train_prop=TRAIN_VAL_PROP, random_state=0 - ) - datasets.append(DatasetSample(train, validation, test)) - - return datasets - - def __call__(self): - return self.get() - - @property - def name(self): - return ( - f"{self._name}_{self._target}_{self.n_prevs}prevs" - if self._name == "rcv1" - else f"{self._name}_{self.n_prevs}prevs" - ) - - -# >>> fetch_rcv1().target_names -# array(['C11', 'C12', 'C13', 'C14', 'C15', 'C151', 'C1511', 'C152', 'C16', -# 'C17', 'C171', 'C172', 'C173', 'C174', 'C18', 'C181', 'C182', -# 'C183', 'C21', 'C22', 'C23', 'C24', 'C31', 'C311', 'C312', 'C313', -# 'C32', 'C33', 'C331', 'C34', 'C41', 'C411', 'C42', 'CCAT', 'E11', -# 'E12', 'E121', 'E13', 'E131', 'E132', 'E14', 'E141', 'E142', -# 'E143', 'E21', 'E211', 'E212', 'E31', 'E311', 'E312', 'E313', -# 'E41', 'E411', 'E51', 'E511', 'E512', 'E513', 'E61', 'E71', 'ECAT', -# 'G15', 'G151', 'G152', 'G153', 'G154', 'G155', 'G156', 'G157', -# 'G158', 'G159', 'GCAT', 'GCRIM', 'GDEF', 'GDIP', 'GDIS', 'GENT', -# 'GENV', 'GFAS', 'GHEA', 'GJOB', 'GMIL', 'GOBIT', 'GODD', 'GPOL', -# 'GPRO', 'GREL', 'GSCI', 'GSPO', 'GTOUR', 'GVIO', 'GVOTE', 'GWEA', -# 'GWELF', 'M11', 'M12', 'M13', 'M131', 'M132', 'M14', 'M141', -# 'M142', 'M143', 'MCAT'], dtype=object) - - -def rcv1_info(): - dataset = fetch_rcv1() - n_train = 23149 - - targets = [] - for target in range(103): - train_t_prev = np.average(dataset.target[:n_train, target].toarray().flatten()) - test_t_prev = np.average(dataset.target[n_train:, target].toarray().flatten()) - targets.append( - ( - dataset.target_names[target], - { - "train": (1.0 - train_t_prev, train_t_prev), - "test": (1.0 - test_t_prev, test_t_prev), - }, - ) - ) - - targets.sort(key=lambda t: t[1]["train"][1]) - for n, d in targets: - print(f"{n}:") - for k, (fp, tp) in d.items(): - print(f"\t{k}: {fp:.4f}, {tp:.4f}") - - -if __name__ == "__main__": - rcv1_info() +import math +from typing import List + +import numpy as np +import quapy as qp +from quapy.data.base import LabelledCollection +from sklearn.conftest import fetch_rcv1 + +TRAIN_VAL_PROP = 0.5 + + +class DatasetSample: + def __init__( + self, + train: LabelledCollection, + validation: LabelledCollection, + test: LabelledCollection, + ): + self.train = train + self.validation = validation + self.test = test + + @property + def train_prev(self): + return self.train.prevalence() + + @property + def validation_prev(self): + return self.validation.prevalence() + + @property + def prevs(self): + return {"train": self.train_prev, "validation": self.validation_prev} + + +class Dataset: + def __init__(self, name, n_prevalences=9, prevs=None, target=None): + self._name = name + self._target = target + + self.prevs = None + self.n_prevs = n_prevalences + if prevs is not None: + prevs = np.unique([p for p in prevs if p > 0.0 and p < 1.0]) + if prevs.shape[0] > 0: + self.prevs = np.sort(prevs) + self.n_prevs = self.prevs.shape[0] + + def __spambase(self): + return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test + + # provare min_df=5 + def __imdb(self): + return qp.datasets.fetch_reviews("imdb", tfidf=True, min_df=3).train_test + + def __rcv1(self): + n_train = 23149 + available_targets = ["CCAT", "GCAT", "MCAT"] + + if self._target is None or self._target not in available_targets: + raise ValueError(f"Invalid target {self._target}") + + dataset = fetch_rcv1() + target_index = np.where(dataset.target_names == self._target)[0] + all_train_d = dataset.data[:n_train, :] + test_d = dataset.data[n_train:, :] + labels = dataset.target[:, target_index].toarray().flatten() + all_train_l, test_l = labels[:n_train], labels[n_train:] + all_train = LabelledCollection(all_train_d, all_train_l, classes=[0, 1]) + test = LabelledCollection(test_d, test_l, classes=[0, 1]) + + return all_train, test + + def get_raw(self) -> DatasetSample: + all_train, test = { + "spambase": self.__spambase, + "imdb": self.__imdb, + "rcv1": self.__rcv1, + }[self._name]() + + train, val = all_train.split_stratified( + train_prop=TRAIN_VAL_PROP, random_state=0 + ) + + return DatasetSample(train, val, test) + + def get(self) -> List[DatasetSample]: + (all_train, test) = { + "spambase": self.__spambase, + "imdb": self.__imdb, + "rcv1": self.__rcv1, + }[self._name]() + + # resample all_train set to have (0.5, 0.5) prevalence + at_positives = np.sum(all_train.y) + all_train = all_train.sampling( + min(at_positives, len(all_train) - at_positives) * 2, 0.5, random_state=0 + ) + + # sample prevalences + if self.prevs is not None: + prevs = self.prevs + else: + prevs = np.linspace(0.0, 1.0, num=self.n_prevs + 1, endpoint=False)[1:] + + at_size = min(math.floor(len(all_train) * 0.5 / p) for p in prevs) + datasets = [] + for p in 1.0 - prevs: + all_train_sampled = all_train.sampling(at_size, p, random_state=0) + train, validation = all_train_sampled.split_stratified( + train_prop=TRAIN_VAL_PROP, random_state=0 + ) + datasets.append(DatasetSample(train, validation, test)) + + return datasets + + def __call__(self): + return self.get() + + @property + def name(self): + return ( + f"{self._name}_{self._target}_{self.n_prevs}prevs" + if self._name == "rcv1" + else f"{self._name}_{self.n_prevs}prevs" + ) + + +# >>> fetch_rcv1().target_names +# array(['C11', 'C12', 'C13', 'C14', 'C15', 'C151', 'C1511', 'C152', 'C16', +# 'C17', 'C171', 'C172', 'C173', 'C174', 'C18', 'C181', 'C182', +# 'C183', 'C21', 'C22', 'C23', 'C24', 'C31', 'C311', 'C312', 'C313', +# 'C32', 'C33', 'C331', 'C34', 'C41', 'C411', 'C42', 'CCAT', 'E11', +# 'E12', 'E121', 'E13', 'E131', 'E132', 'E14', 'E141', 'E142', +# 'E143', 'E21', 'E211', 'E212', 'E31', 'E311', 'E312', 'E313', +# 'E41', 'E411', 'E51', 'E511', 'E512', 'E513', 'E61', 'E71', 'ECAT', +# 'G15', 'G151', 'G152', 'G153', 'G154', 'G155', 'G156', 'G157', +# 'G158', 'G159', 'GCAT', 'GCRIM', 'GDEF', 'GDIP', 'GDIS', 'GENT', +# 'GENV', 'GFAS', 'GHEA', 'GJOB', 'GMIL', 'GOBIT', 'GODD', 'GPOL', +# 'GPRO', 'GREL', 'GSCI', 'GSPO', 'GTOUR', 'GVIO', 'GVOTE', 'GWEA', +# 'GWELF', 'M11', 'M12', 'M13', 'M131', 'M132', 'M14', 'M141', +# 'M142', 'M143', 'MCAT'], dtype=object) + + +def rcv1_info(): + dataset = fetch_rcv1() + n_train = 23149 + + targets = [] + for target in range(103): + train_t_prev = np.average(dataset.target[:n_train, target].toarray().flatten()) + test_t_prev = np.average(dataset.target[n_train:, target].toarray().flatten()) + targets.append( + ( + dataset.target_names[target], + { + "train": (1.0 - train_t_prev, train_t_prev), + "test": (1.0 - test_t_prev, test_t_prev), + }, + ) + ) + + targets.sort(key=lambda t: t[1]["train"][1]) + for n, d in targets: + print(f"{n}:") + for k, (fp, tp) in d.items(): + print(f"\t{k}: {fp:.4f}, {tp:.4f}") + + +if __name__ == "__main__": + rcv1_info() diff --git a/quacc/environment.py b/quacc/environment.py index f22f162..92a039a 100644 --- a/quacc/environment.py +++ b/quacc/environment.py @@ -1,118 +1,118 @@ -import collections as C -import copy -from typing import Any - -import yaml - - -class environ: - _instance = None - _default_env = { - "DATASET_NAME": None, - "DATASET_TARGET": None, - "METRICS": [], - "COMP_ESTIMATORS": [], - "DATASET_N_PREVS": 9, - "DATASET_PREVS": None, - "OUT_DIR_NAME": "output", - "OUT_DIR": None, - "PLOT_DIR_NAME": "plot", - "PLOT_OUT_DIR": None, - "DATASET_DIR_UPDATE": False, - "PROTOCOL_N_PREVS": 21, - "PROTOCOL_REPEATS": 100, - "SAMPLE_SIZE": 1000, - "PLOT_ESTIMATORS": [], - "PLOT_STDEV": False, - } - _keys = list(_default_env.keys()) - - def __init__(self): - self.exec = [] - self.confs = [] - self.load_conf() - self._stack = C.deque([self.__getdict()]) - - def __setdict(self, d): - for k, v in d.items(): - super().__setattr__(k, v) - - def __getdict(self): - return {k: self.__getattribute__(k) for k in environ._keys} - - def __setattr__(self, __name: str, __value: Any) -> None: - if __name in environ._keys: - self._stack[-1][__name] = __value - super().__setattr__(__name, __value) - - def load_conf(self): - self.__setdict(environ._default_env) - - with open("conf.yaml", "r") as f: - confs = yaml.safe_load(f)["exec"] - - _global = confs["global"] - _estimators = set() - for pc in confs["plot_confs"].values(): - _estimators = _estimators.union(set(pc["PLOT_ESTIMATORS"])) - _global["COMP_ESTIMATORS"] = list(_estimators) - - self.__setdict(_global) - - self.confs = confs["confs"] - self.plot_confs = confs["plot_confs"] - - def get_confs(self): - self._stack.append(None) - for _conf in self.confs: - self._stack.pop() - self.__setdict(self._stack[-1]) - self.__setdict(_conf) - self._stack.append(self.__getdict()) - - yield copy.deepcopy(self._stack[-1]) - - self._stack.pop() - - def get_plot_confs(self): - self._stack.append(None) - for k, pc in self.plot_confs.items(): - self._stack.pop() - self.__setdict(self._stack[-1]) - self.__setdict(pc) - self._stack.append(self.__getdict()) - - name = self.DATASET_NAME - if self.DATASET_TARGET is not None: - name += f"_{self.DATASET_TARGET}" - name += f"_{k}" - yield name - - self._stack.pop() - - @property - def current(self): - return copy.deepcopy(self.__getdict()) - - -env = environ() - -if __name__ == "__main__": - stack = C.deque() - stack.append(-1) - - def __gen(stack: C.deque): - stack.append(None) - for i in range(5): - stack.pop() - stack.append(i) - yield stack[-1] - - stack.pop() - - print(stack) - - for i in __gen(stack): - print(stack, i) - - print(stack) +import collections as C +import copy +from typing import Any + +import yaml + + +class environ: + _instance = None + _default_env = { + "DATASET_NAME": None, + "DATASET_TARGET": None, + "METRICS": [], + "COMP_ESTIMATORS": [], + "DATASET_N_PREVS": 9, + "DATASET_PREVS": None, + "OUT_DIR_NAME": "output", + "OUT_DIR": None, + "PLOT_DIR_NAME": "plot", + "PLOT_OUT_DIR": None, + "DATASET_DIR_UPDATE": False, + "PROTOCOL_N_PREVS": 21, + "PROTOCOL_REPEATS": 100, + "SAMPLE_SIZE": 1000, + "PLOT_ESTIMATORS": [], + "PLOT_STDEV": False, + } + _keys = list(_default_env.keys()) + + def __init__(self): + self.exec = [] + self.confs = [] + self.load_conf() + self._stack = C.deque([self.__getdict()]) + + def __setdict(self, d): + for k, v in d.items(): + super().__setattr__(k, v) + + def __getdict(self): + return {k: self.__getattribute__(k) for k in environ._keys} + + def __setattr__(self, __name: str, __value: Any) -> None: + if __name in environ._keys: + self._stack[-1][__name] = __value + super().__setattr__(__name, __value) + + def load_conf(self): + self.__setdict(environ._default_env) + + with open("conf.yaml", "r") as f: + confs = yaml.safe_load(f)["exec"] + + _global = confs["global"] + _estimators = set() + for pc in confs["plot_confs"].values(): + _estimators = _estimators.union(set(pc["PLOT_ESTIMATORS"])) + _global["COMP_ESTIMATORS"] = list(_estimators) + + self.__setdict(_global) + + self.confs = confs["confs"] + self.plot_confs = confs["plot_confs"] + + def get_confs(self): + self._stack.append(None) + for _conf in self.confs: + self._stack.pop() + self.__setdict(self._stack[-1]) + self.__setdict(_conf) + self._stack.append(self.__getdict()) + + yield copy.deepcopy(self._stack[-1]) + + self._stack.pop() + + def get_plot_confs(self): + self._stack.append(None) + for k, pc in self.plot_confs.items(): + self._stack.pop() + self.__setdict(self._stack[-1]) + self.__setdict(pc) + self._stack.append(self.__getdict()) + + name = self.DATASET_NAME + if self.DATASET_TARGET is not None: + name += f"_{self.DATASET_TARGET}" + name += f"_{k}" + yield name + + self._stack.pop() + + @property + def current(self): + return copy.deepcopy(self.__getdict()) + + +env = environ() + +if __name__ == "__main__": + stack = C.deque() + stack.append(-1) + + def __gen(stack: C.deque): + stack.append(None) + for i in range(5): + stack.pop() + stack.append(i) + yield stack[-1] + + stack.pop() + + print(stack) + + for i in __gen(stack): + print(stack, i) + + print(stack) diff --git a/quacc/error.py b/quacc/error.py index 4393d72..748c07a 100644 --- a/quacc/error.py +++ b/quacc/error.py @@ -1,55 +1,55 @@ -import numpy as np - - -def from_name(err_name): - assert err_name in ERROR_NAMES, f"unknown error {err_name}" - callable_error = globals()[err_name] - return callable_error - - -# def f1(prev): -# # https://github.com/dice-group/gerbil/wiki/Precision,-Recall-and-F1-measure -# if prev[0] == 0 and prev[1] == 0 and prev[2] == 0: -# return 1.0 -# elif prev[0] == 0 and prev[1] > 0 and prev[2] == 0: -# return 0.0 -# elif prev[0] == 0 and prev[1] == 0 and prev[2] > 0: -# return float('NaN') -# else: -# recall = prev[0] / (prev[0] + prev[1]) -# precision = prev[0] / (prev[0] + prev[2]) -# return 2 * (precision * recall) / (precision + recall) - - -def f1(prev): - den = (2 * prev[3]) + prev[1] + prev[2] - if den == 0: - return 0.0 - else: - return (2 * prev[3]) / den - - -def f1e(prev): - return 1 - f1(prev) - - -def acc(prev: np.ndarray) -> float: - return (prev[0] + prev[3]) / np.sum(prev) - - -def accd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> np.ndarray: - vacc = np.vectorize(acc, signature="(m)->()") - a_tp = vacc(true_prevs) - a_ep = vacc(estim_prevs) - return np.abs(a_tp - a_ep) - - -def maccd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> float: - return accd(true_prevs, estim_prevs).mean() - - -ACCURACY_ERROR = {maccd} -ACCURACY_ERROR_SINGLE = {accd} -ACCURACY_ERROR_NAMES = {func.__name__ for func in ACCURACY_ERROR} -ACCURACY_ERROR_SINGLE_NAMES = {func.__name__ for func in ACCURACY_ERROR_SINGLE} -ERROR_NAMES = ACCURACY_ERROR_NAMES | ACCURACY_ERROR_SINGLE_NAMES +import numpy as np + + +def from_name(err_name): + assert err_name in ERROR_NAMES, f"unknown error {err_name}" + callable_error = globals()[err_name] + return callable_error + + +# def f1(prev): +# # https://github.com/dice-group/gerbil/wiki/Precision,-Recall-and-F1-measure +# if prev[0] == 0 and prev[1] == 0 and prev[2] == 0: +# return 1.0 +# elif prev[0] == 0 and prev[1] > 0 and prev[2] == 0: +# return 0.0 +# elif prev[0] == 0 and prev[1] == 0 and prev[2] > 0: +# return float('NaN') +# else: +# recall = prev[0] / (prev[0] + prev[1]) +# precision = prev[0] / (prev[0] + prev[2]) +# return 2 * (precision * recall) / (precision + recall) + + +def f1(prev): + den = (2 * prev[3]) + prev[1] + prev[2] + if den == 0: + return 0.0 + else: + return (2 * prev[3]) / den + + +def f1e(prev): + return 1 - f1(prev) + + +def acc(prev: np.ndarray) -> float: + return (prev[0] + prev[3]) / np.sum(prev) + + +def accd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> np.ndarray: + vacc = np.vectorize(acc, signature="(m)->()") + a_tp = vacc(true_prevs) + a_ep = vacc(estim_prevs) + return np.abs(a_tp - a_ep) + + +def maccd(true_prevs: np.ndarray, estim_prevs: np.ndarray) -> float: + return accd(true_prevs, estim_prevs).mean() + + +ACCURACY_ERROR = {maccd} +ACCURACY_ERROR_SINGLE = {accd} +ACCURACY_ERROR_NAMES = {func.__name__ for func in ACCURACY_ERROR} +ACCURACY_ERROR_SINGLE_NAMES = {func.__name__ for func in ACCURACY_ERROR_SINGLE} +ERROR_NAMES = ACCURACY_ERROR_NAMES | ACCURACY_ERROR_SINGLE_NAMES diff --git a/quacc/evaluation/__init__.py b/quacc/evaluation/__init__.py index 1851c4b..aa393ad 100644 --- a/quacc/evaluation/__init__.py +++ b/quacc/evaluation/__init__.py @@ -1,34 +1,34 @@ -from typing import Callable, Union - -import numpy as np -from quapy.protocol import AbstractProtocol, OnLabelledCollectionProtocol - -import quacc as qc - -from ..method.base import BaseAccuracyEstimator - - -def evaluate( - estimator: BaseAccuracyEstimator, - protocol: AbstractProtocol, - error_metric: Union[Callable | str], -) -> float: - if isinstance(error_metric, str): - error_metric = qc.error.from_name(error_metric) - - collator_bck_ = protocol.collator - protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection") - - estim_prevs, true_prevs = [], [] - for sample in protocol(): - e_sample = estimator.extend(sample) - estim_prev = estimator.estimate(e_sample.X, ext=True) - estim_prevs.append(estim_prev) - true_prevs.append(e_sample.prevalence()) - - protocol.collator = collator_bck_ - - true_prevs = np.array(true_prevs) - estim_prevs = np.array(estim_prevs) - - return error_metric(true_prevs, estim_prevs) +from typing import Callable, Union + +import numpy as np +from quapy.protocol import AbstractProtocol, OnLabelledCollectionProtocol + +import quacc as qc + +from ..method.base import BaseAccuracyEstimator + + +def evaluate( + estimator: BaseAccuracyEstimator, + protocol: AbstractProtocol, + error_metric: Union[Callable | str], +) -> float: + if isinstance(error_metric, str): + error_metric = qc.error.from_name(error_metric) + + collator_bck_ = protocol.collator + protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection") + + estim_prevs, true_prevs = [], [] + for sample in protocol(): + e_sample = estimator.extend(sample) + estim_prev = estimator.estimate(e_sample.X, ext=True) + estim_prevs.append(estim_prev) + true_prevs.append(e_sample.prevalence()) + + protocol.collator = collator_bck_ + + true_prevs = np.array(true_prevs) + estim_prevs = np.array(estim_prevs) + + return error_metric(true_prevs, estim_prevs) diff --git a/quacc/evaluation/baseline.py b/quacc/evaluation/baseline.py index c51351e..6532568 100644 --- a/quacc/evaluation/baseline.py +++ b/quacc/evaluation/baseline.py @@ -1,299 +1,299 @@ -from functools import wraps -from statistics import mean - -import numpy as np -import sklearn.metrics as metrics -from quapy.data import LabelledCollection -from quapy.protocol import AbstractStochasticSeededProtocol -from scipy.sparse import issparse -from sklearn.base import BaseEstimator -from sklearn.model_selection import cross_validate - -import baselines.atc as atc -import baselines.doc as doc -import baselines.impweight as iw -import baselines.rca as rcalib - -from .report import EvaluationReport - -_baselines = {} - - -def baseline(func): - @wraps(func) - def wrapper(c_model, validation, protocol): - return func(c_model, validation, protocol) - - _baselines[func.__name__] = wrapper - - return wrapper - - -@baseline -def kfcv( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict", -): - c_model_predict = getattr(c_model, predict_method) - - scoring = ["accuracy", "f1_macro"] - scores = cross_validate(c_model, validation.X, validation.y, scoring=scoring) - acc_score = mean(scores["test_accuracy"]) - f1_score = mean(scores["test_f1_macro"]) - - report = EvaluationReport(name="kfcv") - for test in protocol(): - test_preds = c_model_predict(test.X) - meta_acc = abs(acc_score - metrics.accuracy_score(test.y, test_preds)) - meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) - report.append_row( - test.prevalence(), - acc_score=acc_score, - f1_score=f1_score, - acc=meta_acc, - f1=meta_f1, - ) - - return report - - -@baseline -def ref( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, -): - c_model_predict = getattr(c_model, "predict") - report = EvaluationReport(name="ref") - for test in protocol(): - test_preds = c_model_predict(test.X) - report.append_row( - test.prevalence(), - acc_score=metrics.accuracy_score(test.y, test_preds), - f1_score=metrics.f1_score(test.y, test_preds), - ) - - return report - - -@baseline -def atc_mc( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict_proba", -): - """garg""" - c_model_predict = getattr(c_model, predict_method) - - ## Load ID validation data probs and labels - val_probs, val_labels = c_model_predict(validation.X), validation.y - - ## score function, e.g., negative entropy or argmax confidence - val_scores = atc.get_max_conf(val_probs) - val_preds = np.argmax(val_probs, axis=-1) - _, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds) - - report = EvaluationReport(name="atc_mc") - for test in protocol(): - ## Load OOD test data probs - test_probs = c_model_predict(test.X) - test_preds = np.argmax(test_probs, axis=-1) - test_scores = atc.get_max_conf(test_probs) - atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores) - meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds)) - f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs) - meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) - report.append_row( - test.prevalence(), - acc=meta_acc, - acc_score=atc_accuracy, - f1_score=f1_score, - f1=meta_f1, - ) - - return report - - -@baseline -def atc_ne( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict_proba", -): - """garg""" - c_model_predict = getattr(c_model, predict_method) - - ## Load ID validation data probs and labels - val_probs, val_labels = c_model_predict(validation.X), validation.y - - ## score function, e.g., negative entropy or argmax confidence - val_scores = atc.get_entropy(val_probs) - val_preds = np.argmax(val_probs, axis=-1) - _, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds) - - report = EvaluationReport(name="atc_ne") - for test in protocol(): - ## Load OOD test data probs - test_probs = c_model_predict(test.X) - test_preds = np.argmax(test_probs, axis=-1) - test_scores = atc.get_entropy(test_probs) - atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores) - meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds)) - f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs) - meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) - report.append_row( - test.prevalence(), - acc=meta_acc, - acc_score=atc_accuracy, - f1_score=f1_score, - f1=meta_f1, - ) - - return report - - -@baseline -def doc_feat( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict_proba", -): - c_model_predict = getattr(c_model, predict_method) - - val_probs, val_labels = c_model_predict(validation.X), validation.y - val_scores = np.max(val_probs, axis=-1) - val_preds = np.argmax(val_probs, axis=-1) - v1acc = np.mean(val_preds == val_labels) * 100 - - report = EvaluationReport(name="doc_feat") - for test in protocol(): - test_probs = c_model_predict(test.X) - test_preds = np.argmax(test_probs, axis=-1) - test_scores = np.max(test_probs, axis=-1) - score = (v1acc + doc.get_doc(val_scores, test_scores)) / 100.0 - meta_acc = abs(score - metrics.accuracy_score(test.y, test_preds)) - report.append_row(test.prevalence(), acc=meta_acc, acc_score=score) - - return report - - -@baseline -def rca( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict", -): - """elsahar19""" - c_model_predict = getattr(c_model, predict_method) - val_pred1 = c_model_predict(validation.X) - - report = EvaluationReport(name="rca") - for test in protocol(): - try: - test_pred = c_model_predict(test.X) - c_model2 = rcalib.clone_fit(c_model, test.X, test_pred) - c_model2_predict = getattr(c_model2, predict_method) - val_pred2 = c_model2_predict(validation.X) - rca_score = 1.0 - rcalib.get_score(val_pred1, val_pred2, validation.y) - meta_score = abs(rca_score - metrics.accuracy_score(test.y, test_pred)) - report.append_row(test.prevalence(), acc=meta_score, acc_score=rca_score) - except ValueError: - report.append_row( - test.prevalence(), acc=float("nan"), acc_score=float("nan") - ) - - return report - - -@baseline -def rca_star( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict", -): - """elsahar19""" - c_model_predict = getattr(c_model, predict_method) - validation1, validation2 = validation.split_stratified( - train_prop=0.5, random_state=0 - ) - val1_pred = c_model_predict(validation1.X) - c_model1 = rcalib.clone_fit(c_model, validation1.X, val1_pred) - c_model1_predict = getattr(c_model1, predict_method) - val2_pred1 = c_model1_predict(validation2.X) - - report = EvaluationReport(name="rca_star") - for test in protocol(): - try: - test_pred = c_model_predict(test.X) - c_model2 = rcalib.clone_fit(c_model, test.X, test_pred) - c_model2_predict = getattr(c_model2, predict_method) - val2_pred2 = c_model2_predict(validation2.X) - rca_star_score = 1.0 - rcalib.get_score( - val2_pred1, val2_pred2, validation2.y - ) - meta_score = abs(rca_star_score - metrics.accuracy_score(test.y, test_pred)) - report.append_row( - test.prevalence(), acc=meta_score, acc_score=rca_star_score - ) - except ValueError: - report.append_row( - test.prevalence(), acc=float("nan"), acc_score=float("nan") - ) - - return report - - -@baseline -def logreg( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict", -): - c_model_predict = getattr(c_model, predict_method) - - val_preds = c_model_predict(validation.X) - - report = EvaluationReport(name="logreg") - for test in protocol(): - wx = iw.logreg(validation.X, validation.y, test.X) - test_preds = c_model_predict(test.X) - estim_acc = iw.get_acc(val_preds, validation.y, wx) - true_acc = metrics.accuracy_score(test.y, test_preds) - meta_score = abs(estim_acc - true_acc) - report.append_row(test.prevalence(), acc=meta_score, acc_score=estim_acc) - - return report - - -@baseline -def kdex2( - c_model: BaseEstimator, - validation: LabelledCollection, - protocol: AbstractStochasticSeededProtocol, - predict_method="predict", -): - c_model_predict = getattr(c_model, predict_method) - - val_preds = c_model_predict(validation.X) - log_likelihood_val = iw.kdex2_lltr(validation.X) - Xval = validation.X.toarray() if issparse(validation.X) else validation.X - - report = EvaluationReport(name="kdex2") - for test in protocol(): - Xte = test.X.toarray() if issparse(test.X) else test.X - wx = iw.kdex2_weights(Xval, Xte, log_likelihood_val) - test_preds = c_model_predict(Xte) - estim_acc = iw.get_acc(val_preds, validation.y, wx) - true_acc = metrics.accuracy_score(test.y, test_preds) - meta_score = abs(estim_acc - true_acc) - report.append_row(test.prevalence(), acc=meta_score, acc_score=estim_acc) - - return report +from functools import wraps +from statistics import mean + +import numpy as np +import sklearn.metrics as metrics +from quapy.data import LabelledCollection +from quapy.protocol import AbstractStochasticSeededProtocol +from scipy.sparse import issparse +from sklearn.base import BaseEstimator +from sklearn.model_selection import cross_validate + +import baselines.atc as atc +import baselines.doc as doc +import baselines.impweight as iw +import baselines.rca as rcalib + +from .report import EvaluationReport + +_baselines = {} + + +def baseline(func): + @wraps(func) + def wrapper(c_model, validation, protocol): + return func(c_model, validation, protocol) + + _baselines[func.__name__] = wrapper + + return wrapper + + +@baseline +def kfcv( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict", +): + c_model_predict = getattr(c_model, predict_method) + + scoring = ["accuracy", "f1_macro"] + scores = cross_validate(c_model, validation.X, validation.y, scoring=scoring) + acc_score = mean(scores["test_accuracy"]) + f1_score = mean(scores["test_f1_macro"]) + + report = EvaluationReport(name="kfcv") + for test in protocol(): + test_preds = c_model_predict(test.X) + meta_acc = abs(acc_score - metrics.accuracy_score(test.y, test_preds)) + meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) + report.append_row( + test.prevalence(), + acc_score=acc_score, + f1_score=f1_score, + acc=meta_acc, + f1=meta_f1, + ) + + return report + + +@baseline +def ref( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, +): + c_model_predict = getattr(c_model, "predict") + report = EvaluationReport(name="ref") + for test in protocol(): + test_preds = c_model_predict(test.X) + report.append_row( + test.prevalence(), + acc_score=metrics.accuracy_score(test.y, test_preds), + f1_score=metrics.f1_score(test.y, test_preds), + ) + + return report + + +@baseline +def atc_mc( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict_proba", +): + """garg""" + c_model_predict = getattr(c_model, predict_method) + + ## Load ID validation data probs and labels + val_probs, val_labels = c_model_predict(validation.X), validation.y + + ## score function, e.g., negative entropy or argmax confidence + val_scores = atc.get_max_conf(val_probs) + val_preds = np.argmax(val_probs, axis=-1) + _, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds) + + report = EvaluationReport(name="atc_mc") + for test in protocol(): + ## Load OOD test data probs + test_probs = c_model_predict(test.X) + test_preds = np.argmax(test_probs, axis=-1) + test_scores = atc.get_max_conf(test_probs) + atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores) + meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds)) + f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs) + meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) + report.append_row( + test.prevalence(), + acc=meta_acc, + acc_score=atc_accuracy, + f1_score=f1_score, + f1=meta_f1, + ) + + return report + + +@baseline +def atc_ne( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict_proba", +): + """garg""" + c_model_predict = getattr(c_model, predict_method) + + ## Load ID validation data probs and labels + val_probs, val_labels = c_model_predict(validation.X), validation.y + + ## score function, e.g., negative entropy or argmax confidence + val_scores = atc.get_entropy(val_probs) + val_preds = np.argmax(val_probs, axis=-1) + _, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds) + + report = EvaluationReport(name="atc_ne") + for test in protocol(): + ## Load OOD test data probs + test_probs = c_model_predict(test.X) + test_preds = np.argmax(test_probs, axis=-1) + test_scores = atc.get_entropy(test_probs) + atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores) + meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds)) + f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs) + meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds)) + report.append_row( + test.prevalence(), + acc=meta_acc, + acc_score=atc_accuracy, + f1_score=f1_score, + f1=meta_f1, + ) + + return report + + +@baseline +def doc_feat( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict_proba", +): + c_model_predict = getattr(c_model, predict_method) + + val_probs, val_labels = c_model_predict(validation.X), validation.y + val_scores = np.max(val_probs, axis=-1) + val_preds = np.argmax(val_probs, axis=-1) + v1acc = np.mean(val_preds == val_labels) * 100 + + report = EvaluationReport(name="doc_feat") + for test in protocol(): + test_probs = c_model_predict(test.X) + test_preds = np.argmax(test_probs, axis=-1) + test_scores = np.max(test_probs, axis=-1) + score = (v1acc + doc.get_doc(val_scores, test_scores)) / 100.0 + meta_acc = abs(score - metrics.accuracy_score(test.y, test_preds)) + report.append_row(test.prevalence(), acc=meta_acc, acc_score=score) + + return report + + +@baseline +def rca( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict", +): + """elsahar19""" + c_model_predict = getattr(c_model, predict_method) + val_pred1 = c_model_predict(validation.X) + + report = EvaluationReport(name="rca") + for test in protocol(): + try: + test_pred = c_model_predict(test.X) + c_model2 = rcalib.clone_fit(c_model, test.X, test_pred) + c_model2_predict = getattr(c_model2, predict_method) + val_pred2 = c_model2_predict(validation.X) + rca_score = 1.0 - rcalib.get_score(val_pred1, val_pred2, validation.y) + meta_score = abs(rca_score - metrics.accuracy_score(test.y, test_pred)) + report.append_row(test.prevalence(), acc=meta_score, acc_score=rca_score) + except ValueError: + report.append_row( + test.prevalence(), acc=float("nan"), acc_score=float("nan") + ) + + return report + + +@baseline +def rca_star( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict", +): + """elsahar19""" + c_model_predict = getattr(c_model, predict_method) + validation1, validation2 = validation.split_stratified( + train_prop=0.5, random_state=0 + ) + val1_pred = c_model_predict(validation1.X) + c_model1 = rcalib.clone_fit(c_model, validation1.X, val1_pred) + c_model1_predict = getattr(c_model1, predict_method) + val2_pred1 = c_model1_predict(validation2.X) + + report = EvaluationReport(name="rca_star") + for test in protocol(): + try: + test_pred = c_model_predict(test.X) + c_model2 = rcalib.clone_fit(c_model, test.X, test_pred) + c_model2_predict = getattr(c_model2, predict_method) + val2_pred2 = c_model2_predict(validation2.X) + rca_star_score = 1.0 - rcalib.get_score( + val2_pred1, val2_pred2, validation2.y + ) + meta_score = abs(rca_star_score - metrics.accuracy_score(test.y, test_pred)) + report.append_row( + test.prevalence(), acc=meta_score, acc_score=rca_star_score + ) + except ValueError: + report.append_row( + test.prevalence(), acc=float("nan"), acc_score=float("nan") + ) + + return report + + +@baseline +def logreg( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict", +): + c_model_predict = getattr(c_model, predict_method) + + val_preds = c_model_predict(validation.X) + + report = EvaluationReport(name="logreg") + for test in protocol(): + wx = iw.logreg(validation.X, validation.y, test.X) + test_preds = c_model_predict(test.X) + estim_acc = iw.get_acc(val_preds, validation.y, wx) + true_acc = metrics.accuracy_score(test.y, test_preds) + meta_score = abs(estim_acc - true_acc) + report.append_row(test.prevalence(), acc=meta_score, acc_score=estim_acc) + + return report + + +@baseline +def kdex2( + c_model: BaseEstimator, + validation: LabelledCollection, + protocol: AbstractStochasticSeededProtocol, + predict_method="predict", +): + c_model_predict = getattr(c_model, predict_method) + + val_preds = c_model_predict(validation.X) + log_likelihood_val = iw.kdex2_lltr(validation.X) + Xval = validation.X.toarray() if issparse(validation.X) else validation.X + + report = EvaluationReport(name="kdex2") + for test in protocol(): + Xte = test.X.toarray() if issparse(test.X) else test.X + wx = iw.kdex2_weights(Xval, Xte, log_likelihood_val) + test_preds = c_model_predict(Xte) + estim_acc = iw.get_acc(val_preds, validation.y, wx) + true_acc = metrics.accuracy_score(test.y, test_preds) + meta_score = abs(estim_acc - true_acc) + report.append_row(test.prevalence(), acc=meta_score, acc_score=estim_acc) + + return report diff --git a/quacc/evaluation/comp.py b/quacc/evaluation/comp.py index 6df27a6..37ce1ae 100644 --- a/quacc/evaluation/comp.py +++ b/quacc/evaluation/comp.py @@ -1,128 +1,128 @@ -import multiprocessing -import time -from traceback import print_exception as traceback -from typing import List - -import numpy as np -import pandas as pd -import quapy as qp - -from quacc.dataset import Dataset -from quacc.environment import env -from quacc.evaluation import baseline, method -from quacc.evaluation.report import CompReport, DatasetReport, EvaluationReport -from quacc.evaluation.worker import estimate_worker -from quacc.logger import Logger - -pd.set_option("display.float_format", "{:.4f}".format) -qp.environ["SAMPLE_SIZE"] = env.SAMPLE_SIZE - - -class CompEstimatorName_: - def __init__(self, ce): - self.ce = ce - - def __getitem__(self, e: str | List[str]): - if isinstance(e, str): - return self.ce._CompEstimator__get(e)[0] - elif isinstance(e, list): - return list(self.ce._CompEstimator__get(e).keys()) - - -class CompEstimatorFunc_: - def __init__(self, ce): - self.ce = ce - - def __getitem__(self, e: str | List[str]): - if isinstance(e, str): - return self.ce._CompEstimator__get(e)[1] - elif isinstance(e, list): - return list(self.ce._CompEstimator__get(e).values()) - - -class CompEstimator: - __dict = method._methods | baseline._baselines - - def __get(cls, e: str | List[str]): - if isinstance(e, str): - try: - return (e, cls.__dict[e]) - except KeyError: - raise KeyError(f"Invalid estimator: estimator {e} does not exist") - elif isinstance(e, list): - _subtr = np.setdiff1d(e, list(cls.__dict.keys())) - if len(_subtr) > 0: - raise KeyError( - f"Invalid estimator: estimator {_subtr[0]} does not exist" - ) - - e_fun = {k: fun for k, fun in cls.__dict.items() if k in e} - if "ref" not in e: - e_fun["ref"] = cls.__dict["ref"] - - return e_fun - - @property - def name(self): - return CompEstimatorName_(self) - - @property - def func(self): - return CompEstimatorFunc_(self) - - -CE = CompEstimator() - - -def evaluate_comparison(dataset: Dataset, estimators=None) -> EvaluationReport: - log = Logger.logger() - # with multiprocessing.Pool(1) as pool: - with multiprocessing.Pool(len(estimators)) as pool: - dr = DatasetReport(dataset.name) - log.info(f"dataset {dataset.name}") - for d in dataset(): - log.info( - f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} started" - ) - tstart = time.time() - tasks = [ - (estim, d.train, d.validation, d.test) for estim in CE.func[estimators] - ] - results = [ - pool.apply_async(estimate_worker, t, {"_env": env, "q": Logger.queue()}) - for t in tasks - ] - - results_got = [] - for _r in results: - try: - r = _r.get() - if r["result"] is not None: - results_got.append(r) - except Exception as e: - log.warning( - f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}" - ) - - tend = time.time() - times = {r["name"]: r["time"] for r in results_got} - times["tot"] = tend - tstart - log.info( - f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} finished [took {times['tot']:.4f}s]" - ) - try: - cr = CompReport( - [r["result"] for r in results_got], - name=dataset.name, - train_prev=d.train_prev, - valid_prev=d.validation_prev, - times=times, - ) - except Exception as e: - log.warning( - f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}" - ) - traceback(e) - cr = None - dr += cr - return dr +import multiprocessing +import time +from traceback import print_exception as traceback +from typing import List + +import numpy as np +import pandas as pd +import quapy as qp + +from quacc.dataset import Dataset +from quacc.environment import env +from quacc.evaluation import baseline, method +from quacc.evaluation.report import CompReport, DatasetReport, EvaluationReport +from quacc.evaluation.worker import estimate_worker +from quacc.logger import Logger + +pd.set_option("display.float_format", "{:.4f}".format) +qp.environ["SAMPLE_SIZE"] = env.SAMPLE_SIZE + + +class CompEstimatorName_: + def __init__(self, ce): + self.ce = ce + + def __getitem__(self, e: str | List[str]): + if isinstance(e, str): + return self.ce._CompEstimator__get(e)[0] + elif isinstance(e, list): + return list(self.ce._CompEstimator__get(e).keys()) + + +class CompEstimatorFunc_: + def __init__(self, ce): + self.ce = ce + + def __getitem__(self, e: str | List[str]): + if isinstance(e, str): + return self.ce._CompEstimator__get(e)[1] + elif isinstance(e, list): + return list(self.ce._CompEstimator__get(e).values()) + + +class CompEstimator: + __dict = method._methods | baseline._baselines + + def __get(cls, e: str | List[str]): + if isinstance(e, str): + try: + return (e, cls.__dict[e]) + except KeyError: + raise KeyError(f"Invalid estimator: estimator {e} does not exist") + elif isinstance(e, list): + _subtr = np.setdiff1d(e, list(cls.__dict.keys())) + if len(_subtr) > 0: + raise KeyError( + f"Invalid estimator: estimator {_subtr[0]} does not exist" + ) + + e_fun = {k: fun for k, fun in cls.__dict.items() if k in e} + if "ref" not in e: + e_fun["ref"] = cls.__dict["ref"] + + return e_fun + + @property + def name(self): + return CompEstimatorName_(self) + + @property + def func(self): + return CompEstimatorFunc_(self) + + +CE = CompEstimator() + + +def evaluate_comparison(dataset: Dataset, estimators=None) -> EvaluationReport: + log = Logger.logger() + # with multiprocessing.Pool(1) as pool: + with multiprocessing.Pool(len(estimators)) as pool: + dr = DatasetReport(dataset.name) + log.info(f"dataset {dataset.name}") + for d in dataset(): + log.info( + f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} started" + ) + tstart = time.time() + tasks = [ + (estim, d.train, d.validation, d.test) for estim in CE.func[estimators] + ] + results = [ + pool.apply_async(estimate_worker, t, {"_env": env, "q": Logger.queue()}) + for t in tasks + ] + + results_got = [] + for _r in results: + try: + r = _r.get() + if r["result"] is not None: + results_got.append(r) + except Exception as e: + log.warning( + f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}" + ) + + tend = time.time() + times = {r["name"]: r["time"] for r in results_got} + times["tot"] = tend - tstart + log.info( + f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} finished [took {times['tot']:.4f}s]" + ) + try: + cr = CompReport( + [r["result"] for r in results_got], + name=dataset.name, + train_prev=d.train_prev, + valid_prev=d.validation_prev, + times=times, + ) + except Exception as e: + log.warning( + f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}" + ) + traceback(e) + cr = None + dr += cr + return dr diff --git a/quacc/evaluation/method.py b/quacc/evaluation/method.py index 0caf9c3..8419c52 100644 --- a/quacc/evaluation/method.py +++ b/quacc/evaluation/method.py @@ -1,305 +1,305 @@ -import inspect -from functools import wraps - -import numpy as np -from quapy.method.aggregative import PACC, SLD, CC -from quapy.protocol import UPP, AbstractProtocol -from sklearn.linear_model import LogisticRegression - -import quacc as qc -from quacc.evaluation.report import EvaluationReport -from quacc.method.model_selection import BQAEgsq, GridSearchAE, MCAEgsq - -from ..method.base import BQAE, MCAE, BaseAccuracyEstimator - -_methods = {} -_sld_param_grid = { - "q__classifier__C": np.logspace(-3, 3, 7), - "q__classifier__class_weight": [None, "balanced"], - "q__recalib": [None, "bcts"], - "q__exact_train_prev": [True], - "confidence": [None, "max_conf", "entropy"], -} -_pacc_param_grid = { - "q__classifier__C": np.logspace(-3, 3, 7), - "q__classifier__class_weight": [None, "balanced"], - "confidence": [None, "max_conf", "entropy"], -} -def method(func): - @wraps(func) - def wrapper(c_model, validation, protocol): - return func(c_model, validation, protocol) - - _methods[func.__name__] = wrapper - - return wrapper - - -def evaluation_report( - estimator: BaseAccuracyEstimator, - protocol: AbstractProtocol, -) -> EvaluationReport: - method_name = inspect.stack()[1].function - report = EvaluationReport(name=method_name) - for sample in protocol(): - e_sample = estimator.extend(sample) - estim_prev = estimator.estimate(e_sample.X, ext=True) - acc_score = qc.error.acc(estim_prev) - f1_score = qc.error.f1(estim_prev) - report.append_row( - sample.prevalence(), - acc_score=acc_score, - acc=abs(qc.error.acc(e_sample.prevalence()) - acc_score), - f1_score=f1_score, - f1=abs(qc.error.f1(e_sample.prevalence()) - f1_score), - ) - - return report - - -@method -def bin_sld(c_model, validation, protocol) -> EvaluationReport: - est = BQAE(c_model, SLD(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_sld(c_model, validation, protocol) -> EvaluationReport: - est = MCAE(c_model, SLD(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def binmc_sld(c_model, validation, protocol) -> EvaluationReport: - est = BQAE( - c_model, - SLD(LogisticRegression()), - confidence="max_conf", - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mulmc_sld(c_model, validation, protocol) -> EvaluationReport: - est = MCAE( - c_model, - SLD(LogisticRegression()), - confidence="max_conf", - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def binne_sld(c_model, validation, protocol) -> EvaluationReport: - est = BQAE( - c_model, - SLD(LogisticRegression()), - confidence="entropy", - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mulne_sld(c_model, validation, protocol) -> EvaluationReport: - est = MCAE( - c_model, - SLD(LogisticRegression()), - confidence="entropy", - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def bin_sld_gs(c_model, validation, protocol) -> EvaluationReport: - v_train, v_val = validation.split_stratified(0.6, random_state=0) - model = BQAE(c_model, SLD(LogisticRegression())) - est = GridSearchAE( - model=model, - param_grid=_sld_param_grid, - refit=False, - protocol=UPP(v_val, repeats=100), - verbose=True, - ).fit(v_train) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_sld_gs(c_model, validation, protocol) -> EvaluationReport: - v_train, v_val = validation.split_stratified(0.6, random_state=0) - model = MCAE(c_model, SLD(LogisticRegression())) - est = GridSearchAE( - model=model, - param_grid=_sld_param_grid, - refit=False, - protocol=UPP(v_val, repeats=100), - verbose=True, - ).fit(v_train) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def bin_sld_gsq(c_model, validation, protocol) -> EvaluationReport: - est = BQAEgsq( - c_model, - SLD(LogisticRegression()), - param_grid={ - "classifier__C": np.logspace(-3, 3, 7), - "classifier__class_weight": [None, "balanced"], - "recalib": [None, "bcts", "vs"], - }, - refit=False, - verbose=False, - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_sld_gsq(c_model, validation, protocol) -> EvaluationReport: - est = MCAEgsq( - c_model, - SLD(LogisticRegression()), - param_grid={ - "classifier__C": np.logspace(-3, 3, 7), - "classifier__class_weight": [None, "balanced"], - "recalib": [None, "bcts", "vs"], - }, - refit=False, - verbose=False, - ).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def bin_pacc(c_model, validation, protocol) -> EvaluationReport: - est = BQAE(c_model, PACC(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_pacc(c_model, validation, protocol) -> EvaluationReport: - est = MCAE(c_model, PACC(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def binmc_pacc(c_model, validation, protocol) -> EvaluationReport: - est = BQAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mulmc_pacc(c_model, validation, protocol) -> EvaluationReport: - est = MCAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def binne_pacc(c_model, validation, protocol) -> EvaluationReport: - est = BQAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mulne_pacc(c_model, validation, protocol) -> EvaluationReport: - est = MCAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def bin_pacc_gs(c_model, validation, protocol) -> EvaluationReport: - v_train, v_val = validation.split_stratified(0.6, random_state=0) - model = BQAE(c_model, PACC(LogisticRegression())) - est = GridSearchAE( - model=model, - param_grid=_pacc_param_grid, - refit=False, - protocol=UPP(v_val, repeats=100), - verbose=False, - ).fit(v_train) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport: - v_train, v_val = validation.split_stratified(0.6, random_state=0) - model = MCAE(c_model, PACC(LogisticRegression())) - est = GridSearchAE( - model=model, - param_grid=_pacc_param_grid, - refit=False, - protocol=UPP(v_val, repeats=100), - verbose=False, - ).fit(v_train) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def bin_cc(c_model, validation, protocol) -> EvaluationReport: - est = BQAE(c_model, CC(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) - - -@method -def mul_cc(c_model, validation, protocol) -> EvaluationReport: - est = MCAE(c_model, CC(LogisticRegression())).fit(validation) - return evaluation_report( - estimator=est, - protocol=protocol, - ) +import inspect +from functools import wraps + +import numpy as np +from quapy.method.aggregative import PACC, SLD, CC +from quapy.protocol import UPP, AbstractProtocol +from sklearn.linear_model import LogisticRegression + +import quacc as qc +from quacc.evaluation.report import EvaluationReport +from quacc.method.model_selection import BQAEgsq, GridSearchAE, MCAEgsq + +from ..method.base import BQAE, MCAE, BaseAccuracyEstimator + +_methods = {} +_sld_param_grid = { + "q__classifier__C": np.logspace(-3, 3, 7), + "q__classifier__class_weight": [None, "balanced"], + "q__recalib": [None, "bcts"], + "q__exact_train_prev": [True], + "confidence": [None, "max_conf", "entropy"], +} +_pacc_param_grid = { + "q__classifier__C": np.logspace(-3, 3, 7), + "q__classifier__class_weight": [None, "balanced"], + "confidence": [None, "max_conf", "entropy"], +} +def method(func): + @wraps(func) + def wrapper(c_model, validation, protocol): + return func(c_model, validation, protocol) + + _methods[func.__name__] = wrapper + + return wrapper + + +def evaluation_report( + estimator: BaseAccuracyEstimator, + protocol: AbstractProtocol, +) -> EvaluationReport: + method_name = inspect.stack()[1].function + report = EvaluationReport(name=method_name) + for sample in protocol(): + e_sample = estimator.extend(sample) + estim_prev = estimator.estimate(e_sample.X, ext=True) + acc_score = qc.error.acc(estim_prev) + f1_score = qc.error.f1(estim_prev) + report.append_row( + sample.prevalence(), + acc_score=acc_score, + acc=abs(qc.error.acc(e_sample.prevalence()) - acc_score), + f1_score=f1_score, + f1=abs(qc.error.f1(e_sample.prevalence()) - f1_score), + ) + + return report + + +@method +def bin_sld(c_model, validation, protocol) -> EvaluationReport: + est = BQAE(c_model, SLD(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_sld(c_model, validation, protocol) -> EvaluationReport: + est = MCAE(c_model, SLD(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def binmc_sld(c_model, validation, protocol) -> EvaluationReport: + est = BQAE( + c_model, + SLD(LogisticRegression()), + confidence="max_conf", + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mulmc_sld(c_model, validation, protocol) -> EvaluationReport: + est = MCAE( + c_model, + SLD(LogisticRegression()), + confidence="max_conf", + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def binne_sld(c_model, validation, protocol) -> EvaluationReport: + est = BQAE( + c_model, + SLD(LogisticRegression()), + confidence="entropy", + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mulne_sld(c_model, validation, protocol) -> EvaluationReport: + est = MCAE( + c_model, + SLD(LogisticRegression()), + confidence="entropy", + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def bin_sld_gs(c_model, validation, protocol) -> EvaluationReport: + v_train, v_val = validation.split_stratified(0.6, random_state=0) + model = BQAE(c_model, SLD(LogisticRegression())) + est = GridSearchAE( + model=model, + param_grid=_sld_param_grid, + refit=False, + protocol=UPP(v_val, repeats=100), + verbose=True, + ).fit(v_train) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_sld_gs(c_model, validation, protocol) -> EvaluationReport: + v_train, v_val = validation.split_stratified(0.6, random_state=0) + model = MCAE(c_model, SLD(LogisticRegression())) + est = GridSearchAE( + model=model, + param_grid=_sld_param_grid, + refit=False, + protocol=UPP(v_val, repeats=100), + verbose=True, + ).fit(v_train) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def bin_sld_gsq(c_model, validation, protocol) -> EvaluationReport: + est = BQAEgsq( + c_model, + SLD(LogisticRegression()), + param_grid={ + "classifier__C": np.logspace(-3, 3, 7), + "classifier__class_weight": [None, "balanced"], + "recalib": [None, "bcts", "vs"], + }, + refit=False, + verbose=False, + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_sld_gsq(c_model, validation, protocol) -> EvaluationReport: + est = MCAEgsq( + c_model, + SLD(LogisticRegression()), + param_grid={ + "classifier__C": np.logspace(-3, 3, 7), + "classifier__class_weight": [None, "balanced"], + "recalib": [None, "bcts", "vs"], + }, + refit=False, + verbose=False, + ).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def bin_pacc(c_model, validation, protocol) -> EvaluationReport: + est = BQAE(c_model, PACC(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_pacc(c_model, validation, protocol) -> EvaluationReport: + est = MCAE(c_model, PACC(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def binmc_pacc(c_model, validation, protocol) -> EvaluationReport: + est = BQAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mulmc_pacc(c_model, validation, protocol) -> EvaluationReport: + est = MCAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def binne_pacc(c_model, validation, protocol) -> EvaluationReport: + est = BQAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mulne_pacc(c_model, validation, protocol) -> EvaluationReport: + est = MCAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def bin_pacc_gs(c_model, validation, protocol) -> EvaluationReport: + v_train, v_val = validation.split_stratified(0.6, random_state=0) + model = BQAE(c_model, PACC(LogisticRegression())) + est = GridSearchAE( + model=model, + param_grid=_pacc_param_grid, + refit=False, + protocol=UPP(v_val, repeats=100), + verbose=False, + ).fit(v_train) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport: + v_train, v_val = validation.split_stratified(0.6, random_state=0) + model = MCAE(c_model, PACC(LogisticRegression())) + est = GridSearchAE( + model=model, + param_grid=_pacc_param_grid, + refit=False, + protocol=UPP(v_val, repeats=100), + verbose=False, + ).fit(v_train) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def bin_cc(c_model, validation, protocol) -> EvaluationReport: + est = BQAE(c_model, CC(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) + + +@method +def mul_cc(c_model, validation, protocol) -> EvaluationReport: + est = MCAE(c_model, CC(LogisticRegression())).fit(validation) + return evaluation_report( + estimator=est, + protocol=protocol, + ) diff --git a/quacc/evaluation/report.py b/quacc/evaluation/report.py index 7421a2b..22f16ad 100644 --- a/quacc/evaluation/report.py +++ b/quacc/evaluation/report.py @@ -1,565 +1,565 @@ -from pathlib import Path -from typing import List, Tuple - -import numpy as np -import pandas as pd - -from quacc import plot -from quacc.environment import env -from quacc.utils import fmt_line_md - - -def _get_metric(metric: str): - return slice(None) if metric is None else metric - - -def _get_estimators(estimators: List[str], cols: np.ndarray): - return slice(None) if estimators is None else cols[np.in1d(cols, estimators)] - - -class EvaluationReport: - def __init__(self, name=None): - self.data: pd.DataFrame = None - self.fit_score = None - self.name = name if name is not None else "default" - - def append_row(self, basep: np.ndarray | Tuple, **row): - bp = basep[1] - _keys, _values = zip(*row.items()) - # _keys = list(row.keys()) - # _values = list(row.values()) - - if self.data is None: - _idx = 0 - self.data = pd.DataFrame( - {k: [v] for k, v in row.items()}, - index=pd.MultiIndex.from_tuples([(bp, _idx)]), - columns=_keys, - ) - return - - _idx = len(self.data.loc[(bp,), :]) if (bp,) in self.data.index else 0 - not_in_data = np.setdiff1d(list(row.keys()), self.data.columns.unique(0)) - self.data.loc[:, not_in_data] = np.nan - self.data.loc[(bp, _idx), :] = row - return - - @property - def columns(self) -> np.ndarray: - return self.data.columns.unique(0) - - @property - def prevs(self): - return np.sort(self.data.index.unique(0)) - - -class CompReport: - def __init__( - self, - reports: List[EvaluationReport], - name="default", - train_prev=None, - valid_prev=None, - times=None, - ): - self._data = ( - pd.concat( - [er.data for er in reports], - keys=[er.name for er in reports], - axis=1, - ) - .swaplevel(0, 1, axis=1) - .sort_index(axis=1, level=0, sort_remaining=False) - .sort_index(axis=0, level=0) - ) - - self.fit_scores = { - er.name: er.fit_score for er in reports if er.fit_score is not None - } - self.train_prev = train_prev - self.valid_prev = valid_prev - self.times = times - - @property - def prevs(self) -> np.ndarray: - return np.sort(self._data.index.unique(0)) - - @property - def np_prevs(self) -> np.ndarray: - return np.around([(1.0 - p, p) for p in self.prevs], decimals=2) - - def data(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame: - _metric = _get_metric(metric) - _estimators = _get_estimators(estimators, self._data.columns.unique(1)) - f_data: pd.DataFrame = self._data.copy().loc[:, (_metric, _estimators)] - - if len(f_data.columns.unique(0)) == 1: - f_data = f_data.droplevel(level=0, axis=1) - - return f_data - - def shift_data( - self, metric: str = None, estimators: List[str] = None - ) -> pd.DataFrame: - shift_idx_0 = np.around( - np.abs( - self._data.index.get_level_values(0).to_numpy() - self.train_prev[1] - ), - decimals=2, - ) - - shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype=" pd.DataFrame: - f_dict = self.data(metric=metric, estimators=estimators) - return f_dict.groupby(level=0).mean() - - def stdev_by_prevs( - self, metric: str = None, estimators: List[str] = None - ) -> pd.DataFrame: - f_dict = self.data(metric=metric, estimators=estimators) - return f_dict.groupby(level=0).std() - - def table(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame: - f_data = self.data(metric=metric, estimators=estimators) - avg_p = f_data.groupby(level=0).mean() - avg_p.loc["avg", :] = f_data.mean() - return avg_p - - def get_plots( - self, mode="delta", metric="acc", estimators=None, conf="default", stdev=False - ) -> List[Tuple[str, Path]]: - if mode == "delta": - avg_data = self.avg_by_prevs(metric=metric, estimators=estimators) - return plot.plot_delta( - base_prevs=self.np_prevs, - columns=avg_data.columns.to_numpy(), - data=avg_data.T.to_numpy(), - metric=metric, - name=conf, - train_prev=self.train_prev, - ) - elif mode == "delta_stdev": - avg_data = self.avg_by_prevs(metric=metric, estimators=estimators) - st_data = self.stdev_by_prevs(metric=metric, estimators=estimators) - return plot.plot_delta( - base_prevs=self.np_prevs, - columns=avg_data.columns.to_numpy(), - data=avg_data.T.to_numpy(), - metric=metric, - name=conf, - train_prev=self.train_prev, - stdevs=st_data.T.to_numpy(), - ) - elif mode == "diagonal": - f_data = self.data(metric=metric + "_score", estimators=estimators) - ref: pd.Series = f_data.loc[:, "ref"] - f_data.drop(columns=["ref"], inplace=True) - return plot.plot_diagonal( - reference=ref.to_numpy(), - columns=f_data.columns.to_numpy(), - data=f_data.T.to_numpy(), - metric=metric, - name=conf, - train_prev=self.train_prev, - ) - elif mode == "shift": - _shift_data = self.shift_data(metric=metric, estimators=estimators) - shift_avg = _shift_data.groupby(level=0).mean() - shift_counts = _shift_data.groupby(level=0).count() - shift_prevs = np.around( - [(1.0 - p, p) for p in np.sort(shift_avg.index.unique(0))], - decimals=2, - ) - return plot.plot_shift( - shift_prevs=shift_prevs, - columns=shift_avg.columns.to_numpy(), - data=shift_avg.T.to_numpy(), - metric=metric, - name=conf, - train_prev=self.train_prev, - counts=shift_counts.T.to_numpy(), - ) - - def to_md(self, conf="default", metric="acc", estimators=None, stdev=False) -> str: - res = f"## {int(np.around(self.train_prev, decimals=2)[1]*100)}% positives\n" - res += fmt_line_md(f"train: {str(self.train_prev)}") - res += fmt_line_md(f"validation: {str(self.valid_prev)}") - for k, v in self.times.items(): - res += fmt_line_md(f"{k}: {v:.3f}s") - res += "\n" - res += self.table(metric=metric, estimators=estimators).to_html() + "\n\n" - - plot_modes = np.array(["delta", "diagonal", "shift"], dtype="object") - if stdev: - whd = np.where(plot_modes == "delta")[0] - if len(whd) > 0: - plot_modes = np.insert(plot_modes, whd + 1, "delta_stdev") - for mode in plot_modes: - op = self.get_plots( - mode=mode, - metric=metric, - estimators=estimators, - conf=conf, - stdev=stdev, - ) - res += f"![plot_{mode}]({op.relative_to(env.OUT_DIR).as_posix()})\n" - - return res - - -class DatasetReport: - def __init__(self, name, crs=None): - self.name = name - self.crs: List[CompReport] = [] if crs is None else crs - - def data(self, metric: str = None, estimators: str = None) -> pd.DataFrame: - def _cr_train_prev(cr: CompReport): - return cr.train_prev[1] - - def _cr_data(cr: CompReport): - return cr.data(metric, estimators) - - _crs_sorted = sorted( - [(_cr_train_prev(cr), _cr_data(cr)) for cr in self.crs], - key=lambda cr: len(cr[1].columns), - reverse=True, - ) - _crs_train, _crs_data = zip(*_crs_sorted) - - _data = pd.concat(_crs_data, axis=0, keys=np.around(_crs_train, decimals=2)) - _data = _data.sort_index(axis=0, level=0) - return _data - - def shift_data(self, metric: str = None, estimators: str = None) -> pd.DataFrame: - _shift_data: pd.DataFrame = pd.concat( - sorted( - [cr.shift_data(metric, estimators) for cr in self.crs], - key=lambda d: len(d.columns), - reverse=True, - ), - axis=0, - ) - - shift_idx_0 = _shift_data.index.get_level_values(0) - - shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype=" 0: - a = np.insert(a, whb + 1, "pippo") - print(a) - print("-" * 100) - - dff: pd.DataFrame = df.loc[:, ("a",)] - print(dff.to_dict(orient="list")) - dff = dff.drop(columns=["v"]) - print(dff) - s: pd.Series = dff.loc[:, "e"] - print(s) - print(s.to_numpy()) - print(type(s.to_numpy())) - print("-" * 100) - - df3 = pd.concat([df, df], axis=0, keys=[0.5, 0.3]).sort_index(axis=0, level=0) - print(df3) - df3n = pd.concat([df, df], axis=0).sort_index(axis=0, level=0) - print(df3n) - df = df3 - print("-" * 100) - - print(df.groupby(level=1).mean(), df.groupby(level=1).count()) - print("-" * 100) - - print(df) - for ls in df.T.to_numpy(): - print(ls) - print("-" * 100) - - -if __name__ == "__main__": - __test() +from pathlib import Path +from typing import List, Tuple + +import numpy as np +import pandas as pd + +from quacc import plot +from quacc.environment import env +from quacc.utils import fmt_line_md + + +def _get_metric(metric: str): + return slice(None) if metric is None else metric + + +def _get_estimators(estimators: List[str], cols: np.ndarray): + return slice(None) if estimators is None else cols[np.in1d(cols, estimators)] + + +class EvaluationReport: + def __init__(self, name=None): + self.data: pd.DataFrame = None + self.fit_score = None + self.name = name if name is not None else "default" + + def append_row(self, basep: np.ndarray | Tuple, **row): + bp = basep[1] + _keys, _values = zip(*row.items()) + # _keys = list(row.keys()) + # _values = list(row.values()) + + if self.data is None: + _idx = 0 + self.data = pd.DataFrame( + {k: [v] for k, v in row.items()}, + index=pd.MultiIndex.from_tuples([(bp, _idx)]), + columns=_keys, + ) + return + + _idx = len(self.data.loc[(bp,), :]) if (bp,) in self.data.index else 0 + not_in_data = np.setdiff1d(list(row.keys()), self.data.columns.unique(0)) + self.data.loc[:, not_in_data] = np.nan + self.data.loc[(bp, _idx), :] = row + return + + @property + def columns(self) -> np.ndarray: + return self.data.columns.unique(0) + + @property + def prevs(self): + return np.sort(self.data.index.unique(0)) + + +class CompReport: + def __init__( + self, + reports: List[EvaluationReport], + name="default", + train_prev=None, + valid_prev=None, + times=None, + ): + self._data = ( + pd.concat( + [er.data for er in reports], + keys=[er.name for er in reports], + axis=1, + ) + .swaplevel(0, 1, axis=1) + .sort_index(axis=1, level=0, sort_remaining=False) + .sort_index(axis=0, level=0) + ) + + self.fit_scores = { + er.name: er.fit_score for er in reports if er.fit_score is not None + } + self.train_prev = train_prev + self.valid_prev = valid_prev + self.times = times + + @property + def prevs(self) -> np.ndarray: + return np.sort(self._data.index.unique(0)) + + @property + def np_prevs(self) -> np.ndarray: + return np.around([(1.0 - p, p) for p in self.prevs], decimals=2) + + def data(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame: + _metric = _get_metric(metric) + _estimators = _get_estimators(estimators, self._data.columns.unique(1)) + f_data: pd.DataFrame = self._data.copy().loc[:, (_metric, _estimators)] + + if len(f_data.columns.unique(0)) == 1: + f_data = f_data.droplevel(level=0, axis=1) + + return f_data + + def shift_data( + self, metric: str = None, estimators: List[str] = None + ) -> pd.DataFrame: + shift_idx_0 = np.around( + np.abs( + self._data.index.get_level_values(0).to_numpy() - self.train_prev[1] + ), + decimals=2, + ) + + shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype=" pd.DataFrame: + f_dict = self.data(metric=metric, estimators=estimators) + return f_dict.groupby(level=0).mean() + + def stdev_by_prevs( + self, metric: str = None, estimators: List[str] = None + ) -> pd.DataFrame: + f_dict = self.data(metric=metric, estimators=estimators) + return f_dict.groupby(level=0).std() + + def table(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame: + f_data = self.data(metric=metric, estimators=estimators) + avg_p = f_data.groupby(level=0).mean() + avg_p.loc["avg", :] = f_data.mean() + return avg_p + + def get_plots( + self, mode="delta", metric="acc", estimators=None, conf="default", stdev=False + ) -> List[Tuple[str, Path]]: + if mode == "delta": + avg_data = self.avg_by_prevs(metric=metric, estimators=estimators) + return plot.plot_delta( + base_prevs=self.np_prevs, + columns=avg_data.columns.to_numpy(), + data=avg_data.T.to_numpy(), + metric=metric, + name=conf, + train_prev=self.train_prev, + ) + elif mode == "delta_stdev": + avg_data = self.avg_by_prevs(metric=metric, estimators=estimators) + st_data = self.stdev_by_prevs(metric=metric, estimators=estimators) + return plot.plot_delta( + base_prevs=self.np_prevs, + columns=avg_data.columns.to_numpy(), + data=avg_data.T.to_numpy(), + metric=metric, + name=conf, + train_prev=self.train_prev, + stdevs=st_data.T.to_numpy(), + ) + elif mode == "diagonal": + f_data = self.data(metric=metric + "_score", estimators=estimators) + ref: pd.Series = f_data.loc[:, "ref"] + f_data.drop(columns=["ref"], inplace=True) + return plot.plot_diagonal( + reference=ref.to_numpy(), + columns=f_data.columns.to_numpy(), + data=f_data.T.to_numpy(), + metric=metric, + name=conf, + train_prev=self.train_prev, + ) + elif mode == "shift": + _shift_data = self.shift_data(metric=metric, estimators=estimators) + shift_avg = _shift_data.groupby(level=0).mean() + shift_counts = _shift_data.groupby(level=0).count() + shift_prevs = np.around( + [(1.0 - p, p) for p in np.sort(shift_avg.index.unique(0))], + decimals=2, + ) + return plot.plot_shift( + shift_prevs=shift_prevs, + columns=shift_avg.columns.to_numpy(), + data=shift_avg.T.to_numpy(), + metric=metric, + name=conf, + train_prev=self.train_prev, + counts=shift_counts.T.to_numpy(), + ) + + def to_md(self, conf="default", metric="acc", estimators=None, stdev=False) -> str: + res = f"## {int(np.around(self.train_prev, decimals=2)[1]*100)}% positives\n" + res += fmt_line_md(f"train: {str(self.train_prev)}") + res += fmt_line_md(f"validation: {str(self.valid_prev)}") + for k, v in self.times.items(): + res += fmt_line_md(f"{k}: {v:.3f}s") + res += "\n" + res += self.table(metric=metric, estimators=estimators).to_html() + "\n\n" + + plot_modes = np.array(["delta", "diagonal", "shift"], dtype="object") + if stdev: + whd = np.where(plot_modes == "delta")[0] + if len(whd) > 0: + plot_modes = np.insert(plot_modes, whd + 1, "delta_stdev") + for mode in plot_modes: + op = self.get_plots( + mode=mode, + metric=metric, + estimators=estimators, + conf=conf, + stdev=stdev, + ) + res += f"![plot_{mode}]({op.relative_to(env.OUT_DIR).as_posix()})\n" + + return res + + +class DatasetReport: + def __init__(self, name, crs=None): + self.name = name + self.crs: List[CompReport] = [] if crs is None else crs + + def data(self, metric: str = None, estimators: str = None) -> pd.DataFrame: + def _cr_train_prev(cr: CompReport): + return cr.train_prev[1] + + def _cr_data(cr: CompReport): + return cr.data(metric, estimators) + + _crs_sorted = sorted( + [(_cr_train_prev(cr), _cr_data(cr)) for cr in self.crs], + key=lambda cr: len(cr[1].columns), + reverse=True, + ) + _crs_train, _crs_data = zip(*_crs_sorted) + + _data = pd.concat(_crs_data, axis=0, keys=np.around(_crs_train, decimals=2)) + _data = _data.sort_index(axis=0, level=0) + return _data + + def shift_data(self, metric: str = None, estimators: str = None) -> pd.DataFrame: + _shift_data: pd.DataFrame = pd.concat( + sorted( + [cr.shift_data(metric, estimators) for cr in self.crs], + key=lambda d: len(d.columns), + reverse=True, + ), + axis=0, + ) + + shift_idx_0 = _shift_data.index.get_level_values(0) + + shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype=" 0: + a = np.insert(a, whb + 1, "pippo") + print(a) + print("-" * 100) + + dff: pd.DataFrame = df.loc[:, ("a",)] + print(dff.to_dict(orient="list")) + dff = dff.drop(columns=["v"]) + print(dff) + s: pd.Series = dff.loc[:, "e"] + print(s) + print(s.to_numpy()) + print(type(s.to_numpy())) + print("-" * 100) + + df3 = pd.concat([df, df], axis=0, keys=[0.5, 0.3]).sort_index(axis=0, level=0) + print(df3) + df3n = pd.concat([df, df], axis=0).sort_index(axis=0, level=0) + print(df3n) + df = df3 + print("-" * 100) + + print(df.groupby(level=1).mean(), df.groupby(level=1).count()) + print("-" * 100) + + print(df) + for ls in df.T.to_numpy(): + print(ls) + print("-" * 100) + + +if __name__ == "__main__": + __test() diff --git a/quacc/evaluation/worker.py b/quacc/evaluation/worker.py index 2ff93f6..e1d02b0 100644 --- a/quacc/evaluation/worker.py +++ b/quacc/evaluation/worker.py @@ -1,44 +1,44 @@ -import time -from traceback import print_exception as traceback - -import quapy as qp -from quapy.protocol import APP -from sklearn.linear_model import LogisticRegression - -from quacc.logger import SubLogger - - -def estimate_worker(_estimate, train, validation, test, _env=None, q=None): - qp.environ["SAMPLE_SIZE"] = _env.SAMPLE_SIZE - SubLogger.setup(q) - log = SubLogger.logger() - - model = LogisticRegression() - - model.fit(*train.Xy) - protocol = APP( - test, - n_prevalences=_env.PROTOCOL_N_PREVS, - repeats=_env.PROTOCOL_REPEATS, - return_type="labelled_collection", - ) - start = time.time() - try: - result = _estimate(model, validation, protocol) - except Exception as e: - log.warning(f"Method {_estimate.__name__} failed. Exception: {e}") - traceback(e) - return { - "name": _estimate.__name__, - "result": None, - "time": 0, - } - - end = time.time() - log.info(f"{_estimate.__name__} finished [took {end-start:.4f}s]") - - return { - "name": _estimate.__name__, - "result": result, - "time": end - start, - } +import time +from traceback import print_exception as traceback + +import quapy as qp +from quapy.protocol import APP +from sklearn.linear_model import LogisticRegression + +from quacc.logger import SubLogger + + +def estimate_worker(_estimate, train, validation, test, _env=None, q=None): + qp.environ["SAMPLE_SIZE"] = _env.SAMPLE_SIZE + SubLogger.setup(q) + log = SubLogger.logger() + + model = LogisticRegression() + + model.fit(*train.Xy) + protocol = APP( + test, + n_prevalences=_env.PROTOCOL_N_PREVS, + repeats=_env.PROTOCOL_REPEATS, + return_type="labelled_collection", + ) + start = time.time() + try: + result = _estimate(model, validation, protocol) + except Exception as e: + log.warning(f"Method {_estimate.__name__} failed. Exception: {e}") + traceback(e) + return { + "name": _estimate.__name__, + "result": None, + "time": 0, + } + + end = time.time() + log.info(f"{_estimate.__name__} finished [took {end-start:.4f}s]") + + return { + "name": _estimate.__name__, + "result": result, + "time": end - start, + } diff --git a/quacc/logger.py b/quacc/logger.py index 5725606..acf7eb1 100644 --- a/quacc/logger.py +++ b/quacc/logger.py @@ -1,136 +1,136 @@ -import logging -import logging.handlers -import multiprocessing -import threading -from pathlib import Path - - -class Logger: - __logger_file = "quacc.log" - __logger_name = "queue_logger" - __manager = None - __queue = None - __thread = None - __setup = False - __handlers = [] - - @classmethod - def __logger_listener(cls, q): - while True: - record = q.get() - if record is None: - break - root = logging.getLogger("listener") - root.handle(record) - - @classmethod - def setup(cls): - if cls.__setup: - return - - # setup root - root = logging.getLogger("listener") - root.setLevel(logging.DEBUG) - rh = logging.FileHandler(cls.__logger_file, mode="a") - rh.setLevel(logging.DEBUG) - root.addHandler(rh) - - # setup logger - if cls.__manager is None: - cls.__manager = multiprocessing.Manager() - - if cls.__queue is None: - cls.__queue = cls.__manager.Queue() - - logger = logging.getLogger(cls.__logger_name) - logger.setLevel(logging.DEBUG) - qh = logging.handlers.QueueHandler(cls.__queue) - qh.setLevel(logging.DEBUG) - qh.setFormatter( - logging.Formatter( - fmt="%(asctime)s| %(levelname)-8s %(message)s", - datefmt="%d/%m/%y %H:%M:%S", - ) - ) - logger.addHandler(qh) - - # start listener - cls.__thread = threading.Thread( - target=cls.__logger_listener, - args=(cls.__queue,), - ) - cls.__thread.start() - - cls.__setup = True - - @classmethod - def add_handler(cls, path: Path): - root = logging.getLogger("listener") - rh = logging.FileHandler(path, mode="a") - rh.setLevel(logging.DEBUG) - cls.__handlers.append(rh) - root.addHandler(rh) - - @classmethod - def clear_handlers(cls): - root = logging.getLogger("listener") - for h in cls.__handlers: - root.removeHandler(h) - cls.__handlers.clear() - - @classmethod - def queue(cls): - if not cls.__setup: - cls.setup() - - return cls.__queue - - @classmethod - def logger(cls): - if not cls.__setup: - cls.setup() - - return logging.getLogger(cls.__logger_name) - - @classmethod - def close(cls): - if cls.__setup and cls.__thread is not None: - root = logging.getLogger("listener") - root.info("-" * 100) - cls.__queue.put(None) - cls.__thread.join() - # cls.__manager.close() - - -class SubLogger: - __queue = None - __setup = False - - @classmethod - def setup(cls, q): - if cls.__setup: - return - - cls.__queue = q - - # setup root - root = logging.getLogger() - root.setLevel(logging.DEBUG) - rh = logging.handlers.QueueHandler(q) - rh.setLevel(logging.DEBUG) - rh.setFormatter( - logging.Formatter( - fmt="%(asctime)s| %(levelname)-12s%(message)s", - datefmt="%d/%m/%y %H:%M:%S", - ) - ) - root.addHandler(rh) - - cls.__setup = True - - @classmethod - def logger(cls): - if not cls.__setup: - return None - - return logging.getLogger() +import logging +import logging.handlers +import multiprocessing +import threading +from pathlib import Path + + +class Logger: + __logger_file = "quacc.log" + __logger_name = "queue_logger" + __manager = None + __queue = None + __thread = None + __setup = False + __handlers = [] + + @classmethod + def __logger_listener(cls, q): + while True: + record = q.get() + if record is None: + break + root = logging.getLogger("listener") + root.handle(record) + + @classmethod + def setup(cls): + if cls.__setup: + return + + # setup root + root = logging.getLogger("listener") + root.setLevel(logging.DEBUG) + rh = logging.FileHandler(cls.__logger_file, mode="a") + rh.setLevel(logging.DEBUG) + root.addHandler(rh) + + # setup logger + if cls.__manager is None: + cls.__manager = multiprocessing.Manager() + + if cls.__queue is None: + cls.__queue = cls.__manager.Queue() + + logger = logging.getLogger(cls.__logger_name) + logger.setLevel(logging.DEBUG) + qh = logging.handlers.QueueHandler(cls.__queue) + qh.setLevel(logging.DEBUG) + qh.setFormatter( + logging.Formatter( + fmt="%(asctime)s| %(levelname)-8s %(message)s", + datefmt="%d/%m/%y %H:%M:%S", + ) + ) + logger.addHandler(qh) + + # start listener + cls.__thread = threading.Thread( + target=cls.__logger_listener, + args=(cls.__queue,), + ) + cls.__thread.start() + + cls.__setup = True + + @classmethod + def add_handler(cls, path: Path): + root = logging.getLogger("listener") + rh = logging.FileHandler(path, mode="a") + rh.setLevel(logging.DEBUG) + cls.__handlers.append(rh) + root.addHandler(rh) + + @classmethod + def clear_handlers(cls): + root = logging.getLogger("listener") + for h in cls.__handlers: + root.removeHandler(h) + cls.__handlers.clear() + + @classmethod + def queue(cls): + if not cls.__setup: + cls.setup() + + return cls.__queue + + @classmethod + def logger(cls): + if not cls.__setup: + cls.setup() + + return logging.getLogger(cls.__logger_name) + + @classmethod + def close(cls): + if cls.__setup and cls.__thread is not None: + root = logging.getLogger("listener") + root.info("-" * 100) + cls.__queue.put(None) + cls.__thread.join() + # cls.__manager.close() + + +class SubLogger: + __queue = None + __setup = False + + @classmethod + def setup(cls, q): + if cls.__setup: + return + + cls.__queue = q + + # setup root + root = logging.getLogger() + root.setLevel(logging.DEBUG) + rh = logging.handlers.QueueHandler(q) + rh.setLevel(logging.DEBUG) + rh.setFormatter( + logging.Formatter( + fmt="%(asctime)s| %(levelname)-12s%(message)s", + datefmt="%d/%m/%y %H:%M:%S", + ) + ) + root.addHandler(rh) + + cls.__setup = True + + @classmethod + def logger(cls): + if not cls.__setup: + return None + + return logging.getLogger() diff --git a/quacc/main.py b/quacc/main.py index eaf4067..d8863ee 100644 --- a/quacc/main.py +++ b/quacc/main.py @@ -1,75 +1,75 @@ -from sys import platform -from traceback import print_exception as traceback - -import quacc.evaluation.comp as comp -from quacc.dataset import Dataset -from quacc.environment import env -from quacc.logger import Logger -from quacc.utils import create_dataser_dir - -CE = comp.CompEstimator() - - -def toast(): - if platform == "win32": - import win11toast - - win11toast.notify("Comp", "Completed Execution") - - -def estimate_comparison(): - log = Logger.logger() - for conf in env.get_confs(): - dataset = Dataset( - env.DATASET_NAME, - target=env.DATASET_TARGET, - n_prevalences=env.DATASET_N_PREVS, - prevs=env.DATASET_PREVS, - ) - create_dataser_dir(dataset.name, update=env.DATASET_DIR_UPDATE) - Logger.add_handler(env.OUT_DIR / f"{dataset.name}.log") - try: - dr = comp.evaluate_comparison( - dataset, - estimators=CE.name[env.COMP_ESTIMATORS], - ) - except Exception as e: - log.error(f"Evaluation over {dataset.name} failed. Exception: {e}") - traceback(e) - for plot_conf in env.get_plot_confs(): - for m in env.METRICS: - output_path = env.OUT_DIR / f"{plot_conf}_{m}.md" - try: - _repr = dr.to_md( - conf=plot_conf, - metric=m, - estimators=CE.name[env.PLOT_ESTIMATORS], - stdev=env.PLOT_STDEV, - ) - with open(output_path, "w") as f: - f.write(_repr) - except Exception as e: - log.error( - f"Failed while saving configuration {plot_conf} of {dataset.name}. Exception: {e}" - ) - traceback(e) - Logger.clear_handlers() - - # print(df.to_latex(float_format="{:.4f}".format)) - # print(utils.avg_group_report(df).to_latex(float_format="{:.4f}".format)) - - -def main(): - log = Logger.logger() - try: - estimate_comparison() - except Exception as e: - log.error(f"estimate comparison failed. Exceprion: {e}") - traceback(e) - - toast() - Logger.close() - - -if __name__ == "__main__": - main() +from sys import platform +from traceback import print_exception as traceback + +import quacc.evaluation.comp as comp +from quacc.dataset import Dataset +from quacc.environment import env +from quacc.logger import Logger +from quacc.utils import create_dataser_dir + +CE = comp.CompEstimator() + + +def toast(): + if platform == "win32": + import win11toast + + win11toast.notify("Comp", "Completed Execution") + + +def estimate_comparison(): + log = Logger.logger() + for conf in env.get_confs(): + dataset = Dataset( + env.DATASET_NAME, + target=env.DATASET_TARGET, + n_prevalences=env.DATASET_N_PREVS, + prevs=env.DATASET_PREVS, + ) + create_dataser_dir(dataset.name, update=env.DATASET_DIR_UPDATE) + Logger.add_handler(env.OUT_DIR / f"{dataset.name}.log") + try: + dr = comp.evaluate_comparison( + dataset, + estimators=CE.name[env.COMP_ESTIMATORS], + ) + except Exception as e: + log.error(f"Evaluation over {dataset.name} failed. Exception: {e}") + traceback(e) + for plot_conf in env.get_plot_confs(): + for m in env.METRICS: + output_path = env.OUT_DIR / f"{plot_conf}_{m}.md" + try: + _repr = dr.to_md( + conf=plot_conf, + metric=m, + estimators=CE.name[env.PLOT_ESTIMATORS], + stdev=env.PLOT_STDEV, + ) + with open(output_path, "w") as f: + f.write(_repr) + except Exception as e: + log.error( + f"Failed while saving configuration {plot_conf} of {dataset.name}. Exception: {e}" + ) + traceback(e) + Logger.clear_handlers() + + # print(df.to_latex(float_format="{:.4f}".format)) + # print(utils.avg_group_report(df).to_latex(float_format="{:.4f}".format)) + + +def main(): + log = Logger.logger() + try: + estimate_comparison() + except Exception as e: + log.error(f"estimate comparison failed. Exceprion: {e}") + traceback(e) + + toast() + Logger.close() + + +if __name__ == "__main__": + main() diff --git a/quacc/main_test.py b/quacc/main_test.py index 6e47891..f5b2009 100644 --- a/quacc/main_test.py +++ b/quacc/main_test.py @@ -1,120 +1,120 @@ -from copy import deepcopy -from time import time - -import numpy as np -import win11toast -from quapy.method.aggregative import SLD -from quapy.protocol import APP, UPP -from sklearn.linear_model import LogisticRegression - -import quacc as qc -from quacc.dataset import Dataset -from quacc.error import acc -from quacc.evaluation.baseline import ref -from quacc.evaluation.method import mulmc_sld -from quacc.evaluation.report import CompReport, EvaluationReport -from quacc.method.base import MCAE, BinaryQuantifierAccuracyEstimator -from quacc.method.model_selection import GridSearchAE - - -def test_gs(): - d = Dataset(name="rcv1", target="CCAT", n_prevalences=1).get_raw() - - classifier = LogisticRegression() - classifier.fit(*d.train.Xy) - - quantifier = SLD(LogisticRegression()) - # estimator = MultiClassAccuracyEstimator(classifier, quantifier) - estimator = BinaryQuantifierAccuracyEstimator(classifier, quantifier) - - v_train, v_val = d.validation.split_stratified(0.6, random_state=0) - gs_protocol = UPP(v_val, sample_size=1000, repeats=100) - gs_estimator = GridSearchAE( - model=deepcopy(estimator), - param_grid={ - "q__classifier__C": np.logspace(-3, 3, 7), - "q__classifier__class_weight": [None, "balanced"], - "q__recalib": [None, "bcts", "ts"], - }, - refit=False, - protocol=gs_protocol, - verbose=True, - ).fit(v_train) - - estimator.fit(d.validation) - - tstart = time() - erb, ergs = EvaluationReport("base"), EvaluationReport("gs") - protocol = APP( - d.test, - sample_size=1000, - n_prevalences=21, - repeats=100, - return_type="labelled_collection", - ) - for sample in protocol(): - e_sample = gs_estimator.extend(sample) - estim_prev_b = estimator.estimate(e_sample.X, ext=True) - estim_prev_gs = gs_estimator.estimate(e_sample.X, ext=True) - erb.append_row( - sample.prevalence(), - acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_b)), - ) - ergs.append_row( - sample.prevalence(), - acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_gs)), - ) - - cr = CompReport( - [erb, ergs], - "test", - train_prev=d.train_prev, - valid_prev=d.validation_prev, - ) - - print(cr.table()) - print(f"[took {time() - tstart:.3f}s]") - win11toast.notify("Test", "completed") - - -def test_mc(): - d = Dataset(name="rcv1", target="CCAT", prevs=[0.9]).get()[0] - classifier = LogisticRegression().fit(*d.train.Xy) - protocol = APP( - d.test, - sample_size=1000, - repeats=100, - n_prevalences=21, - return_type="labelled_collection", - ) - - ref_er = ref(classifier, d.validation, protocol) - mulmc_er = mulmc_sld(classifier, d.validation, protocol) - - cr = CompReport( - [mulmc_er, ref_er], - name="test_mc", - train_prev=d.train_prev, - valid_prev=d.validation_prev, - ) - - with open("test_mc.md", "w") as f: - f.write(cr.data().to_markdown()) - - -def test_et(): - d = Dataset(name="imdb", prevs=[0.5]).get()[0] - classifier = LogisticRegression().fit(*d.train.Xy) - estimator = MCAE( - classifier, - SLD(LogisticRegression(), exact_train_prev=False), - confidence="max_conf", - ).fit(d.validation) - e_test = estimator.extend(d.test) - ep = estimator.estimate(e_test.X, ext=True) - print(f"{qc.error.acc(ep) = }") - print(f"{qc.error.acc(e_test.prevalence()) = }") - - -if __name__ == "__main__": - test_et() +from copy import deepcopy +from time import time + +import numpy as np +import win11toast +from quapy.method.aggregative import SLD +from quapy.protocol import APP, UPP +from sklearn.linear_model import LogisticRegression + +import quacc as qc +from quacc.dataset import Dataset +from quacc.error import acc +from quacc.evaluation.baseline import ref +from quacc.evaluation.method import mulmc_sld +from quacc.evaluation.report import CompReport, EvaluationReport +from quacc.method.base import MCAE, BinaryQuantifierAccuracyEstimator +from quacc.method.model_selection import GridSearchAE + + +def test_gs(): + d = Dataset(name="rcv1", target="CCAT", n_prevalences=1).get_raw() + + classifier = LogisticRegression() + classifier.fit(*d.train.Xy) + + quantifier = SLD(LogisticRegression()) + # estimator = MultiClassAccuracyEstimator(classifier, quantifier) + estimator = BinaryQuantifierAccuracyEstimator(classifier, quantifier) + + v_train, v_val = d.validation.split_stratified(0.6, random_state=0) + gs_protocol = UPP(v_val, sample_size=1000, repeats=100) + gs_estimator = GridSearchAE( + model=deepcopy(estimator), + param_grid={ + "q__classifier__C": np.logspace(-3, 3, 7), + "q__classifier__class_weight": [None, "balanced"], + "q__recalib": [None, "bcts", "ts"], + }, + refit=False, + protocol=gs_protocol, + verbose=True, + ).fit(v_train) + + estimator.fit(d.validation) + + tstart = time() + erb, ergs = EvaluationReport("base"), EvaluationReport("gs") + protocol = APP( + d.test, + sample_size=1000, + n_prevalences=21, + repeats=100, + return_type="labelled_collection", + ) + for sample in protocol(): + e_sample = gs_estimator.extend(sample) + estim_prev_b = estimator.estimate(e_sample.X, ext=True) + estim_prev_gs = gs_estimator.estimate(e_sample.X, ext=True) + erb.append_row( + sample.prevalence(), + acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_b)), + ) + ergs.append_row( + sample.prevalence(), + acc=abs(acc(e_sample.prevalence()) - acc(estim_prev_gs)), + ) + + cr = CompReport( + [erb, ergs], + "test", + train_prev=d.train_prev, + valid_prev=d.validation_prev, + ) + + print(cr.table()) + print(f"[took {time() - tstart:.3f}s]") + win11toast.notify("Test", "completed") + + +def test_mc(): + d = Dataset(name="rcv1", target="CCAT", prevs=[0.9]).get()[0] + classifier = LogisticRegression().fit(*d.train.Xy) + protocol = APP( + d.test, + sample_size=1000, + repeats=100, + n_prevalences=21, + return_type="labelled_collection", + ) + + ref_er = ref(classifier, d.validation, protocol) + mulmc_er = mulmc_sld(classifier, d.validation, protocol) + + cr = CompReport( + [mulmc_er, ref_er], + name="test_mc", + train_prev=d.train_prev, + valid_prev=d.validation_prev, + ) + + with open("test_mc.md", "w") as f: + f.write(cr.data().to_markdown()) + + +def test_et(): + d = Dataset(name="imdb", prevs=[0.5]).get()[0] + classifier = LogisticRegression().fit(*d.train.Xy) + estimator = MCAE( + classifier, + SLD(LogisticRegression(), exact_train_prev=False), + confidence="max_conf", + ).fit(d.validation) + e_test = estimator.extend(d.test) + ep = estimator.estimate(e_test.X, ext=True) + print(f"{qc.error.acc(ep) = }") + print(f"{qc.error.acc(e_test.prevalence()) = }") + + +if __name__ == "__main__": + test_et() diff --git a/quacc/method/base.py b/quacc/method/base.py index 670abb7..89ed701 100644 --- a/quacc/method/base.py +++ b/quacc/method/base.py @@ -1,177 +1,177 @@ -import math -from abc import abstractmethod -from copy import deepcopy -from typing import List - -import numpy as np -from quapy.data import LabelledCollection -from quapy.method.aggregative import BaseQuantifier -from scipy.sparse import csr_matrix -from sklearn.base import BaseEstimator - -from quacc.data import ExtendedCollection - - -class BaseAccuracyEstimator(BaseQuantifier): - def __init__( - self, - classifier: BaseEstimator, - quantifier: BaseQuantifier, - confidence=None, - ): - self.__check_classifier(classifier) - self.quantifier = quantifier - self.confidence = confidence - - def __check_classifier(self, classifier): - if not hasattr(classifier, "predict_proba"): - raise ValueError( - f"Passed classifier {classifier.__class__.__name__} cannot predict probabilities." - ) - self.classifier = classifier - - def __get_confidence(self): - def max_conf(probas): - _mc = np.max(probas, axis=-1) - _min = 1.0 / probas.shape[1] - _norm_mc = (_mc - _min) / (1.0 - _min) - return _norm_mc - - def entropy(probas): - _ent = np.sum(np.multiply(probas, np.log(probas + 1e-20)), axis=1) - return _ent - - if self.confidence is None: - return None - - __confs = { - "max_conf": max_conf, - "entropy": entropy, - } - return __confs.get(self.confidence, None) - - def __get_ext(self, pred_proba): - _ext = pred_proba - _f_conf = self.__get_confidence() - if _f_conf is not None: - _confs = _f_conf(pred_proba).reshape((len(pred_proba), 1)) - _ext = np.concatenate((_confs, pred_proba), axis=1) - - return _ext - - def extend(self, coll: LabelledCollection, pred_proba=None) -> ExtendedCollection: - if pred_proba is None: - pred_proba = self.classifier.predict_proba(coll.X) - - _ext = self.__get_ext(pred_proba) - return ExtendedCollection.extend_collection(coll, pred_proba=_ext) - - def _extend_instances(self, instances: np.ndarray | csr_matrix, pred_proba=None): - if pred_proba is None: - pred_proba = self.classifier.predict_proba(instances) - - _ext = self.__get_ext(pred_proba) - return ExtendedCollection.extend_instances(instances, _ext) - - @abstractmethod - def fit(self, train: LabelledCollection | ExtendedCollection): - ... - - @abstractmethod - def estimate(self, instances, ext=False) -> np.ndarray: - ... - - -class MultiClassAccuracyEstimator(BaseAccuracyEstimator): - def __init__( - self, - classifier: BaseEstimator, - quantifier: BaseQuantifier, - confidence: str = None, - ): - super().__init__( - classifier=classifier, - quantifier=quantifier, - confidence=confidence, - ) - self.e_train = None - - def fit(self, train: LabelledCollection): - self.e_train = self.extend(train) - - self.quantifier.fit(self.e_train) - - return self - - def estimate(self, instances, ext=False) -> np.ndarray: - e_inst = instances if ext else self._extend_instances(instances) - - estim_prev = self.quantifier.quantify(e_inst) - return self._check_prevalence_classes(estim_prev, self.quantifier.classes_) - - def _check_prevalence_classes(self, estim_prev, estim_classes) -> np.ndarray: - true_classes = self.e_train.classes_ - for _cls in true_classes: - if _cls not in estim_classes: - estim_prev = np.insert(estim_prev, _cls, [0.0], axis=0) - return estim_prev - - -class BinaryQuantifierAccuracyEstimator(BaseAccuracyEstimator): - def __init__( - self, - classifier: BaseEstimator, - quantifier: BaseAccuracyEstimator, - confidence: str = None, - ): - super().__init__( - classifier=classifier, - quantifier=quantifier, - confidence=confidence, - ) - self.quantifiers = [] - self.e_trains = [] - - def fit(self, train: LabelledCollection | ExtendedCollection): - self.e_train = self.extend(train) - - self.n_classes = self.e_train.n_classes - self.e_trains = self.e_train.split_by_pred() - - self.quantifiers = [] - for train in self.e_trains: - quant = deepcopy(self.quantifier) - quant.fit(train) - self.quantifiers.append(quant) - - return self - - def estimate(self, instances, ext=False): - # TODO: test - e_inst = instances if ext else self._extend_instances(instances) - - _ncl = int(math.sqrt(self.n_classes)) - s_inst, norms = ExtendedCollection.split_inst_by_pred(_ncl, e_inst) - estim_prevs = self._quantify_helper(s_inst, norms) - - estim_prev = np.array([prev_row for prev_row in zip(*estim_prevs)]).flatten() - return estim_prev - - def _quantify_helper( - self, - s_inst: List[np.ndarray | csr_matrix], - norms: List[float], - ): - estim_prevs = [] - for quant, inst, norm in zip(self.quantifiers, s_inst, norms): - if inst.shape[0] > 0: - estim_prevs.append(quant.quantify(inst) * norm) - else: - estim_prevs.append(np.asarray([0.0, 0.0])) - - return estim_prevs - - -BAE = BaseAccuracyEstimator -MCAE = MultiClassAccuracyEstimator -BQAE = BinaryQuantifierAccuracyEstimator +import math +from abc import abstractmethod +from copy import deepcopy +from typing import List + +import numpy as np +from quapy.data import LabelledCollection +from quapy.method.aggregative import BaseQuantifier +from scipy.sparse import csr_matrix +from sklearn.base import BaseEstimator + +from quacc.data import ExtendedCollection + + +class BaseAccuracyEstimator(BaseQuantifier): + def __init__( + self, + classifier: BaseEstimator, + quantifier: BaseQuantifier, + confidence=None, + ): + self.__check_classifier(classifier) + self.quantifier = quantifier + self.confidence = confidence + + def __check_classifier(self, classifier): + if not hasattr(classifier, "predict_proba"): + raise ValueError( + f"Passed classifier {classifier.__class__.__name__} cannot predict probabilities." + ) + self.classifier = classifier + + def __get_confidence(self): + def max_conf(probas): + _mc = np.max(probas, axis=-1) + _min = 1.0 / probas.shape[1] + _norm_mc = (_mc - _min) / (1.0 - _min) + return _norm_mc + + def entropy(probas): + _ent = np.sum(np.multiply(probas, np.log(probas + 1e-20)), axis=1) + return _ent + + if self.confidence is None: + return None + + __confs = { + "max_conf": max_conf, + "entropy": entropy, + } + return __confs.get(self.confidence, None) + + def __get_ext(self, pred_proba): + _ext = pred_proba + _f_conf = self.__get_confidence() + if _f_conf is not None: + _confs = _f_conf(pred_proba).reshape((len(pred_proba), 1)) + _ext = np.concatenate((_confs, pred_proba), axis=1) + + return _ext + + def extend(self, coll: LabelledCollection, pred_proba=None) -> ExtendedCollection: + if pred_proba is None: + pred_proba = self.classifier.predict_proba(coll.X) + + _ext = self.__get_ext(pred_proba) + return ExtendedCollection.extend_collection(coll, pred_proba=_ext) + + def _extend_instances(self, instances: np.ndarray | csr_matrix, pred_proba=None): + if pred_proba is None: + pred_proba = self.classifier.predict_proba(instances) + + _ext = self.__get_ext(pred_proba) + return ExtendedCollection.extend_instances(instances, _ext) + + @abstractmethod + def fit(self, train: LabelledCollection | ExtendedCollection): + ... + + @abstractmethod + def estimate(self, instances, ext=False) -> np.ndarray: + ... + + +class MultiClassAccuracyEstimator(BaseAccuracyEstimator): + def __init__( + self, + classifier: BaseEstimator, + quantifier: BaseQuantifier, + confidence: str = None, + ): + super().__init__( + classifier=classifier, + quantifier=quantifier, + confidence=confidence, + ) + self.e_train = None + + def fit(self, train: LabelledCollection): + self.e_train = self.extend(train) + + self.quantifier.fit(self.e_train) + + return self + + def estimate(self, instances, ext=False) -> np.ndarray: + e_inst = instances if ext else self._extend_instances(instances) + + estim_prev = self.quantifier.quantify(e_inst) + return self._check_prevalence_classes(estim_prev, self.quantifier.classes_) + + def _check_prevalence_classes(self, estim_prev, estim_classes) -> np.ndarray: + true_classes = self.e_train.classes_ + for _cls in true_classes: + if _cls not in estim_classes: + estim_prev = np.insert(estim_prev, _cls, [0.0], axis=0) + return estim_prev + + +class BinaryQuantifierAccuracyEstimator(BaseAccuracyEstimator): + def __init__( + self, + classifier: BaseEstimator, + quantifier: BaseAccuracyEstimator, + confidence: str = None, + ): + super().__init__( + classifier=classifier, + quantifier=quantifier, + confidence=confidence, + ) + self.quantifiers = [] + self.e_trains = [] + + def fit(self, train: LabelledCollection | ExtendedCollection): + self.e_train = self.extend(train) + + self.n_classes = self.e_train.n_classes + self.e_trains = self.e_train.split_by_pred() + + self.quantifiers = [] + for train in self.e_trains: + quant = deepcopy(self.quantifier) + quant.fit(train) + self.quantifiers.append(quant) + + return self + + def estimate(self, instances, ext=False): + # TODO: test + e_inst = instances if ext else self._extend_instances(instances) + + _ncl = int(math.sqrt(self.n_classes)) + s_inst, norms = ExtendedCollection.split_inst_by_pred(_ncl, e_inst) + estim_prevs = self._quantify_helper(s_inst, norms) + + estim_prev = np.array([prev_row for prev_row in zip(*estim_prevs)]).flatten() + return estim_prev + + def _quantify_helper( + self, + s_inst: List[np.ndarray | csr_matrix], + norms: List[float], + ): + estim_prevs = [] + for quant, inst, norm in zip(self.quantifiers, s_inst, norms): + if inst.shape[0] > 0: + estim_prevs.append(quant.quantify(inst) * norm) + else: + estim_prevs.append(np.asarray([0.0, 0.0])) + + return estim_prevs + + +BAE = BaseAccuracyEstimator +MCAE = MultiClassAccuracyEstimator +BQAE = BinaryQuantifierAccuracyEstimator diff --git a/quacc/method/model_selection.py b/quacc/method/model_selection.py index ebc2fb8..4e4df34 100644 --- a/quacc/method/model_selection.py +++ b/quacc/method/model_selection.py @@ -1,307 +1,307 @@ -import itertools -from copy import deepcopy -from time import time -from typing import Callable, Union -import numpy as np - -import quapy as qp -from quapy.data import LabelledCollection -from quapy.model_selection import GridSearchQ -from quapy.protocol import UPP, AbstractProtocol, OnLabelledCollectionProtocol -from sklearn.base import BaseEstimator - -import quacc as qc -import quacc.error -from quacc.data import ExtendedCollection -from quacc.evaluation import evaluate -from quacc.logger import SubLogger -from quacc.method.base import ( - BaseAccuracyEstimator, - BinaryQuantifierAccuracyEstimator, - MultiClassAccuracyEstimator, -) - - -class GridSearchAE(BaseAccuracyEstimator): - def __init__( - self, - model: BaseAccuracyEstimator, - param_grid: dict, - protocol: AbstractProtocol, - error: Union[Callable, str] = qc.error.maccd, - refit=True, - # timeout=-1, - # n_jobs=None, - verbose=False, - ): - self.model = model - self.param_grid = self.__normalize_params(param_grid) - self.protocol = protocol - self.refit = refit - # self.timeout = timeout - # self.n_jobs = qp._get_njobs(n_jobs) - self.verbose = verbose - self.__check_error(error) - assert isinstance(protocol, AbstractProtocol), "unknown protocol" - - def _sout(self, msg): - if self.verbose: - print(f"[{self.__class__.__name__}]: {msg}") - - def __normalize_params(self, params): - __remap = {} - for key in params.keys(): - k, delim, sub_key = key.partition("__") - if delim and k == "q": - __remap[key] = f"quantifier__{sub_key}" - - return {(__remap[k] if k in __remap else k): v for k, v in params.items()} - - def __check_error(self, error): - if error in qc.error.ACCURACY_ERROR: - self.error = error - elif isinstance(error, str): - self.error = qc.error.from_name(error) - elif hasattr(error, "__call__"): - self.error = error - else: - raise ValueError( - f"unexpected error type; must either be a callable function or a str representing\n" - f"the name of an error function in {qc.error.ACCURACY_ERROR_NAMES}" - ) - - def fit(self, training: LabelledCollection): - """Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing - the error metric. - - :param training: the training set on which to optimize the hyperparameters - :return: self - """ - params_keys = list(self.param_grid.keys()) - params_values = list(self.param_grid.values()) - - protocol = self.protocol - - self.param_scores_ = {} - self.best_score_ = None - - tinit = time() - - hyper = [ - dict(zip(params_keys, val)) for val in itertools.product(*params_values) - ] - - # self._sout(f"starting model selection with {self.n_jobs =}") - self._sout("starting model selection") - - scores = [self.__params_eval(params, training) for params in hyper] - - for params, score, model in scores: - if score is not None: - if self.best_score_ is None or score < self.best_score_: - self.best_score_ = score - self.best_params_ = params - self.best_model_ = model - self.param_scores_[str(params)] = score - else: - self.param_scores_[str(params)] = "timeout" - - tend = time() - tinit - - if self.best_score_ is None: - raise TimeoutError("no combination of hyperparameters seem to work") - - self._sout( - f"optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) " - f"[took {tend:.4f}s]" - ) - log = SubLogger.logger() - log.debug( - f"[{self.model.__class__.__name__}] " - f"optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) " - f"[took {tend:.4f}s]" - ) - - if self.refit: - if isinstance(protocol, OnLabelledCollectionProtocol): - self._sout("refitting on the whole development set") - self.best_model_.fit(training + protocol.get_labelled_collection()) - else: - raise RuntimeWarning( - f'"refit" was requested, but the protocol does not ' - f"implement the {OnLabelledCollectionProtocol.__name__} interface" - ) - - return self - - def __params_eval(self, params, training): - protocol = self.protocol - error = self.error - - # if self.timeout > 0: - - # def handler(signum, frame): - # raise TimeoutError() - - # signal.signal(signal.SIGALRM, handler) - - tinit = time() - - # if self.timeout > 0: - # signal.alarm(self.timeout) - - try: - model = deepcopy(self.model) - # overrides default parameters with the parameters being explored at this iteration - model.set_params(**params) - # print({k: v for k, v in model.get_params().items() if k in params}) - model.fit(training) - score = evaluate(model, protocol=protocol, error_metric=error) - - ttime = time() - tinit - self._sout( - f"hyperparams={params}\t got score {score:.5f} [took {ttime:.4f}s]" - ) - - # if self.timeout > 0: - # signal.alarm(0) - # except TimeoutError: - # self._sout(f"timeout ({self.timeout}s) reached for config {params}") - # score = None - except ValueError as e: - self._sout(f"the combination of hyperparameters {params} is invalid") - raise e - except Exception as e: - self._sout(f"something went wrong for config {params}; skipping:") - self._sout(f"\tException: {e}") - score = None - - return params, score, model - - def extend(self, coll: LabelledCollection, pred_proba=None) -> ExtendedCollection: - assert hasattr(self, "best_model_"), "quantify called before fit" - return self.best_model().extend(coll, pred_proba=pred_proba) - - def estimate(self, instances, ext=False): - """Estimate class prevalence values using the best model found after calling the :meth:`fit` method. - - :param instances: sample contanining the instances - :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found - by the model selection process. - """ - - assert hasattr(self, "best_model_"), "estimate called before fit" - return self.best_model().estimate(instances, ext=ext) - - def set_params(self, **parameters): - """Sets the hyper-parameters to explore. - - :param parameters: a dictionary with keys the parameter names and values the list of values to explore - """ - self.param_grid = parameters - - def get_params(self, deep=True): - """Returns the dictionary of hyper-parameters to explore (`param_grid`) - - :param deep: Unused - :return: the dictionary `param_grid` - """ - return self.param_grid - - def best_model(self): - """ - Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination - of hyper-parameters that minimized the error function. - - :return: a trained quantifier - """ - if hasattr(self, "best_model_"): - return self.best_model_ - raise ValueError("best_model called before fit") - - - -class MCAEgsq(MultiClassAccuracyEstimator): - def __init__( - self, - classifier: BaseEstimator, - quantifier: BaseAccuracyEstimator, - param_grid: dict, - error: Union[Callable, str] = qp.error.mae, - refit=True, - timeout=-1, - n_jobs=None, - verbose=False, - ): - self.param_grid = param_grid - self.refit = refit - self.timeout = timeout - self.n_jobs = n_jobs - self.verbose = verbose - self.error = error - super().__init__(classifier, quantifier) - - def fit(self, train: LabelledCollection): - self.e_train = self.extend(train) - t_train, t_val = self.e_train.split_stratified(0.6, random_state=0) - self.quantifier = GridSearchQ( - deepcopy(self.quantifier), - param_grid=self.param_grid, - protocol=UPP(t_val, repeats=100), - error=self.error, - refit=self.refit, - timeout=self.timeout, - n_jobs=self.n_jobs, - verbose=self.verbose, - ).fit(self.e_train) - - return self - - def estimate(self, instances, ext=False) -> np.ndarray: - e_inst = instances if ext else self._extend_instances(instances) - estim_prev = self.quantifier.quantify(e_inst) - return self._check_prevalence_classes(estim_prev, self.quantifier.best_model().classes_) - - -class BQAEgsq(BinaryQuantifierAccuracyEstimator): - def __init__( - self, - classifier: BaseEstimator, - quantifier: BaseAccuracyEstimator, - param_grid: dict, - error: Union[Callable, str] = qp.error.mae, - refit=True, - timeout=-1, - n_jobs=None, - verbose=False, - ): - self.param_grid = param_grid - self.refit = refit - self.timeout = timeout - self.n_jobs = n_jobs - self.verbose = verbose - self.error = error - super().__init__(classifier=classifier, quantifier=quantifier) - - def fit(self, train: LabelledCollection): - self.e_train = self.extend(train) - - self.n_classes = self.e_train.n_classes - self.e_trains = self.e_train.split_by_pred() - - self.quantifiers = [] - for e_train in self.e_trains: - t_train, t_val = e_train.split_stratified(0.6, random_state=0) - quantifier = GridSearchQ( - model=deepcopy(self.quantifier), - param_grid=self.param_grid, - protocol=UPP(t_val, repeats=100), - error=self.error, - refit=self.refit, - timeout=self.timeout, - n_jobs=self.n_jobs, - verbose=self.verbose, - ).fit(t_train) - self.quantifiers.append(quantifier) - - return self +import itertools +from copy import deepcopy +from time import time +from typing import Callable, Union +import numpy as np + +import quapy as qp +from quapy.data import LabelledCollection +from quapy.model_selection import GridSearchQ +from quapy.protocol import UPP, AbstractProtocol, OnLabelledCollectionProtocol +from sklearn.base import BaseEstimator + +import quacc as qc +import quacc.error +from quacc.data import ExtendedCollection +from quacc.evaluation import evaluate +from quacc.logger import SubLogger +from quacc.method.base import ( + BaseAccuracyEstimator, + BinaryQuantifierAccuracyEstimator, + MultiClassAccuracyEstimator, +) + + +class GridSearchAE(BaseAccuracyEstimator): + def __init__( + self, + model: BaseAccuracyEstimator, + param_grid: dict, + protocol: AbstractProtocol, + error: Union[Callable, str] = qc.error.maccd, + refit=True, + # timeout=-1, + # n_jobs=None, + verbose=False, + ): + self.model = model + self.param_grid = self.__normalize_params(param_grid) + self.protocol = protocol + self.refit = refit + # self.timeout = timeout + # self.n_jobs = qp._get_njobs(n_jobs) + self.verbose = verbose + self.__check_error(error) + assert isinstance(protocol, AbstractProtocol), "unknown protocol" + + def _sout(self, msg): + if self.verbose: + print(f"[{self.__class__.__name__}]: {msg}") + + def __normalize_params(self, params): + __remap = {} + for key in params.keys(): + k, delim, sub_key = key.partition("__") + if delim and k == "q": + __remap[key] = f"quantifier__{sub_key}" + + return {(__remap[k] if k in __remap else k): v for k, v in params.items()} + + def __check_error(self, error): + if error in qc.error.ACCURACY_ERROR: + self.error = error + elif isinstance(error, str): + self.error = qc.error.from_name(error) + elif hasattr(error, "__call__"): + self.error = error + else: + raise ValueError( + f"unexpected error type; must either be a callable function or a str representing\n" + f"the name of an error function in {qc.error.ACCURACY_ERROR_NAMES}" + ) + + def fit(self, training: LabelledCollection): + """Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing + the error metric. + + :param training: the training set on which to optimize the hyperparameters + :return: self + """ + params_keys = list(self.param_grid.keys()) + params_values = list(self.param_grid.values()) + + protocol = self.protocol + + self.param_scores_ = {} + self.best_score_ = None + + tinit = time() + + hyper = [ + dict(zip(params_keys, val)) for val in itertools.product(*params_values) + ] + + # self._sout(f"starting model selection with {self.n_jobs =}") + self._sout("starting model selection") + + scores = [self.__params_eval(params, training) for params in hyper] + + for params, score, model in scores: + if score is not None: + if self.best_score_ is None or score < self.best_score_: + self.best_score_ = score + self.best_params_ = params + self.best_model_ = model + self.param_scores_[str(params)] = score + else: + self.param_scores_[str(params)] = "timeout" + + tend = time() - tinit + + if self.best_score_ is None: + raise TimeoutError("no combination of hyperparameters seem to work") + + self._sout( + f"optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) " + f"[took {tend:.4f}s]" + ) + log = SubLogger.logger() + log.debug( + f"[{self.model.__class__.__name__}] " + f"optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) " + f"[took {tend:.4f}s]" + ) + + if self.refit: + if isinstance(protocol, OnLabelledCollectionProtocol): + self._sout("refitting on the whole development set") + self.best_model_.fit(training + protocol.get_labelled_collection()) + else: + raise RuntimeWarning( + f'"refit" was requested, but the protocol does not ' + f"implement the {OnLabelledCollectionProtocol.__name__} interface" + ) + + return self + + def __params_eval(self, params, training): + protocol = self.protocol + error = self.error + + # if self.timeout > 0: + + # def handler(signum, frame): + # raise TimeoutError() + + # signal.signal(signal.SIGALRM, handler) + + tinit = time() + + # if self.timeout > 0: + # signal.alarm(self.timeout) + + try: + model = deepcopy(self.model) + # overrides default parameters with the parameters being explored at this iteration + model.set_params(**params) + # print({k: v for k, v in model.get_params().items() if k in params}) + model.fit(training) + score = evaluate(model, protocol=protocol, error_metric=error) + + ttime = time() - tinit + self._sout( + f"hyperparams={params}\t got score {score:.5f} [took {ttime:.4f}s]" + ) + + # if self.timeout > 0: + # signal.alarm(0) + # except TimeoutError: + # self._sout(f"timeout ({self.timeout}s) reached for config {params}") + # score = None + except ValueError as e: + self._sout(f"the combination of hyperparameters {params} is invalid") + raise e + except Exception as e: + self._sout(f"something went wrong for config {params}; skipping:") + self._sout(f"\tException: {e}") + score = None + + return params, score, model + + def extend(self, coll: LabelledCollection, pred_proba=None) -> ExtendedCollection: + assert hasattr(self, "best_model_"), "quantify called before fit" + return self.best_model().extend(coll, pred_proba=pred_proba) + + def estimate(self, instances, ext=False): + """Estimate class prevalence values using the best model found after calling the :meth:`fit` method. + + :param instances: sample contanining the instances + :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found + by the model selection process. + """ + + assert hasattr(self, "best_model_"), "estimate called before fit" + return self.best_model().estimate(instances, ext=ext) + + def set_params(self, **parameters): + """Sets the hyper-parameters to explore. + + :param parameters: a dictionary with keys the parameter names and values the list of values to explore + """ + self.param_grid = parameters + + def get_params(self, deep=True): + """Returns the dictionary of hyper-parameters to explore (`param_grid`) + + :param deep: Unused + :return: the dictionary `param_grid` + """ + return self.param_grid + + def best_model(self): + """ + Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination + of hyper-parameters that minimized the error function. + + :return: a trained quantifier + """ + if hasattr(self, "best_model_"): + return self.best_model_ + raise ValueError("best_model called before fit") + + + +class MCAEgsq(MultiClassAccuracyEstimator): + def __init__( + self, + classifier: BaseEstimator, + quantifier: BaseAccuracyEstimator, + param_grid: dict, + error: Union[Callable, str] = qp.error.mae, + refit=True, + timeout=-1, + n_jobs=None, + verbose=False, + ): + self.param_grid = param_grid + self.refit = refit + self.timeout = timeout + self.n_jobs = n_jobs + self.verbose = verbose + self.error = error + super().__init__(classifier, quantifier) + + def fit(self, train: LabelledCollection): + self.e_train = self.extend(train) + t_train, t_val = self.e_train.split_stratified(0.6, random_state=0) + self.quantifier = GridSearchQ( + deepcopy(self.quantifier), + param_grid=self.param_grid, + protocol=UPP(t_val, repeats=100), + error=self.error, + refit=self.refit, + timeout=self.timeout, + n_jobs=self.n_jobs, + verbose=self.verbose, + ).fit(self.e_train) + + return self + + def estimate(self, instances, ext=False) -> np.ndarray: + e_inst = instances if ext else self._extend_instances(instances) + estim_prev = self.quantifier.quantify(e_inst) + return self._check_prevalence_classes(estim_prev, self.quantifier.best_model().classes_) + + +class BQAEgsq(BinaryQuantifierAccuracyEstimator): + def __init__( + self, + classifier: BaseEstimator, + quantifier: BaseAccuracyEstimator, + param_grid: dict, + error: Union[Callable, str] = qp.error.mae, + refit=True, + timeout=-1, + n_jobs=None, + verbose=False, + ): + self.param_grid = param_grid + self.refit = refit + self.timeout = timeout + self.n_jobs = n_jobs + self.verbose = verbose + self.error = error + super().__init__(classifier=classifier, quantifier=quantifier) + + def fit(self, train: LabelledCollection): + self.e_train = self.extend(train) + + self.n_classes = self.e_train.n_classes + self.e_trains = self.e_train.split_by_pred() + + self.quantifiers = [] + for e_train in self.e_trains: + t_train, t_val = e_train.split_stratified(0.6, random_state=0) + quantifier = GridSearchQ( + model=deepcopy(self.quantifier), + param_grid=self.param_grid, + protocol=UPP(t_val, repeats=100), + error=self.error, + refit=self.refit, + timeout=self.timeout, + n_jobs=self.n_jobs, + verbose=self.verbose, + ).fit(t_train) + self.quantifiers.append(quantifier) + + return self diff --git a/quacc/plot.py b/quacc/plot.py index 4ccb3fe..471c1cb 100644 --- a/quacc/plot.py +++ b/quacc/plot.py @@ -1,239 +1,239 @@ -from pathlib import Path - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -from cycler import cycler - -from quacc.environment import env - -matplotlib.use("agg") - - -def _get_markers(n: int): - ls = "ovx+sDph*^1234X><.Pd" - if n > len(ls): - ls = ls * (n / len(ls) + 1) - return list(ls)[:n] - - -def plot_delta( - base_prevs, - columns, - data, - *, - stdevs=None, - pos_class=1, - metric="acc", - name="default", - train_prev=None, - legend=True, - avg=None, -) -> Path: - _base_title = "delta_stdev" if stdevs is not None else "delta" - if train_prev is not None: - t_prev_pos = int(round(train_prev[pos_class] * 100)) - title = f"{_base_title}_{name}_{t_prev_pos}_{metric}" - else: - title = f"{_base_title}_{name}_avg_{avg}_{metric}" - - fig, ax = plt.subplots() - ax.set_aspect("auto") - ax.grid() - - NUM_COLORS = len(data) - cm = plt.get_cmap("tab10") - if NUM_COLORS > 10: - cm = plt.get_cmap("tab20") - cy = cycler(color=[cm(i) for i in range(NUM_COLORS)]) - - base_prevs = base_prevs[:, pos_class] - for method, deltas, _cy in zip(columns, data, cy): - ax.plot( - base_prevs, - deltas, - label=method, - color=_cy["color"], - linestyle="-", - marker="o", - markersize=3, - zorder=2, - ) - if stdevs is not None: - _col_idx = np.where(columns == method)[0] - stdev = stdevs[_col_idx].flatten() - nn_idx = np.intersect1d( - np.where(deltas != np.nan)[0], - np.where(stdev != np.nan)[0], - ) - _bps, _ds, _st = base_prevs[nn_idx], deltas[nn_idx], stdev[nn_idx] - ax.fill_between( - _bps, - _ds - _st, - _ds + _st, - color=_cy["color"], - alpha=0.25, - ) - - x_label = "test" if avg is None or avg == "train" else "train" - ax.set( - xlabel=f"{x_label} prevalence", - ylabel=metric, - title=title, - ) - - if legend: - ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) - output_path = env.PLOT_OUT_DIR / f"{title}.png" - fig.savefig(output_path, bbox_inches="tight") - - return output_path - - -def plot_diagonal( - reference, - columns, - data, - *, - pos_class=1, - metric="acc", - name="default", - train_prev=None, - legend=True, -): - if train_prev is not None: - t_prev_pos = int(round(train_prev[pos_class] * 100)) - title = f"diagonal_{name}_{t_prev_pos}_{metric}" - else: - title = f"diagonal_{name}_{metric}" - - fig, ax = plt.subplots() - ax.set_aspect("auto") - ax.grid() - ax.set_aspect("equal") - - NUM_COLORS = len(data) - cm = plt.get_cmap("tab10") - if NUM_COLORS > 10: - cm = plt.get_cmap("tab20") - cy = cycler( - color=[cm(i) for i in range(NUM_COLORS)], - marker=_get_markers(NUM_COLORS), - ) - - reference = np.array(reference) - x_ticks = np.unique(reference) - x_ticks.sort() - - for deltas, _cy in zip(data, cy): - ax.plot( - reference, - deltas, - color=_cy["color"], - linestyle="None", - marker=_cy["marker"], - markersize=3, - zorder=2, - alpha=0.25, - ) - - # ensure limits are equal for both axes - _alims = np.stack(((ax.get_xlim(), ax.get_ylim())), axis=-1) - _lims = np.array([f(ls) for f, ls in zip([np.min, np.max], _alims)]) - ax.set(xlim=tuple(_lims), ylim=tuple(_lims)) - - for method, deltas, _cy in zip(columns, data, cy): - slope, interc = np.polyfit(reference, deltas, 1) - y_lr = np.array([slope * x + interc for x in _lims]) - ax.plot( - _lims, - y_lr, - label=method, - color=_cy["color"], - linestyle="-", - markersize="0", - zorder=1, - ) - - # plot reference line - ax.plot( - _lims, - _lims, - color="black", - linestyle="--", - markersize=0, - zorder=1, - ) - - ax.set(xlabel=f"true {metric}", ylabel=f"estim. {metric}", title=title) - - if legend: - ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) - output_path = env.PLOT_OUT_DIR / f"{title}.png" - fig.savefig(output_path, bbox_inches="tight") - return output_path - - -def plot_shift( - shift_prevs, - columns, - data, - *, - counts=None, - pos_class=1, - metric="acc", - name="default", - train_prev=None, - legend=True, -) -> Path: - if train_prev is not None: - t_prev_pos = int(round(train_prev[pos_class] * 100)) - title = f"shift_{name}_{t_prev_pos}_{metric}" - else: - title = f"shift_{name}_avg_{metric}" - - fig, ax = plt.subplots() - ax.set_aspect("auto") - ax.grid() - - NUM_COLORS = len(data) - cm = plt.get_cmap("tab10") - if NUM_COLORS > 10: - cm = plt.get_cmap("tab20") - cy = cycler(color=[cm(i) for i in range(NUM_COLORS)]) - - shift_prevs = shift_prevs[:, pos_class] - for method, shifts, _cy in zip(columns, data, cy): - ax.plot( - shift_prevs, - shifts, - label=method, - color=_cy["color"], - linestyle="-", - marker="o", - markersize=3, - zorder=2, - ) - if counts is not None: - _col_idx = np.where(columns == method)[0] - count = counts[_col_idx].flatten() - for prev, shift, cnt in zip(shift_prevs, shifts, count): - label = f"{cnt}" - plt.annotate( - label, - (prev, shift), - textcoords="offset points", - xytext=(0, 10), - ha="center", - color=_cy["color"], - fontsize=12.0, - ) - - ax.set(xlabel="dataset shift", ylabel=metric, title=title) - - if legend: - ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) - output_path = env.PLOT_OUT_DIR / f"{title}.png" - fig.savefig(output_path, bbox_inches="tight") - - return output_path +from pathlib import Path + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from cycler import cycler + +from quacc.environment import env + +matplotlib.use("agg") + + +def _get_markers(n: int): + ls = "ovx+sDph*^1234X><.Pd" + if n > len(ls): + ls = ls * (n / len(ls) + 1) + return list(ls)[:n] + + +def plot_delta( + base_prevs, + columns, + data, + *, + stdevs=None, + pos_class=1, + metric="acc", + name="default", + train_prev=None, + legend=True, + avg=None, +) -> Path: + _base_title = "delta_stdev" if stdevs is not None else "delta" + if train_prev is not None: + t_prev_pos = int(round(train_prev[pos_class] * 100)) + title = f"{_base_title}_{name}_{t_prev_pos}_{metric}" + else: + title = f"{_base_title}_{name}_avg_{avg}_{metric}" + + fig, ax = plt.subplots() + ax.set_aspect("auto") + ax.grid() + + NUM_COLORS = len(data) + cm = plt.get_cmap("tab10") + if NUM_COLORS > 10: + cm = plt.get_cmap("tab20") + cy = cycler(color=[cm(i) for i in range(NUM_COLORS)]) + + base_prevs = base_prevs[:, pos_class] + for method, deltas, _cy in zip(columns, data, cy): + ax.plot( + base_prevs, + deltas, + label=method, + color=_cy["color"], + linestyle="-", + marker="o", + markersize=3, + zorder=2, + ) + if stdevs is not None: + _col_idx = np.where(columns == method)[0] + stdev = stdevs[_col_idx].flatten() + nn_idx = np.intersect1d( + np.where(deltas != np.nan)[0], + np.where(stdev != np.nan)[0], + ) + _bps, _ds, _st = base_prevs[nn_idx], deltas[nn_idx], stdev[nn_idx] + ax.fill_between( + _bps, + _ds - _st, + _ds + _st, + color=_cy["color"], + alpha=0.25, + ) + + x_label = "test" if avg is None or avg == "train" else "train" + ax.set( + xlabel=f"{x_label} prevalence", + ylabel=metric, + title=title, + ) + + if legend: + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) + output_path = env.PLOT_OUT_DIR / f"{title}.png" + fig.savefig(output_path, bbox_inches="tight") + + return output_path + + +def plot_diagonal( + reference, + columns, + data, + *, + pos_class=1, + metric="acc", + name="default", + train_prev=None, + legend=True, +): + if train_prev is not None: + t_prev_pos = int(round(train_prev[pos_class] * 100)) + title = f"diagonal_{name}_{t_prev_pos}_{metric}" + else: + title = f"diagonal_{name}_{metric}" + + fig, ax = plt.subplots() + ax.set_aspect("auto") + ax.grid() + ax.set_aspect("equal") + + NUM_COLORS = len(data) + cm = plt.get_cmap("tab10") + if NUM_COLORS > 10: + cm = plt.get_cmap("tab20") + cy = cycler( + color=[cm(i) for i in range(NUM_COLORS)], + marker=_get_markers(NUM_COLORS), + ) + + reference = np.array(reference) + x_ticks = np.unique(reference) + x_ticks.sort() + + for deltas, _cy in zip(data, cy): + ax.plot( + reference, + deltas, + color=_cy["color"], + linestyle="None", + marker=_cy["marker"], + markersize=3, + zorder=2, + alpha=0.25, + ) + + # ensure limits are equal for both axes + _alims = np.stack(((ax.get_xlim(), ax.get_ylim())), axis=-1) + _lims = np.array([f(ls) for f, ls in zip([np.min, np.max], _alims)]) + ax.set(xlim=tuple(_lims), ylim=tuple(_lims)) + + for method, deltas, _cy in zip(columns, data, cy): + slope, interc = np.polyfit(reference, deltas, 1) + y_lr = np.array([slope * x + interc for x in _lims]) + ax.plot( + _lims, + y_lr, + label=method, + color=_cy["color"], + linestyle="-", + markersize="0", + zorder=1, + ) + + # plot reference line + ax.plot( + _lims, + _lims, + color="black", + linestyle="--", + markersize=0, + zorder=1, + ) + + ax.set(xlabel=f"true {metric}", ylabel=f"estim. {metric}", title=title) + + if legend: + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) + output_path = env.PLOT_OUT_DIR / f"{title}.png" + fig.savefig(output_path, bbox_inches="tight") + return output_path + + +def plot_shift( + shift_prevs, + columns, + data, + *, + counts=None, + pos_class=1, + metric="acc", + name="default", + train_prev=None, + legend=True, +) -> Path: + if train_prev is not None: + t_prev_pos = int(round(train_prev[pos_class] * 100)) + title = f"shift_{name}_{t_prev_pos}_{metric}" + else: + title = f"shift_{name}_avg_{metric}" + + fig, ax = plt.subplots() + ax.set_aspect("auto") + ax.grid() + + NUM_COLORS = len(data) + cm = plt.get_cmap("tab10") + if NUM_COLORS > 10: + cm = plt.get_cmap("tab20") + cy = cycler(color=[cm(i) for i in range(NUM_COLORS)]) + + shift_prevs = shift_prevs[:, pos_class] + for method, shifts, _cy in zip(columns, data, cy): + ax.plot( + shift_prevs, + shifts, + label=method, + color=_cy["color"], + linestyle="-", + marker="o", + markersize=3, + zorder=2, + ) + if counts is not None: + _col_idx = np.where(columns == method)[0] + count = counts[_col_idx].flatten() + for prev, shift, cnt in zip(shift_prevs, shifts, count): + label = f"{cnt}" + plt.annotate( + label, + (prev, shift), + textcoords="offset points", + xytext=(0, 10), + ha="center", + color=_cy["color"], + fontsize=12.0, + ) + + ax.set(xlabel="dataset shift", ylabel=metric, title=title) + + if legend: + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) + output_path = env.PLOT_OUT_DIR / f"{title}.png" + fig.savefig(output_path, bbox_inches="tight") + + return output_path diff --git a/quacc/utils.py b/quacc/utils.py index 7989154..52d8e2f 100644 --- a/quacc/utils.py +++ b/quacc/utils.py @@ -1,59 +1,59 @@ -import functools -import os -import shutil -from pathlib import Path - -import pandas as pd - -from quacc.environment import env - - -def combine_dataframes(dfs, df_index=[]) -> pd.DataFrame: - if len(dfs) < 1: - raise ValueError - if len(dfs) == 1: - return dfs[0] - df = dfs[0] - for ndf in dfs[1:]: - df = df.join(ndf.set_index(df_index), on=df_index) - - return df - - -def avg_group_report(df: pd.DataFrame) -> pd.DataFrame: - def _reduce_func(s1, s2): - return {(n1, n2): v + s2[(n1, n2)] for ((n1, n2), v) in s1.items()} - - lst = df.to_dict(orient="records")[1:-1] - summed_series = functools.reduce(_reduce_func, lst) - idx = df.columns.drop([("base", "T"), ("base", "F")]) - avg_report = { - (n1, n2): (v / len(lst)) - for ((n1, n2), v) in summed_series.items() - if n1 != "base" - } - return pd.DataFrame([avg_report], columns=idx) - - -def fmt_line_md(s): - return f"> {s} \n" - - -def create_dataser_dir(dir_name, update=False): - base_out_dir = Path(env.OUT_DIR_NAME) - if not base_out_dir.exists(): - os.mkdir(base_out_dir) - - dataset_dir = base_out_dir / dir_name - env.OUT_DIR = dataset_dir - if update: - if not dataset_dir.exists(): - os.mkdir(dataset_dir) - else: - shutil.rmtree(dataset_dir, ignore_errors=True) - os.mkdir(dataset_dir) - - plot_dir_path = dataset_dir / "plot" - env.PLOT_OUT_DIR = plot_dir_path - if not plot_dir_path.exists(): - os.mkdir(plot_dir_path) +import functools +import os +import shutil +from pathlib import Path + +import pandas as pd + +from quacc.environment import env + + +def combine_dataframes(dfs, df_index=[]) -> pd.DataFrame: + if len(dfs) < 1: + raise ValueError + if len(dfs) == 1: + return dfs[0] + df = dfs[0] + for ndf in dfs[1:]: + df = df.join(ndf.set_index(df_index), on=df_index) + + return df + + +def avg_group_report(df: pd.DataFrame) -> pd.DataFrame: + def _reduce_func(s1, s2): + return {(n1, n2): v + s2[(n1, n2)] for ((n1, n2), v) in s1.items()} + + lst = df.to_dict(orient="records")[1:-1] + summed_series = functools.reduce(_reduce_func, lst) + idx = df.columns.drop([("base", "T"), ("base", "F")]) + avg_report = { + (n1, n2): (v / len(lst)) + for ((n1, n2), v) in summed_series.items() + if n1 != "base" + } + return pd.DataFrame([avg_report], columns=idx) + + +def fmt_line_md(s): + return f"> {s} \n" + + +def create_dataser_dir(dir_name, update=False): + base_out_dir = Path(env.OUT_DIR_NAME) + if not base_out_dir.exists(): + os.mkdir(base_out_dir) + + dataset_dir = base_out_dir / dir_name + env.OUT_DIR = dataset_dir + if update: + if not dataset_dir.exists(): + os.mkdir(dataset_dir) + else: + shutil.rmtree(dataset_dir, ignore_errors=True) + os.mkdir(dataset_dir) + + plot_dir_path = dataset_dir / "plot" + env.PLOT_OUT_DIR = plot_dir_path + if not plot_dir_path.exists(): + os.mkdir(plot_dir_path) diff --git a/roadmap.md b/roadmap.md index 1be8275..f7d7784 100644 --- a/roadmap.md +++ b/roadmap.md @@ -1,40 +1,40 @@ - -## Roadmap - -#### quantificator domain - - - single multilabel quantificator - - - vector of binary quantificators - - | quantificator | | | - |:-------------------:|:--------------:|:--------------:| - | true quantificator | true positive | false positive | - | false quantificator | false negative | true negative | - -#### dataset split - - - train | test - - classificator C is fit on train - - quantificator Q is fit on cross validation of C over train - - train | validation | test - - classificator C is fit on train - - quantificator Q is fit on validation - -#### classificator origin - - - black box - - crystal box - -#### test metrics - - - f1_score - - K - -#### models - - - classificator - - quantificator - - - + +## Roadmap + +#### quantificator domain + + - single multilabel quantificator + + - vector of binary quantificators + + | quantificator | | | + |:-------------------:|:--------------:|:--------------:| + | true quantificator | true positive | false positive | + | false quantificator | false negative | true negative | + +#### dataset split + + - train | test + - classificator C is fit on train + - quantificator Q is fit on cross validation of C over train + - train | validation | test + - classificator C is fit on train + - quantificator Q is fit on validation + +#### classificator origin + + - black box + - crystal box + +#### test metrics + + - f1_score + - K + +#### models + + - classificator + - quantificator + + + diff --git a/test_mc.md b/test_mc.md index c022a66..7ff99bd 100644 --- a/test_mc.md +++ b/test_mc.md @@ -1,2102 +1,2102 @@ -| | ('acc', 'mulmc_sld') | ('acc_score', 'mulmc_sld') | ('acc_score', 'ref') | ('eprevs', 'mulmc_sld') | ('f1', 'mulmc_sld') | ('f1_score', 'mulmc_sld') | ('f1_score', 'ref') | ('prevs', 'mulmc_sld') | ('ref', 'mulmc_sld') | -|:-----------|-----------------------:|-----------------------------:|-----------------------:|:--------------------------|----------------------:|----------------------------:|----------------------:|:-------------------------|-----------------------:| -| (0.0, 0) | 0.117723 | 0.0322767 | 0.15 | [0.15 0.85 0. 0. ] | 1.07331e-70 | 1.07331e-70 | 0 | [0.15 0.85] | 0.15 | -| (0.0, 1) | 0.104077 | 0.0449229 | 0.149 | [0.149 0.851 0. 0. ] | 8.53968e-74 | 8.53968e-74 | 0 | [0.149 0.851] | 0.149 | -| (0.0, 2) | 0.087328 | 0.046672 | 0.134 | [0.134 0.866 0. 0. ] | 1.42651e-85 | 1.42651e-85 | 0 | [0.134 0.866] | 0.134 | -| (0.0, 3) | 0.111971 | 0.0340294 | 0.146 | [0.146 0.854 0. 0. ] | 6.79989e-84 | 6.79989e-84 | 0 | [0.146 0.854] | 0.146 | -| (0.0, 4) | 0.114691 | 0.0233089 | 0.138 | [0.138 0.862 0. 0. ] | 1.46993e-84 | 1.46993e-84 | 0 | [0.138 0.862] | 0.138 | -| (0.0, 5) | 0.103348 | 0.0556519 | 0.159 | [0.159 0.841 0. 0. ] | 3.0317e-73 | 3.0317e-73 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 6) | 0.0685699 | 0.0654301 | 0.134 | [0.134 0.866 0. 0. ] | 1.41915e-80 | 1.41915e-80 | 0 | [0.134 0.866] | 0.134 | -| (0.0, 7) | 0.0672097 | 0.0867903 | 0.154 | [0.154 0.846 0. 0. ] | 1.58212e-72 | 1.58212e-72 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 8) | 0.0906741 | 0.0343259 | 0.125 | [0.125 0.875 0. 0. ] | 2.35037e-81 | 2.35037e-81 | 0 | [0.125 0.875] | 0.125 | -| (0.0, 9) | 0.108344 | 0.0456563 | 0.154 | [0.154 0.846 0. 0. ] | 1.61876e-89 | 1.61876e-89 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 10) | 0.111637 | 0.0303634 | 0.142 | [0.142 0.858 0. 0. ] | 2.34042e-76 | 2.34042e-76 | 0 | [0.142 0.858] | 0.142 | -| (0.0, 11) | 0.0818937 | 0.0631063 | 0.145 | [0.145 0.855 0. 0. ] | 8.88464e-76 | 8.88464e-76 | 0 | [0.145 0.855] | 0.145 | -| (0.0, 12) | 0.080566 | 0.053434 | 0.134 | [0.134 0.866 0. 0. ] | 2.06441e-85 | 2.06441e-85 | 0 | [0.134 0.866] | 0.134 | -| (0.0, 13) | 0.109082 | 0.0259184 | 0.135 | [0.135 0.865 0. 0. ] | 6.90501e-68 | 6.90501e-68 | 0 | [0.135 0.865] | 0.135 | -| (0.0, 14) | 0.104661 | 0.0563386 | 0.161 | [0.161 0.839 0. 0. ] | 1.20084e-80 | 1.20084e-80 | 0 | [0.161 0.839] | 0.161 | -| (0.0, 15) | 0.110168 | 0.0358321 | 0.146 | [0.146 0.854 0. 0. ] | 5.13457e-80 | 5.13457e-80 | 0 | [0.146 0.854] | 0.146 | -| (0.0, 16) | 0.0821699 | 0.0548301 | 0.137 | [0.137 0.863 0. 0. ] | 1.23927e-107 | 1.23927e-107 | 0 | [0.137 0.863] | 0.137 | -| (0.0, 17) | 0.152954 | 0.00104645 | 0.154 | [0.154 0.846 0. 0. ] | 7.76874e-67 | 7.76874e-67 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 18) | 0.101295 | 0.0507053 | 0.152 | [0.152 0.848 0. 0. ] | 5.41613e-70 | 5.41613e-70 | 0 | [0.152 0.848] | 0.152 | -| (0.0, 19) | 0.101427 | 0.0475729 | 0.149 | [0.149 0.851 0. 0. ] | 7.82058e-75 | 7.82058e-75 | 0 | [0.149 0.851] | 0.149 | -| (0.0, 20) | 0.0552487 | 0.111751 | 0.167 | [0.167 0.833 0. 0. ] | 1.48613e-85 | 1.48613e-85 | 0 | [0.167 0.833] | 0.167 | -| (0.0, 21) | 0.105907 | 0.0250927 | 0.131 | [0.131 0.869 0. 0. ] | 4.52759e-74 | 4.52759e-74 | 0 | [0.131 0.869] | 0.131 | -| (0.0, 22) | 0.0702368 | 0.0837632 | 0.154 | [0.154 0.846 0. 0. ] | 4.82942e-111 | 4.82942e-111 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 23) | 0.121287 | 0.0297134 | 0.151 | [0.151 0.849 0. 0. ] | 5.89441e-65 | 5.89441e-65 | 0 | [0.151 0.849] | 0.151 | -| (0.0, 24) | 0.0834857 | 0.0645143 | 0.148 | [0.148 0.852 0. 0. ] | 3.59591e-72 | 3.59591e-72 | 0 | [0.148 0.852] | 0.148 | -| (0.0, 25) | 0.0948862 | 0.0471138 | 0.142 | [0.142 0.858 0. 0. ] | 4.1855e-77 | 4.1855e-77 | 0 | [0.142 0.858] | 0.142 | -| (0.0, 26) | 0.084485 | 0.066515 | 0.151 | [0.151 0.849 0. 0. ] | 3.53958e-93 | 3.53958e-93 | 0 | [0.151 0.849] | 0.151 | -| (0.0, 27) | 0.0788194 | 0.0781806 | 0.157 | [0.157 0.843 0. 0. ] | 7.26205e-93 | 7.26205e-93 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 28) | 0.100112 | 0.0588884 | 0.159 | [0.159 0.841 0. 0. ] | 3.99054e-87 | 3.99054e-87 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 29) | 0.0964799 | 0.0535201 | 0.15 | [0.15 0.85 0. 0. ] | 6.532e-82 | 6.532e-82 | 0 | [0.15 0.85] | 0.15 | -| (0.0, 30) | 0.119753 | 0.0272469 | 0.147 | [0.147 0.853 0. 0. ] | 4.10831e-75 | 4.10831e-75 | 0 | [0.147 0.853] | 0.147 | -| (0.0, 31) | 0.0706346 | 0.0883654 | 0.159 | [0.159 0.841 0. 0. ] | 1.27863e-72 | 1.27863e-72 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 32) | 0.0988101 | 0.0491899 | 0.148 | [0.148 0.852 0. 0. ] | 2.07851e-74 | 2.07851e-74 | 0 | [0.148 0.852] | 0.148 | -| (0.0, 33) | 0.0783135 | 0.0846865 | 0.163 | [0.163 0.837 0. 0. ] | 2.39408e-90 | 2.39408e-90 | 0 | [0.163 0.837] | 0.163 | -| (0.0, 34) | 0.0902689 | 0.0557311 | 0.146 | [0.146 0.854 0. 0. ] | 6.57654e-81 | 6.57654e-81 | 0 | [0.146 0.854] | 0.146 | -| (0.0, 35) | 0.0917235 | 0.0572765 | 0.149 | [0.149 0.851 0. 0. ] | 1.93739e-83 | 1.93739e-83 | 0 | [0.149 0.851] | 0.149 | -| (0.0, 36) | 0.0600061 | 0.108994 | 0.169 | [0.169 0.831 0. 0. ] | 1.4334e-110 | 1.4334e-110 | 0 | [0.169 0.831] | 0.169 | -| (0.0, 37) | 0.0939152 | 0.0700848 | 0.164 | [0.164 0.836 0. 0. ] | 3.97301e-77 | 3.97301e-77 | 0 | [0.164 0.836] | 0.164 | -| (0.0, 38) | 0.0713029 | 0.0846971 | 0.156 | [0.156 0.844 0. 0. ] | 1.62578e-85 | 1.62578e-85 | 0 | [0.156 0.844] | 0.156 | -| (0.0, 39) | 0.10238 | 0.0696199 | 0.172 | [0.172 0.828 0. 0. ] | 1.64608e-71 | 1.64608e-71 | 0 | [0.172 0.828] | 0.172 | -| (0.0, 40) | 0.0808104 | 0.0661896 | 0.147 | [0.147 0.853 0. 0. ] | 7.03173e-76 | 7.03173e-76 | 0 | [0.147 0.853] | 0.147 | -| (0.0, 41) | 0.0477844 | 0.105216 | 0.153 | [0.153 0.847 0. 0. ] | 1.94212e-97 | 1.94212e-97 | 0 | [0.153 0.847] | 0.153 | -| (0.0, 42) | 0.0755388 | 0.0734612 | 0.149 | [0.149 0.851 0. 0. ] | 9.58123e-82 | 9.58123e-82 | 0 | [0.149 0.851] | 0.149 | -| (0.0, 43) | 0.0541017 | 0.0788983 | 0.133 | [0.133 0.867 0. 0. ] | 3.21118e-74 | 3.21118e-74 | 0 | [0.133 0.867] | 0.133 | -| (0.0, 44) | 0.0856808 | 0.0713192 | 0.157 | [0.157 0.843 0. 0. ] | 1.72404e-73 | 1.72404e-73 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 45) | 0.0736663 | 0.0893337 | 0.163 | [0.163 0.837 0. 0. ] | 5.94041e-97 | 5.94041e-97 | 0 | [0.163 0.837] | 0.163 | -| (0.0, 46) | 0.118402 | 0.038598 | 0.157 | [0.157 0.843 0. 0. ] | 6.0959e-69 | 6.0959e-69 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 47) | 0.11078 | 0.0342197 | 0.145 | [0.145 0.855 0. 0. ] | 4.06314e-72 | 4.06314e-72 | 0 | [0.145 0.855] | 0.145 | -| (0.0, 48) | 0.0831503 | 0.0478497 | 0.131 | [0.131 0.869 0. 0. ] | 5.42314e-85 | 5.42314e-85 | 0 | [0.131 0.869] | 0.131 | -| (0.0, 49) | 0.0916819 | 0.0513181 | 0.143 | [0.143 0.857 0. 0. ] | 5.04478e-81 | 5.04478e-81 | 0 | [0.143 0.857] | 0.143 | -| (0.0, 50) | 0.11094 | 0.03506 | 0.146 | [0.146 0.854 0. 0. ] | 2.9869e-75 | 2.9869e-75 | 0 | [0.146 0.854] | 0.146 | -| (0.0, 51) | 0.0908154 | 0.0471846 | 0.138 | [0.138 0.862 0. 0. ] | 7.09617e-64 | 7.09617e-64 | 0 | [0.138 0.862] | 0.138 | -| (0.0, 52) | 0.116686 | 0.0233143 | 0.14 | [0.14 0.86 0. 0. ] | 1.53735e-66 | 1.53735e-66 | 0 | [0.14 0.86] | 0.14 | -| (0.0, 53) | 0.0621283 | 0.104872 | 0.167 | [0.167 0.833 0. 0. ] | 1.32848e-95 | 1.32848e-95 | 0 | [0.167 0.833] | 0.167 | -| (0.0, 54) | 0.0971333 | 0.0688667 | 0.166 | [0.166 0.834 0. 0. ] | 1.60209e-77 | 1.60209e-77 | 0 | [0.166 0.834] | 0.166 | -| (0.0, 55) | 0.0930373 | 0.0839627 | 0.177 | [0.177 0.823 0. 0. ] | 1.6235e-87 | 1.6235e-87 | 0 | [0.177 0.823] | 0.177 | -| (0.0, 56) | 0.0757822 | 0.0812178 | 0.157 | [0.157 0.843 0. 0. ] | 4.25804e-75 | 4.25804e-75 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 57) | 0.0938824 | 0.0411176 | 0.135 | [0.135 0.865 0. 0. ] | 2.09063e-71 | 2.09063e-71 | 0 | [0.135 0.865] | 0.135 | -| (0.0, 58) | 0.106571 | 0.0524287 | 0.159 | [0.159 0.841 0. 0. ] | 7.73697e-68 | 7.73697e-68 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 59) | 0.0897803 | 0.0692197 | 0.159 | [0.159 0.841 0. 0. ] | 9.83727e-73 | 9.83727e-73 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 60) | 0.111984 | 0.0160161 | 0.128 | [0.128 0.872 0. 0. ] | 3.54758e-79 | 3.54758e-79 | 0 | [0.128 0.872] | 0.128 | -| (0.0, 61) | 0.101143 | 0.0308567 | 0.132 | [0.132 0.868 0. 0. ] | 1.72902e-77 | 1.72902e-77 | 0 | [0.132 0.868] | 0.132 | -| (0.0, 62) | 0.115279 | 0.0377211 | 0.153 | [0.153 0.847 0. 0. ] | 7.03603e-77 | 7.03603e-77 | 0 | [0.153 0.847] | 0.153 | -| (0.0, 63) | 0.0948507 | 0.0621493 | 0.157 | [0.157 0.843 0. 0. ] | 1.29883e-86 | 1.29883e-86 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 64) | 0.103509 | 0.0334913 | 0.137 | [0.137 0.863 0. 0. ] | 3.80986e-80 | 3.80986e-80 | 0 | [0.137 0.863] | 0.137 | -| (0.0, 65) | 0.0781409 | 0.0808591 | 0.159 | [0.159 0.841 0. 0. ] | 1.80981e-71 | 1.80981e-71 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 66) | 0.0757231 | 0.0592769 | 0.135 | [0.135 0.865 0. 0. ] | 3.5588e-95 | 3.5588e-95 | 0 | [0.135 0.865] | 0.135 | -| (0.0, 67) | 0.0728872 | 0.0881128 | 0.161 | [0.161 0.839 0. 0. ] | 5.99213e-89 | 5.99213e-89 | 0 | [0.161 0.839] | 0.161 | -| (0.0, 68) | 0.0639539 | 0.0750461 | 0.139 | [0.139 0.861 0. 0. ] | 6.04828e-84 | 6.04828e-84 | 0 | [0.139 0.861] | 0.139 | -| (0.0, 69) | 0.126742 | 0.0192578 | 0.146 | [0.146 0.854 0. 0. ] | 3.02779e-81 | 3.02779e-81 | 0 | [0.146 0.854] | 0.146 | -| (0.0, 70) | 0.0826081 | 0.0673919 | 0.15 | [0.15 0.85 0. 0. ] | 1.46215e-89 | 1.46215e-89 | 0 | [0.15 0.85] | 0.15 | -| (0.0, 71) | 0.0866534 | 0.0793466 | 0.166 | [0.166 0.834 0. 0. ] | 4.1842e-87 | 4.1842e-87 | 0 | [0.166 0.834] | 0.166 | -| (0.0, 72) | 0.0756651 | 0.0873349 | 0.163 | [0.163 0.837 0. 0. ] | 4.15436e-100 | 4.15436e-100 | 0 | [0.163 0.837] | 0.163 | -| (0.0, 73) | 0.0842012 | 0.0727988 | 0.157 | [0.157 0.843 0. 0. ] | 4.33554e-72 | 4.33554e-72 | 0 | [0.157 0.843] | 0.157 | -| (0.0, 74) | 0.0732343 | 0.0537657 | 0.127 | [0.127 0.873 0. 0. ] | 5.78521e-65 | 5.78521e-65 | 0 | [0.127 0.873] | 0.127 | -| (0.0, 75) | 0.0436204 | 0.10138 | 0.145 | [0.145 0.855 0. 0. ] | 5.99248e-08 | 5.99248e-08 | 0 | [0.145 0.855] | 0.145 | -| (0.0, 76) | 0.073634 | 0.071366 | 0.145 | [0.145 0.855 0. 0. ] | 1.33422e-86 | 1.33422e-86 | 0 | [0.145 0.855] | 0.145 | -| (0.0, 77) | 0.114722 | 0.0242778 | 0.139 | [0.139 0.861 0. 0. ] | 1.22296e-69 | 1.22296e-69 | 0 | [0.139 0.861] | 0.139 | -| (0.0, 78) | 0.0910729 | 0.0529271 | 0.144 | [0.144 0.856 0. 0. ] | 3.20385e-81 | 3.20385e-81 | 0 | [0.144 0.856] | 0.144 | -| (0.0, 79) | 0.1199 | 0.0391 | 0.159 | [0.159 0.841 0. 0. ] | 4.88611e-80 | 4.88611e-80 | 0 | [0.159 0.841] | 0.159 | -| (0.0, 80) | 0.101953 | 0.0500466 | 0.152 | [0.152 0.848 0. 0. ] | 1.89129e-75 | 1.89129e-75 | 0 | [0.152 0.848] | 0.152 | -| (0.0, 81) | 0.0889951 | 0.0510049 | 0.14 | [0.14 0.86 0. 0. ] | 1.37045e-70 | 1.37045e-70 | 0 | [0.14 0.86] | 0.14 | -| (0.0, 82) | 0.0998662 | 0.0471338 | 0.147 | [0.147 0.853 0. 0. ] | 3.1581e-76 | 3.1581e-76 | 0 | [0.147 0.853] | 0.147 | -| (0.0, 83) | 0.0831914 | 0.0558086 | 0.139 | [0.139 0.861 0. 0. ] | 1.71931e-86 | 1.71931e-86 | 0 | [0.139 0.861] | 0.139 | -| (0.0, 84) | 0.120851 | 0.0331487 | 0.154 | [0.154 0.846 0. 0. ] | 7.23765e-74 | 7.23765e-74 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 85) | 0.100605 | 0.0433947 | 0.144 | [0.144 0.856 0. 0. ] | 2.67006e-82 | 2.67006e-82 | 0 | [0.144 0.856] | 0.144 | -| (0.0, 86) | 0.0765061 | 0.0734939 | 0.15 | [0.15 0.85 0. 0. ] | 1.61559e-83 | 1.61559e-83 | 0 | [0.15 0.85] | 0.15 | -| (0.0, 87) | 0.0705979 | 0.0724021 | 0.143 | [0.143 0.857 0. 0. ] | 6.80161e-90 | 6.80161e-90 | 0 | [0.143 0.857] | 0.143 | -| (0.0, 88) | 0.133956 | 0.0270437 | 0.161 | [0.161 0.839 0. 0. ] | 4.02932e-85 | 4.02932e-85 | 0 | [0.161 0.839] | 0.161 | -| (0.0, 89) | 0.0874214 | 0.0665786 | 0.154 | [0.154 0.846 0. 0. ] | 5.57476e-68 | 5.57476e-68 | 0 | [0.154 0.846] | 0.154 | -| (0.0, 90) | 0.0715817 | 0.0634183 | 0.135 | [0.135 0.865 0. 0. ] | 4.40147e-76 | 4.40147e-76 | 0 | [0.135 0.865] | 0.135 | -| (0.0, 91) | 0.0905213 | 0.0544787 | 0.145 | [0.145 0.855 0. 0. ] | 5.28556e-78 | 5.28556e-78 | 0 | [0.145 0.855] | 0.145 | -| (0.0, 92) | 0.0795437 | 0.0964563 | 0.176 | [0.176 0.824 0. 0. ] | 1.1435e-87 | 1.1435e-87 | 0 | [0.176 0.824] | 0.176 | -| (0.0, 93) | 0.118409 | 0.0365913 | 0.155 | [0.155 0.845 0. 0. ] | 3.5294e-95 | 3.5294e-95 | 0 | [0.155 0.845] | 0.155 | -| (0.0, 94) | 0.109805 | 0.0591953 | 0.169 | [0.169 0.831 0. 0. ] | 1.80285e-76 | 1.80285e-76 | 0 | [0.169 0.831] | 0.169 | -| (0.0, 95) | 0.098196 | 0.052804 | 0.151 | [0.151 0.849 0. 0. ] | 2.818e-84 | 2.818e-84 | 0 | [0.151 0.849] | 0.151 | -| (0.0, 96) | 0.112439 | 0.0345615 | 0.147 | [0.147 0.853 0. 0. ] | 3.6936e-71 | 3.6936e-71 | 0 | [0.147 0.853] | 0.147 | -| (0.0, 97) | 0.0904939 | 0.0645061 | 0.155 | [0.155 0.845 0. 0. ] | 8.14262e-94 | 8.14262e-94 | 0 | [0.155 0.845] | 0.155 | -| (0.0, 98) | 0.107304 | 0.028696 | 0.136 | [0.136 0.864 0. 0. ] | 7.45097e-62 | 7.45097e-62 | 0 | [0.136 0.864] | 0.136 | -| (0.0, 99) | 0.0726854 | 0.0783146 | 0.151 | [0.151 0.849 0. 0. ] | 1.27965e-92 | 1.27965e-92 | 0 | [0.151 0.849] | 0.151 | -| (0.05, 0) | 0.139695 | 0.0533047 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 1.14176e-19 | 0.110254 | [0.143 0.857] | 0.193 | -| (0.05, 1) | 0.135563 | 0.0344375 | 0.17 | [0.12 0.83 0. 0.05] | 0.107527 | 3.58656e-10 | 0.107527 | [0.12 0.88] | 0.17 | -| (0.05, 2) | 0.140995 | 0.0510053 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 2.74753e-22 | 0.110132 | [0.142 0.858] | 0.192 | -| (0.05, 3) | 0.137862 | 0.0681381 | 0.206 | [0.156 0.794 0. 0.05 ] | 0.111857 | 1.58351e-10 | 0.111857 | [0.156 0.844] | 0.206 | -| (0.05, 4) | 0.106135 | 0.0828645 | 0.189 | [0.139 0.811 0. 0.05 ] | 0.109234 | 0.000535505 | 0.109769 | [0.139 0.861] | 0.189 | -| (0.05, 5) | 0.122517 | 0.0634832 | 0.186 | [0.136 0.814 0. 0.05 ] | 0.109409 | 4.44136e-17 | 0.109409 | [0.136 0.864] | 0.186 | -| (0.05, 6) | 0.156419 | 0.0255809 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 3.43807e-18 | 0.108932 | [0.132 0.868] | 0.182 | -| (0.05, 7) | 0.145084 | 0.0439162 | 0.189 | [0.139 0.811 0. 0.05 ] | 0.109769 | 2.65993e-14 | 0.109769 | [0.139 0.861] | 0.189 | -| (0.05, 8) | 0.155093 | 0.0359071 | 0.191 | [0.142 0.808 0.001 0.049] | 0.108049 | 4.59776e-11 | 0.108049 | [0.143 0.857] | 0.191 | -| (0.05, 9) | 0.182126 | 0.00487421 | 0.187 | [0.137 0.813 0. 0.05 ] | 0.109529 | 1.84496e-14 | 0.109529 | [0.137 0.863] | 0.187 | -| (0.05, 10) | 0.0998061 | 0.0921939 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 4.03461e-21 | 0.110132 | [0.142 0.858] | 0.192 | -| (0.05, 11) | 0.135749 | 0.0592513 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.16984e-21 | 0.110497 | [0.145 0.855] | 0.195 | -| (0.05, 12) | 0.115695 | 0.087305 | 0.203 | [0.153 0.797 0. 0.05 ] | 0.111483 | 1.33398e-15 | 0.111483 | [0.153 0.847] | 0.203 | -| (0.05, 13) | 0.132882 | 0.0491177 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 3.836e-24 | 0.108932 | [0.132 0.868] | 0.182 | -| (0.05, 14) | 0.10577 | 0.0692296 | 0.175 | [0.125 0.825 0. 0.05 ] | 0.108108 | 9.72476e-12 | 0.108108 | [0.125 0.875] | 0.175 | -| (0.05, 15) | 0.156678 | 0.036322 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 2.07272e-23 | 0.110254 | [0.143 0.857] | 0.193 | -| (0.05, 16) | 0.104347 | 0.0826534 | 0.187 | [0.137 0.813 0. 0.05 ] | 0.107933 | 0.00159586 | 0.109529 | [0.137 0.863] | 0.187 | -| (0.05, 17) | 0.169004 | 0.00799577 | 0.177 | [0.127 0.823 0. 0.05 ] | 0.108342 | 7.79333e-11 | 0.108342 | [0.127 0.873] | 0.177 | -| (0.05, 18) | 0.128698 | 0.0653016 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 1.77321e-22 | 0.110375 | [0.144 0.856] | 0.194 | -| (0.05, 19) | 0.111877 | 0.0971234 | 0.209 | [0.159 0.791 0. 0.05 ] | 0.112233 | 2.68131e-19 | 0.112233 | [0.159 0.841] | 0.209 | -| (0.05, 20) | 0.155026 | 0.0209742 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 3.94502e-18 | 0.108225 | [0.126 0.874] | 0.176 | -| (0.05, 21) | 0.124618 | 0.0773818 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 9.0121e-31 | 0.111359 | [0.152 0.848] | 0.202 | -| (0.05, 22) | 0.130664 | 0.0603363 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 3.03675e-22 | 0.110011 | [0.141 0.859] | 0.191 | -| (0.05, 23) | 0.154081 | 0.0469192 | 0.201 | [0.151 0.799 0. 0.05 ] | 0.111235 | 2.16643e-11 | 0.111235 | [0.151 0.849] | 0.201 | -| (0.05, 24) | 0.122652 | 0.059348 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 1.6966e-14 | 0.108932 | [0.132 0.868] | 0.182 | -| (0.05, 25) | 0.146311 | 0.055689 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111355 | 3.46681e-06 | 0.111359 | [0.152 0.848] | 0.202 | -| (0.05, 26) | 0.140658 | 0.0373423 | 0.178 | [0.128 0.822 0. 0.05 ] | 0.10846 | 2.48311e-18 | 0.10846 | [0.128 0.872] | 0.178 | -| (0.05, 27) | 0.134002 | 0.0429984 | 0.177 | [0.127 0.823 0. 0.05 ] | 0.108342 | 9.19174e-20 | 0.108342 | [0.127 0.873] | 0.177 | -| (0.05, 28) | 0.157105 | 0.0368953 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 4.83413e-18 | 0.110375 | [0.144 0.856] | 0.194 | -| (0.05, 29) | 0.149111 | 0.0308887 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 1.16102e-21 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 30) | 0.104365 | 0.0786353 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.10507e-14 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 31) | 0.158109 | 0.041891 | 0.2 | [0.151 0.799 0.001 0.049] | 0.109131 | 8.10113e-19 | 0.109131 | [0.152 0.848] | 0.2 | -| (0.05, 32) | 0.14954 | 0.0294602 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 1.28469e-14 | 0.108578 | [0.129 0.871] | 0.179 | -| (0.05, 33) | 0.157869 | 0.0161307 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 7.84528e-18 | 0.107991 | [0.124 0.876] | 0.174 | -| (0.05, 34) | 0.157753 | 0.039247 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 2.2032e-17 | 0.110742 | [0.147 0.853] | 0.197 | -| (0.05, 35) | 0.134724 | 0.0462759 | 0.181 | [0.131 0.819 0. 0.05 ] | 0.108814 | 1.46e-14 | 0.108814 | [0.131 0.869] | 0.181 | -| (0.05, 36) | 0.14284 | 0.0331603 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 3.56508e-16 | 0.108225 | [0.126 0.874] | 0.176 | -| (0.05, 37) | 0.135851 | 0.0541488 | 0.19 | [0.14 0.81 0. 0.05] | 0.10989 | 1.37439e-14 | 0.10989 | [0.14 0.86] | 0.19 | -| (0.05, 38) | 0.120848 | 0.0871521 | 0.208 | [0.158 0.792 0. 0.05 ] | 0.112108 | 3.07361e-10 | 0.112108 | [0.158 0.842] | 0.208 | -| (0.05, 39) | 0.108411 | 0.071589 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 9.4529e-12 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 40) | 0.138167 | 0.0358328 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 3.92459e-22 | 0.107991 | [0.124 0.876] | 0.174 | -| (0.05, 41) | 0.134067 | 0.0729327 | 0.207 | [0.157 0.793 0. 0.05 ] | 0.111982 | 6.75036e-30 | 0.111982 | [0.157 0.843] | 0.207 | -| (0.05, 42) | 0.172425 | 0.0235747 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 1.7918e-18 | 0.110619 | [0.146 0.854] | 0.196 | -| (0.05, 43) | 0.137972 | 0.0540283 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 6.253e-19 | 0.110132 | [0.142 0.858] | 0.192 | -| (0.05, 44) | 0.128741 | 0.0542591 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.05882e-23 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 45) | 0.138375 | 0.0376248 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 4.84466e-15 | 0.108225 | [0.126 0.874] | 0.176 | -| (0.05, 46) | 0.162737 | 0.0232628 | 0.186 | [0.136 0.814 0. 0.05 ] | 0.109389 | 2.06901e-05 | 0.109409 | [0.136 0.864] | 0.186 | -| (0.05, 47) | 0.145798 | 0.0382015 | 0.184 | [0.134 0.816 0. 0.05 ] | 0.10917 | 2.78441e-27 | 0.10917 | [0.134 0.866] | 0.184 | -| (0.05, 48) | 0.137354 | 0.0726463 | 0.21 | [0.16 0.79 0. 0.05] | 0.11236 | 2.97923e-25 | 0.11236 | [0.16 0.84] | 0.21 | -| (0.05, 49) | 0.155531 | 0.0394693 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.57224e-19 | 0.110497 | [0.145 0.855] | 0.195 | -| (0.05, 50) | 0.165329 | 0.0146715 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 7.05554e-15 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 51) | 0.107478 | 0.0715223 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 2.33682e-16 | 0.108578 | [0.129 0.871] | 0.179 | -| (0.05, 52) | 0.0938284 | 0.0901716 | 0.184 | [0.134 0.816 0. 0.05 ] | 0.108422 | 0.000747907 | 0.10917 | [0.134 0.866] | 0.184 | -| (0.05, 53) | 0.137715 | 0.0582849 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 9.415e-15 | 0.110619 | [0.146 0.854] | 0.196 | -| (0.05, 54) | 0.152119 | 0.0308814 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 3.14673e-08 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 55) | 0.17551 | 0.0224896 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 2.44773e-14 | 0.110865 | [0.148 0.852] | 0.198 | -| (0.05, 56) | 0.180796 | 0.0172042 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 7.30979e-13 | 0.110865 | [0.148 0.852] | 0.198 | -| (0.05, 57) | 0.129629 | 0.0533714 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 7.45796e-26 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 58) | 0.163147 | 0.016853 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 2.66764e-19 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 59) | 0.0869485 | 0.103052 | 0.19 | [0.14 0.81 0. 0.05] | 0.109035 | 0.000854853 | 0.10989 | [0.14 0.86] | 0.19 | -| (0.05, 60) | 0.149196 | 0.0458035 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 1.89128e-15 | 0.110497 | [0.145 0.855] | 0.195 | -| (0.05, 61) | 0.126469 | 0.0565306 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.04909e-16 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 62) | 0.172479 | 0.0155209 | 0.188 | [0.139 0.811 0.001 0.049] | 0.107692 | 3.99895e-09 | 0.107692 | [0.14 0.86] | 0.188 | -| (0.05, 63) | 0.0976526 | 0.0803474 | 0.178 | [0.128 0.822 0. 0.05 ] | 0.10846 | 1.72691e-33 | 0.10846 | [0.128 0.872] | 0.178 | -| (0.05, 64) | 0.118217 | 0.0667828 | 0.185 | [0.135 0.815 0. 0.05 ] | 0.10929 | 3.28193e-14 | 0.10929 | [0.135 0.865] | 0.185 | -| (0.05, 65) | 0.0917532 | 0.0912468 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.1084 | 0.000651129 | 0.109051 | [0.133 0.867] | 0.183 | -| (0.05, 66) | 0.0971105 | 0.0788895 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.107692 | 0.000532613 | 0.108225 | [0.126 0.874] | 0.176 | -| (0.05, 67) | 0.107754 | 0.0802459 | 0.188 | [0.138 0.812 0. 0.05 ] | 0.109269 | 0.000379747 | 0.109649 | [0.138 0.862] | 0.188 | -| (0.05, 68) | 0.153214 | 0.0267861 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 1.06491e-21 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 69) | 0.140475 | 0.0495245 | 0.19 | [0.14 0.81 0. 0.05] | 0.10989 | 2.79207e-20 | 0.10989 | [0.14 0.86] | 0.19 | -| (0.05, 70) | 0.145541 | 0.0454591 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 2.75263e-10 | 0.110011 | [0.141 0.859] | 0.191 | -| (0.05, 71) | 0.155227 | 0.0187734 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 4.71961e-12 | 0.107991 | [0.124 0.876] | 0.174 | -| (0.05, 72) | 0.154241 | 0.0667593 | 0.221 | [0.171 0.779 0. 0.05 ] | 0.113766 | 6.45733e-22 | 0.113766 | [0.171 0.829] | 0.221 | -| (0.05, 73) | 0.149282 | 0.0537176 | 0.203 | [0.153 0.797 0. 0.05 ] | 0.111483 | 5.42307e-08 | 0.111483 | [0.153 0.847] | 0.203 | -| (0.05, 74) | 0.112973 | 0.0600274 | 0.173 | [0.123 0.827 0. 0.05 ] | 0.107875 | 3.72521e-13 | 0.107875 | [0.123 0.877] | 0.173 | -| (0.05, 75) | 0.118677 | 0.0783229 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 2.40661e-16 | 0.110742 | [0.147 0.853] | 0.197 | -| (0.05, 76) | 0.118952 | 0.0800475 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 6.3083e-23 | 0.110988 | [0.149 0.851] | 0.199 | -| (0.05, 77) | 0.147543 | 0.0464572 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 1.67292e-16 | 0.110375 | [0.144 0.856] | 0.194 | -| (0.05, 78) | 0.157932 | 0.0350678 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 1.80691e-27 | 0.110254 | [0.143 0.857] | 0.193 | -| (0.05, 79) | 0.10977 | 0.0872304 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 4.64983e-19 | 0.110742 | [0.147 0.853] | 0.197 | -| (0.05, 80) | 0.110754 | 0.0802464 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 6.39424e-18 | 0.110011 | [0.141 0.859] | 0.191 | -| (0.05, 81) | 0.156431 | 0.041569 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 7.35924e-18 | 0.110865 | [0.148 0.852] | 0.198 | -| (0.05, 82) | 0.154779 | 0.0442212 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 6.42816e-10 | 0.110988 | [0.149 0.851] | 0.199 | -| (0.05, 83) | 0.126509 | 0.0814906 | 0.208 | [0.158 0.792 0. 0.05 ] | 0.112108 | 1.30576e-17 | 0.112108 | [0.158 0.842] | 0.208 | -| (0.05, 84) | 0.136929 | 0.0700712 | 0.207 | [0.158 0.792 0.001 0.049] | 0.109989 | 1.45571e-22 | 0.109989 | [0.159 0.841] | 0.207 | -| (0.05, 85) | 0.150736 | 0.0462638 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 1.26775e-15 | 0.110742 | [0.147 0.853] | 0.197 | -| (0.05, 86) | 0.13535 | 0.0666503 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 1.52258e-16 | 0.111359 | [0.152 0.848] | 0.202 | -| (0.05, 87) | 0.154934 | 0.0240656 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 7.65988e-15 | 0.108578 | [0.129 0.871] | 0.179 | -| (0.05, 88) | 0.107502 | 0.0654985 | 0.173 | [0.123 0.827 0. 0.05 ] | 0.107875 | 1.44443e-19 | 0.107875 | [0.123 0.877] | 0.173 | -| (0.05, 89) | 0.148545 | 0.0474553 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 1.7763e-27 | 0.110619 | [0.146 0.854] | 0.196 | -| (0.05, 90) | 0.118629 | 0.0533714 | 0.172 | [0.122 0.828 0. 0.05 ] | 0.107759 | 1.68968e-12 | 0.107759 | [0.122 0.878] | 0.172 | -| (0.05, 91) | 0.120002 | 0.0739985 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 2.31969e-15 | 0.110375 | [0.144 0.856] | 0.194 | -| (0.05, 92) | 0.11476 | 0.0782402 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 3.75623e-20 | 0.110254 | [0.143 0.857] | 0.193 | -| (0.05, 93) | 0.128018 | 0.0719818 | 0.2 | [0.15 0.8 0. 0.05] | 0.111111 | 3.18359e-20 | 0.111111 | [0.15 0.85] | 0.2 | -| (0.05, 94) | 0.104735 | 0.0972647 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 4.15272e-22 | 0.111359 | [0.152 0.848] | 0.202 | -| (0.05, 95) | 0.14863 | 0.0313704 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 5.0643e-17 | 0.108696 | [0.13 0.87] | 0.18 | -| (0.05, 96) | 0.155999 | 0.0390008 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.38826e-17 | 0.110497 | [0.145 0.855] | 0.195 | -| (0.05, 97) | 0.121559 | 0.0774405 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 8.6301e-15 | 0.110988 | [0.149 0.851] | 0.199 | -| (0.05, 98) | 0.107171 | 0.0908294 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 2.90772e-18 | 0.110865 | [0.148 0.852] | 0.198 | -| (0.05, 99) | 0.155625 | 0.0373746 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 2.24989e-10 | 0.110254 | [0.143 0.857] | 0.193 | -| (0.1, 0) | 0.178993 | 0.0430075 | 0.222 | [0.122 0.778 0. 0.1 ] | 0.144324 | 0.0601746 | 0.204499 | [0.122 0.878] | 0.222 | -| (0.1, 1) | 0.152454 | 0.0715457 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.143947 | 0.0609715 | 0.204918 | [0.124 0.876] | 0.224 | -| (0.1, 2) | 0.157962 | 0.0720383 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.189546 | 0.0166395 | 0.206186 | [0.13 0.87] | 0.23 | -| (0.1, 3) | 0.140091 | 0.103909 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.16705 | 0.042155 | 0.209205 | [0.144 0.856] | 0.244 | -| (0.1, 4) | 0.146193 | 0.0908066 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.144253 | 0.063431 | 0.207684 | [0.137 0.863] | 0.237 | -| (0.1, 5) | 0.149072 | 0.0789285 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.166733 | 0.0390282 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.1, 6) | 0.138868 | 0.0971322 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.146507 | 0.0609615 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 7) | 0.155198 | 0.0728022 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.166977 | 0.0387839 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.1, 8) | 0.155993 | 0.0650068 | 0.221 | [0.121 0.779 0. 0.1 ] | 0.142876 | 0.0614141 | 0.20429 | [0.121 0.879] | 0.221 | -| (0.1, 9) | 0.142102 | 0.0838979 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.161065 | 0.0442743 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 10) | 0.173001 | 0.0689987 | 0.242 | [0.142 0.758 0. 0.1 ] | 0.158182 | 0.0505862 | 0.208768 | [0.142 0.858] | 0.242 | -| (0.1, 11) | 0.137157 | 0.114843 | 0.252 | [0.152 0.748 0. 0.1 ] | 0.17158 | 0.0393908 | 0.21097 | [0.152 0.848] | 0.252 | -| (0.1, 12) | 0.139662 | 0.103338 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.138507 | 0.0704791 | 0.208986 | [0.143 0.857] | 0.243 | -| (0.1, 13) | 0.158423 | 0.0895769 | 0.248 | [0.149 0.751 0.001 0.099] | 0.190184 | 0.0182375 | 0.208421 | [0.15 0.85] | 0.248 | -| (0.1, 14) | 0.12466 | 0.10934 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.139225 | 0.0678139 | 0.207039 | [0.134 0.866] | 0.234 | -| (0.1, 15) | 0.159436 | 0.0755636 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.136966 | 0.0702877 | 0.207254 | [0.135 0.865] | 0.235 | -| (0.1, 16) | 0.140571 | 0.0874287 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.1321 | 0.073661 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.1, 17) | 0.133351 | 0.0936495 | 0.227 | [0.127 0.773 0. 0.1 ] | 0.145652 | 0.0598978 | 0.20555 | [0.127 0.873] | 0.227 | -| (0.1, 18) | 0.153178 | 0.074822 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.15928 | 0.0464809 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.1, 19) | 0.16243 | 0.0635701 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.14073 | 0.0646092 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 20) | 0.162509 | 0.0814913 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.160787 | 0.0484177 | 0.209205 | [0.144 0.856] | 0.244 | -| (0.1, 21) | 0.122019 | 0.109981 | 0.232 | [0.133 0.767 0.001 0.099] | 0.146363 | 0.058606 | 0.204969 | [0.134 0.866] | 0.232 | -| (0.1, 22) | 0.164098 | 0.0729024 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.142794 | 0.0648901 | 0.207684 | [0.137 0.863] | 0.237 | -| (0.1, 23) | 0.13655 | 0.10945 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.133593 | 0.0760508 | 0.209644 | [0.146 0.854] | 0.246 | -| (0.1, 24) | 0.156787 | 0.0702127 | 0.227 | [0.127 0.773 0. 0.1 ] | 0.162406 | 0.0431439 | 0.20555 | [0.127 0.873] | 0.227 | -| (0.1, 25) | 0.142566 | 0.103434 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.161656 | 0.0479876 | 0.209644 | [0.146 0.854] | 0.246 | -| (0.1, 26) | 0.16276 | 0.0772395 | 0.24 | [0.14 0.76 0. 0.1 ] | 0.128966 | 0.0793677 | 0.208333 | [0.14 0.86] | 0.24 | -| (0.1, 27) | 0.125019 | 0.0889812 | 0.214 | [0.114 0.786 0. 0.1 ] | 0.169924 | 0.0329159 | 0.20284 | [0.114 0.886] | 0.214 | -| (0.1, 28) | 0.141623 | 0.101377 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.131695 | 0.077291 | 0.208986 | [0.143 0.857] | 0.243 | -| (0.1, 29) | 0.196429 | 0.0525706 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.155012 | 0.0552926 | 0.210305 | [0.149 0.851] | 0.249 | -| (0.1, 30) | 0.14666 | 0.10034 | 0.247 | [0.147 0.753 0. 0.1 ] | 0.134494 | 0.0753696 | 0.209864 | [0.147 0.853] | 0.247 | -| (0.1, 31) | 0.138889 | 0.111111 | 0.25 | [0.151 0.749 0.001 0.099] | 0.122134 | 0.0867272 | 0.208861 | [0.152 0.848] | 0.25 | -| (0.1, 32) | 0.14235 | 0.10065 | 0.243 | [0.144 0.756 0.001 0.099] | 0.125462 | 0.0818674 | 0.20733 | [0.145 0.855] | 0.243 | -| (0.1, 33) | 0.149198 | 0.0748024 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.138851 | 0.0660671 | 0.204918 | [0.124 0.876] | 0.224 | -| (0.1, 34) | 0.183632 | 0.0423683 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.165968 | 0.0393708 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 35) | 0.109742 | 0.131258 | 0.241 | [0.141 0.759 0. 0.1 ] | 0.124891 | 0.0836592 | 0.208551 | [0.141 0.859] | 0.241 | -| (0.1, 36) | 0.119279 | 0.103721 | 0.223 | [0.123 0.777 0. 0.1 ] | 0.158427 | 0.0462817 | 0.204708 | [0.123 0.877] | 0.223 | -| (0.1, 37) | 0.176492 | 0.0365084 | 0.213 | [0.113 0.787 0. 0.1 ] | 0.158593 | 0.0440417 | 0.202634 | [0.113 0.887] | 0.213 | -| (0.1, 38) | 0.13036 | 0.09864 | 0.229 | [0.129 0.771 0. 0.1 ] | 0.151765 | 0.0542087 | 0.205973 | [0.129 0.871] | 0.229 | -| (0.1, 39) | 0.143694 | 0.0773055 | 0.221 | [0.121 0.779 0. 0.1 ] | 0.152654 | 0.0516359 | 0.20429 | [0.121 0.879] | 0.221 | -| (0.1, 40) | 0.176925 | 0.0620751 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.173127 | 0.0349899 | 0.208117 | [0.139 0.861] | 0.239 | -| (0.1, 41) | 0.161501 | 0.0714994 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.141467 | 0.0653583 | 0.206825 | [0.133 0.867] | 0.233 | -| (0.1, 42) | 0.174647 | 0.0573527 | 0.232 | [0.133 0.767 0.001 0.099] | 0.163408 | 0.0415608 | 0.204969 | [0.134 0.866] | 0.232 | -| (0.1, 43) | 0.167175 | 0.0718247 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.144039 | 0.0640772 | 0.208117 | [0.139 0.861] | 0.239 | -| (0.1, 44) | 0.178495 | 0.0705051 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.167393 | 0.0429116 | 0.210305 | [0.149 0.851] | 0.249 | -| (0.1, 45) | 0.136153 | 0.0988467 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.143173 | 0.0640808 | 0.207254 | [0.135 0.865] | 0.235 | -| (0.1, 46) | 0.160061 | 0.0699394 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.160817 | 0.045369 | 0.206186 | [0.13 0.87] | 0.23 | -| (0.1, 47) | 0.14961 | 0.0743897 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.125738 | 0.07918 | 0.204918 | [0.124 0.876] | 0.224 | -| (0.1, 48) | 0.151283 | 0.0817166 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.15867 | 0.0481552 | 0.206825 | [0.133 0.867] | 0.233 | -| (0.1, 49) | 0.104849 | 0.132151 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.145601 | 0.062083 | 0.207684 | [0.137 0.863] | 0.237 | -| (0.1, 50) | 0.137846 | 0.0781539 | 0.216 | [0.116 0.784 0. 0.1 ] | 0.159667 | 0.0435852 | 0.203252 | [0.116 0.884] | 0.216 | -| (0.1, 51) | 0.165942 | 0.066058 | 0.232 | [0.132 0.768 0. 0.1 ] | 0.172122 | 0.0344898 | 0.206612 | [0.132 0.868] | 0.232 | -| (0.1, 52) | 0.171149 | 0.042851 | 0.214 | [0.114 0.786 0. 0.1 ] | 0.162734 | 0.0401054 | 0.20284 | [0.114 0.886] | 0.214 | -| (0.1, 53) | 0.154402 | 0.0895978 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.145084 | 0.0641207 | 0.209205 | [0.144 0.856] | 0.244 | -| (0.1, 54) | 0.142341 | 0.0896587 | 0.232 | [0.132 0.768 0. 0.1 ] | 0.15741 | 0.0492015 | 0.206612 | [0.132 0.868] | 0.232 | -| (0.1, 55) | 0.125124 | 0.120876 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.152008 | 0.0576355 | 0.209644 | [0.146 0.854] | 0.246 | -| (0.1, 56) | 0.123448 | 0.106552 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.142112 | 0.0640739 | 0.206186 | [0.13 0.87] | 0.23 | -| (0.1, 57) | 0.142486 | 0.0985143 | 0.241 | [0.142 0.758 0.001 0.099] | 0.172454 | 0.0344422 | 0.206897 | [0.143 0.857] | 0.241 | -| (0.1, 58) | 0.1401 | 0.0999001 | 0.24 | [0.14 0.76 0. 0.1 ] | 0.162622 | 0.0457112 | 0.208333 | [0.14 0.86] | 0.24 | -| (0.1, 59) | 0.136005 | 0.0989948 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.113088 | 0.0941657 | 0.207254 | [0.135 0.865] | 0.235 | -| (0.1, 60) | 0.164363 | 0.0656372 | 0.23 | [0.132 0.768 0.002 0.098] | 0.144721 | 0.0581779 | 0.202899 | [0.134 0.866] | 0.23 | -| (0.1, 61) | 0.147789 | 0.102211 | 0.25 | [0.15 0.75 0. 0.1 ] | 0.120903 | 0.0896233 | 0.210526 | [0.15 0.85] | 0.25 | -| (0.1, 62) | 0.171044 | 0.0549561 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.178233 | 0.0271061 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 63) | 0.163507 | 0.0704927 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.158086 | 0.048953 | 0.207039 | [0.134 0.866] | 0.234 | -| (0.1, 64) | 0.110106 | 0.136894 | 0.247 | [0.147 0.753 0. 0.1 ] | 0.131224 | 0.07864 | 0.209864 | [0.147 0.853] | 0.247 | -| (0.1, 65) | 0.116124 | 0.113876 | 0.23 | [0.132 0.768 0.002 0.098] | 0.160052 | 0.0428462 | 0.202899 | [0.134 0.866] | 0.23 | -| (0.1, 66) | 0.148545 | 0.0964552 | 0.245 | [0.145 0.755 0. 0.1 ] | 0.153694 | 0.0557301 | 0.209424 | [0.145 0.855] | 0.245 | -| (0.1, 67) | 0.147949 | 0.0880511 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.120853 | 0.086616 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 68) | 0.159105 | 0.066895 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.166105 | 0.0392333 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 69) | 0.133635 | 0.0993651 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.13905 | 0.0677752 | 0.206825 | [0.133 0.867] | 0.233 | -| (0.1, 70) | 0.136221 | 0.0947786 | 0.231 | [0.131 0.769 0. 0.1 ] | 0.158557 | 0.0478413 | 0.206398 | [0.131 0.869] | 0.231 | -| (0.1, 71) | 0.173515 | 0.0564854 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.154365 | 0.0518201 | 0.206186 | [0.13 0.87] | 0.23 | -| (0.1, 72) | 0.16392 | 0.0540796 | 0.218 | [0.118 0.782 0. 0.1 ] | 0.145843 | 0.0578235 | 0.203666 | [0.118 0.882] | 0.218 | -| (0.1, 73) | 0.15042 | 0.0985805 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.160302 | 0.0500027 | 0.210305 | [0.149 0.851] | 0.249 | -| (0.1, 74) | 0.162813 | 0.0761868 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.158843 | 0.0492734 | 0.208117 | [0.139 0.861] | 0.239 | -| (0.1, 75) | 0.13747 | 0.10053 | 0.238 | [0.138 0.762 0. 0.1 ] | 0.161175 | 0.046725 | 0.2079 | [0.138 0.862] | 0.238 | -| (0.1, 76) | 0.145291 | 0.0577093 | 0.203 | [0.103 0.797 0. 0.1 ] | 0.149275 | 0.0513268 | 0.200602 | [0.103 0.897] | 0.203 | -| (0.1, 77) | 0.162521 | 0.0464792 | 0.209 | [0.109 0.791 0. 0.1 ] | 0.160017 | 0.041799 | 0.201816 | [0.109 0.891] | 0.209 | -| (0.1, 78) | 0.173875 | 0.0691247 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.172443 | 0.0365437 | 0.208986 | [0.143 0.857] | 0.243 | -| (0.1, 79) | 0.167891 | 0.0681092 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.164834 | 0.0426348 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 80) | 0.105674 | 0.122326 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.141365 | 0.0643962 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.1, 81) | 0.146952 | 0.0890479 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.146061 | 0.0614083 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 82) | 0.161609 | 0.0743909 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.157538 | 0.0499308 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 83) | 0.133117 | 0.103883 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.140087 | 0.0675969 | 0.207684 | [0.137 0.863] | 0.237 | -| (0.1, 84) | 0.105845 | 0.133155 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.134486 | 0.0736308 | 0.208117 | [0.139 0.861] | 0.239 | -| (0.1, 85) | 0.149457 | 0.0795428 | 0.229 | [0.129 0.771 0. 0.1 ] | 0.152163 | 0.05381 | 0.205973 | [0.129 0.871] | 0.229 | -| (0.1, 86) | 0.155383 | 0.075617 | 0.231 | [0.131 0.769 0. 0.1 ] | 0.142288 | 0.0641102 | 0.206398 | [0.131 0.869] | 0.231 | -| (0.1, 87) | 0.152283 | 0.0677166 | 0.22 | [0.12 0.78 0. 0.1 ] | 0.136019 | 0.0680626 | 0.204082 | [0.12 0.88] | 0.22 | -| (0.1, 88) | 0.155228 | 0.0857723 | 0.241 | [0.141 0.759 0. 0.1 ] | 0.159894 | 0.0486562 | 0.208551 | [0.141 0.859] | 0.241 | -| (0.1, 89) | 0.173025 | 0.0519748 | 0.225 | [0.125 0.775 0. 0.1 ] | 0.149647 | 0.0554812 | 0.205128 | [0.125 0.875] | 0.225 | -| (0.1, 90) | 0.161536 | 0.0644643 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.164452 | 0.0408866 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 91) | 0.19674 | 0.0392595 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.152299 | 0.0551697 | 0.207469 | [0.136 0.864] | 0.236 | -| (0.1, 92) | 0.1473 | 0.0766998 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.125015 | 0.0799031 | 0.204918 | [0.124 0.876] | 0.224 | -| (0.1, 93) | 0.157492 | 0.0725084 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.161964 | 0.0442218 | 0.206186 | [0.13 0.87] | 0.23 | -| (0.1, 94) | 0.166779 | 0.0672206 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.168283 | 0.0387559 | 0.207039 | [0.134 0.866] | 0.234 | -| (0.1, 95) | 0.170211 | 0.0797889 | 0.25 | [0.15 0.75 0. 0.1 ] | 0.151857 | 0.0586692 | 0.210526 | [0.15 0.85] | 0.25 | -| (0.1, 96) | 0.137124 | 0.0808765 | 0.218 | [0.118 0.782 0. 0.1 ] | 0.154015 | 0.0496513 | 0.203666 | [0.118 0.882] | 0.218 | -| (0.1, 97) | 0.153298 | 0.0727017 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.165413 | 0.0399255 | 0.205339 | [0.126 0.874] | 0.226 | -| (0.1, 98) | 0.141298 | 0.120702 | 0.262 | [0.162 0.738 0. 0.1 ] | 0.135892 | 0.0773272 | 0.21322 | [0.162 0.838] | 0.262 | -| (0.1, 99) | 0.175225 | 0.0527751 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.16037 | 0.045391 | 0.205761 | [0.128 0.872] | 0.228 | -| (0.15, 0) | 0.1203 | 0.1657 | 0.286 | [0.137 0.713 0.001 0.149] | 0.127121 | 0.167346 | 0.294466 | [0.138 0.862] | 0.286 | -| (0.15, 1) | 0.119526 | 0.167474 | 0.287 | [0.137 0.713 0. 0.15 ] | 0.135734 | 0.160416 | 0.29615 | [0.137 0.863] | 0.287 | -| (0.15, 2) | 0.137288 | 0.133712 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.123291 | 0.168254 | 0.291545 | [0.121 0.879] | 0.271 | -| (0.15, 3) | 0.142731 | 0.113269 | 0.256 | [0.106 0.744 0. 0.15 ] | 0.13131 | 0.156047 | 0.287356 | [0.106 0.894] | 0.256 | -| (0.15, 4) | 0.166618 | 0.105382 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.161107 | 0.130722 | 0.291829 | [0.122 0.878] | 0.272 | -| (0.15, 5) | 0.139421 | 0.139579 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.176782 | 0.117048 | 0.29383 | [0.129 0.871] | 0.279 | -| (0.15, 6) | 0.170222 | 0.099778 | 0.27 | [0.12 0.73 0. 0.15] | 0.148121 | 0.143141 | 0.291262 | [0.12 0.88] | 0.27 | -| (0.15, 7) | 0.158061 | 0.114939 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.176748 | 0.115365 | 0.292113 | [0.123 0.877] | 0.273 | -| (0.15, 8) | 0.17871 | 0.11629 | 0.295 | [0.145 0.705 0. 0.15 ] | 0.132824 | 0.165683 | 0.298507 | [0.145 0.855] | 0.295 | -| (0.15, 9) | 0.151538 | 0.119462 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.149422 | 0.142123 | 0.291545 | [0.121 0.879] | 0.271 | -| (0.15, 10) | 0.139049 | 0.130951 | 0.27 | [0.121 0.729 0.001 0.149] | 0.13676 | 0.153123 | 0.289883 | [0.122 0.878] | 0.27 | -| (0.15, 11) | 0.116244 | 0.156756 | 0.273 | [0.124 0.726 0.001 0.149] | 0.141815 | 0.148916 | 0.290732 | [0.125 0.875] | 0.273 | -| (0.15, 12) | 0.162998 | 0.126002 | 0.289 | [0.139 0.711 0. 0.15 ] | 0.156127 | 0.140609 | 0.296736 | [0.139 0.861] | 0.289 | -| (0.15, 13) | 0.128238 | 0.150762 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.134349 | 0.159481 | 0.29383 | [0.129 0.871] | 0.279 | -| (0.15, 14) | 0.117776 | 0.132224 | 0.25 | [0.1 0.75 0. 0.15] | 0.139297 | 0.146418 | 0.285714 | [0.1 0.9] | 0.25 | -| (0.15, 15) | 0.104416 | 0.179584 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.131122 | 0.164154 | 0.295276 | [0.134 0.866] | 0.284 | -| (0.15, 16) | 0.137068 | 0.134932 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.166223 | 0.125605 | 0.291829 | [0.122 0.878] | 0.272 | -| (0.15, 17) | 0.158429 | 0.111571 | 0.27 | [0.12 0.73 0. 0.15] | 0.17491 | 0.116352 | 0.291262 | [0.12 0.88] | 0.27 | -| (0.15, 18) | 0.168285 | 0.0947153 | 0.263 | [0.113 0.737 0. 0.15 ] | 0.173726 | 0.11557 | 0.289296 | [0.113 0.887] | 0.263 | -| (0.15, 19) | 0.171027 | 0.120973 | 0.292 | [0.142 0.708 0. 0.15 ] | 0.142831 | 0.154788 | 0.297619 | [0.142 0.858] | 0.292 | -| (0.15, 20) | 0.148659 | 0.131341 | 0.28 | [0.13 0.72 0. 0.15] | 0.155957 | 0.138161 | 0.294118 | [0.13 0.87] | 0.28 | -| (0.15, 21) | 0.110694 | 0.147306 | 0.258 | [0.108 0.742 0. 0.15 ] | 0.132425 | 0.155482 | 0.287908 | [0.108 0.892] | 0.258 | -| (0.15, 22) | 0.13145 | 0.14855 | 0.28 | [0.13 0.72 0. 0.15] | 0.134374 | 0.159744 | 0.294118 | [0.13 0.87] | 0.28 | -| (0.15, 23) | 0.146038 | 0.126962 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.130698 | 0.161415 | 0.292113 | [0.123 0.877] | 0.273 | -| (0.15, 24) | 0.181347 | 0.108653 | 0.29 | [0.141 0.709 0.001 0.149] | 0.149705 | 0.14593 | 0.295635 | [0.142 0.858] | 0.29 | -| (0.15, 25) | 0.146943 | 0.144057 | 0.291 | [0.141 0.709 0. 0.15 ] | 0.136203 | 0.161121 | 0.297324 | [0.141 0.859] | 0.291 | -| (0.15, 26) | 0.147795 | 0.116205 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.157537 | 0.132038 | 0.289575 | [0.114 0.886] | 0.264 | -| (0.15, 27) | 0.128014 | 0.138986 | 0.267 | [0.117 0.733 0. 0.15 ] | 0.148528 | 0.141888 | 0.290416 | [0.117 0.883] | 0.267 | -| (0.15, 28) | 0.143207 | 0.128793 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.138262 | 0.153567 | 0.291829 | [0.122 0.878] | 0.272 | -| (0.15, 29) | 0.148872 | 0.145128 | 0.294 | [0.144 0.706 0. 0.15 ] | 0.135484 | 0.162727 | 0.298211 | [0.144 0.856] | 0.294 | -| (0.15, 30) | 0.144965 | 0.113035 | 0.258 | [0.108 0.742 0. 0.15 ] | 0.147562 | 0.140346 | 0.287908 | [0.108 0.892] | 0.258 | -| (0.15, 31) | 0.145971 | 0.119029 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.161121 | 0.128734 | 0.289855 | [0.115 0.885] | 0.265 | -| (0.15, 32) | 0.137751 | 0.124249 | 0.262 | [0.113 0.737 0.001 0.149] | 0.145534 | 0.142111 | 0.287645 | [0.114 0.886] | 0.262 | -| (0.15, 33) | 0.11238 | 0.15762 | 0.27 | [0.12 0.73 0. 0.15] | 0.130286 | 0.160976 | 0.291262 | [0.12 0.88] | 0.27 | -| (0.15, 34) | 0.0966508 | 0.179349 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.117277 | 0.175692 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 35) | 0.157902 | 0.113098 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.152535 | 0.13901 | 0.291545 | [0.121 0.879] | 0.271 | -| (0.15, 36) | 0.125849 | 0.166151 | 0.292 | [0.142 0.708 0. 0.15 ] | 0.15111 | 0.146509 | 0.297619 | [0.142 0.858] | 0.292 | -| (0.15, 37) | 0.167764 | 0.137236 | 0.305 | [0.155 0.695 0. 0.15 ] | 0.179681 | 0.121826 | 0.301508 | [0.155 0.845] | 0.305 | -| (0.15, 38) | 0.154211 | 0.128789 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.166432 | 0.128554 | 0.294985 | [0.133 0.867] | 0.283 | -| (0.15, 39) | 0.132705 | 0.143295 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.14616 | 0.146809 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 40) | 0.152512 | 0.123488 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.141054 | 0.151915 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 41) | 0.135058 | 0.127942 | 0.263 | [0.113 0.737 0. 0.15 ] | 0.16119 | 0.128106 | 0.289296 | [0.113 0.887] | 0.263 | -| (0.15, 42) | 0.123784 | 0.152216 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.142844 | 0.150125 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 43) | 0.171101 | 0.0878989 | 0.259 | [0.109 0.741 0. 0.15 ] | 0.164715 | 0.123469 | 0.288184 | [0.109 0.891] | 0.259 | -| (0.15, 44) | 0.16704 | 0.0949603 | 0.262 | [0.114 0.736 0.002 0.148] | 0.142699 | 0.143568 | 0.286267 | [0.116 0.884] | 0.262 | -| (0.15, 45) | 0.116311 | 0.171689 | 0.288 | [0.138 0.712 0. 0.15 ] | 0.141653 | 0.15479 | 0.296443 | [0.138 0.862] | 0.288 | -| (0.15, 46) | 0.154616 | 0.104384 | 0.259 | [0.109 0.741 0. 0.15 ] | 0.138548 | 0.149637 | 0.288184 | [0.109 0.891] | 0.259 | -| (0.15, 47) | 0.144754 | 0.133246 | 0.278 | [0.128 0.722 0. 0.15 ] | 0.12734 | 0.166202 | 0.293542 | [0.128 0.872] | 0.278 | -| (0.15, 48) | 0.175836 | 0.110164 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.153911 | 0.141947 | 0.295858 | [0.136 0.864] | 0.286 | -| (0.15, 49) | 0.162022 | 0.111978 | 0.274 | [0.124 0.726 0. 0.15 ] | 0.150176 | 0.142221 | 0.292398 | [0.124 0.876] | 0.274 | -| (0.15, 50) | 0.177202 | 0.0937983 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.158743 | 0.132803 | 0.291545 | [0.121 0.879] | 0.271 | -| (0.15, 51) | 0.153076 | 0.111924 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.134545 | 0.15531 | 0.289855 | [0.115 0.885] | 0.265 | -| (0.15, 52) | 0.138063 | 0.151937 | 0.29 | [0.14 0.71 0. 0.15] | 0.137941 | 0.159089 | 0.29703 | [0.14 0.86] | 0.29 | -| (0.15, 53) | 0.153557 | 0.122443 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.132467 | 0.160502 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 54) | 0.151345 | 0.114655 | 0.266 | [0.116 0.734 0. 0.15 ] | 0.138179 | 0.151956 | 0.290135 | [0.116 0.884] | 0.266 | -| (0.15, 55) | 0.13427 | 0.14873 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.157275 | 0.137711 | 0.294985 | [0.133 0.867] | 0.283 | -| (0.15, 56) | 0.137219 | 0.134781 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.158478 | 0.133351 | 0.291829 | [0.122 0.878] | 0.272 | -| (0.15, 57) | 0.160927 | 0.140073 | 0.301 | [0.151 0.699 0. 0.15 ] | 0.146971 | 0.153329 | 0.3003 | [0.151 0.849] | 0.301 | -| (0.15, 58) | 0.120219 | 0.163781 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.145434 | 0.149842 | 0.295276 | [0.134 0.866] | 0.284 | -| (0.15, 59) | 0.154083 | 0.126917 | 0.281 | [0.131 0.719 0. 0.15 ] | 0.154977 | 0.139429 | 0.294406 | [0.131 0.869] | 0.281 | -| (0.15, 60) | 0.157883 | 0.111117 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.172352 | 0.118627 | 0.29098 | [0.119 0.881] | 0.269 | -| (0.15, 61) | 0.136203 | 0.143797 | 0.28 | [0.13 0.72 0. 0.15] | 0.156749 | 0.137369 | 0.294118 | [0.13 0.87] | 0.28 | -| (0.15, 62) | 0.121822 | 0.177178 | 0.299 | [0.149 0.701 0. 0.15 ] | 0.158284 | 0.141416 | 0.2997 | [0.149 0.851] | 0.299 | -| (0.15, 63) | 0.134564 | 0.151436 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.122765 | 0.173093 | 0.295858 | [0.136 0.864] | 0.286 | -| (0.15, 64) | 0.138566 | 0.136434 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.164812 | 0.127871 | 0.292683 | [0.125 0.875] | 0.275 | -| (0.15, 65) | 0.141594 | 0.132406 | 0.274 | [0.125 0.725 0.001 0.149] | 0.130073 | 0.160943 | 0.291016 | [0.126 0.874] | 0.274 | -| (0.15, 66) | 0.144607 | 0.135393 | 0.28 | [0.13 0.72 0. 0.15] | 0.119498 | 0.174619 | 0.294118 | [0.13 0.87] | 0.28 | -| (0.15, 67) | 0.178893 | 0.090107 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.171713 | 0.119267 | 0.29098 | [0.119 0.881] | 0.269 | -| (0.15, 68) | 0.139628 | 0.145372 | 0.285 | [0.135 0.715 0. 0.15 ] | 0.135852 | 0.159714 | 0.295567 | [0.135 0.865] | 0.285 | -| (0.15, 69) | 0.1838 | 0.0922 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.172546 | 0.120423 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 70) | 0.125644 | 0.138356 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.160992 | 0.128583 | 0.289575 | [0.114 0.886] | 0.264 | -| (0.15, 71) | 0.160764 | 0.101236 | 0.262 | [0.112 0.738 0. 0.15 ] | 0.161154 | 0.127864 | 0.289017 | [0.112 0.888] | 0.262 | -| (0.15, 72) | 0.161464 | 0.107536 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.159812 | 0.131167 | 0.29098 | [0.119 0.881] | 0.269 | -| (0.15, 73) | 0.121342 | 0.153658 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.107444 | 0.185239 | 0.292683 | [0.125 0.875] | 0.275 | -| (0.15, 74) | 0.145367 | 0.133633 | 0.279 | [0.13 0.72 0.001 0.149] | 0.129848 | 0.162596 | 0.292444 | [0.131 0.869] | 0.279 | -| (0.15, 75) | 0.155114 | 0.117886 | 0.273 | [0.124 0.726 0.001 0.149] | 0.15948 | 0.131252 | 0.290732 | [0.125 0.875] | 0.273 | -| (0.15, 76) | 0.1274 | 0.1606 | 0.288 | [0.138 0.712 0. 0.15 ] | 0.14193 | 0.154513 | 0.296443 | [0.138 0.862] | 0.288 | -| (0.15, 77) | 0.152264 | 0.143736 | 0.296 | [0.146 0.704 0. 0.15 ] | 0.155979 | 0.142826 | 0.298805 | [0.146 0.854] | 0.296 | -| (0.15, 78) | 0.10929 | 0.17271 | 0.282 | [0.132 0.718 0. 0.15 ] | 0.129038 | 0.165657 | 0.294695 | [0.132 0.868] | 0.282 | -| (0.15, 79) | 0.1422 | 0.1328 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.150535 | 0.142148 | 0.292683 | [0.125 0.875] | 0.275 | -| (0.15, 80) | 0.168582 | 0.0974178 | 0.266 | [0.117 0.733 0.001 0.149] | 0.161824 | 0.126936 | 0.28876 | [0.118 0.882] | 0.266 | -| (0.15, 81) | 0.148811 | 0.130189 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.118403 | 0.175427 | 0.29383 | [0.129 0.871] | 0.279 | -| (0.15, 82) | 0.133344 | 0.134656 | 0.268 | [0.118 0.732 0. 0.15 ] | 0.151254 | 0.139444 | 0.290698 | [0.118 0.882] | 0.268 | -| (0.15, 83) | 0.124459 | 0.126541 | 0.251 | [0.101 0.749 0. 0.15 ] | 0.13409 | 0.151896 | 0.285987 | [0.101 0.899] | 0.251 | -| (0.15, 84) | 0.141318 | 0.137682 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.154315 | 0.139515 | 0.29383 | [0.129 0.871] | 0.279 | -| (0.15, 85) | 0.163049 | 0.117951 | 0.281 | [0.131 0.719 0. 0.15 ] | 0.140319 | 0.154088 | 0.294406 | [0.131 0.869] | 0.281 | -| (0.15, 86) | 0.152419 | 0.133581 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.137388 | 0.15847 | 0.295858 | [0.136 0.864] | 0.286 | -| (0.15, 87) | 0.106809 | 0.176191 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.102393 | 0.192592 | 0.294985 | [0.133 0.867] | 0.283 | -| (0.15, 88) | 0.160406 | 0.104594 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.15609 | 0.133765 | 0.289855 | [0.115 0.885] | 0.265 | -| (0.15, 89) | 0.123011 | 0.149989 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.145604 | 0.146509 | 0.292113 | [0.123 0.877] | 0.273 | -| (0.15, 90) | 0.159302 | 0.112698 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.139501 | 0.152328 | 0.291829 | [0.122 0.878] | 0.272 | -| (0.15, 91) | 0.146535 | 0.147465 | 0.294 | [0.144 0.706 0. 0.15 ] | 0.160389 | 0.137822 | 0.298211 | [0.144 0.856] | 0.294 | -| (0.15, 92) | 0.174056 | 0.089944 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.154888 | 0.134687 | 0.289575 | [0.114 0.886] | 0.264 | -| (0.15, 93) | 0.132952 | 0.145048 | 0.278 | [0.128 0.722 0. 0.15 ] | 0.153029 | 0.140513 | 0.293542 | [0.128 0.872] | 0.278 | -| (0.15, 94) | 0.148742 | 0.131258 | 0.28 | [0.131 0.719 0.001 0.149] | 0.15407 | 0.138661 | 0.292731 | [0.132 0.868] | 0.28 | -| (0.15, 95) | 0.160161 | 0.102839 | 0.263 | [0.114 0.736 0.001 0.149] | 0.135026 | 0.152896 | 0.287923 | [0.115 0.885] | 0.263 | -| (0.15, 96) | 0.123638 | 0.160362 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.138609 | 0.156667 | 0.295276 | [0.134 0.866] | 0.284 | -| (0.15, 97) | 0.161662 | 0.105338 | 0.267 | [0.117 0.733 0. 0.15 ] | 0.146184 | 0.144233 | 0.290416 | [0.117 0.883] | 0.267 | -| (0.15, 98) | 0.147239 | 0.128761 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.165486 | 0.127483 | 0.292969 | [0.126 0.874] | 0.276 | -| (0.15, 99) | 0.127231 | 0.141769 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.137447 | 0.153533 | 0.29098 | [0.119 0.881] | 0.269 | -| (0.2, 0) | 0.0932749 | 0.232725 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.107742 | 0.264698 | 0.372439 | [0.126 0.874] | 0.326 | -| (0.2, 1) | 0.103595 | 0.225405 | 0.329 | [0.129 0.671 0. 0.2 ] | 0.11693 | 0.256553 | 0.373483 | [0.129 0.871] | 0.329 | -| (0.2, 2) | 0.146266 | 0.174734 | 0.321 | [0.121 0.679 0. 0.2 ] | 0.13068 | 0.240034 | 0.370714 | [0.121 0.879] | 0.321 | -| (0.2, 3) | 0.158612 | 0.158388 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.140197 | 0.229147 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 4) | 0.13548 | 0.18852 | 0.324 | [0.125 0.675 0.001 0.199] | 0.123131 | 0.247446 | 0.370577 | [0.126 0.874] | 0.324 | -| (0.2, 5) | 0.184383 | 0.139617 | 0.324 | [0.124 0.676 0. 0.2 ] | 0.16367 | 0.208077 | 0.371747 | [0.124 0.876] | 0.324 | -| (0.2, 6) | 0.119201 | 0.191799 | 0.311 | [0.111 0.689 0. 0.2 ] | 0.12538 | 0.24193 | 0.367309 | [0.111 0.889] | 0.311 | -| (0.2, 7) | 0.13074 | 0.18826 | 0.319 | [0.12 0.68 0.001 0.199] | 0.1483 | 0.22056 | 0.36886 | [0.121 0.879] | 0.319 | -| (0.2, 8) | 0.158339 | 0.154661 | 0.313 | [0.113 0.687 0. 0.2 ] | 0.141924 | 0.226061 | 0.367985 | [0.113 0.887] | 0.313 | -| (0.2, 9) | 0.133184 | 0.171816 | 0.305 | [0.107 0.693 0.002 0.198] | 0.155369 | 0.207601 | 0.36297 | [0.109 0.891] | 0.305 | -| (0.2, 10) | 0.152238 | 0.170762 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.141301 | 0.230101 | 0.371402 | [0.123 0.877] | 0.323 | -| (0.2, 11) | 0.147096 | 0.187904 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.126492 | 0.249094 | 0.375587 | [0.135 0.865] | 0.335 | -| (0.2, 12) | 0.139379 | 0.187621 | 0.327 | [0.127 0.673 0. 0.2 ] | 0.140083 | 0.232704 | 0.372787 | [0.127 0.873] | 0.327 | -| (0.2, 13) | 0.157408 | 0.164592 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.128326 | 0.242732 | 0.371058 | [0.122 0.878] | 0.322 | -| (0.2, 14) | 0.165325 | 0.154675 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.141291 | 0.229079 | 0.37037 | [0.12 0.88] | 0.32 | -| (0.2, 15) | 0.155916 | 0.162084 | 0.318 | [0.119 0.681 0.001 0.199] | 0.136168 | 0.232351 | 0.368519 | [0.12 0.88] | 0.318 | -| (0.2, 16) | 0.126583 | 0.190417 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.0939205 | 0.275424 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 17) | 0.132189 | 0.202811 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.109183 | 0.266403 | 0.375587 | [0.135 0.865] | 0.335 | -| (0.2, 18) | 0.129466 | 0.189534 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.115286 | 0.254741 | 0.370028 | [0.119 0.881] | 0.319 | -| (0.2, 19) | 0.128175 | 0.177825 | 0.306 | [0.106 0.694 0. 0.2 ] | 0.133787 | 0.231843 | 0.365631 | [0.106 0.894] | 0.306 | -| (0.2, 20) | 0.145426 | 0.187574 | 0.333 | [0.133 0.667 0. 0.2 ] | 0.151205 | 0.223677 | 0.374883 | [0.133 0.867] | 0.333 | -| (0.2, 21) | 0.163454 | 0.154546 | 0.318 | [0.119 0.681 0.001 0.199] | 0.139559 | 0.228959 | 0.368519 | [0.12 0.88] | 0.318 | -| (0.2, 22) | 0.114168 | 0.215832 | 0.33 | [0.131 0.669 0.001 0.199] | 0.114659 | 0.258001 | 0.372659 | [0.132 0.868] | 0.33 | -| (0.2, 23) | 0.156559 | 0.173441 | 0.33 | [0.13 0.67 0. 0.2 ] | 0.150894 | 0.222937 | 0.373832 | [0.13 0.87] | 0.33 | -| (0.2, 24) | 0.110103 | 0.206897 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.109295 | 0.260049 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 25) | 0.153082 | 0.174918 | 0.328 | [0.128 0.672 0. 0.2 ] | 0.159335 | 0.213799 | 0.373134 | [0.128 0.872] | 0.328 | -| (0.2, 26) | 0.126215 | 0.187785 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.114362 | 0.253962 | 0.368324 | [0.114 0.886] | 0.314 | -| (0.2, 27) | 0.145636 | 0.172364 | 0.318 | [0.118 0.682 0. 0.2 ] | 0.154729 | 0.214956 | 0.369686 | [0.118 0.882] | 0.318 | -| (0.2, 28) | 0.113045 | 0.191955 | 0.305 | [0.105 0.695 0. 0.2 ] | 0.103814 | 0.261482 | 0.365297 | [0.105 0.895] | 0.305 | -| (0.2, 29) | 0.151512 | 0.171488 | 0.323 | [0.124 0.676 0.001 0.199] | 0.134941 | 0.235291 | 0.370233 | [0.125 0.875] | 0.323 | -| (0.2, 30) | 0.124049 | 0.206951 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.130243 | 0.243939 | 0.374181 | [0.131 0.869] | 0.331 | -| (0.2, 31) | 0.14276 | 0.17924 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.147474 | 0.223583 | 0.371058 | [0.122 0.878] | 0.322 | -| (0.2, 32) | 0.184874 | 0.137126 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.162763 | 0.208295 | 0.371058 | [0.122 0.878] | 0.322 | -| (0.2, 33) | 0.113052 | 0.205948 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.125327 | 0.2447 | 0.370028 | [0.119 0.881] | 0.319 | -| (0.2, 34) | 0.11022 | 0.20578 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.134172 | 0.234831 | 0.369004 | [0.116 0.884] | 0.316 | -| (0.2, 35) | 0.15689 | 0.16811 | 0.325 | [0.125 0.675 0. 0.2 ] | 0.139515 | 0.232578 | 0.372093 | [0.125 0.875] | 0.325 | -| (0.2, 36) | 0.12372 | 0.19828 | 0.322 | [0.123 0.677 0.001 0.199] | 0.144416 | 0.225473 | 0.369888 | [0.124 0.876] | 0.322 | -| (0.2, 37) | 0.156173 | 0.149827 | 0.306 | [0.106 0.694 0. 0.2 ] | 0.140543 | 0.225087 | 0.365631 | [0.106 0.894] | 0.306 | -| (0.2, 38) | 0.123178 | 0.191822 | 0.315 | [0.115 0.685 0. 0.2 ] | 0.124918 | 0.243745 | 0.368664 | [0.115 0.885] | 0.315 | -| (0.2, 39) | 0.149818 | 0.155182 | 0.305 | [0.105 0.695 0. 0.2 ] | 0.153473 | 0.211824 | 0.365297 | [0.105 0.895] | 0.305 | -| (0.2, 40) | 0.146996 | 0.151004 | 0.298 | [0.098 0.702 0. 0.2 ] | 0.136716 | 0.226261 | 0.362976 | [0.098 0.902] | 0.298 | -| (0.2, 41) | 0.134983 | 0.174017 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.130812 | 0.235824 | 0.366636 | [0.109 0.891] | 0.309 | -| (0.2, 42) | 0.130339 | 0.204661 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.132934 | 0.242653 | 0.375587 | [0.135 0.865] | 0.335 | -| (0.2, 43) | 0.131752 | 0.197248 | 0.329 | [0.129 0.671 0. 0.2 ] | 0.122563 | 0.25092 | 0.373483 | [0.129 0.871] | 0.329 | -| (0.2, 44) | 0.104892 | 0.213108 | 0.318 | [0.118 0.682 0. 0.2 ] | 0.101844 | 0.267842 | 0.369686 | [0.118 0.882] | 0.318 | -| (0.2, 45) | 0.108924 | 0.208076 | 0.317 | [0.118 0.682 0.001 0.199] | 0.112634 | 0.255543 | 0.368178 | [0.119 0.881] | 0.317 | -| (0.2, 46) | 0.158723 | 0.146277 | 0.305 | [0.106 0.694 0.001 0.199] | 0.14642 | 0.217715 | 0.364135 | [0.107 0.893] | 0.305 | -| (0.2, 47) | 0.181864 | 0.130136 | 0.312 | [0.113 0.687 0.001 0.199] | 0.162233 | 0.204249 | 0.366483 | [0.114 0.886] | 0.312 | -| (0.2, 48) | 0.139857 | 0.177143 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.130469 | 0.238875 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 49) | 0.130555 | 0.185445 | 0.316 | [0.117 0.683 0.001 0.199] | 0.129553 | 0.238285 | 0.367837 | [0.118 0.882] | 0.316 | -| (0.2, 50) | 0.126112 | 0.184888 | 0.311 | [0.111 0.689 0. 0.2 ] | 0.116336 | 0.250973 | 0.367309 | [0.111 0.889] | 0.311 | -| (0.2, 51) | 0.126804 | 0.189196 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.134586 | 0.234417 | 0.369004 | [0.116 0.884] | 0.316 | -| (0.2, 52) | 0.1149 | 0.1861 | 0.301 | [0.101 0.699 0. 0.2 ] | 0.131898 | 0.232069 | 0.363967 | [0.101 0.899] | 0.301 | -| (0.2, 53) | 0.147138 | 0.163862 | 0.311 | [0.112 0.688 0.001 0.199] | 0.130518 | 0.235628 | 0.366145 | [0.113 0.887] | 0.311 | -| (0.2, 54) | 0.126953 | 0.204047 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.103391 | 0.270791 | 0.374181 | [0.131 0.869] | 0.331 | -| (0.2, 55) | 0.154759 | 0.161241 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.143336 | 0.225668 | 0.369004 | [0.116 0.884] | 0.316 | -| (0.2, 56) | 0.148069 | 0.154931 | 0.303 | [0.103 0.697 0. 0.2 ] | 0.13299 | 0.23164 | 0.364631 | [0.103 0.897] | 0.303 | -| (0.2, 57) | 0.14951 | 0.17349 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.124754 | 0.246648 | 0.371402 | [0.123 0.877] | 0.323 | -| (0.2, 58) | 0.124588 | 0.182412 | 0.307 | [0.107 0.693 0. 0.2 ] | 0.135337 | 0.230628 | 0.365965 | [0.107 0.893] | 0.307 | -| (0.2, 59) | 0.106309 | 0.219691 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.124609 | 0.247831 | 0.372439 | [0.126 0.874] | 0.326 | -| (0.2, 60) | 0.163061 | 0.148939 | 0.312 | [0.112 0.688 0. 0.2 ] | 0.125001 | 0.242646 | 0.367647 | [0.112 0.888] | 0.312 | -| (0.2, 61) | 0.098016 | 0.220984 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.111374 | 0.258654 | 0.370028 | [0.119 0.881] | 0.319 | -| (0.2, 62) | 0.133823 | 0.192177 | 0.326 | [0.127 0.673 0.001 0.199] | 0.138711 | 0.232558 | 0.371269 | [0.128 0.872] | 0.326 | -| (0.2, 63) | 0.132592 | 0.193408 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.168652 | 0.203787 | 0.372439 | [0.126 0.874] | 0.326 | -| (0.2, 64) | 0.146847 | 0.167153 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.134352 | 0.233973 | 0.368324 | [0.114 0.886] | 0.314 | -| (0.2, 65) | 0.123678 | 0.209322 | 0.333 | [0.134 0.666 0.001 0.199] | 0.139748 | 0.233961 | 0.373709 | [0.135 0.865] | 0.333 | -| (0.2, 66) | 0.153441 | 0.166559 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.168793 | 0.201578 | 0.37037 | [0.12 0.88] | 0.32 | -| (0.2, 67) | 0.102558 | 0.206442 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.129257 | 0.237379 | 0.366636 | [0.109 0.891] | 0.309 | -| (0.2, 68) | 0.113796 | 0.181204 | 0.295 | [0.095 0.705 0. 0.2 ] | 0.125855 | 0.236136 | 0.361991 | [0.095 0.905] | 0.295 | -| (0.2, 69) | 0.130216 | 0.186784 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.154798 | 0.214547 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 70) | 0.140137 | 0.173863 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.133952 | 0.234372 | 0.368324 | [0.114 0.886] | 0.314 | -| (0.2, 71) | 0.118374 | 0.173626 | 0.292 | [0.093 0.707 0.001 0.199] | 0.102911 | 0.256944 | 0.359855 | [0.094 0.906] | 0.292 | -| (0.2, 72) | 0.142998 | 0.173002 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.13498 | 0.234024 | 0.369004 | [0.116 0.884] | 0.316 | -| (0.2, 73) | 0.11727 | 0.20573 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.100748 | 0.270654 | 0.371402 | [0.123 0.877] | 0.323 | -| (0.2, 74) | 0.13782 | 0.17518 | 0.313 | [0.114 0.686 0.001 0.199] | 0.113838 | 0.252982 | 0.36682 | [0.115 0.885] | 0.313 | -| (0.2, 75) | 0.127086 | 0.203914 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.124813 | 0.249369 | 0.374181 | [0.131 0.869] | 0.331 | -| (0.2, 76) | 0.110385 | 0.192615 | 0.303 | [0.104 0.696 0.001 0.199] | 0.117311 | 0.246159 | 0.36347 | [0.105 0.895] | 0.303 | -| (0.2, 77) | 0.169946 | 0.156054 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.154318 | 0.218121 | 0.372439 | [0.126 0.874] | 0.326 | -| (0.2, 78) | 0.125895 | 0.184105 | 0.31 | [0.11 0.69 0. 0.2 ] | 0.137846 | 0.229127 | 0.366972 | [0.11 0.89] | 0.31 | -| (0.2, 79) | 0.106497 | 0.195503 | 0.302 | [0.102 0.698 0. 0.2 ] | 0.108174 | 0.256124 | 0.364299 | [0.102 0.898] | 0.302 | -| (0.2, 80) | 0.153481 | 0.163519 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.136076 | 0.233269 | 0.369344 | [0.117 0.883] | 0.317 | -| (0.2, 81) | 0.139622 | 0.180378 | 0.32 | [0.121 0.679 0.001 0.199] | 0.126998 | 0.242204 | 0.369202 | [0.122 0.878] | 0.32 | -| (0.2, 82) | 0.131272 | 0.188728 | 0.32 | [0.121 0.679 0.001 0.199] | 0.125249 | 0.243954 | 0.369202 | [0.122 0.878] | 0.32 | -| (0.2, 83) | 0.150497 | 0.172503 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.154524 | 0.216878 | 0.371402 | [0.123 0.877] | 0.323 | -| (0.2, 84) | 0.138086 | 0.180914 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.125401 | 0.244626 | 0.370028 | [0.119 0.881] | 0.319 | -| (0.2, 85) | 0.139788 | 0.187212 | 0.327 | [0.127 0.673 0. 0.2 ] | 0.157005 | 0.215781 | 0.372787 | [0.127 0.873] | 0.327 | -| (0.2, 86) | 0.0908716 | 0.216128 | 0.307 | [0.107 0.693 0. 0.2 ] | 0.0962838 | 0.269681 | 0.365965 | [0.107 0.893] | 0.307 | -| (0.2, 87) | 0.147758 | 0.161242 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.129789 | 0.236848 | 0.366636 | [0.109 0.891] | 0.309 | -| (0.2, 88) | 0.137745 | 0.187255 | 0.325 | [0.125 0.675 0. 0.2 ] | 0.16326 | 0.208833 | 0.372093 | [0.125 0.875] | 0.325 | -| (0.2, 89) | 0.147468 | 0.161532 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.132114 | 0.234522 | 0.366636 | [0.109 0.891] | 0.309 | -| (0.2, 90) | 0.0964468 | 0.224553 | 0.321 | [0.121 0.679 0. 0.2 ] | 0.102582 | 0.268131 | 0.370714 | [0.121 0.879] | 0.321 | -| (0.2, 91) | 0.101547 | 0.214453 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.112843 | 0.25616 | 0.369004 | [0.116 0.884] | 0.316 | -| (0.2, 92) | 0.162379 | 0.157621 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.132839 | 0.237531 | 0.37037 | [0.12 0.88] | 0.32 | -| (0.2, 93) | 0.141758 | 0.189242 | 0.331 | [0.132 0.668 0.001 0.199] | 0.144896 | 0.228113 | 0.373008 | [0.133 0.867] | 0.331 | -| (0.2, 94) | 0.169261 | 0.152739 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.166465 | 0.204593 | 0.371058 | [0.122 0.878] | 0.322 | -| (0.2, 95) | 0.122537 | 0.200463 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.111265 | 0.260137 | 0.371402 | [0.123 0.877] | 0.323 | -| (0.2, 96) | 0.126137 | 0.193863 | 0.32 | [0.121 0.679 0.001 0.199] | 0.130176 | 0.239026 | 0.369202 | [0.122 0.878] | 0.32 | -| (0.2, 97) | 0.0939801 | 0.23702 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.103706 | 0.270476 | 0.374181 | [0.131 0.869] | 0.331 | -| (0.2, 98) | 0.105674 | 0.208326 | 0.314 | [0.115 0.685 0.001 0.199] | 0.115848 | 0.251311 | 0.367159 | [0.116 0.884] | 0.314 | -| (0.2, 99) | 0.107548 | 0.207452 | 0.315 | [0.115 0.685 0. 0.2 ] | 0.118836 | 0.249828 | 0.368664 | [0.115 0.885] | 0.315 | -| (0.25, 0) | 0.124355 | 0.219645 | 0.344 | [0.094 0.656 0. 0.25 ] | 0.128487 | 0.304039 | 0.432526 | [0.094 0.906] | 0.344 | -| (0.25, 1) | 0.123549 | 0.242451 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.123781 | 0.317136 | 0.440917 | [0.116 0.884] | 0.366 | -| (0.25, 2) | 0.11547 | 0.24653 | 0.362 | [0.113 0.637 0.001 0.249] | 0.108391 | 0.32999 | 0.43838 | [0.114 0.886] | 0.362 | -| (0.25, 3) | 0.14217 | 0.23583 | 0.378 | [0.128 0.622 0. 0.25 ] | 0.13991 | 0.305723 | 0.445633 | [0.128 0.872] | 0.378 | -| (0.25, 4) | 0.115606 | 0.240394 | 0.356 | [0.106 0.644 0. 0.25 ] | 0.10033 | 0.336733 | 0.437063 | [0.106 0.894] | 0.356 | -| (0.25, 5) | 0.168544 | 0.212456 | 0.381 | [0.131 0.619 0. 0.25 ] | 0.144145 | 0.302683 | 0.446828 | [0.131 0.869] | 0.381 | -| (0.25, 6) | 0.0947743 | 0.273226 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.095146 | 0.34655 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 7) | 0.113473 | 0.251527 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.111097 | 0.329432 | 0.440529 | [0.115 0.885] | 0.365 | -| (0.25, 8) | 0.155793 | 0.217207 | 0.373 | [0.123 0.627 0. 0.25 ] | 0.136486 | 0.30717 | 0.443656 | [0.123 0.877] | 0.373 | -| (0.25, 9) | 0.129944 | 0.218056 | 0.348 | [0.098 0.652 0. 0.25 ] | 0.124308 | 0.30972 | 0.434028 | [0.098 0.902] | 0.348 | -| (0.25, 10) | 0.137385 | 0.225615 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.134428 | 0.305325 | 0.439754 | [0.113 0.887] | 0.363 | -| (0.25, 11) | 0.129083 | 0.242917 | 0.372 | [0.122 0.628 0. 0.25 ] | 0.136549 | 0.306714 | 0.443262 | [0.122 0.878] | 0.372 | -| (0.25, 12) | 0.116791 | 0.239209 | 0.356 | [0.106 0.644 0. 0.25 ] | 0.130234 | 0.306828 | 0.437063 | [0.106 0.894] | 0.356 | -| (0.25, 13) | 0.126751 | 0.222249 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.113015 | 0.32139 | 0.434405 | [0.099 0.901] | 0.349 | -| (0.25, 14) | 0.128282 | 0.226718 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.120019 | 0.316662 | 0.436681 | [0.105 0.895] | 0.355 | -| (0.25, 15) | 0.143813 | 0.218187 | 0.362 | [0.113 0.637 0.001 0.249] | 0.126338 | 0.312042 | 0.43838 | [0.114 0.886] | 0.362 | -| (0.25, 16) | 0.135428 | 0.238572 | 0.374 | [0.124 0.626 0. 0.25 ] | 0.130811 | 0.313239 | 0.44405 | [0.124 0.876] | 0.374 | -| (0.25, 17) | 0.112018 | 0.241982 | 0.354 | [0.105 0.645 0.001 0.249] | 0.120587 | 0.314728 | 0.435315 | [0.106 0.894] | 0.354 | -| (0.25, 18) | 0.159659 | 0.202341 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.14518 | 0.294188 | 0.439367 | [0.112 0.888] | 0.362 | -| (0.25, 19) | 0.125726 | 0.242274 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.116861 | 0.324835 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 20) | 0.156058 | 0.193942 | 0.35 | [0.1 0.65 0. 0.25] | 0.112552 | 0.32223 | 0.434783 | [0.1 0.9] | 0.35 | -| (0.25, 21) | 0.115433 | 0.259567 | 0.375 | [0.125 0.625 0. 0.25 ] | 0.0991129 | 0.345332 | 0.444444 | [0.125 0.875] | 0.375 | -| (0.25, 22) | 0.127289 | 0.232711 | 0.36 | [0.11 0.64 0. 0.25] | 0.115265 | 0.323332 | 0.438596 | [0.11 0.89] | 0.36 | -| (0.25, 23) | 0.124531 | 0.233469 | 0.358 | [0.109 0.641 0.001 0.249] | 0.0982565 | 0.338586 | 0.436842 | [0.11 0.89] | 0.358 | -| (0.25, 24) | 0.169186 | 0.195814 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.138074 | 0.302455 | 0.440529 | [0.115 0.885] | 0.365 | -| (0.25, 25) | 0.128267 | 0.227733 | 0.356 | [0.107 0.643 0.001 0.249] | 0.148625 | 0.287452 | 0.436077 | [0.108 0.892] | 0.356 | -| (0.25, 26) | 0.126575 | 0.236425 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.133828 | 0.305925 | 0.439754 | [0.113 0.887] | 0.363 | -| (0.25, 27) | 0.129839 | 0.252161 | 0.382 | [0.132 0.618 0. 0.25 ] | 0.115004 | 0.332224 | 0.447227 | [0.132 0.868] | 0.382 | -| (0.25, 28) | 0.10892 | 0.24408 | 0.353 | [0.103 0.647 0. 0.25 ] | 0.112931 | 0.322988 | 0.43592 | [0.103 0.897] | 0.353 | -| (0.25, 29) | 0.121689 | 0.228311 | 0.35 | [0.1 0.65 0. 0.25] | 0.124501 | 0.310282 | 0.434783 | [0.1 0.9] | 0.35 | -| (0.25, 30) | 0.130389 | 0.216611 | 0.347 | [0.098 0.652 0.001 0.249] | 0.137773 | 0.294895 | 0.432667 | [0.099 0.901] | 0.347 | -| (0.25, 31) | 0.109612 | 0.257388 | 0.367 | [0.117 0.633 0. 0.25 ] | 0.100272 | 0.341035 | 0.441306 | [0.117 0.883] | 0.367 | -| (0.25, 32) | 0.123587 | 0.245413 | 0.369 | [0.119 0.631 0. 0.25 ] | 0.133389 | 0.308697 | 0.442087 | [0.119 0.881] | 0.369 | -| (0.25, 33) | 0.162584 | 0.194416 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.134293 | 0.303153 | 0.437445 | [0.107 0.893] | 0.357 | -| (0.25, 34) | 0.148317 | 0.213683 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.110308 | 0.329059 | 0.439367 | [0.112 0.888] | 0.362 | -| (0.25, 35) | 0.11158 | 0.25342 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.110128 | 0.330401 | 0.440529 | [0.115 0.885] | 0.365 | -| (0.25, 36) | 0.154516 | 0.207484 | 0.362 | [0.113 0.637 0.001 0.249] | 0.125018 | 0.313362 | 0.43838 | [0.114 0.886] | 0.362 | -| (0.25, 37) | 0.120364 | 0.245636 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.116338 | 0.32458 | 0.440917 | [0.116 0.884] | 0.366 | -| (0.25, 38) | 0.166437 | 0.190563 | 0.357 | [0.108 0.642 0.001 0.249] | 0.140817 | 0.295642 | 0.436459 | [0.109 0.891] | 0.357 | -| (0.25, 39) | 0.117464 | 0.243536 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.11287 | 0.326111 | 0.438982 | [0.111 0.889] | 0.361 | -| (0.25, 40) | 0.139079 | 0.196921 | 0.336 | [0.087 0.663 0.001 0.249] | 0.132232 | 0.296339 | 0.428571 | [0.088 0.912] | 0.336 | -| (0.25, 41) | 0.106429 | 0.239571 | 0.346 | [0.096 0.654 0. 0.25 ] | 0.113467 | 0.319809 | 0.433276 | [0.096 0.904] | 0.346 | -| (0.25, 42) | 0.130296 | 0.252704 | 0.383 | [0.133 0.617 0. 0.25 ] | 0.0936963 | 0.353931 | 0.447628 | [0.133 0.867] | 0.383 | -| (0.25, 43) | 0.143415 | 0.201585 | 0.345 | [0.095 0.655 0. 0.25 ] | 0.143536 | 0.289364 | 0.4329 | [0.095 0.905] | 0.345 | -| (0.25, 44) | 0.114779 | 0.257221 | 0.372 | [0.123 0.627 0.001 0.249] | 0.103723 | 0.338551 | 0.442274 | [0.124 0.876] | 0.372 | -| (0.25, 45) | 0.16507 | 0.19593 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.149673 | 0.289309 | 0.438982 | [0.111 0.889] | 0.361 | -| (0.25, 46) | 0.158965 | 0.201035 | 0.36 | [0.11 0.64 0. 0.25] | 0.135501 | 0.303095 | 0.438596 | [0.11 0.89] | 0.36 | -| (0.25, 47) | 0.12667 | 0.24133 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.120737 | 0.320959 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 48) | 0.142702 | 0.240298 | 0.383 | [0.133 0.617 0. 0.25 ] | 0.126929 | 0.320699 | 0.447628 | [0.133 0.867] | 0.383 | -| (0.25, 49) | 0.139296 | 0.228704 | 0.368 | [0.12 0.63 0.002 0.248] | 0.116949 | 0.322768 | 0.439716 | [0.122 0.878] | 0.368 | -| (0.25, 50) | 0.130214 | 0.218786 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.139032 | 0.295373 | 0.434405 | [0.099 0.901] | 0.349 | -| (0.25, 51) | 0.126645 | 0.221355 | 0.348 | [0.098 0.652 0. 0.25 ] | 0.111247 | 0.322781 | 0.434028 | [0.098 0.902] | 0.348 | -| (0.25, 52) | 0.162073 | 0.197927 | 0.36 | [0.111 0.639 0.001 0.249] | 0.145148 | 0.292462 | 0.43761 | [0.112 0.888] | 0.36 | -| (0.25, 53) | 0.125951 | 0.237049 | 0.363 | [0.115 0.635 0.002 0.248] | 0.121835 | 0.315941 | 0.437776 | [0.117 0.883] | 0.363 | -| (0.25, 54) | 0.137738 | 0.233262 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.149517 | 0.293353 | 0.44287 | [0.121 0.879] | 0.371 | -| (0.25, 55) | 0.120461 | 0.226539 | 0.347 | [0.098 0.652 0.001 0.249] | 0.126996 | 0.305672 | 0.432667 | [0.099 0.901] | 0.347 | -| (0.25, 56) | 0.083097 | 0.271903 | 0.355 | [0.106 0.644 0.001 0.249] | 0.0829992 | 0.352696 | 0.435696 | [0.107 0.893] | 0.355 | -| (0.25, 57) | 0.152516 | 0.206484 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.156152 | 0.28206 | 0.438212 | [0.109 0.891] | 0.359 | -| (0.25, 58) | 0.15842 | 0.19158 | 0.35 | [0.101 0.649 0.001 0.249] | 0.140964 | 0.292834 | 0.433798 | [0.102 0.898] | 0.35 | -| (0.25, 59) | 0.120642 | 0.251358 | 0.372 | [0.122 0.628 0. 0.25 ] | 0.121374 | 0.321889 | 0.443262 | [0.122 0.878] | 0.372 | -| (0.25, 60) | 0.125145 | 0.231855 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.111299 | 0.326146 | 0.437445 | [0.107 0.893] | 0.357 | -| (0.25, 61) | 0.141589 | 0.228411 | 0.37 | [0.12 0.63 0. 0.25] | 0.146554 | 0.295924 | 0.442478 | [0.12 0.88] | 0.37 | -| (0.25, 62) | 0.129288 | 0.210712 | 0.34 | [0.09 0.66 0. 0.25] | 0.123316 | 0.307719 | 0.431034 | [0.09 0.91] | 0.34 | -| (0.25, 63) | 0.167968 | 0.200032 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.14112 | 0.300576 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 64) | 0.126202 | 0.241798 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.107658 | 0.334039 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 65) | 0.139649 | 0.205351 | 0.345 | [0.095 0.655 0. 0.25 ] | 0.132912 | 0.299989 | 0.4329 | [0.095 0.905] | 0.345 | -| (0.25, 66) | 0.13461 | 0.22139 | 0.356 | [0.107 0.643 0.001 0.249] | 0.120895 | 0.315182 | 0.436077 | [0.108 0.892] | 0.356 | -| (0.25, 67) | 0.112746 | 0.246254 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.108761 | 0.329451 | 0.438212 | [0.109 0.891] | 0.359 | -| (0.25, 68) | 0.125963 | 0.240037 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.12457 | 0.316347 | 0.440917 | [0.116 0.884] | 0.366 | -| (0.25, 69) | 0.117978 | 0.225022 | 0.343 | [0.094 0.656 0.001 0.249] | 0.122437 | 0.308731 | 0.431169 | [0.095 0.905] | 0.343 | -| (0.25, 70) | 0.0978247 | 0.271175 | 0.369 | [0.119 0.631 0. 0.25 ] | 0.10638 | 0.335707 | 0.442087 | [0.119 0.881] | 0.369 | -| (0.25, 71) | 0.122178 | 0.228822 | 0.351 | [0.101 0.649 0. 0.25 ] | 0.112982 | 0.322179 | 0.435161 | [0.101 0.899] | 0.351 | -| (0.25, 72) | 0.116694 | 0.261306 | 0.378 | [0.128 0.622 0. 0.25 ] | 0.129308 | 0.316324 | 0.445633 | [0.128 0.872] | 0.378 | -| (0.25, 73) | 0.142151 | 0.206849 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.127291 | 0.307114 | 0.434405 | [0.099 0.901] | 0.349 | -| (0.25, 74) | 0.119932 | 0.223068 | 0.343 | [0.093 0.657 0. 0.25 ] | 0.118733 | 0.313419 | 0.432152 | [0.093 0.907] | 0.343 | -| (0.25, 75) | 0.161339 | 0.193661 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.13814 | 0.298541 | 0.436681 | [0.105 0.895] | 0.355 | -| (0.25, 76) | 0.102897 | 0.251103 | 0.354 | [0.105 0.645 0.001 0.249] | 0.113351 | 0.321964 | 0.435315 | [0.106 0.894] | 0.354 | -| (0.25, 77) | 0.13454 | 0.22646 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.118977 | 0.320005 | 0.438982 | [0.111 0.889] | 0.361 | -| (0.25, 78) | 0.139003 | 0.228997 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.124439 | 0.317258 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 79) | 0.128929 | 0.230071 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.109666 | 0.328546 | 0.438212 | [0.109 0.891] | 0.359 | -| (0.25, 80) | 0.153328 | 0.220672 | 0.374 | [0.124 0.626 0. 0.25 ] | 0.148008 | 0.296042 | 0.44405 | [0.124 0.876] | 0.374 | -| (0.25, 81) | 0.123484 | 0.247516 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.105126 | 0.337743 | 0.44287 | [0.121 0.879] | 0.371 | -| (0.25, 82) | 0.12095 | 0.23105 | 0.352 | [0.102 0.648 0. 0.25 ] | 0.102409 | 0.333131 | 0.43554 | [0.102 0.898] | 0.352 | -| (0.25, 83) | 0.123315 | 0.239685 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.113052 | 0.326702 | 0.439754 | [0.113 0.887] | 0.363 | -| (0.25, 84) | 0.114865 | 0.244135 | 0.359 | [0.11 0.64 0.001 0.249] | 0.0973808 | 0.339845 | 0.437226 | [0.111 0.889] | 0.359 | -| (0.25, 85) | 0.141829 | 0.225171 | 0.367 | [0.118 0.632 0.001 0.249] | 0.123299 | 0.317019 | 0.440318 | [0.119 0.881] | 0.367 | -| (0.25, 86) | 0.136209 | 0.229791 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.123135 | 0.317782 | 0.440917 | [0.116 0.884] | 0.366 | -| (0.25, 87) | 0.170616 | 0.183384 | 0.354 | [0.104 0.646 0. 0.25 ] | 0.145532 | 0.290768 | 0.4363 | [0.104 0.896] | 0.354 | -| (0.25, 88) | 0.143335 | 0.223665 | 0.367 | [0.117 0.633 0. 0.25 ] | 0.127911 | 0.313396 | 0.441306 | [0.117 0.883] | 0.367 | -| (0.25, 89) | 0.137073 | 0.216927 | 0.354 | [0.104 0.646 0. 0.25 ] | 0.154553 | 0.281747 | 0.4363 | [0.104 0.896] | 0.354 | -| (0.25, 90) | 0.140224 | 0.216776 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.152714 | 0.284731 | 0.437445 | [0.107 0.893] | 0.357 | -| (0.25, 91) | 0.117738 | 0.253262 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.0959792 | 0.346891 | 0.44287 | [0.121 0.879] | 0.371 | -| (0.25, 92) | 0.123906 | 0.252094 | 0.376 | [0.126 0.624 0. 0.25 ] | 0.126874 | 0.317966 | 0.44484 | [0.126 0.874] | 0.376 | -| (0.25, 93) | 0.0661894 | 0.301811 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.0814921 | 0.360204 | 0.441696 | [0.118 0.882] | 0.368 | -| (0.25, 94) | 0.153841 | 0.208159 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.129741 | 0.309626 | 0.439367 | [0.112 0.888] | 0.362 | -| (0.25, 95) | 0.144879 | 0.200121 | 0.345 | [0.096 0.654 0.001 0.249] | 0.139426 | 0.29249 | 0.431917 | [0.097 0.903] | 0.345 | -| (0.25, 96) | 0.151496 | 0.214504 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.112522 | 0.328395 | 0.440917 | [0.116 0.884] | 0.366 | -| (0.25, 97) | 0.135256 | 0.219744 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.126896 | 0.309785 | 0.436681 | [0.105 0.895] | 0.355 | -| (0.25, 98) | 0.148867 | 0.194133 | 0.343 | [0.093 0.657 0. 0.25 ] | 0.128798 | 0.303354 | 0.432152 | [0.093 0.907] | 0.343 | -| (0.25, 99) | 0.137454 | 0.221546 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.108894 | 0.329318 | 0.438212 | [0.109 0.891] | 0.359 | -| (0.3, 0) | 0.115385 | 0.311615 | 0.427 | [0.127 0.573 0. 0.3 ] | 0.103546 | 0.407963 | 0.511509 | [0.127 0.873] | 0.427 | -| (0.3, 1) | 0.115234 | 0.275766 | 0.391 | [0.092 0.608 0.001 0.299] | 0.105087 | 0.390356 | 0.495443 | [0.093 0.907] | 0.391 | -| (0.3, 2) | 0.107756 | 0.296244 | 0.404 | [0.105 0.595 0.001 0.299] | 0.105065 | 0.395773 | 0.500838 | [0.106 0.894] | 0.404 | -| (0.3, 3) | 0.0940979 | 0.301902 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.0939231 | 0.404416 | 0.498339 | [0.096 0.904] | 0.396 | -| (0.3, 4) | 0.129976 | 0.268024 | 0.398 | [0.098 0.602 0. 0.3 ] | 0.104953 | 0.394215 | 0.499168 | [0.098 0.902] | 0.398 | -| (0.3, 5) | 0.128381 | 0.250619 | 0.379 | [0.079 0.621 0. 0.3 ] | 0.101842 | 0.389559 | 0.4914 | [0.079 0.921] | 0.379 | -| (0.3, 6) | 0.131556 | 0.270444 | 0.402 | [0.102 0.598 0. 0.3 ] | 0.117514 | 0.383321 | 0.500835 | [0.102 0.898] | 0.402 | -| (0.3, 7) | 0.112026 | 0.284974 | 0.397 | [0.099 0.601 0.002 0.298] | 0.124003 | 0.373078 | 0.497081 | [0.101 0.899] | 0.397 | -| (0.3, 8) | 0.126207 | 0.290793 | 0.417 | [0.117 0.583 0. 0.3 ] | 0.117671 | 0.389514 | 0.507185 | [0.117 0.883] | 0.417 | -| (0.3, 9) | 0.139192 | 0.253808 | 0.393 | [0.094 0.606 0.001 0.299] | 0.0999559 | 0.39631 | 0.496266 | [0.095 0.905] | 0.393 | -| (0.3, 10) | 0.120012 | 0.281988 | 0.402 | [0.102 0.598 0. 0.3 ] | 0.119377 | 0.381458 | 0.500835 | [0.102 0.898] | 0.402 | -| (0.3, 11) | 0.139279 | 0.260721 | 0.4 | [0.1 0.6 0. 0.3] | 0.117324 | 0.382676 | 0.5 | [0.1 0.9] | 0.4 | -| (0.3, 12) | 0.116443 | 0.276557 | 0.393 | [0.095 0.605 0.002 0.298] | 0.111133 | 0.384295 | 0.495428 | [0.097 0.903] | 0.393 | -| (0.3, 13) | 0.124193 | 0.273807 | 0.398 | [0.099 0.601 0.001 0.299] | 0.108405 | 0.389929 | 0.498333 | [0.1 0.9] | 0.398 | -| (0.3, 14) | 0.12208 | 0.26692 | 0.389 | [0.089 0.611 0. 0.3 ] | 0.127327 | 0.368132 | 0.495458 | [0.089 0.911] | 0.389 | -| (0.3, 15) | 0.103448 | 0.292552 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.100266 | 0.398073 | 0.498339 | [0.096 0.904] | 0.396 | -| (0.3, 16) | 0.14551 | 0.24649 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.120372 | 0.376316 | 0.496689 | [0.092 0.908] | 0.392 | -| (0.3, 17) | 0.141222 | 0.269778 | 0.411 | [0.111 0.589 0. 0.3 ] | 0.118556 | 0.38607 | 0.504626 | [0.111 0.889] | 0.411 | -| (0.3, 18) | 0.142631 | 0.262369 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.0938785 | 0.408214 | 0.502092 | [0.105 0.895] | 0.405 | -| (0.3, 19) | 0.14518 | 0.26782 | 0.413 | [0.114 0.586 0.001 0.299] | 0.125934 | 0.378707 | 0.504641 | [0.115 0.885] | 0.413 | -| (0.3, 20) | 0.117111 | 0.292889 | 0.41 | [0.11 0.59 0. 0.3 ] | 0.100578 | 0.403624 | 0.504202 | [0.11 0.89] | 0.41 | -| (0.3, 21) | 0.147531 | 0.264469 | 0.412 | [0.113 0.587 0.001 0.299] | 0.116326 | 0.387889 | 0.504216 | [0.114 0.886] | 0.412 | -| (0.3, 22) | 0.106063 | 0.292937 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.106593 | 0.39299 | 0.499584 | [0.099 0.901] | 0.399 | -| (0.3, 23) | 0.148613 | 0.250387 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.127635 | 0.371949 | 0.499584 | [0.099 0.901] | 0.399 | -| (0.3, 24) | 0.154573 | 0.248427 | 0.403 | [0.103 0.597 0. 0.3 ] | 0.114545 | 0.386709 | 0.501253 | [0.103 0.897] | 0.403 | -| (0.3, 25) | 0.126976 | 0.267024 | 0.394 | [0.094 0.606 0. 0.3 ] | 0.107205 | 0.390308 | 0.497512 | [0.094 0.906] | 0.394 | -| (0.3, 26) | 0.0936366 | 0.315363 | 0.409 | [0.109 0.591 0. 0.3 ] | 0.0963521 | 0.407426 | 0.503778 | [0.109 0.891] | 0.409 | -| (0.3, 27) | 0.112656 | 0.269344 | 0.382 | [0.083 0.617 0.001 0.299] | 0.104119 | 0.387657 | 0.491776 | [0.084 0.916] | 0.382 | -| (0.3, 28) | 0.162282 | 0.249718 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.125725 | 0.379325 | 0.505051 | [0.112 0.888] | 0.412 | -| (0.3, 29) | 0.144141 | 0.254859 | 0.399 | [0.1 0.6 0.001 0.299] | 0.10784 | 0.390909 | 0.498749 | [0.101 0.899] | 0.399 | -| (0.3, 30) | 0.148497 | 0.254503 | 0.403 | [0.103 0.597 0. 0.3 ] | 0.114328 | 0.386925 | 0.501253 | [0.103 0.897] | 0.403 | -| (0.3, 31) | 0.132418 | 0.280582 | 0.413 | [0.113 0.587 0. 0.3 ] | 0.110571 | 0.394905 | 0.505476 | [0.113 0.887] | 0.413 | -| (0.3, 32) | 0.112737 | 0.277263 | 0.39 | [0.091 0.609 0.001 0.299] | 0.105898 | 0.389135 | 0.495033 | [0.092 0.908] | 0.39 | -| (0.3, 33) | 0.141008 | 0.265992 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.124772 | 0.378162 | 0.502934 | [0.107 0.893] | 0.407 | -| (0.3, 34) | 0.138324 | 0.254676 | 0.393 | [0.093 0.607 0. 0.3 ] | 0.132808 | 0.364293 | 0.4971 | [0.093 0.907] | 0.393 | -| (0.3, 35) | 0.142124 | 0.270876 | 0.413 | [0.113 0.587 0. 0.3 ] | 0.114159 | 0.391317 | 0.505476 | [0.113 0.887] | 0.413 | -| (0.3, 36) | 0.122152 | 0.278848 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.0982835 | 0.402133 | 0.500417 | [0.101 0.899] | 0.401 | -| (0.3, 37) | 0.131292 | 0.261708 | 0.393 | [0.093 0.607 0. 0.3 ] | 0.123554 | 0.373546 | 0.4971 | [0.093 0.907] | 0.393 | -| (0.3, 38) | 0.128837 | 0.288163 | 0.417 | [0.118 0.582 0.001 0.299] | 0.108684 | 0.397666 | 0.506351 | [0.119 0.881] | 0.417 | -| (0.3, 39) | 0.129308 | 0.262692 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.106602 | 0.390086 | 0.496689 | [0.092 0.908] | 0.392 | -| (0.3, 40) | 0.140918 | 0.253082 | 0.394 | [0.095 0.605 0.001 0.299] | 0.112835 | 0.383843 | 0.496678 | [0.096 0.904] | 0.394 | -| (0.3, 41) | 0.116026 | 0.291974 | 0.408 | [0.108 0.592 0. 0.3 ] | 0.0894206 | 0.413935 | 0.503356 | [0.108 0.892] | 0.408 | -| (0.3, 42) | 0.126607 | 0.266393 | 0.393 | [0.095 0.605 0.002 0.298] | 0.112307 | 0.383122 | 0.495428 | [0.097 0.903] | 0.393 | -| (0.3, 43) | 0.100563 | 0.307437 | 0.408 | [0.109 0.591 0.001 0.299] | 0.099999 | 0.402522 | 0.502521 | [0.11 0.89] | 0.408 | -| (0.3, 44) | 0.129683 | 0.262317 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.10938 | 0.387309 | 0.496689 | [0.092 0.908] | 0.392 | -| (0.3, 45) | 0.113322 | 0.281678 | 0.395 | [0.095 0.605 0. 0.3 ] | 0.102612 | 0.395314 | 0.497925 | [0.095 0.905] | 0.395 | -| (0.3, 46) | 0.121586 | 0.283414 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.111687 | 0.390405 | 0.502092 | [0.105 0.895] | 0.405 | -| (0.3, 47) | 0.117878 | 0.303122 | 0.421 | [0.121 0.579 0. 0.3 ] | 0.101569 | 0.407336 | 0.508906 | [0.121 0.879] | 0.421 | -| (0.3, 48) | 0.114245 | 0.286755 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.105145 | 0.395272 | 0.500417 | [0.101 0.899] | 0.401 | -| (0.3, 49) | 0.138991 | 0.244009 | 0.383 | [0.083 0.617 0. 0.3 ] | 0.126666 | 0.366349 | 0.493016 | [0.083 0.917] | 0.383 | -| (0.3, 50) | 0.120115 | 0.271885 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.121823 | 0.374866 | 0.496689 | [0.092 0.908] | 0.392 | -| (0.3, 51) | 0.11579 | 0.26921 | 0.385 | [0.085 0.615 0. 0.3 ] | 0.110148 | 0.383679 | 0.493827 | [0.085 0.915] | 0.385 | -| (0.3, 52) | 0.134915 | 0.275085 | 0.41 | [0.111 0.589 0.001 0.299] | 0.0925126 | 0.410854 | 0.503367 | [0.112 0.888] | 0.41 | -| (0.3, 53) | 0.142717 | 0.261283 | 0.404 | [0.106 0.594 0.002 0.298] | 0.118655 | 0.381345 | 0.5 | [0.108 0.892] | 0.404 | -| (0.3, 54) | 0.154873 | 0.236127 | 0.391 | [0.091 0.609 0. 0.3 ] | 0.115805 | 0.380473 | 0.496278 | [0.091 0.909] | 0.391 | -| (0.3, 55) | 0.117916 | 0.297084 | 0.415 | [0.115 0.585 0. 0.3 ] | 0.113195 | 0.393134 | 0.506329 | [0.115 0.885] | 0.415 | -| (0.3, 56) | 0.119119 | 0.291881 | 0.411 | [0.111 0.589 0. 0.3 ] | 0.0887019 | 0.415924 | 0.504626 | [0.111 0.889] | 0.411 | -| (0.3, 57) | 0.136571 | 0.269429 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.121916 | 0.380597 | 0.502513 | [0.106 0.894] | 0.406 | -| (0.3, 58) | 0.149654 | 0.235346 | 0.385 | [0.086 0.614 0.001 0.299] | 0.118077 | 0.374916 | 0.492993 | [0.087 0.913] | 0.385 | -| (0.3, 59) | 0.110281 | 0.305719 | 0.416 | [0.116 0.584 0. 0.3 ] | 0.113127 | 0.39363 | 0.506757 | [0.116 0.884] | 0.416 | -| (0.3, 60) | 0.105847 | 0.288153 | 0.394 | [0.094 0.606 0. 0.3 ] | 0.108523 | 0.388989 | 0.497512 | [0.094 0.906] | 0.394 | -| (0.3, 61) | 0.118659 | 0.288341 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.085541 | 0.417393 | 0.502934 | [0.107 0.893] | 0.407 | -| (0.3, 62) | 0.129623 | 0.283377 | 0.413 | [0.115 0.585 0.002 0.298] | 0.112032 | 0.391772 | 0.503804 | [0.117 0.883] | 0.413 | -| (0.3, 63) | 0.126394 | 0.269606 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.112563 | 0.385776 | 0.498339 | [0.096 0.904] | 0.396 | -| (0.3, 64) | 0.139585 | 0.264415 | 0.404 | [0.104 0.596 0. 0.3 ] | 0.111884 | 0.389788 | 0.501672 | [0.104 0.896] | 0.404 | -| (0.3, 65) | 0.0951434 | 0.310857 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.0848641 | 0.417648 | 0.502513 | [0.106 0.894] | 0.406 | -| (0.3, 66) | 0.111057 | 0.277943 | 0.389 | [0.089 0.611 0. 0.3 ] | 0.10445 | 0.391009 | 0.495458 | [0.089 0.911] | 0.389 | -| (0.3, 67) | 0.0656081 | 0.346392 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0574239 | 0.447627 | 0.505051 | [0.112 0.888] | 0.412 | -| (0.3, 68) | 0.115383 | 0.293617 | 0.409 | [0.109 0.591 0. 0.3 ] | 0.0971393 | 0.406639 | 0.503778 | [0.109 0.891] | 0.409 | -| (0.3, 69) | 0.11694 | 0.28806 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.118732 | 0.38336 | 0.502092 | [0.105 0.895] | 0.405 | -| (0.3, 70) | 0.129901 | 0.276099 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.11687 | 0.385643 | 0.502513 | [0.106 0.894] | 0.406 | -| (0.3, 71) | 0.12312 | 0.29188 | 0.415 | [0.115 0.585 0. 0.3 ] | 0.0975558 | 0.408773 | 0.506329 | [0.115 0.885] | 0.415 | -| (0.3, 72) | 0.119927 | 0.294073 | 0.414 | [0.114 0.586 0. 0.3 ] | 0.096911 | 0.408991 | 0.505902 | [0.114 0.886] | 0.414 | -| (0.3, 73) | 0.133123 | 0.253877 | 0.387 | [0.087 0.613 0. 0.3 ] | 0.110938 | 0.383703 | 0.494641 | [0.087 0.913] | 0.387 | -| (0.3, 74) | 0.0891778 | 0.333822 | 0.423 | [0.123 0.577 0. 0.3 ] | 0.0806618 | 0.429109 | 0.509771 | [0.123 0.877] | 0.423 | -| (0.3, 75) | 0.0964315 | 0.321569 | 0.418 | [0.118 0.582 0. 0.3 ] | 0.0962955 | 0.411319 | 0.507614 | [0.118 0.882] | 0.418 | -| (0.3, 76) | 0.152005 | 0.253995 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.118398 | 0.384115 | 0.502513 | [0.106 0.894] | 0.406 | -| (0.3, 77) | 0.139943 | 0.259057 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.116276 | 0.383307 | 0.499584 | [0.099 0.901] | 0.399 | -| (0.3, 78) | 0.13643 | 0.26457 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.112532 | 0.387885 | 0.500417 | [0.101 0.899] | 0.401 | -| (0.3, 79) | 0.113834 | 0.290166 | 0.404 | [0.104 0.596 0. 0.3 ] | 0.103453 | 0.398219 | 0.501672 | [0.104 0.896] | 0.404 | -| (0.3, 80) | 0.162074 | 0.225926 | 0.388 | [0.089 0.611 0.001 0.299] | 0.127037 | 0.367178 | 0.494215 | [0.09 0.91] | 0.388 | -| (0.3, 81) | 0.112507 | 0.294493 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.112868 | 0.390066 | 0.502934 | [0.107 0.893] | 0.407 | -| (0.3, 82) | 0.137672 | 0.250328 | 0.388 | [0.088 0.612 0. 0.3 ] | 0.107459 | 0.38759 | 0.49505 | [0.088 0.912] | 0.388 | -| (0.3, 83) | 0.11512 | 0.29688 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0942575 | 0.410793 | 0.505051 | [0.112 0.888] | 0.412 | -| (0.3, 84) | 0.103276 | 0.290724 | 0.394 | [0.096 0.604 0.002 0.298] | 0.0984201 | 0.39742 | 0.49584 | [0.098 0.902] | 0.394 | -| (0.3, 85) | 0.128535 | 0.269465 | 0.398 | [0.099 0.601 0.001 0.299] | 0.102733 | 0.3956 | 0.498333 | [0.1 0.9] | 0.398 | -| (0.3, 86) | 0.117567 | 0.290433 | 0.408 | [0.108 0.592 0. 0.3 ] | 0.11328 | 0.390076 | 0.503356 | [0.108 0.892] | 0.408 | -| (0.3, 87) | 0.141018 | 0.252982 | 0.394 | [0.096 0.604 0.002 0.298] | 0.108792 | 0.387049 | 0.49584 | [0.098 0.902] | 0.394 | -| (0.3, 88) | 0.114183 | 0.286817 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.092482 | 0.407935 | 0.500417 | [0.101 0.899] | 0.401 | -| (0.3, 89) | 0.110207 | 0.302793 | 0.413 | [0.114 0.586 0.001 0.299] | 0.117155 | 0.387487 | 0.504641 | [0.115 0.885] | 0.413 | -| (0.3, 90) | 0.129401 | 0.284599 | 0.414 | [0.114 0.586 0. 0.3 ] | 0.10979 | 0.396113 | 0.505902 | [0.114 0.886] | 0.414 | -| (0.3, 91) | 0.12667 | 0.28733 | 0.414 | [0.115 0.585 0.001 0.299] | 0.109093 | 0.395974 | 0.505068 | [0.116 0.884] | 0.414 | -| (0.3, 92) | 0.0895823 | 0.309418 | 0.399 | [0.1 0.6 0.001 0.299] | 0.0919172 | 0.406832 | 0.498749 | [0.101 0.899] | 0.399 | -| (0.3, 93) | 0.13851 | 0.26349 | 0.402 | [0.103 0.597 0.001 0.299] | 0.116828 | 0.383172 | 0.5 | [0.104 0.896] | 0.402 | -| (0.3, 94) | 0.110087 | 0.308913 | 0.419 | [0.12 0.58 0.001 0.299] | 0.103164 | 0.404046 | 0.507209 | [0.121 0.879] | 0.419 | -| (0.3, 95) | 0.120184 | 0.297816 | 0.418 | [0.118 0.582 0. 0.3 ] | 0.098135 | 0.409479 | 0.507614 | [0.118 0.882] | 0.418 | -| (0.3, 96) | 0.133316 | 0.256684 | 0.39 | [0.09 0.61 0. 0.3 ] | 0.116543 | 0.379325 | 0.495868 | [0.09 0.91] | 0.39 | -| (0.3, 97) | 0.0983323 | 0.313668 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0720765 | 0.432974 | 0.505051 | [0.112 0.888] | 0.412 | -| (0.3, 98) | 0.136707 | 0.279293 | 0.416 | [0.116 0.584 0. 0.3 ] | 0.113834 | 0.392923 | 0.506757 | [0.116 0.884] | 0.416 | -| (0.3, 99) | 0.128764 | 0.277236 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.127621 | 0.374892 | 0.502513 | [0.106 0.894] | 0.406 | -| (0.35, 0) | 0.127734 | 0.323266 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.106 | 0.454448 | 0.560448 | [0.101 0.899] | 0.451 | -| (0.35, 1) | 0.126857 | 0.316143 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.113086 | 0.443795 | 0.556881 | [0.093 0.907] | 0.443 | -| (0.35, 2) | 0.0879283 | 0.358072 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0770572 | 0.480451 | 0.557508 | [0.098 0.902] | 0.446 | -| (0.35, 3) | 0.111434 | 0.340566 | 0.452 | [0.102 0.548 0. 0.35 ] | 0.107314 | 0.453583 | 0.560897 | [0.102 0.898] | 0.452 | -| (0.35, 4) | 0.158193 | 0.295807 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.132322 | 0.429476 | 0.561798 | [0.104 0.896] | 0.454 | -| (0.35, 5) | 0.120109 | 0.325891 | 0.446 | [0.097 0.553 0.001 0.349] | 0.112725 | 0.444783 | 0.557508 | [0.098 0.902] | 0.446 | -| (0.35, 6) | 0.0941796 | 0.35182 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.0882179 | 0.469996 | 0.558214 | [0.096 0.904] | 0.446 | -| (0.35, 7) | 0.103097 | 0.345903 | 0.449 | [0.099 0.551 0. 0.35 ] | 0.099472 | 0.46008 | 0.559552 | [0.099 0.901] | 0.449 | -| (0.35, 8) | 0.138126 | 0.330874 | 0.469 | [0.119 0.531 0. 0.35 ] | 0.100208 | 0.468435 | 0.568643 | [0.119 0.881] | 0.469 | -| (0.35, 9) | 0.107922 | 0.347078 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.0834859 | 0.478763 | 0.562249 | [0.105 0.895] | 0.455 | -| (0.35, 10) | 0.0992061 | 0.337794 | 0.437 | [0.087 0.563 0. 0.35 ] | 0.091621 | 0.462615 | 0.554236 | [0.087 0.913] | 0.437 | -| (0.35, 11) | 0.105041 | 0.344959 | 0.45 | [0.1 0.55 0. 0.35] | 0.0902889 | 0.469711 | 0.56 | [0.1 0.9] | 0.45 | -| (0.35, 12) | 0.100877 | 0.350123 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0870643 | 0.473384 | 0.560448 | [0.101 0.899] | 0.451 | -| (0.35, 13) | 0.0960624 | 0.345938 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0724629 | 0.483976 | 0.556439 | [0.092 0.908] | 0.442 | -| (0.35, 14) | 0.131091 | 0.299909 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.112462 | 0.439153 | 0.551615 | [0.081 0.919] | 0.431 | -| (0.35, 15) | 0.120561 | 0.325439 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0972215 | 0.460286 | 0.557508 | [0.098 0.902] | 0.446 | -| (0.35, 16) | 0.107446 | 0.339554 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.0781809 | 0.480478 | 0.558659 | [0.097 0.903] | 0.447 | -| (0.35, 17) | 0.12172 | 0.33228 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.0898545 | 0.471943 | 0.561798 | [0.104 0.896] | 0.454 | -| (0.35, 18) | 0.110049 | 0.365951 | 0.476 | [0.126 0.524 0. 0.35 ] | 0.0858809 | 0.486014 | 0.571895 | [0.126 0.874] | 0.476 | -| (0.35, 19) | 0.108301 | 0.329699 | 0.438 | [0.089 0.561 0.001 0.349] | 0.0928654 | 0.461103 | 0.553968 | [0.09 0.91] | 0.438 | -| (0.35, 20) | 0.113459 | 0.319541 | 0.433 | [0.083 0.567 0. 0.35 ] | 0.087382 | 0.465104 | 0.552486 | [0.083 0.917] | 0.433 | -| (0.35, 21) | 0.123105 | 0.329895 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.105268 | 0.456079 | 0.561347 | [0.103 0.897] | 0.453 | -| (0.35, 22) | 0.113375 | 0.345625 | 0.459 | [0.109 0.541 0. 0.35 ] | 0.0933376 | 0.470724 | 0.564061 | [0.109 0.891] | 0.459 | -| (0.35, 23) | 0.118606 | 0.329394 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0897363 | 0.469369 | 0.559105 | [0.098 0.902] | 0.448 | -| (0.35, 24) | 0.127113 | 0.306887 | 0.434 | [0.085 0.565 0.001 0.349] | 0.0946077 | 0.457607 | 0.552215 | [0.086 0.914] | 0.434 | -| (0.35, 25) | 0.119535 | 0.320465 | 0.44 | [0.09 0.56 0. 0.35] | 0.115511 | 0.440044 | 0.555556 | [0.09 0.91] | 0.44 | -| (0.35, 26) | 0.0883172 | 0.344683 | 0.433 | [0.084 0.566 0.001 0.349] | 0.0744312 | 0.477347 | 0.551779 | [0.085 0.915] | 0.433 | -| (0.35, 27) | 0.166992 | 0.279008 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.137423 | 0.42079 | 0.558214 | [0.096 0.904] | 0.446 | -| (0.35, 28) | 0.0954868 | 0.340513 | 0.436 | [0.087 0.563 0.001 0.349] | 0.0830672 | 0.470023 | 0.55309 | [0.088 0.912] | 0.436 | -| (0.35, 29) | 0.0778668 | 0.367133 | 0.445 | [0.096 0.554 0.001 0.349] | 0.0649534 | 0.49211 | 0.557063 | [0.097 0.903] | 0.445 | -| (0.35, 30) | 0.107221 | 0.325779 | 0.433 | [0.083 0.567 0. 0.35 ] | 0.0822103 | 0.470276 | 0.552486 | [0.083 0.917] | 0.433 | -| (0.35, 31) | 0.102419 | 0.339581 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0894659 | 0.466973 | 0.556439 | [0.092 0.908] | 0.442 | -| (0.35, 32) | 0.119917 | 0.328083 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0886716 | 0.470434 | 0.559105 | [0.098 0.902] | 0.448 | -| (0.35, 33) | 0.11129 | 0.34371 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.0841548 | 0.478094 | 0.562249 | [0.105 0.895] | 0.455 | -| (0.35, 34) | 0.101306 | 0.349694 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0869425 | 0.473506 | 0.560448 | [0.101 0.899] | 0.451 | -| (0.35, 35) | 0.109456 | 0.333544 | 0.443 | [0.094 0.556 0.001 0.349] | 0.106623 | 0.449552 | 0.556175 | [0.095 0.905] | 0.443 | -| (0.35, 36) | 0.0992197 | 0.34478 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.0943453 | 0.46298 | 0.557325 | [0.094 0.906] | 0.444 | -| (0.35, 37) | 0.150907 | 0.288093 | 0.439 | [0.09 0.56 0.001 0.349] | 0.119051 | 0.435357 | 0.554408 | [0.091 0.909] | 0.439 | -| (0.35, 38) | 0.109748 | 0.325252 | 0.435 | [0.085 0.565 0. 0.35 ] | 0.0863649 | 0.466995 | 0.55336 | [0.085 0.915] | 0.435 | -| (0.35, 39) | 0.145428 | 0.291572 | 0.437 | [0.088 0.562 0.001 0.349] | 0.128978 | 0.424551 | 0.553529 | [0.089 0.911] | 0.437 | -| (0.35, 40) | 0.098426 | 0.360574 | 0.459 | [0.11 0.54 0.001 0.349] | 0.07388 | 0.489478 | 0.563358 | [0.111 0.889] | 0.459 | -| (0.35, 41) | 0.13594 | 0.31106 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.116727 | 0.441932 | 0.558659 | [0.097 0.903] | 0.447 | -| (0.35, 42) | 0.123184 | 0.312816 | 0.436 | [0.086 0.564 0. 0.35 ] | 0.114021 | 0.439777 | 0.553797 | [0.086 0.914] | 0.436 | -| (0.35, 43) | 0.128692 | 0.318308 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.102665 | 0.455994 | 0.558659 | [0.097 0.903] | 0.447 | -| (0.35, 44) | 0.136286 | 0.292714 | 0.429 | [0.08 0.57 0.001 0.349] | 0.104948 | 0.445091 | 0.550039 | [0.081 0.919] | 0.429 | -| (0.35, 45) | 0.108885 | 0.336115 | 0.445 | [0.095 0.555 0. 0.35 ] | 0.0977137 | 0.460055 | 0.557769 | [0.095 0.905] | 0.445 | -| (0.35, 46) | 0.112286 | 0.334714 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.102874 | 0.455785 | 0.558659 | [0.097 0.903] | 0.447 | -| (0.35, 47) | 0.0906932 | 0.369307 | 0.46 | [0.11 0.54 0. 0.35] | 0.0778993 | 0.486617 | 0.564516 | [0.11 0.89] | 0.46 | -| (0.35, 48) | 0.137978 | 0.315022 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.102033 | 0.459314 | 0.561347 | [0.103 0.897] | 0.453 | -| (0.35, 49) | 0.116168 | 0.334832 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0912107 | 0.469238 | 0.560448 | [0.101 0.899] | 0.451 | -| (0.35, 50) | 0.104287 | 0.334713 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.0944456 | 0.460669 | 0.555115 | [0.089 0.911] | 0.439 | -| (0.35, 51) | 0.128891 | 0.313109 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0930384 | 0.4634 | 0.556439 | [0.092 0.908] | 0.442 | -| (0.35, 52) | 0.113322 | 0.357678 | 0.471 | [0.121 0.529 0. 0.35 ] | 0.100441 | 0.469128 | 0.569569 | [0.121 0.879] | 0.471 | -| (0.35, 53) | 0.104489 | 0.331511 | 0.436 | [0.086 0.564 0. 0.35 ] | 0.0901448 | 0.463653 | 0.553797 | [0.086 0.914] | 0.436 | -| (0.35, 54) | 0.0996681 | 0.346332 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.0920318 | 0.466182 | 0.558214 | [0.096 0.904] | 0.446 | -| (0.35, 55) | 0.119316 | 0.311684 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.108829 | 0.442786 | 0.551615 | [0.081 0.919] | 0.431 | -| (0.35, 56) | 0.133962 | 0.304038 | 0.438 | [0.089 0.561 0.001 0.349] | 0.106918 | 0.44705 | 0.553968 | [0.09 0.91] | 0.438 | -| (0.35, 57) | 0.138717 | 0.316283 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.11544 | 0.446809 | 0.562249 | [0.105 0.895] | 0.455 | -| (0.35, 58) | 0.139865 | 0.314135 | 0.454 | [0.105 0.545 0.001 0.349] | 0.0996406 | 0.461453 | 0.561093 | [0.106 0.894] | 0.454 | -| (0.35, 59) | 0.117694 | 0.326306 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.0946397 | 0.462685 | 0.557325 | [0.094 0.906] | 0.444 | -| (0.35, 60) | 0.0823521 | 0.383648 | 0.466 | [0.116 0.534 0. 0.35 ] | 0.0751242 | 0.492137 | 0.567261 | [0.116 0.884] | 0.466 | -| (0.35, 61) | 0.114932 | 0.324068 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.107789 | 0.447326 | 0.555115 | [0.089 0.911] | 0.439 | -| (0.35, 62) | 0.0971008 | 0.341899 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.0794721 | 0.475643 | 0.555115 | [0.089 0.911] | 0.439 | -| (0.35, 63) | 0.114859 | 0.339141 | 0.454 | [0.105 0.545 0.001 0.349] | 0.101821 | 0.459272 | 0.561093 | [0.106 0.894] | 0.454 | -| (0.35, 64) | 0.105616 | 0.336384 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0953635 | 0.461075 | 0.556439 | [0.092 0.908] | 0.442 | -| (0.35, 65) | 0.109411 | 0.330589 | 0.44 | [0.09 0.56 0. 0.35] | 0.0857621 | 0.469794 | 0.555556 | [0.09 0.91] | 0.44 | -| (0.35, 66) | 0.139524 | 0.314476 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.113682 | 0.448115 | 0.561798 | [0.104 0.896] | 0.454 | -| (0.35, 67) | 0.114339 | 0.340661 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.106207 | 0.456042 | 0.562249 | [0.105 0.895] | 0.455 | -| (0.35, 68) | 0.115384 | 0.316616 | 0.432 | [0.082 0.568 0. 0.35 ] | 0.104768 | 0.447283 | 0.55205 | [0.082 0.918] | 0.432 | -| (0.35, 69) | 0.136346 | 0.305654 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.111795 | 0.444644 | 0.556439 | [0.092 0.908] | 0.442 | -| (0.35, 70) | 0.120153 | 0.319847 | 0.44 | [0.09 0.56 0. 0.35] | 0.109676 | 0.44588 | 0.555556 | [0.09 0.91] | 0.44 | -| (0.35, 71) | 0.111197 | 0.350803 | 0.462 | [0.112 0.538 0. 0.35 ] | 0.081468 | 0.48396 | 0.565428 | [0.112 0.888] | 0.462 | -| (0.35, 72) | 0.13692 | 0.30608 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.117318 | 0.439564 | 0.556881 | [0.093 0.907] | 0.443 | -| (0.35, 73) | 0.124503 | 0.328497 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.0945593 | 0.466788 | 0.561347 | [0.103 0.897] | 0.453 | -| (0.35, 74) | 0.136985 | 0.301015 | 0.438 | [0.088 0.562 0. 0.35 ] | 0.0994015 | 0.455274 | 0.554675 | [0.088 0.912] | 0.438 | -| (0.35, 75) | 0.122077 | 0.331923 | 0.454 | [0.106 0.544 0.002 0.348] | 0.0941126 | 0.466274 | 0.560386 | [0.108 0.892] | 0.454 | -| (0.35, 76) | 0.105174 | 0.352826 | 0.458 | [0.109 0.541 0.001 0.349] | 0.0934764 | 0.469427 | 0.562903 | [0.11 0.89] | 0.458 | -| (0.35, 77) | 0.109797 | 0.325203 | 0.435 | [0.086 0.564 0.001 0.349] | 0.0970415 | 0.455611 | 0.552652 | [0.087 0.913] | 0.435 | -| (0.35, 78) | 0.103501 | 0.344499 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0972722 | 0.461833 | 0.559105 | [0.098 0.902] | 0.448 | -| (0.35, 79) | 0.0992824 | 0.347718 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.0883709 | 0.470288 | 0.558659 | [0.097 0.903] | 0.447 | -| (0.35, 80) | 0.0864577 | 0.359542 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0811096 | 0.476398 | 0.557508 | [0.098 0.902] | 0.446 | -| (0.35, 81) | 0.0944448 | 0.362555 | 0.457 | [0.107 0.543 0. 0.35 ] | 0.0824714 | 0.480682 | 0.563154 | [0.107 0.893] | 0.457 | -| (0.35, 82) | 0.131583 | 0.318417 | 0.45 | [0.101 0.549 0.001 0.349] | 0.10794 | 0.451355 | 0.559295 | [0.102 0.898] | 0.45 | -| (0.35, 83) | 0.120998 | 0.331002 | 0.452 | [0.102 0.548 0. 0.35 ] | 0.104399 | 0.456498 | 0.560897 | [0.102 0.898] | 0.452 | -| (0.35, 84) | 0.106906 | 0.345094 | 0.452 | [0.103 0.547 0.001 0.349] | 0.0750503 | 0.485142 | 0.560193 | [0.104 0.896] | 0.452 | -| (0.35, 85) | 0.120944 | 0.337056 | 0.458 | [0.108 0.542 0. 0.35 ] | 0.0885901 | 0.475017 | 0.563607 | [0.108 0.892] | 0.458 | -| (0.35, 86) | 0.105667 | 0.339333 | 0.445 | [0.095 0.555 0. 0.35 ] | 0.0975882 | 0.460181 | 0.557769 | [0.095 0.905] | 0.445 | -| (0.35, 87) | 0.14568 | 0.29532 | 0.441 | [0.091 0.559 0. 0.35 ] | 0.114812 | 0.441184 | 0.555997 | [0.091 0.909] | 0.441 | -| (0.35, 88) | 0.113016 | 0.325984 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.106299 | 0.448816 | 0.555115 | [0.089 0.911] | 0.439 | -| (0.35, 89) | 0.100033 | 0.342967 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.0877762 | 0.469105 | 0.556881 | [0.093 0.907] | 0.443 | -| (0.35, 90) | 0.130903 | 0.300097 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.128361 | 0.423255 | 0.551615 | [0.081 0.919] | 0.431 | -| (0.35, 91) | 0.124577 | 0.332423 | 0.457 | [0.107 0.543 0. 0.35 ] | 0.0952436 | 0.46791 | 0.563154 | [0.107 0.893] | 0.457 | -| (0.35, 92) | 0.105592 | 0.333408 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.098634 | 0.456481 | 0.555115 | [0.089 0.911] | 0.439 | -| (0.35, 93) | 0.140823 | 0.304177 | 0.445 | [0.096 0.554 0.001 0.349] | 0.125783 | 0.43128 | 0.557063 | [0.097 0.903] | 0.445 | -| (0.35, 94) | 0.121894 | 0.316106 | 0.438 | [0.089 0.561 0.001 0.349] | 0.0975567 | 0.456412 | 0.553968 | [0.09 0.91] | 0.438 | -| (0.35, 95) | 0.111933 | 0.332067 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.106763 | 0.450562 | 0.557325 | [0.094 0.906] | 0.444 | -| (0.35, 96) | 0.116825 | 0.334175 | 0.451 | [0.102 0.548 0.001 0.349] | 0.098681 | 0.461062 | 0.559743 | [0.103 0.897] | 0.451 | -| (0.35, 97) | 0.0768109 | 0.383189 | 0.46 | [0.11 0.54 0. 0.35] | 0.0613629 | 0.503153 | 0.564516 | [0.11 0.89] | 0.46 | -| (0.35, 98) | 0.1058 | 0.3342 | 0.44 | [0.09 0.56 0. 0.35] | 0.0989152 | 0.45664 | 0.555556 | [0.09 0.91] | 0.44 | -| (0.35, 99) | 0.117609 | 0.317391 | 0.435 | [0.085 0.565 0. 0.35 ] | 0.0861184 | 0.467241 | 0.55336 | [0.085 0.915] | 0.435 | -| (0.4, 0) | 0.092345 | 0.401655 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0832232 | 0.529334 | 0.612557 | [0.094 0.906] | 0.494 | -| (0.4, 1) | 0.110067 | 0.369933 | 0.48 | [0.081 0.519 0.001 0.399] | 0.0940139 | 0.511449 | 0.605463 | [0.082 0.918] | 0.48 | -| (0.4, 2) | 0.10932 | 0.38868 | 0.498 | [0.098 0.502 0. 0.4 ] | 0.0823125 | 0.532127 | 0.614439 | [0.098 0.902] | 0.498 | -| (0.4, 3) | 0.140325 | 0.344675 | 0.485 | [0.085 0.515 0. 0.4 ] | 0.109529 | 0.498836 | 0.608365 | [0.085 0.915] | 0.485 | -| (0.4, 4) | 0.113004 | 0.369996 | 0.483 | [0.085 0.515 0.002 0.398] | 0.100758 | 0.505487 | 0.606245 | [0.087 0.913] | 0.483 | -| (0.4, 5) | 0.102619 | 0.402381 | 0.505 | [0.106 0.494 0.001 0.399] | 0.0800786 | 0.537091 | 0.617169 | [0.107 0.893] | 0.505 | -| (0.4, 6) | 0.121988 | 0.385012 | 0.507 | [0.107 0.493 0. 0.4 ] | 0.0934156 | 0.525301 | 0.618716 | [0.107 0.893] | 0.507 | -| (0.4, 7) | 0.111719 | 0.373281 | 0.485 | [0.085 0.515 0. 0.4 ] | 0.0860944 | 0.522271 | 0.608365 | [0.085 0.915] | 0.485 | -| (0.4, 8) | 0.103077 | 0.391923 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0934454 | 0.519581 | 0.613027 | [0.095 0.905] | 0.495 | -| (0.4, 9) | 0.09463 | 0.39437 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0841998 | 0.526021 | 0.610221 | [0.089 0.911] | 0.489 | -| (0.4, 10) | 0.114552 | 0.377448 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.0829579 | 0.528663 | 0.611621 | [0.092 0.908] | 0.492 | -| (0.4, 11) | 0.0881744 | 0.402826 | 0.491 | [0.091 0.509 0. 0.4 ] | 0.080588 | 0.530566 | 0.611154 | [0.091 0.909] | 0.491 | -| (0.4, 12) | 0.116386 | 0.369614 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0788718 | 0.529956 | 0.608828 | [0.086 0.914] | 0.486 | -| (0.4, 13) | 0.082174 | 0.422826 | 0.505 | [0.106 0.494 0.001 0.399] | 0.0696855 | 0.547484 | 0.617169 | [0.107 0.893] | 0.505 | -| (0.4, 14) | 0.10176 | 0.38824 | 0.49 | [0.09 0.51 0. 0.4 ] | 0.0850318 | 0.525655 | 0.610687 | [0.09 0.91] | 0.49 | -| (0.4, 15) | 0.0911766 | 0.404823 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0791771 | 0.533726 | 0.612903 | [0.098 0.902] | 0.496 | -| (0.4, 16) | 0.148433 | 0.335567 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.105864 | 0.502038 | 0.607903 | [0.084 0.916] | 0.484 | -| (0.4, 17) | 0.0972479 | 0.395752 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.0776354 | 0.534453 | 0.612089 | [0.093 0.907] | 0.493 | -| (0.4, 18) | 0.0899533 | 0.390047 | 0.48 | [0.08 0.52 0. 0.4 ] | 0.0751172 | 0.530943 | 0.606061 | [0.08 0.92] | 0.48 | -| (0.4, 19) | 0.0985107 | 0.395489 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0900225 | 0.522535 | 0.612557 | [0.094 0.906] | 0.494 | -| (0.4, 20) | 0.133984 | 0.358016 | 0.492 | [0.093 0.507 0.001 0.399] | 0.100027 | 0.510999 | 0.611026 | [0.094 0.906] | 0.492 | -| (0.4, 21) | 0.107137 | 0.388863 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0792101 | 0.534287 | 0.613497 | [0.096 0.904] | 0.496 | -| (0.4, 22) | 0.0939334 | 0.401067 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0811591 | 0.531868 | 0.613027 | [0.095 0.905] | 0.495 | -| (0.4, 23) | 0.119607 | 0.363393 | 0.483 | [0.085 0.515 0.002 0.398] | 0.0741715 | 0.532074 | 0.606245 | [0.087 0.913] | 0.483 | -| (0.4, 24) | 0.0886681 | 0.402332 | 0.491 | [0.092 0.508 0.001 0.399] | 0.0779011 | 0.532657 | 0.610559 | [0.093 0.907] | 0.491 | -| (0.4, 25) | 0.120708 | 0.361292 | 0.482 | [0.083 0.517 0.001 0.399] | 0.0967001 | 0.509683 | 0.606383 | [0.084 0.916] | 0.482 | -| (0.4, 26) | 0.115847 | 0.372153 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.090602 | 0.519154 | 0.609756 | [0.088 0.912] | 0.488 | -| (0.4, 27) | 0.0823224 | 0.418678 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.0663761 | 0.549482 | 0.615858 | [0.101 0.899] | 0.501 | -| (0.4, 28) | 0.126811 | 0.354189 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.0952304 | 0.51129 | 0.60652 | [0.081 0.919] | 0.481 | -| (0.4, 29) | 0.120102 | 0.370898 | 0.491 | [0.091 0.509 0. 0.4 ] | 0.0952146 | 0.515939 | 0.611154 | [0.091 0.909] | 0.491 | -| (0.4, 30) | 0.1235 | 0.3405 | 0.464 | [0.064 0.536 0. 0.4 ] | 0.0918379 | 0.506964 | 0.598802 | [0.064 0.936] | 0.464 | -| (0.4, 31) | 0.116506 | 0.369494 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0997973 | 0.509031 | 0.608828 | [0.086 0.914] | 0.486 | -| (0.4, 32) | 0.129958 | 0.359042 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0956325 | 0.514589 | 0.610221 | [0.089 0.911] | 0.489 | -| (0.4, 33) | 0.101764 | 0.388236 | 0.49 | [0.09 0.51 0. 0.4 ] | 0.0904647 | 0.520222 | 0.610687 | [0.09 0.91] | 0.49 | -| (0.4, 34) | 0.111378 | 0.382622 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0774008 | 0.535157 | 0.612557 | [0.094 0.906] | 0.494 | -| (0.4, 35) | 0.118964 | 0.369036 | 0.488 | [0.089 0.511 0.001 0.399] | 0.0925809 | 0.516579 | 0.60916 | [0.09 0.91] | 0.488 | -| (0.4, 36) | 0.107617 | 0.379383 | 0.487 | [0.087 0.513 0. 0.4 ] | 0.0748225 | 0.534469 | 0.609292 | [0.087 0.913] | 0.487 | -| (0.4, 37) | 0.104499 | 0.377501 | 0.482 | [0.083 0.517 0.001 0.399] | 0.0941583 | 0.512225 | 0.606383 | [0.084 0.916] | 0.482 | -| (0.4, 38) | 0.0908303 | 0.38517 | 0.476 | [0.077 0.523 0.001 0.399] | 0.0805585 | 0.523072 | 0.603631 | [0.078 0.922] | 0.476 | -| (0.4, 39) | 0.141925 | 0.327075 | 0.469 | [0.069 0.531 0. 0.4 ] | 0.109778 | 0.491274 | 0.601052 | [0.069 0.931] | 0.469 | -| (0.4, 40) | 0.113013 | 0.380987 | 0.494 | [0.095 0.505 0.001 0.399] | 0.0873407 | 0.524622 | 0.611963 | [0.096 0.904] | 0.494 | -| (0.4, 41) | 0.103835 | 0.397165 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.0760962 | 0.539762 | 0.615858 | [0.101 0.899] | 0.501 | -| (0.4, 42) | 0.117133 | 0.378867 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0906409 | 0.522262 | 0.612903 | [0.098 0.902] | 0.496 | -| (0.4, 43) | 0.0771431 | 0.416857 | 0.494 | [0.096 0.504 0.002 0.398] | 0.065077 | 0.54629 | 0.611367 | [0.098 0.902] | 0.494 | -| (0.4, 44) | 0.0940821 | 0.384918 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.083618 | 0.521984 | 0.605602 | [0.079 0.921] | 0.479 | -| (0.4, 45) | 0.100887 | 0.379113 | 0.48 | [0.081 0.519 0.001 0.399] | 0.098478 | 0.506985 | 0.605463 | [0.082 0.918] | 0.48 | -| (0.4, 46) | 0.0877998 | 0.4042 | 0.492 | [0.093 0.507 0.001 0.399] | 0.0742074 | 0.536819 | 0.611026 | [0.094 0.906] | 0.492 | -| (0.4, 47) | 0.133793 | 0.343207 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.111633 | 0.493053 | 0.604686 | [0.077 0.923] | 0.477 | -| (0.4, 48) | 0.104751 | 0.391249 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0948151 | 0.518682 | 0.613497 | [0.096 0.904] | 0.496 | -| (0.4, 49) | 0.111086 | 0.372914 | 0.484 | [0.085 0.515 0.001 0.399] | 0.0854655 | 0.52184 | 0.607306 | [0.086 0.914] | 0.484 | -| (0.4, 50) | 0.115748 | 0.367252 | 0.483 | [0.084 0.516 0.001 0.399] | 0.0794044 | 0.52744 | 0.606844 | [0.085 0.915] | 0.483 | -| (0.4, 51) | 0.111478 | 0.389522 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.094504 | 0.521354 | 0.615858 | [0.101 0.899] | 0.501 | -| (0.4, 52) | 0.116461 | 0.378539 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0778475 | 0.535179 | 0.613027 | [0.095 0.905] | 0.495 | -| (0.4, 53) | 0.129073 | 0.340927 | 0.47 | [0.071 0.529 0.001 0.399] | 0.10103 | 0.499874 | 0.600904 | [0.072 0.928] | 0.47 | -| (0.4, 54) | 0.070972 | 0.413028 | 0.484 | [0.085 0.515 0.001 0.399] | 0.0619927 | 0.545313 | 0.607306 | [0.086 0.914] | 0.484 | -| (0.4, 55) | 0.117027 | 0.363973 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.100197 | 0.506323 | 0.60652 | [0.081 0.919] | 0.481 | -| (0.4, 56) | 0.116247 | 0.360753 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.0981762 | 0.50651 | 0.604686 | [0.077 0.923] | 0.477 | -| (0.4, 57) | 0.0605851 | 0.419415 | 0.48 | [0.08 0.52 0. 0.4 ] | 0.0531007 | 0.55296 | 0.606061 | [0.08 0.92] | 0.48 | -| (0.4, 58) | 0.123128 | 0.349872 | 0.473 | [0.073 0.527 0. 0.4 ] | 0.0887936 | 0.51407 | 0.602864 | [0.073 0.927] | 0.473 | -| (0.4, 59) | 0.118622 | 0.362378 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.0999178 | 0.506602 | 0.60652 | [0.081 0.919] | 0.481 | -| (0.4, 60) | 0.127352 | 0.347648 | 0.475 | [0.075 0.525 0. 0.4 ] | 0.0978482 | 0.505925 | 0.603774 | [0.075 0.925] | 0.475 | -| (0.4, 61) | 0.106386 | 0.353614 | 0.46 | [0.061 0.539 0.001 0.399] | 0.0804344 | 0.515978 | 0.596413 | [0.062 0.938] | 0.46 | -| (0.4, 62) | 0.0924329 | 0.395567 | 0.488 | [0.089 0.511 0.001 0.399] | 0.0820076 | 0.527153 | 0.60916 | [0.09 0.91] | 0.488 | -| (0.4, 63) | 0.105464 | 0.381536 | 0.487 | [0.087 0.513 0. 0.4 ] | 0.0768776 | 0.532414 | 0.609292 | [0.087 0.913] | 0.487 | -| (0.4, 64) | 0.103833 | 0.377167 | 0.481 | [0.082 0.518 0.001 0.399] | 0.0846173 | 0.521305 | 0.605923 | [0.083 0.917] | 0.481 | -| (0.4, 65) | 0.099706 | 0.378294 | 0.478 | [0.079 0.521 0.001 0.399] | 0.0838127 | 0.520733 | 0.604545 | [0.08 0.92] | 0.478 | -| (0.4, 66) | 0.138331 | 0.336669 | 0.475 | [0.076 0.524 0.001 0.399] | 0.103986 | 0.499188 | 0.603175 | [0.077 0.923] | 0.475 | -| (0.4, 67) | 0.0768523 | 0.407148 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.0699597 | 0.537943 | 0.607903 | [0.084 0.916] | 0.484 | -| (0.4, 68) | 0.12711 | 0.35589 | 0.483 | [0.083 0.517 0. 0.4 ] | 0.106317 | 0.501125 | 0.607441 | [0.083 0.917] | 0.483 | -| (0.4, 69) | 0.0883312 | 0.388669 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.0754731 | 0.529213 | 0.604686 | [0.077 0.923] | 0.477 | -| (0.4, 70) | 0.0730699 | 0.41593 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0616618 | 0.548559 | 0.610221 | [0.089 0.911] | 0.489 | -| (0.4, 71) | 0.124959 | 0.375041 | 0.5 | [0.1 0.5 0. 0.4] | 0.0857221 | 0.529663 | 0.615385 | [0.1 0.9] | 0.5 | -| (0.4, 72) | 0.113747 | 0.376253 | 0.49 | [0.091 0.509 0.001 0.399] | 0.0965721 | 0.51352 | 0.610092 | [0.092 0.908] | 0.49 | -| (0.4, 73) | 0.133 | 0.358 | 0.491 | [0.092 0.508 0.001 0.399] | 0.0919066 | 0.518652 | 0.610559 | [0.093 0.907] | 0.491 | -| (0.4, 74) | 0.105271 | 0.386729 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.0828093 | 0.528812 | 0.611621 | [0.092 0.908] | 0.492 | -| (0.4, 75) | 0.134828 | 0.358172 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.110178 | 0.50191 | 0.612089 | [0.093 0.907] | 0.493 | -| (0.4, 76) | 0.12011 | 0.37189 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.091034 | 0.520587 | 0.611621 | [0.092 0.908] | 0.492 | -| (0.4, 77) | 0.118663 | 0.359337 | 0.478 | [0.078 0.522 0. 0.4 ] | 0.0957442 | 0.5094 | 0.605144 | [0.078 0.922] | 0.478 | -| (0.4, 78) | 0.0944783 | 0.405522 | 0.5 | [0.1 0.5 0. 0.4] | 0.0700838 | 0.545301 | 0.615385 | [0.1 0.9] | 0.5 | -| (0.4, 79) | 0.116782 | 0.380218 | 0.497 | [0.097 0.503 0. 0.4 ] | 0.0970365 | 0.516931 | 0.613968 | [0.097 0.903] | 0.497 | -| (0.4, 80) | 0.107796 | 0.369204 | 0.477 | [0.079 0.521 0.002 0.398] | 0.0821726 | 0.521315 | 0.603487 | [0.081 0.919] | 0.477 | -| (0.4, 81) | 0.105714 | 0.380286 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0767872 | 0.532041 | 0.608828 | [0.086 0.914] | 0.486 | -| (0.4, 82) | 0.114563 | 0.391437 | 0.506 | [0.106 0.494 0. 0.4 ] | 0.0992908 | 0.518947 | 0.618238 | [0.106 0.894] | 0.506 | -| (0.4, 83) | 0.112762 | 0.366238 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.0831437 | 0.522458 | 0.605602 | [0.079 0.921] | 0.479 | -| (0.4, 84) | 0.118112 | 0.361888 | 0.48 | [0.081 0.519 0.001 0.399] | 0.0910492 | 0.514414 | 0.605463 | [0.082 0.918] | 0.48 | -| (0.4, 85) | 0.109261 | 0.369739 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.0871998 | 0.518402 | 0.605602 | [0.079 0.921] | 0.479 | -| (0.4, 86) | 0.0915574 | 0.404443 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0691098 | 0.544387 | 0.613497 | [0.096 0.904] | 0.496 | -| (0.4, 87) | 0.102728 | 0.399272 | 0.502 | [0.102 0.498 0. 0.4 ] | 0.0756223 | 0.540711 | 0.616333 | [0.102 0.898] | 0.502 | -| (0.4, 88) | 0.073757 | 0.414243 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.0647122 | 0.545044 | 0.609756 | [0.088 0.912] | 0.488 | -| (0.4, 89) | 0.0796764 | 0.404324 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.0650499 | 0.542853 | 0.607903 | [0.084 0.916] | 0.484 | -| (0.4, 90) | 0.109032 | 0.383968 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.0869838 | 0.525105 | 0.612089 | [0.093 0.907] | 0.493 | -| (0.4, 91) | 0.0873812 | 0.403619 | 0.491 | [0.093 0.507 0.002 0.398] | 0.0752138 | 0.534748 | 0.609962 | [0.095 0.905] | 0.491 | -| (0.4, 92) | 0.133381 | 0.342619 | 0.476 | [0.077 0.523 0.001 0.399] | 0.116146 | 0.487485 | 0.603631 | [0.078 0.922] | 0.476 | -| (0.4, 93) | 0.0782402 | 0.39776 | 0.476 | [0.077 0.523 0.001 0.399] | 0.0717926 | 0.531838 | 0.603631 | [0.078 0.922] | 0.476 | -| (0.4, 94) | 0.0698796 | 0.43712 | 0.507 | [0.107 0.493 0. 0.4 ] | 0.056667 | 0.562049 | 0.618716 | [0.107 0.893] | 0.507 | -| (0.4, 95) | 0.0862284 | 0.399772 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0655856 | 0.543242 | 0.608828 | [0.086 0.914] | 0.486 | -| (0.4, 96) | 0.082924 | 0.402076 | 0.485 | [0.086 0.514 0.001 0.399] | 0.0622894 | 0.545479 | 0.607768 | [0.087 0.913] | 0.485 | -| (0.4, 97) | 0.106119 | 0.388881 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0793528 | 0.533674 | 0.613027 | [0.095 0.905] | 0.495 | -| (0.4, 98) | 0.0803093 | 0.415691 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0649711 | 0.547932 | 0.612903 | [0.098 0.902] | 0.496 | -| (0.4, 99) | 0.130854 | 0.357146 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.0929463 | 0.51681 | 0.609756 | [0.088 0.912] | 0.488 | -| (0.45, 0) | 0.144135 | 0.390865 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0978465 | 0.561494 | 0.659341 | [0.085 0.915] | 0.535 | -| (0.45, 1) | 0.0803529 | 0.446647 | 0.527 | [0.078 0.472 0.001 0.449] | 0.0665225 | 0.588474 | 0.654996 | [0.079 0.921] | 0.527 | -| (0.45, 2) | 0.102685 | 0.434315 | 0.537 | [0.087 0.463 0. 0.45 ] | 0.0754333 | 0.584875 | 0.660308 | [0.087 0.913] | 0.537 | -| (0.45, 3) | 0.0687572 | 0.470243 | 0.539 | [0.089 0.461 0. 0.45 ] | 0.0624005 | 0.598878 | 0.661278 | [0.089 0.911] | 0.539 | -| (0.45, 4) | 0.125853 | 0.388147 | 0.514 | [0.064 0.486 0. 0.45 ] | 0.0917095 | 0.557641 | 0.649351 | [0.064 0.936] | 0.514 | -| (0.45, 5) | 0.0746958 | 0.451304 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0637687 | 0.591253 | 0.655022 | [0.076 0.924] | 0.526 | -| (0.45, 6) | 0.110722 | 0.424278 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0802848 | 0.579056 | 0.659341 | [0.085 0.915] | 0.535 | -| (0.45, 7) | 0.0962457 | 0.436754 | 0.533 | [0.084 0.466 0.001 0.449] | 0.0746481 | 0.583227 | 0.657875 | [0.085 0.915] | 0.533 | -| (0.45, 8) | 0.0899108 | 0.463089 | 0.553 | [0.103 0.447 0. 0.45 ] | 0.0678808 | 0.600271 | 0.668151 | [0.103 0.897] | 0.553 | -| (0.45, 9) | 0.0804035 | 0.443597 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0667882 | 0.587282 | 0.65407 | [0.074 0.926] | 0.524 | -| (0.45, 10) | 0.0723278 | 0.458672 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.056579 | 0.600835 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 11) | 0.130674 | 0.398326 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.0896737 | 0.566781 | 0.656455 | [0.079 0.921] | 0.529 | -| (0.45, 12) | 0.0678408 | 0.468159 | 0.536 | [0.088 0.462 0.002 0.448] | 0.0504177 | 0.608406 | 0.658824 | [0.09 0.91] | 0.536 | -| (0.45, 13) | 0.102707 | 0.424293 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0840882 | 0.571411 | 0.655499 | [0.077 0.923] | 0.527 | -| (0.45, 14) | 0.123353 | 0.414647 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0904657 | 0.570327 | 0.660793 | [0.088 0.912] | 0.538 | -| (0.45, 15) | 0.117961 | 0.416039 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.0893819 | 0.569476 | 0.658858 | [0.084 0.916] | 0.534 | -| (0.45, 16) | 0.0889066 | 0.440093 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.06649 | 0.589965 | 0.656455 | [0.079 0.921] | 0.529 | -| (0.45, 17) | 0.093381 | 0.428619 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0608659 | 0.592255 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 18) | 0.0845936 | 0.448406 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0663776 | 0.591998 | 0.658376 | [0.083 0.917] | 0.533 | -| (0.45, 19) | 0.0937979 | 0.442202 | 0.536 | [0.086 0.464 0. 0.45 ] | 0.0702677 | 0.589556 | 0.659824 | [0.086 0.914] | 0.536 | -| (0.45, 20) | 0.0977833 | 0.443217 | 0.541 | [0.091 0.459 0. 0.45 ] | 0.0747889 | 0.587463 | 0.662252 | [0.091 0.909] | 0.541 | -| (0.45, 21) | 0.109247 | 0.412753 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0880567 | 0.565064 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 22) | 0.0977246 | 0.433275 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0722733 | 0.585141 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 23) | 0.0845927 | 0.444407 | 0.529 | [0.08 0.47 0.001 0.449] | 0.063226 | 0.592727 | 0.655953 | [0.081 0.919] | 0.529 | -| (0.45, 24) | 0.0863168 | 0.444683 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0612519 | 0.596162 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 25) | 0.0951726 | 0.430827 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0676557 | 0.587366 | 0.655022 | [0.076 0.924] | 0.526 | -| (0.45, 26) | 0.11682 | 0.41518 | 0.532 | [0.083 0.467 0.001 0.449] | 0.0886786 | 0.568715 | 0.657394 | [0.084 0.916] | 0.532 | -| (0.45, 27) | 0.0810696 | 0.46393 | 0.545 | [0.095 0.455 0. 0.45 ] | 0.0680873 | 0.596119 | 0.664207 | [0.095 0.905] | 0.545 | -| (0.45, 28) | 0.0983129 | 0.434687 | 0.533 | [0.084 0.466 0.001 0.449] | 0.0680319 | 0.589844 | 0.657875 | [0.085 0.915] | 0.533 | -| (0.45, 29) | 0.113898 | 0.434102 | 0.548 | [0.098 0.452 0. 0.45 ] | 0.099043 | 0.566637 | 0.66568 | [0.098 0.902] | 0.548 | -| (0.45, 30) | 0.107967 | 0.436033 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0797048 | 0.584012 | 0.663717 | [0.094 0.906] | 0.544 | -| (0.45, 31) | 0.0910724 | 0.435928 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0792247 | 0.576274 | 0.655499 | [0.077 0.923] | 0.527 | -| (0.45, 32) | 0.0998013 | 0.437199 | 0.537 | [0.088 0.462 0.001 0.449] | 0.0709575 | 0.588851 | 0.659809 | [0.089 0.911] | 0.537 | -| (0.45, 33) | 0.128649 | 0.395351 | 0.524 | [0.075 0.475 0.001 0.449] | 0.0880365 | 0.56553 | 0.653566 | [0.076 0.924] | 0.524 | -| (0.45, 34) | 0.11969 | 0.42331 | 0.543 | [0.093 0.457 0. 0.45 ] | 0.0735173 | 0.58971 | 0.663228 | [0.093 0.907] | 0.543 | -| (0.45, 35) | 0.10032 | 0.43168 | 0.532 | [0.084 0.466 0.002 0.448] | 0.0863961 | 0.570495 | 0.656891 | [0.086 0.914] | 0.532 | -| (0.45, 36) | 0.0874866 | 0.436513 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0645323 | 0.589537 | 0.65407 | [0.074 0.926] | 0.524 | -| (0.45, 37) | 0.0794778 | 0.447522 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0575178 | 0.597981 | 0.655499 | [0.077 0.923] | 0.527 | -| (0.45, 38) | 0.0371157 | 0.498884 | 0.536 | [0.086 0.464 0. 0.45 ] | 0.0320388 | 0.627785 | 0.659824 | [0.086 0.914] | 0.536 | -| (0.45, 39) | 0.0757443 | 0.446256 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0608502 | 0.59227 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 40) | 0.125924 | 0.399076 | 0.525 | [0.077 0.473 0.002 0.448] | 0.0963192 | 0.557218 | 0.653538 | [0.079 0.921] | 0.525 | -| (0.45, 41) | 0.0878115 | 0.450189 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0617661 | 0.599027 | 0.660793 | [0.088 0.912] | 0.538 | -| (0.45, 42) | 0.0975696 | 0.44243 | 0.54 | [0.091 0.459 0.001 0.449] | 0.0706756 | 0.590591 | 0.661267 | [0.092 0.908] | 0.54 | -| (0.45, 43) | 0.0907168 | 0.447283 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0714498 | 0.589343 | 0.660793 | [0.088 0.912] | 0.538 | -| (0.45, 44) | 0.069606 | 0.452394 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0567298 | 0.596391 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 45) | 0.0775188 | 0.460481 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0631226 | 0.59767 | 0.660793 | [0.088 0.912] | 0.538 | -| (0.45, 46) | 0.107153 | 0.428847 | 0.536 | [0.087 0.463 0.001 0.449] | 0.0851788 | 0.574146 | 0.659325 | [0.088 0.912] | 0.536 | -| (0.45, 47) | 0.110526 | 0.409474 | 0.52 | [0.07 0.48 0. 0.45] | 0.0791248 | 0.573049 | 0.652174 | [0.07 0.93] | 0.52 | -| (0.45, 48) | 0.105262 | 0.422738 | 0.528 | [0.078 0.472 0. 0.45 ] | 0.0851603 | 0.570816 | 0.655977 | [0.078 0.922] | 0.528 | -| (0.45, 49) | 0.0977202 | 0.44528 | 0.543 | [0.093 0.457 0. 0.45 ] | 0.0667705 | 0.596457 | 0.663228 | [0.093 0.907] | 0.543 | -| (0.45, 50) | 0.110384 | 0.406616 | 0.517 | [0.067 0.483 0. 0.45 ] | 0.0739936 | 0.576766 | 0.650759 | [0.067 0.933] | 0.517 | -| (0.45, 51) | 0.10361 | 0.42639 | 0.53 | [0.08 0.47 0. 0.45] | 0.0716481 | 0.585286 | 0.656934 | [0.08 0.92] | 0.53 | -| (0.45, 52) | 0.0978246 | 0.433175 | 0.531 | [0.082 0.468 0.001 0.449] | 0.0701777 | 0.586735 | 0.656913 | [0.083 0.917] | 0.531 | -| (0.45, 53) | 0.0900391 | 0.431961 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0735003 | 0.57962 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 54) | 0.0896197 | 0.44938 | 0.539 | [0.09 0.46 0.001 0.449] | 0.0574227 | 0.603357 | 0.66078 | [0.091 0.909] | 0.539 | -| (0.45, 55) | 0.0844244 | 0.429576 | 0.514 | [0.064 0.486 0. 0.45 ] | 0.0684914 | 0.580859 | 0.649351 | [0.064 0.936] | 0.514 | -| (0.45, 56) | 0.0762635 | 0.447736 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0546346 | 0.599435 | 0.65407 | [0.074 0.926] | 0.524 | -| (0.45, 57) | 0.0873352 | 0.442665 | 0.53 | [0.08 0.47 0. 0.45] | 0.0714978 | 0.585436 | 0.656934 | [0.08 0.92] | 0.53 | -| (0.45, 58) | 0.0981975 | 0.432803 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0694249 | 0.587989 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 59) | 0.0862462 | 0.438754 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0721937 | 0.582352 | 0.654545 | [0.075 0.925] | 0.525 | -| (0.45, 60) | 0.0910102 | 0.42499 | 0.516 | [0.066 0.484 0. 0.45 ] | 0.0711453 | 0.579144 | 0.650289 | [0.066 0.934] | 0.516 | -| (0.45, 61) | 0.108033 | 0.420967 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.0712548 | 0.5852 | 0.656455 | [0.079 0.921] | 0.529 | -| (0.45, 62) | 0.0915517 | 0.437448 | 0.529 | [0.08 0.47 0.001 0.449] | 0.0657728 | 0.59018 | 0.655953 | [0.081 0.919] | 0.529 | -| (0.45, 63) | 0.10221 | 0.42779 | 0.53 | [0.08 0.47 0. 0.45] | 0.071132 | 0.585802 | 0.656934 | [0.08 0.92] | 0.53 | -| (0.45, 64) | 0.0737738 | 0.451226 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0595941 | 0.594951 | 0.654545 | [0.075 0.925] | 0.525 | -| (0.45, 65) | 0.108378 | 0.413622 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0710203 | 0.5821 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 66) | 0.0985459 | 0.432454 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0684595 | 0.588955 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 67) | 0.0782579 | 0.460742 | 0.539 | [0.09 0.46 0.001 0.449] | 0.0622707 | 0.598509 | 0.66078 | [0.091 0.909] | 0.539 | -| (0.45, 68) | 0.0895337 | 0.436466 | 0.526 | [0.077 0.473 0.001 0.449] | 0.0572073 | 0.597312 | 0.654519 | [0.078 0.922] | 0.526 | -| (0.45, 69) | 0.116034 | 0.410966 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0800237 | 0.575475 | 0.655499 | [0.077 0.923] | 0.527 | -| (0.45, 70) | 0.0733139 | 0.451686 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0563668 | 0.598179 | 0.654545 | [0.075 0.925] | 0.525 | -| (0.45, 71) | 0.0764108 | 0.454589 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0568153 | 0.600599 | 0.657414 | [0.081 0.919] | 0.531 | -| (0.45, 72) | 0.101457 | 0.421543 | 0.523 | [0.074 0.476 0.001 0.449] | 0.0660446 | 0.587046 | 0.653091 | [0.075 0.925] | 0.523 | -| (0.45, 73) | 0.074457 | 0.445543 | 0.52 | [0.07 0.48 0. 0.45] | 0.0427993 | 0.609375 | 0.652174 | [0.07 0.93] | 0.52 | -| (0.45, 74) | 0.114778 | 0.416222 | 0.531 | [0.082 0.468 0.001 0.449] | 0.0877855 | 0.569127 | 0.656913 | [0.083 0.917] | 0.531 | -| (0.45, 75) | 0.0871245 | 0.453876 | 0.541 | [0.091 0.459 0. 0.45 ] | 0.0578167 | 0.604435 | 0.662252 | [0.091 0.909] | 0.541 | -| (0.45, 76) | 0.0804687 | 0.448531 | 0.529 | [0.08 0.47 0.001 0.449] | 0.0565621 | 0.599391 | 0.655953 | [0.081 0.919] | 0.529 | -| (0.45, 77) | 0.0759259 | 0.472074 | 0.548 | [0.098 0.452 0. 0.45 ] | 0.0714512 | 0.594229 | 0.66568 | [0.098 0.902] | 0.548 | -| (0.45, 78) | 0.0901168 | 0.436883 | 0.527 | [0.078 0.472 0.001 0.449] | 0.0652689 | 0.589727 | 0.654996 | [0.079 0.921] | 0.527 | -| (0.45, 79) | 0.117665 | 0.398335 | 0.516 | [0.066 0.484 0. 0.45 ] | 0.0850179 | 0.565271 | 0.650289 | [0.066 0.934] | 0.516 | -| (0.45, 80) | 0.0838263 | 0.450174 | 0.534 | [0.086 0.464 0.002 0.448] | 0.0586789 | 0.599177 | 0.657856 | [0.088 0.912] | 0.534 | -| (0.45, 81) | 0.117966 | 0.407034 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0916233 | 0.562922 | 0.654545 | [0.075 0.925] | 0.525 | -| (0.45, 82) | 0.0783591 | 0.465641 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0563563 | 0.607361 | 0.663717 | [0.094 0.906] | 0.544 | -| (0.45, 83) | 0.107869 | 0.426131 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.079874 | 0.578984 | 0.658858 | [0.084 0.916] | 0.534 | -| (0.45, 84) | 0.0742541 | 0.457746 | 0.532 | [0.083 0.467 0.001 0.449] | 0.0582135 | 0.59918 | 0.657394 | [0.084 0.916] | 0.532 | -| (0.45, 85) | 0.0889582 | 0.436042 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0641647 | 0.590381 | 0.654545 | [0.075 0.925] | 0.525 | -| (0.45, 86) | 0.105018 | 0.429982 | 0.535 | [0.086 0.464 0.001 0.449] | 0.075934 | 0.582907 | 0.658841 | [0.087 0.913] | 0.535 | -| (0.45, 87) | 0.0880208 | 0.437979 | 0.526 | [0.077 0.473 0.001 0.449] | 0.0573692 | 0.59715 | 0.654519 | [0.078 0.922] | 0.526 | -| (0.45, 88) | 0.0817681 | 0.456232 | 0.538 | [0.09 0.46 0.002 0.448] | 0.056134 | 0.60366 | 0.659794 | [0.092 0.908] | 0.538 | -| (0.45, 89) | 0.113433 | 0.420567 | 0.534 | [0.085 0.465 0.001 0.449] | 0.085868 | 0.57249 | 0.658358 | [0.086 0.914] | 0.534 | -| (0.45, 90) | 0.0828514 | 0.446149 | 0.529 | [0.081 0.469 0.002 0.448] | 0.0623557 | 0.593094 | 0.65545 | [0.083 0.917] | 0.529 | -| (0.45, 91) | 0.0816244 | 0.451376 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0671052 | 0.591271 | 0.658376 | [0.083 0.917] | 0.533 | -| (0.45, 92) | 0.0661671 | 0.459833 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0487002 | 0.606322 | 0.655022 | [0.076 0.924] | 0.526 | -| (0.45, 93) | 0.111753 | 0.412247 | 0.524 | [0.075 0.475 0.001 0.449] | 0.0867837 | 0.566783 | 0.653566 | [0.076 0.924] | 0.524 | -| (0.45, 94) | 0.120017 | 0.414983 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0876596 | 0.571681 | 0.659341 | [0.085 0.915] | 0.535 | -| (0.45, 95) | 0.121518 | 0.412482 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.0959745 | 0.562883 | 0.658858 | [0.084 0.916] | 0.534 | -| (0.45, 96) | 0.0951344 | 0.426866 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0639835 | 0.589137 | 0.65312 | [0.072 0.928] | 0.522 | -| (0.45, 97) | 0.0712756 | 0.461724 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0543197 | 0.604056 | 0.658376 | [0.083 0.917] | 0.533 | -| (0.45, 98) | 0.0954362 | 0.448564 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0673074 | 0.596409 | 0.663717 | [0.094 0.906] | 0.544 | -| (0.45, 99) | 0.103458 | 0.428542 | 0.532 | [0.082 0.468 0. 0.45 ] | 0.0707815 | 0.587113 | 0.657895 | [0.082 0.918] | 0.532 | -| (0.5, 0) | 0.0823199 | 0.48668 | 0.569 | [0.071 0.429 0.002 0.498] | 0.0658081 | 0.63216 | 0.697968 | [0.073 0.927] | 0.569 | -| (0.5, 1) | 0.0714074 | 0.492593 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0533071 | 0.643072 | 0.696379 | [0.064 0.936] | 0.564 | -| (0.5, 2) | 0.0913782 | 0.477622 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0608316 | 0.63798 | 0.698812 | [0.069 0.931] | 0.569 | -| (0.5, 3) | 0.0980838 | 0.467916 | 0.566 | [0.066 0.434 0. 0.5 ] | 0.0728228 | 0.624527 | 0.69735 | [0.066 0.934] | 0.566 | -| (0.5, 4) | 0.0713764 | 0.485624 | 0.557 | [0.06 0.44 0.003 0.497] | 0.0545128 | 0.637206 | 0.691719 | [0.063 0.937] | 0.557 | -| (0.5, 5) | 0.109045 | 0.462955 | 0.572 | [0.073 0.427 0.001 0.499] | 0.069359 | 0.630501 | 0.69986 | [0.074 0.926] | 0.572 | -| (0.5, 6) | 0.0859611 | 0.500039 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0553646 | 0.651849 | 0.707214 | [0.086 0.914] | 0.586 | -| (0.5, 7) | 0.0853441 | 0.483656 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0633528 | 0.635459 | 0.698812 | [0.069 0.931] | 0.569 | -| (0.5, 8) | 0.132703 | 0.442297 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0890792 | 0.612675 | 0.701754 | [0.075 0.925] | 0.575 | -| (0.5, 9) | 0.0901357 | 0.484864 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.061493 | 0.640261 | 0.701754 | [0.075 0.925] | 0.575 | -| (0.5, 10) | 0.0896721 | 0.492328 | 0.582 | [0.083 0.417 0.001 0.499] | 0.05777 | 0.647032 | 0.704802 | [0.084 0.916] | 0.582 | -| (0.5, 11) | 0.104675 | 0.472325 | 0.577 | [0.078 0.422 0.001 0.499] | 0.0722944 | 0.630028 | 0.702322 | [0.079 0.921] | 0.577 | -| (0.5, 12) | 0.106432 | 0.467568 | 0.574 | [0.074 0.426 0. 0.5 ] | 0.0760253 | 0.625237 | 0.701262 | [0.074 0.926] | 0.574 | -| (0.5, 13) | 0.0447724 | 0.536228 | 0.581 | [0.082 0.418 0.001 0.499] | 0.0338064 | 0.670499 | 0.704305 | [0.083 0.917] | 0.581 | -| (0.5, 14) | 0.08127 | 0.49473 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0546865 | 0.647561 | 0.702247 | [0.076 0.924] | 0.576 | -| (0.5, 15) | 0.0927274 | 0.466273 | 0.559 | [0.059 0.441 0. 0.5 ] | 0.0689874 | 0.624975 | 0.693963 | [0.059 0.941] | 0.559 | -| (0.5, 16) | 0.0895199 | 0.49148 | 0.581 | [0.082 0.418 0.001 0.499] | 0.0676034 | 0.636701 | 0.704305 | [0.083 0.917] | 0.581 | -| (0.5, 17) | 0.101514 | 0.471486 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0786432 | 0.622128 | 0.700771 | [0.073 0.927] | 0.573 | -| (0.5, 18) | 0.0802439 | 0.488756 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0639173 | 0.634895 | 0.698812 | [0.069 0.931] | 0.569 | -| (0.5, 19) | 0.094629 | 0.466371 | 0.561 | [0.061 0.439 0. 0.5 ] | 0.0745545 | 0.620373 | 0.694927 | [0.061 0.939] | 0.561 | -| (0.5, 20) | 0.0639745 | 0.506026 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0458685 | 0.653432 | 0.699301 | [0.07 0.93] | 0.57 | -| (0.5, 21) | 0.0647483 | 0.507252 | 0.572 | [0.073 0.427 0.001 0.499] | 0.0419894 | 0.65787 | 0.69986 | [0.074 0.926] | 0.572 | -| (0.5, 22) | 0.0906012 | 0.469399 | 0.56 | [0.06 0.44 0. 0.5 ] | 0.0719325 | 0.622512 | 0.694444 | [0.06 0.94] | 0.56 | -| (0.5, 23) | 0.0792254 | 0.484775 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0515546 | 0.644824 | 0.696379 | [0.064 0.936] | 0.564 | -| (0.5, 24) | 0.089042 | 0.475958 | 0.565 | [0.065 0.435 0. 0.5 ] | 0.0625937 | 0.63427 | 0.696864 | [0.065 0.935] | 0.565 | -| (0.5, 25) | 0.0693021 | 0.508698 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0390147 | 0.66422 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 26) | 0.0826301 | 0.47537 | 0.558 | [0.058 0.442 0. 0.5 ] | 0.0662074 | 0.627274 | 0.693481 | [0.058 0.942] | 0.558 | -| (0.5, 27) | 0.0865851 | 0.486415 | 0.573 | [0.074 0.426 0.001 0.499] | 0.0592513 | 0.6411 | 0.700351 | [0.075 0.925] | 0.573 | -| (0.5, 28) | 0.098346 | 0.477654 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0675682 | 0.634679 | 0.702247 | [0.076 0.924] | 0.576 | -| (0.5, 29) | 0.106672 | 0.465328 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0687628 | 0.631517 | 0.70028 | [0.072 0.928] | 0.572 | -| (0.5, 30) | 0.0814945 | 0.491505 | 0.573 | [0.074 0.426 0.001 0.499] | 0.0639574 | 0.636394 | 0.700351 | [0.075 0.925] | 0.573 | -| (0.5, 31) | 0.089467 | 0.491533 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0609881 | 0.643734 | 0.704722 | [0.081 0.919] | 0.581 | -| (0.5, 32) | 0.118053 | 0.457947 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0806901 | 0.621557 | 0.702247 | [0.076 0.924] | 0.576 | -| (0.5, 33) | 0.0825452 | 0.499455 | 0.582 | [0.082 0.418 0. 0.5 ] | 0.0582277 | 0.646991 | 0.705219 | [0.082 0.918] | 0.582 | -| (0.5, 34) | 0.0417537 | 0.522246 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0305612 | 0.665818 | 0.696379 | [0.064 0.936] | 0.564 | -| (0.5, 35) | 0.0785123 | 0.502488 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0475305 | 0.657191 | 0.704722 | [0.081 0.919] | 0.581 | -| (0.5, 36) | 0.0744245 | 0.511576 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0489571 | 0.658257 | 0.707214 | [0.086 0.914] | 0.586 | -| (0.5, 37) | 0.0843737 | 0.491626 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0653751 | 0.636872 | 0.702247 | [0.076 0.924] | 0.576 | -| (0.5, 38) | 0.09586 | 0.47114 | 0.567 | [0.069 0.431 0.002 0.498] | 0.0624039 | 0.634587 | 0.696991 | [0.071 0.929] | 0.567 | -| (0.5, 39) | 0.0986446 | 0.471355 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0662648 | 0.633036 | 0.699301 | [0.07 0.93] | 0.57 | -| (0.5, 40) | 0.108438 | 0.460562 | 0.569 | [0.07 0.43 0.001 0.499] | 0.0689099 | 0.629481 | 0.69839 | [0.071 0.929] | 0.569 | -| (0.5, 41) | 0.0767079 | 0.524292 | 0.601 | [0.101 0.399 0. 0.5 ] | 0.0610743 | 0.653722 | 0.714796 | [0.101 0.899] | 0.601 | -| (0.5, 42) | 0.0732868 | 0.501713 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0550139 | 0.64674 | 0.701754 | [0.075 0.925] | 0.575 | -| (0.5, 43) | 0.0867752 | 0.495225 | 0.582 | [0.082 0.418 0. 0.5 ] | 0.0691998 | 0.636019 | 0.705219 | [0.082 0.918] | 0.582 | -| (0.5, 44) | 0.0828599 | 0.50414 | 0.587 | [0.089 0.411 0.002 0.498] | 0.0641183 | 0.642766 | 0.706884 | [0.091 0.909] | 0.587 | -| (0.5, 45) | 0.0958384 | 0.493162 | 0.589 | [0.089 0.411 0. 0.5 ] | 0.067415 | 0.641302 | 0.708717 | [0.089 0.911] | 0.589 | -| (0.5, 46) | 0.094656 | 0.483344 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0723339 | 0.630901 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 47) | 0.0912958 | 0.476704 | 0.568 | [0.068 0.432 0. 0.5 ] | 0.0622567 | 0.636067 | 0.698324 | [0.068 0.932] | 0.568 | -| (0.5, 48) | 0.0869253 | 0.491075 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0550731 | 0.648162 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 49) | 0.0468182 | 0.517182 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0355723 | 0.660806 | 0.696379 | [0.064 0.936] | 0.564 | -| (0.5, 50) | 0.0751574 | 0.489843 | 0.565 | [0.066 0.434 0.001 0.499] | 0.0543628 | 0.642078 | 0.696441 | [0.067 0.933] | 0.565 | -| (0.5, 51) | 0.0801626 | 0.511837 | 0.592 | [0.092 0.408 0. 0.5 ] | 0.0670399 | 0.643187 | 0.710227 | [0.092 0.908] | 0.592 | -| (0.5, 52) | 0.0800749 | 0.499925 | 0.58 | [0.08 0.42 0. 0.5 ] | 0.0537815 | 0.650444 | 0.704225 | [0.08 0.92] | 0.58 | -| (0.5, 53) | 0.0895674 | 0.487433 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0634805 | 0.63926 | 0.702741 | [0.077 0.923] | 0.577 | -| (0.5, 54) | 0.083454 | 0.484546 | 0.568 | [0.068 0.432 0. 0.5 ] | 0.061941 | 0.636383 | 0.698324 | [0.068 0.932] | 0.568 | -| (0.5, 55) | 0.0657226 | 0.511277 | 0.577 | [0.078 0.422 0.001 0.499] | 0.053739 | 0.648583 | 0.702322 | [0.079 0.921] | 0.577 | -| (0.5, 56) | 0.0652427 | 0.517757 | 0.583 | [0.083 0.417 0. 0.5 ] | 0.0528151 | 0.652901 | 0.705716 | [0.083 0.917] | 0.583 | -| (0.5, 57) | 0.08749 | 0.49551 | 0.583 | [0.084 0.416 0.001 0.499] | 0.0566554 | 0.648645 | 0.7053 | [0.085 0.915] | 0.583 | -| (0.5, 58) | 0.0876101 | 0.47539 | 0.563 | [0.063 0.437 0. 0.5 ] | 0.0661026 | 0.629792 | 0.695894 | [0.063 0.937] | 0.563 | -| (0.5, 59) | 0.081788 | 0.504212 | 0.586 | [0.087 0.413 0.001 0.499] | 0.063427 | 0.643372 | 0.706799 | [0.088 0.912] | 0.586 | -| (0.5, 60) | 0.08172 | 0.48428 | 0.566 | [0.066 0.434 0. 0.5 ] | 0.0662614 | 0.631089 | 0.69735 | [0.066 0.934] | 0.566 | -| (0.5, 61) | 0.108461 | 0.451539 | 0.56 | [0.061 0.439 0.001 0.499] | 0.0744602 | 0.619559 | 0.694019 | [0.062 0.938] | 0.56 | -| (0.5, 62) | 0.0928987 | 0.476101 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0653223 | 0.63349 | 0.698812 | [0.069 0.931] | 0.569 | -| (0.5, 63) | 0.0916214 | 0.487379 | 0.579 | [0.079 0.421 0. 0.5 ] | 0.0658141 | 0.637916 | 0.70373 | [0.079 0.921] | 0.579 | -| (0.5, 64) | 0.0774471 | 0.500553 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0503743 | 0.652861 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 65) | 0.0649944 | 0.505006 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0464662 | 0.652835 | 0.699301 | [0.07 0.93] | 0.57 | -| (0.5, 66) | 0.112129 | 0.459871 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0782461 | 0.622034 | 0.70028 | [0.072 0.928] | 0.572 | -| (0.5, 67) | 0.0465229 | 0.539477 | 0.586 | [0.087 0.413 0.001 0.499] | 0.0326439 | 0.674155 | 0.706799 | [0.088 0.912] | 0.586 | -| (0.5, 68) | 0.0947336 | 0.477266 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0738824 | 0.626398 | 0.70028 | [0.072 0.928] | 0.572 | -| (0.5, 69) | 0.0904762 | 0.471524 | 0.562 | [0.062 0.438 0. 0.5 ] | 0.0632376 | 0.632173 | 0.69541 | [0.062 0.938] | 0.562 | -| (0.5, 70) | 0.0690796 | 0.51192 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0519332 | 0.652788 | 0.704722 | [0.081 0.919] | 0.581 | -| (0.5, 71) | 0.0983992 | 0.465601 | 0.564 | [0.065 0.435 0.001 0.499] | 0.0771774 | 0.618778 | 0.695955 | [0.066 0.934] | 0.564 | -| (0.5, 72) | 0.0972073 | 0.477793 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0747774 | 0.626977 | 0.701754 | [0.075 0.925] | 0.575 | -| (0.5, 73) | 0.0639479 | 0.518052 | 0.582 | [0.083 0.417 0.001 0.499] | 0.0473908 | 0.657411 | 0.704802 | [0.084 0.916] | 0.582 | -| (0.5, 74) | 0.0487624 | 0.529238 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0355232 | 0.667712 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 75) | 0.0990139 | 0.468986 | 0.568 | [0.069 0.431 0.001 0.499] | 0.0652727 | 0.632629 | 0.697902 | [0.07 0.93] | 0.568 | -| (0.5, 76) | 0.0674569 | 0.510543 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0482796 | 0.654955 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 77) | 0.104605 | 0.473395 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0767052 | 0.62653 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 78) | 0.101666 | 0.483334 | 0.585 | [0.085 0.415 0. 0.5 ] | 0.0646452 | 0.642069 | 0.706714 | [0.085 0.915] | 0.585 | -| (0.5, 79) | 0.0801886 | 0.480811 | 0.561 | [0.061 0.439 0. 0.5 ] | 0.0615175 | 0.63341 | 0.694927 | [0.061 0.939] | 0.561 | -| (0.5, 80) | 0.0946084 | 0.482392 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0679813 | 0.634759 | 0.702741 | [0.077 0.923] | 0.577 | -| (0.5, 81) | 0.106413 | 0.474587 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0724391 | 0.632283 | 0.704722 | [0.081 0.919] | 0.581 | -| (0.5, 82) | 0.0781018 | 0.487898 | 0.566 | [0.067 0.433 0.001 0.499] | 0.0478596 | 0.649068 | 0.696927 | [0.068 0.932] | 0.566 | -| (0.5, 83) | 0.0936024 | 0.473398 | 0.567 | [0.067 0.433 0. 0.5 ] | 0.0720418 | 0.625795 | 0.697837 | [0.067 0.933] | 0.567 | -| (0.5, 84) | 0.111706 | 0.459294 | 0.571 | [0.071 0.429 0. 0.5 ] | 0.0712875 | 0.628503 | 0.69979 | [0.071 0.929] | 0.571 | -| (0.5, 85) | 0.0536686 | 0.513331 | 0.567 | [0.067 0.433 0. 0.5 ] | 0.039865 | 0.657972 | 0.697837 | [0.067 0.933] | 0.567 | -| (0.5, 86) | 0.0633176 | 0.509682 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0460594 | 0.654711 | 0.700771 | [0.073 0.927] | 0.573 | -| (0.5, 87) | 0.0521377 | 0.525862 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0411703 | 0.662065 | 0.703235 | [0.078 0.922] | 0.578 | -| (0.5, 88) | 0.0845024 | 0.501498 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0657795 | 0.641434 | 0.707214 | [0.086 0.914] | 0.586 | -| (0.5, 89) | 0.0716476 | 0.514352 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0514473 | 0.655766 | 0.707214 | [0.086 0.914] | 0.586 | -| (0.5, 90) | 0.0727238 | 0.512276 | 0.585 | [0.085 0.415 0. 0.5 ] | 0.0502498 | 0.656464 | 0.706714 | [0.085 0.915] | 0.585 | -| (0.5, 91) | 0.0693259 | 0.507674 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0515734 | 0.651167 | 0.702741 | [0.077 0.923] | 0.577 | -| (0.5, 92) | 0.0893712 | 0.486629 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0645728 | 0.637674 | 0.702247 | [0.076 0.924] | 0.576 | -| (0.5, 93) | 0.0645983 | 0.509402 | 0.574 | [0.075 0.425 0.001 0.499] | 0.0470981 | 0.653745 | 0.700843 | [0.076 0.924] | 0.574 | -| (0.5, 94) | 0.0577857 | 0.528214 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0477799 | 0.659434 | 0.707214 | [0.086 0.914] | 0.586 | -| (0.5, 95) | 0.0683137 | 0.511686 | 0.58 | [0.08 0.42 0. 0.5 ] | 0.0438813 | 0.660344 | 0.704225 | [0.08 0.92] | 0.58 | -| (0.5, 96) | 0.0836411 | 0.485359 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0653466 | 0.633465 | 0.698812 | [0.069 0.931] | 0.569 | -| (0.5, 97) | 0.0842354 | 0.485765 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.048951 | 0.65035 | 0.699301 | [0.07 0.93] | 0.57 | -| (0.5, 98) | 0.100673 | 0.472327 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0649737 | 0.635797 | 0.700771 | [0.073 0.927] | 0.573 | -| (0.5, 99) | 0.0852149 | 0.486785 | 0.572 | [0.073 0.427 0.001 0.499] | 0.0583452 | 0.641515 | 0.69986 | [0.074 0.926] | 0.572 | -| (0.55, 0) | 0.0730904 | 0.53891 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0428355 | 0.696412 | 0.739247 | [0.062 0.938] | 0.612 | -| (0.55, 1) | 0.0661012 | 0.542899 | 0.609 | [0.059 0.391 0. 0.55 ] | 0.0467022 | 0.691058 | 0.73776 | [0.059 0.941] | 0.609 | -| (0.55, 2) | 0.0644516 | 0.542548 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.046827 | 0.689945 | 0.736772 | [0.057 0.943] | 0.607 | -| (0.55, 3) | 0.0620741 | 0.547926 | 0.61 | [0.06 0.39 0. 0.55] | 0.0387163 | 0.699539 | 0.738255 | [0.06 0.94] | 0.61 | -| (0.55, 4) | 0.0833749 | 0.528625 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.05636 | 0.682887 | 0.739247 | [0.062 0.938] | 0.612 | -| (0.55, 5) | 0.1047 | 0.5133 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0670869 | 0.675153 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 6) | 0.0682258 | 0.538774 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.0365568 | 0.700215 | 0.736772 | [0.057 0.943] | 0.607 | -| (0.55, 7) | 0.0636036 | 0.566396 | 0.63 | [0.081 0.369 0.001 0.549] | 0.0466895 | 0.701267 | 0.747956 | [0.082 0.918] | 0.63 | -| (0.55, 8) | 0.0886129 | 0.523387 | 0.612 | [0.063 0.387 0.001 0.549] | 0.058874 | 0.680022 | 0.738896 | [0.064 0.936] | 0.612 | -| (0.55, 9) | 0.0695911 | 0.535409 | 0.605 | [0.056 0.394 0.001 0.549] | 0.0521205 | 0.683312 | 0.735432 | [0.057 0.943] | 0.605 | -| (0.55, 10) | 0.0832089 | 0.543791 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.0564085 | 0.690367 | 0.746775 | [0.077 0.923] | 0.627 | -| (0.55, 11) | 0.0741853 | 0.553815 | 0.628 | [0.079 0.371 0.001 0.549] | 0.0503493 | 0.696589 | 0.746939 | [0.08 0.92] | 0.628 | -| (0.55, 12) | 0.0767586 | 0.530241 | 0.607 | [0.058 0.392 0.001 0.549] | 0.054906 | 0.681513 | 0.736419 | [0.059 0.941] | 0.607 | -| (0.55, 13) | 0.108092 | 0.515908 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.0698793 | 0.675378 | 0.745257 | [0.074 0.926] | 0.624 | -| (0.55, 14) | 0.0878994 | 0.532101 | 0.62 | [0.071 0.379 0.001 0.549] | 0.0573614 | 0.685534 | 0.742896 | [0.072 0.928] | 0.62 | -| (0.55, 15) | 0.0678694 | 0.559131 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.042195 | 0.70458 | 0.746775 | [0.077 0.923] | 0.627 | -| (0.55, 16) | 0.0683432 | 0.539657 | 0.608 | [0.058 0.392 0. 0.55 ] | 0.0498925 | 0.687373 | 0.737265 | [0.058 0.942] | 0.608 | -| (0.55, 17) | 0.0753853 | 0.536615 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0528654 | 0.686382 | 0.739247 | [0.062 0.938] | 0.612 | -| (0.55, 18) | 0.086495 | 0.527505 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0563929 | 0.683499 | 0.739892 | [0.066 0.934] | 0.614 | -| (0.55, 19) | 0.0657372 | 0.545263 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0404072 | 0.698344 | 0.738751 | [0.061 0.939] | 0.611 | -| (0.55, 20) | 0.108858 | 0.504142 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0840232 | 0.655721 | 0.739744 | [0.063 0.937] | 0.613 | -| (0.55, 21) | 0.0967053 | 0.505295 | 0.602 | [0.053 0.397 0.001 0.549] | 0.066302 | 0.667655 | 0.733957 | [0.054 0.946] | 0.602 | -| (0.55, 22) | 0.0630256 | 0.553974 | 0.617 | [0.068 0.382 0.001 0.549] | 0.045882 | 0.695509 | 0.741391 | [0.069 0.931] | 0.617 | -| (0.55, 23) | 0.0913141 | 0.522686 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0593481 | 0.680894 | 0.740242 | [0.064 0.936] | 0.614 | -| (0.55, 24) | 0.0817313 | 0.530269 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0508022 | 0.688445 | 0.739247 | [0.062 0.938] | 0.612 | -| (0.55, 25) | 0.077761 | 0.540239 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0587776 | 0.683463 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 26) | 0.117069 | 0.500931 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0827969 | 0.659443 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 27) | 0.0470736 | 0.581926 | 0.629 | [0.08 0.37 0.001 0.549] | 0.0371326 | 0.710315 | 0.747447 | [0.081 0.919] | 0.629 | -| (0.55, 28) | 0.0650752 | 0.559925 | 0.625 | [0.076 0.374 0.001 0.549] | 0.0448129 | 0.700605 | 0.745418 | [0.077 0.923] | 0.625 | -| (0.55, 29) | 0.0881858 | 0.528814 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0568185 | 0.684921 | 0.74174 | [0.067 0.933] | 0.617 | -| (0.55, 30) | 0.0977565 | 0.528244 | 0.626 | [0.076 0.374 0. 0.55 ] | 0.0690057 | 0.677263 | 0.746269 | [0.076 0.924] | 0.626 | -| (0.55, 31) | 0.0871869 | 0.523813 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0582915 | 0.680459 | 0.738751 | [0.061 0.939] | 0.611 | -| (0.55, 32) | 0.0728003 | 0.5452 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0543715 | 0.687869 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 33) | 0.0809225 | 0.523078 | 0.604 | [0.054 0.396 0. 0.55 ] | 0.0520946 | 0.6832 | 0.735294 | [0.054 0.946] | 0.604 | -| (0.55, 34) | 0.100559 | 0.526441 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.0630208 | 0.683754 | 0.746775 | [0.077 0.923] | 0.627 | -| (0.55, 35) | 0.103207 | 0.513793 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0645554 | 0.677184 | 0.74174 | [0.067 0.933] | 0.617 | -| (0.55, 36) | 0.0817379 | 0.536262 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0547587 | 0.687481 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 37) | 0.075124 | 0.537876 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0481441 | 0.6916 | 0.739744 | [0.063 0.937] | 0.613 | -| (0.55, 38) | 0.0678917 | 0.557108 | 0.625 | [0.075 0.375 0. 0.55 ] | 0.0452231 | 0.70054 | 0.745763 | [0.075 0.925] | 0.625 | -| (0.55, 39) | 0.0702522 | 0.550748 | 0.621 | [0.072 0.378 0.001 0.549] | 0.0474194 | 0.695979 | 0.743399 | [0.073 0.927] | 0.621 | -| (0.55, 40) | 0.0989642 | 0.522036 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.061899 | 0.681847 | 0.743746 | [0.071 0.929] | 0.621 | -| (0.55, 41) | 0.0844022 | 0.539598 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.0556893 | 0.689568 | 0.745257 | [0.074 0.926] | 0.624 | -| (0.55, 42) | 0.0793985 | 0.555601 | 0.635 | [0.086 0.364 0.001 0.549] | 0.0532486 | 0.697264 | 0.750513 | [0.087 0.913] | 0.635 | -| (0.55, 43) | 0.0422454 | 0.564755 | 0.607 | [0.058 0.392 0.001 0.549] | 0.0257601 | 0.710658 | 0.736419 | [0.059 0.941] | 0.607 | -| (0.55, 44) | 0.0642159 | 0.551784 | 0.616 | [0.066 0.384 0. 0.55 ] | 0.0499338 | 0.691306 | 0.74124 | [0.066 0.934] | 0.616 | -| (0.55, 45) | 0.0775302 | 0.54147 | 0.619 | [0.069 0.381 0. 0.55 ] | 0.0494472 | 0.693294 | 0.742741 | [0.069 0.931] | 0.619 | -| (0.55, 46) | 0.0855431 | 0.530457 | 0.616 | [0.066 0.384 0. 0.55 ] | 0.0539876 | 0.687252 | 0.74124 | [0.066 0.934] | 0.616 | -| (0.55, 47) | 0.0621009 | 0.567899 | 0.63 | [0.081 0.369 0.001 0.549] | 0.0462904 | 0.701666 | 0.747956 | [0.082 0.918] | 0.63 | -| (0.55, 48) | 0.0883259 | 0.526674 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0598105 | 0.680581 | 0.740391 | [0.067 0.933] | 0.615 | -| (0.55, 49) | 0.0951236 | 0.512876 | 0.608 | [0.059 0.391 0.001 0.549] | 0.0690868 | 0.667826 | 0.736913 | [0.06 0.94] | 0.608 | -| (0.55, 50) | 0.0554219 | 0.551578 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.0409083 | 0.695863 | 0.736772 | [0.057 0.943] | 0.607 | -| (0.55, 51) | 0.0839772 | 0.516023 | 0.6 | [0.053 0.397 0.003 0.547] | 0.0600569 | 0.672206 | 0.732262 | [0.056 0.944] | 0.6 | -| (0.55, 52) | 0.12439 | 0.48961 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0840354 | 0.656207 | 0.740242 | [0.064 0.936] | 0.614 | -| (0.55, 53) | 0.0616151 | 0.552385 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0439556 | 0.695937 | 0.739892 | [0.066 0.934] | 0.614 | -| (0.55, 54) | 0.0917346 | 0.531265 | 0.623 | [0.073 0.377 0. 0.55 ] | 0.0659632 | 0.67879 | 0.744753 | [0.073 0.927] | 0.623 | -| (0.55, 55) | 0.0825903 | 0.53441 | 0.617 | [0.068 0.382 0.001 0.549] | 0.0581616 | 0.683229 | 0.741391 | [0.069 0.931] | 0.617 | -| (0.55, 56) | 0.0439757 | 0.564024 | 0.608 | [0.058 0.392 0. 0.55 ] | 0.0307259 | 0.706539 | 0.737265 | [0.058 0.942] | 0.608 | -| (0.55, 57) | 0.0791233 | 0.533877 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0549126 | 0.684832 | 0.739744 | [0.063 0.937] | 0.613 | -| (0.55, 58) | 0.0466274 | 0.568373 | 0.615 | [0.065 0.385 0. 0.55 ] | 0.0319929 | 0.708748 | 0.740741 | [0.065 0.935] | 0.615 | -| (0.55, 59) | 0.0716604 | 0.54134 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0421397 | 0.697605 | 0.739744 | [0.063 0.937] | 0.613 | -| (0.55, 60) | 0.0891312 | 0.525869 | 0.615 | [0.066 0.384 0.001 0.549] | 0.055412 | 0.684979 | 0.740391 | [0.067 0.933] | 0.615 | -| (0.55, 61) | 0.050594 | 0.567406 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0318906 | 0.71035 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 62) | 0.0764588 | 0.536541 | 0.613 | [0.064 0.386 0.001 0.549] | 0.0534303 | 0.685964 | 0.739394 | [0.065 0.935] | 0.613 | -| (0.55, 63) | 0.0825997 | 0.5264 | 0.609 | [0.059 0.391 0. 0.55 ] | 0.0549614 | 0.682798 | 0.73776 | [0.059 0.941] | 0.609 | -| (0.55, 64) | 0.0790597 | 0.53894 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0599859 | 0.682254 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 65) | 0.102482 | 0.509518 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0717024 | 0.667545 | 0.739247 | [0.062 0.938] | 0.612 | -| (0.55, 66) | 0.0946111 | 0.521389 | 0.616 | [0.067 0.383 0.001 0.549] | 0.0639841 | 0.676907 | 0.740891 | [0.068 0.932] | 0.616 | -| (0.55, 67) | 0.0656431 | 0.551357 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0496327 | 0.692107 | 0.74174 | [0.067 0.933] | 0.617 | -| (0.55, 68) | 0.069672 | 0.551328 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.0451221 | 0.698624 | 0.743746 | [0.071 0.929] | 0.621 | -| (0.55, 69) | 0.0642955 | 0.555705 | 0.62 | [0.07 0.38 0. 0.55] | 0.0396424 | 0.703601 | 0.743243 | [0.07 0.93] | 0.62 | -| (0.55, 70) | 0.0843746 | 0.530625 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0508608 | 0.68953 | 0.740391 | [0.067 0.933] | 0.615 | -| (0.55, 71) | 0.0788593 | 0.531141 | 0.61 | [0.06 0.39 0. 0.55] | 0.0476278 | 0.690627 | 0.738255 | [0.06 0.94] | 0.61 | -| (0.55, 72) | 0.0766319 | 0.540368 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0548507 | 0.686889 | 0.74174 | [0.067 0.933] | 0.617 | -| (0.55, 73) | 0.0480531 | 0.566947 | 0.615 | [0.065 0.385 0. 0.55 ] | 0.0363437 | 0.704397 | 0.740741 | [0.065 0.935] | 0.615 | -| (0.55, 74) | 0.101802 | 0.516198 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0708638 | 0.671376 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 75) | 0.0852682 | 0.540732 | 0.626 | [0.076 0.374 0. 0.55 ] | 0.0630882 | 0.68318 | 0.746269 | [0.076 0.924] | 0.626 | -| (0.55, 76) | 0.0654355 | 0.555565 | 0.621 | [0.073 0.377 0.002 0.548] | 0.048294 | 0.694757 | 0.743051 | [0.075 0.925] | 0.621 | -| (0.55, 77) | 0.0788674 | 0.540133 | 0.619 | [0.07 0.38 0.001 0.549] | 0.0505293 | 0.691864 | 0.742394 | [0.071 0.929] | 0.619 | -| (0.55, 78) | 0.0809986 | 0.521001 | 0.602 | [0.052 0.398 0. 0.55 ] | 0.0579031 | 0.676409 | 0.734312 | [0.052 0.948] | 0.602 | -| (0.55, 79) | 0.0710941 | 0.543906 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0542047 | 0.686186 | 0.740391 | [0.067 0.933] | 0.615 | -| (0.55, 80) | 0.104299 | 0.515701 | 0.62 | [0.071 0.379 0.001 0.549] | 0.065018 | 0.677878 | 0.742896 | [0.072 0.928] | 0.62 | -| (0.55, 81) | 0.053892 | 0.565108 | 0.619 | [0.069 0.381 0. 0.55 ] | 0.0370133 | 0.705728 | 0.742741 | [0.069 0.931] | 0.619 | -| (0.55, 82) | 0.0392997 | 0.5717 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0282315 | 0.710519 | 0.738751 | [0.061 0.939] | 0.611 | -| (0.55, 83) | 0.0996358 | 0.514364 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0624861 | 0.677406 | 0.739892 | [0.066 0.934] | 0.614 | -| (0.55, 84) | 0.0668299 | 0.56717 | 0.634 | [0.084 0.366 0. 0.55 ] | 0.049494 | 0.700847 | 0.750341 | [0.084 0.916] | 0.634 | -| (0.55, 85) | 0.0628239 | 0.555176 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0445864 | 0.697654 | 0.74224 | [0.068 0.932] | 0.618 | -| (0.55, 86) | 0.080314 | 0.529686 | 0.61 | [0.06 0.39 0. 0.55] | 0.0544223 | 0.683833 | 0.738255 | [0.06 0.94] | 0.61 | -| (0.55, 87) | 0.0760951 | 0.544905 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.0544041 | 0.689342 | 0.743746 | [0.071 0.929] | 0.621 | -| (0.55, 88) | 0.0713211 | 0.557679 | 0.629 | [0.079 0.371 0. 0.55 ] | 0.0527228 | 0.695068 | 0.747791 | [0.079 0.921] | 0.629 | -| (0.55, 89) | 0.0759801 | 0.53702 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0498224 | 0.689922 | 0.739744 | [0.063 0.937] | 0.613 | -| (0.55, 90) | 0.0725677 | 0.541432 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0533703 | 0.686872 | 0.740242 | [0.064 0.936] | 0.614 | -| (0.55, 91) | 0.0842715 | 0.530729 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0616303 | 0.678761 | 0.740391 | [0.067 0.933] | 0.615 | -| (0.55, 92) | 0.0881018 | 0.525898 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0610744 | 0.678818 | 0.739892 | [0.066 0.934] | 0.614 | -| (0.55, 93) | 0.0972321 | 0.513768 | 0.611 | [0.062 0.388 0.001 0.549] | 0.0641005 | 0.674299 | 0.738399 | [0.063 0.937] | 0.611 | -| (0.55, 94) | 0.0627574 | 0.562243 | 0.625 | [0.075 0.375 0. 0.55 ] | 0.0409383 | 0.704824 | 0.745763 | [0.075 0.925] | 0.625 | -| (0.55, 95) | 0.07313 | 0.53787 | 0.611 | [0.062 0.388 0.001 0.549] | 0.0538739 | 0.684526 | 0.738399 | [0.063 0.937] | 0.611 | -| (0.55, 96) | 0.0938753 | 0.508125 | 0.602 | [0.053 0.397 0.001 0.549] | 0.0607252 | 0.673232 | 0.733957 | [0.054 0.946] | 0.602 | -| (0.55, 97) | 0.0986028 | 0.515397 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0621644 | 0.678078 | 0.740242 | [0.064 0.936] | 0.614 | -| (0.55, 98) | 0.0961309 | 0.527869 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.05855 | 0.686707 | 0.745257 | [0.074 0.926] | 0.624 | -| (0.55, 99) | 0.0959075 | 0.505093 | 0.601 | [0.052 0.398 0.001 0.549] | 0.0639415 | 0.669525 | 0.733467 | [0.053 0.947] | 0.601 | -| (0.6, 0) | 0.0881511 | 0.577849 | 0.666 | [0.067 0.333 0.001 0.599] | 0.055237 | 0.726747 | 0.781984 | [0.068 0.932] | 0.666 | -| (0.6, 1) | 0.0676567 | 0.594343 | 0.662 | [0.063 0.337 0.001 0.599] | 0.0477349 | 0.732213 | 0.779948 | [0.064 0.936] | 0.662 | -| (0.6, 2) | 0.0491176 | 0.612882 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.027934 | 0.7523 | 0.780234 | [0.062 0.938] | 0.662 | -| (0.6, 3) | 0.0568165 | 0.596183 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.035421 | 0.740274 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 4) | 0.0615035 | 0.605496 | 0.667 | [0.068 0.332 0.001 0.599] | 0.040621 | 0.741874 | 0.782495 | [0.069 0.931] | 0.667 | -| (0.6, 5) | 0.0730526 | 0.589947 | 0.663 | [0.063 0.337 0. 0.6 ] | 0.0445382 | 0.736204 | 0.780742 | [0.063 0.937] | 0.663 | -| (0.6, 6) | 0.0522423 | 0.607758 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0293391 | 0.749882 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 7) | 0.0615754 | 0.594425 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0386705 | 0.738532 | 0.777202 | [0.056 0.944] | 0.656 | -| (0.6, 8) | 0.0665795 | 0.582421 | 0.649 | [0.049 0.351 0. 0.6 ] | 0.0407432 | 0.732951 | 0.773694 | [0.049 0.951] | 0.649 | -| (0.6, 9) | 0.0737038 | 0.576296 | 0.65 | [0.051 0.349 0.001 0.599] | 0.0521496 | 0.721752 | 0.773902 | [0.052 0.948] | 0.65 | -| (0.6, 10) | 0.085578 | 0.578422 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.0560622 | 0.725188 | 0.78125 | [0.064 0.936] | 0.664 | -| (0.6, 11) | 0.064456 | 0.598544 | 0.663 | [0.064 0.336 0.001 0.599] | 0.0459933 | 0.734463 | 0.780456 | [0.065 0.935] | 0.663 | -| (0.6, 12) | 0.0562835 | 0.604716 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0319463 | 0.747781 | 0.779727 | [0.061 0.939] | 0.661 | -| (0.6, 13) | 0.0716397 | 0.59336 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.0419245 | 0.739834 | 0.781759 | [0.065 0.935] | 0.665 | -| (0.6, 14) | 0.0583248 | 0.601675 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0437987 | 0.735422 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 15) | 0.0434325 | 0.614568 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0282215 | 0.749989 | 0.77821 | [0.058 0.942] | 0.658 | -| (0.6, 16) | 0.047439 | 0.621561 | 0.669 | [0.07 0.33 0.001 0.599] | 0.0270545 | 0.756464 | 0.783519 | [0.071 0.929] | 0.669 | -| (0.6, 17) | 0.0933332 | 0.582667 | 0.676 | [0.077 0.323 0.001 0.599] | 0.058648 | 0.728474 | 0.787122 | [0.078 0.922] | 0.676 | -| (0.6, 18) | 0.0390986 | 0.632901 | 0.672 | [0.072 0.328 0. 0.6 ] | 0.0259942 | 0.759346 | 0.78534 | [0.072 0.928] | 0.672 | -| (0.6, 19) | 0.0678786 | 0.600121 | 0.668 | [0.068 0.332 0. 0.6 ] | 0.0480079 | 0.735282 | 0.78329 | [0.068 0.932] | 0.668 | -| (0.6, 20) | 0.0539102 | 0.60209 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0370685 | 0.740134 | 0.777202 | [0.056 0.944] | 0.656 | -| (0.6, 21) | 0.0820918 | 0.574908 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0477883 | 0.729629 | 0.777417 | [0.059 0.941] | 0.657 | -| (0.6, 22) | 0.0825172 | 0.577483 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0515437 | 0.727677 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 23) | 0.0568959 | 0.596104 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0359543 | 0.739741 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 24) | 0.0690674 | 0.599933 | 0.669 | [0.069 0.331 0. 0.6 ] | 0.0425673 | 0.741234 | 0.783801 | [0.069 0.931] | 0.669 | -| (0.6, 25) | 0.076607 | 0.581393 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.048413 | 0.729797 | 0.77821 | [0.058 0.942] | 0.658 | -| (0.6, 26) | 0.0757756 | 0.580224 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0556539 | 0.721548 | 0.777202 | [0.056 0.944] | 0.656 | -| (0.6, 27) | 0.0648862 | 0.578114 | 0.643 | [0.044 0.356 0.001 0.599] | 0.0435439 | 0.726874 | 0.770418 | [0.045 0.955] | 0.643 | -| (0.6, 28) | 0.0528143 | 0.600186 | 0.653 | [0.054 0.346 0.001 0.599] | 0.0340945 | 0.74131 | 0.775405 | [0.055 0.945] | 0.653 | -| (0.6, 29) | 0.0667359 | 0.601264 | 0.668 | [0.068 0.332 0. 0.6 ] | 0.0335976 | 0.749692 | 0.78329 | [0.068 0.932] | 0.668 | -| (0.6, 30) | 0.0675497 | 0.58545 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0414239 | 0.734271 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 31) | 0.0660047 | 0.598995 | 0.665 | [0.066 0.334 0.001 0.599] | 0.0426951 | 0.738779 | 0.781474 | [0.067 0.933] | 0.665 | -| (0.6, 32) | 0.0442055 | 0.618795 | 0.663 | [0.063 0.337 0. 0.6 ] | 0.030497 | 0.750245 | 0.780742 | [0.063 0.937] | 0.663 | -| (0.6, 33) | 0.0443751 | 0.609625 | 0.654 | [0.057 0.343 0.003 0.597] | 0.021217 | 0.754108 | 0.775325 | [0.06 0.94] | 0.654 | -| (0.6, 34) | 0.0943248 | 0.576675 | 0.671 | [0.072 0.328 0.001 0.599] | 0.0621463 | 0.722399 | 0.784545 | [0.073 0.927] | 0.671 | -| (0.6, 35) | 0.0535663 | 0.600434 | 0.654 | [0.054 0.346 0. 0.6 ] | 0.0296445 | 0.746552 | 0.776197 | [0.054 0.946] | 0.654 | -| (0.6, 36) | 0.0605562 | 0.604444 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.0392217 | 0.742537 | 0.781759 | [0.065 0.935] | 0.665 | -| (0.6, 37) | 0.0866943 | 0.573306 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0538976 | 0.725323 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 38) | 0.0545823 | 0.593418 | 0.648 | [0.048 0.352 0. 0.6 ] | 0.0352752 | 0.737921 | 0.773196 | [0.048 0.952] | 0.648 | -| (0.6, 39) | 0.0336199 | 0.63738 | 0.671 | [0.071 0.329 0. 0.6 ] | 0.0231063 | 0.76172 | 0.784827 | [0.071 0.929] | 0.671 | -| (0.6, 40) | 0.087924 | 0.585076 | 0.673 | [0.074 0.326 0.001 0.599] | 0.0570552 | 0.728519 | 0.785574 | [0.075 0.925] | 0.673 | -| (0.6, 41) | 0.0627829 | 0.594217 | 0.657 | [0.057 0.343 0. 0.6 ] | 0.0367148 | 0.740991 | 0.777706 | [0.057 0.943] | 0.657 | -| (0.6, 42) | 0.0661217 | 0.588878 | 0.655 | [0.055 0.345 0. 0.6 ] | 0.0462947 | 0.730404 | 0.776699 | [0.055 0.945] | 0.655 | -| (0.6, 43) | 0.0472093 | 0.608791 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0315161 | 0.745686 | 0.777202 | [0.056 0.944] | 0.656 | -| (0.6, 44) | 0.062447 | 0.595553 | 0.658 | [0.059 0.341 0.001 0.599] | 0.0393384 | 0.738584 | 0.777922 | [0.06 0.94] | 0.658 | -| (0.6, 45) | 0.0553339 | 0.597666 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0316878 | 0.744007 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 46) | 0.0603068 | 0.612693 | 0.673 | [0.073 0.327 0. 0.6 ] | 0.0403993 | 0.745455 | 0.785855 | [0.073 0.927] | 0.673 | -| (0.6, 47) | 0.05245 | 0.59255 | 0.645 | [0.045 0.355 0. 0.6 ] | 0.0331539 | 0.73855 | 0.771704 | [0.045 0.955] | 0.645 | -| (0.6, 48) | 0.0380303 | 0.61797 | 0.656 | [0.057 0.343 0.001 0.599] | 0.0253723 | 0.751541 | 0.776913 | [0.058 0.942] | 0.656 | -| (0.6, 49) | 0.0738868 | 0.593113 | 0.667 | [0.067 0.333 0. 0.6 ] | 0.0450897 | 0.737689 | 0.782779 | [0.067 0.933] | 0.667 | -| (0.6, 50) | 0.0354761 | 0.619524 | 0.655 | [0.055 0.345 0. 0.6 ] | 0.0207475 | 0.755952 | 0.776699 | [0.055 0.945] | 0.655 | -| (0.6, 51) | 0.0437408 | 0.600259 | 0.644 | [0.046 0.354 0.002 0.598] | 0.0251752 | 0.745443 | 0.770619 | [0.048 0.952] | 0.644 | -| (0.6, 52) | 0.0453087 | 0.612691 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0260618 | 0.752148 | 0.77821 | [0.058 0.942] | 0.658 | -| (0.6, 53) | 0.0722881 | 0.592712 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.047134 | 0.734625 | 0.781759 | [0.065 0.935] | 0.665 | -| (0.6, 54) | 0.0421048 | 0.615895 | 0.658 | [0.059 0.341 0.001 0.599] | 0.0287259 | 0.749196 | 0.777922 | [0.06 0.94] | 0.658 | -| (0.6, 55) | 0.066419 | 0.595581 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.048597 | 0.731637 | 0.780234 | [0.062 0.938] | 0.662 | -| (0.6, 56) | 0.0490829 | 0.605917 | 0.655 | [0.056 0.344 0.001 0.599] | 0.0286827 | 0.747727 | 0.77641 | [0.057 0.943] | 0.655 | -| (0.6, 57) | 0.066909 | 0.583091 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0391516 | 0.735042 | 0.774194 | [0.05 0.95] | 0.65 | -| (0.6, 58) | 0.0463631 | 0.608637 | 0.655 | [0.057 0.343 0.002 0.598] | 0.0324057 | 0.743714 | 0.776119 | [0.059 0.941] | 0.655 | -| (0.6, 59) | 0.0634421 | 0.586558 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0366035 | 0.73759 | 0.774194 | [0.05 0.95] | 0.65 | -| (0.6, 60) | 0.0772581 | 0.582742 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0456753 | 0.733546 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 61) | 0.0671458 | 0.591854 | 0.659 | [0.06 0.34 0.001 0.599] | 0.0450789 | 0.733349 | 0.778428 | [0.061 0.939] | 0.659 | -| (0.6, 62) | 0.0522664 | 0.599734 | 0.652 | [0.054 0.346 0.002 0.598] | 0.0359455 | 0.738666 | 0.774611 | [0.056 0.944] | 0.652 | -| (0.6, 63) | 0.063116 | 0.588884 | 0.652 | [0.052 0.348 0. 0.6 ] | 0.0464068 | 0.728787 | 0.775194 | [0.052 0.948] | 0.652 | -| (0.6, 64) | 0.0512798 | 0.61072 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.0361072 | 0.744127 | 0.780234 | [0.062 0.938] | 0.662 | -| (0.6, 65) | 0.0398496 | 0.62515 | 0.665 | [0.066 0.334 0.001 0.599] | 0.0283116 | 0.753163 | 0.781474 | [0.067 0.933] | 0.665 | -| (0.6, 66) | 0.05223 | 0.60377 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0330846 | 0.744118 | 0.777202 | [0.056 0.944] | 0.656 | -| (0.6, 67) | 0.0785234 | 0.581477 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0465799 | 0.732641 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 68) | 0.0458468 | 0.604153 | 0.65 | [0.051 0.349 0.001 0.599] | 0.0325445 | 0.741357 | 0.773902 | [0.052 0.948] | 0.65 | -| (0.6, 69) | 0.0860971 | 0.577903 | 0.664 | [0.065 0.335 0.001 0.599] | 0.0522512 | 0.728714 | 0.780965 | [0.066 0.934] | 0.664 | -| (0.6, 70) | 0.0806746 | 0.589325 | 0.67 | [0.071 0.329 0.001 0.599] | 0.0519335 | 0.732098 | 0.784031 | [0.072 0.928] | 0.67 | -| (0.6, 71) | 0.0784531 | 0.577547 | 0.656 | [0.057 0.343 0.001 0.599] | 0.0508206 | 0.726092 | 0.776913 | [0.058 0.942] | 0.656 | -| (0.6, 72) | 0.0399431 | 0.613057 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0248862 | 0.750809 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 73) | 0.0907004 | 0.5673 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0579146 | 0.720295 | 0.77821 | [0.058 0.942] | 0.658 | -| (0.6, 74) | 0.0589133 | 0.596087 | 0.655 | [0.057 0.343 0.002 0.598] | 0.035358 | 0.740761 | 0.776119 | [0.059 0.941] | 0.655 | -| (0.6, 75) | 0.0447162 | 0.624284 | 0.669 | [0.07 0.33 0.001 0.599] | 0.0297811 | 0.753738 | 0.783519 | [0.071 0.929] | 0.669 | -| (0.6, 76) | 0.0764676 | 0.583532 | 0.66 | [0.061 0.339 0.001 0.599] | 0.0455743 | 0.733359 | 0.778934 | [0.062 0.938] | 0.66 | -| (0.6, 77) | 0.0816418 | 0.584358 | 0.666 | [0.066 0.334 0. 0.6 ] | 0.0474277 | 0.734841 | 0.782269 | [0.066 0.934] | 0.666 | -| (0.6, 78) | 0.0735751 | 0.572425 | 0.646 | [0.047 0.353 0.001 0.599] | 0.0540717 | 0.717836 | 0.771907 | [0.048 0.952] | 0.646 | -| (0.6, 79) | 0.0570677 | 0.596932 | 0.654 | [0.055 0.345 0.001 0.599] | 0.0358959 | 0.740011 | 0.775907 | [0.056 0.944] | 0.654 | -| (0.6, 80) | 0.0479097 | 0.60509 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.032508 | 0.743187 | 0.775695 | [0.053 0.947] | 0.653 | -| (0.6, 81) | 0.0575586 | 0.594441 | 0.652 | [0.052 0.348 0. 0.6 ] | 0.038032 | 0.737162 | 0.775194 | [0.052 0.948] | 0.652 | -| (0.6, 82) | 0.0761639 | 0.587836 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.0527736 | 0.728476 | 0.78125 | [0.064 0.936] | 0.664 | -| (0.6, 83) | 0.0529422 | 0.607058 | 0.66 | [0.062 0.338 0.002 0.598] | 0.0371465 | 0.741499 | 0.778646 | [0.064 0.936] | 0.66 | -| (0.6, 84) | 0.0580051 | 0.595995 | 0.654 | [0.055 0.345 0.001 0.599] | 0.0419757 | 0.733931 | 0.775907 | [0.056 0.944] | 0.654 | -| (0.6, 85) | 0.0759486 | 0.587051 | 0.663 | [0.064 0.336 0.001 0.599] | 0.0539609 | 0.726495 | 0.780456 | [0.065 0.935] | 0.663 | -| (0.6, 86) | 0.055749 | 0.619251 | 0.675 | [0.076 0.324 0.001 0.599] | 0.0404567 | 0.746149 | 0.786605 | [0.077 0.923] | 0.675 | -| (0.6, 87) | 0.0509463 | 0.610054 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0335963 | 0.746131 | 0.779727 | [0.061 0.939] | 0.661 | -| (0.6, 88) | 0.0355192 | 0.647481 | 0.683 | [0.083 0.317 0. 0.6 ] | 0.0262492 | 0.764786 | 0.791035 | [0.083 0.917] | 0.683 | -| (0.6, 89) | 0.0640233 | 0.596977 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0340662 | 0.745661 | 0.779727 | [0.061 0.939] | 0.661 | -| (0.6, 90) | 0.0701055 | 0.599894 | 0.67 | [0.07 0.33 0. 0.6 ] | 0.0407704 | 0.743543 | 0.784314 | [0.07 0.93] | 0.67 | -| (0.6, 91) | 0.0341839 | 0.616816 | 0.651 | [0.052 0.348 0.001 0.599] | 0.0247255 | 0.749677 | 0.774402 | [0.053 0.947] | 0.651 | -| (0.6, 92) | 0.0685819 | 0.611418 | 0.68 | [0.08 0.32 0. 0.6 ] | 0.0442119 | 0.745262 | 0.789474 | [0.08 0.92] | 0.68 | -| (0.6, 93) | 0.0894146 | 0.567585 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0539746 | 0.723443 | 0.777417 | [0.059 0.941] | 0.657 | -| (0.6, 94) | 0.0650644 | 0.598936 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.037461 | 0.743789 | 0.78125 | [0.064 0.936] | 0.664 | -| (0.6, 95) | 0.0508844 | 0.599116 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0363045 | 0.737889 | 0.774194 | [0.05 0.95] | 0.65 | -| (0.6, 96) | 0.0661985 | 0.603802 | 0.67 | [0.07 0.33 0. 0.6 ] | 0.039231 | 0.745083 | 0.784314 | [0.07 0.93] | 0.67 | -| (0.6, 97) | 0.0892339 | 0.565766 | 0.655 | [0.057 0.343 0.002 0.598] | 0.0551233 | 0.720996 | 0.776119 | [0.059 0.941] | 0.655 | -| (0.6, 98) | 0.0976272 | 0.562373 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0597714 | 0.719449 | 0.779221 | [0.06 0.94] | 0.66 | -| (0.6, 99) | 0.0636941 | 0.593306 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0418411 | 0.735576 | 0.777417 | [0.059 0.941] | 0.657 | -| (0.65, 0) | 0.0739099 | 0.61709 | 0.691 | [0.041 0.309 0. 0.65 ] | 0.0526754 | 0.75528 | 0.807955 | [0.041 0.959] | 0.691 | -| (0.65, 1) | 0.0477952 | 0.658205 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0325986 | 0.78296 | 0.815558 | [0.056 0.944] | 0.706 | -| (0.65, 2) | 0.0229299 | 0.67707 | 0.7 | [0.05 0.3 0. 0.65] | 0.0159672 | 0.796533 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 3) | 0.0538452 | 0.645155 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0312362 | 0.780756 | 0.811993 | [0.049 0.951] | 0.699 | -| (0.65, 4) | 0.0925105 | 0.61149 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0581155 | 0.756421 | 0.814536 | [0.054 0.946] | 0.704 | -| (0.65, 5) | 0.0578091 | 0.647191 | 0.705 | [0.056 0.294 0.001 0.649] | 0.0351116 | 0.779703 | 0.814815 | [0.057 0.943] | 0.705 | -| (0.65, 6) | 0.062668 | 0.629332 | 0.692 | [0.042 0.308 0. 0.65 ] | 0.0378076 | 0.77065 | 0.808458 | [0.042 0.958] | 0.692 | -| (0.65, 7) | 0.0638892 | 0.645111 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0344787 | 0.782387 | 0.816866 | [0.061 0.939] | 0.709 | -| (0.65, 8) | 0.0890857 | 0.608914 | 0.698 | [0.049 0.301 0.001 0.649] | 0.0547982 | 0.756452 | 0.81125 | [0.05 0.95] | 0.698 | -| (0.65, 9) | 0.0611097 | 0.63789 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0413559 | 0.770637 | 0.811993 | [0.049 0.951] | 0.699 | -| (0.65, 10) | 0.0562112 | 0.632789 | 0.689 | [0.039 0.311 0. 0.65 ] | 0.0323477 | 0.774605 | 0.806952 | [0.039 0.961] | 0.689 | -| (0.65, 11) | 0.0589517 | 0.638048 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0366973 | 0.774282 | 0.810979 | [0.047 0.953] | 0.697 | -| (0.65, 12) | 0.0482449 | 0.654755 | 0.703 | [0.053 0.297 0. 0.65 ] | 0.0320813 | 0.781945 | 0.814026 | [0.053 0.947] | 0.703 | -| (0.65, 13) | 0.0736681 | 0.636332 | 0.71 | [0.061 0.289 0.001 0.649] | 0.0420889 | 0.775291 | 0.81738 | [0.062 0.938] | 0.71 | -| (0.65, 14) | 0.0430404 | 0.66396 | 0.707 | [0.057 0.293 0. 0.65 ] | 0.0287048 | 0.787366 | 0.81607 | [0.057 0.943] | 0.707 | -| (0.65, 15) | 0.0560687 | 0.637931 | 0.694 | [0.045 0.305 0.001 0.649] | 0.0310618 | 0.778165 | 0.809227 | [0.046 0.954] | 0.694 | -| (0.65, 16) | 0.0456324 | 0.653368 | 0.699 | [0.05 0.3 0.001 0.649] | 0.0263883 | 0.785369 | 0.811757 | [0.051 0.949] | 0.699 | -| (0.65, 17) | 0.064613 | 0.632387 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0387813 | 0.772198 | 0.810979 | [0.047 0.953] | 0.697 | -| (0.65, 18) | 0.0734902 | 0.62551 | 0.699 | [0.052 0.298 0.003 0.647] | 0.0453684 | 0.765917 | 0.811285 | [0.055 0.945] | 0.699 | -| (0.65, 19) | 0.0628344 | 0.639166 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0345718 | 0.778477 | 0.813049 | [0.056 0.944] | 0.702 | -| (0.65, 20) | 0.0673456 | 0.640654 | 0.708 | [0.059 0.291 0.001 0.649] | 0.0388652 | 0.777487 | 0.816352 | [0.06 0.94] | 0.708 | -| (0.65, 21) | 0.0752692 | 0.617731 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0459319 | 0.763029 | 0.808961 | [0.043 0.957] | 0.693 | -| (0.65, 22) | 0.0450542 | 0.652946 | 0.698 | [0.049 0.301 0.001 0.649] | 0.0298831 | 0.781367 | 0.81125 | [0.05 0.95] | 0.698 | -| (0.65, 23) | 0.0337217 | 0.675278 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0242411 | 0.792625 | 0.816866 | [0.061 0.939] | 0.709 | -| (0.65, 24) | 0.052651 | 0.649349 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0312041 | 0.781845 | 0.813049 | [0.056 0.944] | 0.702 | -| (0.65, 25) | 0.0663186 | 0.632681 | 0.699 | [0.05 0.3 0.001 0.649] | 0.0405875 | 0.77117 | 0.811757 | [0.051 0.949] | 0.699 | -| (0.65, 26) | 0.0504554 | 0.668545 | 0.719 | [0.069 0.281 0. 0.65 ] | 0.0367366 | 0.785528 | 0.822264 | [0.069 0.931] | 0.719 | -| (0.65, 27) | 0.0624661 | 0.646534 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0340857 | 0.78278 | 0.816866 | [0.061 0.939] | 0.709 | -| (0.65, 28) | 0.0504084 | 0.651592 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0300143 | 0.783035 | 0.813049 | [0.056 0.944] | 0.702 | -| (0.65, 29) | 0.0659238 | 0.629076 | 0.695 | [0.046 0.304 0.001 0.649] | 0.0444182 | 0.765314 | 0.809732 | [0.047 0.953] | 0.695 | -| (0.65, 30) | 0.0823759 | 0.623624 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0473784 | 0.76818 | 0.815558 | [0.056 0.944] | 0.706 | -| (0.65, 31) | 0.06098 | 0.63702 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0393581 | 0.772128 | 0.811486 | [0.048 0.952] | 0.698 | -| (0.65, 32) | 0.0843391 | 0.613661 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0515263 | 0.759959 | 0.811486 | [0.048 0.952] | 0.698 | -| (0.65, 33) | 0.0906488 | 0.620351 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.0539358 | 0.764189 | 0.818125 | [0.061 0.939] | 0.711 | -| (0.65, 34) | 0.051511 | 0.654489 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0330441 | 0.782514 | 0.815558 | [0.056 0.944] | 0.706 | -| (0.65, 35) | 0.068127 | 0.623873 | 0.692 | [0.044 0.306 0.002 0.648] | 0.0475416 | 0.760438 | 0.80798 | [0.046 0.954] | 0.692 | -| (0.65, 36) | 0.05139 | 0.65561 | 0.707 | [0.058 0.292 0.001 0.649] | 0.0342364 | 0.781603 | 0.815839 | [0.059 0.941] | 0.707 | -| (0.65, 37) | 0.0553304 | 0.64467 | 0.7 | [0.05 0.3 0. 0.65] | 0.0339875 | 0.778513 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 38) | 0.0220005 | 0.683 | 0.705 | [0.058 0.292 0.003 0.647] | 0.0105615 | 0.803787 | 0.814349 | [0.061 0.939] | 0.705 | -| (0.65, 39) | 0.0344814 | 0.684519 | 0.719 | [0.069 0.281 0. 0.65 ] | 0.0200217 | 0.802243 | 0.822264 | [0.069 0.931] | 0.719 | -| (0.65, 40) | 0.0192848 | 0.687715 | 0.707 | [0.058 0.292 0.001 0.649] | 0.0118281 | 0.804011 | 0.815839 | [0.059 0.941] | 0.707 | -| (0.65, 41) | 0.0629462 | 0.641054 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0417315 | 0.772805 | 0.814536 | [0.054 0.946] | 0.704 | -| (0.65, 42) | 0.0509591 | 0.648041 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0323075 | 0.779685 | 0.811993 | [0.049 0.951] | 0.699 | -| (0.65, 43) | 0.0284053 | 0.684595 | 0.713 | [0.063 0.287 0. 0.65 ] | 0.0186126 | 0.800543 | 0.819156 | [0.063 0.937] | 0.713 | -| (0.65, 44) | 0.0542485 | 0.646751 | 0.701 | [0.051 0.299 0. 0.65 ] | 0.0377042 | 0.775304 | 0.813008 | [0.051 0.949] | 0.701 | -| (0.65, 45) | 0.0473474 | 0.648653 | 0.696 | [0.046 0.304 0. 0.65 ] | 0.0312429 | 0.779231 | 0.810474 | [0.046 0.954] | 0.696 | -| (0.65, 46) | 0.0573545 | 0.647646 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0328666 | 0.78218 | 0.815047 | [0.055 0.945] | 0.705 | -| (0.65, 47) | 0.0778962 | 0.630104 | 0.708 | [0.058 0.292 0. 0.65 ] | 0.0481544 | 0.768428 | 0.816583 | [0.058 0.942] | 0.708 | -| (0.65, 48) | 0.0636552 | 0.634345 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.042687 | 0.768799 | 0.811486 | [0.048 0.952] | 0.698 | -| (0.65, 49) | 0.0684301 | 0.62757 | 0.696 | [0.046 0.304 0. 0.65 ] | 0.0417784 | 0.768695 | 0.810474 | [0.046 0.954] | 0.696 | -| (0.65, 50) | 0.0600838 | 0.648916 | 0.709 | [0.059 0.291 0. 0.65 ] | 0.0338159 | 0.78328 | 0.817096 | [0.059 0.941] | 0.709 | -| (0.65, 51) | 0.0315922 | 0.670408 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.0197787 | 0.793738 | 0.813517 | [0.052 0.948] | 0.702 | -| (0.65, 52) | 0.0805029 | 0.622497 | 0.703 | [0.053 0.297 0. 0.65 ] | 0.0511013 | 0.762925 | 0.814026 | [0.053 0.947] | 0.703 | -| (0.65, 53) | 0.0684217 | 0.642578 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.0392399 | 0.778885 | 0.818125 | [0.061 0.939] | 0.711 | -| (0.65, 54) | 0.0478134 | 0.656187 | 0.704 | [0.055 0.295 0.001 0.649] | 0.0278887 | 0.786415 | 0.814304 | [0.056 0.944] | 0.704 | -| (0.65, 55) | 0.042591 | 0.654409 | 0.697 | [0.048 0.302 0.001 0.649] | 0.0266163 | 0.784127 | 0.810743 | [0.049 0.951] | 0.697 | -| (0.65, 56) | 0.029453 | 0.676547 | 0.706 | [0.057 0.293 0.001 0.649] | 0.0176713 | 0.797655 | 0.815327 | [0.058 0.942] | 0.706 | -| (0.65, 57) | 0.0740491 | 0.622951 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0467645 | 0.764215 | 0.810979 | [0.047 0.953] | 0.697 | -| (0.65, 58) | 0.0721752 | 0.626825 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0467288 | 0.765264 | 0.811993 | [0.049 0.951] | 0.699 | -| (0.65, 59) | 0.0720319 | 0.631968 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0479897 | 0.766547 | 0.814536 | [0.054 0.946] | 0.704 | -| (0.65, 60) | 0.0632844 | 0.629716 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0401505 | 0.76881 | 0.808961 | [0.043 0.957] | 0.693 | -| (0.65, 61) | 0.0312424 | 0.673758 | 0.705 | [0.056 0.294 0.001 0.649] | 0.0161445 | 0.79867 | 0.814815 | [0.057 0.943] | 0.705 | -| (0.65, 62) | 0.0886932 | 0.611307 | 0.7 | [0.05 0.3 0. 0.65] | 0.0558643 | 0.756636 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 63) | 0.0666069 | 0.627393 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0392557 | 0.770209 | 0.809465 | [0.044 0.956] | 0.694 | -| (0.65, 64) | 0.0493815 | 0.650619 | 0.7 | [0.05 0.3 0. 0.65] | 0.033134 | 0.779366 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 65) | 0.0743474 | 0.627653 | 0.702 | [0.053 0.297 0.001 0.649] | 0.0455442 | 0.767739 | 0.813283 | [0.054 0.946] | 0.702 | -| (0.65, 66) | 0.0590576 | 0.642942 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.0378606 | 0.775656 | 0.813517 | [0.052 0.948] | 0.702 | -| (0.65, 67) | 0.0504622 | 0.640538 | 0.691 | [0.042 0.308 0.001 0.649] | 0.0288882 | 0.778828 | 0.807716 | [0.043 0.957] | 0.691 | -| (0.65, 68) | 0.0491287 | 0.651871 | 0.701 | [0.051 0.299 0. 0.65 ] | 0.0298382 | 0.78317 | 0.813008 | [0.051 0.949] | 0.701 | -| (0.65, 69) | 0.0671233 | 0.625877 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0465888 | 0.762372 | 0.808961 | [0.043 0.957] | 0.693 | -| (0.65, 70) | 0.0581822 | 0.649818 | 0.708 | [0.059 0.291 0.001 0.649] | 0.039018 | 0.777334 | 0.816352 | [0.06 0.94] | 0.708 | -| (0.65, 71) | 0.0656468 | 0.639353 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0450479 | 0.769999 | 0.815047 | [0.055 0.945] | 0.705 | -| (0.65, 72) | 0.042696 | 0.659304 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.029769 | 0.783748 | 0.813517 | [0.052 0.948] | 0.702 | -| (0.65, 73) | 0.0621281 | 0.642872 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0356486 | 0.779398 | 0.815047 | [0.055 0.945] | 0.705 | -| (0.65, 74) | 0.0614174 | 0.638583 | 0.7 | [0.05 0.3 0. 0.65] | 0.0417064 | 0.770794 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 75) | 0.0690815 | 0.636919 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0382886 | 0.77727 | 0.815558 | [0.056 0.944] | 0.706 | -| (0.65, 76) | 0.0663326 | 0.632667 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0417255 | 0.770267 | 0.811993 | [0.049 0.951] | 0.699 | -| (0.65, 77) | 0.0329796 | 0.66402 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0208125 | 0.790167 | 0.810979 | [0.047 0.953] | 0.697 | -| (0.65, 78) | 0.0305704 | 0.66243 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0199287 | 0.789032 | 0.808961 | [0.043 0.957] | 0.693 | -| (0.65, 79) | 0.0591355 | 0.651865 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.033893 | 0.784232 | 0.818125 | [0.061 0.939] | 0.711 | -| (0.65, 80) | 0.0828419 | 0.621158 | 0.704 | [0.055 0.295 0.001 0.649] | 0.0513442 | 0.762959 | 0.814304 | [0.056 0.944] | 0.704 | -| (0.65, 81) | 0.0405789 | 0.653421 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0252821 | 0.784182 | 0.809465 | [0.044 0.956] | 0.694 | -| (0.65, 82) | 0.0552317 | 0.631768 | 0.687 | [0.037 0.313 0. 0.65 ] | 0.0343555 | 0.771596 | 0.805952 | [0.037 0.963] | 0.687 | -| (0.65, 83) | 0.0558149 | 0.656185 | 0.712 | [0.062 0.288 0. 0.65 ] | 0.032602 | 0.786038 | 0.81864 | [0.062 0.938] | 0.712 | -| (0.65, 84) | 0.0477823 | 0.663218 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.025912 | 0.792213 | 0.818125 | [0.061 0.939] | 0.711 | -| (0.65, 85) | 0.0763267 | 0.624673 | 0.701 | [0.052 0.298 0.001 0.649] | 0.0468193 | 0.765955 | 0.812774 | [0.053 0.947] | 0.701 | -| (0.65, 86) | 0.0360446 | 0.657955 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0189634 | 0.790501 | 0.809465 | [0.044 0.956] | 0.694 | -| (0.65, 87) | 0.0756674 | 0.622333 | 0.698 | [0.05 0.3 0.002 0.648] | 0.0459044 | 0.765109 | 0.811014 | [0.052 0.948] | 0.698 | -| (0.65, 88) | 0.071613 | 0.627387 | 0.699 | [0.051 0.299 0.002 0.648] | 0.0411009 | 0.770421 | 0.811522 | [0.053 0.947] | 0.699 | -| (0.65, 89) | 0.0559207 | 0.652079 | 0.708 | [0.059 0.291 0.001 0.649] | 0.0337589 | 0.782593 | 0.816352 | [0.06 0.94] | 0.708 | -| (0.65, 90) | 0.0897853 | 0.600215 | 0.69 | [0.043 0.307 0.003 0.647] | 0.0598717 | 0.746861 | 0.806733 | [0.046 0.954] | 0.69 | -| (0.65, 91) | 0.0652016 | 0.625798 | 0.691 | [0.041 0.309 0. 0.65 ] | 0.0389561 | 0.768999 | 0.807955 | [0.041 0.959] | 0.691 | -| (0.65, 92) | 0.0780821 | 0.632918 | 0.711 | [0.062 0.288 0.001 0.649] | 0.0459551 | 0.77194 | 0.817895 | [0.063 0.937] | 0.711 | -| (0.65, 93) | 0.0369541 | 0.667046 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0196589 | 0.794877 | 0.814536 | [0.054 0.946] | 0.704 | -| (0.65, 94) | 0.0868486 | 0.613151 | 0.7 | [0.05 0.3 0. 0.65] | 0.052447 | 0.760053 | 0.8125 | [0.05 0.95] | 0.7 | -| (0.65, 95) | 0.0358453 | 0.659155 | 0.695 | [0.046 0.304 0.001 0.649] | 0.0240181 | 0.785714 | 0.809732 | [0.047 0.953] | 0.695 | -| (0.65, 96) | 0.0577789 | 0.650221 | 0.708 | [0.058 0.292 0. 0.65 ] | 0.0355455 | 0.781037 | 0.816583 | [0.058 0.942] | 0.708 | -| (0.65, 97) | 0.0486151 | 0.645385 | 0.694 | [0.045 0.305 0.001 0.649] | 0.0316332 | 0.777594 | 0.809227 | [0.046 0.954] | 0.694 | -| (0.65, 98) | 0.04014 | 0.65786 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0231171 | 0.788369 | 0.811486 | [0.048 0.952] | 0.698 | -| (0.65, 99) | 0.0660544 | 0.650946 | 0.717 | [0.069 0.281 0.002 0.648] | 0.0386239 | 0.782149 | 0.820773 | [0.071 0.929] | 0.717 | -| (0.7, 0) | 0.0257237 | 0.713276 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0158348 | 0.827031 | 0.842866 | [0.039 0.961] | 0.739 | -| (0.7, 1) | 0.0460058 | 0.694994 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0269198 | 0.816962 | 0.843882 | [0.041 0.959] | 0.741 | -| (0.7, 2) | 0.042295 | 0.707705 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0283674 | 0.819934 | 0.848301 | [0.052 0.948] | 0.75 | -| (0.7, 3) | 0.0288274 | 0.716173 | 0.745 | [0.048 0.252 0.003 0.697] | 0.0176749 | 0.827686 | 0.845361 | [0.051 0.949] | 0.745 | -| (0.7, 4) | 0.018103 | 0.731897 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0117766 | 0.836524 | 0.848301 | [0.052 0.948] | 0.75 | -| (0.7, 5) | 0.0264212 | 0.724579 | 0.751 | [0.051 0.249 0. 0.7 ] | 0.0160402 | 0.832959 | 0.848999 | [0.051 0.949] | 0.751 | -| (0.7, 6) | 0.0509385 | 0.697061 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0314891 | 0.815784 | 0.847273 | [0.05 0.95] | 0.748 | -| (0.7, 7) | 0.0454696 | 0.71053 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.0292168 | 0.822365 | 0.851582 | [0.056 0.944] | 0.756 | -| (0.7, 8) | 0.0431997 | 0.7028 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0222566 | 0.824176 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 9) | 0.0663396 | 0.67366 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0383664 | 0.805007 | 0.843373 | [0.04 0.96] | 0.74 | -| (0.7, 10) | 0.0436339 | 0.702366 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.021438 | 0.824995 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 11) | 0.0669556 | 0.677044 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0409375 | 0.804286 | 0.845224 | [0.046 0.954] | 0.744 | -| (0.7, 12) | 0.0236731 | 0.732327 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.0114491 | 0.840132 | 0.851582 | [0.056 0.944] | 0.756 | -| (0.7, 13) | 0.0543776 | 0.695622 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0311781 | 0.817123 | 0.848301 | [0.052 0.948] | 0.75 | -| (0.7, 14) | 0.0219647 | 0.722035 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0127436 | 0.83248 | 0.845224 | [0.046 0.954] | 0.744 | -| (0.7, 15) | 0.04204 | 0.70096 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0265375 | 0.818363 | 0.8449 | [0.043 0.957] | 0.743 | -| (0.7, 16) | 0.0428157 | 0.693184 | 0.736 | [0.037 0.263 0.001 0.699] | 0.0243824 | 0.816773 | 0.841155 | [0.038 0.962] | 0.736 | -| (0.7, 17) | 0.0241334 | 0.712867 | 0.737 | [0.038 0.262 0.001 0.699] | 0.0143424 | 0.827319 | 0.841662 | [0.039 0.961] | 0.737 | -| (0.7, 18) | 0.0369606 | 0.702039 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0251595 | 0.817706 | 0.842866 | [0.039 0.961] | 0.739 | -| (0.7, 19) | 0.0357608 | 0.708239 | 0.744 | [0.046 0.254 0.002 0.698] | 0.0229315 | 0.822105 | 0.845036 | [0.048 0.952] | 0.744 | -| (0.7, 20) | 0.0544485 | 0.688552 | 0.743 | [0.044 0.256 0.001 0.699] | 0.0318225 | 0.81289 | 0.844713 | [0.045 0.955] | 0.743 | -| (0.7, 21) | 0.0619287 | 0.696071 | 0.758 | [0.058 0.242 0. 0.7 ] | 0.0351504 | 0.817468 | 0.852619 | [0.058 0.942] | 0.758 | -| (0.7, 22) | 0.04091 | 0.69509 | 0.736 | [0.037 0.263 0.001 0.699] | 0.0261895 | 0.814966 | 0.841155 | [0.038 0.962] | 0.736 | -| (0.7, 23) | 0.0581635 | 0.696836 | 0.755 | [0.055 0.245 0. 0.7 ] | 0.0322321 | 0.818832 | 0.851064 | [0.055 0.945] | 0.755 | -| (0.7, 24) | 0.031157 | 0.713843 | 0.745 | [0.046 0.254 0.001 0.699] | 0.020101 | 0.825634 | 0.845735 | [0.047 0.953] | 0.745 | -| (0.7, 25) | 0.0699032 | 0.679097 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0441162 | 0.803855 | 0.847971 | [0.049 0.951] | 0.749 | -| (0.7, 26) | 0.0365515 | 0.704449 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.02384 | 0.820042 | 0.843882 | [0.041 0.959] | 0.741 | -| (0.7, 27) | 0.051094 | 0.686906 | 0.738 | [0.04 0.26 0.002 0.698] | 0.0296508 | 0.812327 | 0.841978 | [0.042 0.958] | 0.738 | -| (0.7, 28) | 0.0580445 | 0.695955 | 0.754 | [0.054 0.246 0. 0.7 ] | 0.0329645 | 0.817582 | 0.850547 | [0.054 0.946] | 0.754 | -| (0.7, 29) | 0.0581874 | 0.687813 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0329015 | 0.813531 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 30) | 0.0596123 | 0.683388 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0371841 | 0.807716 | 0.8449 | [0.043 0.957] | 0.743 | -| (0.7, 31) | 0.0457276 | 0.695272 | 0.741 | [0.043 0.257 0.002 0.698] | 0.0284045 | 0.8151 | 0.843505 | [0.045 0.955] | 0.741 | -| (0.7, 32) | 0.0302288 | 0.712771 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0195378 | 0.825363 | 0.8449 | [0.043 0.957] | 0.743 | -| (0.7, 33) | 0.0102201 | 0.73978 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.00445961 | 0.844025 | 0.848485 | [0.05 0.95] | 0.75 | -| (0.7, 34) | 0.0382468 | 0.706753 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0235733 | 0.822348 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 35) | 0.0399682 | 0.706032 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0243874 | 0.822045 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 36) | 0.0552478 | 0.685752 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0321503 | 0.811732 | 0.843882 | [0.041 0.959] | 0.741 | -| (0.7, 37) | 0.0626886 | 0.683311 | 0.746 | [0.047 0.253 0.001 0.699] | 0.0357774 | 0.81047 | 0.846247 | [0.048 0.952] | 0.746 | -| (0.7, 38) | 0.0941167 | 0.653883 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0630955 | 0.784362 | 0.847458 | [0.048 0.952] | 0.748 | -| (0.7, 39) | 0.0540688 | 0.684931 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0299878 | 0.812878 | 0.842866 | [0.039 0.961] | 0.739 | -| (0.7, 40) | 0.0768883 | 0.673112 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.0440604 | 0.804424 | 0.848485 | [0.05 0.95] | 0.75 | -| (0.7, 41) | 0.0345316 | 0.705468 | 0.74 | [0.042 0.258 0.002 0.698] | 0.0216253 | 0.82137 | 0.842995 | [0.044 0.956] | 0.74 | -| (0.7, 42) | 0.0386848 | 0.702315 | 0.741 | [0.042 0.258 0.001 0.699] | 0.0241046 | 0.819589 | 0.843693 | [0.043 0.957] | 0.741 | -| (0.7, 43) | 0.0599738 | 0.673026 | 0.733 | [0.034 0.266 0.001 0.699] | 0.0351087 | 0.804531 | 0.83964 | [0.035 0.965] | 0.733 | -| (0.7, 44) | 0.0584178 | 0.686582 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0322826 | 0.813639 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 45) | 0.0447354 | 0.700265 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0272621 | 0.818659 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 46) | 0.0340733 | 0.718927 | 0.753 | [0.053 0.247 0. 0.7 ] | 0.0217421 | 0.828288 | 0.85003 | [0.053 0.947] | 0.753 | -| (0.7, 47) | 0.0335992 | 0.719401 | 0.753 | [0.053 0.247 0. 0.7 ] | 0.020345 | 0.829685 | 0.85003 | [0.053 0.947] | 0.753 | -| (0.7, 48) | 0.0205774 | 0.714423 | 0.735 | [0.036 0.264 0.001 0.699] | 0.0129594 | 0.82769 | 0.840649 | [0.037 0.963] | 0.735 | -| (0.7, 49) | 0.0587974 | 0.686203 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0339661 | 0.811955 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 50) | 0.0351737 | 0.719826 | 0.755 | [0.056 0.244 0.001 0.699] | 0.0199776 | 0.830905 | 0.850883 | [0.057 0.943] | 0.755 | -| (0.7, 51) | 0.0267817 | 0.728218 | 0.755 | [0.055 0.245 0. 0.7 ] | 0.0161224 | 0.834941 | 0.851064 | [0.055 0.945] | 0.755 | -| (0.7, 52) | 0.0472808 | 0.698719 | 0.746 | [0.047 0.253 0.001 0.699] | 0.0257035 | 0.820544 | 0.846247 | [0.048 0.952] | 0.746 | -| (0.7, 53) | 0.0651052 | 0.670895 | 0.736 | [0.036 0.264 0. 0.7 ] | 0.0414622 | 0.799884 | 0.841346 | [0.036 0.964] | 0.736 | -| (0.7, 54) | 0.0266103 | 0.71839 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0163738 | 0.829548 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 55) | 0.0556272 | 0.685373 | 0.741 | [0.042 0.258 0.001 0.699] | 0.0349463 | 0.808747 | 0.843693 | [0.043 0.957] | 0.741 | -| (0.7, 56) | 0.0522764 | 0.689724 | 0.742 | [0.044 0.256 0.002 0.698] | 0.0302049 | 0.81381 | 0.844015 | [0.046 0.954] | 0.742 | -| (0.7, 57) | 0.0489347 | 0.701065 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.0319756 | 0.816509 | 0.848485 | [0.05 0.95] | 0.75 | -| (0.7, 58) | 0.0572132 | 0.690787 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0348496 | 0.812608 | 0.847458 | [0.048 0.952] | 0.748 | -| (0.7, 59) | 0.0616845 | 0.687316 | 0.749 | [0.052 0.248 0.003 0.697] | 0.0364798 | 0.810937 | 0.847416 | [0.055 0.945] | 0.749 | -| (0.7, 60) | 0.0266481 | 0.719352 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0112287 | 0.835204 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 61) | 0.0431471 | 0.703853 | 0.747 | [0.047 0.253 0. 0.7 ] | 0.0277927 | 0.819152 | 0.846945 | [0.047 0.953] | 0.747 | -| (0.7, 62) | 0.058813 | 0.695187 | 0.754 | [0.055 0.245 0.001 0.699] | 0.0385337 | 0.811831 | 0.850365 | [0.056 0.944] | 0.754 | -| (0.7, 63) | 0.044278 | 0.703722 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0263027 | 0.82097 | 0.847273 | [0.05 0.95] | 0.748 | -| (0.7, 64) | 0.0824936 | 0.657506 | 0.74 | [0.041 0.259 0.001 0.699] | 0.0498315 | 0.793353 | 0.843185 | [0.042 0.958] | 0.74 | -| (0.7, 65) | 0.0487397 | 0.69226 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0273824 | 0.8165 | 0.843882 | [0.041 0.959] | 0.741 | -| (0.7, 66) | 0.0456017 | 0.700398 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0291693 | 0.817264 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 67) | 0.0599507 | 0.682049 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.0336685 | 0.810722 | 0.844391 | [0.042 0.958] | 0.742 | -| (0.7, 68) | 0.0606527 | 0.686347 | 0.747 | [0.049 0.251 0.002 0.698] | 0.0362096 | 0.810364 | 0.846574 | [0.051 0.949] | 0.747 | -| (0.7, 69) | 0.0634868 | 0.670513 | 0.734 | [0.034 0.266 0. 0.7 ] | 0.0382688 | 0.802067 | 0.840336 | [0.034 0.966] | 0.734 | -| (0.7, 70) | 0.0314017 | 0.715598 | 0.747 | [0.049 0.251 0.002 0.698] | 0.0198875 | 0.826686 | 0.846574 | [0.051 0.949] | 0.747 | -| (0.7, 71) | 0.0570147 | 0.693985 | 0.751 | [0.051 0.249 0. 0.7 ] | 0.0312233 | 0.817776 | 0.848999 | [0.051 0.949] | 0.751 | -| (0.7, 72) | 0.0439817 | 0.690018 | 0.734 | [0.035 0.265 0.001 0.699] | 0.0238598 | 0.816284 | 0.840144 | [0.036 0.964] | 0.734 | -| (0.7, 73) | 0.0455512 | 0.698449 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0244858 | 0.820738 | 0.845224 | [0.046 0.954] | 0.744 | -| (0.7, 74) | 0.0488415 | 0.697158 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0275018 | 0.818931 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 75) | 0.040398 | 0.701602 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.025014 | 0.819377 | 0.844391 | [0.042 0.958] | 0.742 | -| (0.7, 76) | 0.0338518 | 0.711148 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0211698 | 0.824752 | 0.845921 | [0.045 0.955] | 0.745 | -| (0.7, 77) | 0.0635197 | 0.67848 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.0368786 | 0.807512 | 0.844391 | [0.042 0.958] | 0.742 | -| (0.7, 78) | 0.0168386 | 0.730161 | 0.747 | [0.048 0.252 0.001 0.699] | 0.00937337 | 0.837386 | 0.84676 | [0.049 0.951] | 0.747 | -| (0.7, 79) | 0.0418986 | 0.714101 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.025139 | 0.826443 | 0.851582 | [0.056 0.944] | 0.756 | -| (0.7, 80) | 0.037095 | 0.711905 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0236265 | 0.824344 | 0.847971 | [0.049 0.951] | 0.749 | -| (0.7, 81) | 0.0355185 | 0.710481 | 0.746 | [0.047 0.253 0.001 0.699] | 0.023162 | 0.823085 | 0.846247 | [0.048 0.952] | 0.746 | -| (0.7, 82) | 0.051202 | 0.689798 | 0.741 | [0.043 0.257 0.002 0.698] | 0.0302617 | 0.813243 | 0.843505 | [0.045 0.955] | 0.741 | -| (0.7, 83) | 0.0127804 | 0.72822 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.00508636 | 0.838795 | 0.843882 | [0.041 0.959] | 0.741 | -| (0.7, 84) | 0.0571513 | 0.682849 | 0.74 | [0.042 0.258 0.002 0.698] | 0.0317753 | 0.81122 | 0.842995 | [0.044 0.956] | 0.74 | -| (0.7, 85) | 0.03966 | 0.70434 | 0.744 | [0.044 0.256 0. 0.7 ] | 0.0248263 | 0.820584 | 0.845411 | [0.044 0.956] | 0.744 | -| (0.7, 86) | 0.0395805 | 0.70942 | 0.749 | [0.05 0.25 0.001 0.699] | 0.0253555 | 0.822431 | 0.847787 | [0.051 0.949] | 0.749 | -| (0.7, 87) | 0.0439101 | 0.70109 | 0.745 | [0.046 0.254 0.001 0.699] | 0.0245246 | 0.82121 | 0.845735 | [0.047 0.953] | 0.745 | -| (0.7, 88) | 0.0251373 | 0.720863 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0166981 | 0.829735 | 0.846433 | [0.046 0.954] | 0.746 | -| (0.7, 89) | 0.0427015 | 0.700298 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0273584 | 0.817542 | 0.8449 | [0.043 0.957] | 0.743 | -| (0.7, 90) | 0.0521871 | 0.687813 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0297551 | 0.813618 | 0.843373 | [0.04 0.96] | 0.74 | -| (0.7, 91) | 0.0432122 | 0.695788 | 0.739 | [0.04 0.26 0.001 0.699] | 0.0262949 | 0.816381 | 0.842676 | [0.041 0.959] | 0.739 | -| (0.7, 92) | 0.0378549 | 0.706145 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0179952 | 0.827228 | 0.845224 | [0.046 0.954] | 0.744 | -| (0.7, 93) | 0.0545061 | 0.693494 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0338916 | 0.813566 | 0.847458 | [0.048 0.952] | 0.748 | -| (0.7, 94) | 0.0669098 | 0.67309 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0398733 | 0.8035 | 0.843373 | [0.04 0.96] | 0.74 | -| (0.7, 95) | 0.0615466 | 0.686453 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0419311 | 0.805342 | 0.847273 | [0.05 0.95] | 0.748 | -| (0.7, 96) | 0.0643432 | 0.684657 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0391777 | 0.808793 | 0.847971 | [0.049 0.951] | 0.749 | -| (0.7, 97) | 0.0318799 | 0.69612 | 0.728 | [0.03 0.27 0.002 0.698] | 0.0182705 | 0.81866 | 0.83693 | [0.032 0.968] | 0.728 | -| (0.7, 98) | 0.0286473 | 0.719353 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0153222 | 0.831951 | 0.847273 | [0.05 0.95] | 0.748 | -| (0.7, 99) | 0.048621 | 0.683379 | 0.732 | [0.032 0.268 0. 0.7 ] | 0.0314276 | 0.807901 | 0.839329 | [0.032 0.968] | 0.732 | -| (0.75, 0) | 0.0290489 | 0.763951 | 0.793 | [0.043 0.207 0. 0.75 ] | 0.0188064 | 0.859928 | 0.878735 | [0.043 0.957] | 0.793 | -| (0.75, 1) | 0.0213086 | 0.783691 | 0.805 | [0.055 0.195 0. 0.75 ] | 0.0131107 | 0.871845 | 0.884956 | [0.055 0.945] | 0.805 | -| (0.75, 2) | 0.0258045 | 0.769195 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0152213 | 0.864403 | 0.879624 | [0.047 0.953] | 0.795 | -| (0.75, 3) | 0.0499394 | 0.737061 | 0.787 | [0.037 0.213 0. 0.75 ] | 0.0290134 | 0.846643 | 0.875657 | [0.037 0.963] | 0.787 | -| (0.75, 4) | 0.0376303 | 0.74437 | 0.782 | [0.032 0.218 0. 0.75 ] | 0.0197061 | 0.853402 | 0.873108 | [0.032 0.968] | 0.782 | -| (0.75, 5) | 0.0469517 | 0.733048 | 0.78 | [0.03 0.22 0. 0.75] | 0.0263483 | 0.845745 | 0.872093 | [0.03 0.97] | 0.78 | -| (0.75, 6) | 0.0370853 | 0.757915 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0226526 | 0.856972 | 0.879624 | [0.047 0.953] | 0.795 | -| (0.75, 7) | 0.0310667 | 0.756933 | 0.788 | [0.038 0.212 0. 0.75 ] | 0.0161298 | 0.860038 | 0.876168 | [0.038 0.962] | 0.788 | -| (0.75, 8) | 0.0332692 | 0.761731 | 0.795 | [0.047 0.203 0.002 0.748] | 0.0202977 | 0.859185 | 0.879483 | [0.049 0.951] | 0.795 | -| (0.75, 9) | 0.0136944 | 0.777306 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.00761378 | 0.870092 | 0.877706 | [0.041 0.959] | 0.791 | -| (0.75, 10) | 0.0441472 | 0.740853 | 0.785 | [0.036 0.214 0.001 0.749] | 0.0265147 | 0.847975 | 0.874489 | [0.037 0.963] | 0.785 | -| (0.75, 11) | 0.00733119 | 0.775669 | 0.783 | [0.034 0.216 0.001 0.749] | 0.00279004 | 0.870679 | 0.873469 | [0.035 0.965] | 0.783 | -| (0.75, 12) | 0.0121353 | 0.770865 | 0.783 | [0.035 0.215 0.002 0.748] | 0.00702411 | 0.866298 | 0.873322 | [0.037 0.963] | 0.783 | -| (0.75, 13) | 0.0565286 | 0.731471 | 0.788 | [0.039 0.211 0.001 0.749] | 0.0321032 | 0.84392 | 0.876023 | [0.04 0.96] | 0.788 | -| (0.75, 14) | 0.0152813 | 0.769719 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.00888373 | 0.865752 | 0.874636 | [0.035 0.965] | 0.785 | -| (0.75, 15) | 0.0153936 | 0.772606 | 0.788 | [0.039 0.211 0.001 0.749] | 0.00762887 | 0.868395 | 0.876023 | [0.04 0.96] | 0.788 | -| (0.75, 16) | 0.0119624 | 0.778038 | 0.79 | [0.042 0.208 0.002 0.748] | 0.00732564 | 0.869579 | 0.876905 | [0.044 0.956] | 0.79 | -| (0.75, 17) | 0.0485998 | 0.7364 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.0266139 | 0.848022 | 0.874636 | [0.035 0.965] | 0.785 | -| (0.75, 18) | 0.026567 | 0.751433 | 0.778 | [0.03 0.22 0.002 0.748] | 0.0149359 | 0.855844 | 0.87078 | [0.032 0.968] | 0.778 | -| (0.75, 19) | 0.0434839 | 0.746516 | 0.79 | [0.04 0.21 0. 0.75] | 0.0227465 | 0.854447 | 0.877193 | [0.04 0.96] | 0.79 | -| (0.75, 20) | 0.00358118 | 0.779419 | 0.783 | [0.035 0.215 0.002 0.748] | 0.00161839 | 0.871703 | 0.873322 | [0.037 0.963] | 0.783 | -| (0.75, 21) | 0.0250464 | 0.757954 | 0.783 | [0.033 0.217 0. 0.75 ] | 0.0141667 | 0.85945 | 0.873617 | [0.033 0.967] | 0.783 | -| (0.75, 22) | 0.00593084 | 0.793069 | 0.799 | [0.049 0.201 0. 0.75 ] | 0.00302196 | 0.878812 | 0.881834 | [0.049 0.951] | 0.799 | -| (0.75, 23) | 0.0645649 | 0.724435 | 0.789 | [0.041 0.209 0.002 0.748] | 0.0377325 | 0.838659 | 0.876391 | [0.043 0.957] | 0.789 | -| (0.75, 24) | 0.0404161 | 0.745584 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.022775 | 0.852371 | 0.875146 | [0.036 0.964] | 0.786 | -| (0.75, 25) | 0.0593057 | 0.727694 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0333993 | 0.842112 | 0.875511 | [0.039 0.961] | 0.787 | -| (0.75, 26) | 0.0611953 | 0.737805 | 0.799 | [0.049 0.201 0. 0.75 ] | 0.0339211 | 0.847913 | 0.881834 | [0.049 0.951] | 0.799 | -| (0.75, 27) | 0.0335309 | 0.764469 | 0.798 | [0.049 0.201 0.001 0.749] | 0.0187754 | 0.862401 | 0.881176 | [0.05 0.95] | 0.798 | -| (0.75, 28) | 0.0467563 | 0.734244 | 0.781 | [0.032 0.218 0.001 0.749] | 0.0259504 | 0.846502 | 0.872452 | [0.033 0.967] | 0.781 | -| (0.75, 29) | 0.0367851 | 0.757215 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0220216 | 0.857086 | 0.879108 | [0.046 0.954] | 0.794 | -| (0.75, 30) | 0.0561704 | 0.73983 | 0.796 | [0.047 0.203 0.001 0.749] | 0.0301069 | 0.850034 | 0.880141 | [0.048 0.952] | 0.796 | -| (0.75, 31) | 0.036463 | 0.759537 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0194914 | 0.86079 | 0.880282 | [0.046 0.954] | 0.796 | -| (0.75, 32) | 0.0115699 | 0.76443 | 0.776 | [0.027 0.223 0.001 0.749] | 0.00582438 | 0.864094 | 0.869919 | [0.028 0.972] | 0.776 | -| (0.75, 33) | 0.0385213 | 0.755479 | 0.794 | [0.044 0.206 0. 0.75 ] | 0.0240675 | 0.855182 | 0.87925 | [0.044 0.956] | 0.794 | -| (0.75, 34) | 0.0279748 | 0.762025 | 0.79 | [0.04 0.21 0. 0.75] | 0.0177323 | 0.859461 | 0.877193 | [0.04 0.96] | 0.79 | -| (0.75, 35) | 0.0437401 | 0.73526 | 0.779 | [0.031 0.219 0.002 0.748] | 0.0250926 | 0.846194 | 0.871287 | [0.033 0.967] | 0.779 | -| (0.75, 36) | 0.024253 | 0.758747 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0142197 | 0.85925 | 0.873469 | [0.035 0.965] | 0.783 | -| (0.75, 37) | 0.0511354 | 0.726865 | 0.778 | [0.029 0.221 0.001 0.749] | 0.0319916 | 0.838939 | 0.87093 | [0.03 0.97] | 0.778 | -| (0.75, 38) | 0.0391688 | 0.741831 | 0.781 | [0.032 0.218 0.001 0.749] | 0.0227259 | 0.849726 | 0.872452 | [0.033 0.967] | 0.781 | -| (0.75, 39) | 0.049827 | 0.737173 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0270195 | 0.848492 | 0.875511 | [0.039 0.961] | 0.787 | -| (0.75, 40) | 0.0114272 | 0.785573 | 0.797 | [0.047 0.203 0. 0.75 ] | 0.00614797 | 0.874651 | 0.880799 | [0.047 0.953] | 0.797 | -| (0.75, 41) | 0.0306715 | 0.762329 | 0.793 | [0.044 0.206 0.001 0.749] | 0.0183691 | 0.860223 | 0.878592 | [0.045 0.955] | 0.793 | -| (0.75, 42) | 0.0395072 | 0.747493 | 0.787 | [0.039 0.211 0.002 0.748] | 0.0247259 | 0.85064 | 0.875366 | [0.041 0.959] | 0.787 | -| (0.75, 43) | 0.0434636 | 0.749536 | 0.793 | [0.044 0.206 0.001 0.749] | 0.0248327 | 0.85376 | 0.878592 | [0.045 0.955] | 0.793 | -| (0.75, 44) | 0.0360129 | 0.744987 | 0.781 | [0.031 0.219 0. 0.75 ] | 0.0199793 | 0.852621 | 0.8726 | [0.031 0.969] | 0.781 | -| (0.75, 45) | 0.0280378 | 0.755962 | 0.784 | [0.035 0.215 0.001 0.749] | 0.0137074 | 0.860272 | 0.873979 | [0.036 0.964] | 0.784 | -| (0.75, 46) | 0.0215758 | 0.774424 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0126768 | 0.867605 | 0.880282 | [0.046 0.954] | 0.796 | -| (0.75, 47) | 0.0523155 | 0.739684 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0300079 | 0.848212 | 0.87822 | [0.042 0.958] | 0.792 | -| (0.75, 48) | 0.0602019 | 0.728798 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0383976 | 0.838138 | 0.876536 | [0.041 0.959] | 0.789 | -| (0.75, 49) | 0.047031 | 0.739969 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0290799 | 0.846431 | 0.875511 | [0.039 0.961] | 0.787 | -| (0.75, 50) | 0.0273783 | 0.768622 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0135753 | 0.866706 | 0.880282 | [0.046 0.954] | 0.796 | -| (0.75, 51) | 0.02684 | 0.76216 | 0.789 | [0.041 0.209 0.002 0.748] | 0.0131259 | 0.863265 | 0.876391 | [0.043 0.957] | 0.789 | -| (0.75, 52) | 0.0400329 | 0.747967 | 0.788 | [0.039 0.211 0.001 0.749] | 0.0219925 | 0.854031 | 0.876023 | [0.04 0.96] | 0.788 | -| (0.75, 53) | 0.0356267 | 0.753373 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0208528 | 0.855828 | 0.87668 | [0.039 0.961] | 0.789 | -| (0.75, 54) | 0.0441881 | 0.744812 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0265353 | 0.850001 | 0.876536 | [0.041 0.959] | 0.789 | -| (0.75, 55) | 0.0309762 | 0.749024 | 0.78 | [0.031 0.219 0.001 0.749] | 0.0178195 | 0.854125 | 0.871944 | [0.032 0.968] | 0.78 | -| (0.75, 56) | 0.0161385 | 0.772862 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.00984521 | 0.866835 | 0.87668 | [0.039 0.961] | 0.789 | -| (0.75, 57) | 0.0170935 | 0.761907 | 0.779 | [0.03 0.22 0.001 0.749] | 0.00916119 | 0.862276 | 0.871437 | [0.031 0.969] | 0.779 | -| (0.75, 58) | 0.0354674 | 0.749533 | 0.785 | [0.036 0.214 0.001 0.749] | 0.0220898 | 0.852399 | 0.874489 | [0.037 0.963] | 0.785 | -| (0.75, 59) | 0.025201 | 0.760799 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.0151774 | 0.859968 | 0.875146 | [0.036 0.964] | 0.786 | -| (0.75, 60) | 0.0272636 | 0.755736 | 0.783 | [0.033 0.217 0. 0.75 ] | 0.0156835 | 0.857933 | 0.873617 | [0.033 0.967] | 0.783 | -| (0.75, 61) | 0.0433661 | 0.750634 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0228797 | 0.856228 | 0.879108 | [0.046 0.954] | 0.794 | -| (0.75, 62) | 0.0538387 | 0.737161 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.0292089 | 0.848497 | 0.877706 | [0.041 0.959] | 0.791 | -| (0.75, 63) | 0.0224127 | 0.764587 | 0.787 | [0.037 0.213 0. 0.75 ] | 0.0128879 | 0.862769 | 0.875657 | [0.037 0.963] | 0.787 | -| (0.75, 64) | 0.034333 | 0.754667 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0213609 | 0.855319 | 0.87668 | [0.039 0.961] | 0.789 | -| (0.75, 65) | 0.00924564 | 0.775754 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.00490077 | 0.869735 | 0.874636 | [0.035 0.965] | 0.785 | -| (0.75, 66) | 0.00451873 | 0.786481 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.00214582 | 0.87556 | 0.877706 | [0.041 0.959] | 0.791 | -| (0.75, 67) | 0.0517501 | 0.74025 | 0.792 | [0.044 0.206 0.002 0.748] | 0.028609 | 0.849325 | 0.877934 | [0.046 0.954] | 0.792 | -| (0.75, 68) | 0.0343641 | 0.754636 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0192369 | 0.857299 | 0.876536 | [0.041 0.959] | 0.789 | -| (0.75, 69) | 0.0344314 | 0.750569 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.0205979 | 0.854038 | 0.874636 | [0.035 0.965] | 0.785 | -| (0.75, 70) | 0.0261044 | 0.767896 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0139256 | 0.865182 | 0.879108 | [0.046 0.954] | 0.794 | -| (0.75, 71) | 0.0363507 | 0.753649 | 0.79 | [0.04 0.21 0. 0.75] | 0.0210632 | 0.85613 | 0.877193 | [0.04 0.96] | 0.79 | -| (0.75, 72) | 0.0405785 | 0.741422 | 0.782 | [0.035 0.215 0.003 0.747] | 0.0217776 | 0.850886 | 0.872664 | [0.038 0.962] | 0.782 | -| (0.75, 73) | 0.0170575 | 0.767942 | 0.785 | [0.036 0.214 0.001 0.749] | 0.00850885 | 0.86598 | 0.874489 | [0.037 0.963] | 0.785 | -| (0.75, 74) | 0.0375071 | 0.750493 | 0.788 | [0.039 0.211 0.001 0.749] | 0.021137 | 0.854886 | 0.876023 | [0.04 0.96] | 0.788 | -| (0.75, 75) | 0.0371426 | 0.748857 | 0.786 | [0.037 0.213 0.001 0.749] | 0.0210716 | 0.853928 | 0.875 | [0.038 0.962] | 0.786 | -| (0.75, 76) | 0.0648981 | 0.724102 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0374199 | 0.83926 | 0.87668 | [0.039 0.961] | 0.789 | -| (0.75, 77) | 0.0389063 | 0.741094 | 0.78 | [0.031 0.219 0.001 0.749] | 0.0219382 | 0.850006 | 0.871944 | [0.032 0.968] | 0.78 | -| (0.75, 78) | 0.046141 | 0.745859 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0284288 | 0.849791 | 0.87822 | [0.042 0.958] | 0.792 | -| (0.75, 79) | 0.0459182 | 0.746082 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0254524 | 0.852768 | 0.87822 | [0.042 0.958] | 0.792 | -| (0.75, 80) | 0.0367849 | 0.750215 | 0.787 | [0.039 0.211 0.002 0.748] | 0.0190088 | 0.856357 | 0.875366 | [0.041 0.959] | 0.787 | -| (0.75, 81) | 0.0214734 | 0.769527 | 0.791 | [0.042 0.208 0.001 0.749] | 0.0109792 | 0.866584 | 0.877563 | [0.043 0.957] | 0.791 | -| (0.75, 82) | 0.0606138 | 0.723386 | 0.784 | [0.034 0.216 0. 0.75 ] | 0.0346787 | 0.839447 | 0.874126 | [0.034 0.966] | 0.784 | -| (0.75, 83) | 0.0179434 | 0.767057 | 0.785 | [0.036 0.214 0.001 0.749] | 0.00789914 | 0.86659 | 0.874489 | [0.037 0.963] | 0.785 | -| (0.75, 84) | 0.0356262 | 0.747374 | 0.783 | [0.035 0.215 0.002 0.748] | 0.0210658 | 0.852256 | 0.873322 | [0.037 0.963] | 0.783 | -| (0.75, 85) | 0.0263657 | 0.756634 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0141479 | 0.859321 | 0.873469 | [0.035 0.965] | 0.783 | -| (0.75, 86) | 0.0293021 | 0.743698 | 0.773 | [0.024 0.226 0.001 0.749] | 0.017352 | 0.851054 | 0.868406 | [0.025 0.975] | 0.773 | -| (0.75, 87) | 0.026603 | 0.759397 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.016022 | 0.859124 | 0.875146 | [0.036 0.964] | 0.786 | -| (0.75, 88) | 0.0406937 | 0.747306 | 0.788 | [0.04 0.21 0.002 0.748] | 0.0228616 | 0.853017 | 0.875878 | [0.042 0.958] | 0.788 | -| (0.75, 89) | 0.0458613 | 0.732139 | 0.778 | [0.028 0.222 0. 0.75 ] | 0.0270263 | 0.844054 | 0.87108 | [0.028 0.972] | 0.778 | -| (0.75, 90) | 0.0341579 | 0.760842 | 0.795 | [0.045 0.205 0. 0.75 ] | 0.0208155 | 0.85895 | 0.879765 | [0.045 0.955] | 0.795 | -| (0.75, 91) | 0.038191 | 0.760809 | 0.799 | [0.05 0.2 0.001 0.749] | 0.0213842 | 0.860311 | 0.881695 | [0.051 0.949] | 0.799 | -| (0.75, 92) | 0.0372172 | 0.743783 | 0.781 | [0.031 0.219 0. 0.75 ] | 0.0198746 | 0.852726 | 0.8726 | [0.031 0.969] | 0.781 | -| (0.75, 93) | 0.0567933 | 0.727207 | 0.784 | [0.036 0.214 0.002 0.748] | 0.0351011 | 0.838731 | 0.873832 | [0.038 0.962] | 0.784 | -| (0.75, 94) | 0.0228502 | 0.76015 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0115927 | 0.861877 | 0.873469 | [0.035 0.965] | 0.783 | -| (0.75, 95) | 0.0450282 | 0.738972 | 0.784 | [0.034 0.216 0. 0.75 ] | 0.0242723 | 0.849854 | 0.874126 | [0.034 0.966] | 0.784 | -| (0.75, 96) | 0.0540909 | 0.743909 | 0.798 | [0.048 0.202 0. 0.75 ] | 0.0295038 | 0.851812 | 0.881316 | [0.048 0.952] | 0.798 | -| (0.75, 97) | 0.0563787 | 0.723621 | 0.78 | [0.03 0.22 0. 0.75] | 0.0350677 | 0.837025 | 0.872093 | [0.03 0.97] | 0.78 | -| (0.75, 98) | 0.028261 | 0.766739 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0140084 | 0.865616 | 0.879624 | [0.047 0.953] | 0.795 | -| (0.75, 99) | 0.019607 | 0.776393 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0109948 | 0.869287 | 0.880282 | [0.046 0.954] | 0.796 | -| (0.8, 0) | 0.0409423 | 0.793058 | 0.834 | [0.035 0.165 0.001 0.799] | 0.0217691 | 0.884127 | 0.905896 | [0.036 0.964] | 0.834 | -| (0.8, 1) | 0.00912837 | 0.815872 | 0.825 | [0.026 0.174 0.001 0.799] | 0.0048623 | 0.896435 | 0.901297 | [0.027 0.973] | 0.825 | -| (0.8, 2) | 0.0216766 | 0.806323 | 0.828 | [0.03 0.17 0.002 0.798] | 0.010707 | 0.892008 | 0.902715 | [0.032 0.968] | 0.828 | -| (0.8, 3) | 0.00863246 | 0.816368 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.00504774 | 0.896361 | 0.901408 | [0.025 0.975] | 0.825 | -| (0.8, 4) | 0.0276359 | 0.796364 | 0.824 | [0.025 0.175 0.001 0.799] | 0.0157953 | 0.884994 | 0.900789 | [0.026 0.974] | 0.824 | -| (0.8, 5) | 0.00270277 | 0.815297 | 0.818 | [0.018 0.182 0. 0.8 ] | 0.00111215 | 0.896755 | 0.897868 | [0.018 0.982] | 0.818 | -| (0.8, 6) | 0.0210912 | 0.800909 | 0.822 | [0.023 0.177 0.001 0.799] | 0.0116788 | 0.888096 | 0.899775 | [0.024 0.976] | 0.822 | -| (0.8, 7) | 0.0458864 | 0.780114 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0255575 | 0.876359 | 0.901917 | [0.026 0.974] | 0.826 | -| (0.8, 8) | 0.0325411 | 0.799459 | 0.832 | [0.033 0.167 0.001 0.799] | 0.0179992 | 0.886871 | 0.90487 | [0.034 0.966] | 0.832 | -| (0.8, 9) | 0.0374835 | 0.792517 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0210132 | 0.882942 | 0.903955 | [0.03 0.97] | 0.83 | -| (0.8, 10) | 0.0330656 | 0.787934 | 0.821 | [0.022 0.178 0.001 0.799] | 0.0178779 | 0.88139 | 0.899268 | [0.023 0.977] | 0.821 | -| (0.8, 11) | 0.005432 | 0.826568 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.00301292 | 0.901964 | 0.904977 | [0.032 0.968] | 0.832 | -| (0.8, 12) | 0.00380289 | 0.822197 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.00202778 | 0.899889 | 0.901917 | [0.026 0.974] | 0.826 | -| (0.8, 13) | 0.00720171 | 0.815798 | 0.823 | [0.023 0.177 0. 0.8 ] | 0.00305894 | 0.897335 | 0.900394 | [0.023 0.977] | 0.823 | -| (0.8, 14) | 0.0172814 | 0.807719 | 0.825 | [0.027 0.173 0.002 0.798] | 0.00767473 | 0.893511 | 0.901186 | [0.029 0.971] | 0.825 | -| (0.8, 15) | 0.0120847 | 0.816915 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.00646782 | 0.896977 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 16) | 0.0263668 | 0.792633 | 0.819 | [0.019 0.181 0. 0.8 ] | 0.0145246 | 0.883847 | 0.898372 | [0.019 0.981] | 0.819 | -| (0.8, 17) | 0.032205 | 0.797795 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0186486 | 0.885306 | 0.903955 | [0.03 0.97] | 0.83 | -| (0.8, 18) | 0.0364903 | 0.78451 | 0.821 | [0.023 0.177 0.002 0.798] | 0.0199892 | 0.879166 | 0.899155 | [0.025 0.975] | 0.821 | -| (0.8, 19) | 0.0445238 | 0.794476 | 0.839 | [0.039 0.161 0. 0.8 ] | 0.025349 | 0.883226 | 0.908575 | [0.039 0.961] | 0.839 | -| (0.8, 20) | 0.0180783 | 0.798922 | 0.817 | [0.017 0.183 0. 0.8 ] | 0.0101047 | 0.887259 | 0.897364 | [0.017 0.983] | 0.817 | -| (0.8, 21) | 0.0245807 | 0.804419 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0148101 | 0.888634 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 22) | 0.0244787 | 0.804521 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0137768 | 0.889668 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 23) | 0.0193507 | 0.802649 | 0.822 | [0.022 0.178 0. 0.8 ] | 0.0108588 | 0.889029 | 0.899888 | [0.022 0.978] | 0.822 | -| (0.8, 24) | 0.0354281 | 0.792572 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.019193 | 0.883741 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 25) | 0.021107 | 0.810893 | 0.832 | [0.033 0.167 0.001 0.799] | 0.0122716 | 0.892598 | 0.90487 | [0.034 0.966] | 0.832 | -| (0.8, 26) | 0.0189653 | 0.809035 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0110574 | 0.891877 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 27) | 0.0427835 | 0.783216 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0234921 | 0.878424 | 0.901917 | [0.026 0.974] | 0.826 | -| (0.8, 28) | 0.0388648 | 0.795135 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0206372 | 0.885365 | 0.906002 | [0.034 0.966] | 0.834 | -| (0.8, 29) | 0.0484156 | 0.780584 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0269235 | 0.876412 | 0.903335 | [0.031 0.969] | 0.829 | -| (0.8, 30) | 0.0189334 | 0.809067 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.00894551 | 0.893989 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 31) | 0.0348533 | 0.795147 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0206885 | 0.883266 | 0.903955 | [0.03 0.97] | 0.83 | -| (0.8, 32) | 0.0298236 | 0.797176 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0154133 | 0.887012 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 33) | 0.0308446 | 0.790155 | 0.821 | [0.021 0.179 0. 0.8 ] | 0.0177848 | 0.881597 | 0.899382 | [0.021 0.979] | 0.821 | -| (0.8, 34) | 0.0173788 | 0.815621 | 0.833 | [0.035 0.165 0.002 0.798] | 0.00986453 | 0.895411 | 0.905275 | [0.037 0.963] | 0.833 | -| (0.8, 35) | 0.0274772 | 0.797523 | 0.825 | [0.027 0.173 0.002 0.798] | 0.016222 | 0.884964 | 0.901186 | [0.029 0.971] | 0.825 | -| (0.8, 36) | 0.00890272 | 0.823097 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0056062 | 0.899371 | 0.904977 | [0.032 0.968] | 0.832 | -| (0.8, 37) | 0.0382181 | 0.786782 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0212881 | 0.88012 | 0.901408 | [0.025 0.975] | 0.825 | -| (0.8, 38) | 0.0243138 | 0.802686 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0120784 | 0.890347 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 39) | 0.0384636 | 0.792536 | 0.831 | [0.032 0.168 0.001 0.799] | 0.0225441 | 0.881814 | 0.904358 | [0.033 0.967] | 0.831 | -| (0.8, 40) | 0.0253619 | 0.801638 | 0.827 | [0.028 0.172 0.001 0.799] | 0.0150565 | 0.887259 | 0.902315 | [0.029 0.971] | 0.827 | -| (0.8, 41) | 0.000229561 | 0.82877 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.000929264 | 0.904374 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 42) | 0.0225886 | 0.807411 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0118405 | 0.892006 | 0.903846 | [0.032 0.968] | 0.83 | -| (0.8, 43) | 0.0194196 | 0.80858 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.00990255 | 0.893032 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 44) | 0.00644612 | 0.832554 | 0.839 | [0.04 0.16 0.001 0.799] | 0.00352805 | 0.904943 | 0.908471 | [0.041 0.959] | 0.839 | -| (0.8, 45) | 0.0505133 | 0.779487 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0279151 | 0.87604 | 0.903955 | [0.03 0.97] | 0.83 | -| (0.8, 46) | 0.0373503 | 0.79165 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.020021 | 0.883423 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 47) | 0.0360948 | 0.792905 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0211833 | 0.882152 | 0.903335 | [0.031 0.969] | 0.829 | -| (0.8, 48) | 0.017169 | 0.817831 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.00976995 | 0.896746 | 0.906516 | [0.035 0.965] | 0.835 | -| (0.8, 49) | 0.0305348 | 0.803465 | 0.834 | [0.035 0.165 0.001 0.799] | 0.0180311 | 0.887865 | 0.905896 | [0.036 0.964] | 0.834 | -| (0.8, 50) | 0.0293145 | 0.797686 | 0.827 | [0.029 0.171 0.002 0.798] | 0.0166065 | 0.885598 | 0.902205 | [0.031 0.969] | 0.827 | -| (0.8, 51) | 0.0280583 | 0.802942 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0152134 | 0.889252 | 0.904466 | [0.031 0.969] | 0.831 | -| (0.8, 52) | 0.0273535 | 0.798646 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0140662 | 0.88785 | 0.901917 | [0.026 0.974] | 0.826 | -| (0.8, 53) | 0.037029 | 0.794971 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0200333 | 0.884944 | 0.904977 | [0.032 0.968] | 0.832 | -| (0.8, 54) | 0.02648 | 0.79652 | 0.823 | [0.023 0.177 0. 0.8 ] | 0.0137327 | 0.886661 | 0.900394 | [0.023 0.977] | 0.823 | -| (0.8, 55) | 0.0147235 | 0.812277 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00601093 | 0.896414 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 56) | 0.0248399 | 0.81016 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.0143239 | 0.892192 | 0.906516 | [0.035 0.965] | 0.835 | -| (0.8, 57) | 0.0104378 | 0.837438 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00708952 | 0.909515 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 58) | 0.0244922 | 0.799508 | 0.824 | [0.024 0.176 0. 0.8 ] | 0.0127094 | 0.888191 | 0.900901 | [0.024 0.976] | 0.824 | -| (0.8, 59) | 0.0260415 | 0.800959 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0150998 | 0.887325 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 60) | 0.0267759 | 0.809224 | 0.836 | [0.036 0.164 0. 0.8 ] | 0.0160896 | 0.89094 | 0.907029 | [0.036 0.964] | 0.836 | -| (0.8, 61) | 0.0225115 | 0.797488 | 0.82 | [0.021 0.179 0.001 0.799] | 0.0122826 | 0.88648 | 0.898763 | [0.022 0.978] | 0.82 | -| (0.8, 62) | 0.0343597 | 0.79264 | 0.827 | [0.029 0.171 0.002 0.798] | 0.0190629 | 0.883142 | 0.902205 | [0.031 0.969] | 0.827 | -| (0.8, 63) | 0.0159727 | 0.813027 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0089236 | 0.894521 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 64) | 0.0416964 | 0.791304 | 0.833 | [0.033 0.167 0. 0.8 ] | 0.0222044 | 0.883285 | 0.90549 | [0.033 0.967] | 0.833 | -| (0.8, 65) | 0.0316177 | 0.796382 | 0.828 | [0.031 0.169 0.003 0.797] | 0.0189897 | 0.883615 | 0.902605 | [0.034 0.966] | 0.828 | -| (0.8, 66) | 0.0404031 | 0.794597 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.0213009 | 0.885215 | 0.906516 | [0.035 0.965] | 0.835 | -| (0.8, 67) | 0.0437875 | 0.790213 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0242558 | 0.881746 | 0.906002 | [0.034 0.966] | 0.834 | -| (0.8, 68) | 0.0164454 | 0.817555 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.00894291 | 0.897059 | 0.906002 | [0.034 0.966] | 0.834 | -| (0.8, 69) | 0.0331339 | 0.794866 | 0.828 | [0.029 0.171 0.001 0.799] | 0.0186214 | 0.884203 | 0.902825 | [0.03 0.97] | 0.828 | -| (0.8, 70) | 0.0282686 | 0.803731 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0170943 | 0.887883 | 0.904977 | [0.032 0.968] | 0.832 | -| (0.8, 71) | 0.0266922 | 0.803308 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0152941 | 0.888552 | 0.903846 | [0.032 0.968] | 0.83 | -| (0.8, 72) | 0.0192176 | 0.805782 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0112241 | 0.890184 | 0.901408 | [0.025 0.975] | 0.825 | -| (0.8, 73) | 0.0452923 | 0.778708 | 0.824 | [0.024 0.176 0. 0.8 ] | 0.0253478 | 0.875553 | 0.900901 | [0.024 0.976] | 0.824 | -| (0.8, 74) | 0.0284065 | 0.800593 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0153539 | 0.887981 | 0.903335 | [0.031 0.969] | 0.829 | -| (0.8, 75) | 0.019628 | 0.811372 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0115092 | 0.892957 | 0.904466 | [0.031 0.969] | 0.831 | -| (0.8, 76) | 0.0254376 | 0.802562 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0147003 | 0.888234 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 77) | 0.0274572 | 0.797543 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0140591 | 0.887349 | 0.901408 | [0.025 0.975] | 0.825 | -| (0.8, 78) | 0.0268882 | 0.806112 | 0.833 | [0.035 0.165 0.002 0.798] | 0.0145238 | 0.890751 | 0.905275 | [0.037 0.963] | 0.833 | -| (0.8, 79) | 0.0274169 | 0.803583 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0154312 | 0.889035 | 0.904466 | [0.031 0.969] | 0.831 | -| (0.8, 80) | 0.0421947 | 0.792805 | 0.835 | [0.037 0.163 0.002 0.798] | 0.0234039 | 0.882899 | 0.906303 | [0.039 0.961] | 0.835 | -| (0.8, 81) | 0.0341685 | 0.791831 | 0.826 | [0.029 0.171 0.003 0.797] | 0.0188428 | 0.882741 | 0.901584 | [0.032 0.968] | 0.826 | -| (0.8, 82) | 0.0425206 | 0.791479 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0250496 | 0.880953 | 0.906002 | [0.034 0.966] | 0.834 | -| (0.8, 83) | 0.0240059 | 0.805994 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0139455 | 0.889901 | 0.903846 | [0.032 0.968] | 0.83 | -| (0.8, 84) | 0.0177441 | 0.804256 | 0.822 | [0.022 0.178 0. 0.8 ] | 0.00957679 | 0.890311 | 0.899888 | [0.022 0.978] | 0.822 | -| (0.8, 85) | 0.00355815 | 0.829442 | 0.833 | [0.034 0.166 0.001 0.799] | 0.000679917 | 0.904703 | 0.905382 | [0.035 0.965] | 0.833 | -| (0.8, 86) | 0.0074582 | 0.823542 | 0.831 | [0.032 0.168 0.001 0.799] | 0.00372776 | 0.90063 | 0.904358 | [0.033 0.967] | 0.831 | -| (0.8, 87) | 0.0431926 | 0.785807 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0239931 | 0.879451 | 0.903444 | [0.029 0.971] | 0.829 | -| (0.8, 88) | 0.0312967 | 0.793703 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0164912 | 0.884917 | 0.901408 | [0.025 0.975] | 0.825 | -| (0.8, 89) | 0.0111373 | 0.815863 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00594957 | 0.896476 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 90) | 0.0255766 | 0.802423 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.013677 | 0.889258 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 91) | 0.0221193 | 0.808881 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0110527 | 0.893413 | 0.904466 | [0.031 0.969] | 0.831 | -| (0.8, 92) | 0.0231501 | 0.80585 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0134434 | 0.889892 | 0.903335 | [0.031 0.969] | 0.829 | -| (0.8, 93) | 0.0233668 | 0.797633 | 0.821 | [0.021 0.179 0. 0.8 ] | 0.0137534 | 0.885628 | 0.899382 | [0.021 0.979] | 0.821 | -| (0.8, 94) | 0.00688933 | 0.826111 | 0.833 | [0.034 0.166 0.001 0.799] | 0.0037811 | 0.901601 | 0.905382 | [0.035 0.965] | 0.833 | -| (0.8, 95) | 0.0243997 | 0.8096 | 0.834 | [0.036 0.164 0.002 0.798] | 0.0114362 | 0.894353 | 0.905789 | [0.038 0.962] | 0.834 | -| (0.8, 96) | 0.0228605 | 0.80514 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0123032 | 0.890631 | 0.902935 | [0.028 0.972] | 0.828 | -| (0.8, 97) | 0.0287869 | 0.797213 | 0.826 | [0.027 0.173 0.001 0.799] | 0.0146776 | 0.887128 | 0.901806 | [0.028 0.972] | 0.826 | -| (0.8, 98) | 0.0243104 | 0.80269 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0133648 | 0.88906 | 0.902425 | [0.027 0.973] | 0.827 | -| (0.8, 99) | 0.034638 | 0.793362 | 0.828 | [0.029 0.171 0.001 0.799] | 0.0180954 | 0.884729 | 0.902825 | [0.03 0.97] | 0.828 | -| (0.85, 0) | 0.00916264 | 0.856837 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00395676 | 0.922899 | 0.926856 | [0.018 0.982] | 0.866 | -| (0.85, 1) | 0.0242384 | 0.842762 | 0.867 | [0.019 0.131 0.002 0.848] | 0.0126504 | 0.914632 | 0.927283 | [0.021 0.979] | 0.867 | -| (0.85, 2) | 0.0059672 | 0.874967 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00366687 | 0.932043 | 0.928376 | [0.021 0.979] | 0.869 | -| (0.85, 3) | 0.0185452 | 0.856455 | 0.875 | [0.025 0.125 0. 0.85 ] | 0.00887886 | 0.922628 | 0.931507 | [0.025 0.975] | 0.875 | -| (0.85, 4) | 0.0223926 | 0.854607 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0109393 | 0.921589 | 0.932529 | [0.027 0.973] | 0.877 | -| (0.85, 5) | 0.0275382 | 0.843462 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0144402 | 0.915029 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 6) | 0.0116306 | 0.864369 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0066333 | 0.925384 | 0.932018 | [0.026 0.974] | 0.876 | -| (0.85, 7) | 0.0293276 | 0.839672 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0167791 | 0.911675 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 8) | 0.00853504 | 0.862465 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.00453054 | 0.924939 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 9) | 0.00844529 | 0.877445 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00527631 | 0.933731 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 10) | 0.0174929 | 0.851507 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00949055 | 0.918886 | 0.928376 | [0.021 0.979] | 0.869 | -| (0.85, 11) | 0.032037 | 0.834963 | 0.867 | [0.017 0.133 0. 0.85 ] | 0.0178529 | 0.909588 | 0.927441 | [0.017 0.983] | 0.867 | -| (0.85, 12) | 0.02062 | 0.85538 | 0.876 | [0.027 0.123 0.001 0.849] | 0.0106596 | 0.921283 | 0.931943 | [0.028 0.972] | 0.876 | -| (0.85, 13) | 0.0300872 | 0.839913 | 0.87 | [0.02 0.13 0. 0.85] | 0.0169147 | 0.912047 | 0.928962 | [0.02 0.98] | 0.87 | -| (0.85, 14) | 0.0185164 | 0.847484 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.0095133 | 0.917422 | 0.926936 | [0.016 0.984] | 0.866 | -| (0.85, 15) | 0.00594455 | 0.867055 | 0.873 | [0.023 0.127 0. 0.85 ] | 0.00319687 | 0.92729 | 0.930487 | [0.023 0.977] | 0.873 | -| (0.85, 16) | 0.0159766 | 0.858023 | 0.874 | [0.025 0.125 0.001 0.849] | 0.00772593 | 0.923195 | 0.930921 | [0.026 0.974] | 0.874 | -| (0.85, 17) | 0.0102021 | 0.862798 | 0.873 | [0.024 0.126 0.001 0.849] | 0.00582044 | 0.924591 | 0.930411 | [0.025 0.975] | 0.873 | -| (0.85, 18) | 0.0345923 | 0.835408 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0201934 | 0.908691 | 0.928884 | [0.022 0.978] | 0.87 | -| (0.85, 19) | 0.0164631 | 0.855537 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00789905 | 0.922079 | 0.929978 | [0.022 0.978] | 0.872 | -| (0.85, 20) | 0.0369302 | 0.83707 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.0197393 | 0.911257 | 0.930997 | [0.024 0.976] | 0.874 | -| (0.85, 21) | 0.0297006 | 0.844299 | 0.874 | [0.026 0.124 0.002 0.848] | 0.0157164 | 0.915129 | 0.930845 | [0.028 0.972] | 0.874 | -| (0.85, 22) | 0.00637133 | 0.869629 | 0.876 | [0.027 0.123 0.001 0.849] | 0.00337797 | 0.928565 | 0.931943 | [0.028 0.972] | 0.876 | -| (0.85, 23) | 0.0257953 | 0.836205 | 0.862 | [0.015 0.135 0.003 0.847] | 0.0138807 | 0.910792 | 0.924672 | [0.018 0.982] | 0.862 | -| (0.85, 24) | 0.00337444 | 0.868626 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00166765 | 0.92831 | 0.929978 | [0.022 0.978] | 0.872 | -| (0.85, 25) | 0.024588 | 0.851412 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0122872 | 0.91973 | 0.932018 | [0.026 0.974] | 0.876 | -| (0.85, 26) | 0.0175407 | 0.853459 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00848404 | 0.920908 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 27) | 0.0188113 | 0.853189 | 0.872 | [0.023 0.127 0.001 0.849] | 0.00993147 | 0.91997 | 0.929901 | [0.024 0.976] | 0.872 | -| (0.85, 28) | 0.0102198 | 0.88022 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0060185 | 0.934903 | 0.928884 | [0.022 0.978] | 0.87 | -| (0.85, 29) | 0.0236431 | 0.844357 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.0131277 | 0.91482 | 0.927948 | [0.018 0.982] | 0.868 | -| (0.85, 30) | 0.0294663 | 0.840534 | 0.87 | [0.02 0.13 0. 0.85] | 0.0164557 | 0.912506 | 0.928962 | [0.02 0.98] | 0.87 | -| (0.85, 31) | 0.0125447 | 0.858455 | 0.871 | [0.023 0.127 0.002 0.848] | 0.00581342 | 0.923502 | 0.929315 | [0.025 0.975] | 0.871 | -| (0.85, 32) | 0.0168929 | 0.851107 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.00838249 | 0.919565 | 0.927948 | [0.018 0.982] | 0.868 | -| (0.85, 33) | 0.0416726 | 0.827327 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0237554 | 0.904699 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 34) | 0.00899809 | 0.860002 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00365011 | 0.924726 | 0.928376 | [0.021 0.979] | 0.869 | -| (0.85, 35) | 0.012989 | 0.855011 | 0.868 | [0.02 0.13 0.002 0.848] | 0.00666622 | 0.921124 | 0.92779 | [0.022 0.978] | 0.868 | -| (0.85, 36) | 0.0118965 | 0.859103 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00526969 | 0.924123 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 37) | 0.0351497 | 0.83185 | 0.867 | [0.019 0.131 0.002 0.848] | 0.0192705 | 0.908012 | 0.927283 | [0.021 0.979] | 0.867 | -| (0.85, 38) | 0.000781362 | 0.875781 | 0.875 | [0.025 0.125 0. 0.85 ] | 0.000880589 | 0.932387 | 0.931507 | [0.025 0.975] | 0.875 | -| (0.85, 39) | 0.0148084 | 0.861192 | 0.876 | [0.028 0.122 0.002 0.848] | 0.00707788 | 0.92479 | 0.931868 | [0.03 0.97] | 0.876 | -| (0.85, 40) | 0.033094 | 0.839906 | 0.873 | [0.024 0.126 0.001 0.849] | 0.0174848 | 0.912926 | 0.930411 | [0.025 0.975] | 0.873 | -| (0.85, 41) | 0.00491038 | 0.87191 | 0.867 | [0.018 0.132 0.001 0.849] | 0.00317792 | 0.93054 | 0.927362 | [0.019 0.981] | 0.867 | -| (0.85, 42) | 0.0118285 | 0.859172 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.00646193 | 0.923008 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 43) | 0.0222668 | 0.853733 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0109554 | 0.921062 | 0.932018 | [0.026 0.974] | 0.876 | -| (0.85, 44) | 0.0208303 | 0.85017 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0116156 | 0.917854 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 45) | 0.0232634 | 0.844737 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.0132446 | 0.914703 | 0.927948 | [0.018 0.982] | 0.868 | -| (0.85, 46) | 0.0286176 | 0.837382 | 0.866 | [0.018 0.132 0.002 0.848] | 0.0152861 | 0.91149 | 0.926776 | [0.02 0.98] | 0.866 | -| (0.85, 47) | 0.0189883 | 0.848012 | 0.867 | [0.018 0.132 0.001 0.849] | 0.0098411 | 0.917521 | 0.927362 | [0.019 0.981] | 0.867 | -| (0.85, 48) | 0.0291667 | 0.846833 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0162761 | 0.915741 | 0.932018 | [0.026 0.974] | 0.876 | -| (0.85, 49) | 0.0139737 | 0.852026 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00675732 | 0.920099 | 0.926856 | [0.018 0.982] | 0.866 | -| (0.85, 50) | 0.028988 | 0.840012 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0155634 | 0.912891 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 51) | 0.019931 | 0.850069 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0114954 | 0.917389 | 0.928884 | [0.022 0.978] | 0.87 | -| (0.85, 52) | 0.0126395 | 0.86136 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00694487 | 0.924052 | 0.930997 | [0.024 0.976] | 0.874 | -| (0.85, 53) | 0.0174681 | 0.847532 | 0.865 | [0.016 0.134 0.001 0.849] | 0.00971912 | 0.916631 | 0.92635 | [0.017 0.983] | 0.865 | -| (0.85, 54) | 0.000984246 | 0.871984 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00097317 | 0.930366 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 55) | 0.00821199 | 0.860788 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00444453 | 0.92401 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 56) | 0.00862268 | 0.861377 | 0.87 | [0.02 0.13 0. 0.85] | 0.00347912 | 0.925483 | 0.928962 | [0.02 0.98] | 0.87 | -| (0.85, 57) | 0.00934159 | 0.878342 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00568831 | 0.934143 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 58) | 0.0128164 | 0.857184 | 0.87 | [0.021 0.129 0.001 0.849] | 0.00699737 | 0.921887 | 0.928884 | [0.022 0.978] | 0.87 | -| (0.85, 59) | 0.0331427 | 0.850857 | 0.884 | [0.034 0.116 0. 0.85 ] | 0.0172442 | 0.918879 | 0.936123 | [0.034 0.966] | 0.884 | -| (0.85, 60) | 0.00682919 | 0.856171 | 0.863 | [0.013 0.137 0. 0.85 ] | 0.00356046 | 0.921861 | 0.925422 | [0.013 0.987] | 0.863 | -| (0.85, 61) | 0.0141476 | 0.853852 | 0.868 | [0.019 0.131 0.001 0.849] | 0.00671517 | 0.921154 | 0.927869 | [0.02 0.98] | 0.868 | -| (0.85, 62) | 0.0187097 | 0.85029 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0105429 | 0.917911 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 63) | 0.00948473 | 0.851515 | 0.861 | [0.013 0.137 0.002 0.848] | 0.00445686 | 0.919794 | 0.924251 | [0.015 0.985] | 0.861 | -| (0.85, 64) | 0.0145757 | 0.854424 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00689778 | 0.921478 | 0.928376 | [0.021 0.979] | 0.869 | -| (0.85, 65) | 0.00214502 | 0.869145 | 0.867 | [0.02 0.13 0.003 0.847] | 0.00168839 | 0.928891 | 0.927203 | [0.023 0.977] | 0.867 | -| (0.85, 66) | 0.0170768 | 0.846923 | 0.864 | [0.014 0.136 0. 0.85 ] | 0.00957014 | 0.916356 | 0.925926 | [0.014 0.986] | 0.864 | -| (0.85, 67) | 0.0227065 | 0.850294 | 0.873 | [0.026 0.124 0.003 0.847] | 0.0130022 | 0.917256 | 0.930258 | [0.029 0.971] | 0.873 | -| (0.85, 68) | 0.0145889 | 0.860411 | 0.875 | [0.026 0.124 0.001 0.849] | 0.00836658 | 0.923065 | 0.931432 | [0.027 0.973] | 0.875 | -| (0.85, 69) | 0.0364083 | 0.831592 | 0.868 | [0.02 0.13 0.002 0.848] | 0.019737 | 0.908053 | 0.92779 | [0.022 0.978] | 0.868 | -| (0.85, 70) | 0.0242443 | 0.847756 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.0123723 | 0.917606 | 0.929978 | [0.022 0.978] | 0.872 | -| (0.85, 71) | 0.00301728 | 0.867983 | 0.871 | [0.022 0.128 0.001 0.849] | 0.000460011 | 0.928932 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 72) | 0.027528 | 0.846472 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.0146329 | 0.916364 | 0.930997 | [0.024 0.976] | 0.874 | -| (0.85, 73) | 0.00503926 | 0.868961 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00273599 | 0.928261 | 0.930997 | [0.024 0.976] | 0.874 | -| (0.85, 74) | 0.00180717 | 0.867807 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00122006 | 0.928076 | 0.926856 | [0.018 0.982] | 0.866 | -| (0.85, 75) | 0.0113285 | 0.863672 | 0.875 | [0.029 0.121 0.004 0.846] | 0.00642303 | 0.924782 | 0.931205 | [0.033 0.967] | 0.875 | -| (0.85, 76) | 0.0245245 | 0.853476 | 0.878 | [0.028 0.122 0. 0.85 ] | 0.0126659 | 0.920375 | 0.933041 | [0.028 0.972] | 0.878 | -| (0.85, 77) | 0.0227488 | 0.846251 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0117367 | 0.916718 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 78) | 0.00669301 | 0.875693 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00422216 | 0.932677 | 0.928454 | [0.019 0.981] | 0.869 | -| (0.85, 79) | 0.00956554 | 0.856434 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.00474536 | 0.92219 | 0.926936 | [0.016 0.984] | 0.866 | -| (0.85, 80) | 0.00108528 | 0.872915 | 0.874 | [0.025 0.125 0.001 0.849] | 0.000293925 | 0.930627 | 0.930921 | [0.026 0.974] | 0.874 | -| (0.85, 81) | 0.0139251 | 0.857075 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00655939 | 0.922833 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 82) | 0.0224209 | 0.851579 | 0.874 | [0.026 0.124 0.002 0.848] | 0.0118784 | 0.918967 | 0.930845 | [0.028 0.972] | 0.874 | -| (0.85, 83) | 0.0234424 | 0.843558 | 0.867 | [0.017 0.133 0. 0.85 ] | 0.0127119 | 0.914729 | 0.927441 | [0.017 0.983] | 0.867 | -| (0.85, 84) | 0.0127184 | 0.859282 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00659153 | 0.923387 | 0.929978 | [0.022 0.978] | 0.872 | -| (0.85, 85) | 0.0139557 | 0.858044 | 0.872 | [0.023 0.127 0.001 0.849] | 0.0076882 | 0.922213 | 0.929901 | [0.024 0.976] | 0.872 | -| (0.85, 86) | 0.00034068 | 0.870341 | 0.87 | [0.02 0.13 0. 0.85] | 0.001697 | 0.930659 | 0.928962 | [0.02 0.98] | 0.87 | -| (0.85, 87) | 0.0113094 | 0.862691 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00628484 | 0.924712 | 0.930997 | [0.024 0.976] | 0.874 | -| (0.85, 88) | 0.0272791 | 0.838721 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.015109 | 0.911827 | 0.926936 | [0.016 0.984] | 0.866 | -| (0.85, 89) | 0.014482 | 0.892482 | 0.878 | [0.028 0.122 0. 0.85 ] | 0.00856509 | 0.941606 | 0.933041 | [0.028 0.972] | 0.878 | -| (0.85, 90) | 0.0142999 | 0.8577 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00755693 | 0.922421 | 0.929978 | [0.022 0.978] | 0.872 | -| (0.85, 91) | 0.0421117 | 0.827888 | 0.87 | [0.02 0.13 0. 0.85] | 0.0233165 | 0.905645 | 0.928962 | [0.02 0.98] | 0.87 | -| (0.85, 92) | 0.0311295 | 0.84587 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0160312 | 0.916498 | 0.932529 | [0.027 0.973] | 0.877 | -| (0.85, 93) | 0.0141491 | 0.848851 | 0.863 | [0.015 0.135 0.002 0.848] | 0.00791631 | 0.917343 | 0.925259 | [0.017 0.983] | 0.863 | -| (0.85, 94) | 0.0200191 | 0.851981 | 0.872 | [0.024 0.126 0.002 0.848] | 0.0103975 | 0.919427 | 0.929825 | [0.026 0.974] | 0.872 | -| (0.85, 95) | 0.0261024 | 0.844898 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.014257 | 0.915213 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 96) | 0.0153376 | 0.855662 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0082263 | 0.921243 | 0.92947 | [0.021 0.979] | 0.871 | -| (0.85, 97) | 0.0226085 | 0.845392 | 0.868 | [0.019 0.131 0.001 0.849] | 0.0117309 | 0.916138 | 0.927869 | [0.02 0.98] | 0.868 | -| (0.85, 98) | 0.0373645 | 0.833636 | 0.871 | [0.022 0.128 0.001 0.849] | 0.0202228 | 0.90917 | 0.929392 | [0.023 0.977] | 0.871 | -| (0.85, 99) | 0.0195995 | 0.8574 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0100396 | 0.922489 | 0.932529 | [0.027 0.973] | 0.877 | -| (0.9, 0) | 0.0301308 | 0.880869 | 0.911 | [0.012 0.088 0.001 0.899] | 0.0161845 | 0.936651 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 1) | 0.00656327 | 0.919563 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00410703 | 0.958002 | 0.953895 | [0.013 0.987] | 0.913 | -| (0.9, 2) | 0.0144244 | 0.896576 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00736751 | 0.945468 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 3) | 0.0273018 | 0.891698 | 0.919 | [0.02 0.08 0.001 0.899] | 0.0150242 | 0.941868 | 0.956892 | [0.021 0.979] | 0.919 | -| (0.9, 4) | 0.0168992 | 0.899101 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.0085499 | 0.946864 | 0.955414 | [0.016 0.984] | 0.916 | -| (0.9, 5) | 0.00223583 | 0.908764 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00063496 | 0.9522 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 6) | 0.00952175 | 0.904478 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00494578 | 0.949455 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 7) | 0.014533 | 0.899467 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0074706 | 0.94693 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 8) | 0.01088 | 0.90212 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00584198 | 0.948053 | 0.953895 | [0.013 0.987] | 0.913 | -| (0.9, 9) | 0.00749745 | 0.899503 | 0.907 | [0.008 0.092 0.001 0.899] | 0.00393881 | 0.946881 | 0.95082 | [0.009 0.991] | 0.907 | -| (0.9, 10) | 0.0218657 | 0.890134 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.0115157 | 0.941874 | 0.95339 | [0.012 0.988] | 0.912 | -| (0.9, 11) | 0.00199519 | 0.915005 | 0.917 | [0.018 0.082 0.001 0.899] | 0.0011787 | 0.954696 | 0.955875 | [0.019 0.981] | 0.917 | -| (0.9, 12) | 0.00284903 | 0.924849 | 0.922 | [0.022 0.078 0. 0.9 ] | 0.00168534 | 0.960152 | 0.958466 | [0.022 0.978] | 0.922 | -| (0.9, 13) | 0.00074074 | 0.910741 | 0.91 | [0.012 0.088 0.002 0.898] | 0.000999632 | 0.95328 | 0.95228 | [0.014 0.986] | 0.91 | -| (0.9, 14) | 0.00416334 | 0.911837 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.00221976 | 0.953194 | 0.955414 | [0.016 0.984] | 0.916 | -| (0.9, 15) | 0.0169214 | 0.897079 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00901792 | 0.945383 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 16) | 0.0165161 | 0.900484 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00913514 | 0.946786 | 0.955921 | [0.017 0.983] | 0.917 | -| (0.9, 17) | 0.00866541 | 0.909335 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00399855 | 0.952384 | 0.956383 | [0.02 0.98] | 0.918 | -| (0.9, 18) | 0.011116 | 0.929116 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00686953 | 0.963253 | 0.956383 | [0.02 0.98] | 0.918 | -| (0.9, 19) | 0.00628126 | 0.906719 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00329276 | 0.950602 | 0.953895 | [0.013 0.987] | 0.913 | -| (0.9, 20) | 0.00487117 | 0.909129 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00222355 | 0.952129 | 0.954352 | [0.016 0.984] | 0.914 | -| (0.9, 21) | 0.00958671 | 0.897413 | 0.907 | [0.008 0.092 0.001 0.899] | 0.0050069 | 0.945813 | 0.95082 | [0.009 0.991] | 0.907 | -| (0.9, 22) | 0.00296476 | 0.912035 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00143269 | 0.953427 | 0.954859 | [0.017 0.983] | 0.915 | -| (0.9, 23) | 0.00900789 | 0.907992 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00414382 | 0.951778 | 0.955921 | [0.017 0.983] | 0.917 | -| (0.9, 24) | 0.00113461 | 0.924135 | 0.923 | [0.024 0.076 0.001 0.899] | 0.00144543 | 0.960379 | 0.958933 | [0.025 0.975] | 0.923 | -| (0.9, 25) | 0.0173475 | 0.895653 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00891637 | 0.94493 | 0.953846 | [0.015 0.985] | 0.913 | -| (0.9, 26) | 0.000654213 | 0.909346 | 0.91 | [0.011 0.089 0.001 0.899] | 0.000174876 | 0.952156 | 0.952331 | [0.012 0.988] | 0.91 | -| (0.9, 27) | 0.00520818 | 0.908792 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00248187 | 0.951919 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 28) | 0.00428414 | 0.917284 | 0.913 | [0.015 0.085 0.002 0.898] | 0.00242611 | 0.956223 | 0.953797 | [0.017 0.983] | 0.913 | -| (0.9, 29) | 0.00155474 | 0.922555 | 0.921 | [0.021 0.079 0. 0.9 ] | 0.00142622 | 0.959383 | 0.957956 | [0.021 0.979] | 0.921 | -| (0.9, 30) | 0.015075 | 0.898925 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00843417 | 0.945918 | 0.954352 | [0.016 0.984] | 0.914 | -| (0.9, 31) | 0.00664218 | 0.922642 | 0.916 | [0.017 0.083 0.001 0.899] | 0.00408139 | 0.959448 | 0.955367 | [0.018 0.982] | 0.916 | -| (0.9, 32) | 0.00409654 | 0.908903 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00195499 | 0.951891 | 0.953846 | [0.015 0.985] | 0.913 | -| (0.9, 33) | 0.0100621 | 0.903938 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00487939 | 0.949521 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 34) | 0.00666714 | 0.909333 | 0.916 | [0.018 0.082 0.002 0.898] | 0.00354481 | 0.951774 | 0.955319 | [0.02 0.98] | 0.916 | -| (0.9, 35) | 0.00700774 | 0.904992 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00362466 | 0.949716 | 0.95334 | [0.014 0.986] | 0.912 | -| (0.9, 36) | 0.00340552 | 0.913594 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00179529 | 0.954126 | 0.955921 | [0.017 0.983] | 0.917 | -| (0.9, 37) | 0.00453426 | 0.910466 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00190253 | 0.952957 | 0.954859 | [0.017 0.983] | 0.915 | -| (0.9, 38) | 0.00414505 | 0.916145 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00284759 | 0.956237 | 0.95339 | [0.012 0.988] | 0.912 | -| (0.9, 39) | 0.00205744 | 0.918943 | 0.921 | [0.021 0.079 0. 0.9 ] | 0.000530198 | 0.957426 | 0.957956 | [0.021 0.979] | 0.921 | -| (0.9, 40) | 0.00382936 | 0.915171 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00180558 | 0.955132 | 0.956938 | [0.019 0.981] | 0.919 | -| (0.9, 41) | 0.00497108 | 0.904029 | 0.909 | [0.011 0.089 0.002 0.898] | 0.00217952 | 0.949596 | 0.951775 | [0.013 0.987] | 0.909 | -| (0.9, 42) | 0.0112273 | 0.901773 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00593944 | 0.947956 | 0.953895 | [0.013 0.987] | 0.913 | -| (0.9, 43) | 0.0170636 | 0.892936 | 0.91 | [0.01 0.09 0. 0.9 ] | 0.00909284 | 0.943288 | 0.952381 | [0.01 0.99] | 0.91 | -| (0.9, 44) | 0.0162799 | 0.89772 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00884155 | 0.945511 | 0.954352 | [0.016 0.984] | 0.914 | -| (0.9, 45) | 0.00426367 | 0.922264 | 0.918 | [0.018 0.082 0. 0.9 ] | 0.00234195 | 0.958771 | 0.956429 | [0.018 0.982] | 0.918 | -| (0.9, 46) | 0.00151459 | 0.918485 | 0.92 | [0.02 0.08 0. 0.9 ] | 0.000635751 | 0.956811 | 0.957447 | [0.02 0.98] | 0.92 | -| (0.9, 47) | 0.0134137 | 0.897586 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00688702 | 0.945948 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 48) | 0.0125112 | 0.902489 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00641199 | 0.948495 | 0.954907 | [0.015 0.985] | 0.915 | -| (0.9, 49) | 0.00639305 | 0.914393 | 0.908 | [0.008 0.092 0. 0.9 ] | 0.00362208 | 0.954996 | 0.951374 | [0.008 0.992] | 0.908 | -| (0.9, 50) | 0.00774922 | 0.906251 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0041113 | 0.95029 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 51) | 0.0122481 | 0.898752 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00615901 | 0.946676 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 52) | 0.0058903 | 0.90511 | 0.911 | [0.011 0.089 0. 0.9 ] | 0.00296234 | 0.949923 | 0.952885 | [0.011 0.989] | 0.911 | -| (0.9, 53) | 0.00697218 | 0.912028 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.0035033 | 0.953435 | 0.956938 | [0.019 0.981] | 0.919 | -| (0.9, 54) | 0.00297486 | 0.912025 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00088718 | 0.953972 | 0.954859 | [0.017 0.983] | 0.915 | -| (0.9, 55) | 0.0137413 | 0.898259 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00730465 | 0.946085 | 0.95339 | [0.012 0.988] | 0.912 | -| (0.9, 56) | 0.0064383 | 0.918438 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00363441 | 0.957024 | 0.95339 | [0.012 0.988] | 0.912 | -| (0.9, 57) | 0.00642233 | 0.902578 | 0.909 | [0.012 0.088 0.003 0.897] | 0.00295656 | 0.948768 | 0.951724 | [0.015 0.985] | 0.909 | -| (0.9, 58) | 0.0107958 | 0.920796 | 0.91 | [0.011 0.089 0.001 0.899] | 0.00590905 | 0.95824 | 0.952331 | [0.012 0.988] | 0.91 | -| (0.9, 59) | 0.000578272 | 0.908422 | 0.909 | [0.009 0.091 0. 0.9 ] | 0.000168299 | 0.951709 | 0.951877 | [0.009 0.991] | 0.909 | -| (0.9, 60) | 0.0124924 | 0.906508 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00642065 | 0.950517 | 0.956938 | [0.019 0.981] | 0.919 | -| (0.9, 61) | 0.0155213 | 0.894479 | 0.91 | [0.012 0.088 0.002 0.898] | 0.00832079 | 0.943959 | 0.95228 | [0.014 0.986] | 0.91 | -| (0.9, 62) | 0.0116735 | 0.899327 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00587826 | 0.946957 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 63) | 0.00207603 | 0.911924 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0011058 | 0.953295 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 64) | 0.000689676 | 0.91331 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.000139088 | 0.954262 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 65) | 0.014441 | 0.900559 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00718125 | 0.947678 | 0.954859 | [0.017 0.983] | 0.915 | -| (0.9, 66) | 0.0176499 | 0.89435 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00954539 | 0.943795 | 0.95334 | [0.014 0.986] | 0.912 | -| (0.9, 67) | 0.00501621 | 0.908984 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00243601 | 0.951965 | 0.954401 | [0.014 0.986] | 0.914 | -| (0.9, 68) | 0.00606039 | 0.90894 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00265232 | 0.952255 | 0.954907 | [0.015 0.985] | 0.915 | -| (0.9, 69) | 0.0197135 | 0.898286 | 0.918 | [0.021 0.079 0.003 0.897] | 0.0098805 | 0.946409 | 0.95629 | [0.024 0.976] | 0.918 | -| (0.9, 70) | 0.0116814 | 0.900319 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00584883 | 0.947541 | 0.95339 | [0.012 0.988] | 0.912 | -| (0.9, 71) | 0.00793232 | 0.899068 | 0.907 | [0.008 0.092 0.001 0.899] | 0.00400617 | 0.946813 | 0.95082 | [0.009 0.991] | 0.907 | -| (0.9, 72) | 0.00787936 | 0.909121 | 0.917 | [0.018 0.082 0.001 0.899] | 0.00347757 | 0.952397 | 0.955875 | [0.019 0.981] | 0.917 | -| (0.9, 73) | 0.0128338 | 0.901166 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00633845 | 0.948014 | 0.954352 | [0.016 0.984] | 0.914 | -| (0.9, 74) | 0.000822019 | 0.912822 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00056394 | 0.953904 | 0.95334 | [0.014 0.986] | 0.912 | -| (0.9, 75) | 0.00670409 | 0.917704 | 0.911 | [0.011 0.089 0. 0.9 ] | 0.00386719 | 0.956752 | 0.952885 | [0.011 0.989] | 0.911 | -| (0.9, 76) | 0.0180635 | 0.894936 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00958846 | 0.944258 | 0.953846 | [0.015 0.985] | 0.913 | -| (0.9, 77) | 0.0148393 | 0.900161 | 0.915 | [0.017 0.083 0.002 0.898] | 0.00809086 | 0.94672 | 0.954811 | [0.019 0.981] | 0.915 | -| (0.9, 78) | 0.00479696 | 0.903203 | 0.908 | [0.01 0.09 0.002 0.898] | 0.00213129 | 0.94914 | 0.951271 | [0.012 0.988] | 0.908 | -| (0.9, 79) | 0.00734862 | 0.903651 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00346162 | 0.949374 | 0.952835 | [0.013 0.987] | 0.911 | -| (0.9, 80) | 0.00871425 | 0.906286 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00469793 | 0.950209 | 0.954907 | [0.015 0.985] | 0.915 | -| (0.9, 81) | 0.0288783 | 0.884122 | 0.913 | [0.014 0.086 0.001 0.899] | 0.0153488 | 0.938497 | 0.953846 | [0.015 0.985] | 0.913 | -| (0.9, 82) | 0.00636029 | 0.90364 | 0.91 | [0.011 0.089 0.001 0.899] | 0.00336515 | 0.948965 | 0.952331 | [0.012 0.988] | 0.91 | -| (0.9, 83) | 0.0398032 | 0.876197 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.0216551 | 0.933759 | 0.955414 | [0.016 0.984] | 0.916 | -| (0.9, 84) | 0.00975212 | 0.903248 | 0.913 | [0.015 0.085 0.002 0.898] | 0.00525826 | 0.948539 | 0.953797 | [0.017 0.983] | 0.913 | -| (0.9, 85) | 0.00793273 | 0.920933 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00455126 | 0.958397 | 0.953846 | [0.015 0.985] | 0.913 | -| (0.9, 86) | 0.0174908 | 0.886509 | 0.904 | [0.006 0.094 0.002 0.898] | 0.00949516 | 0.939765 | 0.94926 | [0.008 0.992] | 0.904 | -| (0.9, 87) | 0.0180295 | 0.893971 | 0.912 | [0.014 0.086 0.002 0.898] | 0.00968631 | 0.943605 | 0.953291 | [0.016 0.984] | 0.912 | -| (0.9, 88) | 0.00443638 | 0.909564 | 0.914 | [0.017 0.083 0.003 0.897] | 0.00168449 | 0.952571 | 0.954255 | [0.02 0.98] | 0.914 | -| (0.9, 89) | 0.0109881 | 0.907012 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00541001 | 0.950973 | 0.956383 | [0.02 0.98] | 0.918 | -| (0.9, 90) | 0.00504521 | 0.910045 | 0.905 | [0.006 0.094 0.001 0.899] | 0.00289806 | 0.952713 | 0.949815 | [0.007 0.993] | 0.905 | -| (0.9, 91) | 0.00880224 | 0.910198 | 0.919 | [0.02 0.08 0.001 0.899] | 0.00480602 | 0.952086 | 0.956892 | [0.021 0.979] | 0.919 | -| (0.9, 92) | 0.00684515 | 0.916155 | 0.923 | [0.023 0.077 0. 0.9 ] | 0.00299495 | 0.955982 | 0.958977 | [0.023 0.977] | 0.923 | -| (0.9, 93) | 0.00277936 | 0.916221 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00155942 | 0.955378 | 0.956938 | [0.019 0.981] | 0.919 | -| (0.9, 94) | 0.00155124 | 0.907449 | 0.909 | [0.009 0.091 0. 0.9 ] | 0.000742878 | 0.951134 | 0.951877 | [0.009 0.991] | 0.909 | -| (0.9, 95) | 0.0102061 | 0.926206 | 0.916 | [0.017 0.083 0.001 0.899] | 0.00570406 | 0.961071 | 0.955367 | [0.018 0.982] | 0.916 | -| (0.9, 96) | 0.00659766 | 0.923598 | 0.917 | [0.018 0.082 0.001 0.899] | 0.0037311 | 0.959606 | 0.955875 | [0.019 0.981] | 0.917 | -| (0.9, 97) | 0.0133374 | 0.903663 | 0.917 | [0.018 0.082 0.001 0.899] | 0.00714786 | 0.948727 | 0.955875 | [0.019 0.981] | 0.917 | -| (0.9, 98) | 0.0268155 | 0.890185 | 0.917 | [0.019 0.081 0.002 0.898] | 0.0139259 | 0.941902 | 0.955828 | [0.021 0.979] | 0.917 | -| (0.9, 99) | 0.0100532 | 0.893947 | 0.904 | [0.006 0.094 0.002 0.898] | 0.00525952 | 0.944001 | 0.94926 | [0.008 0.992] | 0.904 | -| (0.95, 0) | 0.0130864 | 0.969086 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00681747 | 0.98416 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 1) | 0.0112369 | 0.945763 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00590501 | 0.971941 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 2) | 0.000913955 | 0.959086 | 0.96 | [0.01 0.04 0. 0.95] | 0.000476725 | 0.978905 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 3) | 0.00282916 | 0.957829 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00161856 | 0.978458 | 0.97684 | [0.007 0.993] | 0.955 | -| (0.95, 4) | 0.00252074 | 0.956479 | 0.959 | [0.01 0.04 0.001 0.949] | 0.00124336 | 0.977612 | 0.978855 | [0.011 0.989] | 0.959 | -| (0.95, 5) | 0.00253113 | 0.957469 | 0.96 | [0.01 0.04 0. 0.95] | 0.00138245 | 0.977999 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 6) | 0.00451495 | 0.951485 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00222678 | 0.975139 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 7) | 0.00417619 | 0.955824 | 0.96 | [0.01 0.04 0. 0.95] | 0.00218906 | 0.977192 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 8) | 0.00512566 | 0.950874 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00255172 | 0.974815 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 9) | 0.00543415 | 0.946566 | 0.952 | [0.005 0.045 0.003 0.947] | 0.00273372 | 0.972549 | 0.975283 | [0.008 0.992] | 0.952 | -| (0.95, 10) | 0.00479567 | 0.960796 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00253629 | 0.979879 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 11) | 0.0059848 | 0.953015 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00308922 | 0.975788 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 12) | 0.0142771 | 0.971277 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00746109 | 0.98533 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 13) | 0.00276339 | 0.956237 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00140162 | 0.977475 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 14) | 0.00478025 | 0.95022 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00252338 | 0.974317 | 0.97684 | [0.007 0.993] | 0.955 | -| (0.95, 15) | 0.0046799 | 0.94932 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00238639 | 0.973951 | 0.976337 | [0.006 0.994] | 0.954 | -| (0.95, 16) | 0.00442474 | 0.955425 | 0.951 | [0.003 0.047 0.002 0.948] | 0.00232928 | 0.977136 | 0.974807 | [0.005 0.995] | 0.951 | -| (0.95, 17) | 0.0114726 | 0.945527 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00597459 | 0.971872 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 18) | 0.00567983 | 0.95132 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00293726 | 0.974909 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 19) | 0.0147145 | 0.939286 | 0.954 | [0.004 0.046 0. 0.95 ] | 0.0076719 | 0.96869 | 0.976362 | [0.004 0.996] | 0.954 | -| (0.95, 20) | 0.0086796 | 0.94732 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00446135 | 0.972905 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 21) | 0.00869415 | 0.951306 | 0.96 | [0.011 0.039 0.001 0.949] | 0.00446133 | 0.974899 | 0.97936 | [0.012 0.988] | 0.96 | -| (0.95, 22) | 0.00115897 | 0.956159 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.000685432 | 0.977549 | 0.976864 | [0.005 0.995] | 0.955 | -| (0.95, 23) | 0.0135314 | 0.950469 | 0.964 | [0.014 0.036 0. 0.95 ] | 0.00711114 | 0.974294 | 0.981405 | [0.014 0.986] | 0.964 | -| (0.95, 24) | 0.000263541 | 0.955736 | 0.956 | [0.007 0.043 0.001 0.949] | 2.1201e-05 | 0.977364 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 25) | 0.0147964 | 0.942204 | 0.957 | [0.01 0.04 0.003 0.947] | 0.00755989 | 0.970241 | 0.977801 | [0.013 0.987] | 0.957 | -| (0.95, 26) | 0.00568312 | 0.951317 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00294096 | 0.974928 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 27) | 0.00402375 | 0.954976 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00190956 | 0.976967 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 28) | 0.00112943 | 0.957129 | 0.956 | [0.008 0.042 0.002 0.948] | 0.000543759 | 0.977863 | 0.97732 | [0.01 0.99] | 0.956 | -| (0.95, 29) | 0.0129274 | 0.950073 | 0.963 | [0.014 0.036 0.001 0.949] | 0.00676198 | 0.974117 | 0.980879 | [0.015 0.985] | 0.963 | -| (0.95, 30) | 0.00401177 | 0.962012 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00225901 | 0.980632 | 0.978373 | [0.008 0.992] | 0.958 | -| (0.95, 31) | 0.0084593 | 0.948541 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00432719 | 0.973519 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 32) | 0.00247352 | 0.959474 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00135301 | 0.979199 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 33) | 0.00287278 | 0.957127 | 0.96 | [0.01 0.04 0. 0.95] | 0.00148084 | 0.977901 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 34) | 0.00586271 | 0.951137 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00297376 | 0.974896 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 35) | 0.00212307 | 0.953877 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00111809 | 0.976225 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 36) | 0.00105894 | 0.961059 | 0.96 | [0.01 0.04 0. 0.95] | 0.000574108 | 0.979956 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 37) | 0.0105679 | 0.943432 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00545768 | 0.97088 | 0.976337 | [0.006 0.994] | 0.954 | -| (0.95, 38) | 0.005027 | 0.963027 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00259891 | 0.980972 | 0.978373 | [0.008 0.992] | 0.958 | -| (0.95, 39) | 0.00677662 | 0.950223 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00342102 | 0.974448 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 40) | 0.0132115 | 0.968212 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00686927 | 0.983709 | 0.97684 | [0.007 0.993] | 0.955 | -| (0.95, 41) | 0.012049 | 0.944951 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00625242 | 0.971594 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 42) | 0.00143589 | 0.960436 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000937985 | 0.979815 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 43) | 0.00898241 | 0.952018 | 0.961 | [0.011 0.039 0. 0.95 ] | 0.00471306 | 0.975173 | 0.979887 | [0.011 0.989] | 0.961 | -| (0.95, 44) | 0.00919265 | 0.947807 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00470138 | 0.973168 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 45) | 0.00175008 | 0.95325 | 0.955 | [0.01 0.04 0.005 0.945] | 0.000737709 | 0.976006 | 0.976744 | [0.015 0.985] | 0.955 | -| (0.95, 46) | 0.00710272 | 0.958103 | 0.951 | [0.005 0.045 0.004 0.946] | 0.00365538 | 0.978411 | 0.974755 | [0.009 0.991] | 0.951 | -| (0.95, 47) | 0.00418356 | 0.955816 | 0.96 | [0.01 0.04 0. 0.95] | 0.00214877 | 0.977233 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 48) | 0.00453778 | 0.960538 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.0023711 | 0.979737 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 49) | 0.0149231 | 0.972923 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00778307 | 0.986134 | 0.978351 | [0.01 0.99] | 0.958 | -| (0.95, 50) | 7.40124e-05 | 0.954926 | 0.955 | [0.005 0.045 0. 0.95 ] | 7.84744e-05 | 0.976942 | 0.976864 | [0.005 0.995] | 0.955 | -| (0.95, 51) | 0.003751 | 0.955751 | 0.952 | [0.002 0.048 0. 0.95 ] | 0.00201513 | 0.977374 | 0.975359 | [0.002 0.998] | 0.952 | -| (0.95, 52) | 0.00456641 | 0.947434 | 0.952 | [0.005 0.045 0.003 0.947] | 0.00227595 | 0.973007 | 0.975283 | [0.008 0.992] | 0.952 | -| (0.95, 53) | 0.00276139 | 0.955239 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00132328 | 0.977027 | 0.978351 | [0.01 0.99] | 0.958 | -| (0.95, 54) | 0.0164937 | 0.941506 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00857274 | 0.9698 | 0.978373 | [0.008 0.992] | 0.958 | -| (0.95, 55) | 0.00856963 | 0.94243 | 0.951 | [0.002 0.048 0.001 0.949] | 0.00451159 | 0.970321 | 0.974833 | [0.003 0.997] | 0.951 | -| (0.95, 56) | 0.00327117 | 0.958271 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.00173844 | 0.978602 | 0.976864 | [0.005 0.995] | 0.955 | -| (0.95, 57) | 0.007871 | 0.944129 | 0.952 | [0.002 0.048 0. 0.95 ] | 0.00409843 | 0.971261 | 0.975359 | [0.002 0.998] | 0.952 | -| (0.95, 58) | 0.000601234 | 0.956601 | 0.956 | [0.008 0.042 0.002 0.948] | 0.000499576 | 0.977819 | 0.97732 | [0.01 0.99] | 0.956 | -| (0.95, 59) | 0.0032114 | 0.959211 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00173182 | 0.979075 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 60) | 0.0155299 | 0.93947 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00804949 | 0.96879 | 0.97684 | [0.007 0.993] | 0.955 | -| (0.95, 61) | 0.0318797 | 0.92512 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0168799 | 0.960944 | 0.977824 | [0.011 0.989] | 0.957 | -| (0.95, 62) | 0.00587987 | 0.96288 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0030603 | 0.980884 | 0.977824 | [0.011 0.989] | 0.957 | -| (0.95, 63) | 0.00842203 | 0.947578 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00443652 | 0.97293 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 64) | 0.00752341 | 0.967523 | 0.96 | [0.01 0.04 0. 0.95] | 0.00394008 | 0.983322 | 0.979381 | [0.01 0.99] | 0.96 | -| (0.95, 65) | 0.000204061 | 0.950204 | 0.95 | [0.005 0.045 0.005 0.945] | 0.000158767 | 0.974386 | 0.974227 | [0.01 0.99] | 0.95 | -| (0.95, 66) | 0.0121659 | 0.945834 | 0.958 | [0.01 0.04 0.002 0.948] | 0.00618715 | 0.972141 | 0.978328 | [0.012 0.988] | 0.958 | -| (0.95, 67) | 0.00567601 | 0.952324 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00277065 | 0.97558 | 0.978351 | [0.01 0.99] | 0.958 | -| (0.95, 68) | 0.000954144 | 0.958046 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000375038 | 0.978502 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 69) | 0.00060172 | 0.957398 | 0.958 | [0.009 0.041 0.001 0.949] | 0.000289562 | 0.978061 | 0.978351 | [0.01 0.99] | 0.958 | -| (0.95, 70) | 0.00426978 | 0.96327 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00227676 | 0.981154 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 71) | 0.019713 | 0.937287 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0104357 | 0.967388 | 0.977824 | [0.011 0.989] | 0.957 | -| (0.95, 72) | 0.00756189 | 0.949438 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00387345 | 0.973973 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 73) | 0.00469595 | 0.951304 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00241727 | 0.974926 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 74) | 0.00317693 | 0.956177 | 0.953 | [0.005 0.045 0.002 0.948] | 0.00168466 | 0.977495 | 0.975811 | [0.007 0.993] | 0.953 | -| (0.95, 75) | 0.00994606 | 0.968946 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.0053118 | 0.984189 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 76) | 0.00516192 | 0.959162 | 0.954 | [0.006 0.044 0.002 0.948] | 0.00269651 | 0.97901 | 0.976313 | [0.008 0.992] | 0.954 | -| (0.95, 77) | 0.000941681 | 0.955058 | 0.956 | [0.007 0.043 0.001 0.949] | 0.000330332 | 0.977013 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 78) | 0.0185866 | 0.980587 | 0.962 | [0.014 0.036 0.002 0.948] | 0.00965988 | 0.990011 | 0.980352 | [0.016 0.984] | 0.962 | -| (0.95, 79) | 0.00133473 | 0.959335 | 0.958 | [0.009 0.041 0.001 0.949] | 0.000654896 | 0.979005 | 0.978351 | [0.01 0.99] | 0.958 | -| (0.95, 80) | 0.00956923 | 0.944431 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00491612 | 0.971421 | 0.976337 | [0.006 0.994] | 0.954 | -| (0.95, 81) | 0.00636464 | 0.950635 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00326916 | 0.974577 | 0.977846 | [0.009 0.991] | 0.957 | -| (0.95, 82) | 0.000789837 | 0.95721 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00023548 | 0.978137 | 0.978373 | [0.008 0.992] | 0.958 | -| (0.95, 83) | 0.0029409 | 0.961941 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00171311 | 0.98059 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 84) | 0.00884236 | 0.946158 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.00453154 | 0.972332 | 0.976864 | [0.005 0.995] | 0.955 | -| (0.95, 85) | 0.000941489 | 0.958941 | 0.958 | [0.01 0.04 0.002 0.948] | 0.000520676 | 0.978849 | 0.978328 | [0.012 0.988] | 0.958 | -| (0.95, 86) | 0.0140395 | 0.93996 | 0.954 | [0.006 0.044 0.002 0.948] | 0.00744394 | 0.968869 | 0.976313 | [0.008 0.992] | 0.954 | -| (0.95, 87) | 0.00377863 | 0.961779 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00200204 | 0.980375 | 0.978373 | [0.008 0.992] | 0.958 | -| (0.95, 88) | 0.000211864 | 0.959212 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000207292 | 0.979084 | 0.978877 | [0.009 0.991] | 0.959 | -| (0.95, 89) | 0.0037105 | 0.952289 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00180449 | 0.975562 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 90) | 0.000369451 | 0.955631 | 0.956 | [0.006 0.044 0. 0.95 ] | 5.4309e-05 | 0.977312 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 91) | 0.00221093 | 0.958211 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00129289 | 0.978659 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 92) | 0.00325314 | 0.958253 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00182254 | 0.978662 | 0.97684 | [0.007 0.993] | 0.955 | -| (0.95, 93) | 0.0021091 | 0.959109 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.0011343 | 0.979004 | 0.977869 | [0.007 0.993] | 0.957 | -| (0.95, 94) | 0.0031392 | 0.952861 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00151907 | 0.975847 | 0.977366 | [0.006 0.994] | 0.956 | -| (0.95, 95) | 0.0117605 | 0.965761 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00613714 | 0.982475 | 0.976337 | [0.006 0.994] | 0.954 | -| (0.95, 96) | 0.0128302 | 0.94117 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00671104 | 0.969626 | 0.976337 | [0.006 0.994] | 0.954 | -| (0.95, 97) | 0.00541577 | 0.955584 | 0.961 | [0.011 0.039 0. 0.95 ] | 0.00261709 | 0.977269 | 0.979887 | [0.011 0.989] | 0.961 | -| (0.95, 98) | 0.0028626 | 0.953137 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00149962 | 0.975843 | 0.977343 | [0.008 0.992] | 0.956 | -| (0.95, 99) | 0.00686275 | 0.949137 | 0.956 | [0.007 0.043 0.001 0.949] | 0.0035354 | 0.973808 | 0.977343 | [0.008 0.992] | 0.956 | -| (1.0, 0) | 0.00038766 | 0.999612 | 1 | [0. 0. 0. 1.] | 0.000193868 | 0.999806 | 1 | [0. 1.] | 1 | -| (1.0, 1) | 0.000768513 | 0.997231 | 0.998 | [0. 0. 0.002 0.998] | 0.000387314 | 0.998612 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 2) | 0.0110783 | 0.985922 | 0.997 | [0. 0. 0.003 0.997] | 0.00558684 | 0.992911 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 3) | 0.00656081 | 0.990439 | 0.997 | [0. 0. 0.003 0.997] | 0.00330234 | 0.995195 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 4) | 0.00261978 | 0.99538 | 0.998 | [0. 0. 0.002 0.998] | 0.00131429 | 0.997685 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 5) | 0.00163194 | 0.998368 | 1 | [0. 0. 0. 1.] | 0.000816636 | 0.999183 | 1 | [0. 1.] | 1 | -| (1.0, 6) | 0.00407542 | 0.995925 | 1 | [0. 0. 0. 1.] | 0.00204187 | 0.997958 | 1 | [0. 1.] | 1 | -| (1.0, 7) | 0.010548 | 0.987452 | 0.998 | [0. 0. 0.002 0.998] | 0.0053126 | 0.993686 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 8) | 0.00242359 | 0.996576 | 0.999 | [0. 0. 0.001 0.999] | 0.00121665 | 0.998283 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 9) | 0.00321056 | 0.995789 | 0.999 | [0. 0. 0.001 0.999] | 0.00160947 | 0.99789 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 10) | 0.00323781 | 0.996762 | 1 | [0. 0. 0. 1.] | 0.00162153 | 0.998378 | 1 | [0. 1.] | 1 | -| (1.0, 11) | 0.000783789 | 0.999216 | 1 | [0. 0. 0. 1.] | 0.000392048 | 0.999608 | 1 | [0. 1.] | 1 | -| (1.0, 12) | 0.00695127 | 0.993049 | 1 | [0. 0. 0. 1.] | 0.00348776 | 0.996512 | 1 | [0. 1.] | 1 | -| (1.0, 13) | 0.00228645 | 0.995714 | 0.998 | [0. 0. 0.002 0.998] | 0.00114683 | 0.997852 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 14) | 0.0112308 | 0.988769 | 1 | [0. 0. 0. 1.] | 0.00564713 | 0.994353 | 1 | [0. 1.] | 1 | -| (1.0, 15) | 0.012585 | 0.984415 | 0.997 | [0. 0. 0.003 0.997] | 0.00635148 | 0.992146 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 16) | 0.00115313 | 0.998847 | 1 | [0. 0. 0. 1.] | 0.000576897 | 0.999423 | 1 | [0. 1.] | 1 | -| (1.0, 17) | 0.00564588 | 0.993354 | 0.999 | [0. 0. 0.001 0.999] | 0.00283377 | 0.996666 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 18) | 0.00350382 | 0.995496 | 0.999 | [0. 0. 0.001 0.999] | 0.00175679 | 0.997743 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 19) | 0.00979927 | 0.990201 | 1 | [0. 0. 0. 1.] | 0.00492376 | 0.995076 | 1 | [0. 1.] | 1 | -| (1.0, 20) | 0.00602819 | 0.991972 | 0.998 | [0. 0. 0.002 0.998] | 0.00302928 | 0.99597 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 21) | 0.00330174 | 0.995698 | 0.999 | [0. 0. 0.001 0.999] | 0.00165551 | 0.997844 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 22) | 0.00237333 | 0.996627 | 0.999 | [0. 0. 0.001 0.999] | 0.00118927 | 0.99831 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 23) | 0.00497561 | 0.994024 | 0.999 | [0. 0. 0.001 0.999] | 0.00249651 | 0.997003 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 24) | 0.0045159 | 0.995484 | 1 | [0. 0. 0. 1.] | 0.00226306 | 0.997737 | 1 | [0. 1.] | 1 | -| (1.0, 25) | 0.00159816 | 0.999598 | 0.998 | [0. 0. 0.002 0.998] | 0.000799555 | 0.999799 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 26) | 0.00282877 | 0.996171 | 0.999 | [0. 0. 0.001 0.999] | 0.00142084 | 0.998079 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 27) | 0.0055852 | 0.992415 | 0.998 | [0. 0. 0.002 0.998] | 0.00280651 | 0.996192 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 28) | 0.00522598 | 0.994774 | 1 | [0. 0. 0. 1.] | 0.00261984 | 0.99738 | 1 | [0. 1.] | 1 | -| (1.0, 29) | 0.00351762 | 0.995482 | 0.999 | [0. 0. 0.001 0.999] | 0.00176397 | 0.997736 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 30) | 0.00530586 | 0.993694 | 0.999 | [0. 0. 0.001 0.999] | 0.00266265 | 0.996837 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 31) | 3.04145e-05 | 0.99897 | 0.999 | [0. 0. 0.001 0.999] | 1.59575e-05 | 0.999484 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 32) | 0.00892355 | 0.990076 | 0.999 | [0. 0. 0.001 0.999] | 0.00448628 | 0.995013 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 33) | 0.00909471 | 0.990905 | 1 | [0. 0. 0. 1.] | 0.00456814 | 0.995432 | 1 | [0. 1.] | 1 | -| (1.0, 34) | 0.00174823 | 0.997252 | 0.999 | [0. 0. 0.001 0.999] | 0.000876536 | 0.998623 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 35) | 0.00144624 | 0.996554 | 0.998 | [0. 0. 0.002 0.998] | 0.000725097 | 0.998274 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 36) | 0.0104275 | 0.989573 | 1 | [0. 0. 0. 1.] | 0.00524108 | 0.994759 | 1 | [0. 1.] | 1 | -| (1.0, 37) | 0.00680342 | 0.992197 | 0.999 | [0. 0. 0.001 0.999] | 0.00341675 | 0.996083 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 38) | 0.0005654 | 0.998565 | 0.998 | [0. 0. 0.002 0.998] | 0.000281429 | 0.99928 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 39) | 0.0103369 | 0.988663 | 0.999 | [0. 0. 0.001 0.999] | 0.0052012 | 0.994299 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 40) | 0.00464786 | 0.994352 | 0.999 | [0. 0. 0.001 0.999] | 0.00233173 | 0.997168 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 41) | 0.00555467 | 0.993445 | 0.999 | [0. 0. 0.001 0.999] | 0.00278786 | 0.996712 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 42) | 0.000538905 | 0.999461 | 1 | [0. 0. 0. 1.] | 0.000269525 | 0.99973 | 1 | [0. 1.] | 1 | -| (1.0, 43) | 0.000103208 | 0.999897 | 1 | [0. 0. 0. 1.] | 5.16066e-05 | 0.999948 | 1 | [0. 1.] | 1 | -| (1.0, 44) | 0.000659831 | 0.99834 | 0.999 | [0. 0. 0.001 0.999] | 0.00033133 | 0.999168 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 45) | 0.00772108 | 0.991279 | 0.999 | [0. 0. 0.001 0.999] | 0.00388279 | 0.995617 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 46) | 0.0105663 | 0.986434 | 0.997 | [0. 0. 0.003 0.997] | 0.00532721 | 0.993171 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 47) | 0.00475672 | 0.995243 | 1 | [0. 0. 0. 1.] | 0.00238403 | 0.997616 | 1 | [0. 1.] | 1 | -| (1.0, 48) | 0.00098032 | 0.99902 | 1 | [0. 0. 0. 1.] | 0.0004904 | 0.99951 | 1 | [0. 1.] | 1 | -| (1.0, 49) | 0.00485684 | 0.995143 | 1 | [0. 0. 0. 1.] | 0.00243433 | 0.997566 | 1 | [0. 1.] | 1 | -| (1.0, 50) | 0.000364171 | 0.999636 | 1 | [0. 0. 0. 1.] | 0.000182119 | 0.999818 | 1 | [0. 1.] | 1 | -| (1.0, 51) | 0.00901725 | 0.989983 | 0.999 | [0. 0. 0.001 0.999] | 0.00453359 | 0.994966 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 52) | 0.00401398 | 0.995986 | 1 | [0. 0. 0. 1.] | 0.00201102 | 0.997989 | 1 | [0. 1.] | 1 | -| (1.0, 53) | 6.02743e-05 | 0.99994 | 1 | [0. 0. 0. 1.] | 3.0138e-05 | 0.99997 | 1 | [0. 1.] | 1 | -| (1.0, 54) | 0.00525217 | 0.993748 | 0.999 | [0. 0. 0.001 0.999] | 0.00263592 | 0.996864 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 55) | 0.00586705 | 0.992133 | 0.998 | [0. 0. 0.002 0.998] | 0.00294815 | 0.996051 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 56) | 0.0047068 | 0.993293 | 0.998 | [0. 0. 0.002 0.998] | 0.00236369 | 0.996635 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 57) | 0.000832389 | 0.999168 | 1 | [0. 0. 0. 1.] | 0.000416368 | 0.999584 | 1 | [0. 1.] | 1 | -| (1.0, 58) | 0.00219301 | 0.997807 | 1 | [0. 0. 0. 1.] | 0.00109771 | 0.998902 | 1 | [0. 1.] | 1 | -| (1.0, 59) | 0.00652092 | 0.991479 | 0.998 | [0. 0. 0.002 0.998] | 0.0032777 | 0.995721 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 60) | 0.00533726 | 0.991663 | 0.997 | [0. 0. 0.003 0.997] | 0.0026845 | 0.995813 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 61) | 0.00188364 | 0.997116 | 0.999 | [0. 0. 0.001 0.999] | 0.000945307 | 0.998554 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 62) | 0.00960516 | 0.990395 | 1 | [0. 0. 0. 1.] | 0.00482576 | 0.995174 | 1 | [0. 1.] | 1 | -| (1.0, 63) | 0.000144585 | 0.999855 | 1 | [0. 0. 0. 1.] | 7.22978e-05 | 0.999928 | 1 | [0. 1.] | 1 | -| (1.0, 64) | 0.0125929 | 0.984407 | 0.997 | [0. 0. 0.003 0.997] | 0.00635546 | 0.992142 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 65) | 0.00338717 | 0.995613 | 0.999 | [0. 0. 0.001 0.999] | 0.00169816 | 0.997802 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 66) | 0.00079801 | 0.998202 | 0.999 | [0. 0. 0.001 0.999] | 0.000401174 | 0.999099 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 67) | 0.00224744 | 0.997753 | 1 | [0. 0. 0. 1.] | 0.00112499 | 0.998875 | 1 | [0. 1.] | 1 | -| (1.0, 68) | 0.00487772 | 0.995122 | 1 | [0. 0. 0. 1.] | 0.00244482 | 0.997555 | 1 | [0. 1.] | 1 | -| (1.0, 69) | 0.000371242 | 0.999629 | 1 | [0. 0. 0. 1.] | 0.000185655 | 0.999814 | 1 | [0. 1.] | 1 | -| (1.0, 70) | 0.00206798 | 0.997932 | 1 | [0. 0. 0. 1.] | 0.00103506 | 0.998965 | 1 | [0. 1.] | 1 | -| (1.0, 71) | 0.000710046 | 0.99929 | 1 | [0. 0. 0. 1.] | 0.000355149 | 0.999645 | 1 | [0. 1.] | 1 | -| (1.0, 72) | 0.00335647 | 0.993644 | 0.997 | [0. 0. 0.003 0.997] | 0.00168683 | 0.996811 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 73) | 0.00405536 | 0.994945 | 0.999 | [0. 0. 0.001 0.999] | 0.00203386 | 0.997466 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 74) | 0.00439108 | 0.995609 | 1 | [0. 0. 0. 1.] | 0.00220037 | 0.9978 | 1 | [0. 1.] | 1 | -| (1.0, 75) | 0.000376378 | 0.999624 | 1 | [0. 0. 0. 1.] | 0.000188225 | 0.999812 | 1 | [0. 1.] | 1 | -| (1.0, 76) | 2.2775e-05 | 0.999977 | 1 | [0. 0. 0. 1.] | 1.13876e-05 | 0.999989 | 1 | [0. 1.] | 1 | -| (1.0, 77) | 0.000470373 | 0.99953 | 1 | [0. 0. 0. 1.] | 0.000235242 | 0.999765 | 1 | [0. 1.] | 1 | -| (1.0, 78) | 0.000160133 | 0.99984 | 1 | [0. 0. 0. 1.] | 8.00731e-05 | 0.99992 | 1 | [0. 1.] | 1 | -| (1.0, 79) | 0.00207087 | 0.997929 | 1 | [0. 0. 0. 1.] | 0.00103651 | 0.998963 | 1 | [0. 1.] | 1 | -| (1.0, 80) | 0.00810417 | 0.989896 | 0.998 | [0. 0. 0.002 0.998] | 0.00407733 | 0.994922 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 81) | 0.00617284 | 0.993827 | 1 | [0. 0. 0. 1.] | 0.00309598 | 0.996904 | 1 | [0. 1.] | 1 | -| (1.0, 82) | 0.000753367 | 0.999247 | 1 | [0. 0. 0. 1.] | 0.000376825 | 0.999623 | 1 | [0. 1.] | 1 | -| (1.0, 83) | 0.00409922 | 0.995901 | 1 | [0. 0. 0. 1.] | 0.00205382 | 0.997946 | 1 | [0. 1.] | 1 | -| (1.0, 84) | 0.00782572 | 0.990174 | 0.998 | [0. 0. 0.002 0.998] | 0.00393612 | 0.995063 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 85) | 0.0045099 | 0.99349 | 0.998 | [0. 0. 0.002 0.998] | 0.0022646 | 0.996734 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 86) | 0.00363821 | 0.994362 | 0.998 | [0. 0. 0.002 0.998] | 0.00182623 | 0.997173 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 87) | 0.00481349 | 0.995187 | 1 | [0. 0. 0. 1.] | 0.00241255 | 0.997587 | 1 | [0. 1.] | 1 | -| (1.0, 88) | 0.00433078 | 0.995669 | 1 | [0. 0. 0. 1.] | 0.00217009 | 0.99783 | 1 | [0. 1.] | 1 | -| (1.0, 89) | 0.00589255 | 0.992107 | 0.998 | [0. 0. 0.002 0.998] | 0.00296091 | 0.996038 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 90) | 0.000687943 | 0.999312 | 1 | [0. 0. 0. 1.] | 0.00034409 | 0.999656 | 1 | [0. 1.] | 1 | -| (1.0, 91) | 0.00126096 | 0.998261 | 0.997 | [0. 0. 0.003 0.997] | 0.000629455 | 0.999127 | 0.998498 | [0.003 0.997] | 0.997 | -| (1.0, 92) | 0.0047099 | 0.99429 | 0.999 | [0. 0. 0.001 0.999] | 0.00236294 | 0.997137 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 93) | 0.00124108 | 0.997759 | 0.999 | [0. 0. 0.001 0.999] | 0.000621546 | 0.998878 | 0.9995 | [0.001 0.999] | 0.999 | -| (1.0, 94) | 0.00111707 | 0.996883 | 0.998 | [0. 0. 0.002 0.998] | 0.00055997 | 0.998439 | 0.998999 | [0.002 0.998] | 0.998 | -| (1.0, 95) | 2.16117e-05 | 0.999978 | 1 | [0. 0. 0. 1.] | 1.0806e-05 | 0.999989 | 1 | [0. 1.] | 1 | -| (1.0, 96) | 0.00580863 | 0.994191 | 1 | [0. 0. 0. 1.] | 0.00291278 | 0.997087 | 1 | [0. 1.] | 1 | -| (1.0, 97) | 0.00636452 | 0.993635 | 1 | [0. 0. 0. 1.] | 0.00319242 | 0.996808 | 1 | [0. 1.] | 1 | -| (1.0, 98) | 0.00321332 | 0.996787 | 1 | [0. 0. 0. 1.] | 0.00160925 | 0.998391 | 1 | [0. 1.] | 1 | +| | ('acc', 'mulmc_sld') | ('acc_score', 'mulmc_sld') | ('acc_score', 'ref') | ('eprevs', 'mulmc_sld') | ('f1', 'mulmc_sld') | ('f1_score', 'mulmc_sld') | ('f1_score', 'ref') | ('prevs', 'mulmc_sld') | ('ref', 'mulmc_sld') | +|:-----------|-----------------------:|-----------------------------:|-----------------------:|:--------------------------|----------------------:|----------------------------:|----------------------:|:-------------------------|-----------------------:| +| (0.0, 0) | 0.117723 | 0.0322767 | 0.15 | [0.15 0.85 0. 0. ] | 1.07331e-70 | 1.07331e-70 | 0 | [0.15 0.85] | 0.15 | +| (0.0, 1) | 0.104077 | 0.0449229 | 0.149 | [0.149 0.851 0. 0. ] | 8.53968e-74 | 8.53968e-74 | 0 | [0.149 0.851] | 0.149 | +| (0.0, 2) | 0.087328 | 0.046672 | 0.134 | [0.134 0.866 0. 0. ] | 1.42651e-85 | 1.42651e-85 | 0 | [0.134 0.866] | 0.134 | +| (0.0, 3) | 0.111971 | 0.0340294 | 0.146 | [0.146 0.854 0. 0. ] | 6.79989e-84 | 6.79989e-84 | 0 | [0.146 0.854] | 0.146 | +| (0.0, 4) | 0.114691 | 0.0233089 | 0.138 | [0.138 0.862 0. 0. ] | 1.46993e-84 | 1.46993e-84 | 0 | [0.138 0.862] | 0.138 | +| (0.0, 5) | 0.103348 | 0.0556519 | 0.159 | [0.159 0.841 0. 0. ] | 3.0317e-73 | 3.0317e-73 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 6) | 0.0685699 | 0.0654301 | 0.134 | [0.134 0.866 0. 0. ] | 1.41915e-80 | 1.41915e-80 | 0 | [0.134 0.866] | 0.134 | +| (0.0, 7) | 0.0672097 | 0.0867903 | 0.154 | [0.154 0.846 0. 0. ] | 1.58212e-72 | 1.58212e-72 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 8) | 0.0906741 | 0.0343259 | 0.125 | [0.125 0.875 0. 0. ] | 2.35037e-81 | 2.35037e-81 | 0 | [0.125 0.875] | 0.125 | +| (0.0, 9) | 0.108344 | 0.0456563 | 0.154 | [0.154 0.846 0. 0. ] | 1.61876e-89 | 1.61876e-89 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 10) | 0.111637 | 0.0303634 | 0.142 | [0.142 0.858 0. 0. ] | 2.34042e-76 | 2.34042e-76 | 0 | [0.142 0.858] | 0.142 | +| (0.0, 11) | 0.0818937 | 0.0631063 | 0.145 | [0.145 0.855 0. 0. ] | 8.88464e-76 | 8.88464e-76 | 0 | [0.145 0.855] | 0.145 | +| (0.0, 12) | 0.080566 | 0.053434 | 0.134 | [0.134 0.866 0. 0. ] | 2.06441e-85 | 2.06441e-85 | 0 | [0.134 0.866] | 0.134 | +| (0.0, 13) | 0.109082 | 0.0259184 | 0.135 | [0.135 0.865 0. 0. ] | 6.90501e-68 | 6.90501e-68 | 0 | [0.135 0.865] | 0.135 | +| (0.0, 14) | 0.104661 | 0.0563386 | 0.161 | [0.161 0.839 0. 0. ] | 1.20084e-80 | 1.20084e-80 | 0 | [0.161 0.839] | 0.161 | +| (0.0, 15) | 0.110168 | 0.0358321 | 0.146 | [0.146 0.854 0. 0. ] | 5.13457e-80 | 5.13457e-80 | 0 | [0.146 0.854] | 0.146 | +| (0.0, 16) | 0.0821699 | 0.0548301 | 0.137 | [0.137 0.863 0. 0. ] | 1.23927e-107 | 1.23927e-107 | 0 | [0.137 0.863] | 0.137 | +| (0.0, 17) | 0.152954 | 0.00104645 | 0.154 | [0.154 0.846 0. 0. ] | 7.76874e-67 | 7.76874e-67 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 18) | 0.101295 | 0.0507053 | 0.152 | [0.152 0.848 0. 0. ] | 5.41613e-70 | 5.41613e-70 | 0 | [0.152 0.848] | 0.152 | +| (0.0, 19) | 0.101427 | 0.0475729 | 0.149 | [0.149 0.851 0. 0. ] | 7.82058e-75 | 7.82058e-75 | 0 | [0.149 0.851] | 0.149 | +| (0.0, 20) | 0.0552487 | 0.111751 | 0.167 | [0.167 0.833 0. 0. ] | 1.48613e-85 | 1.48613e-85 | 0 | [0.167 0.833] | 0.167 | +| (0.0, 21) | 0.105907 | 0.0250927 | 0.131 | [0.131 0.869 0. 0. ] | 4.52759e-74 | 4.52759e-74 | 0 | [0.131 0.869] | 0.131 | +| (0.0, 22) | 0.0702368 | 0.0837632 | 0.154 | [0.154 0.846 0. 0. ] | 4.82942e-111 | 4.82942e-111 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 23) | 0.121287 | 0.0297134 | 0.151 | [0.151 0.849 0. 0. ] | 5.89441e-65 | 5.89441e-65 | 0 | [0.151 0.849] | 0.151 | +| (0.0, 24) | 0.0834857 | 0.0645143 | 0.148 | [0.148 0.852 0. 0. ] | 3.59591e-72 | 3.59591e-72 | 0 | [0.148 0.852] | 0.148 | +| (0.0, 25) | 0.0948862 | 0.0471138 | 0.142 | [0.142 0.858 0. 0. ] | 4.1855e-77 | 4.1855e-77 | 0 | [0.142 0.858] | 0.142 | +| (0.0, 26) | 0.084485 | 0.066515 | 0.151 | [0.151 0.849 0. 0. ] | 3.53958e-93 | 3.53958e-93 | 0 | [0.151 0.849] | 0.151 | +| (0.0, 27) | 0.0788194 | 0.0781806 | 0.157 | [0.157 0.843 0. 0. ] | 7.26205e-93 | 7.26205e-93 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 28) | 0.100112 | 0.0588884 | 0.159 | [0.159 0.841 0. 0. ] | 3.99054e-87 | 3.99054e-87 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 29) | 0.0964799 | 0.0535201 | 0.15 | [0.15 0.85 0. 0. ] | 6.532e-82 | 6.532e-82 | 0 | [0.15 0.85] | 0.15 | +| (0.0, 30) | 0.119753 | 0.0272469 | 0.147 | [0.147 0.853 0. 0. ] | 4.10831e-75 | 4.10831e-75 | 0 | [0.147 0.853] | 0.147 | +| (0.0, 31) | 0.0706346 | 0.0883654 | 0.159 | [0.159 0.841 0. 0. ] | 1.27863e-72 | 1.27863e-72 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 32) | 0.0988101 | 0.0491899 | 0.148 | [0.148 0.852 0. 0. ] | 2.07851e-74 | 2.07851e-74 | 0 | [0.148 0.852] | 0.148 | +| (0.0, 33) | 0.0783135 | 0.0846865 | 0.163 | [0.163 0.837 0. 0. ] | 2.39408e-90 | 2.39408e-90 | 0 | [0.163 0.837] | 0.163 | +| (0.0, 34) | 0.0902689 | 0.0557311 | 0.146 | [0.146 0.854 0. 0. ] | 6.57654e-81 | 6.57654e-81 | 0 | [0.146 0.854] | 0.146 | +| (0.0, 35) | 0.0917235 | 0.0572765 | 0.149 | [0.149 0.851 0. 0. ] | 1.93739e-83 | 1.93739e-83 | 0 | [0.149 0.851] | 0.149 | +| (0.0, 36) | 0.0600061 | 0.108994 | 0.169 | [0.169 0.831 0. 0. ] | 1.4334e-110 | 1.4334e-110 | 0 | [0.169 0.831] | 0.169 | +| (0.0, 37) | 0.0939152 | 0.0700848 | 0.164 | [0.164 0.836 0. 0. ] | 3.97301e-77 | 3.97301e-77 | 0 | [0.164 0.836] | 0.164 | +| (0.0, 38) | 0.0713029 | 0.0846971 | 0.156 | [0.156 0.844 0. 0. ] | 1.62578e-85 | 1.62578e-85 | 0 | [0.156 0.844] | 0.156 | +| (0.0, 39) | 0.10238 | 0.0696199 | 0.172 | [0.172 0.828 0. 0. ] | 1.64608e-71 | 1.64608e-71 | 0 | [0.172 0.828] | 0.172 | +| (0.0, 40) | 0.0808104 | 0.0661896 | 0.147 | [0.147 0.853 0. 0. ] | 7.03173e-76 | 7.03173e-76 | 0 | [0.147 0.853] | 0.147 | +| (0.0, 41) | 0.0477844 | 0.105216 | 0.153 | [0.153 0.847 0. 0. ] | 1.94212e-97 | 1.94212e-97 | 0 | [0.153 0.847] | 0.153 | +| (0.0, 42) | 0.0755388 | 0.0734612 | 0.149 | [0.149 0.851 0. 0. ] | 9.58123e-82 | 9.58123e-82 | 0 | [0.149 0.851] | 0.149 | +| (0.0, 43) | 0.0541017 | 0.0788983 | 0.133 | [0.133 0.867 0. 0. ] | 3.21118e-74 | 3.21118e-74 | 0 | [0.133 0.867] | 0.133 | +| (0.0, 44) | 0.0856808 | 0.0713192 | 0.157 | [0.157 0.843 0. 0. ] | 1.72404e-73 | 1.72404e-73 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 45) | 0.0736663 | 0.0893337 | 0.163 | [0.163 0.837 0. 0. ] | 5.94041e-97 | 5.94041e-97 | 0 | [0.163 0.837] | 0.163 | +| (0.0, 46) | 0.118402 | 0.038598 | 0.157 | [0.157 0.843 0. 0. ] | 6.0959e-69 | 6.0959e-69 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 47) | 0.11078 | 0.0342197 | 0.145 | [0.145 0.855 0. 0. ] | 4.06314e-72 | 4.06314e-72 | 0 | [0.145 0.855] | 0.145 | +| (0.0, 48) | 0.0831503 | 0.0478497 | 0.131 | [0.131 0.869 0. 0. ] | 5.42314e-85 | 5.42314e-85 | 0 | [0.131 0.869] | 0.131 | +| (0.0, 49) | 0.0916819 | 0.0513181 | 0.143 | [0.143 0.857 0. 0. ] | 5.04478e-81 | 5.04478e-81 | 0 | [0.143 0.857] | 0.143 | +| (0.0, 50) | 0.11094 | 0.03506 | 0.146 | [0.146 0.854 0. 0. ] | 2.9869e-75 | 2.9869e-75 | 0 | [0.146 0.854] | 0.146 | +| (0.0, 51) | 0.0908154 | 0.0471846 | 0.138 | [0.138 0.862 0. 0. ] | 7.09617e-64 | 7.09617e-64 | 0 | [0.138 0.862] | 0.138 | +| (0.0, 52) | 0.116686 | 0.0233143 | 0.14 | [0.14 0.86 0. 0. ] | 1.53735e-66 | 1.53735e-66 | 0 | [0.14 0.86] | 0.14 | +| (0.0, 53) | 0.0621283 | 0.104872 | 0.167 | [0.167 0.833 0. 0. ] | 1.32848e-95 | 1.32848e-95 | 0 | [0.167 0.833] | 0.167 | +| (0.0, 54) | 0.0971333 | 0.0688667 | 0.166 | [0.166 0.834 0. 0. ] | 1.60209e-77 | 1.60209e-77 | 0 | [0.166 0.834] | 0.166 | +| (0.0, 55) | 0.0930373 | 0.0839627 | 0.177 | [0.177 0.823 0. 0. ] | 1.6235e-87 | 1.6235e-87 | 0 | [0.177 0.823] | 0.177 | +| (0.0, 56) | 0.0757822 | 0.0812178 | 0.157 | [0.157 0.843 0. 0. ] | 4.25804e-75 | 4.25804e-75 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 57) | 0.0938824 | 0.0411176 | 0.135 | [0.135 0.865 0. 0. ] | 2.09063e-71 | 2.09063e-71 | 0 | [0.135 0.865] | 0.135 | +| (0.0, 58) | 0.106571 | 0.0524287 | 0.159 | [0.159 0.841 0. 0. ] | 7.73697e-68 | 7.73697e-68 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 59) | 0.0897803 | 0.0692197 | 0.159 | [0.159 0.841 0. 0. ] | 9.83727e-73 | 9.83727e-73 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 60) | 0.111984 | 0.0160161 | 0.128 | [0.128 0.872 0. 0. ] | 3.54758e-79 | 3.54758e-79 | 0 | [0.128 0.872] | 0.128 | +| (0.0, 61) | 0.101143 | 0.0308567 | 0.132 | [0.132 0.868 0. 0. ] | 1.72902e-77 | 1.72902e-77 | 0 | [0.132 0.868] | 0.132 | +| (0.0, 62) | 0.115279 | 0.0377211 | 0.153 | [0.153 0.847 0. 0. ] | 7.03603e-77 | 7.03603e-77 | 0 | [0.153 0.847] | 0.153 | +| (0.0, 63) | 0.0948507 | 0.0621493 | 0.157 | [0.157 0.843 0. 0. ] | 1.29883e-86 | 1.29883e-86 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 64) | 0.103509 | 0.0334913 | 0.137 | [0.137 0.863 0. 0. ] | 3.80986e-80 | 3.80986e-80 | 0 | [0.137 0.863] | 0.137 | +| (0.0, 65) | 0.0781409 | 0.0808591 | 0.159 | [0.159 0.841 0. 0. ] | 1.80981e-71 | 1.80981e-71 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 66) | 0.0757231 | 0.0592769 | 0.135 | [0.135 0.865 0. 0. ] | 3.5588e-95 | 3.5588e-95 | 0 | [0.135 0.865] | 0.135 | +| (0.0, 67) | 0.0728872 | 0.0881128 | 0.161 | [0.161 0.839 0. 0. ] | 5.99213e-89 | 5.99213e-89 | 0 | [0.161 0.839] | 0.161 | +| (0.0, 68) | 0.0639539 | 0.0750461 | 0.139 | [0.139 0.861 0. 0. ] | 6.04828e-84 | 6.04828e-84 | 0 | [0.139 0.861] | 0.139 | +| (0.0, 69) | 0.126742 | 0.0192578 | 0.146 | [0.146 0.854 0. 0. ] | 3.02779e-81 | 3.02779e-81 | 0 | [0.146 0.854] | 0.146 | +| (0.0, 70) | 0.0826081 | 0.0673919 | 0.15 | [0.15 0.85 0. 0. ] | 1.46215e-89 | 1.46215e-89 | 0 | [0.15 0.85] | 0.15 | +| (0.0, 71) | 0.0866534 | 0.0793466 | 0.166 | [0.166 0.834 0. 0. ] | 4.1842e-87 | 4.1842e-87 | 0 | [0.166 0.834] | 0.166 | +| (0.0, 72) | 0.0756651 | 0.0873349 | 0.163 | [0.163 0.837 0. 0. ] | 4.15436e-100 | 4.15436e-100 | 0 | [0.163 0.837] | 0.163 | +| (0.0, 73) | 0.0842012 | 0.0727988 | 0.157 | [0.157 0.843 0. 0. ] | 4.33554e-72 | 4.33554e-72 | 0 | [0.157 0.843] | 0.157 | +| (0.0, 74) | 0.0732343 | 0.0537657 | 0.127 | [0.127 0.873 0. 0. ] | 5.78521e-65 | 5.78521e-65 | 0 | [0.127 0.873] | 0.127 | +| (0.0, 75) | 0.0436204 | 0.10138 | 0.145 | [0.145 0.855 0. 0. ] | 5.99248e-08 | 5.99248e-08 | 0 | [0.145 0.855] | 0.145 | +| (0.0, 76) | 0.073634 | 0.071366 | 0.145 | [0.145 0.855 0. 0. ] | 1.33422e-86 | 1.33422e-86 | 0 | [0.145 0.855] | 0.145 | +| (0.0, 77) | 0.114722 | 0.0242778 | 0.139 | [0.139 0.861 0. 0. ] | 1.22296e-69 | 1.22296e-69 | 0 | [0.139 0.861] | 0.139 | +| (0.0, 78) | 0.0910729 | 0.0529271 | 0.144 | [0.144 0.856 0. 0. ] | 3.20385e-81 | 3.20385e-81 | 0 | [0.144 0.856] | 0.144 | +| (0.0, 79) | 0.1199 | 0.0391 | 0.159 | [0.159 0.841 0. 0. ] | 4.88611e-80 | 4.88611e-80 | 0 | [0.159 0.841] | 0.159 | +| (0.0, 80) | 0.101953 | 0.0500466 | 0.152 | [0.152 0.848 0. 0. ] | 1.89129e-75 | 1.89129e-75 | 0 | [0.152 0.848] | 0.152 | +| (0.0, 81) | 0.0889951 | 0.0510049 | 0.14 | [0.14 0.86 0. 0. ] | 1.37045e-70 | 1.37045e-70 | 0 | [0.14 0.86] | 0.14 | +| (0.0, 82) | 0.0998662 | 0.0471338 | 0.147 | [0.147 0.853 0. 0. ] | 3.1581e-76 | 3.1581e-76 | 0 | [0.147 0.853] | 0.147 | +| (0.0, 83) | 0.0831914 | 0.0558086 | 0.139 | [0.139 0.861 0. 0. ] | 1.71931e-86 | 1.71931e-86 | 0 | [0.139 0.861] | 0.139 | +| (0.0, 84) | 0.120851 | 0.0331487 | 0.154 | [0.154 0.846 0. 0. ] | 7.23765e-74 | 7.23765e-74 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 85) | 0.100605 | 0.0433947 | 0.144 | [0.144 0.856 0. 0. ] | 2.67006e-82 | 2.67006e-82 | 0 | [0.144 0.856] | 0.144 | +| (0.0, 86) | 0.0765061 | 0.0734939 | 0.15 | [0.15 0.85 0. 0. ] | 1.61559e-83 | 1.61559e-83 | 0 | [0.15 0.85] | 0.15 | +| (0.0, 87) | 0.0705979 | 0.0724021 | 0.143 | [0.143 0.857 0. 0. ] | 6.80161e-90 | 6.80161e-90 | 0 | [0.143 0.857] | 0.143 | +| (0.0, 88) | 0.133956 | 0.0270437 | 0.161 | [0.161 0.839 0. 0. ] | 4.02932e-85 | 4.02932e-85 | 0 | [0.161 0.839] | 0.161 | +| (0.0, 89) | 0.0874214 | 0.0665786 | 0.154 | [0.154 0.846 0. 0. ] | 5.57476e-68 | 5.57476e-68 | 0 | [0.154 0.846] | 0.154 | +| (0.0, 90) | 0.0715817 | 0.0634183 | 0.135 | [0.135 0.865 0. 0. ] | 4.40147e-76 | 4.40147e-76 | 0 | [0.135 0.865] | 0.135 | +| (0.0, 91) | 0.0905213 | 0.0544787 | 0.145 | [0.145 0.855 0. 0. ] | 5.28556e-78 | 5.28556e-78 | 0 | [0.145 0.855] | 0.145 | +| (0.0, 92) | 0.0795437 | 0.0964563 | 0.176 | [0.176 0.824 0. 0. ] | 1.1435e-87 | 1.1435e-87 | 0 | [0.176 0.824] | 0.176 | +| (0.0, 93) | 0.118409 | 0.0365913 | 0.155 | [0.155 0.845 0. 0. ] | 3.5294e-95 | 3.5294e-95 | 0 | [0.155 0.845] | 0.155 | +| (0.0, 94) | 0.109805 | 0.0591953 | 0.169 | [0.169 0.831 0. 0. ] | 1.80285e-76 | 1.80285e-76 | 0 | [0.169 0.831] | 0.169 | +| (0.0, 95) | 0.098196 | 0.052804 | 0.151 | [0.151 0.849 0. 0. ] | 2.818e-84 | 2.818e-84 | 0 | [0.151 0.849] | 0.151 | +| (0.0, 96) | 0.112439 | 0.0345615 | 0.147 | [0.147 0.853 0. 0. ] | 3.6936e-71 | 3.6936e-71 | 0 | [0.147 0.853] | 0.147 | +| (0.0, 97) | 0.0904939 | 0.0645061 | 0.155 | [0.155 0.845 0. 0. ] | 8.14262e-94 | 8.14262e-94 | 0 | [0.155 0.845] | 0.155 | +| (0.0, 98) | 0.107304 | 0.028696 | 0.136 | [0.136 0.864 0. 0. ] | 7.45097e-62 | 7.45097e-62 | 0 | [0.136 0.864] | 0.136 | +| (0.0, 99) | 0.0726854 | 0.0783146 | 0.151 | [0.151 0.849 0. 0. ] | 1.27965e-92 | 1.27965e-92 | 0 | [0.151 0.849] | 0.151 | +| (0.05, 0) | 0.139695 | 0.0533047 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 1.14176e-19 | 0.110254 | [0.143 0.857] | 0.193 | +| (0.05, 1) | 0.135563 | 0.0344375 | 0.17 | [0.12 0.83 0. 0.05] | 0.107527 | 3.58656e-10 | 0.107527 | [0.12 0.88] | 0.17 | +| (0.05, 2) | 0.140995 | 0.0510053 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 2.74753e-22 | 0.110132 | [0.142 0.858] | 0.192 | +| (0.05, 3) | 0.137862 | 0.0681381 | 0.206 | [0.156 0.794 0. 0.05 ] | 0.111857 | 1.58351e-10 | 0.111857 | [0.156 0.844] | 0.206 | +| (0.05, 4) | 0.106135 | 0.0828645 | 0.189 | [0.139 0.811 0. 0.05 ] | 0.109234 | 0.000535505 | 0.109769 | [0.139 0.861] | 0.189 | +| (0.05, 5) | 0.122517 | 0.0634832 | 0.186 | [0.136 0.814 0. 0.05 ] | 0.109409 | 4.44136e-17 | 0.109409 | [0.136 0.864] | 0.186 | +| (0.05, 6) | 0.156419 | 0.0255809 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 3.43807e-18 | 0.108932 | [0.132 0.868] | 0.182 | +| (0.05, 7) | 0.145084 | 0.0439162 | 0.189 | [0.139 0.811 0. 0.05 ] | 0.109769 | 2.65993e-14 | 0.109769 | [0.139 0.861] | 0.189 | +| (0.05, 8) | 0.155093 | 0.0359071 | 0.191 | [0.142 0.808 0.001 0.049] | 0.108049 | 4.59776e-11 | 0.108049 | [0.143 0.857] | 0.191 | +| (0.05, 9) | 0.182126 | 0.00487421 | 0.187 | [0.137 0.813 0. 0.05 ] | 0.109529 | 1.84496e-14 | 0.109529 | [0.137 0.863] | 0.187 | +| (0.05, 10) | 0.0998061 | 0.0921939 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 4.03461e-21 | 0.110132 | [0.142 0.858] | 0.192 | +| (0.05, 11) | 0.135749 | 0.0592513 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.16984e-21 | 0.110497 | [0.145 0.855] | 0.195 | +| (0.05, 12) | 0.115695 | 0.087305 | 0.203 | [0.153 0.797 0. 0.05 ] | 0.111483 | 1.33398e-15 | 0.111483 | [0.153 0.847] | 0.203 | +| (0.05, 13) | 0.132882 | 0.0491177 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 3.836e-24 | 0.108932 | [0.132 0.868] | 0.182 | +| (0.05, 14) | 0.10577 | 0.0692296 | 0.175 | [0.125 0.825 0. 0.05 ] | 0.108108 | 9.72476e-12 | 0.108108 | [0.125 0.875] | 0.175 | +| (0.05, 15) | 0.156678 | 0.036322 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 2.07272e-23 | 0.110254 | [0.143 0.857] | 0.193 | +| (0.05, 16) | 0.104347 | 0.0826534 | 0.187 | [0.137 0.813 0. 0.05 ] | 0.107933 | 0.00159586 | 0.109529 | [0.137 0.863] | 0.187 | +| (0.05, 17) | 0.169004 | 0.00799577 | 0.177 | [0.127 0.823 0. 0.05 ] | 0.108342 | 7.79333e-11 | 0.108342 | [0.127 0.873] | 0.177 | +| (0.05, 18) | 0.128698 | 0.0653016 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 1.77321e-22 | 0.110375 | [0.144 0.856] | 0.194 | +| (0.05, 19) | 0.111877 | 0.0971234 | 0.209 | [0.159 0.791 0. 0.05 ] | 0.112233 | 2.68131e-19 | 0.112233 | [0.159 0.841] | 0.209 | +| (0.05, 20) | 0.155026 | 0.0209742 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 3.94502e-18 | 0.108225 | [0.126 0.874] | 0.176 | +| (0.05, 21) | 0.124618 | 0.0773818 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 9.0121e-31 | 0.111359 | [0.152 0.848] | 0.202 | +| (0.05, 22) | 0.130664 | 0.0603363 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 3.03675e-22 | 0.110011 | [0.141 0.859] | 0.191 | +| (0.05, 23) | 0.154081 | 0.0469192 | 0.201 | [0.151 0.799 0. 0.05 ] | 0.111235 | 2.16643e-11 | 0.111235 | [0.151 0.849] | 0.201 | +| (0.05, 24) | 0.122652 | 0.059348 | 0.182 | [0.132 0.818 0. 0.05 ] | 0.108932 | 1.6966e-14 | 0.108932 | [0.132 0.868] | 0.182 | +| (0.05, 25) | 0.146311 | 0.055689 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111355 | 3.46681e-06 | 0.111359 | [0.152 0.848] | 0.202 | +| (0.05, 26) | 0.140658 | 0.0373423 | 0.178 | [0.128 0.822 0. 0.05 ] | 0.10846 | 2.48311e-18 | 0.10846 | [0.128 0.872] | 0.178 | +| (0.05, 27) | 0.134002 | 0.0429984 | 0.177 | [0.127 0.823 0. 0.05 ] | 0.108342 | 9.19174e-20 | 0.108342 | [0.127 0.873] | 0.177 | +| (0.05, 28) | 0.157105 | 0.0368953 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 4.83413e-18 | 0.110375 | [0.144 0.856] | 0.194 | +| (0.05, 29) | 0.149111 | 0.0308887 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 1.16102e-21 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 30) | 0.104365 | 0.0786353 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.10507e-14 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 31) | 0.158109 | 0.041891 | 0.2 | [0.151 0.799 0.001 0.049] | 0.109131 | 8.10113e-19 | 0.109131 | [0.152 0.848] | 0.2 | +| (0.05, 32) | 0.14954 | 0.0294602 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 1.28469e-14 | 0.108578 | [0.129 0.871] | 0.179 | +| (0.05, 33) | 0.157869 | 0.0161307 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 7.84528e-18 | 0.107991 | [0.124 0.876] | 0.174 | +| (0.05, 34) | 0.157753 | 0.039247 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 2.2032e-17 | 0.110742 | [0.147 0.853] | 0.197 | +| (0.05, 35) | 0.134724 | 0.0462759 | 0.181 | [0.131 0.819 0. 0.05 ] | 0.108814 | 1.46e-14 | 0.108814 | [0.131 0.869] | 0.181 | +| (0.05, 36) | 0.14284 | 0.0331603 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 3.56508e-16 | 0.108225 | [0.126 0.874] | 0.176 | +| (0.05, 37) | 0.135851 | 0.0541488 | 0.19 | [0.14 0.81 0. 0.05] | 0.10989 | 1.37439e-14 | 0.10989 | [0.14 0.86] | 0.19 | +| (0.05, 38) | 0.120848 | 0.0871521 | 0.208 | [0.158 0.792 0. 0.05 ] | 0.112108 | 3.07361e-10 | 0.112108 | [0.158 0.842] | 0.208 | +| (0.05, 39) | 0.108411 | 0.071589 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 9.4529e-12 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 40) | 0.138167 | 0.0358328 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 3.92459e-22 | 0.107991 | [0.124 0.876] | 0.174 | +| (0.05, 41) | 0.134067 | 0.0729327 | 0.207 | [0.157 0.793 0. 0.05 ] | 0.111982 | 6.75036e-30 | 0.111982 | [0.157 0.843] | 0.207 | +| (0.05, 42) | 0.172425 | 0.0235747 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 1.7918e-18 | 0.110619 | [0.146 0.854] | 0.196 | +| (0.05, 43) | 0.137972 | 0.0540283 | 0.192 | [0.142 0.808 0. 0.05 ] | 0.110132 | 6.253e-19 | 0.110132 | [0.142 0.858] | 0.192 | +| (0.05, 44) | 0.128741 | 0.0542591 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.05882e-23 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 45) | 0.138375 | 0.0376248 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.108225 | 4.84466e-15 | 0.108225 | [0.126 0.874] | 0.176 | +| (0.05, 46) | 0.162737 | 0.0232628 | 0.186 | [0.136 0.814 0. 0.05 ] | 0.109389 | 2.06901e-05 | 0.109409 | [0.136 0.864] | 0.186 | +| (0.05, 47) | 0.145798 | 0.0382015 | 0.184 | [0.134 0.816 0. 0.05 ] | 0.10917 | 2.78441e-27 | 0.10917 | [0.134 0.866] | 0.184 | +| (0.05, 48) | 0.137354 | 0.0726463 | 0.21 | [0.16 0.79 0. 0.05] | 0.11236 | 2.97923e-25 | 0.11236 | [0.16 0.84] | 0.21 | +| (0.05, 49) | 0.155531 | 0.0394693 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.57224e-19 | 0.110497 | [0.145 0.855] | 0.195 | +| (0.05, 50) | 0.165329 | 0.0146715 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 7.05554e-15 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 51) | 0.107478 | 0.0715223 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 2.33682e-16 | 0.108578 | [0.129 0.871] | 0.179 | +| (0.05, 52) | 0.0938284 | 0.0901716 | 0.184 | [0.134 0.816 0. 0.05 ] | 0.108422 | 0.000747907 | 0.10917 | [0.134 0.866] | 0.184 | +| (0.05, 53) | 0.137715 | 0.0582849 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 9.415e-15 | 0.110619 | [0.146 0.854] | 0.196 | +| (0.05, 54) | 0.152119 | 0.0308814 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 3.14673e-08 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 55) | 0.17551 | 0.0224896 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 2.44773e-14 | 0.110865 | [0.148 0.852] | 0.198 | +| (0.05, 56) | 0.180796 | 0.0172042 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 7.30979e-13 | 0.110865 | [0.148 0.852] | 0.198 | +| (0.05, 57) | 0.129629 | 0.0533714 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 7.45796e-26 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 58) | 0.163147 | 0.016853 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 2.66764e-19 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 59) | 0.0869485 | 0.103052 | 0.19 | [0.14 0.81 0. 0.05] | 0.109035 | 0.000854853 | 0.10989 | [0.14 0.86] | 0.19 | +| (0.05, 60) | 0.149196 | 0.0458035 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 1.89128e-15 | 0.110497 | [0.145 0.855] | 0.195 | +| (0.05, 61) | 0.126469 | 0.0565306 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.109051 | 1.04909e-16 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 62) | 0.172479 | 0.0155209 | 0.188 | [0.139 0.811 0.001 0.049] | 0.107692 | 3.99895e-09 | 0.107692 | [0.14 0.86] | 0.188 | +| (0.05, 63) | 0.0976526 | 0.0803474 | 0.178 | [0.128 0.822 0. 0.05 ] | 0.10846 | 1.72691e-33 | 0.10846 | [0.128 0.872] | 0.178 | +| (0.05, 64) | 0.118217 | 0.0667828 | 0.185 | [0.135 0.815 0. 0.05 ] | 0.10929 | 3.28193e-14 | 0.10929 | [0.135 0.865] | 0.185 | +| (0.05, 65) | 0.0917532 | 0.0912468 | 0.183 | [0.133 0.817 0. 0.05 ] | 0.1084 | 0.000651129 | 0.109051 | [0.133 0.867] | 0.183 | +| (0.05, 66) | 0.0971105 | 0.0788895 | 0.176 | [0.126 0.824 0. 0.05 ] | 0.107692 | 0.000532613 | 0.108225 | [0.126 0.874] | 0.176 | +| (0.05, 67) | 0.107754 | 0.0802459 | 0.188 | [0.138 0.812 0. 0.05 ] | 0.109269 | 0.000379747 | 0.109649 | [0.138 0.862] | 0.188 | +| (0.05, 68) | 0.153214 | 0.0267861 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 1.06491e-21 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 69) | 0.140475 | 0.0495245 | 0.19 | [0.14 0.81 0. 0.05] | 0.10989 | 2.79207e-20 | 0.10989 | [0.14 0.86] | 0.19 | +| (0.05, 70) | 0.145541 | 0.0454591 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 2.75263e-10 | 0.110011 | [0.141 0.859] | 0.191 | +| (0.05, 71) | 0.155227 | 0.0187734 | 0.174 | [0.124 0.826 0. 0.05 ] | 0.107991 | 4.71961e-12 | 0.107991 | [0.124 0.876] | 0.174 | +| (0.05, 72) | 0.154241 | 0.0667593 | 0.221 | [0.171 0.779 0. 0.05 ] | 0.113766 | 6.45733e-22 | 0.113766 | [0.171 0.829] | 0.221 | +| (0.05, 73) | 0.149282 | 0.0537176 | 0.203 | [0.153 0.797 0. 0.05 ] | 0.111483 | 5.42307e-08 | 0.111483 | [0.153 0.847] | 0.203 | +| (0.05, 74) | 0.112973 | 0.0600274 | 0.173 | [0.123 0.827 0. 0.05 ] | 0.107875 | 3.72521e-13 | 0.107875 | [0.123 0.877] | 0.173 | +| (0.05, 75) | 0.118677 | 0.0783229 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 2.40661e-16 | 0.110742 | [0.147 0.853] | 0.197 | +| (0.05, 76) | 0.118952 | 0.0800475 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 6.3083e-23 | 0.110988 | [0.149 0.851] | 0.199 | +| (0.05, 77) | 0.147543 | 0.0464572 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 1.67292e-16 | 0.110375 | [0.144 0.856] | 0.194 | +| (0.05, 78) | 0.157932 | 0.0350678 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 1.80691e-27 | 0.110254 | [0.143 0.857] | 0.193 | +| (0.05, 79) | 0.10977 | 0.0872304 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 4.64983e-19 | 0.110742 | [0.147 0.853] | 0.197 | +| (0.05, 80) | 0.110754 | 0.0802464 | 0.191 | [0.141 0.809 0. 0.05 ] | 0.110011 | 6.39424e-18 | 0.110011 | [0.141 0.859] | 0.191 | +| (0.05, 81) | 0.156431 | 0.041569 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 7.35924e-18 | 0.110865 | [0.148 0.852] | 0.198 | +| (0.05, 82) | 0.154779 | 0.0442212 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 6.42816e-10 | 0.110988 | [0.149 0.851] | 0.199 | +| (0.05, 83) | 0.126509 | 0.0814906 | 0.208 | [0.158 0.792 0. 0.05 ] | 0.112108 | 1.30576e-17 | 0.112108 | [0.158 0.842] | 0.208 | +| (0.05, 84) | 0.136929 | 0.0700712 | 0.207 | [0.158 0.792 0.001 0.049] | 0.109989 | 1.45571e-22 | 0.109989 | [0.159 0.841] | 0.207 | +| (0.05, 85) | 0.150736 | 0.0462638 | 0.197 | [0.147 0.803 0. 0.05 ] | 0.110742 | 1.26775e-15 | 0.110742 | [0.147 0.853] | 0.197 | +| (0.05, 86) | 0.13535 | 0.0666503 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 1.52258e-16 | 0.111359 | [0.152 0.848] | 0.202 | +| (0.05, 87) | 0.154934 | 0.0240656 | 0.179 | [0.129 0.821 0. 0.05 ] | 0.108578 | 7.65988e-15 | 0.108578 | [0.129 0.871] | 0.179 | +| (0.05, 88) | 0.107502 | 0.0654985 | 0.173 | [0.123 0.827 0. 0.05 ] | 0.107875 | 1.44443e-19 | 0.107875 | [0.123 0.877] | 0.173 | +| (0.05, 89) | 0.148545 | 0.0474553 | 0.196 | [0.146 0.804 0. 0.05 ] | 0.110619 | 1.7763e-27 | 0.110619 | [0.146 0.854] | 0.196 | +| (0.05, 90) | 0.118629 | 0.0533714 | 0.172 | [0.122 0.828 0. 0.05 ] | 0.107759 | 1.68968e-12 | 0.107759 | [0.122 0.878] | 0.172 | +| (0.05, 91) | 0.120002 | 0.0739985 | 0.194 | [0.144 0.806 0. 0.05 ] | 0.110375 | 2.31969e-15 | 0.110375 | [0.144 0.856] | 0.194 | +| (0.05, 92) | 0.11476 | 0.0782402 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 3.75623e-20 | 0.110254 | [0.143 0.857] | 0.193 | +| (0.05, 93) | 0.128018 | 0.0719818 | 0.2 | [0.15 0.8 0. 0.05] | 0.111111 | 3.18359e-20 | 0.111111 | [0.15 0.85] | 0.2 | +| (0.05, 94) | 0.104735 | 0.0972647 | 0.202 | [0.152 0.798 0. 0.05 ] | 0.111359 | 4.15272e-22 | 0.111359 | [0.152 0.848] | 0.202 | +| (0.05, 95) | 0.14863 | 0.0313704 | 0.18 | [0.13 0.82 0. 0.05] | 0.108696 | 5.0643e-17 | 0.108696 | [0.13 0.87] | 0.18 | +| (0.05, 96) | 0.155999 | 0.0390008 | 0.195 | [0.145 0.805 0. 0.05 ] | 0.110497 | 3.38826e-17 | 0.110497 | [0.145 0.855] | 0.195 | +| (0.05, 97) | 0.121559 | 0.0774405 | 0.199 | [0.149 0.801 0. 0.05 ] | 0.110988 | 8.6301e-15 | 0.110988 | [0.149 0.851] | 0.199 | +| (0.05, 98) | 0.107171 | 0.0908294 | 0.198 | [0.148 0.802 0. 0.05 ] | 0.110865 | 2.90772e-18 | 0.110865 | [0.148 0.852] | 0.198 | +| (0.05, 99) | 0.155625 | 0.0373746 | 0.193 | [0.143 0.807 0. 0.05 ] | 0.110254 | 2.24989e-10 | 0.110254 | [0.143 0.857] | 0.193 | +| (0.1, 0) | 0.178993 | 0.0430075 | 0.222 | [0.122 0.778 0. 0.1 ] | 0.144324 | 0.0601746 | 0.204499 | [0.122 0.878] | 0.222 | +| (0.1, 1) | 0.152454 | 0.0715457 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.143947 | 0.0609715 | 0.204918 | [0.124 0.876] | 0.224 | +| (0.1, 2) | 0.157962 | 0.0720383 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.189546 | 0.0166395 | 0.206186 | [0.13 0.87] | 0.23 | +| (0.1, 3) | 0.140091 | 0.103909 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.16705 | 0.042155 | 0.209205 | [0.144 0.856] | 0.244 | +| (0.1, 4) | 0.146193 | 0.0908066 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.144253 | 0.063431 | 0.207684 | [0.137 0.863] | 0.237 | +| (0.1, 5) | 0.149072 | 0.0789285 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.166733 | 0.0390282 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.1, 6) | 0.138868 | 0.0971322 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.146507 | 0.0609615 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 7) | 0.155198 | 0.0728022 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.166977 | 0.0387839 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.1, 8) | 0.155993 | 0.0650068 | 0.221 | [0.121 0.779 0. 0.1 ] | 0.142876 | 0.0614141 | 0.20429 | [0.121 0.879] | 0.221 | +| (0.1, 9) | 0.142102 | 0.0838979 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.161065 | 0.0442743 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 10) | 0.173001 | 0.0689987 | 0.242 | [0.142 0.758 0. 0.1 ] | 0.158182 | 0.0505862 | 0.208768 | [0.142 0.858] | 0.242 | +| (0.1, 11) | 0.137157 | 0.114843 | 0.252 | [0.152 0.748 0. 0.1 ] | 0.17158 | 0.0393908 | 0.21097 | [0.152 0.848] | 0.252 | +| (0.1, 12) | 0.139662 | 0.103338 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.138507 | 0.0704791 | 0.208986 | [0.143 0.857] | 0.243 | +| (0.1, 13) | 0.158423 | 0.0895769 | 0.248 | [0.149 0.751 0.001 0.099] | 0.190184 | 0.0182375 | 0.208421 | [0.15 0.85] | 0.248 | +| (0.1, 14) | 0.12466 | 0.10934 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.139225 | 0.0678139 | 0.207039 | [0.134 0.866] | 0.234 | +| (0.1, 15) | 0.159436 | 0.0755636 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.136966 | 0.0702877 | 0.207254 | [0.135 0.865] | 0.235 | +| (0.1, 16) | 0.140571 | 0.0874287 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.1321 | 0.073661 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.1, 17) | 0.133351 | 0.0936495 | 0.227 | [0.127 0.773 0. 0.1 ] | 0.145652 | 0.0598978 | 0.20555 | [0.127 0.873] | 0.227 | +| (0.1, 18) | 0.153178 | 0.074822 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.15928 | 0.0464809 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.1, 19) | 0.16243 | 0.0635701 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.14073 | 0.0646092 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 20) | 0.162509 | 0.0814913 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.160787 | 0.0484177 | 0.209205 | [0.144 0.856] | 0.244 | +| (0.1, 21) | 0.122019 | 0.109981 | 0.232 | [0.133 0.767 0.001 0.099] | 0.146363 | 0.058606 | 0.204969 | [0.134 0.866] | 0.232 | +| (0.1, 22) | 0.164098 | 0.0729024 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.142794 | 0.0648901 | 0.207684 | [0.137 0.863] | 0.237 | +| (0.1, 23) | 0.13655 | 0.10945 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.133593 | 0.0760508 | 0.209644 | [0.146 0.854] | 0.246 | +| (0.1, 24) | 0.156787 | 0.0702127 | 0.227 | [0.127 0.773 0. 0.1 ] | 0.162406 | 0.0431439 | 0.20555 | [0.127 0.873] | 0.227 | +| (0.1, 25) | 0.142566 | 0.103434 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.161656 | 0.0479876 | 0.209644 | [0.146 0.854] | 0.246 | +| (0.1, 26) | 0.16276 | 0.0772395 | 0.24 | [0.14 0.76 0. 0.1 ] | 0.128966 | 0.0793677 | 0.208333 | [0.14 0.86] | 0.24 | +| (0.1, 27) | 0.125019 | 0.0889812 | 0.214 | [0.114 0.786 0. 0.1 ] | 0.169924 | 0.0329159 | 0.20284 | [0.114 0.886] | 0.214 | +| (0.1, 28) | 0.141623 | 0.101377 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.131695 | 0.077291 | 0.208986 | [0.143 0.857] | 0.243 | +| (0.1, 29) | 0.196429 | 0.0525706 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.155012 | 0.0552926 | 0.210305 | [0.149 0.851] | 0.249 | +| (0.1, 30) | 0.14666 | 0.10034 | 0.247 | [0.147 0.753 0. 0.1 ] | 0.134494 | 0.0753696 | 0.209864 | [0.147 0.853] | 0.247 | +| (0.1, 31) | 0.138889 | 0.111111 | 0.25 | [0.151 0.749 0.001 0.099] | 0.122134 | 0.0867272 | 0.208861 | [0.152 0.848] | 0.25 | +| (0.1, 32) | 0.14235 | 0.10065 | 0.243 | [0.144 0.756 0.001 0.099] | 0.125462 | 0.0818674 | 0.20733 | [0.145 0.855] | 0.243 | +| (0.1, 33) | 0.149198 | 0.0748024 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.138851 | 0.0660671 | 0.204918 | [0.124 0.876] | 0.224 | +| (0.1, 34) | 0.183632 | 0.0423683 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.165968 | 0.0393708 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 35) | 0.109742 | 0.131258 | 0.241 | [0.141 0.759 0. 0.1 ] | 0.124891 | 0.0836592 | 0.208551 | [0.141 0.859] | 0.241 | +| (0.1, 36) | 0.119279 | 0.103721 | 0.223 | [0.123 0.777 0. 0.1 ] | 0.158427 | 0.0462817 | 0.204708 | [0.123 0.877] | 0.223 | +| (0.1, 37) | 0.176492 | 0.0365084 | 0.213 | [0.113 0.787 0. 0.1 ] | 0.158593 | 0.0440417 | 0.202634 | [0.113 0.887] | 0.213 | +| (0.1, 38) | 0.13036 | 0.09864 | 0.229 | [0.129 0.771 0. 0.1 ] | 0.151765 | 0.0542087 | 0.205973 | [0.129 0.871] | 0.229 | +| (0.1, 39) | 0.143694 | 0.0773055 | 0.221 | [0.121 0.779 0. 0.1 ] | 0.152654 | 0.0516359 | 0.20429 | [0.121 0.879] | 0.221 | +| (0.1, 40) | 0.176925 | 0.0620751 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.173127 | 0.0349899 | 0.208117 | [0.139 0.861] | 0.239 | +| (0.1, 41) | 0.161501 | 0.0714994 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.141467 | 0.0653583 | 0.206825 | [0.133 0.867] | 0.233 | +| (0.1, 42) | 0.174647 | 0.0573527 | 0.232 | [0.133 0.767 0.001 0.099] | 0.163408 | 0.0415608 | 0.204969 | [0.134 0.866] | 0.232 | +| (0.1, 43) | 0.167175 | 0.0718247 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.144039 | 0.0640772 | 0.208117 | [0.139 0.861] | 0.239 | +| (0.1, 44) | 0.178495 | 0.0705051 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.167393 | 0.0429116 | 0.210305 | [0.149 0.851] | 0.249 | +| (0.1, 45) | 0.136153 | 0.0988467 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.143173 | 0.0640808 | 0.207254 | [0.135 0.865] | 0.235 | +| (0.1, 46) | 0.160061 | 0.0699394 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.160817 | 0.045369 | 0.206186 | [0.13 0.87] | 0.23 | +| (0.1, 47) | 0.14961 | 0.0743897 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.125738 | 0.07918 | 0.204918 | [0.124 0.876] | 0.224 | +| (0.1, 48) | 0.151283 | 0.0817166 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.15867 | 0.0481552 | 0.206825 | [0.133 0.867] | 0.233 | +| (0.1, 49) | 0.104849 | 0.132151 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.145601 | 0.062083 | 0.207684 | [0.137 0.863] | 0.237 | +| (0.1, 50) | 0.137846 | 0.0781539 | 0.216 | [0.116 0.784 0. 0.1 ] | 0.159667 | 0.0435852 | 0.203252 | [0.116 0.884] | 0.216 | +| (0.1, 51) | 0.165942 | 0.066058 | 0.232 | [0.132 0.768 0. 0.1 ] | 0.172122 | 0.0344898 | 0.206612 | [0.132 0.868] | 0.232 | +| (0.1, 52) | 0.171149 | 0.042851 | 0.214 | [0.114 0.786 0. 0.1 ] | 0.162734 | 0.0401054 | 0.20284 | [0.114 0.886] | 0.214 | +| (0.1, 53) | 0.154402 | 0.0895978 | 0.244 | [0.144 0.756 0. 0.1 ] | 0.145084 | 0.0641207 | 0.209205 | [0.144 0.856] | 0.244 | +| (0.1, 54) | 0.142341 | 0.0896587 | 0.232 | [0.132 0.768 0. 0.1 ] | 0.15741 | 0.0492015 | 0.206612 | [0.132 0.868] | 0.232 | +| (0.1, 55) | 0.125124 | 0.120876 | 0.246 | [0.146 0.754 0. 0.1 ] | 0.152008 | 0.0576355 | 0.209644 | [0.146 0.854] | 0.246 | +| (0.1, 56) | 0.123448 | 0.106552 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.142112 | 0.0640739 | 0.206186 | [0.13 0.87] | 0.23 | +| (0.1, 57) | 0.142486 | 0.0985143 | 0.241 | [0.142 0.758 0.001 0.099] | 0.172454 | 0.0344422 | 0.206897 | [0.143 0.857] | 0.241 | +| (0.1, 58) | 0.1401 | 0.0999001 | 0.24 | [0.14 0.76 0. 0.1 ] | 0.162622 | 0.0457112 | 0.208333 | [0.14 0.86] | 0.24 | +| (0.1, 59) | 0.136005 | 0.0989948 | 0.235 | [0.135 0.765 0. 0.1 ] | 0.113088 | 0.0941657 | 0.207254 | [0.135 0.865] | 0.235 | +| (0.1, 60) | 0.164363 | 0.0656372 | 0.23 | [0.132 0.768 0.002 0.098] | 0.144721 | 0.0581779 | 0.202899 | [0.134 0.866] | 0.23 | +| (0.1, 61) | 0.147789 | 0.102211 | 0.25 | [0.15 0.75 0. 0.1 ] | 0.120903 | 0.0896233 | 0.210526 | [0.15 0.85] | 0.25 | +| (0.1, 62) | 0.171044 | 0.0549561 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.178233 | 0.0271061 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 63) | 0.163507 | 0.0704927 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.158086 | 0.048953 | 0.207039 | [0.134 0.866] | 0.234 | +| (0.1, 64) | 0.110106 | 0.136894 | 0.247 | [0.147 0.753 0. 0.1 ] | 0.131224 | 0.07864 | 0.209864 | [0.147 0.853] | 0.247 | +| (0.1, 65) | 0.116124 | 0.113876 | 0.23 | [0.132 0.768 0.002 0.098] | 0.160052 | 0.0428462 | 0.202899 | [0.134 0.866] | 0.23 | +| (0.1, 66) | 0.148545 | 0.0964552 | 0.245 | [0.145 0.755 0. 0.1 ] | 0.153694 | 0.0557301 | 0.209424 | [0.145 0.855] | 0.245 | +| (0.1, 67) | 0.147949 | 0.0880511 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.120853 | 0.086616 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 68) | 0.159105 | 0.066895 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.166105 | 0.0392333 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 69) | 0.133635 | 0.0993651 | 0.233 | [0.133 0.767 0. 0.1 ] | 0.13905 | 0.0677752 | 0.206825 | [0.133 0.867] | 0.233 | +| (0.1, 70) | 0.136221 | 0.0947786 | 0.231 | [0.131 0.769 0. 0.1 ] | 0.158557 | 0.0478413 | 0.206398 | [0.131 0.869] | 0.231 | +| (0.1, 71) | 0.173515 | 0.0564854 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.154365 | 0.0518201 | 0.206186 | [0.13 0.87] | 0.23 | +| (0.1, 72) | 0.16392 | 0.0540796 | 0.218 | [0.118 0.782 0. 0.1 ] | 0.145843 | 0.0578235 | 0.203666 | [0.118 0.882] | 0.218 | +| (0.1, 73) | 0.15042 | 0.0985805 | 0.249 | [0.149 0.751 0. 0.1 ] | 0.160302 | 0.0500027 | 0.210305 | [0.149 0.851] | 0.249 | +| (0.1, 74) | 0.162813 | 0.0761868 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.158843 | 0.0492734 | 0.208117 | [0.139 0.861] | 0.239 | +| (0.1, 75) | 0.13747 | 0.10053 | 0.238 | [0.138 0.762 0. 0.1 ] | 0.161175 | 0.046725 | 0.2079 | [0.138 0.862] | 0.238 | +| (0.1, 76) | 0.145291 | 0.0577093 | 0.203 | [0.103 0.797 0. 0.1 ] | 0.149275 | 0.0513268 | 0.200602 | [0.103 0.897] | 0.203 | +| (0.1, 77) | 0.162521 | 0.0464792 | 0.209 | [0.109 0.791 0. 0.1 ] | 0.160017 | 0.041799 | 0.201816 | [0.109 0.891] | 0.209 | +| (0.1, 78) | 0.173875 | 0.0691247 | 0.243 | [0.143 0.757 0. 0.1 ] | 0.172443 | 0.0365437 | 0.208986 | [0.143 0.857] | 0.243 | +| (0.1, 79) | 0.167891 | 0.0681092 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.164834 | 0.0426348 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 80) | 0.105674 | 0.122326 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.141365 | 0.0643962 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.1, 81) | 0.146952 | 0.0890479 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.146061 | 0.0614083 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 82) | 0.161609 | 0.0743909 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.157538 | 0.0499308 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 83) | 0.133117 | 0.103883 | 0.237 | [0.137 0.763 0. 0.1 ] | 0.140087 | 0.0675969 | 0.207684 | [0.137 0.863] | 0.237 | +| (0.1, 84) | 0.105845 | 0.133155 | 0.239 | [0.139 0.761 0. 0.1 ] | 0.134486 | 0.0736308 | 0.208117 | [0.139 0.861] | 0.239 | +| (0.1, 85) | 0.149457 | 0.0795428 | 0.229 | [0.129 0.771 0. 0.1 ] | 0.152163 | 0.05381 | 0.205973 | [0.129 0.871] | 0.229 | +| (0.1, 86) | 0.155383 | 0.075617 | 0.231 | [0.131 0.769 0. 0.1 ] | 0.142288 | 0.0641102 | 0.206398 | [0.131 0.869] | 0.231 | +| (0.1, 87) | 0.152283 | 0.0677166 | 0.22 | [0.12 0.78 0. 0.1 ] | 0.136019 | 0.0680626 | 0.204082 | [0.12 0.88] | 0.22 | +| (0.1, 88) | 0.155228 | 0.0857723 | 0.241 | [0.141 0.759 0. 0.1 ] | 0.159894 | 0.0486562 | 0.208551 | [0.141 0.859] | 0.241 | +| (0.1, 89) | 0.173025 | 0.0519748 | 0.225 | [0.125 0.775 0. 0.1 ] | 0.149647 | 0.0554812 | 0.205128 | [0.125 0.875] | 0.225 | +| (0.1, 90) | 0.161536 | 0.0644643 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.164452 | 0.0408866 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 91) | 0.19674 | 0.0392595 | 0.236 | [0.136 0.764 0. 0.1 ] | 0.152299 | 0.0551697 | 0.207469 | [0.136 0.864] | 0.236 | +| (0.1, 92) | 0.1473 | 0.0766998 | 0.224 | [0.124 0.776 0. 0.1 ] | 0.125015 | 0.0799031 | 0.204918 | [0.124 0.876] | 0.224 | +| (0.1, 93) | 0.157492 | 0.0725084 | 0.23 | [0.13 0.77 0. 0.1 ] | 0.161964 | 0.0442218 | 0.206186 | [0.13 0.87] | 0.23 | +| (0.1, 94) | 0.166779 | 0.0672206 | 0.234 | [0.134 0.766 0. 0.1 ] | 0.168283 | 0.0387559 | 0.207039 | [0.134 0.866] | 0.234 | +| (0.1, 95) | 0.170211 | 0.0797889 | 0.25 | [0.15 0.75 0. 0.1 ] | 0.151857 | 0.0586692 | 0.210526 | [0.15 0.85] | 0.25 | +| (0.1, 96) | 0.137124 | 0.0808765 | 0.218 | [0.118 0.782 0. 0.1 ] | 0.154015 | 0.0496513 | 0.203666 | [0.118 0.882] | 0.218 | +| (0.1, 97) | 0.153298 | 0.0727017 | 0.226 | [0.126 0.774 0. 0.1 ] | 0.165413 | 0.0399255 | 0.205339 | [0.126 0.874] | 0.226 | +| (0.1, 98) | 0.141298 | 0.120702 | 0.262 | [0.162 0.738 0. 0.1 ] | 0.135892 | 0.0773272 | 0.21322 | [0.162 0.838] | 0.262 | +| (0.1, 99) | 0.175225 | 0.0527751 | 0.228 | [0.128 0.772 0. 0.1 ] | 0.16037 | 0.045391 | 0.205761 | [0.128 0.872] | 0.228 | +| (0.15, 0) | 0.1203 | 0.1657 | 0.286 | [0.137 0.713 0.001 0.149] | 0.127121 | 0.167346 | 0.294466 | [0.138 0.862] | 0.286 | +| (0.15, 1) | 0.119526 | 0.167474 | 0.287 | [0.137 0.713 0. 0.15 ] | 0.135734 | 0.160416 | 0.29615 | [0.137 0.863] | 0.287 | +| (0.15, 2) | 0.137288 | 0.133712 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.123291 | 0.168254 | 0.291545 | [0.121 0.879] | 0.271 | +| (0.15, 3) | 0.142731 | 0.113269 | 0.256 | [0.106 0.744 0. 0.15 ] | 0.13131 | 0.156047 | 0.287356 | [0.106 0.894] | 0.256 | +| (0.15, 4) | 0.166618 | 0.105382 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.161107 | 0.130722 | 0.291829 | [0.122 0.878] | 0.272 | +| (0.15, 5) | 0.139421 | 0.139579 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.176782 | 0.117048 | 0.29383 | [0.129 0.871] | 0.279 | +| (0.15, 6) | 0.170222 | 0.099778 | 0.27 | [0.12 0.73 0. 0.15] | 0.148121 | 0.143141 | 0.291262 | [0.12 0.88] | 0.27 | +| (0.15, 7) | 0.158061 | 0.114939 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.176748 | 0.115365 | 0.292113 | [0.123 0.877] | 0.273 | +| (0.15, 8) | 0.17871 | 0.11629 | 0.295 | [0.145 0.705 0. 0.15 ] | 0.132824 | 0.165683 | 0.298507 | [0.145 0.855] | 0.295 | +| (0.15, 9) | 0.151538 | 0.119462 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.149422 | 0.142123 | 0.291545 | [0.121 0.879] | 0.271 | +| (0.15, 10) | 0.139049 | 0.130951 | 0.27 | [0.121 0.729 0.001 0.149] | 0.13676 | 0.153123 | 0.289883 | [0.122 0.878] | 0.27 | +| (0.15, 11) | 0.116244 | 0.156756 | 0.273 | [0.124 0.726 0.001 0.149] | 0.141815 | 0.148916 | 0.290732 | [0.125 0.875] | 0.273 | +| (0.15, 12) | 0.162998 | 0.126002 | 0.289 | [0.139 0.711 0. 0.15 ] | 0.156127 | 0.140609 | 0.296736 | [0.139 0.861] | 0.289 | +| (0.15, 13) | 0.128238 | 0.150762 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.134349 | 0.159481 | 0.29383 | [0.129 0.871] | 0.279 | +| (0.15, 14) | 0.117776 | 0.132224 | 0.25 | [0.1 0.75 0. 0.15] | 0.139297 | 0.146418 | 0.285714 | [0.1 0.9] | 0.25 | +| (0.15, 15) | 0.104416 | 0.179584 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.131122 | 0.164154 | 0.295276 | [0.134 0.866] | 0.284 | +| (0.15, 16) | 0.137068 | 0.134932 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.166223 | 0.125605 | 0.291829 | [0.122 0.878] | 0.272 | +| (0.15, 17) | 0.158429 | 0.111571 | 0.27 | [0.12 0.73 0. 0.15] | 0.17491 | 0.116352 | 0.291262 | [0.12 0.88] | 0.27 | +| (0.15, 18) | 0.168285 | 0.0947153 | 0.263 | [0.113 0.737 0. 0.15 ] | 0.173726 | 0.11557 | 0.289296 | [0.113 0.887] | 0.263 | +| (0.15, 19) | 0.171027 | 0.120973 | 0.292 | [0.142 0.708 0. 0.15 ] | 0.142831 | 0.154788 | 0.297619 | [0.142 0.858] | 0.292 | +| (0.15, 20) | 0.148659 | 0.131341 | 0.28 | [0.13 0.72 0. 0.15] | 0.155957 | 0.138161 | 0.294118 | [0.13 0.87] | 0.28 | +| (0.15, 21) | 0.110694 | 0.147306 | 0.258 | [0.108 0.742 0. 0.15 ] | 0.132425 | 0.155482 | 0.287908 | [0.108 0.892] | 0.258 | +| (0.15, 22) | 0.13145 | 0.14855 | 0.28 | [0.13 0.72 0. 0.15] | 0.134374 | 0.159744 | 0.294118 | [0.13 0.87] | 0.28 | +| (0.15, 23) | 0.146038 | 0.126962 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.130698 | 0.161415 | 0.292113 | [0.123 0.877] | 0.273 | +| (0.15, 24) | 0.181347 | 0.108653 | 0.29 | [0.141 0.709 0.001 0.149] | 0.149705 | 0.14593 | 0.295635 | [0.142 0.858] | 0.29 | +| (0.15, 25) | 0.146943 | 0.144057 | 0.291 | [0.141 0.709 0. 0.15 ] | 0.136203 | 0.161121 | 0.297324 | [0.141 0.859] | 0.291 | +| (0.15, 26) | 0.147795 | 0.116205 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.157537 | 0.132038 | 0.289575 | [0.114 0.886] | 0.264 | +| (0.15, 27) | 0.128014 | 0.138986 | 0.267 | [0.117 0.733 0. 0.15 ] | 0.148528 | 0.141888 | 0.290416 | [0.117 0.883] | 0.267 | +| (0.15, 28) | 0.143207 | 0.128793 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.138262 | 0.153567 | 0.291829 | [0.122 0.878] | 0.272 | +| (0.15, 29) | 0.148872 | 0.145128 | 0.294 | [0.144 0.706 0. 0.15 ] | 0.135484 | 0.162727 | 0.298211 | [0.144 0.856] | 0.294 | +| (0.15, 30) | 0.144965 | 0.113035 | 0.258 | [0.108 0.742 0. 0.15 ] | 0.147562 | 0.140346 | 0.287908 | [0.108 0.892] | 0.258 | +| (0.15, 31) | 0.145971 | 0.119029 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.161121 | 0.128734 | 0.289855 | [0.115 0.885] | 0.265 | +| (0.15, 32) | 0.137751 | 0.124249 | 0.262 | [0.113 0.737 0.001 0.149] | 0.145534 | 0.142111 | 0.287645 | [0.114 0.886] | 0.262 | +| (0.15, 33) | 0.11238 | 0.15762 | 0.27 | [0.12 0.73 0. 0.15] | 0.130286 | 0.160976 | 0.291262 | [0.12 0.88] | 0.27 | +| (0.15, 34) | 0.0966508 | 0.179349 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.117277 | 0.175692 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 35) | 0.157902 | 0.113098 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.152535 | 0.13901 | 0.291545 | [0.121 0.879] | 0.271 | +| (0.15, 36) | 0.125849 | 0.166151 | 0.292 | [0.142 0.708 0. 0.15 ] | 0.15111 | 0.146509 | 0.297619 | [0.142 0.858] | 0.292 | +| (0.15, 37) | 0.167764 | 0.137236 | 0.305 | [0.155 0.695 0. 0.15 ] | 0.179681 | 0.121826 | 0.301508 | [0.155 0.845] | 0.305 | +| (0.15, 38) | 0.154211 | 0.128789 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.166432 | 0.128554 | 0.294985 | [0.133 0.867] | 0.283 | +| (0.15, 39) | 0.132705 | 0.143295 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.14616 | 0.146809 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 40) | 0.152512 | 0.123488 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.141054 | 0.151915 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 41) | 0.135058 | 0.127942 | 0.263 | [0.113 0.737 0. 0.15 ] | 0.16119 | 0.128106 | 0.289296 | [0.113 0.887] | 0.263 | +| (0.15, 42) | 0.123784 | 0.152216 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.142844 | 0.150125 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 43) | 0.171101 | 0.0878989 | 0.259 | [0.109 0.741 0. 0.15 ] | 0.164715 | 0.123469 | 0.288184 | [0.109 0.891] | 0.259 | +| (0.15, 44) | 0.16704 | 0.0949603 | 0.262 | [0.114 0.736 0.002 0.148] | 0.142699 | 0.143568 | 0.286267 | [0.116 0.884] | 0.262 | +| (0.15, 45) | 0.116311 | 0.171689 | 0.288 | [0.138 0.712 0. 0.15 ] | 0.141653 | 0.15479 | 0.296443 | [0.138 0.862] | 0.288 | +| (0.15, 46) | 0.154616 | 0.104384 | 0.259 | [0.109 0.741 0. 0.15 ] | 0.138548 | 0.149637 | 0.288184 | [0.109 0.891] | 0.259 | +| (0.15, 47) | 0.144754 | 0.133246 | 0.278 | [0.128 0.722 0. 0.15 ] | 0.12734 | 0.166202 | 0.293542 | [0.128 0.872] | 0.278 | +| (0.15, 48) | 0.175836 | 0.110164 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.153911 | 0.141947 | 0.295858 | [0.136 0.864] | 0.286 | +| (0.15, 49) | 0.162022 | 0.111978 | 0.274 | [0.124 0.726 0. 0.15 ] | 0.150176 | 0.142221 | 0.292398 | [0.124 0.876] | 0.274 | +| (0.15, 50) | 0.177202 | 0.0937983 | 0.271 | [0.121 0.729 0. 0.15 ] | 0.158743 | 0.132803 | 0.291545 | [0.121 0.879] | 0.271 | +| (0.15, 51) | 0.153076 | 0.111924 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.134545 | 0.15531 | 0.289855 | [0.115 0.885] | 0.265 | +| (0.15, 52) | 0.138063 | 0.151937 | 0.29 | [0.14 0.71 0. 0.15] | 0.137941 | 0.159089 | 0.29703 | [0.14 0.86] | 0.29 | +| (0.15, 53) | 0.153557 | 0.122443 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.132467 | 0.160502 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 54) | 0.151345 | 0.114655 | 0.266 | [0.116 0.734 0. 0.15 ] | 0.138179 | 0.151956 | 0.290135 | [0.116 0.884] | 0.266 | +| (0.15, 55) | 0.13427 | 0.14873 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.157275 | 0.137711 | 0.294985 | [0.133 0.867] | 0.283 | +| (0.15, 56) | 0.137219 | 0.134781 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.158478 | 0.133351 | 0.291829 | [0.122 0.878] | 0.272 | +| (0.15, 57) | 0.160927 | 0.140073 | 0.301 | [0.151 0.699 0. 0.15 ] | 0.146971 | 0.153329 | 0.3003 | [0.151 0.849] | 0.301 | +| (0.15, 58) | 0.120219 | 0.163781 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.145434 | 0.149842 | 0.295276 | [0.134 0.866] | 0.284 | +| (0.15, 59) | 0.154083 | 0.126917 | 0.281 | [0.131 0.719 0. 0.15 ] | 0.154977 | 0.139429 | 0.294406 | [0.131 0.869] | 0.281 | +| (0.15, 60) | 0.157883 | 0.111117 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.172352 | 0.118627 | 0.29098 | [0.119 0.881] | 0.269 | +| (0.15, 61) | 0.136203 | 0.143797 | 0.28 | [0.13 0.72 0. 0.15] | 0.156749 | 0.137369 | 0.294118 | [0.13 0.87] | 0.28 | +| (0.15, 62) | 0.121822 | 0.177178 | 0.299 | [0.149 0.701 0. 0.15 ] | 0.158284 | 0.141416 | 0.2997 | [0.149 0.851] | 0.299 | +| (0.15, 63) | 0.134564 | 0.151436 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.122765 | 0.173093 | 0.295858 | [0.136 0.864] | 0.286 | +| (0.15, 64) | 0.138566 | 0.136434 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.164812 | 0.127871 | 0.292683 | [0.125 0.875] | 0.275 | +| (0.15, 65) | 0.141594 | 0.132406 | 0.274 | [0.125 0.725 0.001 0.149] | 0.130073 | 0.160943 | 0.291016 | [0.126 0.874] | 0.274 | +| (0.15, 66) | 0.144607 | 0.135393 | 0.28 | [0.13 0.72 0. 0.15] | 0.119498 | 0.174619 | 0.294118 | [0.13 0.87] | 0.28 | +| (0.15, 67) | 0.178893 | 0.090107 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.171713 | 0.119267 | 0.29098 | [0.119 0.881] | 0.269 | +| (0.15, 68) | 0.139628 | 0.145372 | 0.285 | [0.135 0.715 0. 0.15 ] | 0.135852 | 0.159714 | 0.295567 | [0.135 0.865] | 0.285 | +| (0.15, 69) | 0.1838 | 0.0922 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.172546 | 0.120423 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 70) | 0.125644 | 0.138356 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.160992 | 0.128583 | 0.289575 | [0.114 0.886] | 0.264 | +| (0.15, 71) | 0.160764 | 0.101236 | 0.262 | [0.112 0.738 0. 0.15 ] | 0.161154 | 0.127864 | 0.289017 | [0.112 0.888] | 0.262 | +| (0.15, 72) | 0.161464 | 0.107536 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.159812 | 0.131167 | 0.29098 | [0.119 0.881] | 0.269 | +| (0.15, 73) | 0.121342 | 0.153658 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.107444 | 0.185239 | 0.292683 | [0.125 0.875] | 0.275 | +| (0.15, 74) | 0.145367 | 0.133633 | 0.279 | [0.13 0.72 0.001 0.149] | 0.129848 | 0.162596 | 0.292444 | [0.131 0.869] | 0.279 | +| (0.15, 75) | 0.155114 | 0.117886 | 0.273 | [0.124 0.726 0.001 0.149] | 0.15948 | 0.131252 | 0.290732 | [0.125 0.875] | 0.273 | +| (0.15, 76) | 0.1274 | 0.1606 | 0.288 | [0.138 0.712 0. 0.15 ] | 0.14193 | 0.154513 | 0.296443 | [0.138 0.862] | 0.288 | +| (0.15, 77) | 0.152264 | 0.143736 | 0.296 | [0.146 0.704 0. 0.15 ] | 0.155979 | 0.142826 | 0.298805 | [0.146 0.854] | 0.296 | +| (0.15, 78) | 0.10929 | 0.17271 | 0.282 | [0.132 0.718 0. 0.15 ] | 0.129038 | 0.165657 | 0.294695 | [0.132 0.868] | 0.282 | +| (0.15, 79) | 0.1422 | 0.1328 | 0.275 | [0.125 0.725 0. 0.15 ] | 0.150535 | 0.142148 | 0.292683 | [0.125 0.875] | 0.275 | +| (0.15, 80) | 0.168582 | 0.0974178 | 0.266 | [0.117 0.733 0.001 0.149] | 0.161824 | 0.126936 | 0.28876 | [0.118 0.882] | 0.266 | +| (0.15, 81) | 0.148811 | 0.130189 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.118403 | 0.175427 | 0.29383 | [0.129 0.871] | 0.279 | +| (0.15, 82) | 0.133344 | 0.134656 | 0.268 | [0.118 0.732 0. 0.15 ] | 0.151254 | 0.139444 | 0.290698 | [0.118 0.882] | 0.268 | +| (0.15, 83) | 0.124459 | 0.126541 | 0.251 | [0.101 0.749 0. 0.15 ] | 0.13409 | 0.151896 | 0.285987 | [0.101 0.899] | 0.251 | +| (0.15, 84) | 0.141318 | 0.137682 | 0.279 | [0.129 0.721 0. 0.15 ] | 0.154315 | 0.139515 | 0.29383 | [0.129 0.871] | 0.279 | +| (0.15, 85) | 0.163049 | 0.117951 | 0.281 | [0.131 0.719 0. 0.15 ] | 0.140319 | 0.154088 | 0.294406 | [0.131 0.869] | 0.281 | +| (0.15, 86) | 0.152419 | 0.133581 | 0.286 | [0.136 0.714 0. 0.15 ] | 0.137388 | 0.15847 | 0.295858 | [0.136 0.864] | 0.286 | +| (0.15, 87) | 0.106809 | 0.176191 | 0.283 | [0.133 0.717 0. 0.15 ] | 0.102393 | 0.192592 | 0.294985 | [0.133 0.867] | 0.283 | +| (0.15, 88) | 0.160406 | 0.104594 | 0.265 | [0.115 0.735 0. 0.15 ] | 0.15609 | 0.133765 | 0.289855 | [0.115 0.885] | 0.265 | +| (0.15, 89) | 0.123011 | 0.149989 | 0.273 | [0.123 0.727 0. 0.15 ] | 0.145604 | 0.146509 | 0.292113 | [0.123 0.877] | 0.273 | +| (0.15, 90) | 0.159302 | 0.112698 | 0.272 | [0.122 0.728 0. 0.15 ] | 0.139501 | 0.152328 | 0.291829 | [0.122 0.878] | 0.272 | +| (0.15, 91) | 0.146535 | 0.147465 | 0.294 | [0.144 0.706 0. 0.15 ] | 0.160389 | 0.137822 | 0.298211 | [0.144 0.856] | 0.294 | +| (0.15, 92) | 0.174056 | 0.089944 | 0.264 | [0.114 0.736 0. 0.15 ] | 0.154888 | 0.134687 | 0.289575 | [0.114 0.886] | 0.264 | +| (0.15, 93) | 0.132952 | 0.145048 | 0.278 | [0.128 0.722 0. 0.15 ] | 0.153029 | 0.140513 | 0.293542 | [0.128 0.872] | 0.278 | +| (0.15, 94) | 0.148742 | 0.131258 | 0.28 | [0.131 0.719 0.001 0.149] | 0.15407 | 0.138661 | 0.292731 | [0.132 0.868] | 0.28 | +| (0.15, 95) | 0.160161 | 0.102839 | 0.263 | [0.114 0.736 0.001 0.149] | 0.135026 | 0.152896 | 0.287923 | [0.115 0.885] | 0.263 | +| (0.15, 96) | 0.123638 | 0.160362 | 0.284 | [0.134 0.716 0. 0.15 ] | 0.138609 | 0.156667 | 0.295276 | [0.134 0.866] | 0.284 | +| (0.15, 97) | 0.161662 | 0.105338 | 0.267 | [0.117 0.733 0. 0.15 ] | 0.146184 | 0.144233 | 0.290416 | [0.117 0.883] | 0.267 | +| (0.15, 98) | 0.147239 | 0.128761 | 0.276 | [0.126 0.724 0. 0.15 ] | 0.165486 | 0.127483 | 0.292969 | [0.126 0.874] | 0.276 | +| (0.15, 99) | 0.127231 | 0.141769 | 0.269 | [0.119 0.731 0. 0.15 ] | 0.137447 | 0.153533 | 0.29098 | [0.119 0.881] | 0.269 | +| (0.2, 0) | 0.0932749 | 0.232725 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.107742 | 0.264698 | 0.372439 | [0.126 0.874] | 0.326 | +| (0.2, 1) | 0.103595 | 0.225405 | 0.329 | [0.129 0.671 0. 0.2 ] | 0.11693 | 0.256553 | 0.373483 | [0.129 0.871] | 0.329 | +| (0.2, 2) | 0.146266 | 0.174734 | 0.321 | [0.121 0.679 0. 0.2 ] | 0.13068 | 0.240034 | 0.370714 | [0.121 0.879] | 0.321 | +| (0.2, 3) | 0.158612 | 0.158388 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.140197 | 0.229147 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 4) | 0.13548 | 0.18852 | 0.324 | [0.125 0.675 0.001 0.199] | 0.123131 | 0.247446 | 0.370577 | [0.126 0.874] | 0.324 | +| (0.2, 5) | 0.184383 | 0.139617 | 0.324 | [0.124 0.676 0. 0.2 ] | 0.16367 | 0.208077 | 0.371747 | [0.124 0.876] | 0.324 | +| (0.2, 6) | 0.119201 | 0.191799 | 0.311 | [0.111 0.689 0. 0.2 ] | 0.12538 | 0.24193 | 0.367309 | [0.111 0.889] | 0.311 | +| (0.2, 7) | 0.13074 | 0.18826 | 0.319 | [0.12 0.68 0.001 0.199] | 0.1483 | 0.22056 | 0.36886 | [0.121 0.879] | 0.319 | +| (0.2, 8) | 0.158339 | 0.154661 | 0.313 | [0.113 0.687 0. 0.2 ] | 0.141924 | 0.226061 | 0.367985 | [0.113 0.887] | 0.313 | +| (0.2, 9) | 0.133184 | 0.171816 | 0.305 | [0.107 0.693 0.002 0.198] | 0.155369 | 0.207601 | 0.36297 | [0.109 0.891] | 0.305 | +| (0.2, 10) | 0.152238 | 0.170762 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.141301 | 0.230101 | 0.371402 | [0.123 0.877] | 0.323 | +| (0.2, 11) | 0.147096 | 0.187904 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.126492 | 0.249094 | 0.375587 | [0.135 0.865] | 0.335 | +| (0.2, 12) | 0.139379 | 0.187621 | 0.327 | [0.127 0.673 0. 0.2 ] | 0.140083 | 0.232704 | 0.372787 | [0.127 0.873] | 0.327 | +| (0.2, 13) | 0.157408 | 0.164592 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.128326 | 0.242732 | 0.371058 | [0.122 0.878] | 0.322 | +| (0.2, 14) | 0.165325 | 0.154675 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.141291 | 0.229079 | 0.37037 | [0.12 0.88] | 0.32 | +| (0.2, 15) | 0.155916 | 0.162084 | 0.318 | [0.119 0.681 0.001 0.199] | 0.136168 | 0.232351 | 0.368519 | [0.12 0.88] | 0.318 | +| (0.2, 16) | 0.126583 | 0.190417 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.0939205 | 0.275424 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 17) | 0.132189 | 0.202811 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.109183 | 0.266403 | 0.375587 | [0.135 0.865] | 0.335 | +| (0.2, 18) | 0.129466 | 0.189534 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.115286 | 0.254741 | 0.370028 | [0.119 0.881] | 0.319 | +| (0.2, 19) | 0.128175 | 0.177825 | 0.306 | [0.106 0.694 0. 0.2 ] | 0.133787 | 0.231843 | 0.365631 | [0.106 0.894] | 0.306 | +| (0.2, 20) | 0.145426 | 0.187574 | 0.333 | [0.133 0.667 0. 0.2 ] | 0.151205 | 0.223677 | 0.374883 | [0.133 0.867] | 0.333 | +| (0.2, 21) | 0.163454 | 0.154546 | 0.318 | [0.119 0.681 0.001 0.199] | 0.139559 | 0.228959 | 0.368519 | [0.12 0.88] | 0.318 | +| (0.2, 22) | 0.114168 | 0.215832 | 0.33 | [0.131 0.669 0.001 0.199] | 0.114659 | 0.258001 | 0.372659 | [0.132 0.868] | 0.33 | +| (0.2, 23) | 0.156559 | 0.173441 | 0.33 | [0.13 0.67 0. 0.2 ] | 0.150894 | 0.222937 | 0.373832 | [0.13 0.87] | 0.33 | +| (0.2, 24) | 0.110103 | 0.206897 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.109295 | 0.260049 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 25) | 0.153082 | 0.174918 | 0.328 | [0.128 0.672 0. 0.2 ] | 0.159335 | 0.213799 | 0.373134 | [0.128 0.872] | 0.328 | +| (0.2, 26) | 0.126215 | 0.187785 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.114362 | 0.253962 | 0.368324 | [0.114 0.886] | 0.314 | +| (0.2, 27) | 0.145636 | 0.172364 | 0.318 | [0.118 0.682 0. 0.2 ] | 0.154729 | 0.214956 | 0.369686 | [0.118 0.882] | 0.318 | +| (0.2, 28) | 0.113045 | 0.191955 | 0.305 | [0.105 0.695 0. 0.2 ] | 0.103814 | 0.261482 | 0.365297 | [0.105 0.895] | 0.305 | +| (0.2, 29) | 0.151512 | 0.171488 | 0.323 | [0.124 0.676 0.001 0.199] | 0.134941 | 0.235291 | 0.370233 | [0.125 0.875] | 0.323 | +| (0.2, 30) | 0.124049 | 0.206951 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.130243 | 0.243939 | 0.374181 | [0.131 0.869] | 0.331 | +| (0.2, 31) | 0.14276 | 0.17924 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.147474 | 0.223583 | 0.371058 | [0.122 0.878] | 0.322 | +| (0.2, 32) | 0.184874 | 0.137126 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.162763 | 0.208295 | 0.371058 | [0.122 0.878] | 0.322 | +| (0.2, 33) | 0.113052 | 0.205948 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.125327 | 0.2447 | 0.370028 | [0.119 0.881] | 0.319 | +| (0.2, 34) | 0.11022 | 0.20578 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.134172 | 0.234831 | 0.369004 | [0.116 0.884] | 0.316 | +| (0.2, 35) | 0.15689 | 0.16811 | 0.325 | [0.125 0.675 0. 0.2 ] | 0.139515 | 0.232578 | 0.372093 | [0.125 0.875] | 0.325 | +| (0.2, 36) | 0.12372 | 0.19828 | 0.322 | [0.123 0.677 0.001 0.199] | 0.144416 | 0.225473 | 0.369888 | [0.124 0.876] | 0.322 | +| (0.2, 37) | 0.156173 | 0.149827 | 0.306 | [0.106 0.694 0. 0.2 ] | 0.140543 | 0.225087 | 0.365631 | [0.106 0.894] | 0.306 | +| (0.2, 38) | 0.123178 | 0.191822 | 0.315 | [0.115 0.685 0. 0.2 ] | 0.124918 | 0.243745 | 0.368664 | [0.115 0.885] | 0.315 | +| (0.2, 39) | 0.149818 | 0.155182 | 0.305 | [0.105 0.695 0. 0.2 ] | 0.153473 | 0.211824 | 0.365297 | [0.105 0.895] | 0.305 | +| (0.2, 40) | 0.146996 | 0.151004 | 0.298 | [0.098 0.702 0. 0.2 ] | 0.136716 | 0.226261 | 0.362976 | [0.098 0.902] | 0.298 | +| (0.2, 41) | 0.134983 | 0.174017 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.130812 | 0.235824 | 0.366636 | [0.109 0.891] | 0.309 | +| (0.2, 42) | 0.130339 | 0.204661 | 0.335 | [0.135 0.665 0. 0.2 ] | 0.132934 | 0.242653 | 0.375587 | [0.135 0.865] | 0.335 | +| (0.2, 43) | 0.131752 | 0.197248 | 0.329 | [0.129 0.671 0. 0.2 ] | 0.122563 | 0.25092 | 0.373483 | [0.129 0.871] | 0.329 | +| (0.2, 44) | 0.104892 | 0.213108 | 0.318 | [0.118 0.682 0. 0.2 ] | 0.101844 | 0.267842 | 0.369686 | [0.118 0.882] | 0.318 | +| (0.2, 45) | 0.108924 | 0.208076 | 0.317 | [0.118 0.682 0.001 0.199] | 0.112634 | 0.255543 | 0.368178 | [0.119 0.881] | 0.317 | +| (0.2, 46) | 0.158723 | 0.146277 | 0.305 | [0.106 0.694 0.001 0.199] | 0.14642 | 0.217715 | 0.364135 | [0.107 0.893] | 0.305 | +| (0.2, 47) | 0.181864 | 0.130136 | 0.312 | [0.113 0.687 0.001 0.199] | 0.162233 | 0.204249 | 0.366483 | [0.114 0.886] | 0.312 | +| (0.2, 48) | 0.139857 | 0.177143 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.130469 | 0.238875 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 49) | 0.130555 | 0.185445 | 0.316 | [0.117 0.683 0.001 0.199] | 0.129553 | 0.238285 | 0.367837 | [0.118 0.882] | 0.316 | +| (0.2, 50) | 0.126112 | 0.184888 | 0.311 | [0.111 0.689 0. 0.2 ] | 0.116336 | 0.250973 | 0.367309 | [0.111 0.889] | 0.311 | +| (0.2, 51) | 0.126804 | 0.189196 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.134586 | 0.234417 | 0.369004 | [0.116 0.884] | 0.316 | +| (0.2, 52) | 0.1149 | 0.1861 | 0.301 | [0.101 0.699 0. 0.2 ] | 0.131898 | 0.232069 | 0.363967 | [0.101 0.899] | 0.301 | +| (0.2, 53) | 0.147138 | 0.163862 | 0.311 | [0.112 0.688 0.001 0.199] | 0.130518 | 0.235628 | 0.366145 | [0.113 0.887] | 0.311 | +| (0.2, 54) | 0.126953 | 0.204047 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.103391 | 0.270791 | 0.374181 | [0.131 0.869] | 0.331 | +| (0.2, 55) | 0.154759 | 0.161241 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.143336 | 0.225668 | 0.369004 | [0.116 0.884] | 0.316 | +| (0.2, 56) | 0.148069 | 0.154931 | 0.303 | [0.103 0.697 0. 0.2 ] | 0.13299 | 0.23164 | 0.364631 | [0.103 0.897] | 0.303 | +| (0.2, 57) | 0.14951 | 0.17349 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.124754 | 0.246648 | 0.371402 | [0.123 0.877] | 0.323 | +| (0.2, 58) | 0.124588 | 0.182412 | 0.307 | [0.107 0.693 0. 0.2 ] | 0.135337 | 0.230628 | 0.365965 | [0.107 0.893] | 0.307 | +| (0.2, 59) | 0.106309 | 0.219691 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.124609 | 0.247831 | 0.372439 | [0.126 0.874] | 0.326 | +| (0.2, 60) | 0.163061 | 0.148939 | 0.312 | [0.112 0.688 0. 0.2 ] | 0.125001 | 0.242646 | 0.367647 | [0.112 0.888] | 0.312 | +| (0.2, 61) | 0.098016 | 0.220984 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.111374 | 0.258654 | 0.370028 | [0.119 0.881] | 0.319 | +| (0.2, 62) | 0.133823 | 0.192177 | 0.326 | [0.127 0.673 0.001 0.199] | 0.138711 | 0.232558 | 0.371269 | [0.128 0.872] | 0.326 | +| (0.2, 63) | 0.132592 | 0.193408 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.168652 | 0.203787 | 0.372439 | [0.126 0.874] | 0.326 | +| (0.2, 64) | 0.146847 | 0.167153 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.134352 | 0.233973 | 0.368324 | [0.114 0.886] | 0.314 | +| (0.2, 65) | 0.123678 | 0.209322 | 0.333 | [0.134 0.666 0.001 0.199] | 0.139748 | 0.233961 | 0.373709 | [0.135 0.865] | 0.333 | +| (0.2, 66) | 0.153441 | 0.166559 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.168793 | 0.201578 | 0.37037 | [0.12 0.88] | 0.32 | +| (0.2, 67) | 0.102558 | 0.206442 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.129257 | 0.237379 | 0.366636 | [0.109 0.891] | 0.309 | +| (0.2, 68) | 0.113796 | 0.181204 | 0.295 | [0.095 0.705 0. 0.2 ] | 0.125855 | 0.236136 | 0.361991 | [0.095 0.905] | 0.295 | +| (0.2, 69) | 0.130216 | 0.186784 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.154798 | 0.214547 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 70) | 0.140137 | 0.173863 | 0.314 | [0.114 0.686 0. 0.2 ] | 0.133952 | 0.234372 | 0.368324 | [0.114 0.886] | 0.314 | +| (0.2, 71) | 0.118374 | 0.173626 | 0.292 | [0.093 0.707 0.001 0.199] | 0.102911 | 0.256944 | 0.359855 | [0.094 0.906] | 0.292 | +| (0.2, 72) | 0.142998 | 0.173002 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.13498 | 0.234024 | 0.369004 | [0.116 0.884] | 0.316 | +| (0.2, 73) | 0.11727 | 0.20573 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.100748 | 0.270654 | 0.371402 | [0.123 0.877] | 0.323 | +| (0.2, 74) | 0.13782 | 0.17518 | 0.313 | [0.114 0.686 0.001 0.199] | 0.113838 | 0.252982 | 0.36682 | [0.115 0.885] | 0.313 | +| (0.2, 75) | 0.127086 | 0.203914 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.124813 | 0.249369 | 0.374181 | [0.131 0.869] | 0.331 | +| (0.2, 76) | 0.110385 | 0.192615 | 0.303 | [0.104 0.696 0.001 0.199] | 0.117311 | 0.246159 | 0.36347 | [0.105 0.895] | 0.303 | +| (0.2, 77) | 0.169946 | 0.156054 | 0.326 | [0.126 0.674 0. 0.2 ] | 0.154318 | 0.218121 | 0.372439 | [0.126 0.874] | 0.326 | +| (0.2, 78) | 0.125895 | 0.184105 | 0.31 | [0.11 0.69 0. 0.2 ] | 0.137846 | 0.229127 | 0.366972 | [0.11 0.89] | 0.31 | +| (0.2, 79) | 0.106497 | 0.195503 | 0.302 | [0.102 0.698 0. 0.2 ] | 0.108174 | 0.256124 | 0.364299 | [0.102 0.898] | 0.302 | +| (0.2, 80) | 0.153481 | 0.163519 | 0.317 | [0.117 0.683 0. 0.2 ] | 0.136076 | 0.233269 | 0.369344 | [0.117 0.883] | 0.317 | +| (0.2, 81) | 0.139622 | 0.180378 | 0.32 | [0.121 0.679 0.001 0.199] | 0.126998 | 0.242204 | 0.369202 | [0.122 0.878] | 0.32 | +| (0.2, 82) | 0.131272 | 0.188728 | 0.32 | [0.121 0.679 0.001 0.199] | 0.125249 | 0.243954 | 0.369202 | [0.122 0.878] | 0.32 | +| (0.2, 83) | 0.150497 | 0.172503 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.154524 | 0.216878 | 0.371402 | [0.123 0.877] | 0.323 | +| (0.2, 84) | 0.138086 | 0.180914 | 0.319 | [0.119 0.681 0. 0.2 ] | 0.125401 | 0.244626 | 0.370028 | [0.119 0.881] | 0.319 | +| (0.2, 85) | 0.139788 | 0.187212 | 0.327 | [0.127 0.673 0. 0.2 ] | 0.157005 | 0.215781 | 0.372787 | [0.127 0.873] | 0.327 | +| (0.2, 86) | 0.0908716 | 0.216128 | 0.307 | [0.107 0.693 0. 0.2 ] | 0.0962838 | 0.269681 | 0.365965 | [0.107 0.893] | 0.307 | +| (0.2, 87) | 0.147758 | 0.161242 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.129789 | 0.236848 | 0.366636 | [0.109 0.891] | 0.309 | +| (0.2, 88) | 0.137745 | 0.187255 | 0.325 | [0.125 0.675 0. 0.2 ] | 0.16326 | 0.208833 | 0.372093 | [0.125 0.875] | 0.325 | +| (0.2, 89) | 0.147468 | 0.161532 | 0.309 | [0.109 0.691 0. 0.2 ] | 0.132114 | 0.234522 | 0.366636 | [0.109 0.891] | 0.309 | +| (0.2, 90) | 0.0964468 | 0.224553 | 0.321 | [0.121 0.679 0. 0.2 ] | 0.102582 | 0.268131 | 0.370714 | [0.121 0.879] | 0.321 | +| (0.2, 91) | 0.101547 | 0.214453 | 0.316 | [0.116 0.684 0. 0.2 ] | 0.112843 | 0.25616 | 0.369004 | [0.116 0.884] | 0.316 | +| (0.2, 92) | 0.162379 | 0.157621 | 0.32 | [0.12 0.68 0. 0.2 ] | 0.132839 | 0.237531 | 0.37037 | [0.12 0.88] | 0.32 | +| (0.2, 93) | 0.141758 | 0.189242 | 0.331 | [0.132 0.668 0.001 0.199] | 0.144896 | 0.228113 | 0.373008 | [0.133 0.867] | 0.331 | +| (0.2, 94) | 0.169261 | 0.152739 | 0.322 | [0.122 0.678 0. 0.2 ] | 0.166465 | 0.204593 | 0.371058 | [0.122 0.878] | 0.322 | +| (0.2, 95) | 0.122537 | 0.200463 | 0.323 | [0.123 0.677 0. 0.2 ] | 0.111265 | 0.260137 | 0.371402 | [0.123 0.877] | 0.323 | +| (0.2, 96) | 0.126137 | 0.193863 | 0.32 | [0.121 0.679 0.001 0.199] | 0.130176 | 0.239026 | 0.369202 | [0.122 0.878] | 0.32 | +| (0.2, 97) | 0.0939801 | 0.23702 | 0.331 | [0.131 0.669 0. 0.2 ] | 0.103706 | 0.270476 | 0.374181 | [0.131 0.869] | 0.331 | +| (0.2, 98) | 0.105674 | 0.208326 | 0.314 | [0.115 0.685 0.001 0.199] | 0.115848 | 0.251311 | 0.367159 | [0.116 0.884] | 0.314 | +| (0.2, 99) | 0.107548 | 0.207452 | 0.315 | [0.115 0.685 0. 0.2 ] | 0.118836 | 0.249828 | 0.368664 | [0.115 0.885] | 0.315 | +| (0.25, 0) | 0.124355 | 0.219645 | 0.344 | [0.094 0.656 0. 0.25 ] | 0.128487 | 0.304039 | 0.432526 | [0.094 0.906] | 0.344 | +| (0.25, 1) | 0.123549 | 0.242451 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.123781 | 0.317136 | 0.440917 | [0.116 0.884] | 0.366 | +| (0.25, 2) | 0.11547 | 0.24653 | 0.362 | [0.113 0.637 0.001 0.249] | 0.108391 | 0.32999 | 0.43838 | [0.114 0.886] | 0.362 | +| (0.25, 3) | 0.14217 | 0.23583 | 0.378 | [0.128 0.622 0. 0.25 ] | 0.13991 | 0.305723 | 0.445633 | [0.128 0.872] | 0.378 | +| (0.25, 4) | 0.115606 | 0.240394 | 0.356 | [0.106 0.644 0. 0.25 ] | 0.10033 | 0.336733 | 0.437063 | [0.106 0.894] | 0.356 | +| (0.25, 5) | 0.168544 | 0.212456 | 0.381 | [0.131 0.619 0. 0.25 ] | 0.144145 | 0.302683 | 0.446828 | [0.131 0.869] | 0.381 | +| (0.25, 6) | 0.0947743 | 0.273226 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.095146 | 0.34655 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 7) | 0.113473 | 0.251527 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.111097 | 0.329432 | 0.440529 | [0.115 0.885] | 0.365 | +| (0.25, 8) | 0.155793 | 0.217207 | 0.373 | [0.123 0.627 0. 0.25 ] | 0.136486 | 0.30717 | 0.443656 | [0.123 0.877] | 0.373 | +| (0.25, 9) | 0.129944 | 0.218056 | 0.348 | [0.098 0.652 0. 0.25 ] | 0.124308 | 0.30972 | 0.434028 | [0.098 0.902] | 0.348 | +| (0.25, 10) | 0.137385 | 0.225615 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.134428 | 0.305325 | 0.439754 | [0.113 0.887] | 0.363 | +| (0.25, 11) | 0.129083 | 0.242917 | 0.372 | [0.122 0.628 0. 0.25 ] | 0.136549 | 0.306714 | 0.443262 | [0.122 0.878] | 0.372 | +| (0.25, 12) | 0.116791 | 0.239209 | 0.356 | [0.106 0.644 0. 0.25 ] | 0.130234 | 0.306828 | 0.437063 | [0.106 0.894] | 0.356 | +| (0.25, 13) | 0.126751 | 0.222249 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.113015 | 0.32139 | 0.434405 | [0.099 0.901] | 0.349 | +| (0.25, 14) | 0.128282 | 0.226718 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.120019 | 0.316662 | 0.436681 | [0.105 0.895] | 0.355 | +| (0.25, 15) | 0.143813 | 0.218187 | 0.362 | [0.113 0.637 0.001 0.249] | 0.126338 | 0.312042 | 0.43838 | [0.114 0.886] | 0.362 | +| (0.25, 16) | 0.135428 | 0.238572 | 0.374 | [0.124 0.626 0. 0.25 ] | 0.130811 | 0.313239 | 0.44405 | [0.124 0.876] | 0.374 | +| (0.25, 17) | 0.112018 | 0.241982 | 0.354 | [0.105 0.645 0.001 0.249] | 0.120587 | 0.314728 | 0.435315 | [0.106 0.894] | 0.354 | +| (0.25, 18) | 0.159659 | 0.202341 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.14518 | 0.294188 | 0.439367 | [0.112 0.888] | 0.362 | +| (0.25, 19) | 0.125726 | 0.242274 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.116861 | 0.324835 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 20) | 0.156058 | 0.193942 | 0.35 | [0.1 0.65 0. 0.25] | 0.112552 | 0.32223 | 0.434783 | [0.1 0.9] | 0.35 | +| (0.25, 21) | 0.115433 | 0.259567 | 0.375 | [0.125 0.625 0. 0.25 ] | 0.0991129 | 0.345332 | 0.444444 | [0.125 0.875] | 0.375 | +| (0.25, 22) | 0.127289 | 0.232711 | 0.36 | [0.11 0.64 0. 0.25] | 0.115265 | 0.323332 | 0.438596 | [0.11 0.89] | 0.36 | +| (0.25, 23) | 0.124531 | 0.233469 | 0.358 | [0.109 0.641 0.001 0.249] | 0.0982565 | 0.338586 | 0.436842 | [0.11 0.89] | 0.358 | +| (0.25, 24) | 0.169186 | 0.195814 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.138074 | 0.302455 | 0.440529 | [0.115 0.885] | 0.365 | +| (0.25, 25) | 0.128267 | 0.227733 | 0.356 | [0.107 0.643 0.001 0.249] | 0.148625 | 0.287452 | 0.436077 | [0.108 0.892] | 0.356 | +| (0.25, 26) | 0.126575 | 0.236425 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.133828 | 0.305925 | 0.439754 | [0.113 0.887] | 0.363 | +| (0.25, 27) | 0.129839 | 0.252161 | 0.382 | [0.132 0.618 0. 0.25 ] | 0.115004 | 0.332224 | 0.447227 | [0.132 0.868] | 0.382 | +| (0.25, 28) | 0.10892 | 0.24408 | 0.353 | [0.103 0.647 0. 0.25 ] | 0.112931 | 0.322988 | 0.43592 | [0.103 0.897] | 0.353 | +| (0.25, 29) | 0.121689 | 0.228311 | 0.35 | [0.1 0.65 0. 0.25] | 0.124501 | 0.310282 | 0.434783 | [0.1 0.9] | 0.35 | +| (0.25, 30) | 0.130389 | 0.216611 | 0.347 | [0.098 0.652 0.001 0.249] | 0.137773 | 0.294895 | 0.432667 | [0.099 0.901] | 0.347 | +| (0.25, 31) | 0.109612 | 0.257388 | 0.367 | [0.117 0.633 0. 0.25 ] | 0.100272 | 0.341035 | 0.441306 | [0.117 0.883] | 0.367 | +| (0.25, 32) | 0.123587 | 0.245413 | 0.369 | [0.119 0.631 0. 0.25 ] | 0.133389 | 0.308697 | 0.442087 | [0.119 0.881] | 0.369 | +| (0.25, 33) | 0.162584 | 0.194416 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.134293 | 0.303153 | 0.437445 | [0.107 0.893] | 0.357 | +| (0.25, 34) | 0.148317 | 0.213683 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.110308 | 0.329059 | 0.439367 | [0.112 0.888] | 0.362 | +| (0.25, 35) | 0.11158 | 0.25342 | 0.365 | [0.115 0.635 0. 0.25 ] | 0.110128 | 0.330401 | 0.440529 | [0.115 0.885] | 0.365 | +| (0.25, 36) | 0.154516 | 0.207484 | 0.362 | [0.113 0.637 0.001 0.249] | 0.125018 | 0.313362 | 0.43838 | [0.114 0.886] | 0.362 | +| (0.25, 37) | 0.120364 | 0.245636 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.116338 | 0.32458 | 0.440917 | [0.116 0.884] | 0.366 | +| (0.25, 38) | 0.166437 | 0.190563 | 0.357 | [0.108 0.642 0.001 0.249] | 0.140817 | 0.295642 | 0.436459 | [0.109 0.891] | 0.357 | +| (0.25, 39) | 0.117464 | 0.243536 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.11287 | 0.326111 | 0.438982 | [0.111 0.889] | 0.361 | +| (0.25, 40) | 0.139079 | 0.196921 | 0.336 | [0.087 0.663 0.001 0.249] | 0.132232 | 0.296339 | 0.428571 | [0.088 0.912] | 0.336 | +| (0.25, 41) | 0.106429 | 0.239571 | 0.346 | [0.096 0.654 0. 0.25 ] | 0.113467 | 0.319809 | 0.433276 | [0.096 0.904] | 0.346 | +| (0.25, 42) | 0.130296 | 0.252704 | 0.383 | [0.133 0.617 0. 0.25 ] | 0.0936963 | 0.353931 | 0.447628 | [0.133 0.867] | 0.383 | +| (0.25, 43) | 0.143415 | 0.201585 | 0.345 | [0.095 0.655 0. 0.25 ] | 0.143536 | 0.289364 | 0.4329 | [0.095 0.905] | 0.345 | +| (0.25, 44) | 0.114779 | 0.257221 | 0.372 | [0.123 0.627 0.001 0.249] | 0.103723 | 0.338551 | 0.442274 | [0.124 0.876] | 0.372 | +| (0.25, 45) | 0.16507 | 0.19593 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.149673 | 0.289309 | 0.438982 | [0.111 0.889] | 0.361 | +| (0.25, 46) | 0.158965 | 0.201035 | 0.36 | [0.11 0.64 0. 0.25] | 0.135501 | 0.303095 | 0.438596 | [0.11 0.89] | 0.36 | +| (0.25, 47) | 0.12667 | 0.24133 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.120737 | 0.320959 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 48) | 0.142702 | 0.240298 | 0.383 | [0.133 0.617 0. 0.25 ] | 0.126929 | 0.320699 | 0.447628 | [0.133 0.867] | 0.383 | +| (0.25, 49) | 0.139296 | 0.228704 | 0.368 | [0.12 0.63 0.002 0.248] | 0.116949 | 0.322768 | 0.439716 | [0.122 0.878] | 0.368 | +| (0.25, 50) | 0.130214 | 0.218786 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.139032 | 0.295373 | 0.434405 | [0.099 0.901] | 0.349 | +| (0.25, 51) | 0.126645 | 0.221355 | 0.348 | [0.098 0.652 0. 0.25 ] | 0.111247 | 0.322781 | 0.434028 | [0.098 0.902] | 0.348 | +| (0.25, 52) | 0.162073 | 0.197927 | 0.36 | [0.111 0.639 0.001 0.249] | 0.145148 | 0.292462 | 0.43761 | [0.112 0.888] | 0.36 | +| (0.25, 53) | 0.125951 | 0.237049 | 0.363 | [0.115 0.635 0.002 0.248] | 0.121835 | 0.315941 | 0.437776 | [0.117 0.883] | 0.363 | +| (0.25, 54) | 0.137738 | 0.233262 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.149517 | 0.293353 | 0.44287 | [0.121 0.879] | 0.371 | +| (0.25, 55) | 0.120461 | 0.226539 | 0.347 | [0.098 0.652 0.001 0.249] | 0.126996 | 0.305672 | 0.432667 | [0.099 0.901] | 0.347 | +| (0.25, 56) | 0.083097 | 0.271903 | 0.355 | [0.106 0.644 0.001 0.249] | 0.0829992 | 0.352696 | 0.435696 | [0.107 0.893] | 0.355 | +| (0.25, 57) | 0.152516 | 0.206484 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.156152 | 0.28206 | 0.438212 | [0.109 0.891] | 0.359 | +| (0.25, 58) | 0.15842 | 0.19158 | 0.35 | [0.101 0.649 0.001 0.249] | 0.140964 | 0.292834 | 0.433798 | [0.102 0.898] | 0.35 | +| (0.25, 59) | 0.120642 | 0.251358 | 0.372 | [0.122 0.628 0. 0.25 ] | 0.121374 | 0.321889 | 0.443262 | [0.122 0.878] | 0.372 | +| (0.25, 60) | 0.125145 | 0.231855 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.111299 | 0.326146 | 0.437445 | [0.107 0.893] | 0.357 | +| (0.25, 61) | 0.141589 | 0.228411 | 0.37 | [0.12 0.63 0. 0.25] | 0.146554 | 0.295924 | 0.442478 | [0.12 0.88] | 0.37 | +| (0.25, 62) | 0.129288 | 0.210712 | 0.34 | [0.09 0.66 0. 0.25] | 0.123316 | 0.307719 | 0.431034 | [0.09 0.91] | 0.34 | +| (0.25, 63) | 0.167968 | 0.200032 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.14112 | 0.300576 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 64) | 0.126202 | 0.241798 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.107658 | 0.334039 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 65) | 0.139649 | 0.205351 | 0.345 | [0.095 0.655 0. 0.25 ] | 0.132912 | 0.299989 | 0.4329 | [0.095 0.905] | 0.345 | +| (0.25, 66) | 0.13461 | 0.22139 | 0.356 | [0.107 0.643 0.001 0.249] | 0.120895 | 0.315182 | 0.436077 | [0.108 0.892] | 0.356 | +| (0.25, 67) | 0.112746 | 0.246254 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.108761 | 0.329451 | 0.438212 | [0.109 0.891] | 0.359 | +| (0.25, 68) | 0.125963 | 0.240037 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.12457 | 0.316347 | 0.440917 | [0.116 0.884] | 0.366 | +| (0.25, 69) | 0.117978 | 0.225022 | 0.343 | [0.094 0.656 0.001 0.249] | 0.122437 | 0.308731 | 0.431169 | [0.095 0.905] | 0.343 | +| (0.25, 70) | 0.0978247 | 0.271175 | 0.369 | [0.119 0.631 0. 0.25 ] | 0.10638 | 0.335707 | 0.442087 | [0.119 0.881] | 0.369 | +| (0.25, 71) | 0.122178 | 0.228822 | 0.351 | [0.101 0.649 0. 0.25 ] | 0.112982 | 0.322179 | 0.435161 | [0.101 0.899] | 0.351 | +| (0.25, 72) | 0.116694 | 0.261306 | 0.378 | [0.128 0.622 0. 0.25 ] | 0.129308 | 0.316324 | 0.445633 | [0.128 0.872] | 0.378 | +| (0.25, 73) | 0.142151 | 0.206849 | 0.349 | [0.099 0.651 0. 0.25 ] | 0.127291 | 0.307114 | 0.434405 | [0.099 0.901] | 0.349 | +| (0.25, 74) | 0.119932 | 0.223068 | 0.343 | [0.093 0.657 0. 0.25 ] | 0.118733 | 0.313419 | 0.432152 | [0.093 0.907] | 0.343 | +| (0.25, 75) | 0.161339 | 0.193661 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.13814 | 0.298541 | 0.436681 | [0.105 0.895] | 0.355 | +| (0.25, 76) | 0.102897 | 0.251103 | 0.354 | [0.105 0.645 0.001 0.249] | 0.113351 | 0.321964 | 0.435315 | [0.106 0.894] | 0.354 | +| (0.25, 77) | 0.13454 | 0.22646 | 0.361 | [0.111 0.639 0. 0.25 ] | 0.118977 | 0.320005 | 0.438982 | [0.111 0.889] | 0.361 | +| (0.25, 78) | 0.139003 | 0.228997 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.124439 | 0.317258 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 79) | 0.128929 | 0.230071 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.109666 | 0.328546 | 0.438212 | [0.109 0.891] | 0.359 | +| (0.25, 80) | 0.153328 | 0.220672 | 0.374 | [0.124 0.626 0. 0.25 ] | 0.148008 | 0.296042 | 0.44405 | [0.124 0.876] | 0.374 | +| (0.25, 81) | 0.123484 | 0.247516 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.105126 | 0.337743 | 0.44287 | [0.121 0.879] | 0.371 | +| (0.25, 82) | 0.12095 | 0.23105 | 0.352 | [0.102 0.648 0. 0.25 ] | 0.102409 | 0.333131 | 0.43554 | [0.102 0.898] | 0.352 | +| (0.25, 83) | 0.123315 | 0.239685 | 0.363 | [0.113 0.637 0. 0.25 ] | 0.113052 | 0.326702 | 0.439754 | [0.113 0.887] | 0.363 | +| (0.25, 84) | 0.114865 | 0.244135 | 0.359 | [0.11 0.64 0.001 0.249] | 0.0973808 | 0.339845 | 0.437226 | [0.111 0.889] | 0.359 | +| (0.25, 85) | 0.141829 | 0.225171 | 0.367 | [0.118 0.632 0.001 0.249] | 0.123299 | 0.317019 | 0.440318 | [0.119 0.881] | 0.367 | +| (0.25, 86) | 0.136209 | 0.229791 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.123135 | 0.317782 | 0.440917 | [0.116 0.884] | 0.366 | +| (0.25, 87) | 0.170616 | 0.183384 | 0.354 | [0.104 0.646 0. 0.25 ] | 0.145532 | 0.290768 | 0.4363 | [0.104 0.896] | 0.354 | +| (0.25, 88) | 0.143335 | 0.223665 | 0.367 | [0.117 0.633 0. 0.25 ] | 0.127911 | 0.313396 | 0.441306 | [0.117 0.883] | 0.367 | +| (0.25, 89) | 0.137073 | 0.216927 | 0.354 | [0.104 0.646 0. 0.25 ] | 0.154553 | 0.281747 | 0.4363 | [0.104 0.896] | 0.354 | +| (0.25, 90) | 0.140224 | 0.216776 | 0.357 | [0.107 0.643 0. 0.25 ] | 0.152714 | 0.284731 | 0.437445 | [0.107 0.893] | 0.357 | +| (0.25, 91) | 0.117738 | 0.253262 | 0.371 | [0.121 0.629 0. 0.25 ] | 0.0959792 | 0.346891 | 0.44287 | [0.121 0.879] | 0.371 | +| (0.25, 92) | 0.123906 | 0.252094 | 0.376 | [0.126 0.624 0. 0.25 ] | 0.126874 | 0.317966 | 0.44484 | [0.126 0.874] | 0.376 | +| (0.25, 93) | 0.0661894 | 0.301811 | 0.368 | [0.118 0.632 0. 0.25 ] | 0.0814921 | 0.360204 | 0.441696 | [0.118 0.882] | 0.368 | +| (0.25, 94) | 0.153841 | 0.208159 | 0.362 | [0.112 0.638 0. 0.25 ] | 0.129741 | 0.309626 | 0.439367 | [0.112 0.888] | 0.362 | +| (0.25, 95) | 0.144879 | 0.200121 | 0.345 | [0.096 0.654 0.001 0.249] | 0.139426 | 0.29249 | 0.431917 | [0.097 0.903] | 0.345 | +| (0.25, 96) | 0.151496 | 0.214504 | 0.366 | [0.116 0.634 0. 0.25 ] | 0.112522 | 0.328395 | 0.440917 | [0.116 0.884] | 0.366 | +| (0.25, 97) | 0.135256 | 0.219744 | 0.355 | [0.105 0.645 0. 0.25 ] | 0.126896 | 0.309785 | 0.436681 | [0.105 0.895] | 0.355 | +| (0.25, 98) | 0.148867 | 0.194133 | 0.343 | [0.093 0.657 0. 0.25 ] | 0.128798 | 0.303354 | 0.432152 | [0.093 0.907] | 0.343 | +| (0.25, 99) | 0.137454 | 0.221546 | 0.359 | [0.109 0.641 0. 0.25 ] | 0.108894 | 0.329318 | 0.438212 | [0.109 0.891] | 0.359 | +| (0.3, 0) | 0.115385 | 0.311615 | 0.427 | [0.127 0.573 0. 0.3 ] | 0.103546 | 0.407963 | 0.511509 | [0.127 0.873] | 0.427 | +| (0.3, 1) | 0.115234 | 0.275766 | 0.391 | [0.092 0.608 0.001 0.299] | 0.105087 | 0.390356 | 0.495443 | [0.093 0.907] | 0.391 | +| (0.3, 2) | 0.107756 | 0.296244 | 0.404 | [0.105 0.595 0.001 0.299] | 0.105065 | 0.395773 | 0.500838 | [0.106 0.894] | 0.404 | +| (0.3, 3) | 0.0940979 | 0.301902 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.0939231 | 0.404416 | 0.498339 | [0.096 0.904] | 0.396 | +| (0.3, 4) | 0.129976 | 0.268024 | 0.398 | [0.098 0.602 0. 0.3 ] | 0.104953 | 0.394215 | 0.499168 | [0.098 0.902] | 0.398 | +| (0.3, 5) | 0.128381 | 0.250619 | 0.379 | [0.079 0.621 0. 0.3 ] | 0.101842 | 0.389559 | 0.4914 | [0.079 0.921] | 0.379 | +| (0.3, 6) | 0.131556 | 0.270444 | 0.402 | [0.102 0.598 0. 0.3 ] | 0.117514 | 0.383321 | 0.500835 | [0.102 0.898] | 0.402 | +| (0.3, 7) | 0.112026 | 0.284974 | 0.397 | [0.099 0.601 0.002 0.298] | 0.124003 | 0.373078 | 0.497081 | [0.101 0.899] | 0.397 | +| (0.3, 8) | 0.126207 | 0.290793 | 0.417 | [0.117 0.583 0. 0.3 ] | 0.117671 | 0.389514 | 0.507185 | [0.117 0.883] | 0.417 | +| (0.3, 9) | 0.139192 | 0.253808 | 0.393 | [0.094 0.606 0.001 0.299] | 0.0999559 | 0.39631 | 0.496266 | [0.095 0.905] | 0.393 | +| (0.3, 10) | 0.120012 | 0.281988 | 0.402 | [0.102 0.598 0. 0.3 ] | 0.119377 | 0.381458 | 0.500835 | [0.102 0.898] | 0.402 | +| (0.3, 11) | 0.139279 | 0.260721 | 0.4 | [0.1 0.6 0. 0.3] | 0.117324 | 0.382676 | 0.5 | [0.1 0.9] | 0.4 | +| (0.3, 12) | 0.116443 | 0.276557 | 0.393 | [0.095 0.605 0.002 0.298] | 0.111133 | 0.384295 | 0.495428 | [0.097 0.903] | 0.393 | +| (0.3, 13) | 0.124193 | 0.273807 | 0.398 | [0.099 0.601 0.001 0.299] | 0.108405 | 0.389929 | 0.498333 | [0.1 0.9] | 0.398 | +| (0.3, 14) | 0.12208 | 0.26692 | 0.389 | [0.089 0.611 0. 0.3 ] | 0.127327 | 0.368132 | 0.495458 | [0.089 0.911] | 0.389 | +| (0.3, 15) | 0.103448 | 0.292552 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.100266 | 0.398073 | 0.498339 | [0.096 0.904] | 0.396 | +| (0.3, 16) | 0.14551 | 0.24649 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.120372 | 0.376316 | 0.496689 | [0.092 0.908] | 0.392 | +| (0.3, 17) | 0.141222 | 0.269778 | 0.411 | [0.111 0.589 0. 0.3 ] | 0.118556 | 0.38607 | 0.504626 | [0.111 0.889] | 0.411 | +| (0.3, 18) | 0.142631 | 0.262369 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.0938785 | 0.408214 | 0.502092 | [0.105 0.895] | 0.405 | +| (0.3, 19) | 0.14518 | 0.26782 | 0.413 | [0.114 0.586 0.001 0.299] | 0.125934 | 0.378707 | 0.504641 | [0.115 0.885] | 0.413 | +| (0.3, 20) | 0.117111 | 0.292889 | 0.41 | [0.11 0.59 0. 0.3 ] | 0.100578 | 0.403624 | 0.504202 | [0.11 0.89] | 0.41 | +| (0.3, 21) | 0.147531 | 0.264469 | 0.412 | [0.113 0.587 0.001 0.299] | 0.116326 | 0.387889 | 0.504216 | [0.114 0.886] | 0.412 | +| (0.3, 22) | 0.106063 | 0.292937 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.106593 | 0.39299 | 0.499584 | [0.099 0.901] | 0.399 | +| (0.3, 23) | 0.148613 | 0.250387 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.127635 | 0.371949 | 0.499584 | [0.099 0.901] | 0.399 | +| (0.3, 24) | 0.154573 | 0.248427 | 0.403 | [0.103 0.597 0. 0.3 ] | 0.114545 | 0.386709 | 0.501253 | [0.103 0.897] | 0.403 | +| (0.3, 25) | 0.126976 | 0.267024 | 0.394 | [0.094 0.606 0. 0.3 ] | 0.107205 | 0.390308 | 0.497512 | [0.094 0.906] | 0.394 | +| (0.3, 26) | 0.0936366 | 0.315363 | 0.409 | [0.109 0.591 0. 0.3 ] | 0.0963521 | 0.407426 | 0.503778 | [0.109 0.891] | 0.409 | +| (0.3, 27) | 0.112656 | 0.269344 | 0.382 | [0.083 0.617 0.001 0.299] | 0.104119 | 0.387657 | 0.491776 | [0.084 0.916] | 0.382 | +| (0.3, 28) | 0.162282 | 0.249718 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.125725 | 0.379325 | 0.505051 | [0.112 0.888] | 0.412 | +| (0.3, 29) | 0.144141 | 0.254859 | 0.399 | [0.1 0.6 0.001 0.299] | 0.10784 | 0.390909 | 0.498749 | [0.101 0.899] | 0.399 | +| (0.3, 30) | 0.148497 | 0.254503 | 0.403 | [0.103 0.597 0. 0.3 ] | 0.114328 | 0.386925 | 0.501253 | [0.103 0.897] | 0.403 | +| (0.3, 31) | 0.132418 | 0.280582 | 0.413 | [0.113 0.587 0. 0.3 ] | 0.110571 | 0.394905 | 0.505476 | [0.113 0.887] | 0.413 | +| (0.3, 32) | 0.112737 | 0.277263 | 0.39 | [0.091 0.609 0.001 0.299] | 0.105898 | 0.389135 | 0.495033 | [0.092 0.908] | 0.39 | +| (0.3, 33) | 0.141008 | 0.265992 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.124772 | 0.378162 | 0.502934 | [0.107 0.893] | 0.407 | +| (0.3, 34) | 0.138324 | 0.254676 | 0.393 | [0.093 0.607 0. 0.3 ] | 0.132808 | 0.364293 | 0.4971 | [0.093 0.907] | 0.393 | +| (0.3, 35) | 0.142124 | 0.270876 | 0.413 | [0.113 0.587 0. 0.3 ] | 0.114159 | 0.391317 | 0.505476 | [0.113 0.887] | 0.413 | +| (0.3, 36) | 0.122152 | 0.278848 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.0982835 | 0.402133 | 0.500417 | [0.101 0.899] | 0.401 | +| (0.3, 37) | 0.131292 | 0.261708 | 0.393 | [0.093 0.607 0. 0.3 ] | 0.123554 | 0.373546 | 0.4971 | [0.093 0.907] | 0.393 | +| (0.3, 38) | 0.128837 | 0.288163 | 0.417 | [0.118 0.582 0.001 0.299] | 0.108684 | 0.397666 | 0.506351 | [0.119 0.881] | 0.417 | +| (0.3, 39) | 0.129308 | 0.262692 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.106602 | 0.390086 | 0.496689 | [0.092 0.908] | 0.392 | +| (0.3, 40) | 0.140918 | 0.253082 | 0.394 | [0.095 0.605 0.001 0.299] | 0.112835 | 0.383843 | 0.496678 | [0.096 0.904] | 0.394 | +| (0.3, 41) | 0.116026 | 0.291974 | 0.408 | [0.108 0.592 0. 0.3 ] | 0.0894206 | 0.413935 | 0.503356 | [0.108 0.892] | 0.408 | +| (0.3, 42) | 0.126607 | 0.266393 | 0.393 | [0.095 0.605 0.002 0.298] | 0.112307 | 0.383122 | 0.495428 | [0.097 0.903] | 0.393 | +| (0.3, 43) | 0.100563 | 0.307437 | 0.408 | [0.109 0.591 0.001 0.299] | 0.099999 | 0.402522 | 0.502521 | [0.11 0.89] | 0.408 | +| (0.3, 44) | 0.129683 | 0.262317 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.10938 | 0.387309 | 0.496689 | [0.092 0.908] | 0.392 | +| (0.3, 45) | 0.113322 | 0.281678 | 0.395 | [0.095 0.605 0. 0.3 ] | 0.102612 | 0.395314 | 0.497925 | [0.095 0.905] | 0.395 | +| (0.3, 46) | 0.121586 | 0.283414 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.111687 | 0.390405 | 0.502092 | [0.105 0.895] | 0.405 | +| (0.3, 47) | 0.117878 | 0.303122 | 0.421 | [0.121 0.579 0. 0.3 ] | 0.101569 | 0.407336 | 0.508906 | [0.121 0.879] | 0.421 | +| (0.3, 48) | 0.114245 | 0.286755 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.105145 | 0.395272 | 0.500417 | [0.101 0.899] | 0.401 | +| (0.3, 49) | 0.138991 | 0.244009 | 0.383 | [0.083 0.617 0. 0.3 ] | 0.126666 | 0.366349 | 0.493016 | [0.083 0.917] | 0.383 | +| (0.3, 50) | 0.120115 | 0.271885 | 0.392 | [0.092 0.608 0. 0.3 ] | 0.121823 | 0.374866 | 0.496689 | [0.092 0.908] | 0.392 | +| (0.3, 51) | 0.11579 | 0.26921 | 0.385 | [0.085 0.615 0. 0.3 ] | 0.110148 | 0.383679 | 0.493827 | [0.085 0.915] | 0.385 | +| (0.3, 52) | 0.134915 | 0.275085 | 0.41 | [0.111 0.589 0.001 0.299] | 0.0925126 | 0.410854 | 0.503367 | [0.112 0.888] | 0.41 | +| (0.3, 53) | 0.142717 | 0.261283 | 0.404 | [0.106 0.594 0.002 0.298] | 0.118655 | 0.381345 | 0.5 | [0.108 0.892] | 0.404 | +| (0.3, 54) | 0.154873 | 0.236127 | 0.391 | [0.091 0.609 0. 0.3 ] | 0.115805 | 0.380473 | 0.496278 | [0.091 0.909] | 0.391 | +| (0.3, 55) | 0.117916 | 0.297084 | 0.415 | [0.115 0.585 0. 0.3 ] | 0.113195 | 0.393134 | 0.506329 | [0.115 0.885] | 0.415 | +| (0.3, 56) | 0.119119 | 0.291881 | 0.411 | [0.111 0.589 0. 0.3 ] | 0.0887019 | 0.415924 | 0.504626 | [0.111 0.889] | 0.411 | +| (0.3, 57) | 0.136571 | 0.269429 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.121916 | 0.380597 | 0.502513 | [0.106 0.894] | 0.406 | +| (0.3, 58) | 0.149654 | 0.235346 | 0.385 | [0.086 0.614 0.001 0.299] | 0.118077 | 0.374916 | 0.492993 | [0.087 0.913] | 0.385 | +| (0.3, 59) | 0.110281 | 0.305719 | 0.416 | [0.116 0.584 0. 0.3 ] | 0.113127 | 0.39363 | 0.506757 | [0.116 0.884] | 0.416 | +| (0.3, 60) | 0.105847 | 0.288153 | 0.394 | [0.094 0.606 0. 0.3 ] | 0.108523 | 0.388989 | 0.497512 | [0.094 0.906] | 0.394 | +| (0.3, 61) | 0.118659 | 0.288341 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.085541 | 0.417393 | 0.502934 | [0.107 0.893] | 0.407 | +| (0.3, 62) | 0.129623 | 0.283377 | 0.413 | [0.115 0.585 0.002 0.298] | 0.112032 | 0.391772 | 0.503804 | [0.117 0.883] | 0.413 | +| (0.3, 63) | 0.126394 | 0.269606 | 0.396 | [0.096 0.604 0. 0.3 ] | 0.112563 | 0.385776 | 0.498339 | [0.096 0.904] | 0.396 | +| (0.3, 64) | 0.139585 | 0.264415 | 0.404 | [0.104 0.596 0. 0.3 ] | 0.111884 | 0.389788 | 0.501672 | [0.104 0.896] | 0.404 | +| (0.3, 65) | 0.0951434 | 0.310857 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.0848641 | 0.417648 | 0.502513 | [0.106 0.894] | 0.406 | +| (0.3, 66) | 0.111057 | 0.277943 | 0.389 | [0.089 0.611 0. 0.3 ] | 0.10445 | 0.391009 | 0.495458 | [0.089 0.911] | 0.389 | +| (0.3, 67) | 0.0656081 | 0.346392 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0574239 | 0.447627 | 0.505051 | [0.112 0.888] | 0.412 | +| (0.3, 68) | 0.115383 | 0.293617 | 0.409 | [0.109 0.591 0. 0.3 ] | 0.0971393 | 0.406639 | 0.503778 | [0.109 0.891] | 0.409 | +| (0.3, 69) | 0.11694 | 0.28806 | 0.405 | [0.105 0.595 0. 0.3 ] | 0.118732 | 0.38336 | 0.502092 | [0.105 0.895] | 0.405 | +| (0.3, 70) | 0.129901 | 0.276099 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.11687 | 0.385643 | 0.502513 | [0.106 0.894] | 0.406 | +| (0.3, 71) | 0.12312 | 0.29188 | 0.415 | [0.115 0.585 0. 0.3 ] | 0.0975558 | 0.408773 | 0.506329 | [0.115 0.885] | 0.415 | +| (0.3, 72) | 0.119927 | 0.294073 | 0.414 | [0.114 0.586 0. 0.3 ] | 0.096911 | 0.408991 | 0.505902 | [0.114 0.886] | 0.414 | +| (0.3, 73) | 0.133123 | 0.253877 | 0.387 | [0.087 0.613 0. 0.3 ] | 0.110938 | 0.383703 | 0.494641 | [0.087 0.913] | 0.387 | +| (0.3, 74) | 0.0891778 | 0.333822 | 0.423 | [0.123 0.577 0. 0.3 ] | 0.0806618 | 0.429109 | 0.509771 | [0.123 0.877] | 0.423 | +| (0.3, 75) | 0.0964315 | 0.321569 | 0.418 | [0.118 0.582 0. 0.3 ] | 0.0962955 | 0.411319 | 0.507614 | [0.118 0.882] | 0.418 | +| (0.3, 76) | 0.152005 | 0.253995 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.118398 | 0.384115 | 0.502513 | [0.106 0.894] | 0.406 | +| (0.3, 77) | 0.139943 | 0.259057 | 0.399 | [0.099 0.601 0. 0.3 ] | 0.116276 | 0.383307 | 0.499584 | [0.099 0.901] | 0.399 | +| (0.3, 78) | 0.13643 | 0.26457 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.112532 | 0.387885 | 0.500417 | [0.101 0.899] | 0.401 | +| (0.3, 79) | 0.113834 | 0.290166 | 0.404 | [0.104 0.596 0. 0.3 ] | 0.103453 | 0.398219 | 0.501672 | [0.104 0.896] | 0.404 | +| (0.3, 80) | 0.162074 | 0.225926 | 0.388 | [0.089 0.611 0.001 0.299] | 0.127037 | 0.367178 | 0.494215 | [0.09 0.91] | 0.388 | +| (0.3, 81) | 0.112507 | 0.294493 | 0.407 | [0.107 0.593 0. 0.3 ] | 0.112868 | 0.390066 | 0.502934 | [0.107 0.893] | 0.407 | +| (0.3, 82) | 0.137672 | 0.250328 | 0.388 | [0.088 0.612 0. 0.3 ] | 0.107459 | 0.38759 | 0.49505 | [0.088 0.912] | 0.388 | +| (0.3, 83) | 0.11512 | 0.29688 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0942575 | 0.410793 | 0.505051 | [0.112 0.888] | 0.412 | +| (0.3, 84) | 0.103276 | 0.290724 | 0.394 | [0.096 0.604 0.002 0.298] | 0.0984201 | 0.39742 | 0.49584 | [0.098 0.902] | 0.394 | +| (0.3, 85) | 0.128535 | 0.269465 | 0.398 | [0.099 0.601 0.001 0.299] | 0.102733 | 0.3956 | 0.498333 | [0.1 0.9] | 0.398 | +| (0.3, 86) | 0.117567 | 0.290433 | 0.408 | [0.108 0.592 0. 0.3 ] | 0.11328 | 0.390076 | 0.503356 | [0.108 0.892] | 0.408 | +| (0.3, 87) | 0.141018 | 0.252982 | 0.394 | [0.096 0.604 0.002 0.298] | 0.108792 | 0.387049 | 0.49584 | [0.098 0.902] | 0.394 | +| (0.3, 88) | 0.114183 | 0.286817 | 0.401 | [0.101 0.599 0. 0.3 ] | 0.092482 | 0.407935 | 0.500417 | [0.101 0.899] | 0.401 | +| (0.3, 89) | 0.110207 | 0.302793 | 0.413 | [0.114 0.586 0.001 0.299] | 0.117155 | 0.387487 | 0.504641 | [0.115 0.885] | 0.413 | +| (0.3, 90) | 0.129401 | 0.284599 | 0.414 | [0.114 0.586 0. 0.3 ] | 0.10979 | 0.396113 | 0.505902 | [0.114 0.886] | 0.414 | +| (0.3, 91) | 0.12667 | 0.28733 | 0.414 | [0.115 0.585 0.001 0.299] | 0.109093 | 0.395974 | 0.505068 | [0.116 0.884] | 0.414 | +| (0.3, 92) | 0.0895823 | 0.309418 | 0.399 | [0.1 0.6 0.001 0.299] | 0.0919172 | 0.406832 | 0.498749 | [0.101 0.899] | 0.399 | +| (0.3, 93) | 0.13851 | 0.26349 | 0.402 | [0.103 0.597 0.001 0.299] | 0.116828 | 0.383172 | 0.5 | [0.104 0.896] | 0.402 | +| (0.3, 94) | 0.110087 | 0.308913 | 0.419 | [0.12 0.58 0.001 0.299] | 0.103164 | 0.404046 | 0.507209 | [0.121 0.879] | 0.419 | +| (0.3, 95) | 0.120184 | 0.297816 | 0.418 | [0.118 0.582 0. 0.3 ] | 0.098135 | 0.409479 | 0.507614 | [0.118 0.882] | 0.418 | +| (0.3, 96) | 0.133316 | 0.256684 | 0.39 | [0.09 0.61 0. 0.3 ] | 0.116543 | 0.379325 | 0.495868 | [0.09 0.91] | 0.39 | +| (0.3, 97) | 0.0983323 | 0.313668 | 0.412 | [0.112 0.588 0. 0.3 ] | 0.0720765 | 0.432974 | 0.505051 | [0.112 0.888] | 0.412 | +| (0.3, 98) | 0.136707 | 0.279293 | 0.416 | [0.116 0.584 0. 0.3 ] | 0.113834 | 0.392923 | 0.506757 | [0.116 0.884] | 0.416 | +| (0.3, 99) | 0.128764 | 0.277236 | 0.406 | [0.106 0.594 0. 0.3 ] | 0.127621 | 0.374892 | 0.502513 | [0.106 0.894] | 0.406 | +| (0.35, 0) | 0.127734 | 0.323266 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.106 | 0.454448 | 0.560448 | [0.101 0.899] | 0.451 | +| (0.35, 1) | 0.126857 | 0.316143 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.113086 | 0.443795 | 0.556881 | [0.093 0.907] | 0.443 | +| (0.35, 2) | 0.0879283 | 0.358072 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0770572 | 0.480451 | 0.557508 | [0.098 0.902] | 0.446 | +| (0.35, 3) | 0.111434 | 0.340566 | 0.452 | [0.102 0.548 0. 0.35 ] | 0.107314 | 0.453583 | 0.560897 | [0.102 0.898] | 0.452 | +| (0.35, 4) | 0.158193 | 0.295807 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.132322 | 0.429476 | 0.561798 | [0.104 0.896] | 0.454 | +| (0.35, 5) | 0.120109 | 0.325891 | 0.446 | [0.097 0.553 0.001 0.349] | 0.112725 | 0.444783 | 0.557508 | [0.098 0.902] | 0.446 | +| (0.35, 6) | 0.0941796 | 0.35182 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.0882179 | 0.469996 | 0.558214 | [0.096 0.904] | 0.446 | +| (0.35, 7) | 0.103097 | 0.345903 | 0.449 | [0.099 0.551 0. 0.35 ] | 0.099472 | 0.46008 | 0.559552 | [0.099 0.901] | 0.449 | +| (0.35, 8) | 0.138126 | 0.330874 | 0.469 | [0.119 0.531 0. 0.35 ] | 0.100208 | 0.468435 | 0.568643 | [0.119 0.881] | 0.469 | +| (0.35, 9) | 0.107922 | 0.347078 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.0834859 | 0.478763 | 0.562249 | [0.105 0.895] | 0.455 | +| (0.35, 10) | 0.0992061 | 0.337794 | 0.437 | [0.087 0.563 0. 0.35 ] | 0.091621 | 0.462615 | 0.554236 | [0.087 0.913] | 0.437 | +| (0.35, 11) | 0.105041 | 0.344959 | 0.45 | [0.1 0.55 0. 0.35] | 0.0902889 | 0.469711 | 0.56 | [0.1 0.9] | 0.45 | +| (0.35, 12) | 0.100877 | 0.350123 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0870643 | 0.473384 | 0.560448 | [0.101 0.899] | 0.451 | +| (0.35, 13) | 0.0960624 | 0.345938 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0724629 | 0.483976 | 0.556439 | [0.092 0.908] | 0.442 | +| (0.35, 14) | 0.131091 | 0.299909 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.112462 | 0.439153 | 0.551615 | [0.081 0.919] | 0.431 | +| (0.35, 15) | 0.120561 | 0.325439 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0972215 | 0.460286 | 0.557508 | [0.098 0.902] | 0.446 | +| (0.35, 16) | 0.107446 | 0.339554 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.0781809 | 0.480478 | 0.558659 | [0.097 0.903] | 0.447 | +| (0.35, 17) | 0.12172 | 0.33228 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.0898545 | 0.471943 | 0.561798 | [0.104 0.896] | 0.454 | +| (0.35, 18) | 0.110049 | 0.365951 | 0.476 | [0.126 0.524 0. 0.35 ] | 0.0858809 | 0.486014 | 0.571895 | [0.126 0.874] | 0.476 | +| (0.35, 19) | 0.108301 | 0.329699 | 0.438 | [0.089 0.561 0.001 0.349] | 0.0928654 | 0.461103 | 0.553968 | [0.09 0.91] | 0.438 | +| (0.35, 20) | 0.113459 | 0.319541 | 0.433 | [0.083 0.567 0. 0.35 ] | 0.087382 | 0.465104 | 0.552486 | [0.083 0.917] | 0.433 | +| (0.35, 21) | 0.123105 | 0.329895 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.105268 | 0.456079 | 0.561347 | [0.103 0.897] | 0.453 | +| (0.35, 22) | 0.113375 | 0.345625 | 0.459 | [0.109 0.541 0. 0.35 ] | 0.0933376 | 0.470724 | 0.564061 | [0.109 0.891] | 0.459 | +| (0.35, 23) | 0.118606 | 0.329394 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0897363 | 0.469369 | 0.559105 | [0.098 0.902] | 0.448 | +| (0.35, 24) | 0.127113 | 0.306887 | 0.434 | [0.085 0.565 0.001 0.349] | 0.0946077 | 0.457607 | 0.552215 | [0.086 0.914] | 0.434 | +| (0.35, 25) | 0.119535 | 0.320465 | 0.44 | [0.09 0.56 0. 0.35] | 0.115511 | 0.440044 | 0.555556 | [0.09 0.91] | 0.44 | +| (0.35, 26) | 0.0883172 | 0.344683 | 0.433 | [0.084 0.566 0.001 0.349] | 0.0744312 | 0.477347 | 0.551779 | [0.085 0.915] | 0.433 | +| (0.35, 27) | 0.166992 | 0.279008 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.137423 | 0.42079 | 0.558214 | [0.096 0.904] | 0.446 | +| (0.35, 28) | 0.0954868 | 0.340513 | 0.436 | [0.087 0.563 0.001 0.349] | 0.0830672 | 0.470023 | 0.55309 | [0.088 0.912] | 0.436 | +| (0.35, 29) | 0.0778668 | 0.367133 | 0.445 | [0.096 0.554 0.001 0.349] | 0.0649534 | 0.49211 | 0.557063 | [0.097 0.903] | 0.445 | +| (0.35, 30) | 0.107221 | 0.325779 | 0.433 | [0.083 0.567 0. 0.35 ] | 0.0822103 | 0.470276 | 0.552486 | [0.083 0.917] | 0.433 | +| (0.35, 31) | 0.102419 | 0.339581 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0894659 | 0.466973 | 0.556439 | [0.092 0.908] | 0.442 | +| (0.35, 32) | 0.119917 | 0.328083 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0886716 | 0.470434 | 0.559105 | [0.098 0.902] | 0.448 | +| (0.35, 33) | 0.11129 | 0.34371 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.0841548 | 0.478094 | 0.562249 | [0.105 0.895] | 0.455 | +| (0.35, 34) | 0.101306 | 0.349694 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0869425 | 0.473506 | 0.560448 | [0.101 0.899] | 0.451 | +| (0.35, 35) | 0.109456 | 0.333544 | 0.443 | [0.094 0.556 0.001 0.349] | 0.106623 | 0.449552 | 0.556175 | [0.095 0.905] | 0.443 | +| (0.35, 36) | 0.0992197 | 0.34478 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.0943453 | 0.46298 | 0.557325 | [0.094 0.906] | 0.444 | +| (0.35, 37) | 0.150907 | 0.288093 | 0.439 | [0.09 0.56 0.001 0.349] | 0.119051 | 0.435357 | 0.554408 | [0.091 0.909] | 0.439 | +| (0.35, 38) | 0.109748 | 0.325252 | 0.435 | [0.085 0.565 0. 0.35 ] | 0.0863649 | 0.466995 | 0.55336 | [0.085 0.915] | 0.435 | +| (0.35, 39) | 0.145428 | 0.291572 | 0.437 | [0.088 0.562 0.001 0.349] | 0.128978 | 0.424551 | 0.553529 | [0.089 0.911] | 0.437 | +| (0.35, 40) | 0.098426 | 0.360574 | 0.459 | [0.11 0.54 0.001 0.349] | 0.07388 | 0.489478 | 0.563358 | [0.111 0.889] | 0.459 | +| (0.35, 41) | 0.13594 | 0.31106 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.116727 | 0.441932 | 0.558659 | [0.097 0.903] | 0.447 | +| (0.35, 42) | 0.123184 | 0.312816 | 0.436 | [0.086 0.564 0. 0.35 ] | 0.114021 | 0.439777 | 0.553797 | [0.086 0.914] | 0.436 | +| (0.35, 43) | 0.128692 | 0.318308 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.102665 | 0.455994 | 0.558659 | [0.097 0.903] | 0.447 | +| (0.35, 44) | 0.136286 | 0.292714 | 0.429 | [0.08 0.57 0.001 0.349] | 0.104948 | 0.445091 | 0.550039 | [0.081 0.919] | 0.429 | +| (0.35, 45) | 0.108885 | 0.336115 | 0.445 | [0.095 0.555 0. 0.35 ] | 0.0977137 | 0.460055 | 0.557769 | [0.095 0.905] | 0.445 | +| (0.35, 46) | 0.112286 | 0.334714 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.102874 | 0.455785 | 0.558659 | [0.097 0.903] | 0.447 | +| (0.35, 47) | 0.0906932 | 0.369307 | 0.46 | [0.11 0.54 0. 0.35] | 0.0778993 | 0.486617 | 0.564516 | [0.11 0.89] | 0.46 | +| (0.35, 48) | 0.137978 | 0.315022 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.102033 | 0.459314 | 0.561347 | [0.103 0.897] | 0.453 | +| (0.35, 49) | 0.116168 | 0.334832 | 0.451 | [0.101 0.549 0. 0.35 ] | 0.0912107 | 0.469238 | 0.560448 | [0.101 0.899] | 0.451 | +| (0.35, 50) | 0.104287 | 0.334713 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.0944456 | 0.460669 | 0.555115 | [0.089 0.911] | 0.439 | +| (0.35, 51) | 0.128891 | 0.313109 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0930384 | 0.4634 | 0.556439 | [0.092 0.908] | 0.442 | +| (0.35, 52) | 0.113322 | 0.357678 | 0.471 | [0.121 0.529 0. 0.35 ] | 0.100441 | 0.469128 | 0.569569 | [0.121 0.879] | 0.471 | +| (0.35, 53) | 0.104489 | 0.331511 | 0.436 | [0.086 0.564 0. 0.35 ] | 0.0901448 | 0.463653 | 0.553797 | [0.086 0.914] | 0.436 | +| (0.35, 54) | 0.0996681 | 0.346332 | 0.446 | [0.096 0.554 0. 0.35 ] | 0.0920318 | 0.466182 | 0.558214 | [0.096 0.904] | 0.446 | +| (0.35, 55) | 0.119316 | 0.311684 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.108829 | 0.442786 | 0.551615 | [0.081 0.919] | 0.431 | +| (0.35, 56) | 0.133962 | 0.304038 | 0.438 | [0.089 0.561 0.001 0.349] | 0.106918 | 0.44705 | 0.553968 | [0.09 0.91] | 0.438 | +| (0.35, 57) | 0.138717 | 0.316283 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.11544 | 0.446809 | 0.562249 | [0.105 0.895] | 0.455 | +| (0.35, 58) | 0.139865 | 0.314135 | 0.454 | [0.105 0.545 0.001 0.349] | 0.0996406 | 0.461453 | 0.561093 | [0.106 0.894] | 0.454 | +| (0.35, 59) | 0.117694 | 0.326306 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.0946397 | 0.462685 | 0.557325 | [0.094 0.906] | 0.444 | +| (0.35, 60) | 0.0823521 | 0.383648 | 0.466 | [0.116 0.534 0. 0.35 ] | 0.0751242 | 0.492137 | 0.567261 | [0.116 0.884] | 0.466 | +| (0.35, 61) | 0.114932 | 0.324068 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.107789 | 0.447326 | 0.555115 | [0.089 0.911] | 0.439 | +| (0.35, 62) | 0.0971008 | 0.341899 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.0794721 | 0.475643 | 0.555115 | [0.089 0.911] | 0.439 | +| (0.35, 63) | 0.114859 | 0.339141 | 0.454 | [0.105 0.545 0.001 0.349] | 0.101821 | 0.459272 | 0.561093 | [0.106 0.894] | 0.454 | +| (0.35, 64) | 0.105616 | 0.336384 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.0953635 | 0.461075 | 0.556439 | [0.092 0.908] | 0.442 | +| (0.35, 65) | 0.109411 | 0.330589 | 0.44 | [0.09 0.56 0. 0.35] | 0.0857621 | 0.469794 | 0.555556 | [0.09 0.91] | 0.44 | +| (0.35, 66) | 0.139524 | 0.314476 | 0.454 | [0.104 0.546 0. 0.35 ] | 0.113682 | 0.448115 | 0.561798 | [0.104 0.896] | 0.454 | +| (0.35, 67) | 0.114339 | 0.340661 | 0.455 | [0.105 0.545 0. 0.35 ] | 0.106207 | 0.456042 | 0.562249 | [0.105 0.895] | 0.455 | +| (0.35, 68) | 0.115384 | 0.316616 | 0.432 | [0.082 0.568 0. 0.35 ] | 0.104768 | 0.447283 | 0.55205 | [0.082 0.918] | 0.432 | +| (0.35, 69) | 0.136346 | 0.305654 | 0.442 | [0.092 0.558 0. 0.35 ] | 0.111795 | 0.444644 | 0.556439 | [0.092 0.908] | 0.442 | +| (0.35, 70) | 0.120153 | 0.319847 | 0.44 | [0.09 0.56 0. 0.35] | 0.109676 | 0.44588 | 0.555556 | [0.09 0.91] | 0.44 | +| (0.35, 71) | 0.111197 | 0.350803 | 0.462 | [0.112 0.538 0. 0.35 ] | 0.081468 | 0.48396 | 0.565428 | [0.112 0.888] | 0.462 | +| (0.35, 72) | 0.13692 | 0.30608 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.117318 | 0.439564 | 0.556881 | [0.093 0.907] | 0.443 | +| (0.35, 73) | 0.124503 | 0.328497 | 0.453 | [0.103 0.547 0. 0.35 ] | 0.0945593 | 0.466788 | 0.561347 | [0.103 0.897] | 0.453 | +| (0.35, 74) | 0.136985 | 0.301015 | 0.438 | [0.088 0.562 0. 0.35 ] | 0.0994015 | 0.455274 | 0.554675 | [0.088 0.912] | 0.438 | +| (0.35, 75) | 0.122077 | 0.331923 | 0.454 | [0.106 0.544 0.002 0.348] | 0.0941126 | 0.466274 | 0.560386 | [0.108 0.892] | 0.454 | +| (0.35, 76) | 0.105174 | 0.352826 | 0.458 | [0.109 0.541 0.001 0.349] | 0.0934764 | 0.469427 | 0.562903 | [0.11 0.89] | 0.458 | +| (0.35, 77) | 0.109797 | 0.325203 | 0.435 | [0.086 0.564 0.001 0.349] | 0.0970415 | 0.455611 | 0.552652 | [0.087 0.913] | 0.435 | +| (0.35, 78) | 0.103501 | 0.344499 | 0.448 | [0.098 0.552 0. 0.35 ] | 0.0972722 | 0.461833 | 0.559105 | [0.098 0.902] | 0.448 | +| (0.35, 79) | 0.0992824 | 0.347718 | 0.447 | [0.097 0.553 0. 0.35 ] | 0.0883709 | 0.470288 | 0.558659 | [0.097 0.903] | 0.447 | +| (0.35, 80) | 0.0864577 | 0.359542 | 0.446 | [0.097 0.553 0.001 0.349] | 0.0811096 | 0.476398 | 0.557508 | [0.098 0.902] | 0.446 | +| (0.35, 81) | 0.0944448 | 0.362555 | 0.457 | [0.107 0.543 0. 0.35 ] | 0.0824714 | 0.480682 | 0.563154 | [0.107 0.893] | 0.457 | +| (0.35, 82) | 0.131583 | 0.318417 | 0.45 | [0.101 0.549 0.001 0.349] | 0.10794 | 0.451355 | 0.559295 | [0.102 0.898] | 0.45 | +| (0.35, 83) | 0.120998 | 0.331002 | 0.452 | [0.102 0.548 0. 0.35 ] | 0.104399 | 0.456498 | 0.560897 | [0.102 0.898] | 0.452 | +| (0.35, 84) | 0.106906 | 0.345094 | 0.452 | [0.103 0.547 0.001 0.349] | 0.0750503 | 0.485142 | 0.560193 | [0.104 0.896] | 0.452 | +| (0.35, 85) | 0.120944 | 0.337056 | 0.458 | [0.108 0.542 0. 0.35 ] | 0.0885901 | 0.475017 | 0.563607 | [0.108 0.892] | 0.458 | +| (0.35, 86) | 0.105667 | 0.339333 | 0.445 | [0.095 0.555 0. 0.35 ] | 0.0975882 | 0.460181 | 0.557769 | [0.095 0.905] | 0.445 | +| (0.35, 87) | 0.14568 | 0.29532 | 0.441 | [0.091 0.559 0. 0.35 ] | 0.114812 | 0.441184 | 0.555997 | [0.091 0.909] | 0.441 | +| (0.35, 88) | 0.113016 | 0.325984 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.106299 | 0.448816 | 0.555115 | [0.089 0.911] | 0.439 | +| (0.35, 89) | 0.100033 | 0.342967 | 0.443 | [0.093 0.557 0. 0.35 ] | 0.0877762 | 0.469105 | 0.556881 | [0.093 0.907] | 0.443 | +| (0.35, 90) | 0.130903 | 0.300097 | 0.431 | [0.081 0.569 0. 0.35 ] | 0.128361 | 0.423255 | 0.551615 | [0.081 0.919] | 0.431 | +| (0.35, 91) | 0.124577 | 0.332423 | 0.457 | [0.107 0.543 0. 0.35 ] | 0.0952436 | 0.46791 | 0.563154 | [0.107 0.893] | 0.457 | +| (0.35, 92) | 0.105592 | 0.333408 | 0.439 | [0.089 0.561 0. 0.35 ] | 0.098634 | 0.456481 | 0.555115 | [0.089 0.911] | 0.439 | +| (0.35, 93) | 0.140823 | 0.304177 | 0.445 | [0.096 0.554 0.001 0.349] | 0.125783 | 0.43128 | 0.557063 | [0.097 0.903] | 0.445 | +| (0.35, 94) | 0.121894 | 0.316106 | 0.438 | [0.089 0.561 0.001 0.349] | 0.0975567 | 0.456412 | 0.553968 | [0.09 0.91] | 0.438 | +| (0.35, 95) | 0.111933 | 0.332067 | 0.444 | [0.094 0.556 0. 0.35 ] | 0.106763 | 0.450562 | 0.557325 | [0.094 0.906] | 0.444 | +| (0.35, 96) | 0.116825 | 0.334175 | 0.451 | [0.102 0.548 0.001 0.349] | 0.098681 | 0.461062 | 0.559743 | [0.103 0.897] | 0.451 | +| (0.35, 97) | 0.0768109 | 0.383189 | 0.46 | [0.11 0.54 0. 0.35] | 0.0613629 | 0.503153 | 0.564516 | [0.11 0.89] | 0.46 | +| (0.35, 98) | 0.1058 | 0.3342 | 0.44 | [0.09 0.56 0. 0.35] | 0.0989152 | 0.45664 | 0.555556 | [0.09 0.91] | 0.44 | +| (0.35, 99) | 0.117609 | 0.317391 | 0.435 | [0.085 0.565 0. 0.35 ] | 0.0861184 | 0.467241 | 0.55336 | [0.085 0.915] | 0.435 | +| (0.4, 0) | 0.092345 | 0.401655 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0832232 | 0.529334 | 0.612557 | [0.094 0.906] | 0.494 | +| (0.4, 1) | 0.110067 | 0.369933 | 0.48 | [0.081 0.519 0.001 0.399] | 0.0940139 | 0.511449 | 0.605463 | [0.082 0.918] | 0.48 | +| (0.4, 2) | 0.10932 | 0.38868 | 0.498 | [0.098 0.502 0. 0.4 ] | 0.0823125 | 0.532127 | 0.614439 | [0.098 0.902] | 0.498 | +| (0.4, 3) | 0.140325 | 0.344675 | 0.485 | [0.085 0.515 0. 0.4 ] | 0.109529 | 0.498836 | 0.608365 | [0.085 0.915] | 0.485 | +| (0.4, 4) | 0.113004 | 0.369996 | 0.483 | [0.085 0.515 0.002 0.398] | 0.100758 | 0.505487 | 0.606245 | [0.087 0.913] | 0.483 | +| (0.4, 5) | 0.102619 | 0.402381 | 0.505 | [0.106 0.494 0.001 0.399] | 0.0800786 | 0.537091 | 0.617169 | [0.107 0.893] | 0.505 | +| (0.4, 6) | 0.121988 | 0.385012 | 0.507 | [0.107 0.493 0. 0.4 ] | 0.0934156 | 0.525301 | 0.618716 | [0.107 0.893] | 0.507 | +| (0.4, 7) | 0.111719 | 0.373281 | 0.485 | [0.085 0.515 0. 0.4 ] | 0.0860944 | 0.522271 | 0.608365 | [0.085 0.915] | 0.485 | +| (0.4, 8) | 0.103077 | 0.391923 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0934454 | 0.519581 | 0.613027 | [0.095 0.905] | 0.495 | +| (0.4, 9) | 0.09463 | 0.39437 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0841998 | 0.526021 | 0.610221 | [0.089 0.911] | 0.489 | +| (0.4, 10) | 0.114552 | 0.377448 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.0829579 | 0.528663 | 0.611621 | [0.092 0.908] | 0.492 | +| (0.4, 11) | 0.0881744 | 0.402826 | 0.491 | [0.091 0.509 0. 0.4 ] | 0.080588 | 0.530566 | 0.611154 | [0.091 0.909] | 0.491 | +| (0.4, 12) | 0.116386 | 0.369614 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0788718 | 0.529956 | 0.608828 | [0.086 0.914] | 0.486 | +| (0.4, 13) | 0.082174 | 0.422826 | 0.505 | [0.106 0.494 0.001 0.399] | 0.0696855 | 0.547484 | 0.617169 | [0.107 0.893] | 0.505 | +| (0.4, 14) | 0.10176 | 0.38824 | 0.49 | [0.09 0.51 0. 0.4 ] | 0.0850318 | 0.525655 | 0.610687 | [0.09 0.91] | 0.49 | +| (0.4, 15) | 0.0911766 | 0.404823 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0791771 | 0.533726 | 0.612903 | [0.098 0.902] | 0.496 | +| (0.4, 16) | 0.148433 | 0.335567 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.105864 | 0.502038 | 0.607903 | [0.084 0.916] | 0.484 | +| (0.4, 17) | 0.0972479 | 0.395752 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.0776354 | 0.534453 | 0.612089 | [0.093 0.907] | 0.493 | +| (0.4, 18) | 0.0899533 | 0.390047 | 0.48 | [0.08 0.52 0. 0.4 ] | 0.0751172 | 0.530943 | 0.606061 | [0.08 0.92] | 0.48 | +| (0.4, 19) | 0.0985107 | 0.395489 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0900225 | 0.522535 | 0.612557 | [0.094 0.906] | 0.494 | +| (0.4, 20) | 0.133984 | 0.358016 | 0.492 | [0.093 0.507 0.001 0.399] | 0.100027 | 0.510999 | 0.611026 | [0.094 0.906] | 0.492 | +| (0.4, 21) | 0.107137 | 0.388863 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0792101 | 0.534287 | 0.613497 | [0.096 0.904] | 0.496 | +| (0.4, 22) | 0.0939334 | 0.401067 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0811591 | 0.531868 | 0.613027 | [0.095 0.905] | 0.495 | +| (0.4, 23) | 0.119607 | 0.363393 | 0.483 | [0.085 0.515 0.002 0.398] | 0.0741715 | 0.532074 | 0.606245 | [0.087 0.913] | 0.483 | +| (0.4, 24) | 0.0886681 | 0.402332 | 0.491 | [0.092 0.508 0.001 0.399] | 0.0779011 | 0.532657 | 0.610559 | [0.093 0.907] | 0.491 | +| (0.4, 25) | 0.120708 | 0.361292 | 0.482 | [0.083 0.517 0.001 0.399] | 0.0967001 | 0.509683 | 0.606383 | [0.084 0.916] | 0.482 | +| (0.4, 26) | 0.115847 | 0.372153 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.090602 | 0.519154 | 0.609756 | [0.088 0.912] | 0.488 | +| (0.4, 27) | 0.0823224 | 0.418678 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.0663761 | 0.549482 | 0.615858 | [0.101 0.899] | 0.501 | +| (0.4, 28) | 0.126811 | 0.354189 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.0952304 | 0.51129 | 0.60652 | [0.081 0.919] | 0.481 | +| (0.4, 29) | 0.120102 | 0.370898 | 0.491 | [0.091 0.509 0. 0.4 ] | 0.0952146 | 0.515939 | 0.611154 | [0.091 0.909] | 0.491 | +| (0.4, 30) | 0.1235 | 0.3405 | 0.464 | [0.064 0.536 0. 0.4 ] | 0.0918379 | 0.506964 | 0.598802 | [0.064 0.936] | 0.464 | +| (0.4, 31) | 0.116506 | 0.369494 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0997973 | 0.509031 | 0.608828 | [0.086 0.914] | 0.486 | +| (0.4, 32) | 0.129958 | 0.359042 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0956325 | 0.514589 | 0.610221 | [0.089 0.911] | 0.489 | +| (0.4, 33) | 0.101764 | 0.388236 | 0.49 | [0.09 0.51 0. 0.4 ] | 0.0904647 | 0.520222 | 0.610687 | [0.09 0.91] | 0.49 | +| (0.4, 34) | 0.111378 | 0.382622 | 0.494 | [0.094 0.506 0. 0.4 ] | 0.0774008 | 0.535157 | 0.612557 | [0.094 0.906] | 0.494 | +| (0.4, 35) | 0.118964 | 0.369036 | 0.488 | [0.089 0.511 0.001 0.399] | 0.0925809 | 0.516579 | 0.60916 | [0.09 0.91] | 0.488 | +| (0.4, 36) | 0.107617 | 0.379383 | 0.487 | [0.087 0.513 0. 0.4 ] | 0.0748225 | 0.534469 | 0.609292 | [0.087 0.913] | 0.487 | +| (0.4, 37) | 0.104499 | 0.377501 | 0.482 | [0.083 0.517 0.001 0.399] | 0.0941583 | 0.512225 | 0.606383 | [0.084 0.916] | 0.482 | +| (0.4, 38) | 0.0908303 | 0.38517 | 0.476 | [0.077 0.523 0.001 0.399] | 0.0805585 | 0.523072 | 0.603631 | [0.078 0.922] | 0.476 | +| (0.4, 39) | 0.141925 | 0.327075 | 0.469 | [0.069 0.531 0. 0.4 ] | 0.109778 | 0.491274 | 0.601052 | [0.069 0.931] | 0.469 | +| (0.4, 40) | 0.113013 | 0.380987 | 0.494 | [0.095 0.505 0.001 0.399] | 0.0873407 | 0.524622 | 0.611963 | [0.096 0.904] | 0.494 | +| (0.4, 41) | 0.103835 | 0.397165 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.0760962 | 0.539762 | 0.615858 | [0.101 0.899] | 0.501 | +| (0.4, 42) | 0.117133 | 0.378867 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0906409 | 0.522262 | 0.612903 | [0.098 0.902] | 0.496 | +| (0.4, 43) | 0.0771431 | 0.416857 | 0.494 | [0.096 0.504 0.002 0.398] | 0.065077 | 0.54629 | 0.611367 | [0.098 0.902] | 0.494 | +| (0.4, 44) | 0.0940821 | 0.384918 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.083618 | 0.521984 | 0.605602 | [0.079 0.921] | 0.479 | +| (0.4, 45) | 0.100887 | 0.379113 | 0.48 | [0.081 0.519 0.001 0.399] | 0.098478 | 0.506985 | 0.605463 | [0.082 0.918] | 0.48 | +| (0.4, 46) | 0.0877998 | 0.4042 | 0.492 | [0.093 0.507 0.001 0.399] | 0.0742074 | 0.536819 | 0.611026 | [0.094 0.906] | 0.492 | +| (0.4, 47) | 0.133793 | 0.343207 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.111633 | 0.493053 | 0.604686 | [0.077 0.923] | 0.477 | +| (0.4, 48) | 0.104751 | 0.391249 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0948151 | 0.518682 | 0.613497 | [0.096 0.904] | 0.496 | +| (0.4, 49) | 0.111086 | 0.372914 | 0.484 | [0.085 0.515 0.001 0.399] | 0.0854655 | 0.52184 | 0.607306 | [0.086 0.914] | 0.484 | +| (0.4, 50) | 0.115748 | 0.367252 | 0.483 | [0.084 0.516 0.001 0.399] | 0.0794044 | 0.52744 | 0.606844 | [0.085 0.915] | 0.483 | +| (0.4, 51) | 0.111478 | 0.389522 | 0.501 | [0.101 0.499 0. 0.4 ] | 0.094504 | 0.521354 | 0.615858 | [0.101 0.899] | 0.501 | +| (0.4, 52) | 0.116461 | 0.378539 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0778475 | 0.535179 | 0.613027 | [0.095 0.905] | 0.495 | +| (0.4, 53) | 0.129073 | 0.340927 | 0.47 | [0.071 0.529 0.001 0.399] | 0.10103 | 0.499874 | 0.600904 | [0.072 0.928] | 0.47 | +| (0.4, 54) | 0.070972 | 0.413028 | 0.484 | [0.085 0.515 0.001 0.399] | 0.0619927 | 0.545313 | 0.607306 | [0.086 0.914] | 0.484 | +| (0.4, 55) | 0.117027 | 0.363973 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.100197 | 0.506323 | 0.60652 | [0.081 0.919] | 0.481 | +| (0.4, 56) | 0.116247 | 0.360753 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.0981762 | 0.50651 | 0.604686 | [0.077 0.923] | 0.477 | +| (0.4, 57) | 0.0605851 | 0.419415 | 0.48 | [0.08 0.52 0. 0.4 ] | 0.0531007 | 0.55296 | 0.606061 | [0.08 0.92] | 0.48 | +| (0.4, 58) | 0.123128 | 0.349872 | 0.473 | [0.073 0.527 0. 0.4 ] | 0.0887936 | 0.51407 | 0.602864 | [0.073 0.927] | 0.473 | +| (0.4, 59) | 0.118622 | 0.362378 | 0.481 | [0.081 0.519 0. 0.4 ] | 0.0999178 | 0.506602 | 0.60652 | [0.081 0.919] | 0.481 | +| (0.4, 60) | 0.127352 | 0.347648 | 0.475 | [0.075 0.525 0. 0.4 ] | 0.0978482 | 0.505925 | 0.603774 | [0.075 0.925] | 0.475 | +| (0.4, 61) | 0.106386 | 0.353614 | 0.46 | [0.061 0.539 0.001 0.399] | 0.0804344 | 0.515978 | 0.596413 | [0.062 0.938] | 0.46 | +| (0.4, 62) | 0.0924329 | 0.395567 | 0.488 | [0.089 0.511 0.001 0.399] | 0.0820076 | 0.527153 | 0.60916 | [0.09 0.91] | 0.488 | +| (0.4, 63) | 0.105464 | 0.381536 | 0.487 | [0.087 0.513 0. 0.4 ] | 0.0768776 | 0.532414 | 0.609292 | [0.087 0.913] | 0.487 | +| (0.4, 64) | 0.103833 | 0.377167 | 0.481 | [0.082 0.518 0.001 0.399] | 0.0846173 | 0.521305 | 0.605923 | [0.083 0.917] | 0.481 | +| (0.4, 65) | 0.099706 | 0.378294 | 0.478 | [0.079 0.521 0.001 0.399] | 0.0838127 | 0.520733 | 0.604545 | [0.08 0.92] | 0.478 | +| (0.4, 66) | 0.138331 | 0.336669 | 0.475 | [0.076 0.524 0.001 0.399] | 0.103986 | 0.499188 | 0.603175 | [0.077 0.923] | 0.475 | +| (0.4, 67) | 0.0768523 | 0.407148 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.0699597 | 0.537943 | 0.607903 | [0.084 0.916] | 0.484 | +| (0.4, 68) | 0.12711 | 0.35589 | 0.483 | [0.083 0.517 0. 0.4 ] | 0.106317 | 0.501125 | 0.607441 | [0.083 0.917] | 0.483 | +| (0.4, 69) | 0.0883312 | 0.388669 | 0.477 | [0.077 0.523 0. 0.4 ] | 0.0754731 | 0.529213 | 0.604686 | [0.077 0.923] | 0.477 | +| (0.4, 70) | 0.0730699 | 0.41593 | 0.489 | [0.089 0.511 0. 0.4 ] | 0.0616618 | 0.548559 | 0.610221 | [0.089 0.911] | 0.489 | +| (0.4, 71) | 0.124959 | 0.375041 | 0.5 | [0.1 0.5 0. 0.4] | 0.0857221 | 0.529663 | 0.615385 | [0.1 0.9] | 0.5 | +| (0.4, 72) | 0.113747 | 0.376253 | 0.49 | [0.091 0.509 0.001 0.399] | 0.0965721 | 0.51352 | 0.610092 | [0.092 0.908] | 0.49 | +| (0.4, 73) | 0.133 | 0.358 | 0.491 | [0.092 0.508 0.001 0.399] | 0.0919066 | 0.518652 | 0.610559 | [0.093 0.907] | 0.491 | +| (0.4, 74) | 0.105271 | 0.386729 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.0828093 | 0.528812 | 0.611621 | [0.092 0.908] | 0.492 | +| (0.4, 75) | 0.134828 | 0.358172 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.110178 | 0.50191 | 0.612089 | [0.093 0.907] | 0.493 | +| (0.4, 76) | 0.12011 | 0.37189 | 0.492 | [0.092 0.508 0. 0.4 ] | 0.091034 | 0.520587 | 0.611621 | [0.092 0.908] | 0.492 | +| (0.4, 77) | 0.118663 | 0.359337 | 0.478 | [0.078 0.522 0. 0.4 ] | 0.0957442 | 0.5094 | 0.605144 | [0.078 0.922] | 0.478 | +| (0.4, 78) | 0.0944783 | 0.405522 | 0.5 | [0.1 0.5 0. 0.4] | 0.0700838 | 0.545301 | 0.615385 | [0.1 0.9] | 0.5 | +| (0.4, 79) | 0.116782 | 0.380218 | 0.497 | [0.097 0.503 0. 0.4 ] | 0.0970365 | 0.516931 | 0.613968 | [0.097 0.903] | 0.497 | +| (0.4, 80) | 0.107796 | 0.369204 | 0.477 | [0.079 0.521 0.002 0.398] | 0.0821726 | 0.521315 | 0.603487 | [0.081 0.919] | 0.477 | +| (0.4, 81) | 0.105714 | 0.380286 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0767872 | 0.532041 | 0.608828 | [0.086 0.914] | 0.486 | +| (0.4, 82) | 0.114563 | 0.391437 | 0.506 | [0.106 0.494 0. 0.4 ] | 0.0992908 | 0.518947 | 0.618238 | [0.106 0.894] | 0.506 | +| (0.4, 83) | 0.112762 | 0.366238 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.0831437 | 0.522458 | 0.605602 | [0.079 0.921] | 0.479 | +| (0.4, 84) | 0.118112 | 0.361888 | 0.48 | [0.081 0.519 0.001 0.399] | 0.0910492 | 0.514414 | 0.605463 | [0.082 0.918] | 0.48 | +| (0.4, 85) | 0.109261 | 0.369739 | 0.479 | [0.079 0.521 0. 0.4 ] | 0.0871998 | 0.518402 | 0.605602 | [0.079 0.921] | 0.479 | +| (0.4, 86) | 0.0915574 | 0.404443 | 0.496 | [0.096 0.504 0. 0.4 ] | 0.0691098 | 0.544387 | 0.613497 | [0.096 0.904] | 0.496 | +| (0.4, 87) | 0.102728 | 0.399272 | 0.502 | [0.102 0.498 0. 0.4 ] | 0.0756223 | 0.540711 | 0.616333 | [0.102 0.898] | 0.502 | +| (0.4, 88) | 0.073757 | 0.414243 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.0647122 | 0.545044 | 0.609756 | [0.088 0.912] | 0.488 | +| (0.4, 89) | 0.0796764 | 0.404324 | 0.484 | [0.084 0.516 0. 0.4 ] | 0.0650499 | 0.542853 | 0.607903 | [0.084 0.916] | 0.484 | +| (0.4, 90) | 0.109032 | 0.383968 | 0.493 | [0.093 0.507 0. 0.4 ] | 0.0869838 | 0.525105 | 0.612089 | [0.093 0.907] | 0.493 | +| (0.4, 91) | 0.0873812 | 0.403619 | 0.491 | [0.093 0.507 0.002 0.398] | 0.0752138 | 0.534748 | 0.609962 | [0.095 0.905] | 0.491 | +| (0.4, 92) | 0.133381 | 0.342619 | 0.476 | [0.077 0.523 0.001 0.399] | 0.116146 | 0.487485 | 0.603631 | [0.078 0.922] | 0.476 | +| (0.4, 93) | 0.0782402 | 0.39776 | 0.476 | [0.077 0.523 0.001 0.399] | 0.0717926 | 0.531838 | 0.603631 | [0.078 0.922] | 0.476 | +| (0.4, 94) | 0.0698796 | 0.43712 | 0.507 | [0.107 0.493 0. 0.4 ] | 0.056667 | 0.562049 | 0.618716 | [0.107 0.893] | 0.507 | +| (0.4, 95) | 0.0862284 | 0.399772 | 0.486 | [0.086 0.514 0. 0.4 ] | 0.0655856 | 0.543242 | 0.608828 | [0.086 0.914] | 0.486 | +| (0.4, 96) | 0.082924 | 0.402076 | 0.485 | [0.086 0.514 0.001 0.399] | 0.0622894 | 0.545479 | 0.607768 | [0.087 0.913] | 0.485 | +| (0.4, 97) | 0.106119 | 0.388881 | 0.495 | [0.095 0.505 0. 0.4 ] | 0.0793528 | 0.533674 | 0.613027 | [0.095 0.905] | 0.495 | +| (0.4, 98) | 0.0803093 | 0.415691 | 0.496 | [0.097 0.503 0.001 0.399] | 0.0649711 | 0.547932 | 0.612903 | [0.098 0.902] | 0.496 | +| (0.4, 99) | 0.130854 | 0.357146 | 0.488 | [0.088 0.512 0. 0.4 ] | 0.0929463 | 0.51681 | 0.609756 | [0.088 0.912] | 0.488 | +| (0.45, 0) | 0.144135 | 0.390865 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0978465 | 0.561494 | 0.659341 | [0.085 0.915] | 0.535 | +| (0.45, 1) | 0.0803529 | 0.446647 | 0.527 | [0.078 0.472 0.001 0.449] | 0.0665225 | 0.588474 | 0.654996 | [0.079 0.921] | 0.527 | +| (0.45, 2) | 0.102685 | 0.434315 | 0.537 | [0.087 0.463 0. 0.45 ] | 0.0754333 | 0.584875 | 0.660308 | [0.087 0.913] | 0.537 | +| (0.45, 3) | 0.0687572 | 0.470243 | 0.539 | [0.089 0.461 0. 0.45 ] | 0.0624005 | 0.598878 | 0.661278 | [0.089 0.911] | 0.539 | +| (0.45, 4) | 0.125853 | 0.388147 | 0.514 | [0.064 0.486 0. 0.45 ] | 0.0917095 | 0.557641 | 0.649351 | [0.064 0.936] | 0.514 | +| (0.45, 5) | 0.0746958 | 0.451304 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0637687 | 0.591253 | 0.655022 | [0.076 0.924] | 0.526 | +| (0.45, 6) | 0.110722 | 0.424278 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0802848 | 0.579056 | 0.659341 | [0.085 0.915] | 0.535 | +| (0.45, 7) | 0.0962457 | 0.436754 | 0.533 | [0.084 0.466 0.001 0.449] | 0.0746481 | 0.583227 | 0.657875 | [0.085 0.915] | 0.533 | +| (0.45, 8) | 0.0899108 | 0.463089 | 0.553 | [0.103 0.447 0. 0.45 ] | 0.0678808 | 0.600271 | 0.668151 | [0.103 0.897] | 0.553 | +| (0.45, 9) | 0.0804035 | 0.443597 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0667882 | 0.587282 | 0.65407 | [0.074 0.926] | 0.524 | +| (0.45, 10) | 0.0723278 | 0.458672 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.056579 | 0.600835 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 11) | 0.130674 | 0.398326 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.0896737 | 0.566781 | 0.656455 | [0.079 0.921] | 0.529 | +| (0.45, 12) | 0.0678408 | 0.468159 | 0.536 | [0.088 0.462 0.002 0.448] | 0.0504177 | 0.608406 | 0.658824 | [0.09 0.91] | 0.536 | +| (0.45, 13) | 0.102707 | 0.424293 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0840882 | 0.571411 | 0.655499 | [0.077 0.923] | 0.527 | +| (0.45, 14) | 0.123353 | 0.414647 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0904657 | 0.570327 | 0.660793 | [0.088 0.912] | 0.538 | +| (0.45, 15) | 0.117961 | 0.416039 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.0893819 | 0.569476 | 0.658858 | [0.084 0.916] | 0.534 | +| (0.45, 16) | 0.0889066 | 0.440093 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.06649 | 0.589965 | 0.656455 | [0.079 0.921] | 0.529 | +| (0.45, 17) | 0.093381 | 0.428619 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0608659 | 0.592255 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 18) | 0.0845936 | 0.448406 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0663776 | 0.591998 | 0.658376 | [0.083 0.917] | 0.533 | +| (0.45, 19) | 0.0937979 | 0.442202 | 0.536 | [0.086 0.464 0. 0.45 ] | 0.0702677 | 0.589556 | 0.659824 | [0.086 0.914] | 0.536 | +| (0.45, 20) | 0.0977833 | 0.443217 | 0.541 | [0.091 0.459 0. 0.45 ] | 0.0747889 | 0.587463 | 0.662252 | [0.091 0.909] | 0.541 | +| (0.45, 21) | 0.109247 | 0.412753 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0880567 | 0.565064 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 22) | 0.0977246 | 0.433275 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0722733 | 0.585141 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 23) | 0.0845927 | 0.444407 | 0.529 | [0.08 0.47 0.001 0.449] | 0.063226 | 0.592727 | 0.655953 | [0.081 0.919] | 0.529 | +| (0.45, 24) | 0.0863168 | 0.444683 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0612519 | 0.596162 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 25) | 0.0951726 | 0.430827 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0676557 | 0.587366 | 0.655022 | [0.076 0.924] | 0.526 | +| (0.45, 26) | 0.11682 | 0.41518 | 0.532 | [0.083 0.467 0.001 0.449] | 0.0886786 | 0.568715 | 0.657394 | [0.084 0.916] | 0.532 | +| (0.45, 27) | 0.0810696 | 0.46393 | 0.545 | [0.095 0.455 0. 0.45 ] | 0.0680873 | 0.596119 | 0.664207 | [0.095 0.905] | 0.545 | +| (0.45, 28) | 0.0983129 | 0.434687 | 0.533 | [0.084 0.466 0.001 0.449] | 0.0680319 | 0.589844 | 0.657875 | [0.085 0.915] | 0.533 | +| (0.45, 29) | 0.113898 | 0.434102 | 0.548 | [0.098 0.452 0. 0.45 ] | 0.099043 | 0.566637 | 0.66568 | [0.098 0.902] | 0.548 | +| (0.45, 30) | 0.107967 | 0.436033 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0797048 | 0.584012 | 0.663717 | [0.094 0.906] | 0.544 | +| (0.45, 31) | 0.0910724 | 0.435928 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0792247 | 0.576274 | 0.655499 | [0.077 0.923] | 0.527 | +| (0.45, 32) | 0.0998013 | 0.437199 | 0.537 | [0.088 0.462 0.001 0.449] | 0.0709575 | 0.588851 | 0.659809 | [0.089 0.911] | 0.537 | +| (0.45, 33) | 0.128649 | 0.395351 | 0.524 | [0.075 0.475 0.001 0.449] | 0.0880365 | 0.56553 | 0.653566 | [0.076 0.924] | 0.524 | +| (0.45, 34) | 0.11969 | 0.42331 | 0.543 | [0.093 0.457 0. 0.45 ] | 0.0735173 | 0.58971 | 0.663228 | [0.093 0.907] | 0.543 | +| (0.45, 35) | 0.10032 | 0.43168 | 0.532 | [0.084 0.466 0.002 0.448] | 0.0863961 | 0.570495 | 0.656891 | [0.086 0.914] | 0.532 | +| (0.45, 36) | 0.0874866 | 0.436513 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0645323 | 0.589537 | 0.65407 | [0.074 0.926] | 0.524 | +| (0.45, 37) | 0.0794778 | 0.447522 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0575178 | 0.597981 | 0.655499 | [0.077 0.923] | 0.527 | +| (0.45, 38) | 0.0371157 | 0.498884 | 0.536 | [0.086 0.464 0. 0.45 ] | 0.0320388 | 0.627785 | 0.659824 | [0.086 0.914] | 0.536 | +| (0.45, 39) | 0.0757443 | 0.446256 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0608502 | 0.59227 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 40) | 0.125924 | 0.399076 | 0.525 | [0.077 0.473 0.002 0.448] | 0.0963192 | 0.557218 | 0.653538 | [0.079 0.921] | 0.525 | +| (0.45, 41) | 0.0878115 | 0.450189 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0617661 | 0.599027 | 0.660793 | [0.088 0.912] | 0.538 | +| (0.45, 42) | 0.0975696 | 0.44243 | 0.54 | [0.091 0.459 0.001 0.449] | 0.0706756 | 0.590591 | 0.661267 | [0.092 0.908] | 0.54 | +| (0.45, 43) | 0.0907168 | 0.447283 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0714498 | 0.589343 | 0.660793 | [0.088 0.912] | 0.538 | +| (0.45, 44) | 0.069606 | 0.452394 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0567298 | 0.596391 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 45) | 0.0775188 | 0.460481 | 0.538 | [0.088 0.462 0. 0.45 ] | 0.0631226 | 0.59767 | 0.660793 | [0.088 0.912] | 0.538 | +| (0.45, 46) | 0.107153 | 0.428847 | 0.536 | [0.087 0.463 0.001 0.449] | 0.0851788 | 0.574146 | 0.659325 | [0.088 0.912] | 0.536 | +| (0.45, 47) | 0.110526 | 0.409474 | 0.52 | [0.07 0.48 0. 0.45] | 0.0791248 | 0.573049 | 0.652174 | [0.07 0.93] | 0.52 | +| (0.45, 48) | 0.105262 | 0.422738 | 0.528 | [0.078 0.472 0. 0.45 ] | 0.0851603 | 0.570816 | 0.655977 | [0.078 0.922] | 0.528 | +| (0.45, 49) | 0.0977202 | 0.44528 | 0.543 | [0.093 0.457 0. 0.45 ] | 0.0667705 | 0.596457 | 0.663228 | [0.093 0.907] | 0.543 | +| (0.45, 50) | 0.110384 | 0.406616 | 0.517 | [0.067 0.483 0. 0.45 ] | 0.0739936 | 0.576766 | 0.650759 | [0.067 0.933] | 0.517 | +| (0.45, 51) | 0.10361 | 0.42639 | 0.53 | [0.08 0.47 0. 0.45] | 0.0716481 | 0.585286 | 0.656934 | [0.08 0.92] | 0.53 | +| (0.45, 52) | 0.0978246 | 0.433175 | 0.531 | [0.082 0.468 0.001 0.449] | 0.0701777 | 0.586735 | 0.656913 | [0.083 0.917] | 0.531 | +| (0.45, 53) | 0.0900391 | 0.431961 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0735003 | 0.57962 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 54) | 0.0896197 | 0.44938 | 0.539 | [0.09 0.46 0.001 0.449] | 0.0574227 | 0.603357 | 0.66078 | [0.091 0.909] | 0.539 | +| (0.45, 55) | 0.0844244 | 0.429576 | 0.514 | [0.064 0.486 0. 0.45 ] | 0.0684914 | 0.580859 | 0.649351 | [0.064 0.936] | 0.514 | +| (0.45, 56) | 0.0762635 | 0.447736 | 0.524 | [0.074 0.476 0. 0.45 ] | 0.0546346 | 0.599435 | 0.65407 | [0.074 0.926] | 0.524 | +| (0.45, 57) | 0.0873352 | 0.442665 | 0.53 | [0.08 0.47 0. 0.45] | 0.0714978 | 0.585436 | 0.656934 | [0.08 0.92] | 0.53 | +| (0.45, 58) | 0.0981975 | 0.432803 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0694249 | 0.587989 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 59) | 0.0862462 | 0.438754 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0721937 | 0.582352 | 0.654545 | [0.075 0.925] | 0.525 | +| (0.45, 60) | 0.0910102 | 0.42499 | 0.516 | [0.066 0.484 0. 0.45 ] | 0.0711453 | 0.579144 | 0.650289 | [0.066 0.934] | 0.516 | +| (0.45, 61) | 0.108033 | 0.420967 | 0.529 | [0.079 0.471 0. 0.45 ] | 0.0712548 | 0.5852 | 0.656455 | [0.079 0.921] | 0.529 | +| (0.45, 62) | 0.0915517 | 0.437448 | 0.529 | [0.08 0.47 0.001 0.449] | 0.0657728 | 0.59018 | 0.655953 | [0.081 0.919] | 0.529 | +| (0.45, 63) | 0.10221 | 0.42779 | 0.53 | [0.08 0.47 0. 0.45] | 0.071132 | 0.585802 | 0.656934 | [0.08 0.92] | 0.53 | +| (0.45, 64) | 0.0737738 | 0.451226 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0595941 | 0.594951 | 0.654545 | [0.075 0.925] | 0.525 | +| (0.45, 65) | 0.108378 | 0.413622 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0710203 | 0.5821 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 66) | 0.0985459 | 0.432454 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0684595 | 0.588955 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 67) | 0.0782579 | 0.460742 | 0.539 | [0.09 0.46 0.001 0.449] | 0.0622707 | 0.598509 | 0.66078 | [0.091 0.909] | 0.539 | +| (0.45, 68) | 0.0895337 | 0.436466 | 0.526 | [0.077 0.473 0.001 0.449] | 0.0572073 | 0.597312 | 0.654519 | [0.078 0.922] | 0.526 | +| (0.45, 69) | 0.116034 | 0.410966 | 0.527 | [0.077 0.473 0. 0.45 ] | 0.0800237 | 0.575475 | 0.655499 | [0.077 0.923] | 0.527 | +| (0.45, 70) | 0.0733139 | 0.451686 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0563668 | 0.598179 | 0.654545 | [0.075 0.925] | 0.525 | +| (0.45, 71) | 0.0764108 | 0.454589 | 0.531 | [0.081 0.469 0. 0.45 ] | 0.0568153 | 0.600599 | 0.657414 | [0.081 0.919] | 0.531 | +| (0.45, 72) | 0.101457 | 0.421543 | 0.523 | [0.074 0.476 0.001 0.449] | 0.0660446 | 0.587046 | 0.653091 | [0.075 0.925] | 0.523 | +| (0.45, 73) | 0.074457 | 0.445543 | 0.52 | [0.07 0.48 0. 0.45] | 0.0427993 | 0.609375 | 0.652174 | [0.07 0.93] | 0.52 | +| (0.45, 74) | 0.114778 | 0.416222 | 0.531 | [0.082 0.468 0.001 0.449] | 0.0877855 | 0.569127 | 0.656913 | [0.083 0.917] | 0.531 | +| (0.45, 75) | 0.0871245 | 0.453876 | 0.541 | [0.091 0.459 0. 0.45 ] | 0.0578167 | 0.604435 | 0.662252 | [0.091 0.909] | 0.541 | +| (0.45, 76) | 0.0804687 | 0.448531 | 0.529 | [0.08 0.47 0.001 0.449] | 0.0565621 | 0.599391 | 0.655953 | [0.081 0.919] | 0.529 | +| (0.45, 77) | 0.0759259 | 0.472074 | 0.548 | [0.098 0.452 0. 0.45 ] | 0.0714512 | 0.594229 | 0.66568 | [0.098 0.902] | 0.548 | +| (0.45, 78) | 0.0901168 | 0.436883 | 0.527 | [0.078 0.472 0.001 0.449] | 0.0652689 | 0.589727 | 0.654996 | [0.079 0.921] | 0.527 | +| (0.45, 79) | 0.117665 | 0.398335 | 0.516 | [0.066 0.484 0. 0.45 ] | 0.0850179 | 0.565271 | 0.650289 | [0.066 0.934] | 0.516 | +| (0.45, 80) | 0.0838263 | 0.450174 | 0.534 | [0.086 0.464 0.002 0.448] | 0.0586789 | 0.599177 | 0.657856 | [0.088 0.912] | 0.534 | +| (0.45, 81) | 0.117966 | 0.407034 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0916233 | 0.562922 | 0.654545 | [0.075 0.925] | 0.525 | +| (0.45, 82) | 0.0783591 | 0.465641 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0563563 | 0.607361 | 0.663717 | [0.094 0.906] | 0.544 | +| (0.45, 83) | 0.107869 | 0.426131 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.079874 | 0.578984 | 0.658858 | [0.084 0.916] | 0.534 | +| (0.45, 84) | 0.0742541 | 0.457746 | 0.532 | [0.083 0.467 0.001 0.449] | 0.0582135 | 0.59918 | 0.657394 | [0.084 0.916] | 0.532 | +| (0.45, 85) | 0.0889582 | 0.436042 | 0.525 | [0.075 0.475 0. 0.45 ] | 0.0641647 | 0.590381 | 0.654545 | [0.075 0.925] | 0.525 | +| (0.45, 86) | 0.105018 | 0.429982 | 0.535 | [0.086 0.464 0.001 0.449] | 0.075934 | 0.582907 | 0.658841 | [0.087 0.913] | 0.535 | +| (0.45, 87) | 0.0880208 | 0.437979 | 0.526 | [0.077 0.473 0.001 0.449] | 0.0573692 | 0.59715 | 0.654519 | [0.078 0.922] | 0.526 | +| (0.45, 88) | 0.0817681 | 0.456232 | 0.538 | [0.09 0.46 0.002 0.448] | 0.056134 | 0.60366 | 0.659794 | [0.092 0.908] | 0.538 | +| (0.45, 89) | 0.113433 | 0.420567 | 0.534 | [0.085 0.465 0.001 0.449] | 0.085868 | 0.57249 | 0.658358 | [0.086 0.914] | 0.534 | +| (0.45, 90) | 0.0828514 | 0.446149 | 0.529 | [0.081 0.469 0.002 0.448] | 0.0623557 | 0.593094 | 0.65545 | [0.083 0.917] | 0.529 | +| (0.45, 91) | 0.0816244 | 0.451376 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0671052 | 0.591271 | 0.658376 | [0.083 0.917] | 0.533 | +| (0.45, 92) | 0.0661671 | 0.459833 | 0.526 | [0.076 0.474 0. 0.45 ] | 0.0487002 | 0.606322 | 0.655022 | [0.076 0.924] | 0.526 | +| (0.45, 93) | 0.111753 | 0.412247 | 0.524 | [0.075 0.475 0.001 0.449] | 0.0867837 | 0.566783 | 0.653566 | [0.076 0.924] | 0.524 | +| (0.45, 94) | 0.120017 | 0.414983 | 0.535 | [0.085 0.465 0. 0.45 ] | 0.0876596 | 0.571681 | 0.659341 | [0.085 0.915] | 0.535 | +| (0.45, 95) | 0.121518 | 0.412482 | 0.534 | [0.084 0.466 0. 0.45 ] | 0.0959745 | 0.562883 | 0.658858 | [0.084 0.916] | 0.534 | +| (0.45, 96) | 0.0951344 | 0.426866 | 0.522 | [0.072 0.478 0. 0.45 ] | 0.0639835 | 0.589137 | 0.65312 | [0.072 0.928] | 0.522 | +| (0.45, 97) | 0.0712756 | 0.461724 | 0.533 | [0.083 0.467 0. 0.45 ] | 0.0543197 | 0.604056 | 0.658376 | [0.083 0.917] | 0.533 | +| (0.45, 98) | 0.0954362 | 0.448564 | 0.544 | [0.094 0.456 0. 0.45 ] | 0.0673074 | 0.596409 | 0.663717 | [0.094 0.906] | 0.544 | +| (0.45, 99) | 0.103458 | 0.428542 | 0.532 | [0.082 0.468 0. 0.45 ] | 0.0707815 | 0.587113 | 0.657895 | [0.082 0.918] | 0.532 | +| (0.5, 0) | 0.0823199 | 0.48668 | 0.569 | [0.071 0.429 0.002 0.498] | 0.0658081 | 0.63216 | 0.697968 | [0.073 0.927] | 0.569 | +| (0.5, 1) | 0.0714074 | 0.492593 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0533071 | 0.643072 | 0.696379 | [0.064 0.936] | 0.564 | +| (0.5, 2) | 0.0913782 | 0.477622 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0608316 | 0.63798 | 0.698812 | [0.069 0.931] | 0.569 | +| (0.5, 3) | 0.0980838 | 0.467916 | 0.566 | [0.066 0.434 0. 0.5 ] | 0.0728228 | 0.624527 | 0.69735 | [0.066 0.934] | 0.566 | +| (0.5, 4) | 0.0713764 | 0.485624 | 0.557 | [0.06 0.44 0.003 0.497] | 0.0545128 | 0.637206 | 0.691719 | [0.063 0.937] | 0.557 | +| (0.5, 5) | 0.109045 | 0.462955 | 0.572 | [0.073 0.427 0.001 0.499] | 0.069359 | 0.630501 | 0.69986 | [0.074 0.926] | 0.572 | +| (0.5, 6) | 0.0859611 | 0.500039 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0553646 | 0.651849 | 0.707214 | [0.086 0.914] | 0.586 | +| (0.5, 7) | 0.0853441 | 0.483656 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0633528 | 0.635459 | 0.698812 | [0.069 0.931] | 0.569 | +| (0.5, 8) | 0.132703 | 0.442297 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0890792 | 0.612675 | 0.701754 | [0.075 0.925] | 0.575 | +| (0.5, 9) | 0.0901357 | 0.484864 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.061493 | 0.640261 | 0.701754 | [0.075 0.925] | 0.575 | +| (0.5, 10) | 0.0896721 | 0.492328 | 0.582 | [0.083 0.417 0.001 0.499] | 0.05777 | 0.647032 | 0.704802 | [0.084 0.916] | 0.582 | +| (0.5, 11) | 0.104675 | 0.472325 | 0.577 | [0.078 0.422 0.001 0.499] | 0.0722944 | 0.630028 | 0.702322 | [0.079 0.921] | 0.577 | +| (0.5, 12) | 0.106432 | 0.467568 | 0.574 | [0.074 0.426 0. 0.5 ] | 0.0760253 | 0.625237 | 0.701262 | [0.074 0.926] | 0.574 | +| (0.5, 13) | 0.0447724 | 0.536228 | 0.581 | [0.082 0.418 0.001 0.499] | 0.0338064 | 0.670499 | 0.704305 | [0.083 0.917] | 0.581 | +| (0.5, 14) | 0.08127 | 0.49473 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0546865 | 0.647561 | 0.702247 | [0.076 0.924] | 0.576 | +| (0.5, 15) | 0.0927274 | 0.466273 | 0.559 | [0.059 0.441 0. 0.5 ] | 0.0689874 | 0.624975 | 0.693963 | [0.059 0.941] | 0.559 | +| (0.5, 16) | 0.0895199 | 0.49148 | 0.581 | [0.082 0.418 0.001 0.499] | 0.0676034 | 0.636701 | 0.704305 | [0.083 0.917] | 0.581 | +| (0.5, 17) | 0.101514 | 0.471486 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0786432 | 0.622128 | 0.700771 | [0.073 0.927] | 0.573 | +| (0.5, 18) | 0.0802439 | 0.488756 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0639173 | 0.634895 | 0.698812 | [0.069 0.931] | 0.569 | +| (0.5, 19) | 0.094629 | 0.466371 | 0.561 | [0.061 0.439 0. 0.5 ] | 0.0745545 | 0.620373 | 0.694927 | [0.061 0.939] | 0.561 | +| (0.5, 20) | 0.0639745 | 0.506026 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0458685 | 0.653432 | 0.699301 | [0.07 0.93] | 0.57 | +| (0.5, 21) | 0.0647483 | 0.507252 | 0.572 | [0.073 0.427 0.001 0.499] | 0.0419894 | 0.65787 | 0.69986 | [0.074 0.926] | 0.572 | +| (0.5, 22) | 0.0906012 | 0.469399 | 0.56 | [0.06 0.44 0. 0.5 ] | 0.0719325 | 0.622512 | 0.694444 | [0.06 0.94] | 0.56 | +| (0.5, 23) | 0.0792254 | 0.484775 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0515546 | 0.644824 | 0.696379 | [0.064 0.936] | 0.564 | +| (0.5, 24) | 0.089042 | 0.475958 | 0.565 | [0.065 0.435 0. 0.5 ] | 0.0625937 | 0.63427 | 0.696864 | [0.065 0.935] | 0.565 | +| (0.5, 25) | 0.0693021 | 0.508698 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0390147 | 0.66422 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 26) | 0.0826301 | 0.47537 | 0.558 | [0.058 0.442 0. 0.5 ] | 0.0662074 | 0.627274 | 0.693481 | [0.058 0.942] | 0.558 | +| (0.5, 27) | 0.0865851 | 0.486415 | 0.573 | [0.074 0.426 0.001 0.499] | 0.0592513 | 0.6411 | 0.700351 | [0.075 0.925] | 0.573 | +| (0.5, 28) | 0.098346 | 0.477654 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0675682 | 0.634679 | 0.702247 | [0.076 0.924] | 0.576 | +| (0.5, 29) | 0.106672 | 0.465328 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0687628 | 0.631517 | 0.70028 | [0.072 0.928] | 0.572 | +| (0.5, 30) | 0.0814945 | 0.491505 | 0.573 | [0.074 0.426 0.001 0.499] | 0.0639574 | 0.636394 | 0.700351 | [0.075 0.925] | 0.573 | +| (0.5, 31) | 0.089467 | 0.491533 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0609881 | 0.643734 | 0.704722 | [0.081 0.919] | 0.581 | +| (0.5, 32) | 0.118053 | 0.457947 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0806901 | 0.621557 | 0.702247 | [0.076 0.924] | 0.576 | +| (0.5, 33) | 0.0825452 | 0.499455 | 0.582 | [0.082 0.418 0. 0.5 ] | 0.0582277 | 0.646991 | 0.705219 | [0.082 0.918] | 0.582 | +| (0.5, 34) | 0.0417537 | 0.522246 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0305612 | 0.665818 | 0.696379 | [0.064 0.936] | 0.564 | +| (0.5, 35) | 0.0785123 | 0.502488 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0475305 | 0.657191 | 0.704722 | [0.081 0.919] | 0.581 | +| (0.5, 36) | 0.0744245 | 0.511576 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0489571 | 0.658257 | 0.707214 | [0.086 0.914] | 0.586 | +| (0.5, 37) | 0.0843737 | 0.491626 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0653751 | 0.636872 | 0.702247 | [0.076 0.924] | 0.576 | +| (0.5, 38) | 0.09586 | 0.47114 | 0.567 | [0.069 0.431 0.002 0.498] | 0.0624039 | 0.634587 | 0.696991 | [0.071 0.929] | 0.567 | +| (0.5, 39) | 0.0986446 | 0.471355 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0662648 | 0.633036 | 0.699301 | [0.07 0.93] | 0.57 | +| (0.5, 40) | 0.108438 | 0.460562 | 0.569 | [0.07 0.43 0.001 0.499] | 0.0689099 | 0.629481 | 0.69839 | [0.071 0.929] | 0.569 | +| (0.5, 41) | 0.0767079 | 0.524292 | 0.601 | [0.101 0.399 0. 0.5 ] | 0.0610743 | 0.653722 | 0.714796 | [0.101 0.899] | 0.601 | +| (0.5, 42) | 0.0732868 | 0.501713 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0550139 | 0.64674 | 0.701754 | [0.075 0.925] | 0.575 | +| (0.5, 43) | 0.0867752 | 0.495225 | 0.582 | [0.082 0.418 0. 0.5 ] | 0.0691998 | 0.636019 | 0.705219 | [0.082 0.918] | 0.582 | +| (0.5, 44) | 0.0828599 | 0.50414 | 0.587 | [0.089 0.411 0.002 0.498] | 0.0641183 | 0.642766 | 0.706884 | [0.091 0.909] | 0.587 | +| (0.5, 45) | 0.0958384 | 0.493162 | 0.589 | [0.089 0.411 0. 0.5 ] | 0.067415 | 0.641302 | 0.708717 | [0.089 0.911] | 0.589 | +| (0.5, 46) | 0.094656 | 0.483344 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0723339 | 0.630901 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 47) | 0.0912958 | 0.476704 | 0.568 | [0.068 0.432 0. 0.5 ] | 0.0622567 | 0.636067 | 0.698324 | [0.068 0.932] | 0.568 | +| (0.5, 48) | 0.0869253 | 0.491075 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0550731 | 0.648162 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 49) | 0.0468182 | 0.517182 | 0.564 | [0.064 0.436 0. 0.5 ] | 0.0355723 | 0.660806 | 0.696379 | [0.064 0.936] | 0.564 | +| (0.5, 50) | 0.0751574 | 0.489843 | 0.565 | [0.066 0.434 0.001 0.499] | 0.0543628 | 0.642078 | 0.696441 | [0.067 0.933] | 0.565 | +| (0.5, 51) | 0.0801626 | 0.511837 | 0.592 | [0.092 0.408 0. 0.5 ] | 0.0670399 | 0.643187 | 0.710227 | [0.092 0.908] | 0.592 | +| (0.5, 52) | 0.0800749 | 0.499925 | 0.58 | [0.08 0.42 0. 0.5 ] | 0.0537815 | 0.650444 | 0.704225 | [0.08 0.92] | 0.58 | +| (0.5, 53) | 0.0895674 | 0.487433 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0634805 | 0.63926 | 0.702741 | [0.077 0.923] | 0.577 | +| (0.5, 54) | 0.083454 | 0.484546 | 0.568 | [0.068 0.432 0. 0.5 ] | 0.061941 | 0.636383 | 0.698324 | [0.068 0.932] | 0.568 | +| (0.5, 55) | 0.0657226 | 0.511277 | 0.577 | [0.078 0.422 0.001 0.499] | 0.053739 | 0.648583 | 0.702322 | [0.079 0.921] | 0.577 | +| (0.5, 56) | 0.0652427 | 0.517757 | 0.583 | [0.083 0.417 0. 0.5 ] | 0.0528151 | 0.652901 | 0.705716 | [0.083 0.917] | 0.583 | +| (0.5, 57) | 0.08749 | 0.49551 | 0.583 | [0.084 0.416 0.001 0.499] | 0.0566554 | 0.648645 | 0.7053 | [0.085 0.915] | 0.583 | +| (0.5, 58) | 0.0876101 | 0.47539 | 0.563 | [0.063 0.437 0. 0.5 ] | 0.0661026 | 0.629792 | 0.695894 | [0.063 0.937] | 0.563 | +| (0.5, 59) | 0.081788 | 0.504212 | 0.586 | [0.087 0.413 0.001 0.499] | 0.063427 | 0.643372 | 0.706799 | [0.088 0.912] | 0.586 | +| (0.5, 60) | 0.08172 | 0.48428 | 0.566 | [0.066 0.434 0. 0.5 ] | 0.0662614 | 0.631089 | 0.69735 | [0.066 0.934] | 0.566 | +| (0.5, 61) | 0.108461 | 0.451539 | 0.56 | [0.061 0.439 0.001 0.499] | 0.0744602 | 0.619559 | 0.694019 | [0.062 0.938] | 0.56 | +| (0.5, 62) | 0.0928987 | 0.476101 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0653223 | 0.63349 | 0.698812 | [0.069 0.931] | 0.569 | +| (0.5, 63) | 0.0916214 | 0.487379 | 0.579 | [0.079 0.421 0. 0.5 ] | 0.0658141 | 0.637916 | 0.70373 | [0.079 0.921] | 0.579 | +| (0.5, 64) | 0.0774471 | 0.500553 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0503743 | 0.652861 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 65) | 0.0649944 | 0.505006 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.0464662 | 0.652835 | 0.699301 | [0.07 0.93] | 0.57 | +| (0.5, 66) | 0.112129 | 0.459871 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0782461 | 0.622034 | 0.70028 | [0.072 0.928] | 0.572 | +| (0.5, 67) | 0.0465229 | 0.539477 | 0.586 | [0.087 0.413 0.001 0.499] | 0.0326439 | 0.674155 | 0.706799 | [0.088 0.912] | 0.586 | +| (0.5, 68) | 0.0947336 | 0.477266 | 0.572 | [0.072 0.428 0. 0.5 ] | 0.0738824 | 0.626398 | 0.70028 | [0.072 0.928] | 0.572 | +| (0.5, 69) | 0.0904762 | 0.471524 | 0.562 | [0.062 0.438 0. 0.5 ] | 0.0632376 | 0.632173 | 0.69541 | [0.062 0.938] | 0.562 | +| (0.5, 70) | 0.0690796 | 0.51192 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0519332 | 0.652788 | 0.704722 | [0.081 0.919] | 0.581 | +| (0.5, 71) | 0.0983992 | 0.465601 | 0.564 | [0.065 0.435 0.001 0.499] | 0.0771774 | 0.618778 | 0.695955 | [0.066 0.934] | 0.564 | +| (0.5, 72) | 0.0972073 | 0.477793 | 0.575 | [0.075 0.425 0. 0.5 ] | 0.0747774 | 0.626977 | 0.701754 | [0.075 0.925] | 0.575 | +| (0.5, 73) | 0.0639479 | 0.518052 | 0.582 | [0.083 0.417 0.001 0.499] | 0.0473908 | 0.657411 | 0.704802 | [0.084 0.916] | 0.582 | +| (0.5, 74) | 0.0487624 | 0.529238 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0355232 | 0.667712 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 75) | 0.0990139 | 0.468986 | 0.568 | [0.069 0.431 0.001 0.499] | 0.0652727 | 0.632629 | 0.697902 | [0.07 0.93] | 0.568 | +| (0.5, 76) | 0.0674569 | 0.510543 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0482796 | 0.654955 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 77) | 0.104605 | 0.473395 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0767052 | 0.62653 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 78) | 0.101666 | 0.483334 | 0.585 | [0.085 0.415 0. 0.5 ] | 0.0646452 | 0.642069 | 0.706714 | [0.085 0.915] | 0.585 | +| (0.5, 79) | 0.0801886 | 0.480811 | 0.561 | [0.061 0.439 0. 0.5 ] | 0.0615175 | 0.63341 | 0.694927 | [0.061 0.939] | 0.561 | +| (0.5, 80) | 0.0946084 | 0.482392 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0679813 | 0.634759 | 0.702741 | [0.077 0.923] | 0.577 | +| (0.5, 81) | 0.106413 | 0.474587 | 0.581 | [0.081 0.419 0. 0.5 ] | 0.0724391 | 0.632283 | 0.704722 | [0.081 0.919] | 0.581 | +| (0.5, 82) | 0.0781018 | 0.487898 | 0.566 | [0.067 0.433 0.001 0.499] | 0.0478596 | 0.649068 | 0.696927 | [0.068 0.932] | 0.566 | +| (0.5, 83) | 0.0936024 | 0.473398 | 0.567 | [0.067 0.433 0. 0.5 ] | 0.0720418 | 0.625795 | 0.697837 | [0.067 0.933] | 0.567 | +| (0.5, 84) | 0.111706 | 0.459294 | 0.571 | [0.071 0.429 0. 0.5 ] | 0.0712875 | 0.628503 | 0.69979 | [0.071 0.929] | 0.571 | +| (0.5, 85) | 0.0536686 | 0.513331 | 0.567 | [0.067 0.433 0. 0.5 ] | 0.039865 | 0.657972 | 0.697837 | [0.067 0.933] | 0.567 | +| (0.5, 86) | 0.0633176 | 0.509682 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0460594 | 0.654711 | 0.700771 | [0.073 0.927] | 0.573 | +| (0.5, 87) | 0.0521377 | 0.525862 | 0.578 | [0.078 0.422 0. 0.5 ] | 0.0411703 | 0.662065 | 0.703235 | [0.078 0.922] | 0.578 | +| (0.5, 88) | 0.0845024 | 0.501498 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0657795 | 0.641434 | 0.707214 | [0.086 0.914] | 0.586 | +| (0.5, 89) | 0.0716476 | 0.514352 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0514473 | 0.655766 | 0.707214 | [0.086 0.914] | 0.586 | +| (0.5, 90) | 0.0727238 | 0.512276 | 0.585 | [0.085 0.415 0. 0.5 ] | 0.0502498 | 0.656464 | 0.706714 | [0.085 0.915] | 0.585 | +| (0.5, 91) | 0.0693259 | 0.507674 | 0.577 | [0.077 0.423 0. 0.5 ] | 0.0515734 | 0.651167 | 0.702741 | [0.077 0.923] | 0.577 | +| (0.5, 92) | 0.0893712 | 0.486629 | 0.576 | [0.076 0.424 0. 0.5 ] | 0.0645728 | 0.637674 | 0.702247 | [0.076 0.924] | 0.576 | +| (0.5, 93) | 0.0645983 | 0.509402 | 0.574 | [0.075 0.425 0.001 0.499] | 0.0470981 | 0.653745 | 0.700843 | [0.076 0.924] | 0.574 | +| (0.5, 94) | 0.0577857 | 0.528214 | 0.586 | [0.086 0.414 0. 0.5 ] | 0.0477799 | 0.659434 | 0.707214 | [0.086 0.914] | 0.586 | +| (0.5, 95) | 0.0683137 | 0.511686 | 0.58 | [0.08 0.42 0. 0.5 ] | 0.0438813 | 0.660344 | 0.704225 | [0.08 0.92] | 0.58 | +| (0.5, 96) | 0.0836411 | 0.485359 | 0.569 | [0.069 0.431 0. 0.5 ] | 0.0653466 | 0.633465 | 0.698812 | [0.069 0.931] | 0.569 | +| (0.5, 97) | 0.0842354 | 0.485765 | 0.57 | [0.07 0.43 0. 0.5 ] | 0.048951 | 0.65035 | 0.699301 | [0.07 0.93] | 0.57 | +| (0.5, 98) | 0.100673 | 0.472327 | 0.573 | [0.073 0.427 0. 0.5 ] | 0.0649737 | 0.635797 | 0.700771 | [0.073 0.927] | 0.573 | +| (0.5, 99) | 0.0852149 | 0.486785 | 0.572 | [0.073 0.427 0.001 0.499] | 0.0583452 | 0.641515 | 0.69986 | [0.074 0.926] | 0.572 | +| (0.55, 0) | 0.0730904 | 0.53891 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0428355 | 0.696412 | 0.739247 | [0.062 0.938] | 0.612 | +| (0.55, 1) | 0.0661012 | 0.542899 | 0.609 | [0.059 0.391 0. 0.55 ] | 0.0467022 | 0.691058 | 0.73776 | [0.059 0.941] | 0.609 | +| (0.55, 2) | 0.0644516 | 0.542548 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.046827 | 0.689945 | 0.736772 | [0.057 0.943] | 0.607 | +| (0.55, 3) | 0.0620741 | 0.547926 | 0.61 | [0.06 0.39 0. 0.55] | 0.0387163 | 0.699539 | 0.738255 | [0.06 0.94] | 0.61 | +| (0.55, 4) | 0.0833749 | 0.528625 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.05636 | 0.682887 | 0.739247 | [0.062 0.938] | 0.612 | +| (0.55, 5) | 0.1047 | 0.5133 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0670869 | 0.675153 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 6) | 0.0682258 | 0.538774 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.0365568 | 0.700215 | 0.736772 | [0.057 0.943] | 0.607 | +| (0.55, 7) | 0.0636036 | 0.566396 | 0.63 | [0.081 0.369 0.001 0.549] | 0.0466895 | 0.701267 | 0.747956 | [0.082 0.918] | 0.63 | +| (0.55, 8) | 0.0886129 | 0.523387 | 0.612 | [0.063 0.387 0.001 0.549] | 0.058874 | 0.680022 | 0.738896 | [0.064 0.936] | 0.612 | +| (0.55, 9) | 0.0695911 | 0.535409 | 0.605 | [0.056 0.394 0.001 0.549] | 0.0521205 | 0.683312 | 0.735432 | [0.057 0.943] | 0.605 | +| (0.55, 10) | 0.0832089 | 0.543791 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.0564085 | 0.690367 | 0.746775 | [0.077 0.923] | 0.627 | +| (0.55, 11) | 0.0741853 | 0.553815 | 0.628 | [0.079 0.371 0.001 0.549] | 0.0503493 | 0.696589 | 0.746939 | [0.08 0.92] | 0.628 | +| (0.55, 12) | 0.0767586 | 0.530241 | 0.607 | [0.058 0.392 0.001 0.549] | 0.054906 | 0.681513 | 0.736419 | [0.059 0.941] | 0.607 | +| (0.55, 13) | 0.108092 | 0.515908 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.0698793 | 0.675378 | 0.745257 | [0.074 0.926] | 0.624 | +| (0.55, 14) | 0.0878994 | 0.532101 | 0.62 | [0.071 0.379 0.001 0.549] | 0.0573614 | 0.685534 | 0.742896 | [0.072 0.928] | 0.62 | +| (0.55, 15) | 0.0678694 | 0.559131 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.042195 | 0.70458 | 0.746775 | [0.077 0.923] | 0.627 | +| (0.55, 16) | 0.0683432 | 0.539657 | 0.608 | [0.058 0.392 0. 0.55 ] | 0.0498925 | 0.687373 | 0.737265 | [0.058 0.942] | 0.608 | +| (0.55, 17) | 0.0753853 | 0.536615 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0528654 | 0.686382 | 0.739247 | [0.062 0.938] | 0.612 | +| (0.55, 18) | 0.086495 | 0.527505 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0563929 | 0.683499 | 0.739892 | [0.066 0.934] | 0.614 | +| (0.55, 19) | 0.0657372 | 0.545263 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0404072 | 0.698344 | 0.738751 | [0.061 0.939] | 0.611 | +| (0.55, 20) | 0.108858 | 0.504142 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0840232 | 0.655721 | 0.739744 | [0.063 0.937] | 0.613 | +| (0.55, 21) | 0.0967053 | 0.505295 | 0.602 | [0.053 0.397 0.001 0.549] | 0.066302 | 0.667655 | 0.733957 | [0.054 0.946] | 0.602 | +| (0.55, 22) | 0.0630256 | 0.553974 | 0.617 | [0.068 0.382 0.001 0.549] | 0.045882 | 0.695509 | 0.741391 | [0.069 0.931] | 0.617 | +| (0.55, 23) | 0.0913141 | 0.522686 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0593481 | 0.680894 | 0.740242 | [0.064 0.936] | 0.614 | +| (0.55, 24) | 0.0817313 | 0.530269 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0508022 | 0.688445 | 0.739247 | [0.062 0.938] | 0.612 | +| (0.55, 25) | 0.077761 | 0.540239 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0587776 | 0.683463 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 26) | 0.117069 | 0.500931 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0827969 | 0.659443 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 27) | 0.0470736 | 0.581926 | 0.629 | [0.08 0.37 0.001 0.549] | 0.0371326 | 0.710315 | 0.747447 | [0.081 0.919] | 0.629 | +| (0.55, 28) | 0.0650752 | 0.559925 | 0.625 | [0.076 0.374 0.001 0.549] | 0.0448129 | 0.700605 | 0.745418 | [0.077 0.923] | 0.625 | +| (0.55, 29) | 0.0881858 | 0.528814 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0568185 | 0.684921 | 0.74174 | [0.067 0.933] | 0.617 | +| (0.55, 30) | 0.0977565 | 0.528244 | 0.626 | [0.076 0.374 0. 0.55 ] | 0.0690057 | 0.677263 | 0.746269 | [0.076 0.924] | 0.626 | +| (0.55, 31) | 0.0871869 | 0.523813 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0582915 | 0.680459 | 0.738751 | [0.061 0.939] | 0.611 | +| (0.55, 32) | 0.0728003 | 0.5452 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0543715 | 0.687869 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 33) | 0.0809225 | 0.523078 | 0.604 | [0.054 0.396 0. 0.55 ] | 0.0520946 | 0.6832 | 0.735294 | [0.054 0.946] | 0.604 | +| (0.55, 34) | 0.100559 | 0.526441 | 0.627 | [0.077 0.373 0. 0.55 ] | 0.0630208 | 0.683754 | 0.746775 | [0.077 0.923] | 0.627 | +| (0.55, 35) | 0.103207 | 0.513793 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0645554 | 0.677184 | 0.74174 | [0.067 0.933] | 0.617 | +| (0.55, 36) | 0.0817379 | 0.536262 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0547587 | 0.687481 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 37) | 0.075124 | 0.537876 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0481441 | 0.6916 | 0.739744 | [0.063 0.937] | 0.613 | +| (0.55, 38) | 0.0678917 | 0.557108 | 0.625 | [0.075 0.375 0. 0.55 ] | 0.0452231 | 0.70054 | 0.745763 | [0.075 0.925] | 0.625 | +| (0.55, 39) | 0.0702522 | 0.550748 | 0.621 | [0.072 0.378 0.001 0.549] | 0.0474194 | 0.695979 | 0.743399 | [0.073 0.927] | 0.621 | +| (0.55, 40) | 0.0989642 | 0.522036 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.061899 | 0.681847 | 0.743746 | [0.071 0.929] | 0.621 | +| (0.55, 41) | 0.0844022 | 0.539598 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.0556893 | 0.689568 | 0.745257 | [0.074 0.926] | 0.624 | +| (0.55, 42) | 0.0793985 | 0.555601 | 0.635 | [0.086 0.364 0.001 0.549] | 0.0532486 | 0.697264 | 0.750513 | [0.087 0.913] | 0.635 | +| (0.55, 43) | 0.0422454 | 0.564755 | 0.607 | [0.058 0.392 0.001 0.549] | 0.0257601 | 0.710658 | 0.736419 | [0.059 0.941] | 0.607 | +| (0.55, 44) | 0.0642159 | 0.551784 | 0.616 | [0.066 0.384 0. 0.55 ] | 0.0499338 | 0.691306 | 0.74124 | [0.066 0.934] | 0.616 | +| (0.55, 45) | 0.0775302 | 0.54147 | 0.619 | [0.069 0.381 0. 0.55 ] | 0.0494472 | 0.693294 | 0.742741 | [0.069 0.931] | 0.619 | +| (0.55, 46) | 0.0855431 | 0.530457 | 0.616 | [0.066 0.384 0. 0.55 ] | 0.0539876 | 0.687252 | 0.74124 | [0.066 0.934] | 0.616 | +| (0.55, 47) | 0.0621009 | 0.567899 | 0.63 | [0.081 0.369 0.001 0.549] | 0.0462904 | 0.701666 | 0.747956 | [0.082 0.918] | 0.63 | +| (0.55, 48) | 0.0883259 | 0.526674 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0598105 | 0.680581 | 0.740391 | [0.067 0.933] | 0.615 | +| (0.55, 49) | 0.0951236 | 0.512876 | 0.608 | [0.059 0.391 0.001 0.549] | 0.0690868 | 0.667826 | 0.736913 | [0.06 0.94] | 0.608 | +| (0.55, 50) | 0.0554219 | 0.551578 | 0.607 | [0.057 0.393 0. 0.55 ] | 0.0409083 | 0.695863 | 0.736772 | [0.057 0.943] | 0.607 | +| (0.55, 51) | 0.0839772 | 0.516023 | 0.6 | [0.053 0.397 0.003 0.547] | 0.0600569 | 0.672206 | 0.732262 | [0.056 0.944] | 0.6 | +| (0.55, 52) | 0.12439 | 0.48961 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0840354 | 0.656207 | 0.740242 | [0.064 0.936] | 0.614 | +| (0.55, 53) | 0.0616151 | 0.552385 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0439556 | 0.695937 | 0.739892 | [0.066 0.934] | 0.614 | +| (0.55, 54) | 0.0917346 | 0.531265 | 0.623 | [0.073 0.377 0. 0.55 ] | 0.0659632 | 0.67879 | 0.744753 | [0.073 0.927] | 0.623 | +| (0.55, 55) | 0.0825903 | 0.53441 | 0.617 | [0.068 0.382 0.001 0.549] | 0.0581616 | 0.683229 | 0.741391 | [0.069 0.931] | 0.617 | +| (0.55, 56) | 0.0439757 | 0.564024 | 0.608 | [0.058 0.392 0. 0.55 ] | 0.0307259 | 0.706539 | 0.737265 | [0.058 0.942] | 0.608 | +| (0.55, 57) | 0.0791233 | 0.533877 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0549126 | 0.684832 | 0.739744 | [0.063 0.937] | 0.613 | +| (0.55, 58) | 0.0466274 | 0.568373 | 0.615 | [0.065 0.385 0. 0.55 ] | 0.0319929 | 0.708748 | 0.740741 | [0.065 0.935] | 0.615 | +| (0.55, 59) | 0.0716604 | 0.54134 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0421397 | 0.697605 | 0.739744 | [0.063 0.937] | 0.613 | +| (0.55, 60) | 0.0891312 | 0.525869 | 0.615 | [0.066 0.384 0.001 0.549] | 0.055412 | 0.684979 | 0.740391 | [0.067 0.933] | 0.615 | +| (0.55, 61) | 0.050594 | 0.567406 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0318906 | 0.71035 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 62) | 0.0764588 | 0.536541 | 0.613 | [0.064 0.386 0.001 0.549] | 0.0534303 | 0.685964 | 0.739394 | [0.065 0.935] | 0.613 | +| (0.55, 63) | 0.0825997 | 0.5264 | 0.609 | [0.059 0.391 0. 0.55 ] | 0.0549614 | 0.682798 | 0.73776 | [0.059 0.941] | 0.609 | +| (0.55, 64) | 0.0790597 | 0.53894 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0599859 | 0.682254 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 65) | 0.102482 | 0.509518 | 0.612 | [0.062 0.388 0. 0.55 ] | 0.0717024 | 0.667545 | 0.739247 | [0.062 0.938] | 0.612 | +| (0.55, 66) | 0.0946111 | 0.521389 | 0.616 | [0.067 0.383 0.001 0.549] | 0.0639841 | 0.676907 | 0.740891 | [0.068 0.932] | 0.616 | +| (0.55, 67) | 0.0656431 | 0.551357 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0496327 | 0.692107 | 0.74174 | [0.067 0.933] | 0.617 | +| (0.55, 68) | 0.069672 | 0.551328 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.0451221 | 0.698624 | 0.743746 | [0.071 0.929] | 0.621 | +| (0.55, 69) | 0.0642955 | 0.555705 | 0.62 | [0.07 0.38 0. 0.55] | 0.0396424 | 0.703601 | 0.743243 | [0.07 0.93] | 0.62 | +| (0.55, 70) | 0.0843746 | 0.530625 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0508608 | 0.68953 | 0.740391 | [0.067 0.933] | 0.615 | +| (0.55, 71) | 0.0788593 | 0.531141 | 0.61 | [0.06 0.39 0. 0.55] | 0.0476278 | 0.690627 | 0.738255 | [0.06 0.94] | 0.61 | +| (0.55, 72) | 0.0766319 | 0.540368 | 0.617 | [0.067 0.383 0. 0.55 ] | 0.0548507 | 0.686889 | 0.74174 | [0.067 0.933] | 0.617 | +| (0.55, 73) | 0.0480531 | 0.566947 | 0.615 | [0.065 0.385 0. 0.55 ] | 0.0363437 | 0.704397 | 0.740741 | [0.065 0.935] | 0.615 | +| (0.55, 74) | 0.101802 | 0.516198 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0708638 | 0.671376 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 75) | 0.0852682 | 0.540732 | 0.626 | [0.076 0.374 0. 0.55 ] | 0.0630882 | 0.68318 | 0.746269 | [0.076 0.924] | 0.626 | +| (0.55, 76) | 0.0654355 | 0.555565 | 0.621 | [0.073 0.377 0.002 0.548] | 0.048294 | 0.694757 | 0.743051 | [0.075 0.925] | 0.621 | +| (0.55, 77) | 0.0788674 | 0.540133 | 0.619 | [0.07 0.38 0.001 0.549] | 0.0505293 | 0.691864 | 0.742394 | [0.071 0.929] | 0.619 | +| (0.55, 78) | 0.0809986 | 0.521001 | 0.602 | [0.052 0.398 0. 0.55 ] | 0.0579031 | 0.676409 | 0.734312 | [0.052 0.948] | 0.602 | +| (0.55, 79) | 0.0710941 | 0.543906 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0542047 | 0.686186 | 0.740391 | [0.067 0.933] | 0.615 | +| (0.55, 80) | 0.104299 | 0.515701 | 0.62 | [0.071 0.379 0.001 0.549] | 0.065018 | 0.677878 | 0.742896 | [0.072 0.928] | 0.62 | +| (0.55, 81) | 0.053892 | 0.565108 | 0.619 | [0.069 0.381 0. 0.55 ] | 0.0370133 | 0.705728 | 0.742741 | [0.069 0.931] | 0.619 | +| (0.55, 82) | 0.0392997 | 0.5717 | 0.611 | [0.061 0.389 0. 0.55 ] | 0.0282315 | 0.710519 | 0.738751 | [0.061 0.939] | 0.611 | +| (0.55, 83) | 0.0996358 | 0.514364 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0624861 | 0.677406 | 0.739892 | [0.066 0.934] | 0.614 | +| (0.55, 84) | 0.0668299 | 0.56717 | 0.634 | [0.084 0.366 0. 0.55 ] | 0.049494 | 0.700847 | 0.750341 | [0.084 0.916] | 0.634 | +| (0.55, 85) | 0.0628239 | 0.555176 | 0.618 | [0.068 0.382 0. 0.55 ] | 0.0445864 | 0.697654 | 0.74224 | [0.068 0.932] | 0.618 | +| (0.55, 86) | 0.080314 | 0.529686 | 0.61 | [0.06 0.39 0. 0.55] | 0.0544223 | 0.683833 | 0.738255 | [0.06 0.94] | 0.61 | +| (0.55, 87) | 0.0760951 | 0.544905 | 0.621 | [0.071 0.379 0. 0.55 ] | 0.0544041 | 0.689342 | 0.743746 | [0.071 0.929] | 0.621 | +| (0.55, 88) | 0.0713211 | 0.557679 | 0.629 | [0.079 0.371 0. 0.55 ] | 0.0527228 | 0.695068 | 0.747791 | [0.079 0.921] | 0.629 | +| (0.55, 89) | 0.0759801 | 0.53702 | 0.613 | [0.063 0.387 0. 0.55 ] | 0.0498224 | 0.689922 | 0.739744 | [0.063 0.937] | 0.613 | +| (0.55, 90) | 0.0725677 | 0.541432 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0533703 | 0.686872 | 0.740242 | [0.064 0.936] | 0.614 | +| (0.55, 91) | 0.0842715 | 0.530729 | 0.615 | [0.066 0.384 0.001 0.549] | 0.0616303 | 0.678761 | 0.740391 | [0.067 0.933] | 0.615 | +| (0.55, 92) | 0.0881018 | 0.525898 | 0.614 | [0.065 0.385 0.001 0.549] | 0.0610744 | 0.678818 | 0.739892 | [0.066 0.934] | 0.614 | +| (0.55, 93) | 0.0972321 | 0.513768 | 0.611 | [0.062 0.388 0.001 0.549] | 0.0641005 | 0.674299 | 0.738399 | [0.063 0.937] | 0.611 | +| (0.55, 94) | 0.0627574 | 0.562243 | 0.625 | [0.075 0.375 0. 0.55 ] | 0.0409383 | 0.704824 | 0.745763 | [0.075 0.925] | 0.625 | +| (0.55, 95) | 0.07313 | 0.53787 | 0.611 | [0.062 0.388 0.001 0.549] | 0.0538739 | 0.684526 | 0.738399 | [0.063 0.937] | 0.611 | +| (0.55, 96) | 0.0938753 | 0.508125 | 0.602 | [0.053 0.397 0.001 0.549] | 0.0607252 | 0.673232 | 0.733957 | [0.054 0.946] | 0.602 | +| (0.55, 97) | 0.0986028 | 0.515397 | 0.614 | [0.064 0.386 0. 0.55 ] | 0.0621644 | 0.678078 | 0.740242 | [0.064 0.936] | 0.614 | +| (0.55, 98) | 0.0961309 | 0.527869 | 0.624 | [0.074 0.376 0. 0.55 ] | 0.05855 | 0.686707 | 0.745257 | [0.074 0.926] | 0.624 | +| (0.55, 99) | 0.0959075 | 0.505093 | 0.601 | [0.052 0.398 0.001 0.549] | 0.0639415 | 0.669525 | 0.733467 | [0.053 0.947] | 0.601 | +| (0.6, 0) | 0.0881511 | 0.577849 | 0.666 | [0.067 0.333 0.001 0.599] | 0.055237 | 0.726747 | 0.781984 | [0.068 0.932] | 0.666 | +| (0.6, 1) | 0.0676567 | 0.594343 | 0.662 | [0.063 0.337 0.001 0.599] | 0.0477349 | 0.732213 | 0.779948 | [0.064 0.936] | 0.662 | +| (0.6, 2) | 0.0491176 | 0.612882 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.027934 | 0.7523 | 0.780234 | [0.062 0.938] | 0.662 | +| (0.6, 3) | 0.0568165 | 0.596183 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.035421 | 0.740274 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 4) | 0.0615035 | 0.605496 | 0.667 | [0.068 0.332 0.001 0.599] | 0.040621 | 0.741874 | 0.782495 | [0.069 0.931] | 0.667 | +| (0.6, 5) | 0.0730526 | 0.589947 | 0.663 | [0.063 0.337 0. 0.6 ] | 0.0445382 | 0.736204 | 0.780742 | [0.063 0.937] | 0.663 | +| (0.6, 6) | 0.0522423 | 0.607758 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0293391 | 0.749882 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 7) | 0.0615754 | 0.594425 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0386705 | 0.738532 | 0.777202 | [0.056 0.944] | 0.656 | +| (0.6, 8) | 0.0665795 | 0.582421 | 0.649 | [0.049 0.351 0. 0.6 ] | 0.0407432 | 0.732951 | 0.773694 | [0.049 0.951] | 0.649 | +| (0.6, 9) | 0.0737038 | 0.576296 | 0.65 | [0.051 0.349 0.001 0.599] | 0.0521496 | 0.721752 | 0.773902 | [0.052 0.948] | 0.65 | +| (0.6, 10) | 0.085578 | 0.578422 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.0560622 | 0.725188 | 0.78125 | [0.064 0.936] | 0.664 | +| (0.6, 11) | 0.064456 | 0.598544 | 0.663 | [0.064 0.336 0.001 0.599] | 0.0459933 | 0.734463 | 0.780456 | [0.065 0.935] | 0.663 | +| (0.6, 12) | 0.0562835 | 0.604716 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0319463 | 0.747781 | 0.779727 | [0.061 0.939] | 0.661 | +| (0.6, 13) | 0.0716397 | 0.59336 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.0419245 | 0.739834 | 0.781759 | [0.065 0.935] | 0.665 | +| (0.6, 14) | 0.0583248 | 0.601675 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0437987 | 0.735422 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 15) | 0.0434325 | 0.614568 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0282215 | 0.749989 | 0.77821 | [0.058 0.942] | 0.658 | +| (0.6, 16) | 0.047439 | 0.621561 | 0.669 | [0.07 0.33 0.001 0.599] | 0.0270545 | 0.756464 | 0.783519 | [0.071 0.929] | 0.669 | +| (0.6, 17) | 0.0933332 | 0.582667 | 0.676 | [0.077 0.323 0.001 0.599] | 0.058648 | 0.728474 | 0.787122 | [0.078 0.922] | 0.676 | +| (0.6, 18) | 0.0390986 | 0.632901 | 0.672 | [0.072 0.328 0. 0.6 ] | 0.0259942 | 0.759346 | 0.78534 | [0.072 0.928] | 0.672 | +| (0.6, 19) | 0.0678786 | 0.600121 | 0.668 | [0.068 0.332 0. 0.6 ] | 0.0480079 | 0.735282 | 0.78329 | [0.068 0.932] | 0.668 | +| (0.6, 20) | 0.0539102 | 0.60209 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0370685 | 0.740134 | 0.777202 | [0.056 0.944] | 0.656 | +| (0.6, 21) | 0.0820918 | 0.574908 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0477883 | 0.729629 | 0.777417 | [0.059 0.941] | 0.657 | +| (0.6, 22) | 0.0825172 | 0.577483 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0515437 | 0.727677 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 23) | 0.0568959 | 0.596104 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0359543 | 0.739741 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 24) | 0.0690674 | 0.599933 | 0.669 | [0.069 0.331 0. 0.6 ] | 0.0425673 | 0.741234 | 0.783801 | [0.069 0.931] | 0.669 | +| (0.6, 25) | 0.076607 | 0.581393 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.048413 | 0.729797 | 0.77821 | [0.058 0.942] | 0.658 | +| (0.6, 26) | 0.0757756 | 0.580224 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0556539 | 0.721548 | 0.777202 | [0.056 0.944] | 0.656 | +| (0.6, 27) | 0.0648862 | 0.578114 | 0.643 | [0.044 0.356 0.001 0.599] | 0.0435439 | 0.726874 | 0.770418 | [0.045 0.955] | 0.643 | +| (0.6, 28) | 0.0528143 | 0.600186 | 0.653 | [0.054 0.346 0.001 0.599] | 0.0340945 | 0.74131 | 0.775405 | [0.055 0.945] | 0.653 | +| (0.6, 29) | 0.0667359 | 0.601264 | 0.668 | [0.068 0.332 0. 0.6 ] | 0.0335976 | 0.749692 | 0.78329 | [0.068 0.932] | 0.668 | +| (0.6, 30) | 0.0675497 | 0.58545 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0414239 | 0.734271 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 31) | 0.0660047 | 0.598995 | 0.665 | [0.066 0.334 0.001 0.599] | 0.0426951 | 0.738779 | 0.781474 | [0.067 0.933] | 0.665 | +| (0.6, 32) | 0.0442055 | 0.618795 | 0.663 | [0.063 0.337 0. 0.6 ] | 0.030497 | 0.750245 | 0.780742 | [0.063 0.937] | 0.663 | +| (0.6, 33) | 0.0443751 | 0.609625 | 0.654 | [0.057 0.343 0.003 0.597] | 0.021217 | 0.754108 | 0.775325 | [0.06 0.94] | 0.654 | +| (0.6, 34) | 0.0943248 | 0.576675 | 0.671 | [0.072 0.328 0.001 0.599] | 0.0621463 | 0.722399 | 0.784545 | [0.073 0.927] | 0.671 | +| (0.6, 35) | 0.0535663 | 0.600434 | 0.654 | [0.054 0.346 0. 0.6 ] | 0.0296445 | 0.746552 | 0.776197 | [0.054 0.946] | 0.654 | +| (0.6, 36) | 0.0605562 | 0.604444 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.0392217 | 0.742537 | 0.781759 | [0.065 0.935] | 0.665 | +| (0.6, 37) | 0.0866943 | 0.573306 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0538976 | 0.725323 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 38) | 0.0545823 | 0.593418 | 0.648 | [0.048 0.352 0. 0.6 ] | 0.0352752 | 0.737921 | 0.773196 | [0.048 0.952] | 0.648 | +| (0.6, 39) | 0.0336199 | 0.63738 | 0.671 | [0.071 0.329 0. 0.6 ] | 0.0231063 | 0.76172 | 0.784827 | [0.071 0.929] | 0.671 | +| (0.6, 40) | 0.087924 | 0.585076 | 0.673 | [0.074 0.326 0.001 0.599] | 0.0570552 | 0.728519 | 0.785574 | [0.075 0.925] | 0.673 | +| (0.6, 41) | 0.0627829 | 0.594217 | 0.657 | [0.057 0.343 0. 0.6 ] | 0.0367148 | 0.740991 | 0.777706 | [0.057 0.943] | 0.657 | +| (0.6, 42) | 0.0661217 | 0.588878 | 0.655 | [0.055 0.345 0. 0.6 ] | 0.0462947 | 0.730404 | 0.776699 | [0.055 0.945] | 0.655 | +| (0.6, 43) | 0.0472093 | 0.608791 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0315161 | 0.745686 | 0.777202 | [0.056 0.944] | 0.656 | +| (0.6, 44) | 0.062447 | 0.595553 | 0.658 | [0.059 0.341 0.001 0.599] | 0.0393384 | 0.738584 | 0.777922 | [0.06 0.94] | 0.658 | +| (0.6, 45) | 0.0553339 | 0.597666 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0316878 | 0.744007 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 46) | 0.0603068 | 0.612693 | 0.673 | [0.073 0.327 0. 0.6 ] | 0.0403993 | 0.745455 | 0.785855 | [0.073 0.927] | 0.673 | +| (0.6, 47) | 0.05245 | 0.59255 | 0.645 | [0.045 0.355 0. 0.6 ] | 0.0331539 | 0.73855 | 0.771704 | [0.045 0.955] | 0.645 | +| (0.6, 48) | 0.0380303 | 0.61797 | 0.656 | [0.057 0.343 0.001 0.599] | 0.0253723 | 0.751541 | 0.776913 | [0.058 0.942] | 0.656 | +| (0.6, 49) | 0.0738868 | 0.593113 | 0.667 | [0.067 0.333 0. 0.6 ] | 0.0450897 | 0.737689 | 0.782779 | [0.067 0.933] | 0.667 | +| (0.6, 50) | 0.0354761 | 0.619524 | 0.655 | [0.055 0.345 0. 0.6 ] | 0.0207475 | 0.755952 | 0.776699 | [0.055 0.945] | 0.655 | +| (0.6, 51) | 0.0437408 | 0.600259 | 0.644 | [0.046 0.354 0.002 0.598] | 0.0251752 | 0.745443 | 0.770619 | [0.048 0.952] | 0.644 | +| (0.6, 52) | 0.0453087 | 0.612691 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0260618 | 0.752148 | 0.77821 | [0.058 0.942] | 0.658 | +| (0.6, 53) | 0.0722881 | 0.592712 | 0.665 | [0.065 0.335 0. 0.6 ] | 0.047134 | 0.734625 | 0.781759 | [0.065 0.935] | 0.665 | +| (0.6, 54) | 0.0421048 | 0.615895 | 0.658 | [0.059 0.341 0.001 0.599] | 0.0287259 | 0.749196 | 0.777922 | [0.06 0.94] | 0.658 | +| (0.6, 55) | 0.066419 | 0.595581 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.048597 | 0.731637 | 0.780234 | [0.062 0.938] | 0.662 | +| (0.6, 56) | 0.0490829 | 0.605917 | 0.655 | [0.056 0.344 0.001 0.599] | 0.0286827 | 0.747727 | 0.77641 | [0.057 0.943] | 0.655 | +| (0.6, 57) | 0.066909 | 0.583091 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0391516 | 0.735042 | 0.774194 | [0.05 0.95] | 0.65 | +| (0.6, 58) | 0.0463631 | 0.608637 | 0.655 | [0.057 0.343 0.002 0.598] | 0.0324057 | 0.743714 | 0.776119 | [0.059 0.941] | 0.655 | +| (0.6, 59) | 0.0634421 | 0.586558 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0366035 | 0.73759 | 0.774194 | [0.05 0.95] | 0.65 | +| (0.6, 60) | 0.0772581 | 0.582742 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0456753 | 0.733546 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 61) | 0.0671458 | 0.591854 | 0.659 | [0.06 0.34 0.001 0.599] | 0.0450789 | 0.733349 | 0.778428 | [0.061 0.939] | 0.659 | +| (0.6, 62) | 0.0522664 | 0.599734 | 0.652 | [0.054 0.346 0.002 0.598] | 0.0359455 | 0.738666 | 0.774611 | [0.056 0.944] | 0.652 | +| (0.6, 63) | 0.063116 | 0.588884 | 0.652 | [0.052 0.348 0. 0.6 ] | 0.0464068 | 0.728787 | 0.775194 | [0.052 0.948] | 0.652 | +| (0.6, 64) | 0.0512798 | 0.61072 | 0.662 | [0.062 0.338 0. 0.6 ] | 0.0361072 | 0.744127 | 0.780234 | [0.062 0.938] | 0.662 | +| (0.6, 65) | 0.0398496 | 0.62515 | 0.665 | [0.066 0.334 0.001 0.599] | 0.0283116 | 0.753163 | 0.781474 | [0.067 0.933] | 0.665 | +| (0.6, 66) | 0.05223 | 0.60377 | 0.656 | [0.056 0.344 0. 0.6 ] | 0.0330846 | 0.744118 | 0.777202 | [0.056 0.944] | 0.656 | +| (0.6, 67) | 0.0785234 | 0.581477 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0465799 | 0.732641 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 68) | 0.0458468 | 0.604153 | 0.65 | [0.051 0.349 0.001 0.599] | 0.0325445 | 0.741357 | 0.773902 | [0.052 0.948] | 0.65 | +| (0.6, 69) | 0.0860971 | 0.577903 | 0.664 | [0.065 0.335 0.001 0.599] | 0.0522512 | 0.728714 | 0.780965 | [0.066 0.934] | 0.664 | +| (0.6, 70) | 0.0806746 | 0.589325 | 0.67 | [0.071 0.329 0.001 0.599] | 0.0519335 | 0.732098 | 0.784031 | [0.072 0.928] | 0.67 | +| (0.6, 71) | 0.0784531 | 0.577547 | 0.656 | [0.057 0.343 0.001 0.599] | 0.0508206 | 0.726092 | 0.776913 | [0.058 0.942] | 0.656 | +| (0.6, 72) | 0.0399431 | 0.613057 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.0248862 | 0.750809 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 73) | 0.0907004 | 0.5673 | 0.658 | [0.058 0.342 0. 0.6 ] | 0.0579146 | 0.720295 | 0.77821 | [0.058 0.942] | 0.658 | +| (0.6, 74) | 0.0589133 | 0.596087 | 0.655 | [0.057 0.343 0.002 0.598] | 0.035358 | 0.740761 | 0.776119 | [0.059 0.941] | 0.655 | +| (0.6, 75) | 0.0447162 | 0.624284 | 0.669 | [0.07 0.33 0.001 0.599] | 0.0297811 | 0.753738 | 0.783519 | [0.071 0.929] | 0.669 | +| (0.6, 76) | 0.0764676 | 0.583532 | 0.66 | [0.061 0.339 0.001 0.599] | 0.0455743 | 0.733359 | 0.778934 | [0.062 0.938] | 0.66 | +| (0.6, 77) | 0.0816418 | 0.584358 | 0.666 | [0.066 0.334 0. 0.6 ] | 0.0474277 | 0.734841 | 0.782269 | [0.066 0.934] | 0.666 | +| (0.6, 78) | 0.0735751 | 0.572425 | 0.646 | [0.047 0.353 0.001 0.599] | 0.0540717 | 0.717836 | 0.771907 | [0.048 0.952] | 0.646 | +| (0.6, 79) | 0.0570677 | 0.596932 | 0.654 | [0.055 0.345 0.001 0.599] | 0.0358959 | 0.740011 | 0.775907 | [0.056 0.944] | 0.654 | +| (0.6, 80) | 0.0479097 | 0.60509 | 0.653 | [0.053 0.347 0. 0.6 ] | 0.032508 | 0.743187 | 0.775695 | [0.053 0.947] | 0.653 | +| (0.6, 81) | 0.0575586 | 0.594441 | 0.652 | [0.052 0.348 0. 0.6 ] | 0.038032 | 0.737162 | 0.775194 | [0.052 0.948] | 0.652 | +| (0.6, 82) | 0.0761639 | 0.587836 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.0527736 | 0.728476 | 0.78125 | [0.064 0.936] | 0.664 | +| (0.6, 83) | 0.0529422 | 0.607058 | 0.66 | [0.062 0.338 0.002 0.598] | 0.0371465 | 0.741499 | 0.778646 | [0.064 0.936] | 0.66 | +| (0.6, 84) | 0.0580051 | 0.595995 | 0.654 | [0.055 0.345 0.001 0.599] | 0.0419757 | 0.733931 | 0.775907 | [0.056 0.944] | 0.654 | +| (0.6, 85) | 0.0759486 | 0.587051 | 0.663 | [0.064 0.336 0.001 0.599] | 0.0539609 | 0.726495 | 0.780456 | [0.065 0.935] | 0.663 | +| (0.6, 86) | 0.055749 | 0.619251 | 0.675 | [0.076 0.324 0.001 0.599] | 0.0404567 | 0.746149 | 0.786605 | [0.077 0.923] | 0.675 | +| (0.6, 87) | 0.0509463 | 0.610054 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0335963 | 0.746131 | 0.779727 | [0.061 0.939] | 0.661 | +| (0.6, 88) | 0.0355192 | 0.647481 | 0.683 | [0.083 0.317 0. 0.6 ] | 0.0262492 | 0.764786 | 0.791035 | [0.083 0.917] | 0.683 | +| (0.6, 89) | 0.0640233 | 0.596977 | 0.661 | [0.061 0.339 0. 0.6 ] | 0.0340662 | 0.745661 | 0.779727 | [0.061 0.939] | 0.661 | +| (0.6, 90) | 0.0701055 | 0.599894 | 0.67 | [0.07 0.33 0. 0.6 ] | 0.0407704 | 0.743543 | 0.784314 | [0.07 0.93] | 0.67 | +| (0.6, 91) | 0.0341839 | 0.616816 | 0.651 | [0.052 0.348 0.001 0.599] | 0.0247255 | 0.749677 | 0.774402 | [0.053 0.947] | 0.651 | +| (0.6, 92) | 0.0685819 | 0.611418 | 0.68 | [0.08 0.32 0. 0.6 ] | 0.0442119 | 0.745262 | 0.789474 | [0.08 0.92] | 0.68 | +| (0.6, 93) | 0.0894146 | 0.567585 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0539746 | 0.723443 | 0.777417 | [0.059 0.941] | 0.657 | +| (0.6, 94) | 0.0650644 | 0.598936 | 0.664 | [0.064 0.336 0. 0.6 ] | 0.037461 | 0.743789 | 0.78125 | [0.064 0.936] | 0.664 | +| (0.6, 95) | 0.0508844 | 0.599116 | 0.65 | [0.05 0.35 0. 0.6 ] | 0.0363045 | 0.737889 | 0.774194 | [0.05 0.95] | 0.65 | +| (0.6, 96) | 0.0661985 | 0.603802 | 0.67 | [0.07 0.33 0. 0.6 ] | 0.039231 | 0.745083 | 0.784314 | [0.07 0.93] | 0.67 | +| (0.6, 97) | 0.0892339 | 0.565766 | 0.655 | [0.057 0.343 0.002 0.598] | 0.0551233 | 0.720996 | 0.776119 | [0.059 0.941] | 0.655 | +| (0.6, 98) | 0.0976272 | 0.562373 | 0.66 | [0.06 0.34 0. 0.6 ] | 0.0597714 | 0.719449 | 0.779221 | [0.06 0.94] | 0.66 | +| (0.6, 99) | 0.0636941 | 0.593306 | 0.657 | [0.058 0.342 0.001 0.599] | 0.0418411 | 0.735576 | 0.777417 | [0.059 0.941] | 0.657 | +| (0.65, 0) | 0.0739099 | 0.61709 | 0.691 | [0.041 0.309 0. 0.65 ] | 0.0526754 | 0.75528 | 0.807955 | [0.041 0.959] | 0.691 | +| (0.65, 1) | 0.0477952 | 0.658205 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0325986 | 0.78296 | 0.815558 | [0.056 0.944] | 0.706 | +| (0.65, 2) | 0.0229299 | 0.67707 | 0.7 | [0.05 0.3 0. 0.65] | 0.0159672 | 0.796533 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 3) | 0.0538452 | 0.645155 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0312362 | 0.780756 | 0.811993 | [0.049 0.951] | 0.699 | +| (0.65, 4) | 0.0925105 | 0.61149 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0581155 | 0.756421 | 0.814536 | [0.054 0.946] | 0.704 | +| (0.65, 5) | 0.0578091 | 0.647191 | 0.705 | [0.056 0.294 0.001 0.649] | 0.0351116 | 0.779703 | 0.814815 | [0.057 0.943] | 0.705 | +| (0.65, 6) | 0.062668 | 0.629332 | 0.692 | [0.042 0.308 0. 0.65 ] | 0.0378076 | 0.77065 | 0.808458 | [0.042 0.958] | 0.692 | +| (0.65, 7) | 0.0638892 | 0.645111 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0344787 | 0.782387 | 0.816866 | [0.061 0.939] | 0.709 | +| (0.65, 8) | 0.0890857 | 0.608914 | 0.698 | [0.049 0.301 0.001 0.649] | 0.0547982 | 0.756452 | 0.81125 | [0.05 0.95] | 0.698 | +| (0.65, 9) | 0.0611097 | 0.63789 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0413559 | 0.770637 | 0.811993 | [0.049 0.951] | 0.699 | +| (0.65, 10) | 0.0562112 | 0.632789 | 0.689 | [0.039 0.311 0. 0.65 ] | 0.0323477 | 0.774605 | 0.806952 | [0.039 0.961] | 0.689 | +| (0.65, 11) | 0.0589517 | 0.638048 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0366973 | 0.774282 | 0.810979 | [0.047 0.953] | 0.697 | +| (0.65, 12) | 0.0482449 | 0.654755 | 0.703 | [0.053 0.297 0. 0.65 ] | 0.0320813 | 0.781945 | 0.814026 | [0.053 0.947] | 0.703 | +| (0.65, 13) | 0.0736681 | 0.636332 | 0.71 | [0.061 0.289 0.001 0.649] | 0.0420889 | 0.775291 | 0.81738 | [0.062 0.938] | 0.71 | +| (0.65, 14) | 0.0430404 | 0.66396 | 0.707 | [0.057 0.293 0. 0.65 ] | 0.0287048 | 0.787366 | 0.81607 | [0.057 0.943] | 0.707 | +| (0.65, 15) | 0.0560687 | 0.637931 | 0.694 | [0.045 0.305 0.001 0.649] | 0.0310618 | 0.778165 | 0.809227 | [0.046 0.954] | 0.694 | +| (0.65, 16) | 0.0456324 | 0.653368 | 0.699 | [0.05 0.3 0.001 0.649] | 0.0263883 | 0.785369 | 0.811757 | [0.051 0.949] | 0.699 | +| (0.65, 17) | 0.064613 | 0.632387 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0387813 | 0.772198 | 0.810979 | [0.047 0.953] | 0.697 | +| (0.65, 18) | 0.0734902 | 0.62551 | 0.699 | [0.052 0.298 0.003 0.647] | 0.0453684 | 0.765917 | 0.811285 | [0.055 0.945] | 0.699 | +| (0.65, 19) | 0.0628344 | 0.639166 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0345718 | 0.778477 | 0.813049 | [0.056 0.944] | 0.702 | +| (0.65, 20) | 0.0673456 | 0.640654 | 0.708 | [0.059 0.291 0.001 0.649] | 0.0388652 | 0.777487 | 0.816352 | [0.06 0.94] | 0.708 | +| (0.65, 21) | 0.0752692 | 0.617731 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0459319 | 0.763029 | 0.808961 | [0.043 0.957] | 0.693 | +| (0.65, 22) | 0.0450542 | 0.652946 | 0.698 | [0.049 0.301 0.001 0.649] | 0.0298831 | 0.781367 | 0.81125 | [0.05 0.95] | 0.698 | +| (0.65, 23) | 0.0337217 | 0.675278 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0242411 | 0.792625 | 0.816866 | [0.061 0.939] | 0.709 | +| (0.65, 24) | 0.052651 | 0.649349 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0312041 | 0.781845 | 0.813049 | [0.056 0.944] | 0.702 | +| (0.65, 25) | 0.0663186 | 0.632681 | 0.699 | [0.05 0.3 0.001 0.649] | 0.0405875 | 0.77117 | 0.811757 | [0.051 0.949] | 0.699 | +| (0.65, 26) | 0.0504554 | 0.668545 | 0.719 | [0.069 0.281 0. 0.65 ] | 0.0367366 | 0.785528 | 0.822264 | [0.069 0.931] | 0.719 | +| (0.65, 27) | 0.0624661 | 0.646534 | 0.709 | [0.06 0.29 0.001 0.649] | 0.0340857 | 0.78278 | 0.816866 | [0.061 0.939] | 0.709 | +| (0.65, 28) | 0.0504084 | 0.651592 | 0.702 | [0.054 0.296 0.002 0.648] | 0.0300143 | 0.783035 | 0.813049 | [0.056 0.944] | 0.702 | +| (0.65, 29) | 0.0659238 | 0.629076 | 0.695 | [0.046 0.304 0.001 0.649] | 0.0444182 | 0.765314 | 0.809732 | [0.047 0.953] | 0.695 | +| (0.65, 30) | 0.0823759 | 0.623624 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0473784 | 0.76818 | 0.815558 | [0.056 0.944] | 0.706 | +| (0.65, 31) | 0.06098 | 0.63702 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0393581 | 0.772128 | 0.811486 | [0.048 0.952] | 0.698 | +| (0.65, 32) | 0.0843391 | 0.613661 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0515263 | 0.759959 | 0.811486 | [0.048 0.952] | 0.698 | +| (0.65, 33) | 0.0906488 | 0.620351 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.0539358 | 0.764189 | 0.818125 | [0.061 0.939] | 0.711 | +| (0.65, 34) | 0.051511 | 0.654489 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0330441 | 0.782514 | 0.815558 | [0.056 0.944] | 0.706 | +| (0.65, 35) | 0.068127 | 0.623873 | 0.692 | [0.044 0.306 0.002 0.648] | 0.0475416 | 0.760438 | 0.80798 | [0.046 0.954] | 0.692 | +| (0.65, 36) | 0.05139 | 0.65561 | 0.707 | [0.058 0.292 0.001 0.649] | 0.0342364 | 0.781603 | 0.815839 | [0.059 0.941] | 0.707 | +| (0.65, 37) | 0.0553304 | 0.64467 | 0.7 | [0.05 0.3 0. 0.65] | 0.0339875 | 0.778513 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 38) | 0.0220005 | 0.683 | 0.705 | [0.058 0.292 0.003 0.647] | 0.0105615 | 0.803787 | 0.814349 | [0.061 0.939] | 0.705 | +| (0.65, 39) | 0.0344814 | 0.684519 | 0.719 | [0.069 0.281 0. 0.65 ] | 0.0200217 | 0.802243 | 0.822264 | [0.069 0.931] | 0.719 | +| (0.65, 40) | 0.0192848 | 0.687715 | 0.707 | [0.058 0.292 0.001 0.649] | 0.0118281 | 0.804011 | 0.815839 | [0.059 0.941] | 0.707 | +| (0.65, 41) | 0.0629462 | 0.641054 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0417315 | 0.772805 | 0.814536 | [0.054 0.946] | 0.704 | +| (0.65, 42) | 0.0509591 | 0.648041 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0323075 | 0.779685 | 0.811993 | [0.049 0.951] | 0.699 | +| (0.65, 43) | 0.0284053 | 0.684595 | 0.713 | [0.063 0.287 0. 0.65 ] | 0.0186126 | 0.800543 | 0.819156 | [0.063 0.937] | 0.713 | +| (0.65, 44) | 0.0542485 | 0.646751 | 0.701 | [0.051 0.299 0. 0.65 ] | 0.0377042 | 0.775304 | 0.813008 | [0.051 0.949] | 0.701 | +| (0.65, 45) | 0.0473474 | 0.648653 | 0.696 | [0.046 0.304 0. 0.65 ] | 0.0312429 | 0.779231 | 0.810474 | [0.046 0.954] | 0.696 | +| (0.65, 46) | 0.0573545 | 0.647646 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0328666 | 0.78218 | 0.815047 | [0.055 0.945] | 0.705 | +| (0.65, 47) | 0.0778962 | 0.630104 | 0.708 | [0.058 0.292 0. 0.65 ] | 0.0481544 | 0.768428 | 0.816583 | [0.058 0.942] | 0.708 | +| (0.65, 48) | 0.0636552 | 0.634345 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.042687 | 0.768799 | 0.811486 | [0.048 0.952] | 0.698 | +| (0.65, 49) | 0.0684301 | 0.62757 | 0.696 | [0.046 0.304 0. 0.65 ] | 0.0417784 | 0.768695 | 0.810474 | [0.046 0.954] | 0.696 | +| (0.65, 50) | 0.0600838 | 0.648916 | 0.709 | [0.059 0.291 0. 0.65 ] | 0.0338159 | 0.78328 | 0.817096 | [0.059 0.941] | 0.709 | +| (0.65, 51) | 0.0315922 | 0.670408 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.0197787 | 0.793738 | 0.813517 | [0.052 0.948] | 0.702 | +| (0.65, 52) | 0.0805029 | 0.622497 | 0.703 | [0.053 0.297 0. 0.65 ] | 0.0511013 | 0.762925 | 0.814026 | [0.053 0.947] | 0.703 | +| (0.65, 53) | 0.0684217 | 0.642578 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.0392399 | 0.778885 | 0.818125 | [0.061 0.939] | 0.711 | +| (0.65, 54) | 0.0478134 | 0.656187 | 0.704 | [0.055 0.295 0.001 0.649] | 0.0278887 | 0.786415 | 0.814304 | [0.056 0.944] | 0.704 | +| (0.65, 55) | 0.042591 | 0.654409 | 0.697 | [0.048 0.302 0.001 0.649] | 0.0266163 | 0.784127 | 0.810743 | [0.049 0.951] | 0.697 | +| (0.65, 56) | 0.029453 | 0.676547 | 0.706 | [0.057 0.293 0.001 0.649] | 0.0176713 | 0.797655 | 0.815327 | [0.058 0.942] | 0.706 | +| (0.65, 57) | 0.0740491 | 0.622951 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0467645 | 0.764215 | 0.810979 | [0.047 0.953] | 0.697 | +| (0.65, 58) | 0.0721752 | 0.626825 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0467288 | 0.765264 | 0.811993 | [0.049 0.951] | 0.699 | +| (0.65, 59) | 0.0720319 | 0.631968 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0479897 | 0.766547 | 0.814536 | [0.054 0.946] | 0.704 | +| (0.65, 60) | 0.0632844 | 0.629716 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0401505 | 0.76881 | 0.808961 | [0.043 0.957] | 0.693 | +| (0.65, 61) | 0.0312424 | 0.673758 | 0.705 | [0.056 0.294 0.001 0.649] | 0.0161445 | 0.79867 | 0.814815 | [0.057 0.943] | 0.705 | +| (0.65, 62) | 0.0886932 | 0.611307 | 0.7 | [0.05 0.3 0. 0.65] | 0.0558643 | 0.756636 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 63) | 0.0666069 | 0.627393 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0392557 | 0.770209 | 0.809465 | [0.044 0.956] | 0.694 | +| (0.65, 64) | 0.0493815 | 0.650619 | 0.7 | [0.05 0.3 0. 0.65] | 0.033134 | 0.779366 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 65) | 0.0743474 | 0.627653 | 0.702 | [0.053 0.297 0.001 0.649] | 0.0455442 | 0.767739 | 0.813283 | [0.054 0.946] | 0.702 | +| (0.65, 66) | 0.0590576 | 0.642942 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.0378606 | 0.775656 | 0.813517 | [0.052 0.948] | 0.702 | +| (0.65, 67) | 0.0504622 | 0.640538 | 0.691 | [0.042 0.308 0.001 0.649] | 0.0288882 | 0.778828 | 0.807716 | [0.043 0.957] | 0.691 | +| (0.65, 68) | 0.0491287 | 0.651871 | 0.701 | [0.051 0.299 0. 0.65 ] | 0.0298382 | 0.78317 | 0.813008 | [0.051 0.949] | 0.701 | +| (0.65, 69) | 0.0671233 | 0.625877 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0465888 | 0.762372 | 0.808961 | [0.043 0.957] | 0.693 | +| (0.65, 70) | 0.0581822 | 0.649818 | 0.708 | [0.059 0.291 0.001 0.649] | 0.039018 | 0.777334 | 0.816352 | [0.06 0.94] | 0.708 | +| (0.65, 71) | 0.0656468 | 0.639353 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0450479 | 0.769999 | 0.815047 | [0.055 0.945] | 0.705 | +| (0.65, 72) | 0.042696 | 0.659304 | 0.702 | [0.052 0.298 0. 0.65 ] | 0.029769 | 0.783748 | 0.813517 | [0.052 0.948] | 0.702 | +| (0.65, 73) | 0.0621281 | 0.642872 | 0.705 | [0.055 0.295 0. 0.65 ] | 0.0356486 | 0.779398 | 0.815047 | [0.055 0.945] | 0.705 | +| (0.65, 74) | 0.0614174 | 0.638583 | 0.7 | [0.05 0.3 0. 0.65] | 0.0417064 | 0.770794 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 75) | 0.0690815 | 0.636919 | 0.706 | [0.056 0.294 0. 0.65 ] | 0.0382886 | 0.77727 | 0.815558 | [0.056 0.944] | 0.706 | +| (0.65, 76) | 0.0663326 | 0.632667 | 0.699 | [0.049 0.301 0. 0.65 ] | 0.0417255 | 0.770267 | 0.811993 | [0.049 0.951] | 0.699 | +| (0.65, 77) | 0.0329796 | 0.66402 | 0.697 | [0.047 0.303 0. 0.65 ] | 0.0208125 | 0.790167 | 0.810979 | [0.047 0.953] | 0.697 | +| (0.65, 78) | 0.0305704 | 0.66243 | 0.693 | [0.043 0.307 0. 0.65 ] | 0.0199287 | 0.789032 | 0.808961 | [0.043 0.957] | 0.693 | +| (0.65, 79) | 0.0591355 | 0.651865 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.033893 | 0.784232 | 0.818125 | [0.061 0.939] | 0.711 | +| (0.65, 80) | 0.0828419 | 0.621158 | 0.704 | [0.055 0.295 0.001 0.649] | 0.0513442 | 0.762959 | 0.814304 | [0.056 0.944] | 0.704 | +| (0.65, 81) | 0.0405789 | 0.653421 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0252821 | 0.784182 | 0.809465 | [0.044 0.956] | 0.694 | +| (0.65, 82) | 0.0552317 | 0.631768 | 0.687 | [0.037 0.313 0. 0.65 ] | 0.0343555 | 0.771596 | 0.805952 | [0.037 0.963] | 0.687 | +| (0.65, 83) | 0.0558149 | 0.656185 | 0.712 | [0.062 0.288 0. 0.65 ] | 0.032602 | 0.786038 | 0.81864 | [0.062 0.938] | 0.712 | +| (0.65, 84) | 0.0477823 | 0.663218 | 0.711 | [0.061 0.289 0. 0.65 ] | 0.025912 | 0.792213 | 0.818125 | [0.061 0.939] | 0.711 | +| (0.65, 85) | 0.0763267 | 0.624673 | 0.701 | [0.052 0.298 0.001 0.649] | 0.0468193 | 0.765955 | 0.812774 | [0.053 0.947] | 0.701 | +| (0.65, 86) | 0.0360446 | 0.657955 | 0.694 | [0.044 0.306 0. 0.65 ] | 0.0189634 | 0.790501 | 0.809465 | [0.044 0.956] | 0.694 | +| (0.65, 87) | 0.0756674 | 0.622333 | 0.698 | [0.05 0.3 0.002 0.648] | 0.0459044 | 0.765109 | 0.811014 | [0.052 0.948] | 0.698 | +| (0.65, 88) | 0.071613 | 0.627387 | 0.699 | [0.051 0.299 0.002 0.648] | 0.0411009 | 0.770421 | 0.811522 | [0.053 0.947] | 0.699 | +| (0.65, 89) | 0.0559207 | 0.652079 | 0.708 | [0.059 0.291 0.001 0.649] | 0.0337589 | 0.782593 | 0.816352 | [0.06 0.94] | 0.708 | +| (0.65, 90) | 0.0897853 | 0.600215 | 0.69 | [0.043 0.307 0.003 0.647] | 0.0598717 | 0.746861 | 0.806733 | [0.046 0.954] | 0.69 | +| (0.65, 91) | 0.0652016 | 0.625798 | 0.691 | [0.041 0.309 0. 0.65 ] | 0.0389561 | 0.768999 | 0.807955 | [0.041 0.959] | 0.691 | +| (0.65, 92) | 0.0780821 | 0.632918 | 0.711 | [0.062 0.288 0.001 0.649] | 0.0459551 | 0.77194 | 0.817895 | [0.063 0.937] | 0.711 | +| (0.65, 93) | 0.0369541 | 0.667046 | 0.704 | [0.054 0.296 0. 0.65 ] | 0.0196589 | 0.794877 | 0.814536 | [0.054 0.946] | 0.704 | +| (0.65, 94) | 0.0868486 | 0.613151 | 0.7 | [0.05 0.3 0. 0.65] | 0.052447 | 0.760053 | 0.8125 | [0.05 0.95] | 0.7 | +| (0.65, 95) | 0.0358453 | 0.659155 | 0.695 | [0.046 0.304 0.001 0.649] | 0.0240181 | 0.785714 | 0.809732 | [0.047 0.953] | 0.695 | +| (0.65, 96) | 0.0577789 | 0.650221 | 0.708 | [0.058 0.292 0. 0.65 ] | 0.0355455 | 0.781037 | 0.816583 | [0.058 0.942] | 0.708 | +| (0.65, 97) | 0.0486151 | 0.645385 | 0.694 | [0.045 0.305 0.001 0.649] | 0.0316332 | 0.777594 | 0.809227 | [0.046 0.954] | 0.694 | +| (0.65, 98) | 0.04014 | 0.65786 | 0.698 | [0.048 0.302 0. 0.65 ] | 0.0231171 | 0.788369 | 0.811486 | [0.048 0.952] | 0.698 | +| (0.65, 99) | 0.0660544 | 0.650946 | 0.717 | [0.069 0.281 0.002 0.648] | 0.0386239 | 0.782149 | 0.820773 | [0.071 0.929] | 0.717 | +| (0.7, 0) | 0.0257237 | 0.713276 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0158348 | 0.827031 | 0.842866 | [0.039 0.961] | 0.739 | +| (0.7, 1) | 0.0460058 | 0.694994 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0269198 | 0.816962 | 0.843882 | [0.041 0.959] | 0.741 | +| (0.7, 2) | 0.042295 | 0.707705 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0283674 | 0.819934 | 0.848301 | [0.052 0.948] | 0.75 | +| (0.7, 3) | 0.0288274 | 0.716173 | 0.745 | [0.048 0.252 0.003 0.697] | 0.0176749 | 0.827686 | 0.845361 | [0.051 0.949] | 0.745 | +| (0.7, 4) | 0.018103 | 0.731897 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0117766 | 0.836524 | 0.848301 | [0.052 0.948] | 0.75 | +| (0.7, 5) | 0.0264212 | 0.724579 | 0.751 | [0.051 0.249 0. 0.7 ] | 0.0160402 | 0.832959 | 0.848999 | [0.051 0.949] | 0.751 | +| (0.7, 6) | 0.0509385 | 0.697061 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0314891 | 0.815784 | 0.847273 | [0.05 0.95] | 0.748 | +| (0.7, 7) | 0.0454696 | 0.71053 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.0292168 | 0.822365 | 0.851582 | [0.056 0.944] | 0.756 | +| (0.7, 8) | 0.0431997 | 0.7028 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0222566 | 0.824176 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 9) | 0.0663396 | 0.67366 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0383664 | 0.805007 | 0.843373 | [0.04 0.96] | 0.74 | +| (0.7, 10) | 0.0436339 | 0.702366 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.021438 | 0.824995 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 11) | 0.0669556 | 0.677044 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0409375 | 0.804286 | 0.845224 | [0.046 0.954] | 0.744 | +| (0.7, 12) | 0.0236731 | 0.732327 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.0114491 | 0.840132 | 0.851582 | [0.056 0.944] | 0.756 | +| (0.7, 13) | 0.0543776 | 0.695622 | 0.75 | [0.051 0.249 0.001 0.699] | 0.0311781 | 0.817123 | 0.848301 | [0.052 0.948] | 0.75 | +| (0.7, 14) | 0.0219647 | 0.722035 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0127436 | 0.83248 | 0.845224 | [0.046 0.954] | 0.744 | +| (0.7, 15) | 0.04204 | 0.70096 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0265375 | 0.818363 | 0.8449 | [0.043 0.957] | 0.743 | +| (0.7, 16) | 0.0428157 | 0.693184 | 0.736 | [0.037 0.263 0.001 0.699] | 0.0243824 | 0.816773 | 0.841155 | [0.038 0.962] | 0.736 | +| (0.7, 17) | 0.0241334 | 0.712867 | 0.737 | [0.038 0.262 0.001 0.699] | 0.0143424 | 0.827319 | 0.841662 | [0.039 0.961] | 0.737 | +| (0.7, 18) | 0.0369606 | 0.702039 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0251595 | 0.817706 | 0.842866 | [0.039 0.961] | 0.739 | +| (0.7, 19) | 0.0357608 | 0.708239 | 0.744 | [0.046 0.254 0.002 0.698] | 0.0229315 | 0.822105 | 0.845036 | [0.048 0.952] | 0.744 | +| (0.7, 20) | 0.0544485 | 0.688552 | 0.743 | [0.044 0.256 0.001 0.699] | 0.0318225 | 0.81289 | 0.844713 | [0.045 0.955] | 0.743 | +| (0.7, 21) | 0.0619287 | 0.696071 | 0.758 | [0.058 0.242 0. 0.7 ] | 0.0351504 | 0.817468 | 0.852619 | [0.058 0.942] | 0.758 | +| (0.7, 22) | 0.04091 | 0.69509 | 0.736 | [0.037 0.263 0.001 0.699] | 0.0261895 | 0.814966 | 0.841155 | [0.038 0.962] | 0.736 | +| (0.7, 23) | 0.0581635 | 0.696836 | 0.755 | [0.055 0.245 0. 0.7 ] | 0.0322321 | 0.818832 | 0.851064 | [0.055 0.945] | 0.755 | +| (0.7, 24) | 0.031157 | 0.713843 | 0.745 | [0.046 0.254 0.001 0.699] | 0.020101 | 0.825634 | 0.845735 | [0.047 0.953] | 0.745 | +| (0.7, 25) | 0.0699032 | 0.679097 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0441162 | 0.803855 | 0.847971 | [0.049 0.951] | 0.749 | +| (0.7, 26) | 0.0365515 | 0.704449 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.02384 | 0.820042 | 0.843882 | [0.041 0.959] | 0.741 | +| (0.7, 27) | 0.051094 | 0.686906 | 0.738 | [0.04 0.26 0.002 0.698] | 0.0296508 | 0.812327 | 0.841978 | [0.042 0.958] | 0.738 | +| (0.7, 28) | 0.0580445 | 0.695955 | 0.754 | [0.054 0.246 0. 0.7 ] | 0.0329645 | 0.817582 | 0.850547 | [0.054 0.946] | 0.754 | +| (0.7, 29) | 0.0581874 | 0.687813 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0329015 | 0.813531 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 30) | 0.0596123 | 0.683388 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0371841 | 0.807716 | 0.8449 | [0.043 0.957] | 0.743 | +| (0.7, 31) | 0.0457276 | 0.695272 | 0.741 | [0.043 0.257 0.002 0.698] | 0.0284045 | 0.8151 | 0.843505 | [0.045 0.955] | 0.741 | +| (0.7, 32) | 0.0302288 | 0.712771 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0195378 | 0.825363 | 0.8449 | [0.043 0.957] | 0.743 | +| (0.7, 33) | 0.0102201 | 0.73978 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.00445961 | 0.844025 | 0.848485 | [0.05 0.95] | 0.75 | +| (0.7, 34) | 0.0382468 | 0.706753 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0235733 | 0.822348 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 35) | 0.0399682 | 0.706032 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0243874 | 0.822045 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 36) | 0.0552478 | 0.685752 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0321503 | 0.811732 | 0.843882 | [0.041 0.959] | 0.741 | +| (0.7, 37) | 0.0626886 | 0.683311 | 0.746 | [0.047 0.253 0.001 0.699] | 0.0357774 | 0.81047 | 0.846247 | [0.048 0.952] | 0.746 | +| (0.7, 38) | 0.0941167 | 0.653883 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0630955 | 0.784362 | 0.847458 | [0.048 0.952] | 0.748 | +| (0.7, 39) | 0.0540688 | 0.684931 | 0.739 | [0.039 0.261 0. 0.7 ] | 0.0299878 | 0.812878 | 0.842866 | [0.039 0.961] | 0.739 | +| (0.7, 40) | 0.0768883 | 0.673112 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.0440604 | 0.804424 | 0.848485 | [0.05 0.95] | 0.75 | +| (0.7, 41) | 0.0345316 | 0.705468 | 0.74 | [0.042 0.258 0.002 0.698] | 0.0216253 | 0.82137 | 0.842995 | [0.044 0.956] | 0.74 | +| (0.7, 42) | 0.0386848 | 0.702315 | 0.741 | [0.042 0.258 0.001 0.699] | 0.0241046 | 0.819589 | 0.843693 | [0.043 0.957] | 0.741 | +| (0.7, 43) | 0.0599738 | 0.673026 | 0.733 | [0.034 0.266 0.001 0.699] | 0.0351087 | 0.804531 | 0.83964 | [0.035 0.965] | 0.733 | +| (0.7, 44) | 0.0584178 | 0.686582 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0322826 | 0.813639 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 45) | 0.0447354 | 0.700265 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0272621 | 0.818659 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 46) | 0.0340733 | 0.718927 | 0.753 | [0.053 0.247 0. 0.7 ] | 0.0217421 | 0.828288 | 0.85003 | [0.053 0.947] | 0.753 | +| (0.7, 47) | 0.0335992 | 0.719401 | 0.753 | [0.053 0.247 0. 0.7 ] | 0.020345 | 0.829685 | 0.85003 | [0.053 0.947] | 0.753 | +| (0.7, 48) | 0.0205774 | 0.714423 | 0.735 | [0.036 0.264 0.001 0.699] | 0.0129594 | 0.82769 | 0.840649 | [0.037 0.963] | 0.735 | +| (0.7, 49) | 0.0587974 | 0.686203 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0339661 | 0.811955 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 50) | 0.0351737 | 0.719826 | 0.755 | [0.056 0.244 0.001 0.699] | 0.0199776 | 0.830905 | 0.850883 | [0.057 0.943] | 0.755 | +| (0.7, 51) | 0.0267817 | 0.728218 | 0.755 | [0.055 0.245 0. 0.7 ] | 0.0161224 | 0.834941 | 0.851064 | [0.055 0.945] | 0.755 | +| (0.7, 52) | 0.0472808 | 0.698719 | 0.746 | [0.047 0.253 0.001 0.699] | 0.0257035 | 0.820544 | 0.846247 | [0.048 0.952] | 0.746 | +| (0.7, 53) | 0.0651052 | 0.670895 | 0.736 | [0.036 0.264 0. 0.7 ] | 0.0414622 | 0.799884 | 0.841346 | [0.036 0.964] | 0.736 | +| (0.7, 54) | 0.0266103 | 0.71839 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0163738 | 0.829548 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 55) | 0.0556272 | 0.685373 | 0.741 | [0.042 0.258 0.001 0.699] | 0.0349463 | 0.808747 | 0.843693 | [0.043 0.957] | 0.741 | +| (0.7, 56) | 0.0522764 | 0.689724 | 0.742 | [0.044 0.256 0.002 0.698] | 0.0302049 | 0.81381 | 0.844015 | [0.046 0.954] | 0.742 | +| (0.7, 57) | 0.0489347 | 0.701065 | 0.75 | [0.05 0.25 0. 0.7 ] | 0.0319756 | 0.816509 | 0.848485 | [0.05 0.95] | 0.75 | +| (0.7, 58) | 0.0572132 | 0.690787 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0348496 | 0.812608 | 0.847458 | [0.048 0.952] | 0.748 | +| (0.7, 59) | 0.0616845 | 0.687316 | 0.749 | [0.052 0.248 0.003 0.697] | 0.0364798 | 0.810937 | 0.847416 | [0.055 0.945] | 0.749 | +| (0.7, 60) | 0.0266481 | 0.719352 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0112287 | 0.835204 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 61) | 0.0431471 | 0.703853 | 0.747 | [0.047 0.253 0. 0.7 ] | 0.0277927 | 0.819152 | 0.846945 | [0.047 0.953] | 0.747 | +| (0.7, 62) | 0.058813 | 0.695187 | 0.754 | [0.055 0.245 0.001 0.699] | 0.0385337 | 0.811831 | 0.850365 | [0.056 0.944] | 0.754 | +| (0.7, 63) | 0.044278 | 0.703722 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0263027 | 0.82097 | 0.847273 | [0.05 0.95] | 0.748 | +| (0.7, 64) | 0.0824936 | 0.657506 | 0.74 | [0.041 0.259 0.001 0.699] | 0.0498315 | 0.793353 | 0.843185 | [0.042 0.958] | 0.74 | +| (0.7, 65) | 0.0487397 | 0.69226 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.0273824 | 0.8165 | 0.843882 | [0.041 0.959] | 0.741 | +| (0.7, 66) | 0.0456017 | 0.700398 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0291693 | 0.817264 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 67) | 0.0599507 | 0.682049 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.0336685 | 0.810722 | 0.844391 | [0.042 0.958] | 0.742 | +| (0.7, 68) | 0.0606527 | 0.686347 | 0.747 | [0.049 0.251 0.002 0.698] | 0.0362096 | 0.810364 | 0.846574 | [0.051 0.949] | 0.747 | +| (0.7, 69) | 0.0634868 | 0.670513 | 0.734 | [0.034 0.266 0. 0.7 ] | 0.0382688 | 0.802067 | 0.840336 | [0.034 0.966] | 0.734 | +| (0.7, 70) | 0.0314017 | 0.715598 | 0.747 | [0.049 0.251 0.002 0.698] | 0.0198875 | 0.826686 | 0.846574 | [0.051 0.949] | 0.747 | +| (0.7, 71) | 0.0570147 | 0.693985 | 0.751 | [0.051 0.249 0. 0.7 ] | 0.0312233 | 0.817776 | 0.848999 | [0.051 0.949] | 0.751 | +| (0.7, 72) | 0.0439817 | 0.690018 | 0.734 | [0.035 0.265 0.001 0.699] | 0.0238598 | 0.816284 | 0.840144 | [0.036 0.964] | 0.734 | +| (0.7, 73) | 0.0455512 | 0.698449 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0244858 | 0.820738 | 0.845224 | [0.046 0.954] | 0.744 | +| (0.7, 74) | 0.0488415 | 0.697158 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0275018 | 0.818931 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 75) | 0.040398 | 0.701602 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.025014 | 0.819377 | 0.844391 | [0.042 0.958] | 0.742 | +| (0.7, 76) | 0.0338518 | 0.711148 | 0.745 | [0.045 0.255 0. 0.7 ] | 0.0211698 | 0.824752 | 0.845921 | [0.045 0.955] | 0.745 | +| (0.7, 77) | 0.0635197 | 0.67848 | 0.742 | [0.042 0.258 0. 0.7 ] | 0.0368786 | 0.807512 | 0.844391 | [0.042 0.958] | 0.742 | +| (0.7, 78) | 0.0168386 | 0.730161 | 0.747 | [0.048 0.252 0.001 0.699] | 0.00937337 | 0.837386 | 0.84676 | [0.049 0.951] | 0.747 | +| (0.7, 79) | 0.0418986 | 0.714101 | 0.756 | [0.056 0.244 0. 0.7 ] | 0.025139 | 0.826443 | 0.851582 | [0.056 0.944] | 0.756 | +| (0.7, 80) | 0.037095 | 0.711905 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0236265 | 0.824344 | 0.847971 | [0.049 0.951] | 0.749 | +| (0.7, 81) | 0.0355185 | 0.710481 | 0.746 | [0.047 0.253 0.001 0.699] | 0.023162 | 0.823085 | 0.846247 | [0.048 0.952] | 0.746 | +| (0.7, 82) | 0.051202 | 0.689798 | 0.741 | [0.043 0.257 0.002 0.698] | 0.0302617 | 0.813243 | 0.843505 | [0.045 0.955] | 0.741 | +| (0.7, 83) | 0.0127804 | 0.72822 | 0.741 | [0.041 0.259 0. 0.7 ] | 0.00508636 | 0.838795 | 0.843882 | [0.041 0.959] | 0.741 | +| (0.7, 84) | 0.0571513 | 0.682849 | 0.74 | [0.042 0.258 0.002 0.698] | 0.0317753 | 0.81122 | 0.842995 | [0.044 0.956] | 0.74 | +| (0.7, 85) | 0.03966 | 0.70434 | 0.744 | [0.044 0.256 0. 0.7 ] | 0.0248263 | 0.820584 | 0.845411 | [0.044 0.956] | 0.744 | +| (0.7, 86) | 0.0395805 | 0.70942 | 0.749 | [0.05 0.25 0.001 0.699] | 0.0253555 | 0.822431 | 0.847787 | [0.051 0.949] | 0.749 | +| (0.7, 87) | 0.0439101 | 0.70109 | 0.745 | [0.046 0.254 0.001 0.699] | 0.0245246 | 0.82121 | 0.845735 | [0.047 0.953] | 0.745 | +| (0.7, 88) | 0.0251373 | 0.720863 | 0.746 | [0.046 0.254 0. 0.7 ] | 0.0166981 | 0.829735 | 0.846433 | [0.046 0.954] | 0.746 | +| (0.7, 89) | 0.0427015 | 0.700298 | 0.743 | [0.043 0.257 0. 0.7 ] | 0.0273584 | 0.817542 | 0.8449 | [0.043 0.957] | 0.743 | +| (0.7, 90) | 0.0521871 | 0.687813 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0297551 | 0.813618 | 0.843373 | [0.04 0.96] | 0.74 | +| (0.7, 91) | 0.0432122 | 0.695788 | 0.739 | [0.04 0.26 0.001 0.699] | 0.0262949 | 0.816381 | 0.842676 | [0.041 0.959] | 0.739 | +| (0.7, 92) | 0.0378549 | 0.706145 | 0.744 | [0.045 0.255 0.001 0.699] | 0.0179952 | 0.827228 | 0.845224 | [0.046 0.954] | 0.744 | +| (0.7, 93) | 0.0545061 | 0.693494 | 0.748 | [0.048 0.252 0. 0.7 ] | 0.0338916 | 0.813566 | 0.847458 | [0.048 0.952] | 0.748 | +| (0.7, 94) | 0.0669098 | 0.67309 | 0.74 | [0.04 0.26 0. 0.7 ] | 0.0398733 | 0.8035 | 0.843373 | [0.04 0.96] | 0.74 | +| (0.7, 95) | 0.0615466 | 0.686453 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0419311 | 0.805342 | 0.847273 | [0.05 0.95] | 0.748 | +| (0.7, 96) | 0.0643432 | 0.684657 | 0.749 | [0.049 0.251 0. 0.7 ] | 0.0391777 | 0.808793 | 0.847971 | [0.049 0.951] | 0.749 | +| (0.7, 97) | 0.0318799 | 0.69612 | 0.728 | [0.03 0.27 0.002 0.698] | 0.0182705 | 0.81866 | 0.83693 | [0.032 0.968] | 0.728 | +| (0.7, 98) | 0.0286473 | 0.719353 | 0.748 | [0.049 0.251 0.001 0.699] | 0.0153222 | 0.831951 | 0.847273 | [0.05 0.95] | 0.748 | +| (0.7, 99) | 0.048621 | 0.683379 | 0.732 | [0.032 0.268 0. 0.7 ] | 0.0314276 | 0.807901 | 0.839329 | [0.032 0.968] | 0.732 | +| (0.75, 0) | 0.0290489 | 0.763951 | 0.793 | [0.043 0.207 0. 0.75 ] | 0.0188064 | 0.859928 | 0.878735 | [0.043 0.957] | 0.793 | +| (0.75, 1) | 0.0213086 | 0.783691 | 0.805 | [0.055 0.195 0. 0.75 ] | 0.0131107 | 0.871845 | 0.884956 | [0.055 0.945] | 0.805 | +| (0.75, 2) | 0.0258045 | 0.769195 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0152213 | 0.864403 | 0.879624 | [0.047 0.953] | 0.795 | +| (0.75, 3) | 0.0499394 | 0.737061 | 0.787 | [0.037 0.213 0. 0.75 ] | 0.0290134 | 0.846643 | 0.875657 | [0.037 0.963] | 0.787 | +| (0.75, 4) | 0.0376303 | 0.74437 | 0.782 | [0.032 0.218 0. 0.75 ] | 0.0197061 | 0.853402 | 0.873108 | [0.032 0.968] | 0.782 | +| (0.75, 5) | 0.0469517 | 0.733048 | 0.78 | [0.03 0.22 0. 0.75] | 0.0263483 | 0.845745 | 0.872093 | [0.03 0.97] | 0.78 | +| (0.75, 6) | 0.0370853 | 0.757915 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0226526 | 0.856972 | 0.879624 | [0.047 0.953] | 0.795 | +| (0.75, 7) | 0.0310667 | 0.756933 | 0.788 | [0.038 0.212 0. 0.75 ] | 0.0161298 | 0.860038 | 0.876168 | [0.038 0.962] | 0.788 | +| (0.75, 8) | 0.0332692 | 0.761731 | 0.795 | [0.047 0.203 0.002 0.748] | 0.0202977 | 0.859185 | 0.879483 | [0.049 0.951] | 0.795 | +| (0.75, 9) | 0.0136944 | 0.777306 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.00761378 | 0.870092 | 0.877706 | [0.041 0.959] | 0.791 | +| (0.75, 10) | 0.0441472 | 0.740853 | 0.785 | [0.036 0.214 0.001 0.749] | 0.0265147 | 0.847975 | 0.874489 | [0.037 0.963] | 0.785 | +| (0.75, 11) | 0.00733119 | 0.775669 | 0.783 | [0.034 0.216 0.001 0.749] | 0.00279004 | 0.870679 | 0.873469 | [0.035 0.965] | 0.783 | +| (0.75, 12) | 0.0121353 | 0.770865 | 0.783 | [0.035 0.215 0.002 0.748] | 0.00702411 | 0.866298 | 0.873322 | [0.037 0.963] | 0.783 | +| (0.75, 13) | 0.0565286 | 0.731471 | 0.788 | [0.039 0.211 0.001 0.749] | 0.0321032 | 0.84392 | 0.876023 | [0.04 0.96] | 0.788 | +| (0.75, 14) | 0.0152813 | 0.769719 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.00888373 | 0.865752 | 0.874636 | [0.035 0.965] | 0.785 | +| (0.75, 15) | 0.0153936 | 0.772606 | 0.788 | [0.039 0.211 0.001 0.749] | 0.00762887 | 0.868395 | 0.876023 | [0.04 0.96] | 0.788 | +| (0.75, 16) | 0.0119624 | 0.778038 | 0.79 | [0.042 0.208 0.002 0.748] | 0.00732564 | 0.869579 | 0.876905 | [0.044 0.956] | 0.79 | +| (0.75, 17) | 0.0485998 | 0.7364 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.0266139 | 0.848022 | 0.874636 | [0.035 0.965] | 0.785 | +| (0.75, 18) | 0.026567 | 0.751433 | 0.778 | [0.03 0.22 0.002 0.748] | 0.0149359 | 0.855844 | 0.87078 | [0.032 0.968] | 0.778 | +| (0.75, 19) | 0.0434839 | 0.746516 | 0.79 | [0.04 0.21 0. 0.75] | 0.0227465 | 0.854447 | 0.877193 | [0.04 0.96] | 0.79 | +| (0.75, 20) | 0.00358118 | 0.779419 | 0.783 | [0.035 0.215 0.002 0.748] | 0.00161839 | 0.871703 | 0.873322 | [0.037 0.963] | 0.783 | +| (0.75, 21) | 0.0250464 | 0.757954 | 0.783 | [0.033 0.217 0. 0.75 ] | 0.0141667 | 0.85945 | 0.873617 | [0.033 0.967] | 0.783 | +| (0.75, 22) | 0.00593084 | 0.793069 | 0.799 | [0.049 0.201 0. 0.75 ] | 0.00302196 | 0.878812 | 0.881834 | [0.049 0.951] | 0.799 | +| (0.75, 23) | 0.0645649 | 0.724435 | 0.789 | [0.041 0.209 0.002 0.748] | 0.0377325 | 0.838659 | 0.876391 | [0.043 0.957] | 0.789 | +| (0.75, 24) | 0.0404161 | 0.745584 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.022775 | 0.852371 | 0.875146 | [0.036 0.964] | 0.786 | +| (0.75, 25) | 0.0593057 | 0.727694 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0333993 | 0.842112 | 0.875511 | [0.039 0.961] | 0.787 | +| (0.75, 26) | 0.0611953 | 0.737805 | 0.799 | [0.049 0.201 0. 0.75 ] | 0.0339211 | 0.847913 | 0.881834 | [0.049 0.951] | 0.799 | +| (0.75, 27) | 0.0335309 | 0.764469 | 0.798 | [0.049 0.201 0.001 0.749] | 0.0187754 | 0.862401 | 0.881176 | [0.05 0.95] | 0.798 | +| (0.75, 28) | 0.0467563 | 0.734244 | 0.781 | [0.032 0.218 0.001 0.749] | 0.0259504 | 0.846502 | 0.872452 | [0.033 0.967] | 0.781 | +| (0.75, 29) | 0.0367851 | 0.757215 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0220216 | 0.857086 | 0.879108 | [0.046 0.954] | 0.794 | +| (0.75, 30) | 0.0561704 | 0.73983 | 0.796 | [0.047 0.203 0.001 0.749] | 0.0301069 | 0.850034 | 0.880141 | [0.048 0.952] | 0.796 | +| (0.75, 31) | 0.036463 | 0.759537 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0194914 | 0.86079 | 0.880282 | [0.046 0.954] | 0.796 | +| (0.75, 32) | 0.0115699 | 0.76443 | 0.776 | [0.027 0.223 0.001 0.749] | 0.00582438 | 0.864094 | 0.869919 | [0.028 0.972] | 0.776 | +| (0.75, 33) | 0.0385213 | 0.755479 | 0.794 | [0.044 0.206 0. 0.75 ] | 0.0240675 | 0.855182 | 0.87925 | [0.044 0.956] | 0.794 | +| (0.75, 34) | 0.0279748 | 0.762025 | 0.79 | [0.04 0.21 0. 0.75] | 0.0177323 | 0.859461 | 0.877193 | [0.04 0.96] | 0.79 | +| (0.75, 35) | 0.0437401 | 0.73526 | 0.779 | [0.031 0.219 0.002 0.748] | 0.0250926 | 0.846194 | 0.871287 | [0.033 0.967] | 0.779 | +| (0.75, 36) | 0.024253 | 0.758747 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0142197 | 0.85925 | 0.873469 | [0.035 0.965] | 0.783 | +| (0.75, 37) | 0.0511354 | 0.726865 | 0.778 | [0.029 0.221 0.001 0.749] | 0.0319916 | 0.838939 | 0.87093 | [0.03 0.97] | 0.778 | +| (0.75, 38) | 0.0391688 | 0.741831 | 0.781 | [0.032 0.218 0.001 0.749] | 0.0227259 | 0.849726 | 0.872452 | [0.033 0.967] | 0.781 | +| (0.75, 39) | 0.049827 | 0.737173 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0270195 | 0.848492 | 0.875511 | [0.039 0.961] | 0.787 | +| (0.75, 40) | 0.0114272 | 0.785573 | 0.797 | [0.047 0.203 0. 0.75 ] | 0.00614797 | 0.874651 | 0.880799 | [0.047 0.953] | 0.797 | +| (0.75, 41) | 0.0306715 | 0.762329 | 0.793 | [0.044 0.206 0.001 0.749] | 0.0183691 | 0.860223 | 0.878592 | [0.045 0.955] | 0.793 | +| (0.75, 42) | 0.0395072 | 0.747493 | 0.787 | [0.039 0.211 0.002 0.748] | 0.0247259 | 0.85064 | 0.875366 | [0.041 0.959] | 0.787 | +| (0.75, 43) | 0.0434636 | 0.749536 | 0.793 | [0.044 0.206 0.001 0.749] | 0.0248327 | 0.85376 | 0.878592 | [0.045 0.955] | 0.793 | +| (0.75, 44) | 0.0360129 | 0.744987 | 0.781 | [0.031 0.219 0. 0.75 ] | 0.0199793 | 0.852621 | 0.8726 | [0.031 0.969] | 0.781 | +| (0.75, 45) | 0.0280378 | 0.755962 | 0.784 | [0.035 0.215 0.001 0.749] | 0.0137074 | 0.860272 | 0.873979 | [0.036 0.964] | 0.784 | +| (0.75, 46) | 0.0215758 | 0.774424 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0126768 | 0.867605 | 0.880282 | [0.046 0.954] | 0.796 | +| (0.75, 47) | 0.0523155 | 0.739684 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0300079 | 0.848212 | 0.87822 | [0.042 0.958] | 0.792 | +| (0.75, 48) | 0.0602019 | 0.728798 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0383976 | 0.838138 | 0.876536 | [0.041 0.959] | 0.789 | +| (0.75, 49) | 0.047031 | 0.739969 | 0.787 | [0.038 0.212 0.001 0.749] | 0.0290799 | 0.846431 | 0.875511 | [0.039 0.961] | 0.787 | +| (0.75, 50) | 0.0273783 | 0.768622 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0135753 | 0.866706 | 0.880282 | [0.046 0.954] | 0.796 | +| (0.75, 51) | 0.02684 | 0.76216 | 0.789 | [0.041 0.209 0.002 0.748] | 0.0131259 | 0.863265 | 0.876391 | [0.043 0.957] | 0.789 | +| (0.75, 52) | 0.0400329 | 0.747967 | 0.788 | [0.039 0.211 0.001 0.749] | 0.0219925 | 0.854031 | 0.876023 | [0.04 0.96] | 0.788 | +| (0.75, 53) | 0.0356267 | 0.753373 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0208528 | 0.855828 | 0.87668 | [0.039 0.961] | 0.789 | +| (0.75, 54) | 0.0441881 | 0.744812 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0265353 | 0.850001 | 0.876536 | [0.041 0.959] | 0.789 | +| (0.75, 55) | 0.0309762 | 0.749024 | 0.78 | [0.031 0.219 0.001 0.749] | 0.0178195 | 0.854125 | 0.871944 | [0.032 0.968] | 0.78 | +| (0.75, 56) | 0.0161385 | 0.772862 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.00984521 | 0.866835 | 0.87668 | [0.039 0.961] | 0.789 | +| (0.75, 57) | 0.0170935 | 0.761907 | 0.779 | [0.03 0.22 0.001 0.749] | 0.00916119 | 0.862276 | 0.871437 | [0.031 0.969] | 0.779 | +| (0.75, 58) | 0.0354674 | 0.749533 | 0.785 | [0.036 0.214 0.001 0.749] | 0.0220898 | 0.852399 | 0.874489 | [0.037 0.963] | 0.785 | +| (0.75, 59) | 0.025201 | 0.760799 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.0151774 | 0.859968 | 0.875146 | [0.036 0.964] | 0.786 | +| (0.75, 60) | 0.0272636 | 0.755736 | 0.783 | [0.033 0.217 0. 0.75 ] | 0.0156835 | 0.857933 | 0.873617 | [0.033 0.967] | 0.783 | +| (0.75, 61) | 0.0433661 | 0.750634 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0228797 | 0.856228 | 0.879108 | [0.046 0.954] | 0.794 | +| (0.75, 62) | 0.0538387 | 0.737161 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.0292089 | 0.848497 | 0.877706 | [0.041 0.959] | 0.791 | +| (0.75, 63) | 0.0224127 | 0.764587 | 0.787 | [0.037 0.213 0. 0.75 ] | 0.0128879 | 0.862769 | 0.875657 | [0.037 0.963] | 0.787 | +| (0.75, 64) | 0.034333 | 0.754667 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0213609 | 0.855319 | 0.87668 | [0.039 0.961] | 0.789 | +| (0.75, 65) | 0.00924564 | 0.775754 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.00490077 | 0.869735 | 0.874636 | [0.035 0.965] | 0.785 | +| (0.75, 66) | 0.00451873 | 0.786481 | 0.791 | [0.041 0.209 0. 0.75 ] | 0.00214582 | 0.87556 | 0.877706 | [0.041 0.959] | 0.791 | +| (0.75, 67) | 0.0517501 | 0.74025 | 0.792 | [0.044 0.206 0.002 0.748] | 0.028609 | 0.849325 | 0.877934 | [0.046 0.954] | 0.792 | +| (0.75, 68) | 0.0343641 | 0.754636 | 0.789 | [0.04 0.21 0.001 0.749] | 0.0192369 | 0.857299 | 0.876536 | [0.041 0.959] | 0.789 | +| (0.75, 69) | 0.0344314 | 0.750569 | 0.785 | [0.035 0.215 0. 0.75 ] | 0.0205979 | 0.854038 | 0.874636 | [0.035 0.965] | 0.785 | +| (0.75, 70) | 0.0261044 | 0.767896 | 0.794 | [0.045 0.205 0.001 0.749] | 0.0139256 | 0.865182 | 0.879108 | [0.046 0.954] | 0.794 | +| (0.75, 71) | 0.0363507 | 0.753649 | 0.79 | [0.04 0.21 0. 0.75] | 0.0210632 | 0.85613 | 0.877193 | [0.04 0.96] | 0.79 | +| (0.75, 72) | 0.0405785 | 0.741422 | 0.782 | [0.035 0.215 0.003 0.747] | 0.0217776 | 0.850886 | 0.872664 | [0.038 0.962] | 0.782 | +| (0.75, 73) | 0.0170575 | 0.767942 | 0.785 | [0.036 0.214 0.001 0.749] | 0.00850885 | 0.86598 | 0.874489 | [0.037 0.963] | 0.785 | +| (0.75, 74) | 0.0375071 | 0.750493 | 0.788 | [0.039 0.211 0.001 0.749] | 0.021137 | 0.854886 | 0.876023 | [0.04 0.96] | 0.788 | +| (0.75, 75) | 0.0371426 | 0.748857 | 0.786 | [0.037 0.213 0.001 0.749] | 0.0210716 | 0.853928 | 0.875 | [0.038 0.962] | 0.786 | +| (0.75, 76) | 0.0648981 | 0.724102 | 0.789 | [0.039 0.211 0. 0.75 ] | 0.0374199 | 0.83926 | 0.87668 | [0.039 0.961] | 0.789 | +| (0.75, 77) | 0.0389063 | 0.741094 | 0.78 | [0.031 0.219 0.001 0.749] | 0.0219382 | 0.850006 | 0.871944 | [0.032 0.968] | 0.78 | +| (0.75, 78) | 0.046141 | 0.745859 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0284288 | 0.849791 | 0.87822 | [0.042 0.958] | 0.792 | +| (0.75, 79) | 0.0459182 | 0.746082 | 0.792 | [0.042 0.208 0. 0.75 ] | 0.0254524 | 0.852768 | 0.87822 | [0.042 0.958] | 0.792 | +| (0.75, 80) | 0.0367849 | 0.750215 | 0.787 | [0.039 0.211 0.002 0.748] | 0.0190088 | 0.856357 | 0.875366 | [0.041 0.959] | 0.787 | +| (0.75, 81) | 0.0214734 | 0.769527 | 0.791 | [0.042 0.208 0.001 0.749] | 0.0109792 | 0.866584 | 0.877563 | [0.043 0.957] | 0.791 | +| (0.75, 82) | 0.0606138 | 0.723386 | 0.784 | [0.034 0.216 0. 0.75 ] | 0.0346787 | 0.839447 | 0.874126 | [0.034 0.966] | 0.784 | +| (0.75, 83) | 0.0179434 | 0.767057 | 0.785 | [0.036 0.214 0.001 0.749] | 0.00789914 | 0.86659 | 0.874489 | [0.037 0.963] | 0.785 | +| (0.75, 84) | 0.0356262 | 0.747374 | 0.783 | [0.035 0.215 0.002 0.748] | 0.0210658 | 0.852256 | 0.873322 | [0.037 0.963] | 0.783 | +| (0.75, 85) | 0.0263657 | 0.756634 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0141479 | 0.859321 | 0.873469 | [0.035 0.965] | 0.783 | +| (0.75, 86) | 0.0293021 | 0.743698 | 0.773 | [0.024 0.226 0.001 0.749] | 0.017352 | 0.851054 | 0.868406 | [0.025 0.975] | 0.773 | +| (0.75, 87) | 0.026603 | 0.759397 | 0.786 | [0.036 0.214 0. 0.75 ] | 0.016022 | 0.859124 | 0.875146 | [0.036 0.964] | 0.786 | +| (0.75, 88) | 0.0406937 | 0.747306 | 0.788 | [0.04 0.21 0.002 0.748] | 0.0228616 | 0.853017 | 0.875878 | [0.042 0.958] | 0.788 | +| (0.75, 89) | 0.0458613 | 0.732139 | 0.778 | [0.028 0.222 0. 0.75 ] | 0.0270263 | 0.844054 | 0.87108 | [0.028 0.972] | 0.778 | +| (0.75, 90) | 0.0341579 | 0.760842 | 0.795 | [0.045 0.205 0. 0.75 ] | 0.0208155 | 0.85895 | 0.879765 | [0.045 0.955] | 0.795 | +| (0.75, 91) | 0.038191 | 0.760809 | 0.799 | [0.05 0.2 0.001 0.749] | 0.0213842 | 0.860311 | 0.881695 | [0.051 0.949] | 0.799 | +| (0.75, 92) | 0.0372172 | 0.743783 | 0.781 | [0.031 0.219 0. 0.75 ] | 0.0198746 | 0.852726 | 0.8726 | [0.031 0.969] | 0.781 | +| (0.75, 93) | 0.0567933 | 0.727207 | 0.784 | [0.036 0.214 0.002 0.748] | 0.0351011 | 0.838731 | 0.873832 | [0.038 0.962] | 0.784 | +| (0.75, 94) | 0.0228502 | 0.76015 | 0.783 | [0.034 0.216 0.001 0.749] | 0.0115927 | 0.861877 | 0.873469 | [0.035 0.965] | 0.783 | +| (0.75, 95) | 0.0450282 | 0.738972 | 0.784 | [0.034 0.216 0. 0.75 ] | 0.0242723 | 0.849854 | 0.874126 | [0.034 0.966] | 0.784 | +| (0.75, 96) | 0.0540909 | 0.743909 | 0.798 | [0.048 0.202 0. 0.75 ] | 0.0295038 | 0.851812 | 0.881316 | [0.048 0.952] | 0.798 | +| (0.75, 97) | 0.0563787 | 0.723621 | 0.78 | [0.03 0.22 0. 0.75] | 0.0350677 | 0.837025 | 0.872093 | [0.03 0.97] | 0.78 | +| (0.75, 98) | 0.028261 | 0.766739 | 0.795 | [0.046 0.204 0.001 0.749] | 0.0140084 | 0.865616 | 0.879624 | [0.047 0.953] | 0.795 | +| (0.75, 99) | 0.019607 | 0.776393 | 0.796 | [0.046 0.204 0. 0.75 ] | 0.0109948 | 0.869287 | 0.880282 | [0.046 0.954] | 0.796 | +| (0.8, 0) | 0.0409423 | 0.793058 | 0.834 | [0.035 0.165 0.001 0.799] | 0.0217691 | 0.884127 | 0.905896 | [0.036 0.964] | 0.834 | +| (0.8, 1) | 0.00912837 | 0.815872 | 0.825 | [0.026 0.174 0.001 0.799] | 0.0048623 | 0.896435 | 0.901297 | [0.027 0.973] | 0.825 | +| (0.8, 2) | 0.0216766 | 0.806323 | 0.828 | [0.03 0.17 0.002 0.798] | 0.010707 | 0.892008 | 0.902715 | [0.032 0.968] | 0.828 | +| (0.8, 3) | 0.00863246 | 0.816368 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.00504774 | 0.896361 | 0.901408 | [0.025 0.975] | 0.825 | +| (0.8, 4) | 0.0276359 | 0.796364 | 0.824 | [0.025 0.175 0.001 0.799] | 0.0157953 | 0.884994 | 0.900789 | [0.026 0.974] | 0.824 | +| (0.8, 5) | 0.00270277 | 0.815297 | 0.818 | [0.018 0.182 0. 0.8 ] | 0.00111215 | 0.896755 | 0.897868 | [0.018 0.982] | 0.818 | +| (0.8, 6) | 0.0210912 | 0.800909 | 0.822 | [0.023 0.177 0.001 0.799] | 0.0116788 | 0.888096 | 0.899775 | [0.024 0.976] | 0.822 | +| (0.8, 7) | 0.0458864 | 0.780114 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0255575 | 0.876359 | 0.901917 | [0.026 0.974] | 0.826 | +| (0.8, 8) | 0.0325411 | 0.799459 | 0.832 | [0.033 0.167 0.001 0.799] | 0.0179992 | 0.886871 | 0.90487 | [0.034 0.966] | 0.832 | +| (0.8, 9) | 0.0374835 | 0.792517 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0210132 | 0.882942 | 0.903955 | [0.03 0.97] | 0.83 | +| (0.8, 10) | 0.0330656 | 0.787934 | 0.821 | [0.022 0.178 0.001 0.799] | 0.0178779 | 0.88139 | 0.899268 | [0.023 0.977] | 0.821 | +| (0.8, 11) | 0.005432 | 0.826568 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.00301292 | 0.901964 | 0.904977 | [0.032 0.968] | 0.832 | +| (0.8, 12) | 0.00380289 | 0.822197 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.00202778 | 0.899889 | 0.901917 | [0.026 0.974] | 0.826 | +| (0.8, 13) | 0.00720171 | 0.815798 | 0.823 | [0.023 0.177 0. 0.8 ] | 0.00305894 | 0.897335 | 0.900394 | [0.023 0.977] | 0.823 | +| (0.8, 14) | 0.0172814 | 0.807719 | 0.825 | [0.027 0.173 0.002 0.798] | 0.00767473 | 0.893511 | 0.901186 | [0.029 0.971] | 0.825 | +| (0.8, 15) | 0.0120847 | 0.816915 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.00646782 | 0.896977 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 16) | 0.0263668 | 0.792633 | 0.819 | [0.019 0.181 0. 0.8 ] | 0.0145246 | 0.883847 | 0.898372 | [0.019 0.981] | 0.819 | +| (0.8, 17) | 0.032205 | 0.797795 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0186486 | 0.885306 | 0.903955 | [0.03 0.97] | 0.83 | +| (0.8, 18) | 0.0364903 | 0.78451 | 0.821 | [0.023 0.177 0.002 0.798] | 0.0199892 | 0.879166 | 0.899155 | [0.025 0.975] | 0.821 | +| (0.8, 19) | 0.0445238 | 0.794476 | 0.839 | [0.039 0.161 0. 0.8 ] | 0.025349 | 0.883226 | 0.908575 | [0.039 0.961] | 0.839 | +| (0.8, 20) | 0.0180783 | 0.798922 | 0.817 | [0.017 0.183 0. 0.8 ] | 0.0101047 | 0.887259 | 0.897364 | [0.017 0.983] | 0.817 | +| (0.8, 21) | 0.0245807 | 0.804419 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0148101 | 0.888634 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 22) | 0.0244787 | 0.804521 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0137768 | 0.889668 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 23) | 0.0193507 | 0.802649 | 0.822 | [0.022 0.178 0. 0.8 ] | 0.0108588 | 0.889029 | 0.899888 | [0.022 0.978] | 0.822 | +| (0.8, 24) | 0.0354281 | 0.792572 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.019193 | 0.883741 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 25) | 0.021107 | 0.810893 | 0.832 | [0.033 0.167 0.001 0.799] | 0.0122716 | 0.892598 | 0.90487 | [0.034 0.966] | 0.832 | +| (0.8, 26) | 0.0189653 | 0.809035 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0110574 | 0.891877 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 27) | 0.0427835 | 0.783216 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0234921 | 0.878424 | 0.901917 | [0.026 0.974] | 0.826 | +| (0.8, 28) | 0.0388648 | 0.795135 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0206372 | 0.885365 | 0.906002 | [0.034 0.966] | 0.834 | +| (0.8, 29) | 0.0484156 | 0.780584 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0269235 | 0.876412 | 0.903335 | [0.031 0.969] | 0.829 | +| (0.8, 30) | 0.0189334 | 0.809067 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.00894551 | 0.893989 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 31) | 0.0348533 | 0.795147 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0206885 | 0.883266 | 0.903955 | [0.03 0.97] | 0.83 | +| (0.8, 32) | 0.0298236 | 0.797176 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0154133 | 0.887012 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 33) | 0.0308446 | 0.790155 | 0.821 | [0.021 0.179 0. 0.8 ] | 0.0177848 | 0.881597 | 0.899382 | [0.021 0.979] | 0.821 | +| (0.8, 34) | 0.0173788 | 0.815621 | 0.833 | [0.035 0.165 0.002 0.798] | 0.00986453 | 0.895411 | 0.905275 | [0.037 0.963] | 0.833 | +| (0.8, 35) | 0.0274772 | 0.797523 | 0.825 | [0.027 0.173 0.002 0.798] | 0.016222 | 0.884964 | 0.901186 | [0.029 0.971] | 0.825 | +| (0.8, 36) | 0.00890272 | 0.823097 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0056062 | 0.899371 | 0.904977 | [0.032 0.968] | 0.832 | +| (0.8, 37) | 0.0382181 | 0.786782 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0212881 | 0.88012 | 0.901408 | [0.025 0.975] | 0.825 | +| (0.8, 38) | 0.0243138 | 0.802686 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0120784 | 0.890347 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 39) | 0.0384636 | 0.792536 | 0.831 | [0.032 0.168 0.001 0.799] | 0.0225441 | 0.881814 | 0.904358 | [0.033 0.967] | 0.831 | +| (0.8, 40) | 0.0253619 | 0.801638 | 0.827 | [0.028 0.172 0.001 0.799] | 0.0150565 | 0.887259 | 0.902315 | [0.029 0.971] | 0.827 | +| (0.8, 41) | 0.000229561 | 0.82877 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.000929264 | 0.904374 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 42) | 0.0225886 | 0.807411 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0118405 | 0.892006 | 0.903846 | [0.032 0.968] | 0.83 | +| (0.8, 43) | 0.0194196 | 0.80858 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.00990255 | 0.893032 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 44) | 0.00644612 | 0.832554 | 0.839 | [0.04 0.16 0.001 0.799] | 0.00352805 | 0.904943 | 0.908471 | [0.041 0.959] | 0.839 | +| (0.8, 45) | 0.0505133 | 0.779487 | 0.83 | [0.03 0.17 0. 0.8 ] | 0.0279151 | 0.87604 | 0.903955 | [0.03 0.97] | 0.83 | +| (0.8, 46) | 0.0373503 | 0.79165 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.020021 | 0.883423 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 47) | 0.0360948 | 0.792905 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0211833 | 0.882152 | 0.903335 | [0.031 0.969] | 0.829 | +| (0.8, 48) | 0.017169 | 0.817831 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.00976995 | 0.896746 | 0.906516 | [0.035 0.965] | 0.835 | +| (0.8, 49) | 0.0305348 | 0.803465 | 0.834 | [0.035 0.165 0.001 0.799] | 0.0180311 | 0.887865 | 0.905896 | [0.036 0.964] | 0.834 | +| (0.8, 50) | 0.0293145 | 0.797686 | 0.827 | [0.029 0.171 0.002 0.798] | 0.0166065 | 0.885598 | 0.902205 | [0.031 0.969] | 0.827 | +| (0.8, 51) | 0.0280583 | 0.802942 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0152134 | 0.889252 | 0.904466 | [0.031 0.969] | 0.831 | +| (0.8, 52) | 0.0273535 | 0.798646 | 0.826 | [0.026 0.174 0. 0.8 ] | 0.0140662 | 0.88785 | 0.901917 | [0.026 0.974] | 0.826 | +| (0.8, 53) | 0.037029 | 0.794971 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0200333 | 0.884944 | 0.904977 | [0.032 0.968] | 0.832 | +| (0.8, 54) | 0.02648 | 0.79652 | 0.823 | [0.023 0.177 0. 0.8 ] | 0.0137327 | 0.886661 | 0.900394 | [0.023 0.977] | 0.823 | +| (0.8, 55) | 0.0147235 | 0.812277 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00601093 | 0.896414 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 56) | 0.0248399 | 0.81016 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.0143239 | 0.892192 | 0.906516 | [0.035 0.965] | 0.835 | +| (0.8, 57) | 0.0104378 | 0.837438 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00708952 | 0.909515 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 58) | 0.0244922 | 0.799508 | 0.824 | [0.024 0.176 0. 0.8 ] | 0.0127094 | 0.888191 | 0.900901 | [0.024 0.976] | 0.824 | +| (0.8, 59) | 0.0260415 | 0.800959 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0150998 | 0.887325 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 60) | 0.0267759 | 0.809224 | 0.836 | [0.036 0.164 0. 0.8 ] | 0.0160896 | 0.89094 | 0.907029 | [0.036 0.964] | 0.836 | +| (0.8, 61) | 0.0225115 | 0.797488 | 0.82 | [0.021 0.179 0.001 0.799] | 0.0122826 | 0.88648 | 0.898763 | [0.022 0.978] | 0.82 | +| (0.8, 62) | 0.0343597 | 0.79264 | 0.827 | [0.029 0.171 0.002 0.798] | 0.0190629 | 0.883142 | 0.902205 | [0.031 0.969] | 0.827 | +| (0.8, 63) | 0.0159727 | 0.813027 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0089236 | 0.894521 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 64) | 0.0416964 | 0.791304 | 0.833 | [0.033 0.167 0. 0.8 ] | 0.0222044 | 0.883285 | 0.90549 | [0.033 0.967] | 0.833 | +| (0.8, 65) | 0.0316177 | 0.796382 | 0.828 | [0.031 0.169 0.003 0.797] | 0.0189897 | 0.883615 | 0.902605 | [0.034 0.966] | 0.828 | +| (0.8, 66) | 0.0404031 | 0.794597 | 0.835 | [0.035 0.165 0. 0.8 ] | 0.0213009 | 0.885215 | 0.906516 | [0.035 0.965] | 0.835 | +| (0.8, 67) | 0.0437875 | 0.790213 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0242558 | 0.881746 | 0.906002 | [0.034 0.966] | 0.834 | +| (0.8, 68) | 0.0164454 | 0.817555 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.00894291 | 0.897059 | 0.906002 | [0.034 0.966] | 0.834 | +| (0.8, 69) | 0.0331339 | 0.794866 | 0.828 | [0.029 0.171 0.001 0.799] | 0.0186214 | 0.884203 | 0.902825 | [0.03 0.97] | 0.828 | +| (0.8, 70) | 0.0282686 | 0.803731 | 0.832 | [0.032 0.168 0. 0.8 ] | 0.0170943 | 0.887883 | 0.904977 | [0.032 0.968] | 0.832 | +| (0.8, 71) | 0.0266922 | 0.803308 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0152941 | 0.888552 | 0.903846 | [0.032 0.968] | 0.83 | +| (0.8, 72) | 0.0192176 | 0.805782 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0112241 | 0.890184 | 0.901408 | [0.025 0.975] | 0.825 | +| (0.8, 73) | 0.0452923 | 0.778708 | 0.824 | [0.024 0.176 0. 0.8 ] | 0.0253478 | 0.875553 | 0.900901 | [0.024 0.976] | 0.824 | +| (0.8, 74) | 0.0284065 | 0.800593 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0153539 | 0.887981 | 0.903335 | [0.031 0.969] | 0.829 | +| (0.8, 75) | 0.019628 | 0.811372 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0115092 | 0.892957 | 0.904466 | [0.031 0.969] | 0.831 | +| (0.8, 76) | 0.0254376 | 0.802562 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0147003 | 0.888234 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 77) | 0.0274572 | 0.797543 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0140591 | 0.887349 | 0.901408 | [0.025 0.975] | 0.825 | +| (0.8, 78) | 0.0268882 | 0.806112 | 0.833 | [0.035 0.165 0.002 0.798] | 0.0145238 | 0.890751 | 0.905275 | [0.037 0.963] | 0.833 | +| (0.8, 79) | 0.0274169 | 0.803583 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0154312 | 0.889035 | 0.904466 | [0.031 0.969] | 0.831 | +| (0.8, 80) | 0.0421947 | 0.792805 | 0.835 | [0.037 0.163 0.002 0.798] | 0.0234039 | 0.882899 | 0.906303 | [0.039 0.961] | 0.835 | +| (0.8, 81) | 0.0341685 | 0.791831 | 0.826 | [0.029 0.171 0.003 0.797] | 0.0188428 | 0.882741 | 0.901584 | [0.032 0.968] | 0.826 | +| (0.8, 82) | 0.0425206 | 0.791479 | 0.834 | [0.034 0.166 0. 0.8 ] | 0.0250496 | 0.880953 | 0.906002 | [0.034 0.966] | 0.834 | +| (0.8, 83) | 0.0240059 | 0.805994 | 0.83 | [0.031 0.169 0.001 0.799] | 0.0139455 | 0.889901 | 0.903846 | [0.032 0.968] | 0.83 | +| (0.8, 84) | 0.0177441 | 0.804256 | 0.822 | [0.022 0.178 0. 0.8 ] | 0.00957679 | 0.890311 | 0.899888 | [0.022 0.978] | 0.822 | +| (0.8, 85) | 0.00355815 | 0.829442 | 0.833 | [0.034 0.166 0.001 0.799] | 0.000679917 | 0.904703 | 0.905382 | [0.035 0.965] | 0.833 | +| (0.8, 86) | 0.0074582 | 0.823542 | 0.831 | [0.032 0.168 0.001 0.799] | 0.00372776 | 0.90063 | 0.904358 | [0.033 0.967] | 0.831 | +| (0.8, 87) | 0.0431926 | 0.785807 | 0.829 | [0.029 0.171 0. 0.8 ] | 0.0239931 | 0.879451 | 0.903444 | [0.029 0.971] | 0.829 | +| (0.8, 88) | 0.0312967 | 0.793703 | 0.825 | [0.025 0.175 0. 0.8 ] | 0.0164912 | 0.884917 | 0.901408 | [0.025 0.975] | 0.825 | +| (0.8, 89) | 0.0111373 | 0.815863 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.00594957 | 0.896476 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 90) | 0.0255766 | 0.802423 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.013677 | 0.889258 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 91) | 0.0221193 | 0.808881 | 0.831 | [0.031 0.169 0. 0.8 ] | 0.0110527 | 0.893413 | 0.904466 | [0.031 0.969] | 0.831 | +| (0.8, 92) | 0.0231501 | 0.80585 | 0.829 | [0.03 0.17 0.001 0.799] | 0.0134434 | 0.889892 | 0.903335 | [0.031 0.969] | 0.829 | +| (0.8, 93) | 0.0233668 | 0.797633 | 0.821 | [0.021 0.179 0. 0.8 ] | 0.0137534 | 0.885628 | 0.899382 | [0.021 0.979] | 0.821 | +| (0.8, 94) | 0.00688933 | 0.826111 | 0.833 | [0.034 0.166 0.001 0.799] | 0.0037811 | 0.901601 | 0.905382 | [0.035 0.965] | 0.833 | +| (0.8, 95) | 0.0243997 | 0.8096 | 0.834 | [0.036 0.164 0.002 0.798] | 0.0114362 | 0.894353 | 0.905789 | [0.038 0.962] | 0.834 | +| (0.8, 96) | 0.0228605 | 0.80514 | 0.828 | [0.028 0.172 0. 0.8 ] | 0.0123032 | 0.890631 | 0.902935 | [0.028 0.972] | 0.828 | +| (0.8, 97) | 0.0287869 | 0.797213 | 0.826 | [0.027 0.173 0.001 0.799] | 0.0146776 | 0.887128 | 0.901806 | [0.028 0.972] | 0.826 | +| (0.8, 98) | 0.0243104 | 0.80269 | 0.827 | [0.027 0.173 0. 0.8 ] | 0.0133648 | 0.88906 | 0.902425 | [0.027 0.973] | 0.827 | +| (0.8, 99) | 0.034638 | 0.793362 | 0.828 | [0.029 0.171 0.001 0.799] | 0.0180954 | 0.884729 | 0.902825 | [0.03 0.97] | 0.828 | +| (0.85, 0) | 0.00916264 | 0.856837 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00395676 | 0.922899 | 0.926856 | [0.018 0.982] | 0.866 | +| (0.85, 1) | 0.0242384 | 0.842762 | 0.867 | [0.019 0.131 0.002 0.848] | 0.0126504 | 0.914632 | 0.927283 | [0.021 0.979] | 0.867 | +| (0.85, 2) | 0.0059672 | 0.874967 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00366687 | 0.932043 | 0.928376 | [0.021 0.979] | 0.869 | +| (0.85, 3) | 0.0185452 | 0.856455 | 0.875 | [0.025 0.125 0. 0.85 ] | 0.00887886 | 0.922628 | 0.931507 | [0.025 0.975] | 0.875 | +| (0.85, 4) | 0.0223926 | 0.854607 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0109393 | 0.921589 | 0.932529 | [0.027 0.973] | 0.877 | +| (0.85, 5) | 0.0275382 | 0.843462 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0144402 | 0.915029 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 6) | 0.0116306 | 0.864369 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0066333 | 0.925384 | 0.932018 | [0.026 0.974] | 0.876 | +| (0.85, 7) | 0.0293276 | 0.839672 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0167791 | 0.911675 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 8) | 0.00853504 | 0.862465 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.00453054 | 0.924939 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 9) | 0.00844529 | 0.877445 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00527631 | 0.933731 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 10) | 0.0174929 | 0.851507 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00949055 | 0.918886 | 0.928376 | [0.021 0.979] | 0.869 | +| (0.85, 11) | 0.032037 | 0.834963 | 0.867 | [0.017 0.133 0. 0.85 ] | 0.0178529 | 0.909588 | 0.927441 | [0.017 0.983] | 0.867 | +| (0.85, 12) | 0.02062 | 0.85538 | 0.876 | [0.027 0.123 0.001 0.849] | 0.0106596 | 0.921283 | 0.931943 | [0.028 0.972] | 0.876 | +| (0.85, 13) | 0.0300872 | 0.839913 | 0.87 | [0.02 0.13 0. 0.85] | 0.0169147 | 0.912047 | 0.928962 | [0.02 0.98] | 0.87 | +| (0.85, 14) | 0.0185164 | 0.847484 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.0095133 | 0.917422 | 0.926936 | [0.016 0.984] | 0.866 | +| (0.85, 15) | 0.00594455 | 0.867055 | 0.873 | [0.023 0.127 0. 0.85 ] | 0.00319687 | 0.92729 | 0.930487 | [0.023 0.977] | 0.873 | +| (0.85, 16) | 0.0159766 | 0.858023 | 0.874 | [0.025 0.125 0.001 0.849] | 0.00772593 | 0.923195 | 0.930921 | [0.026 0.974] | 0.874 | +| (0.85, 17) | 0.0102021 | 0.862798 | 0.873 | [0.024 0.126 0.001 0.849] | 0.00582044 | 0.924591 | 0.930411 | [0.025 0.975] | 0.873 | +| (0.85, 18) | 0.0345923 | 0.835408 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0201934 | 0.908691 | 0.928884 | [0.022 0.978] | 0.87 | +| (0.85, 19) | 0.0164631 | 0.855537 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00789905 | 0.922079 | 0.929978 | [0.022 0.978] | 0.872 | +| (0.85, 20) | 0.0369302 | 0.83707 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.0197393 | 0.911257 | 0.930997 | [0.024 0.976] | 0.874 | +| (0.85, 21) | 0.0297006 | 0.844299 | 0.874 | [0.026 0.124 0.002 0.848] | 0.0157164 | 0.915129 | 0.930845 | [0.028 0.972] | 0.874 | +| (0.85, 22) | 0.00637133 | 0.869629 | 0.876 | [0.027 0.123 0.001 0.849] | 0.00337797 | 0.928565 | 0.931943 | [0.028 0.972] | 0.876 | +| (0.85, 23) | 0.0257953 | 0.836205 | 0.862 | [0.015 0.135 0.003 0.847] | 0.0138807 | 0.910792 | 0.924672 | [0.018 0.982] | 0.862 | +| (0.85, 24) | 0.00337444 | 0.868626 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00166765 | 0.92831 | 0.929978 | [0.022 0.978] | 0.872 | +| (0.85, 25) | 0.024588 | 0.851412 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0122872 | 0.91973 | 0.932018 | [0.026 0.974] | 0.876 | +| (0.85, 26) | 0.0175407 | 0.853459 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00848404 | 0.920908 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 27) | 0.0188113 | 0.853189 | 0.872 | [0.023 0.127 0.001 0.849] | 0.00993147 | 0.91997 | 0.929901 | [0.024 0.976] | 0.872 | +| (0.85, 28) | 0.0102198 | 0.88022 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0060185 | 0.934903 | 0.928884 | [0.022 0.978] | 0.87 | +| (0.85, 29) | 0.0236431 | 0.844357 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.0131277 | 0.91482 | 0.927948 | [0.018 0.982] | 0.868 | +| (0.85, 30) | 0.0294663 | 0.840534 | 0.87 | [0.02 0.13 0. 0.85] | 0.0164557 | 0.912506 | 0.928962 | [0.02 0.98] | 0.87 | +| (0.85, 31) | 0.0125447 | 0.858455 | 0.871 | [0.023 0.127 0.002 0.848] | 0.00581342 | 0.923502 | 0.929315 | [0.025 0.975] | 0.871 | +| (0.85, 32) | 0.0168929 | 0.851107 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.00838249 | 0.919565 | 0.927948 | [0.018 0.982] | 0.868 | +| (0.85, 33) | 0.0416726 | 0.827327 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0237554 | 0.904699 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 34) | 0.00899809 | 0.860002 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00365011 | 0.924726 | 0.928376 | [0.021 0.979] | 0.869 | +| (0.85, 35) | 0.012989 | 0.855011 | 0.868 | [0.02 0.13 0.002 0.848] | 0.00666622 | 0.921124 | 0.92779 | [0.022 0.978] | 0.868 | +| (0.85, 36) | 0.0118965 | 0.859103 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00526969 | 0.924123 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 37) | 0.0351497 | 0.83185 | 0.867 | [0.019 0.131 0.002 0.848] | 0.0192705 | 0.908012 | 0.927283 | [0.021 0.979] | 0.867 | +| (0.85, 38) | 0.000781362 | 0.875781 | 0.875 | [0.025 0.125 0. 0.85 ] | 0.000880589 | 0.932387 | 0.931507 | [0.025 0.975] | 0.875 | +| (0.85, 39) | 0.0148084 | 0.861192 | 0.876 | [0.028 0.122 0.002 0.848] | 0.00707788 | 0.92479 | 0.931868 | [0.03 0.97] | 0.876 | +| (0.85, 40) | 0.033094 | 0.839906 | 0.873 | [0.024 0.126 0.001 0.849] | 0.0174848 | 0.912926 | 0.930411 | [0.025 0.975] | 0.873 | +| (0.85, 41) | 0.00491038 | 0.87191 | 0.867 | [0.018 0.132 0.001 0.849] | 0.00317792 | 0.93054 | 0.927362 | [0.019 0.981] | 0.867 | +| (0.85, 42) | 0.0118285 | 0.859172 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.00646193 | 0.923008 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 43) | 0.0222668 | 0.853733 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0109554 | 0.921062 | 0.932018 | [0.026 0.974] | 0.876 | +| (0.85, 44) | 0.0208303 | 0.85017 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0116156 | 0.917854 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 45) | 0.0232634 | 0.844737 | 0.868 | [0.018 0.132 0. 0.85 ] | 0.0132446 | 0.914703 | 0.927948 | [0.018 0.982] | 0.868 | +| (0.85, 46) | 0.0286176 | 0.837382 | 0.866 | [0.018 0.132 0.002 0.848] | 0.0152861 | 0.91149 | 0.926776 | [0.02 0.98] | 0.866 | +| (0.85, 47) | 0.0189883 | 0.848012 | 0.867 | [0.018 0.132 0.001 0.849] | 0.0098411 | 0.917521 | 0.927362 | [0.019 0.981] | 0.867 | +| (0.85, 48) | 0.0291667 | 0.846833 | 0.876 | [0.026 0.124 0. 0.85 ] | 0.0162761 | 0.915741 | 0.932018 | [0.026 0.974] | 0.876 | +| (0.85, 49) | 0.0139737 | 0.852026 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00675732 | 0.920099 | 0.926856 | [0.018 0.982] | 0.866 | +| (0.85, 50) | 0.028988 | 0.840012 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0155634 | 0.912891 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 51) | 0.019931 | 0.850069 | 0.87 | [0.021 0.129 0.001 0.849] | 0.0114954 | 0.917389 | 0.928884 | [0.022 0.978] | 0.87 | +| (0.85, 52) | 0.0126395 | 0.86136 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00694487 | 0.924052 | 0.930997 | [0.024 0.976] | 0.874 | +| (0.85, 53) | 0.0174681 | 0.847532 | 0.865 | [0.016 0.134 0.001 0.849] | 0.00971912 | 0.916631 | 0.92635 | [0.017 0.983] | 0.865 | +| (0.85, 54) | 0.000984246 | 0.871984 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00097317 | 0.930366 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 55) | 0.00821199 | 0.860788 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00444453 | 0.92401 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 56) | 0.00862268 | 0.861377 | 0.87 | [0.02 0.13 0. 0.85] | 0.00347912 | 0.925483 | 0.928962 | [0.02 0.98] | 0.87 | +| (0.85, 57) | 0.00934159 | 0.878342 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00568831 | 0.934143 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 58) | 0.0128164 | 0.857184 | 0.87 | [0.021 0.129 0.001 0.849] | 0.00699737 | 0.921887 | 0.928884 | [0.022 0.978] | 0.87 | +| (0.85, 59) | 0.0331427 | 0.850857 | 0.884 | [0.034 0.116 0. 0.85 ] | 0.0172442 | 0.918879 | 0.936123 | [0.034 0.966] | 0.884 | +| (0.85, 60) | 0.00682919 | 0.856171 | 0.863 | [0.013 0.137 0. 0.85 ] | 0.00356046 | 0.921861 | 0.925422 | [0.013 0.987] | 0.863 | +| (0.85, 61) | 0.0141476 | 0.853852 | 0.868 | [0.019 0.131 0.001 0.849] | 0.00671517 | 0.921154 | 0.927869 | [0.02 0.98] | 0.868 | +| (0.85, 62) | 0.0187097 | 0.85029 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0105429 | 0.917911 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 63) | 0.00948473 | 0.851515 | 0.861 | [0.013 0.137 0.002 0.848] | 0.00445686 | 0.919794 | 0.924251 | [0.015 0.985] | 0.861 | +| (0.85, 64) | 0.0145757 | 0.854424 | 0.869 | [0.02 0.13 0.001 0.849] | 0.00689778 | 0.921478 | 0.928376 | [0.021 0.979] | 0.869 | +| (0.85, 65) | 0.00214502 | 0.869145 | 0.867 | [0.02 0.13 0.003 0.847] | 0.00168839 | 0.928891 | 0.927203 | [0.023 0.977] | 0.867 | +| (0.85, 66) | 0.0170768 | 0.846923 | 0.864 | [0.014 0.136 0. 0.85 ] | 0.00957014 | 0.916356 | 0.925926 | [0.014 0.986] | 0.864 | +| (0.85, 67) | 0.0227065 | 0.850294 | 0.873 | [0.026 0.124 0.003 0.847] | 0.0130022 | 0.917256 | 0.930258 | [0.029 0.971] | 0.873 | +| (0.85, 68) | 0.0145889 | 0.860411 | 0.875 | [0.026 0.124 0.001 0.849] | 0.00836658 | 0.923065 | 0.931432 | [0.027 0.973] | 0.875 | +| (0.85, 69) | 0.0364083 | 0.831592 | 0.868 | [0.02 0.13 0.002 0.848] | 0.019737 | 0.908053 | 0.92779 | [0.022 0.978] | 0.868 | +| (0.85, 70) | 0.0242443 | 0.847756 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.0123723 | 0.917606 | 0.929978 | [0.022 0.978] | 0.872 | +| (0.85, 71) | 0.00301728 | 0.867983 | 0.871 | [0.022 0.128 0.001 0.849] | 0.000460011 | 0.928932 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 72) | 0.027528 | 0.846472 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.0146329 | 0.916364 | 0.930997 | [0.024 0.976] | 0.874 | +| (0.85, 73) | 0.00503926 | 0.868961 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00273599 | 0.928261 | 0.930997 | [0.024 0.976] | 0.874 | +| (0.85, 74) | 0.00180717 | 0.867807 | 0.866 | [0.017 0.133 0.001 0.849] | 0.00122006 | 0.928076 | 0.926856 | [0.018 0.982] | 0.866 | +| (0.85, 75) | 0.0113285 | 0.863672 | 0.875 | [0.029 0.121 0.004 0.846] | 0.00642303 | 0.924782 | 0.931205 | [0.033 0.967] | 0.875 | +| (0.85, 76) | 0.0245245 | 0.853476 | 0.878 | [0.028 0.122 0. 0.85 ] | 0.0126659 | 0.920375 | 0.933041 | [0.028 0.972] | 0.878 | +| (0.85, 77) | 0.0227488 | 0.846251 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.0117367 | 0.916718 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 78) | 0.00669301 | 0.875693 | 0.869 | [0.019 0.131 0. 0.85 ] | 0.00422216 | 0.932677 | 0.928454 | [0.019 0.981] | 0.869 | +| (0.85, 79) | 0.00956554 | 0.856434 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.00474536 | 0.92219 | 0.926936 | [0.016 0.984] | 0.866 | +| (0.85, 80) | 0.00108528 | 0.872915 | 0.874 | [0.025 0.125 0.001 0.849] | 0.000293925 | 0.930627 | 0.930921 | [0.026 0.974] | 0.874 | +| (0.85, 81) | 0.0139251 | 0.857075 | 0.871 | [0.022 0.128 0.001 0.849] | 0.00655939 | 0.922833 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 82) | 0.0224209 | 0.851579 | 0.874 | [0.026 0.124 0.002 0.848] | 0.0118784 | 0.918967 | 0.930845 | [0.028 0.972] | 0.874 | +| (0.85, 83) | 0.0234424 | 0.843558 | 0.867 | [0.017 0.133 0. 0.85 ] | 0.0127119 | 0.914729 | 0.927441 | [0.017 0.983] | 0.867 | +| (0.85, 84) | 0.0127184 | 0.859282 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00659153 | 0.923387 | 0.929978 | [0.022 0.978] | 0.872 | +| (0.85, 85) | 0.0139557 | 0.858044 | 0.872 | [0.023 0.127 0.001 0.849] | 0.0076882 | 0.922213 | 0.929901 | [0.024 0.976] | 0.872 | +| (0.85, 86) | 0.00034068 | 0.870341 | 0.87 | [0.02 0.13 0. 0.85] | 0.001697 | 0.930659 | 0.928962 | [0.02 0.98] | 0.87 | +| (0.85, 87) | 0.0113094 | 0.862691 | 0.874 | [0.024 0.126 0. 0.85 ] | 0.00628484 | 0.924712 | 0.930997 | [0.024 0.976] | 0.874 | +| (0.85, 88) | 0.0272791 | 0.838721 | 0.866 | [0.016 0.134 0. 0.85 ] | 0.015109 | 0.911827 | 0.926936 | [0.016 0.984] | 0.866 | +| (0.85, 89) | 0.014482 | 0.892482 | 0.878 | [0.028 0.122 0. 0.85 ] | 0.00856509 | 0.941606 | 0.933041 | [0.028 0.972] | 0.878 | +| (0.85, 90) | 0.0142999 | 0.8577 | 0.872 | [0.022 0.128 0. 0.85 ] | 0.00755693 | 0.922421 | 0.929978 | [0.022 0.978] | 0.872 | +| (0.85, 91) | 0.0421117 | 0.827888 | 0.87 | [0.02 0.13 0. 0.85] | 0.0233165 | 0.905645 | 0.928962 | [0.02 0.98] | 0.87 | +| (0.85, 92) | 0.0311295 | 0.84587 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0160312 | 0.916498 | 0.932529 | [0.027 0.973] | 0.877 | +| (0.85, 93) | 0.0141491 | 0.848851 | 0.863 | [0.015 0.135 0.002 0.848] | 0.00791631 | 0.917343 | 0.925259 | [0.017 0.983] | 0.863 | +| (0.85, 94) | 0.0200191 | 0.851981 | 0.872 | [0.024 0.126 0.002 0.848] | 0.0103975 | 0.919427 | 0.929825 | [0.026 0.974] | 0.872 | +| (0.85, 95) | 0.0261024 | 0.844898 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.014257 | 0.915213 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 96) | 0.0153376 | 0.855662 | 0.871 | [0.021 0.129 0. 0.85 ] | 0.0082263 | 0.921243 | 0.92947 | [0.021 0.979] | 0.871 | +| (0.85, 97) | 0.0226085 | 0.845392 | 0.868 | [0.019 0.131 0.001 0.849] | 0.0117309 | 0.916138 | 0.927869 | [0.02 0.98] | 0.868 | +| (0.85, 98) | 0.0373645 | 0.833636 | 0.871 | [0.022 0.128 0.001 0.849] | 0.0202228 | 0.90917 | 0.929392 | [0.023 0.977] | 0.871 | +| (0.85, 99) | 0.0195995 | 0.8574 | 0.877 | [0.027 0.123 0. 0.85 ] | 0.0100396 | 0.922489 | 0.932529 | [0.027 0.973] | 0.877 | +| (0.9, 0) | 0.0301308 | 0.880869 | 0.911 | [0.012 0.088 0.001 0.899] | 0.0161845 | 0.936651 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 1) | 0.00656327 | 0.919563 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00410703 | 0.958002 | 0.953895 | [0.013 0.987] | 0.913 | +| (0.9, 2) | 0.0144244 | 0.896576 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00736751 | 0.945468 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 3) | 0.0273018 | 0.891698 | 0.919 | [0.02 0.08 0.001 0.899] | 0.0150242 | 0.941868 | 0.956892 | [0.021 0.979] | 0.919 | +| (0.9, 4) | 0.0168992 | 0.899101 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.0085499 | 0.946864 | 0.955414 | [0.016 0.984] | 0.916 | +| (0.9, 5) | 0.00223583 | 0.908764 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00063496 | 0.9522 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 6) | 0.00952175 | 0.904478 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00494578 | 0.949455 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 7) | 0.014533 | 0.899467 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0074706 | 0.94693 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 8) | 0.01088 | 0.90212 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00584198 | 0.948053 | 0.953895 | [0.013 0.987] | 0.913 | +| (0.9, 9) | 0.00749745 | 0.899503 | 0.907 | [0.008 0.092 0.001 0.899] | 0.00393881 | 0.946881 | 0.95082 | [0.009 0.991] | 0.907 | +| (0.9, 10) | 0.0218657 | 0.890134 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.0115157 | 0.941874 | 0.95339 | [0.012 0.988] | 0.912 | +| (0.9, 11) | 0.00199519 | 0.915005 | 0.917 | [0.018 0.082 0.001 0.899] | 0.0011787 | 0.954696 | 0.955875 | [0.019 0.981] | 0.917 | +| (0.9, 12) | 0.00284903 | 0.924849 | 0.922 | [0.022 0.078 0. 0.9 ] | 0.00168534 | 0.960152 | 0.958466 | [0.022 0.978] | 0.922 | +| (0.9, 13) | 0.00074074 | 0.910741 | 0.91 | [0.012 0.088 0.002 0.898] | 0.000999632 | 0.95328 | 0.95228 | [0.014 0.986] | 0.91 | +| (0.9, 14) | 0.00416334 | 0.911837 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.00221976 | 0.953194 | 0.955414 | [0.016 0.984] | 0.916 | +| (0.9, 15) | 0.0169214 | 0.897079 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00901792 | 0.945383 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 16) | 0.0165161 | 0.900484 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00913514 | 0.946786 | 0.955921 | [0.017 0.983] | 0.917 | +| (0.9, 17) | 0.00866541 | 0.909335 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00399855 | 0.952384 | 0.956383 | [0.02 0.98] | 0.918 | +| (0.9, 18) | 0.011116 | 0.929116 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00686953 | 0.963253 | 0.956383 | [0.02 0.98] | 0.918 | +| (0.9, 19) | 0.00628126 | 0.906719 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00329276 | 0.950602 | 0.953895 | [0.013 0.987] | 0.913 | +| (0.9, 20) | 0.00487117 | 0.909129 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00222355 | 0.952129 | 0.954352 | [0.016 0.984] | 0.914 | +| (0.9, 21) | 0.00958671 | 0.897413 | 0.907 | [0.008 0.092 0.001 0.899] | 0.0050069 | 0.945813 | 0.95082 | [0.009 0.991] | 0.907 | +| (0.9, 22) | 0.00296476 | 0.912035 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00143269 | 0.953427 | 0.954859 | [0.017 0.983] | 0.915 | +| (0.9, 23) | 0.00900789 | 0.907992 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00414382 | 0.951778 | 0.955921 | [0.017 0.983] | 0.917 | +| (0.9, 24) | 0.00113461 | 0.924135 | 0.923 | [0.024 0.076 0.001 0.899] | 0.00144543 | 0.960379 | 0.958933 | [0.025 0.975] | 0.923 | +| (0.9, 25) | 0.0173475 | 0.895653 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00891637 | 0.94493 | 0.953846 | [0.015 0.985] | 0.913 | +| (0.9, 26) | 0.000654213 | 0.909346 | 0.91 | [0.011 0.089 0.001 0.899] | 0.000174876 | 0.952156 | 0.952331 | [0.012 0.988] | 0.91 | +| (0.9, 27) | 0.00520818 | 0.908792 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00248187 | 0.951919 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 28) | 0.00428414 | 0.917284 | 0.913 | [0.015 0.085 0.002 0.898] | 0.00242611 | 0.956223 | 0.953797 | [0.017 0.983] | 0.913 | +| (0.9, 29) | 0.00155474 | 0.922555 | 0.921 | [0.021 0.079 0. 0.9 ] | 0.00142622 | 0.959383 | 0.957956 | [0.021 0.979] | 0.921 | +| (0.9, 30) | 0.015075 | 0.898925 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00843417 | 0.945918 | 0.954352 | [0.016 0.984] | 0.914 | +| (0.9, 31) | 0.00664218 | 0.922642 | 0.916 | [0.017 0.083 0.001 0.899] | 0.00408139 | 0.959448 | 0.955367 | [0.018 0.982] | 0.916 | +| (0.9, 32) | 0.00409654 | 0.908903 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00195499 | 0.951891 | 0.953846 | [0.015 0.985] | 0.913 | +| (0.9, 33) | 0.0100621 | 0.903938 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00487939 | 0.949521 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 34) | 0.00666714 | 0.909333 | 0.916 | [0.018 0.082 0.002 0.898] | 0.00354481 | 0.951774 | 0.955319 | [0.02 0.98] | 0.916 | +| (0.9, 35) | 0.00700774 | 0.904992 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00362466 | 0.949716 | 0.95334 | [0.014 0.986] | 0.912 | +| (0.9, 36) | 0.00340552 | 0.913594 | 0.917 | [0.017 0.083 0. 0.9 ] | 0.00179529 | 0.954126 | 0.955921 | [0.017 0.983] | 0.917 | +| (0.9, 37) | 0.00453426 | 0.910466 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00190253 | 0.952957 | 0.954859 | [0.017 0.983] | 0.915 | +| (0.9, 38) | 0.00414505 | 0.916145 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00284759 | 0.956237 | 0.95339 | [0.012 0.988] | 0.912 | +| (0.9, 39) | 0.00205744 | 0.918943 | 0.921 | [0.021 0.079 0. 0.9 ] | 0.000530198 | 0.957426 | 0.957956 | [0.021 0.979] | 0.921 | +| (0.9, 40) | 0.00382936 | 0.915171 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00180558 | 0.955132 | 0.956938 | [0.019 0.981] | 0.919 | +| (0.9, 41) | 0.00497108 | 0.904029 | 0.909 | [0.011 0.089 0.002 0.898] | 0.00217952 | 0.949596 | 0.951775 | [0.013 0.987] | 0.909 | +| (0.9, 42) | 0.0112273 | 0.901773 | 0.913 | [0.013 0.087 0. 0.9 ] | 0.00593944 | 0.947956 | 0.953895 | [0.013 0.987] | 0.913 | +| (0.9, 43) | 0.0170636 | 0.892936 | 0.91 | [0.01 0.09 0. 0.9 ] | 0.00909284 | 0.943288 | 0.952381 | [0.01 0.99] | 0.91 | +| (0.9, 44) | 0.0162799 | 0.89772 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00884155 | 0.945511 | 0.954352 | [0.016 0.984] | 0.914 | +| (0.9, 45) | 0.00426367 | 0.922264 | 0.918 | [0.018 0.082 0. 0.9 ] | 0.00234195 | 0.958771 | 0.956429 | [0.018 0.982] | 0.918 | +| (0.9, 46) | 0.00151459 | 0.918485 | 0.92 | [0.02 0.08 0. 0.9 ] | 0.000635751 | 0.956811 | 0.957447 | [0.02 0.98] | 0.92 | +| (0.9, 47) | 0.0134137 | 0.897586 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00688702 | 0.945948 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 48) | 0.0125112 | 0.902489 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00641199 | 0.948495 | 0.954907 | [0.015 0.985] | 0.915 | +| (0.9, 49) | 0.00639305 | 0.914393 | 0.908 | [0.008 0.092 0. 0.9 ] | 0.00362208 | 0.954996 | 0.951374 | [0.008 0.992] | 0.908 | +| (0.9, 50) | 0.00774922 | 0.906251 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0041113 | 0.95029 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 51) | 0.0122481 | 0.898752 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00615901 | 0.946676 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 52) | 0.0058903 | 0.90511 | 0.911 | [0.011 0.089 0. 0.9 ] | 0.00296234 | 0.949923 | 0.952885 | [0.011 0.989] | 0.911 | +| (0.9, 53) | 0.00697218 | 0.912028 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.0035033 | 0.953435 | 0.956938 | [0.019 0.981] | 0.919 | +| (0.9, 54) | 0.00297486 | 0.912025 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00088718 | 0.953972 | 0.954859 | [0.017 0.983] | 0.915 | +| (0.9, 55) | 0.0137413 | 0.898259 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00730465 | 0.946085 | 0.95339 | [0.012 0.988] | 0.912 | +| (0.9, 56) | 0.0064383 | 0.918438 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00363441 | 0.957024 | 0.95339 | [0.012 0.988] | 0.912 | +| (0.9, 57) | 0.00642233 | 0.902578 | 0.909 | [0.012 0.088 0.003 0.897] | 0.00295656 | 0.948768 | 0.951724 | [0.015 0.985] | 0.909 | +| (0.9, 58) | 0.0107958 | 0.920796 | 0.91 | [0.011 0.089 0.001 0.899] | 0.00590905 | 0.95824 | 0.952331 | [0.012 0.988] | 0.91 | +| (0.9, 59) | 0.000578272 | 0.908422 | 0.909 | [0.009 0.091 0. 0.9 ] | 0.000168299 | 0.951709 | 0.951877 | [0.009 0.991] | 0.909 | +| (0.9, 60) | 0.0124924 | 0.906508 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00642065 | 0.950517 | 0.956938 | [0.019 0.981] | 0.919 | +| (0.9, 61) | 0.0155213 | 0.894479 | 0.91 | [0.012 0.088 0.002 0.898] | 0.00832079 | 0.943959 | 0.95228 | [0.014 0.986] | 0.91 | +| (0.9, 62) | 0.0116735 | 0.899327 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00587826 | 0.946957 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 63) | 0.00207603 | 0.911924 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.0011058 | 0.953295 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 64) | 0.000689676 | 0.91331 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.000139088 | 0.954262 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 65) | 0.014441 | 0.900559 | 0.915 | [0.016 0.084 0.001 0.899] | 0.00718125 | 0.947678 | 0.954859 | [0.017 0.983] | 0.915 | +| (0.9, 66) | 0.0176499 | 0.89435 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00954539 | 0.943795 | 0.95334 | [0.014 0.986] | 0.912 | +| (0.9, 67) | 0.00501621 | 0.908984 | 0.914 | [0.014 0.086 0. 0.9 ] | 0.00243601 | 0.951965 | 0.954401 | [0.014 0.986] | 0.914 | +| (0.9, 68) | 0.00606039 | 0.90894 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00265232 | 0.952255 | 0.954907 | [0.015 0.985] | 0.915 | +| (0.9, 69) | 0.0197135 | 0.898286 | 0.918 | [0.021 0.079 0.003 0.897] | 0.0098805 | 0.946409 | 0.95629 | [0.024 0.976] | 0.918 | +| (0.9, 70) | 0.0116814 | 0.900319 | 0.912 | [0.012 0.088 0. 0.9 ] | 0.00584883 | 0.947541 | 0.95339 | [0.012 0.988] | 0.912 | +| (0.9, 71) | 0.00793232 | 0.899068 | 0.907 | [0.008 0.092 0.001 0.899] | 0.00400617 | 0.946813 | 0.95082 | [0.009 0.991] | 0.907 | +| (0.9, 72) | 0.00787936 | 0.909121 | 0.917 | [0.018 0.082 0.001 0.899] | 0.00347757 | 0.952397 | 0.955875 | [0.019 0.981] | 0.917 | +| (0.9, 73) | 0.0128338 | 0.901166 | 0.914 | [0.015 0.085 0.001 0.899] | 0.00633845 | 0.948014 | 0.954352 | [0.016 0.984] | 0.914 | +| (0.9, 74) | 0.000822019 | 0.912822 | 0.912 | [0.013 0.087 0.001 0.899] | 0.00056394 | 0.953904 | 0.95334 | [0.014 0.986] | 0.912 | +| (0.9, 75) | 0.00670409 | 0.917704 | 0.911 | [0.011 0.089 0. 0.9 ] | 0.00386719 | 0.956752 | 0.952885 | [0.011 0.989] | 0.911 | +| (0.9, 76) | 0.0180635 | 0.894936 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00958846 | 0.944258 | 0.953846 | [0.015 0.985] | 0.913 | +| (0.9, 77) | 0.0148393 | 0.900161 | 0.915 | [0.017 0.083 0.002 0.898] | 0.00809086 | 0.94672 | 0.954811 | [0.019 0.981] | 0.915 | +| (0.9, 78) | 0.00479696 | 0.903203 | 0.908 | [0.01 0.09 0.002 0.898] | 0.00213129 | 0.94914 | 0.951271 | [0.012 0.988] | 0.908 | +| (0.9, 79) | 0.00734862 | 0.903651 | 0.911 | [0.012 0.088 0.001 0.899] | 0.00346162 | 0.949374 | 0.952835 | [0.013 0.987] | 0.911 | +| (0.9, 80) | 0.00871425 | 0.906286 | 0.915 | [0.015 0.085 0. 0.9 ] | 0.00469793 | 0.950209 | 0.954907 | [0.015 0.985] | 0.915 | +| (0.9, 81) | 0.0288783 | 0.884122 | 0.913 | [0.014 0.086 0.001 0.899] | 0.0153488 | 0.938497 | 0.953846 | [0.015 0.985] | 0.913 | +| (0.9, 82) | 0.00636029 | 0.90364 | 0.91 | [0.011 0.089 0.001 0.899] | 0.00336515 | 0.948965 | 0.952331 | [0.012 0.988] | 0.91 | +| (0.9, 83) | 0.0398032 | 0.876197 | 0.916 | [0.016 0.084 0. 0.9 ] | 0.0216551 | 0.933759 | 0.955414 | [0.016 0.984] | 0.916 | +| (0.9, 84) | 0.00975212 | 0.903248 | 0.913 | [0.015 0.085 0.002 0.898] | 0.00525826 | 0.948539 | 0.953797 | [0.017 0.983] | 0.913 | +| (0.9, 85) | 0.00793273 | 0.920933 | 0.913 | [0.014 0.086 0.001 0.899] | 0.00455126 | 0.958397 | 0.953846 | [0.015 0.985] | 0.913 | +| (0.9, 86) | 0.0174908 | 0.886509 | 0.904 | [0.006 0.094 0.002 0.898] | 0.00949516 | 0.939765 | 0.94926 | [0.008 0.992] | 0.904 | +| (0.9, 87) | 0.0180295 | 0.893971 | 0.912 | [0.014 0.086 0.002 0.898] | 0.00968631 | 0.943605 | 0.953291 | [0.016 0.984] | 0.912 | +| (0.9, 88) | 0.00443638 | 0.909564 | 0.914 | [0.017 0.083 0.003 0.897] | 0.00168449 | 0.952571 | 0.954255 | [0.02 0.98] | 0.914 | +| (0.9, 89) | 0.0109881 | 0.907012 | 0.918 | [0.019 0.081 0.001 0.899] | 0.00541001 | 0.950973 | 0.956383 | [0.02 0.98] | 0.918 | +| (0.9, 90) | 0.00504521 | 0.910045 | 0.905 | [0.006 0.094 0.001 0.899] | 0.00289806 | 0.952713 | 0.949815 | [0.007 0.993] | 0.905 | +| (0.9, 91) | 0.00880224 | 0.910198 | 0.919 | [0.02 0.08 0.001 0.899] | 0.00480602 | 0.952086 | 0.956892 | [0.021 0.979] | 0.919 | +| (0.9, 92) | 0.00684515 | 0.916155 | 0.923 | [0.023 0.077 0. 0.9 ] | 0.00299495 | 0.955982 | 0.958977 | [0.023 0.977] | 0.923 | +| (0.9, 93) | 0.00277936 | 0.916221 | 0.919 | [0.019 0.081 0. 0.9 ] | 0.00155942 | 0.955378 | 0.956938 | [0.019 0.981] | 0.919 | +| (0.9, 94) | 0.00155124 | 0.907449 | 0.909 | [0.009 0.091 0. 0.9 ] | 0.000742878 | 0.951134 | 0.951877 | [0.009 0.991] | 0.909 | +| (0.9, 95) | 0.0102061 | 0.926206 | 0.916 | [0.017 0.083 0.001 0.899] | 0.00570406 | 0.961071 | 0.955367 | [0.018 0.982] | 0.916 | +| (0.9, 96) | 0.00659766 | 0.923598 | 0.917 | [0.018 0.082 0.001 0.899] | 0.0037311 | 0.959606 | 0.955875 | [0.019 0.981] | 0.917 | +| (0.9, 97) | 0.0133374 | 0.903663 | 0.917 | [0.018 0.082 0.001 0.899] | 0.00714786 | 0.948727 | 0.955875 | [0.019 0.981] | 0.917 | +| (0.9, 98) | 0.0268155 | 0.890185 | 0.917 | [0.019 0.081 0.002 0.898] | 0.0139259 | 0.941902 | 0.955828 | [0.021 0.979] | 0.917 | +| (0.9, 99) | 0.0100532 | 0.893947 | 0.904 | [0.006 0.094 0.002 0.898] | 0.00525952 | 0.944001 | 0.94926 | [0.008 0.992] | 0.904 | +| (0.95, 0) | 0.0130864 | 0.969086 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00681747 | 0.98416 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 1) | 0.0112369 | 0.945763 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00590501 | 0.971941 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 2) | 0.000913955 | 0.959086 | 0.96 | [0.01 0.04 0. 0.95] | 0.000476725 | 0.978905 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 3) | 0.00282916 | 0.957829 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00161856 | 0.978458 | 0.97684 | [0.007 0.993] | 0.955 | +| (0.95, 4) | 0.00252074 | 0.956479 | 0.959 | [0.01 0.04 0.001 0.949] | 0.00124336 | 0.977612 | 0.978855 | [0.011 0.989] | 0.959 | +| (0.95, 5) | 0.00253113 | 0.957469 | 0.96 | [0.01 0.04 0. 0.95] | 0.00138245 | 0.977999 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 6) | 0.00451495 | 0.951485 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00222678 | 0.975139 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 7) | 0.00417619 | 0.955824 | 0.96 | [0.01 0.04 0. 0.95] | 0.00218906 | 0.977192 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 8) | 0.00512566 | 0.950874 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00255172 | 0.974815 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 9) | 0.00543415 | 0.946566 | 0.952 | [0.005 0.045 0.003 0.947] | 0.00273372 | 0.972549 | 0.975283 | [0.008 0.992] | 0.952 | +| (0.95, 10) | 0.00479567 | 0.960796 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00253629 | 0.979879 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 11) | 0.0059848 | 0.953015 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00308922 | 0.975788 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 12) | 0.0142771 | 0.971277 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00746109 | 0.98533 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 13) | 0.00276339 | 0.956237 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00140162 | 0.977475 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 14) | 0.00478025 | 0.95022 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00252338 | 0.974317 | 0.97684 | [0.007 0.993] | 0.955 | +| (0.95, 15) | 0.0046799 | 0.94932 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00238639 | 0.973951 | 0.976337 | [0.006 0.994] | 0.954 | +| (0.95, 16) | 0.00442474 | 0.955425 | 0.951 | [0.003 0.047 0.002 0.948] | 0.00232928 | 0.977136 | 0.974807 | [0.005 0.995] | 0.951 | +| (0.95, 17) | 0.0114726 | 0.945527 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00597459 | 0.971872 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 18) | 0.00567983 | 0.95132 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00293726 | 0.974909 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 19) | 0.0147145 | 0.939286 | 0.954 | [0.004 0.046 0. 0.95 ] | 0.0076719 | 0.96869 | 0.976362 | [0.004 0.996] | 0.954 | +| (0.95, 20) | 0.0086796 | 0.94732 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00446135 | 0.972905 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 21) | 0.00869415 | 0.951306 | 0.96 | [0.011 0.039 0.001 0.949] | 0.00446133 | 0.974899 | 0.97936 | [0.012 0.988] | 0.96 | +| (0.95, 22) | 0.00115897 | 0.956159 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.000685432 | 0.977549 | 0.976864 | [0.005 0.995] | 0.955 | +| (0.95, 23) | 0.0135314 | 0.950469 | 0.964 | [0.014 0.036 0. 0.95 ] | 0.00711114 | 0.974294 | 0.981405 | [0.014 0.986] | 0.964 | +| (0.95, 24) | 0.000263541 | 0.955736 | 0.956 | [0.007 0.043 0.001 0.949] | 2.1201e-05 | 0.977364 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 25) | 0.0147964 | 0.942204 | 0.957 | [0.01 0.04 0.003 0.947] | 0.00755989 | 0.970241 | 0.977801 | [0.013 0.987] | 0.957 | +| (0.95, 26) | 0.00568312 | 0.951317 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00294096 | 0.974928 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 27) | 0.00402375 | 0.954976 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00190956 | 0.976967 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 28) | 0.00112943 | 0.957129 | 0.956 | [0.008 0.042 0.002 0.948] | 0.000543759 | 0.977863 | 0.97732 | [0.01 0.99] | 0.956 | +| (0.95, 29) | 0.0129274 | 0.950073 | 0.963 | [0.014 0.036 0.001 0.949] | 0.00676198 | 0.974117 | 0.980879 | [0.015 0.985] | 0.963 | +| (0.95, 30) | 0.00401177 | 0.962012 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00225901 | 0.980632 | 0.978373 | [0.008 0.992] | 0.958 | +| (0.95, 31) | 0.0084593 | 0.948541 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00432719 | 0.973519 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 32) | 0.00247352 | 0.959474 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00135301 | 0.979199 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 33) | 0.00287278 | 0.957127 | 0.96 | [0.01 0.04 0. 0.95] | 0.00148084 | 0.977901 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 34) | 0.00586271 | 0.951137 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00297376 | 0.974896 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 35) | 0.00212307 | 0.953877 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00111809 | 0.976225 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 36) | 0.00105894 | 0.961059 | 0.96 | [0.01 0.04 0. 0.95] | 0.000574108 | 0.979956 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 37) | 0.0105679 | 0.943432 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00545768 | 0.97088 | 0.976337 | [0.006 0.994] | 0.954 | +| (0.95, 38) | 0.005027 | 0.963027 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00259891 | 0.980972 | 0.978373 | [0.008 0.992] | 0.958 | +| (0.95, 39) | 0.00677662 | 0.950223 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00342102 | 0.974448 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 40) | 0.0132115 | 0.968212 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00686927 | 0.983709 | 0.97684 | [0.007 0.993] | 0.955 | +| (0.95, 41) | 0.012049 | 0.944951 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00625242 | 0.971594 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 42) | 0.00143589 | 0.960436 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000937985 | 0.979815 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 43) | 0.00898241 | 0.952018 | 0.961 | [0.011 0.039 0. 0.95 ] | 0.00471306 | 0.975173 | 0.979887 | [0.011 0.989] | 0.961 | +| (0.95, 44) | 0.00919265 | 0.947807 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.00470138 | 0.973168 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 45) | 0.00175008 | 0.95325 | 0.955 | [0.01 0.04 0.005 0.945] | 0.000737709 | 0.976006 | 0.976744 | [0.015 0.985] | 0.955 | +| (0.95, 46) | 0.00710272 | 0.958103 | 0.951 | [0.005 0.045 0.004 0.946] | 0.00365538 | 0.978411 | 0.974755 | [0.009 0.991] | 0.951 | +| (0.95, 47) | 0.00418356 | 0.955816 | 0.96 | [0.01 0.04 0. 0.95] | 0.00214877 | 0.977233 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 48) | 0.00453778 | 0.960538 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.0023711 | 0.979737 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 49) | 0.0149231 | 0.972923 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00778307 | 0.986134 | 0.978351 | [0.01 0.99] | 0.958 | +| (0.95, 50) | 7.40124e-05 | 0.954926 | 0.955 | [0.005 0.045 0. 0.95 ] | 7.84744e-05 | 0.976942 | 0.976864 | [0.005 0.995] | 0.955 | +| (0.95, 51) | 0.003751 | 0.955751 | 0.952 | [0.002 0.048 0. 0.95 ] | 0.00201513 | 0.977374 | 0.975359 | [0.002 0.998] | 0.952 | +| (0.95, 52) | 0.00456641 | 0.947434 | 0.952 | [0.005 0.045 0.003 0.947] | 0.00227595 | 0.973007 | 0.975283 | [0.008 0.992] | 0.952 | +| (0.95, 53) | 0.00276139 | 0.955239 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00132328 | 0.977027 | 0.978351 | [0.01 0.99] | 0.958 | +| (0.95, 54) | 0.0164937 | 0.941506 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00857274 | 0.9698 | 0.978373 | [0.008 0.992] | 0.958 | +| (0.95, 55) | 0.00856963 | 0.94243 | 0.951 | [0.002 0.048 0.001 0.949] | 0.00451159 | 0.970321 | 0.974833 | [0.003 0.997] | 0.951 | +| (0.95, 56) | 0.00327117 | 0.958271 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.00173844 | 0.978602 | 0.976864 | [0.005 0.995] | 0.955 | +| (0.95, 57) | 0.007871 | 0.944129 | 0.952 | [0.002 0.048 0. 0.95 ] | 0.00409843 | 0.971261 | 0.975359 | [0.002 0.998] | 0.952 | +| (0.95, 58) | 0.000601234 | 0.956601 | 0.956 | [0.008 0.042 0.002 0.948] | 0.000499576 | 0.977819 | 0.97732 | [0.01 0.99] | 0.956 | +| (0.95, 59) | 0.0032114 | 0.959211 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00173182 | 0.979075 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 60) | 0.0155299 | 0.93947 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00804949 | 0.96879 | 0.97684 | [0.007 0.993] | 0.955 | +| (0.95, 61) | 0.0318797 | 0.92512 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0168799 | 0.960944 | 0.977824 | [0.011 0.989] | 0.957 | +| (0.95, 62) | 0.00587987 | 0.96288 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0030603 | 0.980884 | 0.977824 | [0.011 0.989] | 0.957 | +| (0.95, 63) | 0.00842203 | 0.947578 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00443652 | 0.97293 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 64) | 0.00752341 | 0.967523 | 0.96 | [0.01 0.04 0. 0.95] | 0.00394008 | 0.983322 | 0.979381 | [0.01 0.99] | 0.96 | +| (0.95, 65) | 0.000204061 | 0.950204 | 0.95 | [0.005 0.045 0.005 0.945] | 0.000158767 | 0.974386 | 0.974227 | [0.01 0.99] | 0.95 | +| (0.95, 66) | 0.0121659 | 0.945834 | 0.958 | [0.01 0.04 0.002 0.948] | 0.00618715 | 0.972141 | 0.978328 | [0.012 0.988] | 0.958 | +| (0.95, 67) | 0.00567601 | 0.952324 | 0.958 | [0.009 0.041 0.001 0.949] | 0.00277065 | 0.97558 | 0.978351 | [0.01 0.99] | 0.958 | +| (0.95, 68) | 0.000954144 | 0.958046 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000375038 | 0.978502 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 69) | 0.00060172 | 0.957398 | 0.958 | [0.009 0.041 0.001 0.949] | 0.000289562 | 0.978061 | 0.978351 | [0.01 0.99] | 0.958 | +| (0.95, 70) | 0.00426978 | 0.96327 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00227676 | 0.981154 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 71) | 0.019713 | 0.937287 | 0.957 | [0.009 0.041 0.002 0.948] | 0.0104357 | 0.967388 | 0.977824 | [0.011 0.989] | 0.957 | +| (0.95, 72) | 0.00756189 | 0.949438 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00387345 | 0.973973 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 73) | 0.00469595 | 0.951304 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00241727 | 0.974926 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 74) | 0.00317693 | 0.956177 | 0.953 | [0.005 0.045 0.002 0.948] | 0.00168466 | 0.977495 | 0.975811 | [0.007 0.993] | 0.953 | +| (0.95, 75) | 0.00994606 | 0.968946 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.0053118 | 0.984189 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 76) | 0.00516192 | 0.959162 | 0.954 | [0.006 0.044 0.002 0.948] | 0.00269651 | 0.97901 | 0.976313 | [0.008 0.992] | 0.954 | +| (0.95, 77) | 0.000941681 | 0.955058 | 0.956 | [0.007 0.043 0.001 0.949] | 0.000330332 | 0.977013 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 78) | 0.0185866 | 0.980587 | 0.962 | [0.014 0.036 0.002 0.948] | 0.00965988 | 0.990011 | 0.980352 | [0.016 0.984] | 0.962 | +| (0.95, 79) | 0.00133473 | 0.959335 | 0.958 | [0.009 0.041 0.001 0.949] | 0.000654896 | 0.979005 | 0.978351 | [0.01 0.99] | 0.958 | +| (0.95, 80) | 0.00956923 | 0.944431 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00491612 | 0.971421 | 0.976337 | [0.006 0.994] | 0.954 | +| (0.95, 81) | 0.00636464 | 0.950635 | 0.957 | [0.008 0.042 0.001 0.949] | 0.00326916 | 0.974577 | 0.977846 | [0.009 0.991] | 0.957 | +| (0.95, 82) | 0.000789837 | 0.95721 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00023548 | 0.978137 | 0.978373 | [0.008 0.992] | 0.958 | +| (0.95, 83) | 0.0029409 | 0.961941 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.00171311 | 0.98059 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 84) | 0.00884236 | 0.946158 | 0.955 | [0.005 0.045 0. 0.95 ] | 0.00453154 | 0.972332 | 0.976864 | [0.005 0.995] | 0.955 | +| (0.95, 85) | 0.000941489 | 0.958941 | 0.958 | [0.01 0.04 0.002 0.948] | 0.000520676 | 0.978849 | 0.978328 | [0.012 0.988] | 0.958 | +| (0.95, 86) | 0.0140395 | 0.93996 | 0.954 | [0.006 0.044 0.002 0.948] | 0.00744394 | 0.968869 | 0.976313 | [0.008 0.992] | 0.954 | +| (0.95, 87) | 0.00377863 | 0.961779 | 0.958 | [0.008 0.042 0. 0.95 ] | 0.00200204 | 0.980375 | 0.978373 | [0.008 0.992] | 0.958 | +| (0.95, 88) | 0.000211864 | 0.959212 | 0.959 | [0.009 0.041 0. 0.95 ] | 0.000207292 | 0.979084 | 0.978877 | [0.009 0.991] | 0.959 | +| (0.95, 89) | 0.0037105 | 0.952289 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00180449 | 0.975562 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 90) | 0.000369451 | 0.955631 | 0.956 | [0.006 0.044 0. 0.95 ] | 5.4309e-05 | 0.977312 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 91) | 0.00221093 | 0.958211 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00129289 | 0.978659 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 92) | 0.00325314 | 0.958253 | 0.955 | [0.006 0.044 0.001 0.949] | 0.00182254 | 0.978662 | 0.97684 | [0.007 0.993] | 0.955 | +| (0.95, 93) | 0.0021091 | 0.959109 | 0.957 | [0.007 0.043 0. 0.95 ] | 0.0011343 | 0.979004 | 0.977869 | [0.007 0.993] | 0.957 | +| (0.95, 94) | 0.0031392 | 0.952861 | 0.956 | [0.006 0.044 0. 0.95 ] | 0.00151907 | 0.975847 | 0.977366 | [0.006 0.994] | 0.956 | +| (0.95, 95) | 0.0117605 | 0.965761 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00613714 | 0.982475 | 0.976337 | [0.006 0.994] | 0.954 | +| (0.95, 96) | 0.0128302 | 0.94117 | 0.954 | [0.005 0.045 0.001 0.949] | 0.00671104 | 0.969626 | 0.976337 | [0.006 0.994] | 0.954 | +| (0.95, 97) | 0.00541577 | 0.955584 | 0.961 | [0.011 0.039 0. 0.95 ] | 0.00261709 | 0.977269 | 0.979887 | [0.011 0.989] | 0.961 | +| (0.95, 98) | 0.0028626 | 0.953137 | 0.956 | [0.007 0.043 0.001 0.949] | 0.00149962 | 0.975843 | 0.977343 | [0.008 0.992] | 0.956 | +| (0.95, 99) | 0.00686275 | 0.949137 | 0.956 | [0.007 0.043 0.001 0.949] | 0.0035354 | 0.973808 | 0.977343 | [0.008 0.992] | 0.956 | +| (1.0, 0) | 0.00038766 | 0.999612 | 1 | [0. 0. 0. 1.] | 0.000193868 | 0.999806 | 1 | [0. 1.] | 1 | +| (1.0, 1) | 0.000768513 | 0.997231 | 0.998 | [0. 0. 0.002 0.998] | 0.000387314 | 0.998612 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 2) | 0.0110783 | 0.985922 | 0.997 | [0. 0. 0.003 0.997] | 0.00558684 | 0.992911 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 3) | 0.00656081 | 0.990439 | 0.997 | [0. 0. 0.003 0.997] | 0.00330234 | 0.995195 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 4) | 0.00261978 | 0.99538 | 0.998 | [0. 0. 0.002 0.998] | 0.00131429 | 0.997685 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 5) | 0.00163194 | 0.998368 | 1 | [0. 0. 0. 1.] | 0.000816636 | 0.999183 | 1 | [0. 1.] | 1 | +| (1.0, 6) | 0.00407542 | 0.995925 | 1 | [0. 0. 0. 1.] | 0.00204187 | 0.997958 | 1 | [0. 1.] | 1 | +| (1.0, 7) | 0.010548 | 0.987452 | 0.998 | [0. 0. 0.002 0.998] | 0.0053126 | 0.993686 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 8) | 0.00242359 | 0.996576 | 0.999 | [0. 0. 0.001 0.999] | 0.00121665 | 0.998283 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 9) | 0.00321056 | 0.995789 | 0.999 | [0. 0. 0.001 0.999] | 0.00160947 | 0.99789 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 10) | 0.00323781 | 0.996762 | 1 | [0. 0. 0. 1.] | 0.00162153 | 0.998378 | 1 | [0. 1.] | 1 | +| (1.0, 11) | 0.000783789 | 0.999216 | 1 | [0. 0. 0. 1.] | 0.000392048 | 0.999608 | 1 | [0. 1.] | 1 | +| (1.0, 12) | 0.00695127 | 0.993049 | 1 | [0. 0. 0. 1.] | 0.00348776 | 0.996512 | 1 | [0. 1.] | 1 | +| (1.0, 13) | 0.00228645 | 0.995714 | 0.998 | [0. 0. 0.002 0.998] | 0.00114683 | 0.997852 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 14) | 0.0112308 | 0.988769 | 1 | [0. 0. 0. 1.] | 0.00564713 | 0.994353 | 1 | [0. 1.] | 1 | +| (1.0, 15) | 0.012585 | 0.984415 | 0.997 | [0. 0. 0.003 0.997] | 0.00635148 | 0.992146 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 16) | 0.00115313 | 0.998847 | 1 | [0. 0. 0. 1.] | 0.000576897 | 0.999423 | 1 | [0. 1.] | 1 | +| (1.0, 17) | 0.00564588 | 0.993354 | 0.999 | [0. 0. 0.001 0.999] | 0.00283377 | 0.996666 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 18) | 0.00350382 | 0.995496 | 0.999 | [0. 0. 0.001 0.999] | 0.00175679 | 0.997743 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 19) | 0.00979927 | 0.990201 | 1 | [0. 0. 0. 1.] | 0.00492376 | 0.995076 | 1 | [0. 1.] | 1 | +| (1.0, 20) | 0.00602819 | 0.991972 | 0.998 | [0. 0. 0.002 0.998] | 0.00302928 | 0.99597 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 21) | 0.00330174 | 0.995698 | 0.999 | [0. 0. 0.001 0.999] | 0.00165551 | 0.997844 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 22) | 0.00237333 | 0.996627 | 0.999 | [0. 0. 0.001 0.999] | 0.00118927 | 0.99831 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 23) | 0.00497561 | 0.994024 | 0.999 | [0. 0. 0.001 0.999] | 0.00249651 | 0.997003 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 24) | 0.0045159 | 0.995484 | 1 | [0. 0. 0. 1.] | 0.00226306 | 0.997737 | 1 | [0. 1.] | 1 | +| (1.0, 25) | 0.00159816 | 0.999598 | 0.998 | [0. 0. 0.002 0.998] | 0.000799555 | 0.999799 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 26) | 0.00282877 | 0.996171 | 0.999 | [0. 0. 0.001 0.999] | 0.00142084 | 0.998079 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 27) | 0.0055852 | 0.992415 | 0.998 | [0. 0. 0.002 0.998] | 0.00280651 | 0.996192 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 28) | 0.00522598 | 0.994774 | 1 | [0. 0. 0. 1.] | 0.00261984 | 0.99738 | 1 | [0. 1.] | 1 | +| (1.0, 29) | 0.00351762 | 0.995482 | 0.999 | [0. 0. 0.001 0.999] | 0.00176397 | 0.997736 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 30) | 0.00530586 | 0.993694 | 0.999 | [0. 0. 0.001 0.999] | 0.00266265 | 0.996837 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 31) | 3.04145e-05 | 0.99897 | 0.999 | [0. 0. 0.001 0.999] | 1.59575e-05 | 0.999484 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 32) | 0.00892355 | 0.990076 | 0.999 | [0. 0. 0.001 0.999] | 0.00448628 | 0.995013 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 33) | 0.00909471 | 0.990905 | 1 | [0. 0. 0. 1.] | 0.00456814 | 0.995432 | 1 | [0. 1.] | 1 | +| (1.0, 34) | 0.00174823 | 0.997252 | 0.999 | [0. 0. 0.001 0.999] | 0.000876536 | 0.998623 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 35) | 0.00144624 | 0.996554 | 0.998 | [0. 0. 0.002 0.998] | 0.000725097 | 0.998274 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 36) | 0.0104275 | 0.989573 | 1 | [0. 0. 0. 1.] | 0.00524108 | 0.994759 | 1 | [0. 1.] | 1 | +| (1.0, 37) | 0.00680342 | 0.992197 | 0.999 | [0. 0. 0.001 0.999] | 0.00341675 | 0.996083 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 38) | 0.0005654 | 0.998565 | 0.998 | [0. 0. 0.002 0.998] | 0.000281429 | 0.99928 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 39) | 0.0103369 | 0.988663 | 0.999 | [0. 0. 0.001 0.999] | 0.0052012 | 0.994299 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 40) | 0.00464786 | 0.994352 | 0.999 | [0. 0. 0.001 0.999] | 0.00233173 | 0.997168 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 41) | 0.00555467 | 0.993445 | 0.999 | [0. 0. 0.001 0.999] | 0.00278786 | 0.996712 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 42) | 0.000538905 | 0.999461 | 1 | [0. 0. 0. 1.] | 0.000269525 | 0.99973 | 1 | [0. 1.] | 1 | +| (1.0, 43) | 0.000103208 | 0.999897 | 1 | [0. 0. 0. 1.] | 5.16066e-05 | 0.999948 | 1 | [0. 1.] | 1 | +| (1.0, 44) | 0.000659831 | 0.99834 | 0.999 | [0. 0. 0.001 0.999] | 0.00033133 | 0.999168 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 45) | 0.00772108 | 0.991279 | 0.999 | [0. 0. 0.001 0.999] | 0.00388279 | 0.995617 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 46) | 0.0105663 | 0.986434 | 0.997 | [0. 0. 0.003 0.997] | 0.00532721 | 0.993171 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 47) | 0.00475672 | 0.995243 | 1 | [0. 0. 0. 1.] | 0.00238403 | 0.997616 | 1 | [0. 1.] | 1 | +| (1.0, 48) | 0.00098032 | 0.99902 | 1 | [0. 0. 0. 1.] | 0.0004904 | 0.99951 | 1 | [0. 1.] | 1 | +| (1.0, 49) | 0.00485684 | 0.995143 | 1 | [0. 0. 0. 1.] | 0.00243433 | 0.997566 | 1 | [0. 1.] | 1 | +| (1.0, 50) | 0.000364171 | 0.999636 | 1 | [0. 0. 0. 1.] | 0.000182119 | 0.999818 | 1 | [0. 1.] | 1 | +| (1.0, 51) | 0.00901725 | 0.989983 | 0.999 | [0. 0. 0.001 0.999] | 0.00453359 | 0.994966 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 52) | 0.00401398 | 0.995986 | 1 | [0. 0. 0. 1.] | 0.00201102 | 0.997989 | 1 | [0. 1.] | 1 | +| (1.0, 53) | 6.02743e-05 | 0.99994 | 1 | [0. 0. 0. 1.] | 3.0138e-05 | 0.99997 | 1 | [0. 1.] | 1 | +| (1.0, 54) | 0.00525217 | 0.993748 | 0.999 | [0. 0. 0.001 0.999] | 0.00263592 | 0.996864 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 55) | 0.00586705 | 0.992133 | 0.998 | [0. 0. 0.002 0.998] | 0.00294815 | 0.996051 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 56) | 0.0047068 | 0.993293 | 0.998 | [0. 0. 0.002 0.998] | 0.00236369 | 0.996635 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 57) | 0.000832389 | 0.999168 | 1 | [0. 0. 0. 1.] | 0.000416368 | 0.999584 | 1 | [0. 1.] | 1 | +| (1.0, 58) | 0.00219301 | 0.997807 | 1 | [0. 0. 0. 1.] | 0.00109771 | 0.998902 | 1 | [0. 1.] | 1 | +| (1.0, 59) | 0.00652092 | 0.991479 | 0.998 | [0. 0. 0.002 0.998] | 0.0032777 | 0.995721 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 60) | 0.00533726 | 0.991663 | 0.997 | [0. 0. 0.003 0.997] | 0.0026845 | 0.995813 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 61) | 0.00188364 | 0.997116 | 0.999 | [0. 0. 0.001 0.999] | 0.000945307 | 0.998554 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 62) | 0.00960516 | 0.990395 | 1 | [0. 0. 0. 1.] | 0.00482576 | 0.995174 | 1 | [0. 1.] | 1 | +| (1.0, 63) | 0.000144585 | 0.999855 | 1 | [0. 0. 0. 1.] | 7.22978e-05 | 0.999928 | 1 | [0. 1.] | 1 | +| (1.0, 64) | 0.0125929 | 0.984407 | 0.997 | [0. 0. 0.003 0.997] | 0.00635546 | 0.992142 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 65) | 0.00338717 | 0.995613 | 0.999 | [0. 0. 0.001 0.999] | 0.00169816 | 0.997802 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 66) | 0.00079801 | 0.998202 | 0.999 | [0. 0. 0.001 0.999] | 0.000401174 | 0.999099 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 67) | 0.00224744 | 0.997753 | 1 | [0. 0. 0. 1.] | 0.00112499 | 0.998875 | 1 | [0. 1.] | 1 | +| (1.0, 68) | 0.00487772 | 0.995122 | 1 | [0. 0. 0. 1.] | 0.00244482 | 0.997555 | 1 | [0. 1.] | 1 | +| (1.0, 69) | 0.000371242 | 0.999629 | 1 | [0. 0. 0. 1.] | 0.000185655 | 0.999814 | 1 | [0. 1.] | 1 | +| (1.0, 70) | 0.00206798 | 0.997932 | 1 | [0. 0. 0. 1.] | 0.00103506 | 0.998965 | 1 | [0. 1.] | 1 | +| (1.0, 71) | 0.000710046 | 0.99929 | 1 | [0. 0. 0. 1.] | 0.000355149 | 0.999645 | 1 | [0. 1.] | 1 | +| (1.0, 72) | 0.00335647 | 0.993644 | 0.997 | [0. 0. 0.003 0.997] | 0.00168683 | 0.996811 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 73) | 0.00405536 | 0.994945 | 0.999 | [0. 0. 0.001 0.999] | 0.00203386 | 0.997466 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 74) | 0.00439108 | 0.995609 | 1 | [0. 0. 0. 1.] | 0.00220037 | 0.9978 | 1 | [0. 1.] | 1 | +| (1.0, 75) | 0.000376378 | 0.999624 | 1 | [0. 0. 0. 1.] | 0.000188225 | 0.999812 | 1 | [0. 1.] | 1 | +| (1.0, 76) | 2.2775e-05 | 0.999977 | 1 | [0. 0. 0. 1.] | 1.13876e-05 | 0.999989 | 1 | [0. 1.] | 1 | +| (1.0, 77) | 0.000470373 | 0.99953 | 1 | [0. 0. 0. 1.] | 0.000235242 | 0.999765 | 1 | [0. 1.] | 1 | +| (1.0, 78) | 0.000160133 | 0.99984 | 1 | [0. 0. 0. 1.] | 8.00731e-05 | 0.99992 | 1 | [0. 1.] | 1 | +| (1.0, 79) | 0.00207087 | 0.997929 | 1 | [0. 0. 0. 1.] | 0.00103651 | 0.998963 | 1 | [0. 1.] | 1 | +| (1.0, 80) | 0.00810417 | 0.989896 | 0.998 | [0. 0. 0.002 0.998] | 0.00407733 | 0.994922 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 81) | 0.00617284 | 0.993827 | 1 | [0. 0. 0. 1.] | 0.00309598 | 0.996904 | 1 | [0. 1.] | 1 | +| (1.0, 82) | 0.000753367 | 0.999247 | 1 | [0. 0. 0. 1.] | 0.000376825 | 0.999623 | 1 | [0. 1.] | 1 | +| (1.0, 83) | 0.00409922 | 0.995901 | 1 | [0. 0. 0. 1.] | 0.00205382 | 0.997946 | 1 | [0. 1.] | 1 | +| (1.0, 84) | 0.00782572 | 0.990174 | 0.998 | [0. 0. 0.002 0.998] | 0.00393612 | 0.995063 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 85) | 0.0045099 | 0.99349 | 0.998 | [0. 0. 0.002 0.998] | 0.0022646 | 0.996734 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 86) | 0.00363821 | 0.994362 | 0.998 | [0. 0. 0.002 0.998] | 0.00182623 | 0.997173 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 87) | 0.00481349 | 0.995187 | 1 | [0. 0. 0. 1.] | 0.00241255 | 0.997587 | 1 | [0. 1.] | 1 | +| (1.0, 88) | 0.00433078 | 0.995669 | 1 | [0. 0. 0. 1.] | 0.00217009 | 0.99783 | 1 | [0. 1.] | 1 | +| (1.0, 89) | 0.00589255 | 0.992107 | 0.998 | [0. 0. 0.002 0.998] | 0.00296091 | 0.996038 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 90) | 0.000687943 | 0.999312 | 1 | [0. 0. 0. 1.] | 0.00034409 | 0.999656 | 1 | [0. 1.] | 1 | +| (1.0, 91) | 0.00126096 | 0.998261 | 0.997 | [0. 0. 0.003 0.997] | 0.000629455 | 0.999127 | 0.998498 | [0.003 0.997] | 0.997 | +| (1.0, 92) | 0.0047099 | 0.99429 | 0.999 | [0. 0. 0.001 0.999] | 0.00236294 | 0.997137 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 93) | 0.00124108 | 0.997759 | 0.999 | [0. 0. 0.001 0.999] | 0.000621546 | 0.998878 | 0.9995 | [0.001 0.999] | 0.999 | +| (1.0, 94) | 0.00111707 | 0.996883 | 0.998 | [0. 0. 0.002 0.998] | 0.00055997 | 0.998439 | 0.998999 | [0.002 0.998] | 0.998 | +| (1.0, 95) | 2.16117e-05 | 0.999978 | 1 | [0. 0. 0. 1.] | 1.0806e-05 | 0.999989 | 1 | [0. 1.] | 1 | +| (1.0, 96) | 0.00580863 | 0.994191 | 1 | [0. 0. 0. 1.] | 0.00291278 | 0.997087 | 1 | [0. 1.] | 1 | +| (1.0, 97) | 0.00636452 | 0.993635 | 1 | [0. 0. 0. 1.] | 0.00319242 | 0.996808 | 1 | [0. 1.] | 1 | +| (1.0, 98) | 0.00321332 | 0.996787 | 1 | [0. 0. 0. 1.] | 0.00160925 | 0.998391 | 1 | [0. 1.] | 1 | | (1.0, 99) | 0.00562354 | 0.992376 | 0.998 | [0. 0. 0.002 0.998] | 0.00282537 | 0.996174 | 0.998999 | [0.002 0.998] | 0.998 | \ No newline at end of file diff --git a/tests/test_data.py b/tests/test_data.py index d8e6b3c..53c6d30 100644 --- a/tests/test_data.py +++ b/tests/test_data.py @@ -1,225 +1,225 @@ -import pytest -from quacc.data import ExClassManager as ECM, ExtendedCollection -import numpy as np -import scipy.sparse as sp - - -class TestExClassManager: - @pytest.mark.parametrize( - "true_class,pred_class,result", - [ - (0, 0, 0), - (0, 1, 1), - (1, 0, 2), - (1, 1, 3), - ], - ) - def test_get_ex(self, true_class, pred_class, result): - ncl = 2 - assert ECM.get_ex(ncl, true_class, pred_class) == result - - @pytest.mark.parametrize( - "ex_class,result", - [ - (0, 0), - (1, 1), - (2, 0), - (3, 1), - ], - ) - def test_get_pred(self, ex_class, result): - ncl = 2 - assert ECM.get_pred(ncl, ex_class) == result - - @pytest.mark.parametrize( - "ex_class,result", - [ - (0, 0), - (1, 0), - (2, 1), - (3, 1), - ], - ) - def test_get_true(self, ex_class, result): - ncl = 2 - assert ECM.get_true(ncl, ex_class) == result - - -class TestExtendedCollection: - @pytest.mark.parametrize( - "instances,result", - [ - ( - np.asarray( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - [np.asarray([1, 3]), np.asarray([0, 2])], - ), - ( - sp.csr_matrix( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - [np.asarray([1, 3]), np.asarray([0, 2])], - ), - ( - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - [np.asarray([], dtype=int), np.asarray([0, 1])], - ), - ( - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - [np.asarray([], dtype=int), np.asarray([0, 1])], - ), - ( - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - [np.asarray([0, 1]), np.asarray([], dtype=int)], - ), - ( - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - [np.asarray([0, 1]), np.asarray([], dtype=int)], - ), - ], - ) - def test__split_index_by_pred(self, instances, result): - ncl = 2 - assert all( - np.array_equal(a, b) - for (a, b) in zip( - ExtendedCollection._split_index_by_pred(ncl, instances), - result, - ) - ) - - @pytest.mark.parametrize( - "instances,s_inst,norms", - [ - ( - np.asarray( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - [ - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - ], - [0.5, 0.5], - ), - ( - sp.csr_matrix( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - [ - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - ], - [0.5, 0.5], - ), - ( - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - [ - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([], dtype=int), - ], - [1.0, 0.0], - ), - ( - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - [ - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - sp.csr_matrix([], dtype=int), - ], - [1.0, 0.0], - ), - ( - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - [ - np.asarray([], dtype=int), - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - ], - [0.0, 1.0], - ), - ( - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - [ - sp.csr_matrix([], dtype=int), - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - ], - [0.0, 1.0], - ), - ], - ) - def test_split_inst_by_pred(self, instances, s_inst, norms): - ncl = 2 - _s_inst, _norms = ExtendedCollection.split_inst_by_pred(ncl, instances) - if isinstance(s_inst, np.ndarray): - assert all(np.array_equal(a, b) for (a, b) in zip(_s_inst, s_inst)) - if isinstance(s_inst, sp.csr_matrix): - assert all((a != b).nnz == 0 for (a, b) in zip(_s_inst, s_inst)) - assert all(a == b for (a, b) in zip(_norms, norms)) - - @pytest.mark.parametrize( - "instances,labels,inst0,lbl0,inst1,lbl1", - [ - ( - np.asarray( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - np.asarray([3, 0, 1, 2]), - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 1]), - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([1, 0]), - ), - ( - sp.csr_matrix( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - np.asarray([3, 0, 1, 2]), - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 1]), - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([1, 0]), - ), - ( - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([3, 1]), - np.asarray([], dtype=int), - np.asarray([], dtype=int), - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([1, 0]), - ), - ( - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([3, 1]), - sp.csr_matrix(np.empty((0, 0), dtype=int)), - np.asarray([], dtype=int), - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([1, 0]), - ), - ( - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 2]), - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 1]), - np.asarray([], dtype=int), - np.asarray([], dtype=int), - ), - ( - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 2]), - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0, 1]), - sp.csr_matrix(np.empty((0, 0), dtype=int)), - np.asarray([], dtype=int), - ), - ], - ) - def test_split_by_pred(self, instances, labels, inst0, lbl0, inst1, lbl1): - ec = ExtendedCollection(instances, labels, classes=range(0, 4)) - [ec0, ec1] = ec.split_by_pred() - if isinstance(instances, np.ndarray): - assert np.array_equal(ec0.X, inst0) - assert np.array_equal(ec1.X, inst1) - if isinstance(instances, sp.csr_matrix): - assert (ec0.X != inst0).nnz == 0 - assert (ec1.X != inst1).nnz == 0 - assert np.array_equal(ec0.y, lbl0) - assert np.array_equal(ec1.y, lbl1) +import pytest +from quacc.data import ExClassManager as ECM, ExtendedCollection +import numpy as np +import scipy.sparse as sp + + +class TestExClassManager: + @pytest.mark.parametrize( + "true_class,pred_class,result", + [ + (0, 0, 0), + (0, 1, 1), + (1, 0, 2), + (1, 1, 3), + ], + ) + def test_get_ex(self, true_class, pred_class, result): + ncl = 2 + assert ECM.get_ex(ncl, true_class, pred_class) == result + + @pytest.mark.parametrize( + "ex_class,result", + [ + (0, 0), + (1, 1), + (2, 0), + (3, 1), + ], + ) + def test_get_pred(self, ex_class, result): + ncl = 2 + assert ECM.get_pred(ncl, ex_class) == result + + @pytest.mark.parametrize( + "ex_class,result", + [ + (0, 0), + (1, 0), + (2, 1), + (3, 1), + ], + ) + def test_get_true(self, ex_class, result): + ncl = 2 + assert ECM.get_true(ncl, ex_class) == result + + +class TestExtendedCollection: + @pytest.mark.parametrize( + "instances,result", + [ + ( + np.asarray( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + [np.asarray([1, 3]), np.asarray([0, 2])], + ), + ( + sp.csr_matrix( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + [np.asarray([1, 3]), np.asarray([0, 2])], + ), + ( + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + [np.asarray([], dtype=int), np.asarray([0, 1])], + ), + ( + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + [np.asarray([], dtype=int), np.asarray([0, 1])], + ), + ( + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + [np.asarray([0, 1]), np.asarray([], dtype=int)], + ), + ( + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + [np.asarray([0, 1]), np.asarray([], dtype=int)], + ), + ], + ) + def test__split_index_by_pred(self, instances, result): + ncl = 2 + assert all( + np.array_equal(a, b) + for (a, b) in zip( + ExtendedCollection._split_index_by_pred(ncl, instances), + result, + ) + ) + + @pytest.mark.parametrize( + "instances,s_inst,norms", + [ + ( + np.asarray( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + [ + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + ], + [0.5, 0.5], + ), + ( + sp.csr_matrix( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + [ + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + ], + [0.5, 0.5], + ), + ( + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + [ + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([], dtype=int), + ], + [1.0, 0.0], + ), + ( + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + [ + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + sp.csr_matrix([], dtype=int), + ], + [1.0, 0.0], + ), + ( + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + [ + np.asarray([], dtype=int), + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + ], + [0.0, 1.0], + ), + ( + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + [ + sp.csr_matrix([], dtype=int), + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + ], + [0.0, 1.0], + ), + ], + ) + def test_split_inst_by_pred(self, instances, s_inst, norms): + ncl = 2 + _s_inst, _norms = ExtendedCollection.split_inst_by_pred(ncl, instances) + if isinstance(s_inst, np.ndarray): + assert all(np.array_equal(a, b) for (a, b) in zip(_s_inst, s_inst)) + if isinstance(s_inst, sp.csr_matrix): + assert all((a != b).nnz == 0 for (a, b) in zip(_s_inst, s_inst)) + assert all(a == b for (a, b) in zip(_norms, norms)) + + @pytest.mark.parametrize( + "instances,labels,inst0,lbl0,inst1,lbl1", + [ + ( + np.asarray( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + np.asarray([3, 0, 1, 2]), + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 1]), + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([1, 0]), + ), + ( + sp.csr_matrix( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + np.asarray([3, 0, 1, 2]), + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 1]), + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([1, 0]), + ), + ( + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([3, 1]), + np.asarray([], dtype=int), + np.asarray([], dtype=int), + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([1, 0]), + ), + ( + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([3, 1]), + sp.csr_matrix(np.empty((0, 0), dtype=int)), + np.asarray([], dtype=int), + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([1, 0]), + ), + ( + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 2]), + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 1]), + np.asarray([], dtype=int), + np.asarray([], dtype=int), + ), + ( + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 2]), + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0, 1]), + sp.csr_matrix(np.empty((0, 0), dtype=int)), + np.asarray([], dtype=int), + ), + ], + ) + def test_split_by_pred(self, instances, labels, inst0, lbl0, inst1, lbl1): + ec = ExtendedCollection(instances, labels, classes=range(0, 4)) + [ec0, ec1] = ec.split_by_pred() + if isinstance(instances, np.ndarray): + assert np.array_equal(ec0.X, inst0) + assert np.array_equal(ec1.X, inst1) + if isinstance(instances, sp.csr_matrix): + assert (ec0.X != inst0).nnz == 0 + assert (ec1.X != inst1).nnz == 0 + assert np.array_equal(ec0.y, lbl0) + assert np.array_equal(ec1.y, lbl1) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index b3ffda5..9b2a72a 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,3 +1,3 @@ - -class TestDataset: + +class TestDataset: pass \ No newline at end of file diff --git a/tests/test_evaluation/test_baseline.py b/tests/test_evaluation/test_baseline.py index 20fac98..eab79c2 100644 --- a/tests/test_evaluation/test_baseline.py +++ b/tests/test_evaluation/test_baseline.py @@ -1,12 +1,12 @@ -from sklearn.linear_model import LogisticRegression - -from quacc.dataset import Dataset -from quacc.evaluation.baseline import kfcv - - -class TestBaseline: - def test_kfcv(self): - spambase = Dataset("spambase", n_prevalences=1).get_raw() - c_model = LogisticRegression() - c_model.fit(spambase.train.X, spambase.train.y) - assert "f1_score" in kfcv(c_model, spambase.validation) +from sklearn.linear_model import LogisticRegression + +from quacc.dataset import Dataset +from quacc.evaluation.baseline import kfcv + + +class TestBaseline: + def test_kfcv(self): + spambase = Dataset("spambase", n_prevalences=1).get_raw() + c_model = LogisticRegression() + c_model.fit(spambase.train.X, spambase.train.y) + assert "f1_score" in kfcv(c_model, spambase.validation) diff --git a/tests/test_method/test_base/test_BQAE.py b/tests/test_method/test_base/test_BQAE.py index f28c71b..426b08f 100644 --- a/tests/test_method/test_base/test_BQAE.py +++ b/tests/test_method/test_base/test_BQAE.py @@ -1,66 +1,66 @@ -import numpy as np -import pytest -import scipy.sparse as sp -from sklearn.linear_model import LogisticRegression - -from quacc.method.base import BinaryQuantifierAccuracyEstimator - - -class TestBQAE: - @pytest.mark.parametrize( - "instances,preds0,preds1,result", - [ - ( - np.asarray( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.15, 0.2, 0.35, 0.3]), - ), - ( - sp.csr_matrix( - [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] - ), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.15, 0.2, 0.35, 0.3]), - ), - ( - np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.0, 0.4, 0.0, 0.6]), - ), - ( - sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.0, 0.4, 0.0, 0.6]), - ), - ( - np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.3, 0.0, 0.7, 0.0]), - ), - ( - sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), - np.asarray([0.3, 0.7]), - np.asarray([0.4, 0.6]), - np.asarray([0.3, 0.0, 0.7, 0.0]), - ), - ], - ) - def test_estimate_ndarray(self, mocker, instances, preds0, preds1, result): - estimator = BinaryQuantifierAccuracyEstimator(LogisticRegression()) - estimator.n_classes = 4 - with mocker.patch.object(estimator.q_model_0, "quantify"), mocker.patch.object( - estimator.q_model_1, "quantify" - ): - estimator.q_model_0.quantify.return_value = preds0 - estimator.q_model_1.quantify.return_value = preds1 - assert np.array_equal( - estimator.estimate(instances, ext=True), - result, - ) +import numpy as np +import pytest +import scipy.sparse as sp +from sklearn.linear_model import LogisticRegression + +from quacc.method.base import BinaryQuantifierAccuracyEstimator + + +class TestBQAE: + @pytest.mark.parametrize( + "instances,preds0,preds1,result", + [ + ( + np.asarray( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.15, 0.2, 0.35, 0.3]), + ), + ( + sp.csr_matrix( + [[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]] + ), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.15, 0.2, 0.35, 0.3]), + ), + ( + np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.0, 0.4, 0.0, 0.6]), + ), + ( + sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.0, 0.4, 0.0, 0.6]), + ), + ( + np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.3, 0.0, 0.7, 0.0]), + ), + ( + sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]), + np.asarray([0.3, 0.7]), + np.asarray([0.4, 0.6]), + np.asarray([0.3, 0.0, 0.7, 0.0]), + ), + ], + ) + def test_estimate_ndarray(self, mocker, instances, preds0, preds1, result): + estimator = BinaryQuantifierAccuracyEstimator(LogisticRegression()) + estimator.n_classes = 4 + with mocker.patch.object(estimator.q_model_0, "quantify"), mocker.patch.object( + estimator.q_model_1, "quantify" + ): + estimator.q_model_0.quantify.return_value = preds0 + estimator.q_model_1.quantify.return_value = preds1 + assert np.array_equal( + estimator.estimate(instances, ext=True), + result, + ) diff --git a/tests/test_method/test_base/test_MCAE.py b/tests/test_method/test_base/test_MCAE.py index b0784a2..a6cae5e 100644 --- a/tests/test_method/test_base/test_MCAE.py +++ b/tests/test_method/test_base/test_MCAE.py @@ -1,2 +1,2 @@ -class TestMCAE: - pass +class TestMCAE: + pass