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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.svm import LinearSVC
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from fgsld.fgsld_quantifiers import FakeFGLSD
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from method.aggregative import EMQ, CC
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import quapy as qp
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qp.environ['SAMPLE_SIZE'] = 500
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dataset = qp.datasets.fetch_reviews('kindle')
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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training = dataset.training
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test = dataset.test
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cls = CalibratedClassifierCV(LinearSVC())
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method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
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for model, model_name in [
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(CC(cls), 'CC'),
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# (FakeFGLSD(cls, nbins=5, isomerous=False, recompute_bins=False), 'FGSLD-isometric-stat-5'),
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(FakeFGLSD(cls, nbins=5, isomerous=True, recompute_bins=True), 'FGSLD-isometric-dyn-5'),
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# (FakeFGLSD(cls, nbins=5, isomerous=True, recompute_bins=False), 'FGSLD-isomerous-stat-5'),
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# (FakeFGLSD(cls, nbins=10, isomerous=True, recompute_bins=True), 'FGSLD-isomerous-dyn-10'),
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#(FakeFGLSD(cls, nbins=5, isomerous=False), 'FGSLD-5'),
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#(FakeFGLSD(cls, nbins=10, isomerous=False), 'FGSLD-10'),
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#(FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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#(FakeFGLSD(cls, nbins=100, isomerous=False), 'FGSLD-100'),
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# (FakeFGLSD(cls, nbins=1, isomerous=False), 'FGSLD-1'),
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#(FakeFGLSD(cls, nbins=10, isomerous=True), 'FGSLD-10-ISO'),
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# (FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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(EMQ(cls), 'SLD'),
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]:
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print('running ', model_name)
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model.fit(training)
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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
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model, test, qp.environ['SAMPLE_SIZE'], n_repetitions=5, n_prevpoints=11, n_jobs=-1
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)
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method_names.append(model_name)
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true_prevs.append(true_prev)
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estim_prevs.append(estim_prev)
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tr_prevs.append(training.prevalence())
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, train_prev=tr_prevs[0], savepath='./plot_fglsd.png')
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import numpy as np
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from metrics import isomerous_bins, isometric_bins
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from em import History, get_measures_single_history
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from sklearn.model_selection import cross_val_predict
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import math
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class FineGrainedSLD:
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def __init__(self, x_tr, x_te, y_tr, tr_priors, clf, n_bins=10):
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self.y_tr = y_tr
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self.clf = clf
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self.tr_priors = tr_priors
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self.te_preds = clf.predict_proba(x_te)
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self.tr_preds = cross_val_predict(clf, x_tr, y_tr, method='predict_proba', n_jobs=10)
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self.n_bins = n_bins
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self.history: [History] = []
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self.multi_class = False
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def run(self, isomerous_binning, epsilon=1e-6, compute_bins_at_every_iter=True, return_posteriors_hist=False):
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"""
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Run the FGSLD algorithm.
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:param isomerous_binning: whether to use isomerous or isometric binning.
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:param epsilon: stopping condition.
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:param compute_bins_at_every_iter: whether FGSLD should recompute the posterior bins at every iteration or not.
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:param return_posteriors_hist: whether to return posteriors at every iteration or not.
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:return: If `return_posteriors_hist` is true, the returned posteriors will be a list of numpy arrays, else a single numpy array with posteriors at last iteration.
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"""
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smoothing_tr = 1 / (2 * self.tr_preds.shape[0])
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smoothing_te = 1 / (2 * self.te_preds.shape[0])
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s = 0
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tr_bin_priors = np.zeros((self.n_bins, self.tr_preds.shape[1]), dtype=np.float)
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te_bin_priors = np.zeros((self.n_bins, self.te_preds.shape[1]), dtype=np.float)
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tr_bins = self.__create_bins(training=True, isomerous_binning=isomerous_binning)
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te_bins = self.__create_bins(training=False, isomerous_binning=isomerous_binning)
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self.__compute_bins_priors(tr_bin_priors, self.tr_preds, tr_bins, smoothing_tr)
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val = 2 * epsilon
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if return_posteriors_hist:
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posteriors_hist = [self.te_preds.copy()]
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while not val < epsilon and s < 1000:
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assert np.all(np.around(self.te_preds.sum(axis=1), 4) == 1), f"Probabilities do not sum to 1:\ns={s}, " \
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f"probs={self.te_preds.sum(axis=1)}"
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if compute_bins_at_every_iter:
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te_bins = self.__create_bins(training=False, isomerous_binning=isomerous_binning)
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if s == 0:
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te_bin_priors_prev = tr_bin_priors.copy()
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else:
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te_bin_priors_prev = te_bin_priors.copy()
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self.__compute_bins_priors(te_bin_priors, self.te_preds, te_bins, smoothing_te)
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te_preds_cp = self.te_preds.copy()
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for label_idx, bins in te_bins.items():
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for i, bin_ in enumerate(bins):
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if bin_.shape[0] == 0:
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continue
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te = te_bin_priors[i][label_idx]
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tr = tr_bin_priors[i][label_idx]
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# local_min = (math.floor(tr * 10) / 10)
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# local_max = local_min + .1
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# trans = lambda l: min(max((l - local_min) / 1, 0), 1)
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trans = lambda l: l
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self.te_preds[:, label_idx][bin_] = (te_preds_cp[:, label_idx][bin_]) * \
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(trans(te) / trans(tr))
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# Normalization step
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self.te_preds = (self.te_preds / self.te_preds.sum(axis=1, keepdims=True))
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val = 0
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for label_idx in range(te_bin_priors.shape[1]):
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temp = max(abs((te_bin_priors[:, label_idx] / te_bin_priors_prev[:, label_idx]) - 1))
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if temp > val:
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val = temp
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s += 1
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if return_posteriors_hist:
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posteriors_hist.append(self.te_preds.copy())
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if return_posteriors_hist:
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return self.te_preds.mean(axis=0), posteriors_hist
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return self.te_preds.mean(axis=0), self.te_preds
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def __compute_bins_priors(self, bin_priors_placeholder, posteriors, bins, smoothing):
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for label_idx, bins in bins.items():
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for i, bin_ in enumerate(bins):
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if bin_.shape[0] == 0:
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bin_priors_placeholder[i, label_idx] = smoothing
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continue
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numerator = posteriors[:, label_idx][bin_].mean()
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bin_prior = (numerator + smoothing) / (1 + self.n_bins * smoothing) # normalize priors
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bin_priors_placeholder[i, label_idx] = bin_prior
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def __find_bin_idx(self, label_bins: [np.array], idx: int or list):
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if hasattr(idx, '__len__'):
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idxs = np.zeros(len(idx), dtype=np.int)
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for i, bin_ in enumerate(label_bins):
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for j, id_ in enumerate(idx):
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if id_ in bin_:
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idxs[j] = i
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return idxs
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else:
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for i, bin_ in enumerate(label_bins):
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if idx in bin_:
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return i
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def __create_bins(self, training: bool, isomerous_binning: bool):
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bins = {}
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preds = self.tr_preds if training else self.te_preds
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if isomerous_binning:
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for label_idx in range(preds.shape[1]):
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bins[label_idx] = isomerous_bins(label_idx, preds, self.n_bins)
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else:
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intervals = np.linspace(0., 1., num=self.n_bins, endpoint=False)
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for label_idx in range(preds.shape[1]):
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bins_ = isometric_bins(label_idx, preds, intervals, 0.1)
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bins[label_idx] = [bins_[i] for i in intervals]
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return bins
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import quapy as qp
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import numpy as np
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from os import makedirs
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import sys, os
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import pickle
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from experiments import result_path
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from tabular import Table
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import argparse
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tables_path = './tables'
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MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results
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makedirs(tables_path, exist_ok=True)
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sample_size = 100
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qp.environ['SAMPLE_SIZE'] = sample_size
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nice = {
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'mae':'AE',
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'mrae':'RAE',
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'ae':'AE',
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'rae':'RAE',
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'svmkld': 'SVM(KLD)',
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'svmnkld': 'SVM(NKLD)',
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'svmq': 'SVM(Q)',
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'svmae': 'SVM(AE)',
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'svmnae': 'SVM(NAE)',
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'svmmae': 'SVM(AE)',
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'svmmrae': 'SVM(RAE)',
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'quanet': 'QuaNet',
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'hdy': 'HDy',
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'hdysld': 'HDy-SLD',
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'dys': 'DyS',
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'svmperf':'',
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'sanders': 'Sanders',
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'semeval13': 'SemEval13',
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'semeval14': 'SemEval14',
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'semeval15': 'SemEval15',
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'semeval16': 'SemEval16',
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'Average': 'Average'
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}
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def save_table(path, table):
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print(f'saving results in {path}')
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with open(path, 'wt') as foo:
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foo.write(table)
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def experiment_errors(path, dataset, method, loss):
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path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss)
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if os.path.exists(path):
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true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
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err_fn = getattr(qp.error, loss)
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errors = err_fn(true_prevs, estim_prevs)
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return errors
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return None
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def nicerm(key):
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return '\mathrm{'+nice[key]+'}'
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Generate tables for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str,
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help='path to the directory containing the results of the methods tested in Gao & Sebastiani')
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parser.add_argument('newresults', metavar='RESULT_PATH', type=str,
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help='path to the directory containing the results for the experimental methods')
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args = parser.parse_args()
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
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evaluation_measures = [qp.error.ae, qp.error.rae]
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gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
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new_methods = ['hdy'] # methods added to the Gao & Sebastiani methods
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experimental_methods = ['hdysld'] # experimental
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for i, eval_func in enumerate(evaluation_measures):
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# Tables evaluation scores for AE and RAE (two tables)
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# ----------------------------------------------------
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eval_name = eval_func.__name__
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added_methods = ['svmm' + eval_name] + new_methods
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methods = gao_seb_methods + added_methods + experimental_methods
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nold_methods = len(gao_seb_methods)
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nnew_methods = len(added_methods)
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nexp_methods = len(experimental_methods)
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# fill data table
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table = Table(benchmarks=datasets, methods=methods)
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for dataset in datasets:
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for method in methods:
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if method in experimental_methods:
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path = args.newresults
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else:
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path = args.results
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table.add(dataset, method, experiment_errors(path, dataset, method, eval_name))
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# write the latex table
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tabular = """
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\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods) + '|' + ('Y|'*nnew_methods) + '|' + ('Y|'*nexp_methods) + """} \hline
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& \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
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\multicolumn{"""+str(nnew_methods)+"""}{c|}{} &
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\multicolumn{"""+str(nexp_methods)+"""}{c|}{}\\\\ \hline
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"""
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rowreplace={dataset: nice.get(dataset, dataset.upper()) for dataset in datasets}
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colreplace={method:'\side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} ' for method in methods}
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tabular += table.latexTabular(benchmark_replace=rowreplace, method_replace=colreplace)
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tabular += "\n\end{tabularx}"
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save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
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# Tables ranks for AE and RAE (two tables)
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# ----------------------------------------------------
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# fill the data table
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ranktable = Table(benchmarks=datasets, methods=methods, missing='--')
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for dataset in datasets:
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for method in methods:
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ranktable.add(dataset, method, values=table.get(dataset, method, 'rank'))
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# write the latex table
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tabular = """
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\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods) + '|' + ('Y|'*nnew_methods) + '|' + ('Y|'*nexp_methods) + """} \hline
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& \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
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\multicolumn{"""+str(nnew_methods)+"""}{c|}{} &
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\multicolumn{"""+str(nexp_methods)+"""}{c|}{}\\\\ \hline
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"""
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for method in methods:
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tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} '
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tabular += '\\\\\hline\n'
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for dataset in datasets:
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tabular += nice.get(dataset, dataset.upper()) + ' '
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for method in methods:
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newrank = ranktable.get(dataset, method)
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if newrank != '--':
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newrank = f'{int(newrank)}'
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color = ranktable.get_color(dataset, method)
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if color == '--':
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color = ''
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tabular += ' & ' + f'{newrank}' + color
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tabular += '\\\\\hline\n'
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tabular += '\hline\n'
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tabular += 'Average '
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for method in methods:
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newrank = ranktable.get_average(method)
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if newrank != '--':
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newrank = f'{newrank:.1f}'
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color = ranktable.get_average(method, 'color')
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if color == '--':
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color = ''
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tabular += ' & ' + f'{newrank}' + color
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tabular += '\\\\\hline\n'
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tabular += "\end{tabularx}"
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save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
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print("[Done]")
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import multiprocessing
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N_JOBS = -2 #multiprocessing.cpu_count()
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ENSEMBLE_N_JOBS=1
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SAMPLE_SIZE = 100
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