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QuaPy/LeQua2022/baselinesSVD_T1A.py

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2.4 KiB
Python

import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants
from sklearn.decomposition import TruncatedSVD
# LeQua official baselines for task T1A (Binary/Vector)
# =====================================================
predictions_path = os.path.join('predictions', 'T1A')
os.makedirs(predictions_path, exist_ok=True)
models_path = os.path.join('models', 'T1A')
os.makedirs(models_path, exist_ok=True)
pathT1A = './data/T1A/public'
T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors')
T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv')
T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt')
train = LabelledCollection.load(T1A_trainpath, load_binary_vectors)
nF = train.instances.shape[1]
svd = TruncatedSVD(n_components=300)
train.instances = svd.fit_transform(train.instances)
qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
# classifier = CalibratedClassifierCV(LogisticRegression())
classifier = LogisticRegression()
model = quantifier(classifier).fit(train)
quantifier_name = model.__class__.__name__
predictions = ResultSubmission(categories=['negative', 'positive'])
for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF),
desc=quantifier_name, total=len(true_prevalence)):
sample = svd.transform(sample)
predictions.add(samplename, model.quantify(sample))
predictions.dump(os.path.join(predictions_path, quantifier_name + '.svd.csv'))
pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.svd.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
mae, mrae = evaluate_submission(true_prevalence, predictions)
print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}')
"""
validation
CC 0.1862 1.9587
ACC 0.0394 0.2669
PCC 0.1789 2.1383
PACC 0.0354 0.1587
EMQ 0.0224 0.0960
HDy 0.0467 0.2121
"""