def submit():
if request.form['text_input'] == "" or len(
request.form['text_input']) < 10:
return "Please provide input large enough, Classifier can understand :)"
# todo: change column name to be dynamically taken from training file
test_data = pd.DataFrame([request.form['text_input']],
columns=['Document'])
session_id = request.cookies['session']
path = os.path.join(app.config['UPLOAD_FOLDER'], session_id)
trained_classifier = [i for i in os.listdir(path) if '.pkl' in i]
vectorizer = os.path.join(path, 'tfidf_vectorizer.pk')
tfidf_transformer = joblib.load(vectorizer)
pre_processor = PreProcess(test_data, column_name='Document')
test_data = pre_processor.clean_html()
test_data = pre_processor.remove_non_ascii()
test_data = pre_processor.remove_spaces()
test_data = pre_processor.remove_punctuation()
test_data = pre_processor.stemming()
test_data = pre_processor.lemmatization()
test_data = pre_processor.stop_words()
test_data1 = tfidf_transformer.transform(test_data.Document)
result = {}
for clf in trained_classifier:
model = joblib.load(os.path.join(path, clf))
print(clf, model.predict(test_data1)[0])
classifier_name = clf.split('/')[-1].split('.')[0]
result[classifier_name] = model.predict(test_data1)[0]
print(result)
return render_template('results.html', result=result)
def main():
data = get_data(
'/Users/aditya1/Documents/Document_Classification/bbc-dataset')
###############################################################################
# Data Pre-processing steps
###############################################################################
column_name = data.columns[0]
# print(column_name)
pre_processor = PreProcess(data, column_name)
# todo: change code to provide all functions in class definition.
pre_processor_operations = ['clean_html']
data = pre_processor.clean_html()
data = pre_processor.remove_non_ascii()
data = pre_processor.remove_spaces()
data = pre_processor.remove_punctuation()
data = pre_processor.stemming()
data = pre_processor.lemmatization()
data = pre_processor.stop_words()
###############################################################################
# Feature extraction
###############################################################################
train_x, test_x, train_y, test_y = train_test_split(data.Document,
data.Category,
test_size=0.20)
# print(train_x.shape, train_y.shape)
# print(test_x.shape, test_y.shape)
tfidf_transformer = TfidfVectorizer(min_df=1)
train_vectors = tfidf_transformer.fit_transform(train_x)
joblib.dump(tfidf_transformer, 'vectorizer.pkl')
test_vectors = tfidf_transformer.transform(test_x)
print(data.head())
###############################################################################
# Perform classification with SVM, kernel=linear
model1 = svm.SVC(kernel='linear')
model1.fit(train_vectors, train_y)
joblib.dump(model1, 'SVM.pkl')
y_pred_class = model1.predict(test_vectors)
print(metrics.accuracy_score(test_y, y_pred_class))
print("Prediction score for classifier %s:\n%s\n" %
(model1, metrics.accuracy_score(test_y, y_pred_class)))
print("Classification report for classifier %s:\n%s\n" %
(model1, metrics.classification_report(test_y, y_pred_class)))
model2 = MultinomialNB()
model2.fit(train_vectors, train_y)
joblib.dump(model2, 'MultinomialNB.pkl')
y_pred_class = model2.predict(test_vectors)
print("Accuracy score:", metrics.accuracy_score(test_y, y_pred_class))
print("Confusion Matrix for classifier %s:\n%s\n" %
(model2, metrics.confusion_matrix(test_y, y_pred_class)))
print("Classification report for classifier %s:\n%s\n" %
(model2, metrics.classification_report(test_y, y_pred_class)))
def read_process_data(path, files_path):
data = pd.read_csv(path)
column_name = data.columns[0]
# print(column_name)
pre_processor = PreProcess(data, column_name)
# todo: change code to provide all functions in class definition.
data = pre_processor.clean_html()
data = pre_processor.remove_non_ascii()
data = pre_processor.remove_spaces()
data = pre_processor.remove_punctuation()
data = pre_processor.stemming()
data = pre_processor.lemmatization()
data = pre_processor.stop_words()
train_x, test_x, train_y, test_y = train_test_split(data.Document,
data.Category,
test_size=0.20)
tfidf_transformer = TfidfVectorizer(min_df=1)
train_vectors = tfidf_transformer.fit_transform(train_x)
vectorizer_path = os.path.join(files_path, 'tfidf_vectorizer.pk')
joblib.dump(tfidf_transformer, vectorizer_path)
return train_vectors, train_y