75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
import os
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from time import time
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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 LocalStack.method import *
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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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METHODS = [
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('PACC', PACC(), {}),
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('EMQ', EMQ(), {}),
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('KDEy-ML', KDEyML(), {}),
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]
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TRANSDUCTIVE_METHODS = [
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('LSQ', LocalStackingQuantification(EMQ()), {}),
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('LSQ2', LocalStackingQuantification2(EMQ()), {}),
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('LSQ-torch', LocalStackingQuantification3(EMQ()), {})
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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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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 = 25
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n_bags_test = 100
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result_dir = f'results_quantification/localstack'
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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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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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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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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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show_results(global_result_path) |