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Training_10fold.py
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Training_10fold.py
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import numpy as np
import gensim
from sklearn.model_selection import KFold
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import accuracy_score, confusion_matrix
from gensim.models import Word2Vec
from sklearn.neighbors import KNeighborsClassifier
from sklearn import linear_model
from nltk.corpus import stopwords
from gensim.models import KeyedVectors
from sklearn.metrics import classification_report
import gc
import scipy.sparse as sp
import time
from sklearn.naive_bayes import MultinomialNB
from imblearn.over_sampling import SMOTE
from representations import Representations
from Models import Models
from obter_dados import dados
from tratar_texto import limpe_texto
def Classify(X, Y, cls, rep, k=5000):
# Start moment
Start_moment = time.time()
title = 'Classificando com {} e {} k={}'.format(cls, rep, k)
print(title)
# Creating the K-fold cross validator
K_fold = KFold(n_splits=10, shuffle=True)
# Labels
test_labels = np.array([], 'int32')
test_pred = np.array([], 'int32')
# Confusion Matrix
confusion = np.array([[0, 0], [ 0, 0]])
# The test
for train_indices, test_indices in K_fold.split(X):
print('Running .... =)')
X_train = [X[i] for i in train_indices]
Y_train = [Y[i] for i in train_indices]
X_test = [X[i] for i in test_indices]
Y_test = [Y[i] for i in test_indices]
train_x, train_y, test_x, test_y = Representations().get_representation(
rep=rep, train_x=X_train, train_y=Y_train, test_x=X_test, test_y=Y_test, k=k, cat=None)
# c = Counter(Y_train)
# print(Counter(train_y))
# print({1:c.most_common(1)[0][1], 0:c.most_common(1)[0][1], 2:c.most_common(1)[0][1]})
sm = SMOTE(sampling_strategy='minority',
random_state=None)
# sm = SMOTE(sampling_strategy={1:c.most_common(1)[0][1], 0:c.most_common(1)[0][1], 2:c.most_common(1)[0][1]}, random_state=None)
# print(len(train_y))
train_x, train_y = sm.fit_sample(train_x, train_y)
# print(Counter(train_y))
test_labels = np.append(test_labels, Y_test)
classifier = Models().get_classifier(cls)
classifier.fit(train_x, train_y)
# Train_Classifier(classifier, X_train, Y_train)
pred = classifier.predict(test_x)
test_pred = np.append(test_pred, pred)
# print(test_y)
# print(pred)
confusion += confusion_matrix(test_y, pred)
# report = classification_report(test_labels, test_pred, target_names=['Contrário', 'Favorável'] if plb =='polaridade' else ['neutro', 'opiniao'])
report = classification_report(test_labels, test_pred,
target_names=['no','yes'])
print(report)
print("Confusion matrix:")
print(confusion)
Finish_moment = time.time()
tm = "It took " + str((Finish_moment - Start_moment)) + " seconds"
print(tm)
#
# f = open(Output_string(cls, clf), 'w+')
# f.write('Word2vec with ' + clf + '\n Classifying ' + cls + '\n')
# f.write(title + '\n \n')
# f.write(report + '\n \n')
# f.write("Confusion Matrix: \n")
# f.write(np.array_str(confusion) + '\n \n')
# f.write(tm)
# f.close()
classificadores = ['LogReg', 'NB', 'MLP']
representacao = ['TFidf', 'CountVec']
textos, tags = dados()
textos, tags = limpe_texto(textos, tags)
print(len(textos))
print(len([t for t in tags if t =='no']))
for k in range(5000, 6000, 1000):
for c in classificadores:
for r in representacao:
Classify(textos, tags, c, r, k)