forked from moreo/QuaPy
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
import pickle
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import numpy as np
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import os
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import pandas as pd
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from distribution_matching.commons import METHODS, BIN_METHODS, new_method, show_results
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import quapy as qp
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from quapy.model_selection import GridSearchQ
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if __name__ == '__main__':
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for task in ['T1A', 'T1B']:
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qp.environ['SAMPLE_SIZE'] = qp.datasets.LEQUA2022_SAMPLE_SIZE[task]
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qp.environ['N_JOBS'] = -1
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for optim in ['mae', 'mrae']:
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result_dir = f'results/lequa/{task}/{optim}'
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os.makedirs(result_dir, exist_ok=True)
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for method in (METHODS if task=='T1B' else BIN_METHODS):
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print('Init method', method)
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result_path = f'{result_dir}/{method}'
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if os.path.exists(result_path+'.csv'):
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print(f'file {result_path}.csv already exist; skipping')
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continue
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with open(result_path+'.csv', 'wt') as csv:
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csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\n')
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dataset = task
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train, val_gen, test_gen = qp.datasets.fetch_lequa2022(dataset)
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print(f'init {dataset} #instances: {len(train)}')
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param_grid, quantifier = new_method(method)
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if param_grid is not None:
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modsel = GridSearchQ(quantifier, param_grid, protocol=val_gen, refit=False, n_jobs=-1, verbose=1, error=optim)
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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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pickle.dump(
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(modsel.best_params_, modsel.best_score_,),
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open(f'{result_path}.hyper.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
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quantifier = modsel.best_model()
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else:
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print('debug mode... skipping model selection')
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quantifier.fit(train)
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report = qp.evaluation.evaluation_report(
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quantifier, protocol=test_gen, error_metrics=['mae', 'mrae', 'kld'],
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verbose=True, verbose_error=optim[1:], n_jobs=-1
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)
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means = report.mean()
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report.to_csv(result_path+'.dataframe')
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csv.write(f'{method}\tLeQua-{task}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\n')
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csv.flush()
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print(means)
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show_results(result_path)
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