def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
val_messages, val_labels = util.load_spam_dataset('spam_val.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
train_matrix = transform_text(train_messages, dictionary)
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
optimal_radius = compute_best_svm_radius(train_matrix, train_labels,
val_matrix, val_labels,
[0.01, 0.1, 1, 10])
print('The optimal SVM radius was {}'.format(optimal_radius))
svm_predictions = svm.train_and_predict_svm(train_matrix, train_labels,
test_matrix, optimal_radius)
np.savetxt('svm_predictions.txt', svm_predictions)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(
svm_accuracy))
def main():
train_messages, train_labels = util.load_spam_dataset(
'../data/ds6_train.tsv')
val_messages, val_labels = util.load_spam_dataset('../data/ds6_val.tsv')
test_messages, test_labels = util.load_spam_dataset('../data/ds6_test.tsv')
dictionary = create_dictionary(train_messages)
util.write_json('./output/p06_dictionary', dictionary)
train_matrix = transform_text(train_messages, dictionary)
np.savetxt('./output/p06_sample_train_matrix', train_matrix[:100, :])
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(
naive_bayes_model, test_matrix)
np.savetxt('./output/p06_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
naive_bayes_accuracy))
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
util.write_json('./output/p06_top_indicative_words', top_5_words)
"""
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
val_messages, val_labels = util.load_spam_dataset('spam_val.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
### Q3.1 ###
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
util.write_json('spam_dictionary', dictionary)
train_matrix = transform_text(train_messages, dictionary)
np.savetxt('spam_sample_train_matrix', train_matrix[:100, :])
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
### Q3.2 ###
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(
naive_bayes_model, test_matrix)
np.savetxt('spam_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
naive_bayes_accuracy))
### Q3.3 ###
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
util.write_json('spam_top_indicative_words', top_5_words)
### Q3.4 ###
optimal_radius = compute_best_svm_radius(train_matrix, train_labels,
val_matrix, val_labels,
[0.01, 0.1, 1, 10])
# optimal_radius = compute_best_svm_radius(train_matrix, train_labels, val_matrix, val_labels, [0.1])
util.write_json('spam_optimal_radius', optimal_radius)
print('The optimal SVM radius was {}'.format(optimal_radius))
svm_predictions = svm.train_and_predict_svm(train_matrix, train_labels,
test_matrix, optimal_radius)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(
svm_accuracy, optimal_radius))
def experimenting_without_punctuation():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
val_messages, val_labels = util.load_spam_dataset('spam_val.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
print(
'EXPERIMENT: WHAT HAPPENS WHEN WE DELETE PUNCTUATION FROM OUR MESSAGES'
)
### Q3.1 ###
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
train_matrix = transform_text(train_messages, dictionary)
np.savetxt('spam_sample_train_matrix', train_matrix[:100, :])
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
### Q3.2 ###
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(
naive_bayes_model, test_matrix)
np.savetxt('spam_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
naive_bayes_accuracy))
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
### Q3.4 ###
optimal_radius = compute_best_svm_radius(train_matrix, train_labels,
val_matrix, val_labels,
[0.01, 0.1, 1, 10])
print('The optimal SVM radius was {}'.format(optimal_radius))
svm_predictions = svm.train_and_predict_svm(train_matrix, train_labels,
test_matrix, optimal_radius)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(
svm_accuracy, optimal_radius))
def main():
train_messages, train_labels = util.load_spam_dataset(
'../data/ds6_train.tsv')
val_messages, val_labels = util.load_spam_dataset('../data/ds6_val.tsv')
test_messages, test_labels = util.load_spam_dataset('../data/ds6_test.tsv')
dictionary = create_dictionary(train_messages)
util.write_json('./output/p06_dictionary', dictionary)
train_matrix = transform_text(train_messages, dictionary)
np.savetxt('./output/p06_sample_train_matrix', train_matrix[:100, :])
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(
naive_bayes_model, test_matrix)
np.savetxt('./output/p06_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
naive_bayes_accuracy))
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
util.write_json('./output/p06_top_indicative_words', top_5_words)
optimal_radius = compute_best_svm_radius(train_matrix, train_labels,
val_matrix, val_labels,
[0.01, 0.1, 1, 10])
util.write_json('./output/p06_optimal_radius', optimal_radius)
print('The optimal SVM radius was {}'.format(optimal_radius))
svm_predictions = svm.train_and_predict_svm(train_matrix, train_labels,
test_matrix, optimal_radius)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(
svm_accuracy, optimal_radius))
end = time.time()
print("Execution Time: ", end - start)
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
train_matrix = transform_text(train_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
gnb = GaussianNB()
naive_bayes_predictions = gnb.fit(train_matrix,
train_labels).predict(test_matrix)
np.savetxt('vscikit-learn_spam_naive_bayes_predictions',
naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
naive_bayes_accuracy))
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
train_matrix = transform_text(train_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(naive_bayes_model, test_matrix)
np.savetxt('spam_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(naive_bayes_accuracy))
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
train_matrix = transform_text(train_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
clf = svm.SVC()
clf.fit(train_matrix, train_labels)
svm_predictions = clf.predict(test_matrix)
np.savetxt('vscikit-learn_svm_predictions.txt', svm_predictions)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(
svm_accuracy))
def main(train_path, test_path):
train_messages, train_labels = util.load_spam_dataset(train_path)
test_messages, test_labels = util.load_spam_dataset(test_path)
dictionary = create_dictionary(train_messages)
util.write_json('./output/p06_dictionary', dictionary)
x_train = transform_text(train_messages, dictionary)
x_test = transform_text(test_messages, dictionary)
np.savetxt('./output/p06_sample_train_matrix', x_train[:100, :])
nb = NaiveBayes()
nb.fit(x_train, train_labels)
y_pred = nb.predict(x_test)
np.savetxt('./output/p06_naive_bayes_predictions', y_pred)
accuracy = np.mean(y_pred == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(
accuracy))
top_5_words = nb.top_words(5, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
val_messages, val_labels = util.load_spam_dataset('spam_val.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
print('Size of dictionary: ', len(dictionary))
util.write_json('spam_dictionary', dictionary)
train_matrix = transform_text(train_messages, dictionary)
np.savetxt('spam_sample_train_matrix', train_matrix[:100,:])
val_matrix = transform_text(val_messages, dictionary)
test_matrix = transform_text(test_messages, dictionary)
naive_bayes_model = fit_naive_bayes_model(train_matrix, train_labels)
naive_bayes_predictions = predict_from_naive_bayes_model(naive_bayes_model, test_matrix)
np.savetxt('spam_naive_bayes_predictions', naive_bayes_predictions)
naive_bayes_accuracy = np.mean(naive_bayes_predictions == test_labels)
print('Naive Bayes had an accuracy of {} on the testing set'.format(naive_bayes_accuracy))
top_5_words = get_top_five_naive_bayes_words(naive_bayes_model, dictionary)
print('The top 5 indicative words for Naive Bayes are: ', top_5_words)
util.write_json('spam_top_indicative_words', top_5_words)
optimal_radius = compute_best_svm_radius(train_matrix, train_labels, val_matrix, val_labels, [0.01, 0.1, 1, 10])
util.write_json('spam_optimal_radius', optimal_radius)
print('The optimal SVM radius was {}'.format(optimal_radius))
svm_predictions = svm.train_and_predict_svm(train_matrix, train_labels, test_matrix, optimal_radius)
svm_accuracy = np.mean(svm_predictions == test_labels)
print('The SVM model had an accuracy of {} on the testing set'.format(svm_accuracy, optimal_radius))
train_matrix = util.load_bert_encoding('bert_train_matrix.tsv.bz2')
val_matrix = util.load_bert_encoding('bert_val_matrix.tsv.bz2')
test_matrix = util.load_bert_encoding('bert_test_matrix.tsv.bz2')
best_learning_rate = compute_best_logreg_learning_rate(train_matrix, train_labels, val_matrix, val_labels, [0.01, 0.001, 0.0001, 0.00001, 0.000001])
print('The best learning rate for logistic regression is {}'.format(best_learning_rate))
logreg_predictions = logreg.train_and_predict_logreg(train_matrix, train_labels, test_matrix, best_learning_rate)
logreg_accuracy = np.mean(logreg_predictions == test_labels)
print('The Logistic Regression model with BERT encodings had an accuracy of {} on the testing set'.format(logreg_accuracy))
def main():
train_messages, train_labels = util.load_spam_dataset('spam_train.tsv')
val_messages, val_labels = util.load_spam_dataset('spam_val.tsv')
test_messages, test_labels = util.load_spam_dataset('spam_test.tsv')
dictionary = create_dictionary(train_messages)
