forked from moreo/QuaPy
80 lines
2.3 KiB
Python
80 lines
2.3 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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from tqdm import tqdm
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import pandas as pd
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import quapy as qp
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from quapy.data import LabelledCollection
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from quapy.method.aggregative import *
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import quapy.functional as F
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from data import *
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import os
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import constants
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# LeQua official baselines for task T1A (Binary/Vector)
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# =====================================================
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predictions_path = os.path.join('predictions', 'T1A')
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os.makedirs(predictions_path, exist_ok=True)
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models_path = os.path.join('models', 'T1A')
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os.makedirs(models_path, exist_ok=True)
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pathT1A = './data/T1A/public'
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T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors')
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T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv')
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T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt')
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train = LabelledCollection.load(T1A_trainpath, load_binary_vectors)
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nF = train.instances.shape[1]
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qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
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print(f'number of classes: {len(train.classes_)}')
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print(f'number of training documents: {len(train)}')
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print(f'training prevalence: {F.strprev(train.prevalence())}')
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print(f'training matrix shape: {train.instances.shape}')
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true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
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for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
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# classifier = CalibratedClassifierCV(LogisticRegression(C=1))
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classifier = LogisticRegression(C=1)
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model = quantifier(classifier).fit(train)
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quantifier_name = model.__class__.__name__
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predictions = ResultSubmission(categories=['negative', 'positive'])
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for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF),
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desc=quantifier_name, total=len(true_prevalence)):
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predictions.add(samplename, model.quantify(sample))
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predictions.dump(os.path.join(predictions_path, quantifier_name + '.csv'))
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pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
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mae, mrae = evaluate_submission(true_prevalence, predictions)
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print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}')
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"""
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test:
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CC 0.1859 1.5406
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ACC 0.0453 0.2840
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PCC 0.1793 1.7187
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PACC 0.0287 0.1494
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EMQ 0.0225 0.1020
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HDy 0.0631 0.2307
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validation
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CC 0.1862 1.9587
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ACC 0.0394 0.2669
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PCC 0.1789 2.1383
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PACC 0.0354 0.1587
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EMQ 0.0224 0.0960
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HDy 0.0467 0.2121
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"""
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