def problem_3b_sizes(training_set_sizes, testing_set_size=None):
results = []
for training_set_size in training_set_sizes:
print "Starting", training_set_size
print 'loading data'
training_set, training_labels, testing_set, testing_labels = c1.select_data(training_set_size,
testing_set_size)
testing_set = pickle.load(open('testing_set.p'))
testing_labels = pickle.load(open('testing_labels.p'))
print 'preprocessing data'
training_set = c1.preprocess(training_set)
testing_set = c1.preprocess(testing_set)
print 'building classifier'
classifier = c1.build_classifier(training_set, training_labels)
print 'predicting'
predicted = c1.classify(testing_set, classifier)
error = c1.error_measure(predicted, testing_labels)
print 'error:', error
results.append(error)
print zip(training_set_sizes, results)
plt.plot(training_set_sizes, results, 'o')
plt.xscale('log')
plt.yscale('log')
plt.title('Naive Bayes Error vs Training set size')
plt.xlabel('Training set size')
plt.ylabel('Error')
plt.show()
def problem_3b_parameters():
results = []
alphas = xrange(0, 100, 4)
binarizes = np.array(xrange(0, 10))/10.0
for binarize in binarizes:
print "\nStarting Binarize:", binarize
print 'loading data'
training_set = pickle.load(open('training_set.p'))
training_labels = pickle.load(open('training_labels.p'))
testing_set = pickle.load(open('testing_set.p'))
testing_labels = pickle.load(open('testing_labels.p'))
print 'preprocessing data'
training_set = c1.preprocess(training_set)
testing_set = c1.preprocess(testing_set)
print 'building classifier'
classifier = c1.build_classifier(training_set, training_labels, binarize=binarize)
print 'predicting'
predicted = c1.classify(testing_set, classifier)
error = c1.error_measure(predicted, testing_labels)
print 'error:', error
results.append(error)
print zip(binarizes, results)
plt.plot(binarizes, results, 'o')
plt.title('Naive Bayes Error vs Threshold parameter')
plt.xlabel('Threshold parameter')
plt.ylabel('Error')
plt.show()
results = []
for alpha in alphas:
print "\nStarting Alpha:", alpha
print 'loading data'
training_set = pickle.load(open('training_set.p'))
training_labels = pickle.load(open('training_labels.p'))
testing_set = pickle.load(open('testing_set.p'))
testing_labels = pickle.load(open('testing_labels.p'))
print 'preprocessing data'
training_set = c1.preprocess(training_set)
testing_set = c1.preprocess(testing_set)
print 'building classifier'
classifier = c1.build_classifier(training_set, training_labels, alpha=alpha)
print 'predicting'
predicted = c1.classify(testing_set, classifier)
error = c1.error_measure(predicted, testing_labels)
print 'error:', error
results.append(error)
print zip(alphas, results)
plt.plot(alphas, results, 'o')
plt.title('Naive Bayes Error vs Alpha parameter')
plt.xlabel('Alpha Value')
plt.ylabel('Error')
plt.show()