def tokenize(self, writeDictionaryToCsv=False):
print('Tokenizing')
print('adding n-grams')
self.trainData = self.addNgGrams(self.trainData)
self.testData = self.addNgGrams(self.testData)
all_reviews = self.trainData.append(self.testData)
tokenizer = Tokenizer(num_words=30000)
print('fitting')
tokenizer.fit_on_texts(all_reviews)
tokenizer.fit_on_sequences(all_reviews)
print('texts_to_sequences')
self.trainData = tokenizer.texts_to_sequences(self.trainData)
self.testData = tokenizer.texts_to_sequences(self.testData)
print('sequences_to_matrix')
self.trainData = tokenizer.sequences_to_matrix(self.trainData)
self.testData = tokenizer.sequences_to_matrix(self.testData)
all_reviews = np.vstack((self.trainData, self.testData))
#does not work with svm no time to change code
self.trainData = self.trainData / self.trainData.sum(axis=1)[:, None]
self.testData = self.testData / self.testData.sum(axis=1)[:, None]
if (writeDictionaryToCsv):
self.ExportFeatureSpace(tokenizer)
print('Finished tokenizing')
def closure(mu):
(x_train, y_train), (_, _) = imdb.load_data()
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_sequences(x_train)
x_train = tokenizer.sequences_to_matrix(x_train, "tfidf")
# Note: svd_solver=full is needed on GPU server
x_train = PCA(n_components=100,
svd_solver='full').fit_transform(x_train)
ds = {"data": x_train, "target": y_train}
# Apply noise and return
res = preprocess_and_noise(dataset=ds, mu=mu)
return res
