77 lines
2.4 KiB
Python
77 lines
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
|
|
"""
|
|
|
|
|