print('No data detected in input_data folder')
exit()
if os.path.exists(output_directory):
shutil.rmtree(output_directory)
os.makedirs(output_directory)
else:
os.makedirs(output_directory)
# Run Script for ps1 (Perceptron Implementation)
import split_training_data
import create_feature_vectors
import perceptron
# Part 1: Split Training data into training and validation set
split_training_data.run()
# Part 2: Transform each email in the training set into a feature vector
(feature_vector_list_training,
is_spam_list_training,
vocabulary_list) = create_feature_vectors.run('./output_data/training_set')
# Part 3/4: Train the data on the training set and return the last weight vector. Test the percent
# error when this weight is run on the validation set
print('\n=========================================================================================')
print('Problem 4:')
(weight_vector,
total_number_of_misclassifications,
number_of_runs) = perceptron.perceptron_train(feature_vector_list_training, is_spam_list_training)
(feature_vector_list_validation,
def run():
# Part 1
print('\n=====================================================================================')
print('Problem 1 Preparing data:')
# Split Training data into training and validation set
split_training_data.run()
# Transform each email in the training set into a feature vector
(feature_vector_list_training,
is_spam_list_training,
vocabulary_list) = create_feature_vectors.run_spam('./output_data/training_set')
print('\n=====================================================================================')
print('Problem 1 Creating "svm objective as a function of iterations" graph:')
# Part 1a: Plot the svm objective as a function of iterations
(weight_vector, svm_objective_list) = pegasos_svm.pegasos_svm_train(feature_vector_list_training,
is_spam_list_training,
pow(2, -5))
# Plot data ------------------------------------------------------------------------------------
m = len(feature_vector_list_training)
iterations = [(i+1)*m for i in range(0, len(svm_objective_list))]
pylab.plot(iterations, svm_objective_list)
pylab.xlabel('Number of Iterations')
pylab.ylabel('SVM Objective')
pylab.title('SVM Objective as a function of iterations')
pylab.grid(True)
pylab.savefig("SVM_Objective_Graph.png")
#pylab.show()
pylab.close()
pylab.clf()
# End plot data --------------------------------------------------------------------------------
print('\n=====================================================================================')
print('Problem 1 Creating "Training Error as a function of lambda" graph:')
# Part 1b/c:
# Setup validation error data
(feature_vector_list_validation,
is_spam_list_validation,
_) = create_feature_vectors.run_spam('./output_data/validation_set', vocabulary_list)
# Setup data for loop
lambda_set = [pow(2,power) for power in range(-9, 2)]
weight_list = []
average_training_error_list = []
average_hinge_loss_error_list = []
average_validation_error_list = []
# Get data for varying values of lambda
for lambda_ in lambda_set:
print('Processing data for lambda: ' + str(lambda_))
(this_weight_vector, _) = pegasos_svm.pegasos_svm_train(feature_vector_list_training,
is_spam_list_training, lambda_)
weight_list.append(this_weight_vector)
# Average training error
average_training_error = pegasos_svm.pegasos_svm_test(this_weight_vector,
feature_vector_list_training,
is_spam_list_training)
average_training_error_list.append(average_training_error)
# Average hinge error
average_hinge_loss_error = pegasos_svm.calculate_average_hinge_loss_error(
this_weight_vector, feature_vector_list_training,
is_spam_list_training)
average_hinge_loss_error_list.append(average_hinge_loss_error)
# Average validation error
average_validation_error = pegasos_svm.pegasos_svm_test(this_weight_vector,
feature_vector_list_validation,
is_spam_list_validation)
average_validation_error_list.append(average_validation_error)
# Normalize to percentage out of 100
average_training_error_list = [100*average_training_error
for average_training_error
in average_training_error_list]
average_hinge_loss_error_list = [100*average_hinge_loss_error
for average_hinge_loss_error
in average_hinge_loss_error_list]
average_validation_error_list = [100*average_validation_error
for average_validation_error
in average_validation_error_list]
# Plot data ------------------------------------------------------------------------------------
log_lambda = [math.log(lambda_, 2) for lambda_ in lambda_set]
# Plot logic
pylab.plot(log_lambda, average_training_error_list)
pylab.plot(log_lambda, average_hinge_loss_error_list)
pylab.plot(log_lambda, average_validation_error_list)
pylab.xlabel('log base 2 of lambda')
pylab.ylabel('Average error (% out of 100')
pylab.title('Average Errors as a function of log base 2 of lambda')
pylab.legend(['Training Error', 'Hinge Loss Error', 'Validation Error'], loc=2)
pylab.grid(True)
pylab.savefig("Average_Error_Over_Log2_Lambda.png")
#pylab.show()
pylab.close()
pylab.clf()
# End plot data --------------------------------------------------------------------------------
print('\n=====================================================================================')
print('Problem 1 Evaluating values:')
# Print results for part 1:
(minimum_index, minimum_average_validation_error) = min(enumerate(average_validation_error_list),
key = itemgetter(1))
print('The minimum of the validation error was: ' + str(minimum_average_validation_error) + '%')
# Setup test set data
(feature_vector_list_training,
is_spam_list_training,
vocabulary_list) = create_feature_vectors.run_spam('./input_data/spam_train.txt')
(feature_vector_list_test,
is_spam_list_test,
_) = create_feature_vectors.run_spam('./input_data/spam_test.txt', vocabulary_list)
# Calculate Test Error
minimum_lambda = lambda_set[minimum_index]
(minimum_validation_error_classifier, _) = pegasos_svm.pegasos_svm_train(
feature_vector_list_training,
is_spam_list_training, minimum_lambda)
test_set_error = pegasos_svm.pegasos_svm_test(minimum_validation_error_classifier,
feature_vector_list_test,
is_spam_list_test)
print('Error on the test set: ' + str(test_set_error*100) + '%')
# Calculate the number of support vectors
is_support_vector = [1 if is_spam_list_training[i]*np.dot(minimum_validation_error_classifier,
feature_vector_list_training[i]) <= 1
else 0 for i in range(0, len(feature_vector_list_training))]
number_of_support_vectors = sum(is_support_vector)
print('Number of support vectors: ' + str(number_of_support_vectors))