results = []
for perc in range(1, 100, 5):
p = np.empty([numFolds])
ch2 = SelectPercentile(chi2, percentile=perc)
#perfrom 5folds cross-validation
for i in range(0, numFolds):
#data_txt preproccessing - tokenization, selecting 90% of the best features
vectorizer = TfidfVectorizer(tokenizer=uux_preprocessing.tokenize)
X_train_features = vectorizer.fit_transform(x_train_folds[i])
X_train_features_names = vectorizer.fit(x_train_folds[i]).vocabulary_
X_train_features = ch2.fit_transform(X_train_features,
y_train_folds[i])
selected_features_names = np.asarray(
vectorizer.get_feature_names())[ch2.get_support()]
classifier = Pipeline([('tfidf', vectorizer), ('chi2', ch2),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(x_train_folds[i], y_train_folds[i])
predicted = classifier.predict(x_test_folds[i])
print metrics.precision_score(y_test_folds[i], predicted)
p[i] = metrics.precision_score(y_test_folds[i], predicted)
print p
results = np.append(results, p.mean())
print "Results"
from sklearn.externals import joblib
#get the data_txt from DB
numDimensions = 22
numFolds = 5
X_train = uux_data.getUUXSentences(numDimensions)
y_train = uux_data.getUUXSentenceDimension(numDimensions)
y_train_binary = MultiLabelBinarizer().fit_transform(y_train)
target_names = uux_data.getUUXDimensions(numDimensions)
#data_txt preproccessing - tokenization, selecting 90% of the best features
vectorizer = TfidfVectorizer(tokenizer=uux_preprocessing.tokenize)
X_train_features = vectorizer.fit_transform(X_train)
X_train_features_names = vectorizer.fit(X_train).vocabulary_
ch2 = SelectPercentile(chi2, percentile=16)
X_train_features = ch2.fit_transform(X_train_features, y_train_binary)
selected_features_names = np.asarray(vectorizer.get_feature_names())[ch2.get_support()]
print str(len(selected_features_names))
classifier = Pipeline([
('tfidf', vectorizer),
('chi2', ch2),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, y_train_binary)
joblib.dump(classifier, 'classifier/uux_classifier.pkl')
#Conctatenate the 4 folds for training
for j in range(0, numFolds):
if (i != j):
fold_x_train = np.concatenate((fold_x_train, folds_X_train[j]))
fold_y_train = np.concatenate((fold_y_train, folds_y_train[j]))
fold_x_test = folds_X_train[i]
fold_y_test = folds_y_train[i]
#data_txt preproccessing - tokenization, selecting 90% of the best features
vectorizer = TfidfVectorizer(tokenizer=uux_preprocessing.tokenize)
X_train_features = vectorizer.fit_transform(fold_x_train)
X_train_features_names = vectorizer.fit(fold_x_train).vocabulary_
ch2 = SelectPercentile(chi2, percentile=16)
X_train_features = ch2.fit_transform(X_train_features, fold_y_train)
selected_features_names = np.asarray(
vectorizer.get_feature_names())[ch2.get_support()]
print str(len(selected_features_names))
classifier = Pipeline([('tfidf', vectorizer), ('chi2', ch2),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(fold_x_train, fold_y_train)
predicted = classifier.predict(fold_x_test)
print classification_report(fold_y_test,
predicted,
target_names=target_names)
p[i], r[i], f1[i], s[i] = metrics.precision_recall_fscore_support(
