# predict the results for the test data
print("Generating probability prediction for the test data...\n")
y_pred = LRC.predict(X_test)
# print testing time
time_test_end = time.clock()
print("Testing finished, testing time: %g seconds \n" %
(time_test_end - time_test_start))
'''
### print the classification results ###
print ("The probabilities for each instance in the test set are:\n")
for prob in LRC.predict_proba(X_test):
print (prob)
'''
# print simple precision metric to the console
print('Accuracy: ' + str(fipr.compute_accuracy(y_test, y_pred)))
# predict the results for the test data
print("Generating probability prediction for the EVEN test data...\n")
y_pred_even = LRC.predict(X_test_even)
# print simple precision metric to the console
print('Accuracy: ' + str(fipr.compute_accuracy(y_test_even, y_pred_even)))
# write the model to the model file
# LRC.print_model(features, model_file)
# Calculate total running time
print("--- Total running time: %g seconds ---" % (time.clock() - start_time))
"""
if __name__ == '__main__':
def main():
# open and load csv files
time_load_start = time.clock()
X_train, y_train = fipr.load_csv("train_file.csv", True)
X_test, y_test = fipr.load_csv("test_file.csv", True)
#y_train = y_train.flatten()
#y_test = y_test.flatten()
time_load_end = time.clock()
print("Loading finished, loading time: %g seconds" %
(time_load_end - time_load_start))
X_test_even, y_test_even = fipr.load_csv("test_file_even.csv", True)
training_data = X_train
training_labels = y_train
test_data = X_test
test_labels = y_test
test_data_even = X_test_even
test_labels_even = y_test_even
# building the SDA
sDA = StackedDA([100])
# start counting time for training
time_train_start = time.clock()
print('Pre-training...')
# pre-trainning the SDA
sDA.pre_train(training_data[:1000], noise_rate=0.3, epochs=100)
print('Training Network...')
# adding the final layer
sDA.finalLayer(training_data, training_labels, epochs=500)
# trainning the whole network
sDA.fine_tune(training_data, training_labels, epochs=500)
# print training time
time_train_end = time.clock()
print("Training finished, training time: %g seconds \n" %
(time_train_end - time_train_start))
# start counting time for testing
time_test_start = time.clock()
print('Testing performance...')
# predicting using the SDA
y_pred = sDA.predict(test_data).argmax(1)
# print simple precision metric to the console
print('Accuracy: ' + str(fipr.compute_accuracy(y_test, y_pred)))
# print testing time
time_test_end = time.clock()
print("Testing finished, testing time: %g seconds \n" %
(time_test_end - time_test_start))
# Even set test
y_pred_even = sDA.predict(test_data_even).argmax(1)
# print simple precision metric to the console
print('Accuracy on EVEN set: ' +
str(fipr.compute_accuracy(y_test_even, y_pred_even)))
return sDA
