forked from moreo/QuaPy
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
import pickle
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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import os
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import sys
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import pandas as pd
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import quapy as qp
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from quapy.method.aggregative import EMQ, DistributionMatching, PACC, ACC, CC, PCC, HDy, OneVsAllAggregative
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from method_kdey import KDEy
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from method_dirichlety import DIRy
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import UPP
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SEED=1
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if __name__ == '__main__':
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qp.environ['SAMPLE_SIZE'] = 100
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qp.environ['N_JOBS'] = -1
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n_bags_val = 250
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n_bags_test = 1000
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result_dir = f'results_tweet_sensibility'
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os.makedirs(result_dir, exist_ok=True)
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method = 'KDEy-MLE'
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global_result_path = f'{result_dir}/{method}'
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if not os.path.exists(global_result_path+'.csv'):
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with open(global_result_path+'.csv', 'wt') as csv:
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csv.write(f'Method\tDataset\tBandwidth\tMAE\tMRAE\tKLD\n')
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with open(global_result_path+'.csv', 'at') as csv:
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for bandwidth in np.linspace(0.01, 0.2, 20):
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for dataset in qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST:
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print('init', dataset)
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local_result_path = global_result_path + '_' + dataset + f'_{bandwidth:.3f}'
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with qp.util.temp_seed(SEED):
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data = qp.datasets.fetch_twitter(dataset, min_df=3, pickle=True, for_model_selection=False)
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quantifier = KDEy(LogisticRegression(), target='max_likelihood', val_split=10, bandwidth=bandwidth)
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quantifier.fit(data.training)
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protocol = UPP(data.test, repeats=n_bags_test)
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report = qp.evaluation.evaluation_report(quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'], verbose=True)
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report.to_csv(f'{local_result_path}.dataframe')
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means = report.mean()
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csv.write(f'{method}\t{data.name}\t{bandwidth}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\n')
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csv.flush()
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df = pd.read_csv(global_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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pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE", "MRAE"])
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print(pv)
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