def learn():
stoplist = makeStoplist()
features = extractFeaturesFromFile(stoplist=stoplist)
vectorizer = TfidfVectorizer(encoding=ENCODING)
X_train = vectorizer.fit_transform(
[" ".join(feature[1:]) for feature in features])
y_train = np.zeros(len(features))
for i in range(len(features)):
if features[i][0] == "+1":
y_train[i] = 1
clf = LogisticRegression()
clf.fit(X_train, y_train)
io.savemat("X_train", {"X_train": X_train})
np.save("y_train", y_train)
joblib.dump(vectorizer, "tfidf.vec")
clf.save("logreg")
