def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
#remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
#prepare trainning and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
# # random shuffle
df.sample(frac=1)
train_df = df[0:TRAINING_DATA_SIZE]
test_df = df.drop(train_df.index)
# 2 - news description, 0 - class
x_train = train_df[2]
x_test = test_df[2]
y_train = train_df[0]
y_test = test_df[0]
# tokenize sentences
x_train = [word_tokenize(s) for s in x_train.tolist()]
x_test = [word_tokenize(s) for s in x_test.tolist()]
# stemming words
x_train = stemWords(x_train)
x_test = stemWords(x_test)
# process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
LOGGER.debug('Total words: %d', n_words)
# saving n_words and vocab_processor:
# we need to use the same vocabulary processor
# each word the same index
# we also need to save n_words itself for news_cnn_model
with open(VARS_FILE, 'wb') as f: # needs to be opened in binary mode
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
LOGGER.info('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# 导入训练数据集和测试数据集，
# 数据集的格式为：topic，title，description，source
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x代表news的title，y代表news的topic（类别）
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
# x_train经过fit_transform后转化为以下形式：
# array([[ 1, 2, 3, ..., 0, 0, 0],
# [ 1, 8, 9, ..., 0, 0, 0],
# [ 17, 1, 18, ..., 0, 0, 0],
# ...,
# [2112, 2113, 1417, ..., 0, 0, 0],
# [2120, 49, 2121, ..., 0, 0, 0],
# [2123, 1895, 2124, ..., 0, 0, 0]])
# 每一条news的title由字符串序列转换为数字序列，
# 400条news的title转变为400个数字序列
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
# 总共有2127个unique words
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# 将unique words的总数以及vocab_processor存到文件中
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# 构建模型
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# 训练
classifier.fit(x_train, y_train, steps=STEPS)
# 对测试集进行预测，评估泛化性能
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
if os.path.exists(MODEL_OUTPUT_DIR):
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:700]
# x - news title, y - class
x_train = train_df[2]
# y_train [1, entry amount in x_train]
y_train = train_df[0]
test_df = df.drop(train_df.index)
x_test = test_df[2]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
# fit_transform: vocab_processor recognize words in x_train
# x_train [entry amount in x_train, MAX_DOCUMENT_LENGTH]
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
# transform: vocab_processor only mark words in x_test only appeared in x_train
# x_test [entry amount in x_test, MAX_DOCUMENT_LENGTH]
x_test = np.array(list(vocab_processor.transform(x_test)))
# unique words amount in x_train
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
print('hello')
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE)
df['class'] = df['class'].map(CLASS_ENCODING)
df['title'].fillna('Untitled', inplace=True)
num_train = int(len(df) * TRAIN_TEST_SPLIT)
train_df = df[0:num_train]
test_df = df.drop(train_df.index)
# x - news title, y - class
# use only title to train the topic model
x_train = train_df['title']
y_train = train_df['class']
x_test = test_df['title']
y_test = test_df['class']
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'wb') as f: # needs to be opened in binary mode.
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
# This is to create customized estimator
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
# x_train = train_df[1] + ". " + train_df[2]
# x_train = x_train.astype(str)
# y_train = train_df[0]
# x_test = test_df[1] + ". " + test_df[2]
# x_test = x_test.astype(str)
# y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'wb') as f: # needs to be opened in binary mode.
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def loadModel():
global classifier
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_DIR)
df = pd.read_csv('../data/labeled_news.csv', header=None)
train_df = df[0:400]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)))
y_train = train_df[0]
classifier.evaluate(x_train, y_train)
print 'Model update'
def loadModel():
global classifier
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_DIR)
df = pd.read_csv('../data/labeled_news.csv', header=None)
# We have to call evaluate or predict at least once to make the restored Estimator work.
train_df = df[0:400]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)))
y_train = train_df[0]
classifier.evaluate(x_train, y_train)
print("Model updated!")
def loadModel():
global classifier
classifier = estimator.SKCompat(estimator.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_DIR
))
df = pd.read_csv(CSV_FILE, header=None)
train_df = df[0:1]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)), dtype=int)
y_train = np.array(train_df[0], dtype=int)
classifier.score(x_train, y_train)
print 'Model updated'
def loadModel():
global classifier
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_DIR)
# Prepare training and testing
df = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'labeled_news.csv'), header=None)
train_df = df[0:1]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)))
y_train = train_df[0]
classifier.evaluate(x_train, y_train)
print "Model updated."
def loadModel():
global classifier
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
number_classes, n_words, learning_rate),
model_dir=MODEL_DIR)
# Prepare training and testing
df = pd.read_csv(DATA_FILE, header=None)
# TODO: fix this until https://github.com/tensorflow/tensorflow/issues/5548 is solved.
# We have to call evaluate or predict at least once to make the restored Estimator work.
test_df = df[0:testing_index_end]
x_test = test_df[1]
x_test = np.array(list(vocab_processor.transform(x_test)))
y_test = test_df[0]
classifier.evaluate(x_test, y_test)
print "Model updated."
logging.info('news_topic_modeling: model updated')
def loadModel():
global classifier
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(number_classes, n_words, learning_rate),
model_dir=MODEL_DIR)
# Prepare training and testing
df = pd.read_csv(DATA_FILE, header=None)
# TODO: fix this until https://github.com/tensorflow/tensorflow/issues/5548 is solved.
# We have to call evaluate or predict at least once to make the restored Estimator work.
test_df = df[0:testing_index_end]
x_test = test_df[1]
x_test = np.array(list(vocab_processor.transform(x_test)))
y_test = test_df[0]
classifier.evaluate(x_test, y_test)
print "Model updated."
logging.info('news_topic_modeling: model updated')
def loadModel():
global classifier
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_DIR)
# Prepare training and testing
df = pd.read_csv('../data/labeled_news.csv', header=None)
# TODO: fix this until https://github.com/tensorflow/tensorflow/issues/5548 is solved.
# We have to call evaluate or predict at least once to make the restored Estimator work.
train_df = df[0:400]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)))
y_train = train_df[0]
classifier.evaluate(x_train, y_train)
print("Model update.")
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def loadModel():
global classifier
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_DIR)
# Prepare training and testing
df = pd.read_csv('../data/labeled_news.csv', header=None)
# TODO: fix this until https://github.com/tensorflow/tensorflow/issues/5548 is solved.
# We have to call evaluate or predict at least once to make the restored Estimator work.
train_df = df[0:400]
x_train = train_df[1]
x_train = np.array(list(vocab_processor.transform(x_train)))
y_train = train_df[0]
classifier.evaluate(x_train, y_train)
print "Model update."
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
print("Removing previous model...")
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x - title
x_train = train_df[1]
x_test = test_df[1]
# y - classes
y_train = train_df[0]
y_test = test_df[0]
vocab = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
# print(x_train)
x_train = np.array(list(vocab.fit_transform(x_train)))
x_test = np.array(list(vocab.transform(x_test)))
n_words = len(vocab.vocabulary_)
print("Total words: %d" % n_words)
# save the vocabulary
with open(VARS_FILE, 'wb') as f:
pickle.dump(n_words, f)
vocab.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
model = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# train model
model.fit(x_train, y_train, steps=STEPS)
# evaluate model
y_predict = [p['class'] for p in model.predict(x_test, as_iterable=True)]
model_score = metrics.accuracy_score(y_test, y_predict)
print("Accuracy of the model: {0:f}".format(model_score))
def loadModel():
global classifier
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(N_CLASSES, n_words),
model_dir=MODEL_DIR)
# TODO: fix this until https://github.com/tensorflow/tensorflow/issues/5548 is solved.
# We have to call evaluate or predict at least once to make the restored Estimator work.
# the below is just a dummy evaluate
df = pd.read_csv('../data/labeled_news1.csv')
df['class'] = df['class'].map(CLASS_ENCODING)
df['title'].fillna('Untitled', inplace = True)
num_train = int(len(df) * TRAIN_TEST_SPLIT)
train_df = df[0:num_train]
x_train = train_df['title']
y_train = train_df['class']
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
classifier.evaluate(x_train, y_train)
print("Model update.")
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
'''
### brian 07/11/2017
news = pd.read_csv("../training_data/labeled_news_title2.csv")
news['TITLE'] = news['TITLE'].apply(nltk.word_tokenize)
news['DESCRIPTION'] = news['DESCRIPTION'].apply(nltk.word_tokenize)
news['DESCRIPTION'] = news['DESCRIPTION'] + news['TITLE']
news['TEXT'] = [normalize_text(s) for s in news['DESCRIPTION']]
# pull the data into vectors
vectorizer = CountVectorizer()
x = vectorizer.fit_transform(news['TEXT'])
encoder = LabelEncoder()
y = encoder.fit_transform(news['CATEGORY'])
# split into train and test sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
nb = MultinomialNB()
nb.fit(x_train, y_train)
with open(MODEL_OUTPUT_FOLDER, 'wb') as f:
pickle.dump(nb, f)
print "save NB model"
with open(MODEL_FEATURE_FOLDER, 'wb') as f1:
pickle.dump(vectorizer.vocabulary_, f1)
print "save Vocabulary feature"
print "nb score: ", nb.score(x_test, y_test)
#loaded_model = pickle.load(open(MODEL_OUTPUT_FOLDER, 'rb'))
#print "load model score ",loaded_model.score(x_test, y_test)
'''
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:3300]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
x_train = x_train.str.replace('[^\x00-\x7F]', '')
#####################################
'''
x_train = train_df[2]
x_train = x_train.str.replace('[^\x00-\x7F]','')
tokenizer = RegexpTokenizer(r"\w+")
stemmer = PorterStemmer()
#wnl = WordNetLemmatizer()
for i in xrange(0,3000):
x_train[i] = str(x_train[i])
x_train[i] = tokenizer.tokenize(x_train[i])
x_train[i] = list(word for word in x_train[i] if word not in stopwords.words('english'))
x_train[i] = [stemmer.stem(word) for word in x_train[i]]
#x_train[i] = [wnl.lemmatize(word) for word in x_train[i]]
x_train[i] = " ".join(str(word) for word in x_train[i])
'''
###########################################################
y_train = np.array(train_df[0], dtype=int)
x_test = test_df[1]
y_test = np.array(test_df[0], dtype=int)
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = estimator.SKCompat(
estimator.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR,
config=learn.RunConfig(save_checkpoints_secs=10,
save_summary_steps=10)))
# Set up logging for predictions
tensors_to_log = {"prob": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=100)
# Train and predict
classifier.fit(x_train,
y_train,
batch_size=BATCH,
steps=STEPS,
monitors=[logging_hook])
# Configure the accuracy metric
metrics = {
"accuracy":
learn.MetricSpec(metric_fn=tf.metrics.accuracy, prediction_key="class")
}
# Evaluate the model
eval_results = classifier.score(x=x_test, y=y_test, metrics=metrics)
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
df.sample(frac=1)
stemmer = SnowballStemmer('english')
for i in range(0, len(df)):
print i
df_line = str(df.loc[i, 1]) + ' ' + str(df.loc[i, 2]) + ' ' + str(
df.loc[i, 3])
df_line = df_line.translate(None, string.punctuation)
df_line = df_line.lower()
df_tokens = word_tokenize(df_line)
df_filtered_tokens = []
for word in df_tokens:
if word not in stopwords.words('english'):
df_filtered_tokens.append(stemmer.stem(word.decode('utf-8')))
df_filtered_line = ' '.join(df_filtered_tokens)
#print df_filtered_line
df.loc[i, 1] = df_filtered_line
#df.loc[i, 2] = df.loc[i, 3]
train_df = df[0:training_index_end]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
max_document_length, min_frequency, None, None)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
number_classes, n_words, learning_rate),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=steps)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
train_df = df[0:400]
test_df = df.drop(train_df.index)
# x - news description, y - class
x_train = train_df[2]
y_train = train_df[0]
x_test = test_df[2]
y_test = test_df[0]
# tokenize sentences
x_train = [word_tokenize(sentence) for sentence in x_train.tolist()]
x_test = [word_tokenize(sentence) for sentence in x_test.tolist()]
# Stemming words.
norm_x_train = []
norm_x_test = []
for tokens in x_train:
stemmed_tokens = [
stemmer.stem(w.lower()) for w in tokens if not w in stop_words
]
norm_sentence = ' '.join(stemmed_tokens)
norm_x_train.append(norm_sentence)
for tokens in x_test:
stemmed_tokens = [
stemmer.stem(w.lower()) for w in tokens if not w in stop_words
]
norm_sentence = ' '.join(stemmed_tokens)
norm_x_test.append(norm_sentence)
x_train = norm_x_train
x_test = norm_x_test
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
LOGGER.debug('Total words: %d', n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'wb') as f: # needs to be opened in binary mode.
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=STEPS)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
LOGGER.info('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
df.sample(frac=1)
stemmer = SnowballStemmer('english')
for i in range(0, len(df)):
print i
df_line = str(df.loc[i, 1]) + ' ' + str(df.loc[i, 2]) + ' ' + str(df.loc[i, 3])
df_line = df_line.translate(None, string.punctuation)
df_line = df_line.lower()
df_tokens = word_tokenize(df_line)
df_filtered_tokens = []
for word in df_tokens:
if word not in stopwords.words('english'):
df_filtered_tokens.append(stemmer.stem(word.decode('utf-8')))
df_filtered_line = ' '.join(df_filtered_tokens)
#print df_filtered_line
df.loc[i, 1] = df_filtered_line
#df.loc[i, 2] = df.loc[i, 3]
train_df = df[0:training_index_end]
test_df = df.drop(train_df.index)
# x - news title, y - class
x_train = train_df[1]
y_train = train_df[0]
x_test = test_df[1]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length, min_frequency, None, None)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Build model
classifier = learn.Estimator(
model_fn=news_cnn_model.generate_cnn_model(number_classes, n_words, learning_rate),
model_dir=MODEL_OUTPUT_DIR)
# Train and predict
classifier.fit(x_train, y_train, steps=steps)
# Evaluate model
y_predicted = [
p['class'] for p in classifier.predict(x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
def loopFunction(steps, docLength, iteration):
if REMOVE_PREVIOUS_MODEL:
if os.path.exists(MODEL_OUTPUT_DIR):
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
# x - news title, y - class
# training data
train_df = df[0:1100]
x_train = train_df[1]
# y_train [1, entry amount in x_train]
y_train = train_df[0]
# testing data
test_df = df.drop(train_df.index)
x_test = test_df[1]
y_test = test_df[0]
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(docLength)
# fit_transform: vocab_processor recognize words in x_train
# x_train [entry amount in x_train, MAX_DOCUMENT_LENGTH]
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
# transform: vocab_processor only mark words in x_test only appeared in x_train
# x_test [entry amount in x_test, MAX_DOCUMENT_LENGTH]
x_test = np.array(list(vocab_processor.transform(x_test)))
# unique words amount in x_train
n_words = len(vocab_processor.vocabulary_)
# print('Total words: %d' % n_words)
# Saving n_words and vocab_processor:
with open(VARS_FILE, 'w') as f:
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
# Set up logging for predictions
tensors_to_log = {"opt": "softmax"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=20)
# validation monitor, log the metrics
# https://www.tensorflow.org/get_started/monitors
validation_metrics = {
"accuracy":
MetricSpec(metric_fn=tf.contrib.metrics.streaming_accuracy,
prediction_key=learn.PredictionKey.CLASSES),
"precision":
MetricSpec(metric_fn=tf.contrib.metrics.streaming_precision,
prediction_key=learn.PredictionKey.CLASSES)
}
validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(
x_test, y_test, every_n_steps=11, metrics=validation_metrics)
# Build model
classifier = learn.SKCompat(
learn.Estimator(model_fn=news_cnn_model.generate_cnn_model(
N_CLASSES, n_words),
model_dir=MODEL_OUTPUT_DIR,
config=learn.RunConfig(save_checkpoints_secs=None,
save_checkpoints_steps=10)))
# Train
classifier.fit(x_train,
y_train,
steps=steps,
monitors=[validation_monitor])
# Evaluate model
prediction = classifier.predict(x_test)
y_predicted = prediction['classes']
score = metrics.accuracy_score(y_test, y_predicted)
# with open('night_test.csv','ab') as f:
# writer=csv.writer(f, delimiter=',')
# writer.writerow([iteration, steps, docLength, score])
print('Accuracy: {0:f}'.format(score))
def main(unused_argv):
if REMOVE_PREVIOUS_MODEL:
# Remove old model
shutil.rmtree(MODEL_OUTPUT_DIR)
os.mkdir(MODEL_OUTPUT_DIR)
# Prepare training and testing data
df = pd.read_csv(DATA_SET_FILE, header=None)
# df = df.sample(frac=1).reset_index(drop=True)
train_df = df[0:1800]
test_df = df.drop(train_df.index)
# x - news title + source, y - class
x_train = train_df[1] + ' ' + train_df[3].astype(str).map(str.strip)
y_train = train_df[0]
x_test = test_df[1] + ' ' + test_df[3].astype(str).map(str.strip)
y_test = list(test_df[0])
# Process vocabulary
vocab_processor = learn.preprocessing.VocabularyProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(vocab_processor.fit_transform(x_train)))
x_test = np.array(list(vocab_processor.transform(x_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
# Saving n_words and vocab_processor for serving:
with open(VARS_FILE, 'wb') as f: # needs to be opened in binary mode.
pickle.dump(n_words, f)
vocab_processor.save(VOCAB_PROCESSOR_SAVE_FILE)
'''
Version 2
'''
# Build model
model_fn = news_cnn_model.generate_cnn_model(N_CLASSES, n_words)
classifier = tf.estimator.Estimator(model_fn=model_fn)
# Train
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={WORDS_FEATURE: x_train},
y=y_train,
batch_size=BATCH_SIZE,
num_epochs=None,
shuffle=True)
classifier.train(input_fn=train_input_fn, steps=STEPS)
# Predict
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={WORDS_FEATURE: x_test}, num_epochs=1, shuffle=False)
y_predicted = list(classifier.predict(input_fn=predict_input_fn))
predicted_classes = [p['class'] for p in y_predicted]
# Evaluate w/o 'sports'
for i in range(30):
print(predicted_classes[i])
count = 0
sports = 0
for i in range(len(predicted_classes)):
if (y_test[i] == 6):
sports += 1
continue
if (predicted_classes[i] == y_test[i]):
count += 1
total = len(predicted_classes) - sports
print('Augmented Accuracy: {0:f}'.format(float(count) / total))
# Evaluate.
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={WORDS_FEATURE: x_test},
y=np.array(y_test),
num_epochs=1,
shuffle=False)
scores = classifier.evaluate(input_fn=test_input_fn)
print('Overall Accuracy: {0:f}'.format(scores['accuracy']))
# Export
export_dir = classifier.export_savedmodel(MODEL_OUTPUT_DIR,
serving_input_receiver_fn)
print('Model exported to %s' % export_dir)
with open(EXPORT_DIR_FILE,
'wb') as f: # needs to be opened in binary mode.
pickle.dump(export_dir, f)
'''
Version 1
'''
'''
