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(
"Accuracy", 100 * accuracy_score(test_classes, predicted_classes))
)
print("{:<25}: {:>6.2f} %".format(
"Kappa statistics", 100 * kappa(
category_to_number(test_classes, categories),
category_to_number(predicted_classes, categories)
)
))
print("{:<25}: {:>6} articles".format("Total", total_docs))
print("{:<25}: {:>6} words".format(
"Number of words", classifier.bag.shape[1]
))
print("{:<25}: {:>6.2f} seconds".format(
"Parse time", time.time() - start_time
))
print("-" * 42)
# -------------- Classify --------------- #
print("> Start classify data")
start_time = time.time()
if options.test:
predicted_classes = classifier.classify(test_docs)
print(classification_report(test_classes, predicted_classes))
print('-' * 42)
print("{:<25}: {:>6} articles".format("Test data size", len(test_classes)))
print("{:<25}: {:>6.2f} %".format(
"Accuracy", 100 * accuracy_score(test_classes, predicted_classes))
)
print("{:<25}: {:>6.2f} %".format(
"Kappa statistics", 100 * kappa(test_classes, predicted_classes)
))
elif options.predict:
predicted_classes = classifier.classify(test_data.review)
print("> Save predicted results")
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)
print("Total", total_docs, " tweets")
print("Number of words", classifier.bag.shape[1], " words")
print("Parse time", time.time() - start_time, "seconds")
print("-" * 42)
# -------------- Classify --------------- #
print("> Start classify data")
start_time = time.time()
test_docs = test_docs.fillna('1')
test_classes = test_classes.fillna('1')
predicted_classes = classifier.classify(test_docs)
print((predicted_classes), (test_classes))
print(classification_report(test_classes, predicted_classes))
print('-' * 42)
print("Test data size", len(test_classes), "articles")
print("Accuracy", 100 * accuracy_score(test_classes, predicted_classes), "%")
end_time = time.time()
print("Computation time", end_time - start_time, "seconds")
print('-' * 42)
total_docs = len(train_data)
print("-" * 42)
print("{:<25}: {:>6} articles".format("Total", total_docs))
print("{:<25}: {:>6} words".format("Number of words", classifier.bag.shape[1]))
print("{:<25}: {:>6.2f} seconds".format("Parse time",
time.time() - start_time))
print("-" * 42)
# -------------- Classify --------------- #
print("> Start classify data")
start_time = time.time()
if options.test:
predicted_classes = classifier.classify(test_docs)
print(classification_report(test_classes, predicted_classes))
print('-' * 42)
print("{:<25}: {:>6} articles".format("Test data size", len(test_classes)))
print("{:<25}: {:>6.2f} %".format(
"Accuracy", 100 * accuracy_score(test_classes, predicted_classes)))
print("{:<25}: {:>6.2f} %".format(
"Kappa statistics", 100 * kappa(test_classes, predicted_classes)))
elif options.predict:
predicted_classes = classifier.classify(test_data.review)
print("> Save predicted results")
print("> {}".format(PREDICTED_DATA_FILE))
np.savetxt(PREDICTED_DATA_FILE,
