170 lines
7.3 KiB
Python
170 lines
7.3 KiB
Python
import pickle
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import os
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from time import time
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from collections import defaultdict
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
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from quapy.method.aggregative import PACC, EMQ, KDEyML
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import UPP
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from pathlib import Path
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SEED = 1
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def newLR():
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return LogisticRegression(max_iter=3000)
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# typical hyperparameters explored for Logistic Regression
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logreg_grid = {
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'C': np.logspace(-4,4,9),
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'class_weight': [None, 'balanced']
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}
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def wrap_hyper(classifier_hyper_grid: dict):
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return {'classifier__' + k: v for k, v in classifier_hyper_grid.items()}
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METHODS = [
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('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
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('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
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('KDEy', KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
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# ('KDEy-MLred', KDEyMLred(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.logspace(-4, np.log10(0.2), 20)}}),
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('KDEy-scott', KDEyML(newLR(), bandwidth='scott'), wrap_hyper(logreg_grid)),
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('KDEy-silver', KDEyML(newLR(), bandwidth='silverman'), wrap_hyper(logreg_grid)),
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('KDEy-NLL', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-NLL+', KDEyMLauto2(newLR(), bandwidth='auto', target='likelihood', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-AE', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-AE+', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='optim'), wrap_hyper(logreg_grid)),
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]
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"""
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TKDEyML era primero bandwidth (init 0.05) y luego prevalence (init uniform)
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TKDEyML2 era primero prevalence (init uniform) y luego bandwidth (init 0.05)
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TKDEyML3 era primero prevalence (init uniform) y luego bandwidth (init 0.1)
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TKDEyML4 es como ML2 pero max 5 iteraciones por optimización
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"""
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TRANSDUCTIVE_METHODS = [
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#('TKDEy-ML', KDEyMLauto(newLR()), None),
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# ('TKDEy-MLboth', KDEyMLauto(newLR(), optim='both'), None),
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# ('TKDEy-MLbothfine', KDEyMLauto(newLR(), optim='both_fine'), None),
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# ('TKDEy-ML2', KDEyMLauto(newLR(), optim='two_steps'), None),
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# ('TKDEy-MLike', KDEyMLauto(newLR(), optim='max_likelihood'), None),
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# ('TKDEy-MLike2', KDEyMLauto(newLR(), optim='max_likelihood2'), None),
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#('TKDEy-ML3', KDEyMLauto(newLR()), None),
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#('TKDEy-ML4', KDEyMLauto(newLR()), None),
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]
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def show_results(result_path):
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import pandas as pd
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df = pd.read_csv(result_path + '.csv', sep='\t')
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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pd.set_option('display.width', 1000) # Ajustar el ancho máximo
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pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE"], margins=True)
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print(pv)
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pv = df.pivot_table(index='Dataset', columns="Method", values=["MRAE"], margins=True)
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print(pv)
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pv = df.pivot_table(index='Dataset', columns="Method", values=["KLD"], margins=True)
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print(pv)
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pv = df.pivot_table(index='Dataset', columns="Method", values=["TR-TIME"], margins=True)
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print(pv)
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pv = df.pivot_table(index='Dataset', columns="Method", values=["TE-TIME"], margins=True)
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print(pv)
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def run_experiment(method_name, quantifier, param_grid):
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print('Init method', method_name)
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with open(global_result_path + '.csv', 'at') as csv:
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for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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print('init', dataset)
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# run_experiment(global_result_path, method_name, quantifier, param_grid, dataset)
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local_result_path = os.path.join(Path(global_result_path).parent, method_name + '_' + dataset + '.dataframe')
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if os.path.exists(local_result_path):
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print(f'result file {local_result_path} already exist; skipping')
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report = qp.util.load_report(local_result_path)
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else:
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with qp.util.temp_seed(SEED):
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset, verbose=True)
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train, test = data.train_test
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transductive_names = [name for (name, *_) in TRANSDUCTIVE_METHODS]
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if method_name not in transductive_names:
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if len(param_grid) == 0:
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t_init = time()
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# model selection (train)
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train, val = train.split_stratified(random_state=SEED)
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protocol = UPP(val, repeats=n_bags_val)
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modsel = GridSearchQ(
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quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error='mae'
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)
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t_init = time()
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try:
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modsel.fit(train)
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print(f'best params {modsel.best_params_}')
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print(f'best score {modsel.best_score_}')
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quantifier = modsel.best_model()
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except:
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print('something went wrong... trying to fit the default model')
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quantifier.fit(train)
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train_time = time() - t_init
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else:
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# transductive
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t_init = time()
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quantifier.fit(train) # <-- nothing actually (proyects the X into posteriors only)
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train_time = time() - t_init
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# test
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t_init = time()
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protocol = UPP(test, repeats=n_bags_test)
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report = qp.evaluation.evaluation_report(
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quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True
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)
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test_time = time() - t_init
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report['tr_time'] = train_time
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report['te_time'] = test_time
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report.to_csv(local_result_path)
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means = report.mean(numeric_only=True)
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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')
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csv.flush()
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if __name__ == '__main__':
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qp.environ['SAMPLE_SIZE'] = 500
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qp.environ['N_JOBS'] = -1
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n_bags_val = 100
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n_bags_test = 500
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result_dir = f'results_quantification/ucimulti'
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os.makedirs(result_dir, exist_ok=True)
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global_result_path = f'{result_dir}/allmethods'
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with open(global_result_path + '.csv', 'wt') as csv:
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csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\tTR-TIME\tTE-TIME\n')
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for method_name, quantifier, param_grid in METHODS + TRANSDUCTIVE_METHODS:
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run_experiment(method_name, quantifier, param_grid)
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show_results(global_result_path) |