forked from moreo/QuaPy
56 lines
1.9 KiB
Python
56 lines
1.9 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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predictions_path = os.path.join('predictions', 'T1B') # multiclass - vector
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os.makedirs(predictions_path, exist_ok=True)
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pathT1B = './data/T1B/public'
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T1B_devvectors_path = os.path.join(pathT1B, 'dev_vectors')
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T1B_devprevalence_path = os.path.join(pathT1B, 'dev_prevalences.csv')
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T1B_trainpath = os.path.join(pathT1B, 'training_vectors.txt')
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T1B_catmap = os.path.join(pathT1B, 'training_vectors_label_map.txt')
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train = LabelledCollection.load(T1B_trainpath, load_binary_vectors)
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nF = train.instances.shape[1]
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qp.environ['SAMPLE_SIZE'] = constants.T1B_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(T1B_devprevalence_path)
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cat2code, categories = load_category_map(T1B_catmap)
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for quantifier in [PACC]: # [CC, ACC, PCC, PACC, EMQ]:
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classifier = CalibratedClassifierCV(LogisticRegression())
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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=categories)
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for samplename, sample in tqdm(gen_load_samples_T1(T1B_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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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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