return documents, classes
def category_to_number(classes, category_type):
return list(map(category_type.index, classes))
print('> Read files...')
categories = os.listdir(data_dir)
print('> Split data to test and train')
documents, classes = get_texts(categories)
train_docs, test_docs, train_classes, test_classes = train_test_split(
documents, classes, train_size=0.7)
classifier = NaiveBayesTextClassifier(
categories=categories,
min_df=1,
lowercase=True,
stop_words=stopwords.words('english')
)
print('> Train classifier')
classifier.train(train_docs, train_classes)
print('> Classify test data...')
predicted_classes = classifier.classify(test_docs)
print('> Complete.')
print(classification_report(test_classes, predicted_classes))
print('-' * 42)
print("{:<25}: {:>4} articles".format("Test data size", len(test_classes)))
print("{:<25}: {:>6.2f} %".format(
important_files = (TEST_DATA_FILE, LABELED_TRAIN_DATA_FILE)
for tsv_file in important_files:
if not os.path.exists(tsv_file):
raise EnvironmentError("File {} doesn't exist at {}.".format(
ntpath.basename(tsv_file), DATADIR
))
print("> Read train data")
train_data = read_csv(LABELED_TRAIN_DATA_FILE, sep='\t')
print("> Init classifier")
start_time = time.time()
classifier = NaiveBayesTextClassifier(
categories=[0, 1],
min_df=1,
lowercase=True,
# 127 English stop words
stop_words=stopwords.words('english')
)
if options.test:
print("> Split data to test and train")
train_docs, test_docs, train_classes, test_classes = train_test_split(
train_data.review, train_data.sentiment, train_size=0.7
)
print("> Train classifier")
classifier.train(train_docs, train_classes)
total_docs = len(train_docs)
elif options.predict:
def non_shuffling_train_test_split(X, y, test_size=0.2):
i = int((1 - test_size) * X.shape[0]) + 1
X_train, X_test = np.split(X, [i])
y_train, y_test = np.split(y, [i])
return X_train, X_test, y_train, y_test
print("> Read train data")
train_data = read_csv('train.csv')
print("> Init classifier")
start_time = time.time()
classifier = NaiveBayesTextClassifier(
categories=['1', '0', 't'],
min_df=1,
lowercase=True,
# 127 English stop words
stop_words=stopwords.words('english'))
print("> Split data to test and train")
train_docs, test_docs, train_classes, test_classes = non_shuffling_train_test_split(
train_data['tweet'], train_data['label'])
train_docs = train_docs.fillna('1')
train_classes = train_classes.fillna('1')
print(train_docs.isnull().any())
print(train_classes.isnull().any())
print(type(train_docs))
print("> Train classifier")
classifier.train(train_docs, train_classes)
total_docs = len(train_docs)
print("-" * 42)
"and put it in {}.".format(DATADIR))
important_files = (TEST_DATA_FILE, LABELED_TRAIN_DATA_FILE)
for tsv_file in important_files:
if not os.path.exists(tsv_file):
raise EnvironmentError("File {} doesn't exist at {}.".format(
ntpath.basename(tsv_file), DATADIR))
print("> Read train data")
train_data = read_csv(LABELED_TRAIN_DATA_FILE, sep='\t')
print("> Init classifier")
start_time = time.time()
classifier = NaiveBayesTextClassifier(
categories=[0, 1],
min_df=1,
lowercase=True,
# 127 English stop words
stop_words=stopwords.words('english'))
if options.test:
print("> Split data to test and train")
train_docs, test_docs, train_classes, test_classes = train_test_split(
train_data.review, train_data.sentiment, train_size=0.7)
print("> Train classifier")
classifier.train(train_docs, train_classes)
total_docs = len(train_docs)
elif options.predict:
print("> Read test data")
test_data = read_csv(TEST_DATA_FILE, sep='\t')
