trying optimizing both prev and band at the same time, and per-class bandwidth

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "result_table"]
+	path = result_table
+	url = gitea@gitea-s2i2s.isti.cnr.it:moreo/result_table.git

KDEy/constants.py

@@ -0,0 +1,2 @@
+
+DEBUG = False

KDEy/experiments.py

@@ -1,13 +1,17 @@
 import os
-
+import pickle
+import shutil
 import numpy as np
 from sklearn.linear_model import LogisticRegression
 from os.path import join
 import quapy as qp
-from quapy.protocol import UPP
 from quapy.method.aggregative import KDEyML
+from quapy.protocol import UPP
+from kdey_devel import KDEyMLauto
+from utils import *
+from constants import *
+import quapy.functional as F
 
-DEBUG = False
 
 qp.environ["SAMPLE_SIZE"] = 100 if DEBUG else 500
 val_repeats  = 100 if DEBUG else 500
@@ -20,21 +24,24 @@ val_choice = {}
 
 bandwidth_range = np.linspace(0.01, 0.20, 20)
 if DEBUG:
-    bandwidth_range = np.linspace(0.01, 0.20, 10)
+    bandwidth_range = np.linspace(0.01, 0.20, 5)
+
 
 def datasets():
-    for dataset_name in qp.datasets.UCI_MULTICLASS_DATASETS:
+    dataset_list = qp.datasets.UCI_MULTICLASS_DATASETS[:4]
+    if DEBUG:
+        dataset_list = dataset_list[:4]
+    for dataset_name in dataset_list:
         dataset = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
         if DEBUG:
             dataset = dataset.reduce(random_state=0)
         yield dataset
 
 
-def experiment_dataset(dataset):
-    train, test = dataset.train_test
-    test_gen = UPP(test, repeats=test_repeats)
-
+@measuretime
+def predict_b_modsel(dataset):
     # bandwidth chosen during model selection in validation
+    train = dataset.training
     train_tr, train_va = train.split_stratified(random_state=0)
     kdey = KDEyML(random_state=0)
     modsel = qp.model_selection.GridSearchQ(
@@ -47,66 +54,69 @@ def experiment_dataset(dataset):
     ).fit(train_tr)
     chosen_bandwidth = modsel.best_params_['bandwidth']
     modsel_choice = float(chosen_bandwidth)
+    # kdey.set_params(bandwidth=chosen_bandwidth)
+    # kdey.fit(train)
+    # kdey.qua
+    return modsel_choice
 
-    # results in test
-    print(f"testing KDEy in {dataset.name}")
-    dataset_results = []
-    for b in bandwidth_range:
-        kdey = KDEyML(bandwidth=b, random_state=0)
+@measuretime
+def predict_b_kdeymlauto(dataset):
+    # bandwidth chosen during model selection in validation
+    train, test = dataset.train_test
+    kdey = KDEyMLauto(random_state=0)
+    print(f'true-prevalence: {F.strprev(test.prevalence())}')
+    chosen_bandwidth, _ = kdey.chose_bandwidth(train, test.X)
+    auto_bandwidth = float(chosen_bandwidth)
+    return auto_bandwidth
+
+
+def in_test_search(dataset, n_jobs=-1):
+    train, test = dataset.train_test
+
+    print(f"generating true tests scores using KDEy in {dataset.name}")
+
+    def experiment_job(bandwidth):
+        kdey = KDEyML(bandwidth=bandwidth, random_state=0)
         kdey.fit(train)
-
+        test_gen = UPP(test, repeats=test_repeats)
         mae = qp.evaluation.evaluate(kdey, protocol=test_gen, error_metric='mae', verbose=True)
-        print(f'bandwidth={b}: {mae:.5f}')
-        dataset_results.append((float(b), float(mae)))
+        print(f'{bandwidth=}: {mae:.5f}')
+        return float(mae)
 
-    return modsel_choice, dataset_results
+    dataset_results = qp.util.parallel(experiment_job, bandwidth_range, n_jobs=n_jobs)
+    return dataset_results, bandwidth_range
 
-def plot_bandwidth(val_choice, test_results):
-    for dataset_name in val_choice.keys():
-        import matplotlib.pyplot as plt
 
-        bandwidths, results = zip(*test_results[dataset_name])
 
-        # Crear la gráfica
-        plt.figure(figsize=(8, 6))
-
-        # Graficar los puntos de datos
-        plt.plot(bandwidths, results, marker='o')
-
-        # Agregar la línea vertical en bandwidth_chosen
-        plt.axvline(x=val_choice[dataset_name], color='r', linestyle='--', label=f'Bandwidth elegido: {val_choice[dataset_name]}')
-
-        # Agregar etiquetas y título
-        plt.xlabel('Bandwidth')
-        plt.ylabel('Resultado')
-        plt.title('Gráfica de Bandwidth vs Resultado')
-
-        # Mostrar la leyenda
-        plt.legend()
-
-        # Mostrar la gráfica
-        plt.grid(True)
-        # plt.show()
-        os.makedirs('./plots', exist_ok=True)
-        plt.savefig(f'./plots/{dataset_name}.png')
 
 
 
 for dataset in datasets():
-    if DEBUG:
-        result_path = f'./results/debug/{dataset.name}.pkl'
-    else:
-        result_path = f'./results/{dataset.name}.pkl'
+    print('NAME', dataset.name)
+    print(len(dataset.training))
+    print(len(dataset.test))
 
-    modsel_choice, dataset_results = qp.util.pickled_resource(result_path, experiment_dataset, dataset)
-    val_choice[dataset.name] = modsel_choice
-    test_results[dataset.name] = dataset_results
+    result_path = f'./results/{dataset.name}/'
+    if DEBUG:
+        result_path = result_path.replace('results', 'results_debug')
+        if os.path.exists(result_path):
+            shutil.rmtree(result_path)
+
+    dataset_results, bandwidth_range = qp.util.pickled_resource(join(result_path, 'test.pkl'), in_test_search, dataset)
+
+    triplet_list_results = []
+    modsel_choice, modsel_time = qp.util.pickled_resource(join(result_path, 'modsel.pkl'), predict_b_modsel, dataset)
+    triplet_list_results.append(('modsel', modsel_choice, modsel_time,))
+    auto_choice, auto_time = qp.util.pickled_resource(join(result_path, 'auto.pkl'), predict_b_kdeymlauto, dataset)
+    triplet_list_results.append(('auto', auto_choice, auto_time,))
 
     print(f'Dataset = {dataset.name}')
     print(modsel_choice)
     print(dataset_results)
 
-plot_bandwidth(val_choice, test_results)
+    plot_bandwidth(dataset.name, dataset_results, bandwidth_range, triplet_list_results)
+    error_table(dataset.name, dataset_results, bandwidth_range, triplet_list_results)
+    # time_table(dataset.name, dataset_results, bandwidth_range, triplet_list_results)
 
 
 

KDEy/kdey_devel.py

@@ -0,0 +1,171 @@
+from typing import Union, Callable
+import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.neighbors import KernelDensity
+
+import quapy as qp
+from quapy.data import LabelledCollection
+from quapy.method.aggregative import AggregativeSoftQuantifier, KDEyML
+import quapy.functional as F
+
+from sklearn.metrics.pairwise import rbf_kernel
+from scipy import optimize
+
+
+
+
+class KDEyMLauto(KDEyML):
+    def __init__(self, classifier: BaseEstimator = None, val_split=5, random_state=None, optim='two_steps'):
+        self.classifier = qp._get_classifier(classifier)
+        self.val_split = val_split
+        self.bandwidth = None
+        self.random_state = random_state
+        self.optim = optim
+
+    def chose_bandwidth(self, train, test_instances):
+        classif_predictions = self.classifier_fit_predict(train, fit_classifier=True, predict_on=self.val_split)
+        te_posteriors = self.classify(test_instances)
+        return self.transduce(classif_predictions, te_posteriors)
+
+    def transduce(self, classif_predictions, te_posteriors):
+        tr_posteriors, tr_y = classif_predictions.Xy
+        classes = classif_predictions.classes_
+        n_classes = len(classes)
+
+        current_bandwidth = 0.05
+        if self.optim == 'both_fine':
+            current_bandwidth = np.full(fill_value=current_bandwidth, shape=(n_classes,))
+        current_prevalence = np.full(fill_value=1 / n_classes, shape=(n_classes,))
+
+        iterations = 0
+        convergence = False
+        with qp.util.temp_seed(self.random_state):
+
+            while not convergence:
+                previous_bandwidth = current_bandwidth
+                previous_prevalence = current_prevalence
+
+                iterations += 1
+                print(f'{iterations}:')
+
+                if self.optim == 'two_steps':
+                    current_prevalence = self.optim_minimize_prevalence(current_bandwidth, current_prevalence, tr_posteriors, tr_y, te_posteriors, classes)
+                    print(f'\testim-prev={F.strprev(current_prevalence)}')
+
+                    current_bandwidth = self.optim_minimize_bandwidth(current_bandwidth, current_prevalence, tr_posteriors, tr_y, te_posteriors, classes)
+                    print(f'\tbandwidth={current_bandwidth}')
+                    if np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001) and all(
+                            np.isclose(previous_prevalence, current_prevalence, atol=0.0001)):
+                        convergence = True
+                elif self.optim == 'both':
+                    current_prevalence, current_bandwidth = self.optim_minimize_both(current_bandwidth, current_prevalence, tr_posteriors, tr_y, te_posteriors, classes)
+                    if np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001) and all(np.isclose(previous_prevalence, current_prevalence, atol=0.0001)):
+                        convergence = True
+                elif self.optim == 'both_fine':
+                    current_prevalence, current_bandwidth = self.optim_minimize_both_fine(current_bandwidth, current_prevalence, tr_posteriors, tr_y,
+                                                         te_posteriors, classes)
+
+                    if all(np.isclose(previous_bandwidth, current_bandwidth, atol=0.0001)) and all(np.isclose(previous_prevalence, current_prevalence, atol=0.0001)):
+                        convergence = True
+
+        self.bandwidth = current_bandwidth
+        print('bandwidth=', current_bandwidth)
+        print('prevalence=', current_prevalence)
+        return current_prevalence
+
+    def optim_minimize_prevalence(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
+        epsilon = 1e-10
+        mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, current_bandwidth)
+        test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
+
+        def neg_loglikelihood_prev(prev):
+            test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prev, test_densities))
+            test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
+            return -np.sum(test_loglikelihood)
+
+        return optim_minimize(neg_loglikelihood_prev, current_prev)
+
+    def optim_minimize_bandwidth(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
+        epsilon = 1e-10
+        def neg_loglikelihood_bandwidth(bandwidth):
+            mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth[0])
+            test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
+            test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(current_prev, test_densities))
+            test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
+            return -np.sum(test_loglikelihood)
+
+        bounds = [(0.00001, 1)]
+        r = optimize.minimize(neg_loglikelihood_bandwidth, x0=[current_bandwidth], method='SLSQP', bounds=bounds)
+        print(f'iterations-bandwidth={r.nit}')
+        return r.x[0]
+
+    def optim_minimize_both(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
+        epsilon = 1e-10
+        n_classes = len(current_prev)
+        def neg_loglikelihood_bandwidth(prevalence_bandwidth):
+            bandwidth = prevalence_bandwidth[-1]
+            prevalence = prevalence_bandwidth[:-1]
+            mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
+            test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
+            test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prevalence, test_densities))
+            test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
+            return -np.sum(test_loglikelihood)
+
+        bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 1)]
+        constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x[:n_classes])})
+        prevalence_bandwidth = np.append(current_prev, current_bandwidth)
+        r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)
+        print(f'iterations-both={r.nit}')
+        prev_band = r.x
+        current_prevalence = prev_band[:-1]
+        current_bandwidth = prev_band[-1]
+        return current_prevalence, current_bandwidth
+
+    def optim_minimize_both_fine(self, current_bandwidth, current_prev, tr_posteriors, tr_y, te_posteriors, classes):
+        epsilon = 1e-10
+        n_classes = len(current_bandwidth)
+        def neg_loglikelihood_bandwidth(prevalence_bandwidth):
+            prevalence = prevalence_bandwidth[:n_classes]
+            bandwidth = prevalence_bandwidth[n_classes:]
+            mix_densities = self.get_mixture_components(tr_posteriors, tr_y, classes, bandwidth)
+            test_densities = [self.pdf(kde_i, te_posteriors) for kde_i in mix_densities]
+            test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip(prevalence, test_densities))
+            test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
+            return -np.sum(test_loglikelihood)
+
+        bounds = [(0, 1) for _ in range(n_classes)] + [(0.00001, 1) for _ in range(n_classes)]
+        constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x[:n_classes])})
+        prevalence_bandwidth = np.concatenate((current_prev, current_bandwidth))
+        r = optimize.minimize(neg_loglikelihood_bandwidth, x0=prevalence_bandwidth, method='SLSQP', bounds=bounds, constraints=constraints)
+        print(f'iterations-both-fine={r.nit}')
+        prev_band = r.x
+        current_prevalence = prev_band[:n_classes]
+        current_bandwidth = prev_band[n_classes:]
+        return current_prevalence, current_bandwidth
+
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
+        self.classif_predictions = classif_predictions
+        return self
+
+    def aggregate(self, posteriors: np.ndarray):
+        return self.transduce(self.classif_predictions, posteriors)
+
+
+def optim_minimize(loss: Callable, init_prev: np.ndarray):
+    """
+    Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
+    that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
+    SLSQP routine.
+
+    :param loss: (callable) the function to minimize
+    :return: (ndarray) the best prevalence vector found
+    """
+
+    n_classes = len(init_prev)
+    # solutions are bounded to those contained in the unit-simplex
+    bounds = tuple((0, 1) for _ in range(n_classes))  # values in [0,1]
+    constraints = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)})  # values summing up to 1
+    r = optimize.minimize(loss, x0=init_prev, method='SLSQP', bounds=bounds, constraints=constraints)
+    print(f'iterations-prevalence={r.nit}')
+    return r.x
+

KDEy/quantification_evaluation.py

@@ -0,0 +1,156 @@
+import pickle
+import os
+from time import time
+from collections import defaultdict
+
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+import quapy as qp
+from KDEy.kdey_devel import KDEyMLauto
+from quapy.method.aggregative import PACC, EMQ, KDEyML
+from quapy.model_selection import GridSearchQ
+from quapy.protocol import UPP
+from pathlib import Path
+
+SEED = 1
+
+
+def newLR():
+    return LogisticRegression(max_iter=3000)
+
+
+# typical hyperparameters explored for Logistic Regression
+logreg_grid = {
+    'C': [1],
+    'class_weight': [None]
+}
+
+
+def wrap_hyper(classifier_hyper_grid: dict):
+    return {'classifier__' + k: v for k, v in classifier_hyper_grid.items()}
+
+
+METHODS = [
+    ('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
+    ('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
+    ('KDEy-ML',  KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.linspace(0.01, 0.2, 20)}}),
+]
+
+
+"""
+TKDEyML era primero bandwidth (init 0.05) y luego prevalence (init uniform)
+TKDEyML2 era primero prevalence (init uniform) y luego bandwidth (init 0.05)
+TKDEyML3 era primero prevalence (init uniform) y luego bandwidth (init 0.1)
+TKDEyML4 es como ML2 pero max 5 iteraciones por optimización 
+"""
+TRANSDUCTIVE_METHODS = [
+    #('TKDEy-ML',  KDEyMLauto(newLR()), None),
+    ('TKDEy-MLboth',  KDEyMLauto(newLR(), optim='both'), None),
+    ('TKDEy-MLbothfine',  KDEyMLauto(newLR(), optim='both_fine'), None),
+    ('TKDEy-ML2',  KDEyMLauto(newLR()), None),
+    #('TKDEy-ML3',  KDEyMLauto(newLR()), None),
+    #('TKDEy-ML4',  KDEyMLauto(newLR()), None),
+]
+
+def show_results(result_path):
+    import pandas as pd
+    df = pd.read_csv(result_path + '.csv', sep='\t')
+    pd.set_option('display.max_columns', None)
+    pd.set_option('display.max_rows', None)
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE", "MRAE", "t_train"], margins=True)
+    print(pv)
+
+
+def load_timings(result_path):
+    import pandas as pd
+    timings = defaultdict(lambda: {})
+    if not Path(result_path + '.csv').exists():
+        return timings
+
+    df = pd.read_csv(result_path + '.csv', sep='\t')
+    return timings | df.pivot_table(index='Dataset', columns='Method', values='t_train').to_dict()
+
+
+if __name__ == '__main__':
+
+    qp.environ['SAMPLE_SIZE'] = 500
+    qp.environ['N_JOBS'] = -1
+    n_bags_val = 25
+    n_bags_test = 100
+    result_dir = f'results_quantification/ucimulti'
+
+    os.makedirs(result_dir, exist_ok=True)
+
+    global_result_path = f'{result_dir}/allmethods'
+    timings = load_timings(global_result_path)
+    with open(global_result_path + '.csv', 'wt') as csv:
+        csv.write(f'Method\tDataset\tMAE\tMRAE\tt_train\n')
+
+    for method_name, quantifier, param_grid in METHODS + TRANSDUCTIVE_METHODS:
+
+        print('Init method', method_name)
+
+        with open(global_result_path + '.csv', 'at') as csv:
+            for dataset in qp.datasets.UCI_MULTICLASS_DATASETS[:4]:
+                print('init', dataset)
+
+                local_result_path = os.path.join(Path(global_result_path).parent,
+                                                 method_name + '_' + dataset + '.dataframe')
+
+                if os.path.exists(local_result_path):
+                    print(f'result file {local_result_path} already exist; skipping')
+                    report = qp.util.load_report(local_result_path)
+
+                else:
+                    with qp.util.temp_seed(SEED):
+
+                        data = qp.datasets.fetch_UCIMulticlassDataset(dataset, verbose=True)
+
+                        if not method_name.startswith("TKDEy-ML"):
+                            # model selection
+                            train, test = data.train_test
+                            train, val = train.split_stratified(random_state=SEED)
+
+                            protocol = UPP(val, repeats=n_bags_val)
+                            modsel = GridSearchQ(
+                                quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error='mae'
+                            )
+
+                            t_init = time()
+                            try:
+                                modsel.fit(train)
+
+                                print(f'best params {modsel.best_params_}')
+                                print(f'best score {modsel.best_score_}')
+
+                                quantifier = modsel.best_model()
+                            except:
+                                print('something went wrong... trying to fit the default model')
+                                quantifier.fit(train)
+                            timings[method_name][dataset] = time() - t_init
+
+                            protocol = UPP(test, repeats=n_bags_test)
+                            report = qp.evaluation.evaluation_report(
+                                quantifier, protocol, error_metrics=['mae', 'mrae'], verbose=True
+                            )
+                            report.to_csv(local_result_path)
+                        else:
+                            # model selection
+                            train, test = data.train_test
+                            t_init = time()
+                            quantifier.fit(train)
+                            timings[method_name][dataset] = time() - t_init
+
+                            protocol = UPP(test, repeats=n_bags_test)
+                            report = qp.evaluation.evaluation_report(
+                                quantifier, protocol, error_metrics=['mae', 'mrae'], verbose=True
+                            )
+                            report.to_csv(local_result_path)
+
+                means = report.mean(numeric_only=True)
+                csv.write(
+                    f'{method_name}\t{dataset}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\n')#\t{timings[method_name][dataset]:.3f}\n')
+                csv.flush()
+
+    show_results(global_result_path)

KDEy/utils.py

@@ -0,0 +1,81 @@
+import time
+from functools import wraps
+import os
+from os.path import join
+from result_table.src.table import Table
+import numpy as np
+from constants import *
+
+def measuretime(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        time_it_took = end_time - start_time
+        if isinstance(result, tuple):
+            return (*result, time_it_took)
+        else:
+            return result, time_it_took
+    return wrapper
+
+
+def plot_bandwidth(dataset_name, test_results, bandwidths, triplet_list_results):
+    import matplotlib.pyplot as plt
+
+    print("PLOT", dataset_name)
+    print(dataset_name)
+
+    plt.figure(figsize=(8, 6))
+
+    # show test results
+    plt.plot(bandwidths, test_results, marker='o', color='k')
+
+    colors = plt.cm.tab10(np.linspace(0, 1, len(triplet_list_results)))
+    for i, (method_name, method_choice, method_time) in enumerate(triplet_list_results):
+        plt.axvline(x=method_choice, linestyle='--', label=method_name, color=colors[i])
+
+    # Agregar etiquetas y título
+    plt.xlabel('Bandwidth')
+    plt.ylabel('MAE')
+    plt.title(dataset_name)
+
+    # Mostrar la leyenda
+    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+    # Mostrar la gráfica
+    plt.grid(True)
+
+    plotdir = './plots'
+    if DEBUG:
+        plotdir = './plots_debug'
+    os.makedirs(plotdir, exist_ok=True)
+    plt.tight_layout()
+    plt.savefig(f'{plotdir}/{dataset_name}.png')
+    plt.close()
+
+def error_table(dataset_name, test_results, bandwidth_range, triplet_list_results):
+    best_bandwidth = bandwidth_range[np.argmin(test_results)]
+    best_score = np.min(test_results)
+    print(f'Method\tChoice\tAE\tTime')
+    table=Table(name=dataset_name)
+    table.format.with_mean=False
+    table.format.with_rank_mean = False
+    table.format.show_std = False
+    for method_name, method_choice, took in triplet_list_results:
+        if method_choice in bandwidth_range:
+            index = np.where(bandwidth_range == method_choice)[0][0]
+            method_score = test_results[index]
+        else:
+            method_score = 1
+        error = np.abs(best_score-method_score)
+        table.add(benchmark='Choice', method=method_name, v=method_choice)
+        table.add(benchmark='ScoreChoice', method=method_name, v=method_score)
+        table.add(benchmark='Best', method=method_name, v=best_bandwidth)
+        table.add(benchmark='ScoreBest', method=method_name, v=best_score)
+        table.add(benchmark='AE', method=method_name, v=error)
+        table.add(benchmark='Time', method=method_name, v=took)
+    outpath = './tables'
+    if DEBUG:
+        outpath = './tables_debug'
+    table.latexPDF(join(outpath, dataset_name+'.pdf'), transpose=True)

quapy/__init__.py

@@ -14,7 +14,7 @@ from . import model_selection
 from . import classification
 import os
 
-__version__ = '0.1.9'
+__version__ = '0.1.10'
 
 environ = {
     'SAMPLE_SIZE': None,

quapy/data/datasets.py

@@ -3,6 +3,7 @@ from contextlib import contextmanager
 import zipfile
 from os.path import join
 import pandas as pd
+import sklearn.datasets
 from ucimlrepo import fetch_ucirepo
 from quapy.data.base import Dataset, LabelledCollection
 from quapy.data.preprocessing import text2tfidf, reduce_columns
@@ -1004,3 +1005,49 @@ def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=No
         return train, test_gen
     else:
         return train_gen, test_gen
+
+
+def syntheticUniformLabelledCollection(n_samples, n_features, n_classes, n_clusters_per_class=1, **kwargs):
+    """
+    Generates a synthetic labelled collection with uniform priors and
+    of `n_samples` instances, `n_features` features, and `n_classes` classes.
+    The underlying generator relies on the function
+    `sklearn.datasets.make_classification`. Other options can be specified using the `kwargs`;
+    see the `scikit-learn documentation
+    <https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html>`_
+    for a full list of optional parameters.
+
+    :param n_samples: number of instances
+    :param n_features: number of features
+    :param n_classes: number of classes
+    """
+    X, y = sklearn.datasets.make_classification(
+        n_samples=n_samples,
+        n_features=n_features,
+        n_classes=n_classes,
+        n_clusters_per_class=n_clusters_per_class,
+        **kwargs
+    )
+
+    return LabelledCollection(X, y)
+
+def syntheticUniformDataset(n_samples, n_features, n_classes, test_split=0.3, **kwargs):
+    """
+    Generates a synthetic Dataset with approximately uniform priors and
+    of `n_samples` instances, `n_features` features, and `n_classes` classes.
+    The underlying generator relies on the function
+    `sklearn.datasets.make_classification`. Other options can be specified using the `kwargs`;
+    see the `scikit-learn documentation
+    <https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html>`_
+    for a full list of optional parameters.
+
+    :param n_samples: number of instances
+    :param n_features: number of features
+    :param n_classes: number of classes
+    :param test_split: proportion of test instances
+    """
+    assert 0. < test_split < 1., "invalid proportion of test instances; the value must be in (0, 1)"
+    lc = syntheticUniformLabelledCollection(n_samples, n_features, n_classes, **kwargs)
+    training, test = lc.split_stratified(train_prop=1-test_split, random_state=kwargs.get('random_state', None))
+    dataset = Dataset(training=training, test=test, name=f'synthetic(nF={n_features},nC={n_classes})')
+    return dataset

quapy/method/_kdey.py

@@ -66,11 +66,13 @@ class KDEBase:
         """
         class_cond_X = []
         for cat in classes:
-            selX = X[y==cat]
-            if selX.size==0:
+            selX = X[y == cat]
+            if selX.size == 0:
                 selX = [F.uniform_prevalence(len(classes))]
             class_cond_X.append(selX)
-        return [self.get_kde_function(X_cond_yi, bandwidth) for X_cond_yi in class_cond_X]
+        if isinstance(bandwidth, float):
+            bandwidth = np.full(fill_value=bandwidth, shape=(len(classes),))
+        return [self.get_kde_function(X_cond_yi, band_i) for X_cond_yi, band_i in zip(class_cond_X, bandwidth)]
 
 
 class KDEyML(AggregativeSoftQuantifier, KDEBase):
@@ -188,7 +190,7 @@ class KDEyHD(AggregativeSoftQuantifier, KDEBase):
 
     def __init__(self, classifier: BaseEstimator=None, val_split=5, divergence: str='HD',
                  bandwidth=0.1, random_state=None, montecarlo_trials=10000):
-        
+
         self.classifier = qp._get_classifier(classifier)
         self.val_split = val_split
         self.divergence = divergence
@@ -218,7 +220,7 @@ class KDEyHD(AggregativeSoftQuantifier, KDEBase):
 
         def f_squared_hellinger(u):
             return (np.sqrt(u)-1)**2
-        
+
         # todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway
         if self.divergence.lower() == 'hd':
             f = f_squared_hellinger
@@ -283,7 +285,7 @@ class KDEyCS(AggregativeSoftQuantifier):
 
     def gram_matrix_mix_sum(self, X, Y=None):
         # this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))
-        # to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are 
+        # to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are
         # two "scalar matrices" (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)
         h = self.bandwidth
         variance = 2 * (h**2)
@@ -342,7 +344,7 @@ class KDEyCS(AggregativeSoftQuantifier):
         # at each iteration of the optimization phase)
         tr_te_sums = np.zeros(shape=n, dtype=float)
         for i in range(n):
-            tr_te_sums[i] = self.gram_matrix_mix_sum(Ptr[y==i], Pte) 
+            tr_te_sums[i] = self.gram_matrix_mix_sum(Ptr[y==i], Pte)
 
         def divergence(alpha):
             # called \overline{r} in the paper

result_table

@@ -0,0 +1 @@
+Subproject commit c223c9f1fe3c9708e8c5a5c56e438cdaaa857be4