local stack

LocalStack/experiments.py

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+import os
+from time import time
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+import quapy as qp
+from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
+from LocalStack.method import LocalStackingQuantification, LocalStackingQuantification2
+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
+
+
+
+METHODS = [
+    ('PACC', PACC(), {}),
+    ('EMQ', EMQ(), {}),
+    ('KDEy-ML',  KDEyML(), {}),
+]
+
+TRANSDUCTIVE_METHODS = [
+    ('LSQ',  LocalStackingQuantification(EMQ()), {}),
+    ('LSQ2',  LocalStackingQuantification2(EMQ()), {})
+]
+
+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)
+    pd.set_option('display.width', 1000)  # Ajustar el ancho máximo
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE"], margins=True)
+    print(pv)
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["MRAE"], margins=True)
+    print(pv)
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["KLD"], margins=True)
+    print(pv)
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["TR-TIME"], margins=True)
+    print(pv)
+    pv = df.pivot_table(index='Dataset', columns="Method", values=["TE-TIME"], margins=True)
+    print(pv)
+
+
+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/localstack'
+
+    os.makedirs(result_dir, exist_ok=True)
+
+    global_result_path = f'{result_dir}/allmethods'
+    with open(global_result_path + '.csv', 'wt') as csv:
+        csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\tTR-TIME\tTE-TIME\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:
+                print('init', dataset)
+
+                # run_experiment(global_result_path, method_name, quantifier, param_grid, 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)
+                        train, test = data.train_test
+
+                        transductive_names = [name for (name, *_) in TRANSDUCTIVE_METHODS]
+
+                        if method_name not in transductive_names:
+                            if len(param_grid) == 0:
+                                t_init = time()
+                                quantifier.fit(train)
+                                train_time = time() - t_init
+                            else:
+                                # model selection (train)
+                                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)
+                                train_time = time() - t_init
+                        else:
+                            # transductive
+                            t_init = time()
+                            quantifier.fit(train)  # <-- nothing actually (proyects the X into posteriors only)
+                            train_time = time() - t_init
+
+                        # test
+                        t_init = time()
+                        protocol = UPP(test, repeats=n_bags_test)
+                        report = qp.evaluation.evaluation_report(
+                            quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True
+                        )
+                        test_time = time() - t_init
+                        report['tr_time'] = train_time
+                        report['te_time'] = test_time
+                        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}\t{means["kld"]:.5f}\t{means["tr_time"]:.3f}\t{means["te_time"]:.3f}\n')
+                csv.flush()
+
+    show_results(global_result_path)

LocalStack/method.py

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+import numpy as np
+import quapy as qp
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.svm import SVR
+
+from data import LabelledCollection
+from quapy.method.base import BaseQuantifier
+from quapy.method.aggregative import AggregativeSoftQuantifier
+
+
+class LocalStackingQuantification(BaseQuantifier):
+
+    def __init__(self, surrogate_quantifier, n_samples_gen=200, n_samples_sel=50, comparison_measure='ae', random_state=None):
+        assert isinstance(surrogate_quantifier, AggregativeSoftQuantifier), \
+            f'the surrogate quantifier must be of type {AggregativeSoftQuantifier.__class__.__name__}'
+        self.surrogate_quantifier = surrogate_quantifier
+        self.n_samples_gen = n_samples_gen
+        self.n_samples_sel = n_samples_sel
+        self.comparison_measure = qp.error.from_name(comparison_measure)
+        self.random_state = random_state
+
+    def fit(self, data: LabelledCollection):
+        train, val = data.split_stratified()
+        self.surrogate_quantifier.fit(train)
+        self.val_data = val
+        return self
+
+    def normalize(self, out_simplex:np.ndarray):
+        in_simplex = out_simplex/out_simplex.sum()
+        return in_simplex
+
+    def quantify(self, instances: np.ndarray):
+        assert hasattr(self, 'val_data'), 'quantify called before fit'
+        pred_prevs = self.surrogate_quantifier.quantify(instances)
+        test_size = instances.shape[0]
+
+        samples = []
+        samples_pred_prevs = []
+        samples_distance = []
+        for i in range(self.n_samples_gen):
+            sample_i = self.val_data.sampling(test_size, *pred_prevs, random_state=self.random_state)
+            pred_prev_sample_i = self.surrogate_quantifier.quantify(sample_i.X)
+            err_dist = self.comparison_measure(pred_prevs, pred_prev_sample_i)
+
+            samples.append(sample_i)
+            samples_pred_prevs.append(pred_prev_sample_i)
+            samples_distance.append(err_dist)
+
+        ord_distances = np.argsort(samples_distance)
+        samples_sel = np.asarray(samples)[ord_distances][:self.n_samples_sel]
+        samples_pred_prevs_sel = np.asarray(samples_pred_prevs)[ord_distances][:self.n_samples_sel]
+
+        reg = MultiOutputRegressor(SVR())
+        reg_X = samples_pred_prevs_sel
+        reg_y = [s.prevalence() for s in samples_sel]
+        reg.fit(reg_X, reg_y)
+
+        corrected_prev = reg.predict([pred_prevs])[0]
+
+        corrected_prev = self.normalize(corrected_prev)
+        return corrected_prev
+
+
+
+class LocalStackingQuantification2(BaseQuantifier):
+
+    """
+    Este en vez de seleccionar samples de training para los que la prevalencia predicha se parece a la prevalencia
+    predica en test, saca directamente samples de training con la prevalencia predicha en test
+    """
+
+    def __init__(self, surrogate_quantifier, n_samples_gen=200, n_samples_sel=50, comparison_measure='ae', random_state=None):
+        assert isinstance(surrogate_quantifier, AggregativeSoftQuantifier), \
+            f'the surrogate quantifier must be of type {AggregativeSoftQuantifier.__class__.__name__}'
+        self.surrogate_quantifier = surrogate_quantifier
+        self.n_samples_gen = n_samples_gen
+        self.n_samples_sel = n_samples_sel
+        self.comparison_measure = qp.error.from_name(comparison_measure)
+        self.random_state = random_state
+
+    def fit(self, data: LabelledCollection):
+        train, val = data.split_stratified()
+        self.surrogate_quantifier.fit(train)
+        self.val_data = val
+        return self
+
+    def normalize(self, out_simplex:np.ndarray):
+        in_simplex = out_simplex/out_simplex.sum()
+        return in_simplex
+
+    def quantify(self, instances: np.ndarray):
+        assert hasattr(self, 'val_data'), 'quantify called before fit'
+        pred_prevs = self.surrogate_quantifier.quantify(instances)
+        test_size = instances.shape[0]
+
+        samples = []
+        samples_pred_prevs = []
+        for i in range(self.n_samples_gen):
+            sample_i = self.val_data.sampling(test_size, *pred_prevs, random_state=self.random_state)
+            pred_prev_sample_i = self.surrogate_quantifier.quantify(sample_i.X)
+            samples.append(sample_i)
+            samples_pred_prevs.append(pred_prev_sample_i)
+
+        reg = MultiOutputRegressor(SVR())
+        reg_X = samples_pred_prevs
+        reg_y = [s.prevalence() for s in samples]
+        reg.fit(reg_X, reg_y)
+
+        corrected_prev = reg.predict([pred_prevs])[0]
+
+        corrected_prev = self.normalize(corrected_prev)
+        return corrected_prev