def predict(self, X):
#print X
data_parser = DataParser()
Xtrain = pd.Series(X)
input_texts = []
for input_text in Xtrain:
input_texts.append(data_parser.split(input_text.decode('utf-8')))
count_vectorizer = CountVectorizer(
vocabulary=self.vocabulary # 学習時の vocabulary を指定する
)
feature_vectors = count_vectorizer.fit_transform(input_texts)
return self.estimator.predict(feature_vectors)
def predict(self, X):
#print X
data_parser = DataParser()
Xtrain = pd.Series(X)
input_texts = []
for input_text in Xtrain:
input_texts.append(data_parser.split(input_text[1].decode('utf-8')))
print input_texts
count_vectorizer = CountVectorizer(
vocabulary=self.vocabulary # 学習時の vocabulary を指定する
)
feature_vectors = count_vectorizer.fit_transform(input_texts)
# featureにcategory_idを追加
features_array = feature_vectors.toarray()
category_ids = np.array(X)[:, 0].T
features_array = np.c_[category_ids, features_array]
return self.estimator.predict(features_array)
