def model_with_all_preprocessing_and_features(no_bag_of_words=False):
print('## Download and load the data')
dataset = get_dataset()
print('## Clean the data')
cleaned_X = clean_data(dataset['X'])
print('## Feature Extraction')
all_features_X = extract_features(cleaned_X, dataset['X'], no_bag_of_words=no_bag_of_words)
print('## Training and Evaluation')
train_the_model(all_features_X, dataset['Y'])
def model_with_only_features():
print('## Download and load the data')
dataset = get_dataset()
print('## Only separate using spaces')
cleaned_X = [val.split(' ') for val in dataset['X']]
print('## Feature Extraction')
all_features_X = extract_features(cleaned_X, dataset['X'])
print('## Training and Evaluation')
train_the_model(all_features_X, dataset['Y'])
def model_with_only_preprocessing():
print('## Download and load the data')
dataset = get_dataset()
print('## Clean the data')
cleaned_X = clean_data(dataset['X'])
print('## Only perform BoW')
all_features_X = perform_bag_of_words(cleaned_X)
print('## Training and Evaluation')
train_the_model(all_features_X, dataset['Y'])
def model_with_no_preprocessing_or_features():
print('## Download and load the data')
dataset = get_dataset()
print('## Only separate using spaces')
cleaned_X = [val.split(' ') for val in dataset['X']]
print('## Only perform BoW')
all_features_X = perform_bag_of_words(cleaned_X)
print('## Training and Evaluation')
train_the_model(all_features_X, dataset['Y'])