def test_tokenizer_serde_fitting():
sample_texts = [
'There was a time that the pieces fit, but I watched them fall away',
'Mildewed and smoldering, strangled by our coveting',
'I\'ve done the math enough to know the dangers of our second guessing'
]
tokenizer = text.Tokenizer(num_words=100)
tokenizer.fit_on_texts(sample_texts)
seq_generator = tokenizer.texts_to_sequences_generator(sample_texts)
sequences = [seq for seq in seq_generator]
tokenizer.fit_on_sequences(sequences)
tokenizer_json = tokenizer.to_json()
recovered = text.tokenizer_from_json(tokenizer_json)
assert tokenizer.char_level == recovered.char_level
assert tokenizer.document_count == recovered.document_count
assert tokenizer.filters == recovered.filters
assert tokenizer.lower == recovered.lower
assert tokenizer.num_words == recovered.num_words
assert tokenizer.oov_token == recovered.oov_token
assert tokenizer.word_docs == recovered.word_docs
assert tokenizer.word_counts == recovered.word_counts
assert tokenizer.word_index == recovered.word_index
assert tokenizer.index_word == recovered.index_word
assert tokenizer.index_docs == recovered.index_docs
def get_tokenizer(self):
'''
:param embedded_matrix_size: 嵌入矩阵大小
:return: tokenizer
'''
if not os.path.exists(self.tokenizer_path):
self.remove_stop_word_list = self.get_remove_stop_word()
tokenizer = text.Tokenizer(num_words=self.embedded_matrix_size,
lower=False,
char_level=False)
tokenizer.fit_on_texts(self.remove_stop_word_list)
tokenizer_json = tokenizer.to_json()
with open(self.tokenizer_path, "w") as f:
f.write(tokenizer_json)
print("save tokenizer_json success as '{}'".format(
self.tokenizer_path))
return tokenizer
else:
print("更换数据集需手动删除{}此文件,并重新运行代码后会自动生成tokenizer.".format(
self.tokenizer_path))
with open(self.tokenizer_path, "r") as f:
tokenizer_json = f.read()
tokenizer = text.tokenizer_from_json(tokenizer_json)
print("load tokenizer_json success as '{}'".format(
self.tokenizer_path))
return tokenizer
def __init__(self,
train_data_path='data/token_train.tsv',
max_document_length=100,
vocabulary_size=5000,
embedding_size=300,
dropout_keep_prob=0.5,
lr=1e-4,
batch_size=50,
num_epochs=5,
dev_size=0.2):
self.dev_size = dev_size
self.num_epochs = num_epochs
self.batch_size = batch_size
self.lr = lr
self.dropout_keep_prob = dropout_keep_prob
self.embedding_size = embedding_size
self.vocabulary_size = vocabulary_size
self.max_document_length = max_document_length
self.train_data_path = train_data_path
self.pickle_path = os.path.splitext(train_data_path)[0] + '.model.p'
self._tokenizer = text.Tokenizer(num_words=vocabulary_size,
char_level=False,
filters='')
raw_x, raw_y = self._aggregate_raw_data_from_dir(self.train_data_path)
self._tokenizer.fit_on_texts(raw_x)
self.x_train, self.y_train = self._prepare_from_raw_data(raw_x, raw_y)
self.model = self._create_model()
def test_tokenizer_unicode():
sample_texts = [u'ali veli kırk dokuz elli',
u'ali veli kırk dokuz elli veli kırk dokuz']
tokenizer = text.Tokenizer(num_words=5)
tokenizer.fit_on_texts(sample_texts)
assert len(tokenizer.word_counts) == 5
def test_tokenizer_oov_flag():
"""Test of Out of Vocabulary (OOV) flag in text.Tokenizer
"""
x_train = ['This text has only known words']
x_test = ['This text has some unknown words'] # 2 OOVs: some, unknown
# Default, without OOV flag
tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
assert len(x_test_seq[0]) == 4 # discards 2 OOVs
# With OOV feature
tokenizer = text.Tokenizer(oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
assert len(x_test_seq[0]) == 6 # OOVs marked in place
def test_tokenizer_serde_no_fitting():
tokenizer = text.Tokenizer(num_words=100)
tokenizer_json = tokenizer.to_json()
recovered = text.tokenizer_from_json(tokenizer_json)
assert tokenizer.get_config() == recovered.get_config()
assert tokenizer.word_docs == recovered.word_docs
assert tokenizer.word_counts == recovered.word_counts
assert tokenizer.word_index == recovered.word_index
assert tokenizer.index_word == recovered.index_word
assert tokenizer.index_docs == recovered.index_docs
def test_tokenizer_lower_flag():
"""Tests for `lower` flag in text.Tokenizer
"""
# word level tokenizer with sentences as texts
word_tokenizer = text.Tokenizer(lower=True)
texts = [
'The cat sat on the mat.', 'The dog sat on the log.',
'Dog and Cat living Together.'
]
word_tokenizer.fit_on_texts(texts)
expected_word_counts = OrderedDict([('the', 4), ('cat', 2), ('sat', 2),
('on', 2), ('mat', 1), ('dog', 2),
('log', 1), ('and', 1), ('living', 1),
('together', 1)])
assert word_tokenizer.word_counts == expected_word_counts
# word level tokenizer with word_sequences as texts
word_tokenizer = text.Tokenizer(lower=True)
word_sequences = [['The', 'cat', 'is', 'sitting'],
['The', 'dog', 'is', 'standing']]
word_tokenizer.fit_on_texts(word_sequences)
expected_word_counts = OrderedDict([('the', 2), ('cat', 1), ('is', 2),
('sitting', 1), ('dog', 1),
('standing', 1)])
assert word_tokenizer.word_counts == expected_word_counts
# char level tokenizer with sentences as texts
char_tokenizer = text.Tokenizer(lower=True, char_level=True)
texts = [
'The cat sat on the mat.', 'The dog sat on the log.',
'Dog and Cat living Together.'
]
char_tokenizer.fit_on_texts(texts)
expected_word_counts = OrderedDict([('t', 11), ('h', 5), ('e', 6),
(' ', 14), ('c', 2), ('a', 6),
('s', 2), ('o', 6), ('n', 4), ('m', 1),
('.', 3), ('d', 3), ('g', 5), ('l', 2),
('i', 2), ('v', 1), ('r', 1)])
assert char_tokenizer.word_counts == expected_word_counts
def test_sequential_fit():
texts = [
'The cat sat on the mat.', 'The dog sat on the log.',
'Dogs and cats living together.'
]
word_sequences = [['The', 'cat', 'is', 'sitting'],
['The', 'dog', 'is', 'standing']]
tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(texts)
tokenizer.fit_on_texts(word_sequences)
assert tokenizer.document_count == 5
tokenizer.texts_to_matrix(texts)
tokenizer.texts_to_matrix(word_sequences)
def test_tokenizer():
sample_texts = ['The cat sat on the mat.',
'The dog sat on the log.',
'Dogs and cats living together.']
tokenizer = text.Tokenizer(num_words=10)
tokenizer.fit_on_texts(sample_texts)
sequences = []
for seq in tokenizer.texts_to_sequences_generator(sample_texts):
sequences.append(seq)
assert np.max(np.max(sequences)) < 10
assert np.min(np.min(sequences)) == 1
tokenizer.fit_on_sequences(sequences)
for mode in ['binary', 'count', 'tfidf', 'freq']:
tokenizer.texts_to_matrix(sample_texts, mode)
def get_word2vec(dataframe):
#词向量
# jieba分词
wordList = dataframe["title"].apply(lambda x: list(jieba.cut(x)))
#words_dict =[]
texts = []
stoplist = []
# 去掉停用词
for words in wordList:
line = [word for word in words if word not in stoplist]
#words_dict.extend([word for word in line])
texts.append(line)
maxlen = 0
for line in texts:
if maxlen < len(line):
maxlen = len(line)
max_words = 50000
# 利用keras的Tokenizer进行onehot，并调整未等长数组
tokenizer = text.Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(texts)
data_w = tokenizer.texts_to_sequences(texts)
word2vec = sequence.pad_sequences(data_w, maxlen=maxlen)
return word2vec
def __init__(self):
"""Constructor for the selector."""
logging.info('Initializing tokenizer..')
words, embedding_matrix = self._build_embedding_matrix()
self.tokenizer = text.Tokenizer(num_words=len(words), lower=False)
# Tokenizer treats each item in a nested list as a token.
self.tokenizer.fit_on_texts([[word] for word in words])
# Preppend a array of zeros to the embeddings matrix that will be used by
# out-of-vocabulary words.
embedding_matrix = np.concatenate(
[np.zeros((1, embedding_matrix.shape[1])), embedding_matrix])
assert len(words) == len(self.tokenizer.word_index), (
'embeddings_matrix and tokenizer.word_index do not have the same size:'
' {} and {}, respectively'.format(len(words),
len(self.tokenizer.word_index)))
assert all([
self.tokenizer.word_index[word] == i + 1
for i, word in enumerate(words)
]), ('embeddings_matrix and tokenizer.word_index are not aligned.')
self.model = self._build_model(embedding_matrix)
def load_dataset(test_sen=None):
EMBEDDING_FILE = config.EMBEDDING_300
df = pd.read_csv(config.DATA_PATH)
X = df["content"].values
Y = df["label"].values
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=0.3,
random_state=109)
# data preprocessing
print(X[0])
puncList = ["।", "”", "“", "’"]
x = "".join(puncList)
filterString = x + '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n০১২৩৪৫৬৭৮৯'
tokenizer = text.Tokenizer(
num_words=50000,
filters=filterString,
lower=False,
)
tokenizer.fit_on_texts(x_train)
train_idx = tokenizer.texts_to_sequences(x_train)
test_idx = tokenizer.texts_to_sequences(x_test)
word_index = tokenizer.word_index
embeddings_index = {}
for i, line in enumerate(open(EMBEDDING_FILE, encoding="utf-8")):
val = line.split()
embeddings_index[val[0]] = np.asarray(val[1:], dtype='float32')
embedding_matrix = np.zeros((len(word_index) + 1, 300))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
x_train = sequence.pad_sequences(train_idx,
maxlen=32,
padding='post',
truncating='post')
x_test = sequence.pad_sequences(test_idx,
maxlen=32,
padding='post',
truncating='post')
test_size = len(x_test)
dev_size = (int)(test_size * 0.1)
x_dev = x_test[:dev_size]
x_test = x_test[dev_size:]
y_dev = y_test[:dev_size]
y_test = y_test[dev_size:]
x_train = torch.tensor(x_train, dtype=torch.long)
y_train = torch.tensor(y_train, dtype=torch.float32)
train = TensorDataset(x_train, y_train)
train_iter = DataLoader(train, batch_size=32)
x_test = torch.tensor(x_test, dtype=torch.long)
y_test = torch.tensor(y_test, dtype=torch.float32)
test = TensorDataset(x_test, y_test)
test_iter = DataLoader(test, batch_size=32)
x_dev = torch.tensor(x_dev, dtype=torch.long)
y_dev = torch.tensor(y_dev, dtype=torch.float32)
valid = TensorDataset(x_dev, y_dev)
valid_iter = DataLoader(valid, batch_size=32)
word_embeddings = embedding_matrix
vocab_size = 50000
return vocab_size, word_embeddings, train_iter, valid_iter, test_iter
# Data Loading
train = pd.read_csv('../Data/MELD.Raw/train_sent_emo.csv', dtype=str)
validation = pd.read_csv('../Data/MELD.Raw/dev_sent_emo.csv', dtype=str)
test = pd.read_csv('../Data/MELD.Raw/test_sent_emo.csv', dtype=str)
# Create mapping to identify audio files
train["ID"] = 'dia' + train["Dialogue_ID"] + '_utt' + train[
"Utterance_ID"] + '.jpg'
validation["ID"] = 'dia' + validation["Dialogue_ID"] + '_utt' + validation[
"Utterance_ID"] + '.jpg'
test["ID"] = 'dia' + test["Dialogue_ID"] + '_utt' + test[
"Utterance_ID"] + '.jpg'
# Text Features
tokenizer = kt.Tokenizer(num_words=5000)
tokenizer.fit_on_texts(train['Utterance'])
vocab_size = len(tokenizer.word_index) + 1
train_tokens = tokenizer.texts_to_sequences(train['Utterance'])
text_features = pd.DataFrame(ks.pad_sequences(train_tokens, maxlen=200))
validation_tokens = tokenizer.texts_to_sequences(validation['Utterance'])
validation_features = pd.DataFrame(
ks.pad_sequences(validation_tokens, maxlen=200))
# Data Pipeline
def train_generator(features, batch):
train_generator = ki.ImageDataGenerator(rescale=1. / 255.)
def trainer(dict_csv='test.csv'):
data = pd.read_csv(dict_csv, engine='python')
train_size = int(len(data) * .7)
train_posts = data['documents']
train_tags = data['tags']
test_posts = data['documents'][train_size:]
test_tags = data['tags'][train_size:]
posts = data['documents']
dlp_data = {'filename': [], 'tags': []}
vocab_size = 10000
tokenize = text.Tokenizer(num_words=vocab_size)
tokenize.fit_on_texts(train_posts)
# save token
with open('tokenizer.pickle', 'wb') as handle:
pickle.dump(tokenize, handle, protocol=pickle.HIGHEST_PROTOCOL)
print("Saving tokenizer with name tokenizer.pickle")
x_train = tokenize.texts_to_matrix(train_posts)
x_test = tokenize.texts_to_matrix(test_posts)
x_post = tokenize.texts_to_matrix(posts)
encoder = preprocessing.LabelBinarizer()
encoder.fit(train_tags)
y_train = encoder.transform(train_tags)
y_test = encoder.transform(test_tags)
text_labels = encoder.classes_
num_labels = len(np.unique(y_train))
batch_size = 1024
model = Sequential()
##Buat hidden layer, gunanya buat naikin akurasi
model.add(Dense(512, input_shape=(vocab_size,)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(128))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(512))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(128))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(64))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(num_labels))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=256,
verbose=1,
validation_split=0.1)
score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
print("\n Saved h5 json model to disk with name model.json ")
model.save_weights("model.h5")
print("\n Saved model to disk with name model.h5")
print("Training done")
pred = model.predict(np.array(x_post))
pred = pred > 0.5
for i in range(0, len(posts)):
print('Document name: %s, is %s' % (data['filename'][i], text_labels[np.argmax(pred[i])]))
dlp_data['filename'].append(data['filename'][i])
dlp_data['tags'].append(text_labels[np.argmax(pred[i])])
df = pd.DataFrame(dlp_data, columns=['filename', 'tags'])
df.to_csv('dlp.csv', encoding="utf-8")
print('Saved CSV model')
json_file = open('model.json', 'r')
loaded_json_model = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_json_model)
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(x_test, y_test, verbose=1)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1] * 100))
def LSTM(positive_tweets, negative_tweets):
""" Return a LSTM model fitted on postive_tweets and negative_tweets
Keyword arguments:
positive_tweets -- the file (.csv) that contains the positive tweets
negative_tweets -- the file (.csv) that contains the negative tweets
"""
pos_df = pd.read_csv(positive_tweets, index_col=0)
neg_df = pd.read_csv(negative_tweets, index_col=0)
train = pd.concat([pos_df, neg_df])
#Randomize order
train = train.sample(frac=1, random_state=1)
train = train.dropna()
# The maximum number of words to be used. (most frequent)
MAX_NB_WORDS = 450000
# Max number of words in each complaint.
MAX_SEQUENCE_LENGTH = 50
EMBEDDING_DIM = 300
tokenizer = text.Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(train.tweets.values)
word_index = tokenizer.word_index
#number of word with that appears at least 5 times
words = len([
k for k in tokenizer.word_index.keys() if tokenizer.word_counts[k] > 4
])
# The maximum number of words to be used. (most frequent)
MAX_NB_WORDS = words
# Max number of words in each complaint.
MAX_SEQUENCE_LENGTH = 50
EMBEDDING_DIM = 300
tokenizer = text.Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(train.tweets.values)
word_index = tokenizer.word_index
#Create sequence of index
X = tokenizer.texts_to_sequences(train.tweets.values)
X = sequence.pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)
Y = train.label
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=0.20,
random_state=1)
#Build model
batch_size = 8192
model = Sequential()
model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=X.shape[1]))
model.add(LSTM(256, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#To have our best model
es = EarlyStopping(monitor='val_acc', mode='max', verbose=1, patience=5)
mc = ModelCheckpoint('best_model.h5',
monitor='val_acc',
mode='max',
verbose=1,
save_best_only=True)
#Training
model.fit(X_train,
Y_train,
batch_size=batch_size,
epochs=250,
validation_data=(X_test, Y_test),
callbacks=[es, mc])
saved_model = load_model('best_model.h5')
return saved_model