def part2():
training_emails = readTrainingEmails()
spam_emails = []
reg_emails = []
for email in training_emails:
if(email.label == 0):
reg_emails.append(email)
else:
spam_emails.append(email)
emails_classes = []
emails_classes.append(emailClass(reg_emails))
emails_classes.append(emailClass(spam_emails))
multinomial(emails_classes[0])
multinomial(emails_classes[1])
bernouilli(emails_classes[0])
bernouilli(emails_classes[1])
file = open("multinomial_regular.txt", "w")
print>>file, emails_classes[0].m_likelihood
file.close()
file = open("multinomial_spam.txt", "w")
print>>file, emails_classes[1].m_likelihood
file.close()
file = open("bernouilli_regular.txt", "w")
print>>file, emails_classes[0].b_likelihood
file.close()
file = open("bernouilli_spam.txt", "w")
print>>file, emails_classes[1].b_likelihood
file.close()
actual_labels, testing_emails = readTestingEmails()
def main():
training_emails = readTrainingReviews()
neg_emails = []
pos_emails = []
for email in training_emails:
if (email.label == 1):
pos_emails.append(email)
else:
neg_emails.append(email)
emails_classes = []
emails_classes.append(emailClass(neg_emails))
emails_classes.append(emailClass(pos_emails))
multinomial(emails_classes[0])
multinomial(emails_classes[1])
bernouilli(emails_classes[0])
bernouilli(emails_classes[1])
file = open("multinomial_negative.txt", "w")
print >> file, emails_classes[0].m_likelihood
file.close()
file = open("multinomial_positive.txt", "w")
print >> file, emails_classes[1].m_likelihood
file.close()
file = open("bernouilli_negative.txt", "w")
print >> file, emails_classes[0].b_likelihood
file.close()
file = open("bernouilli_positive.txt", "w")
print >> file, emails_classes[1].b_likelihood
file.close()
actual_labels, testing_emails = readTestingReviews()
confusionMatrixM = zeros((2, 2))
confusionMatrixB = zeros((2, 2))
hypothetical_m_classifier = []
hypothetical_b_classifier = []
map_m_estimate = []
map_b_estimate = []
for x in xrange(0, len(testing_emails)):
map_estimate, label = multinomial_classifier(testing_emails[x],
emails_classes[0],
emails_classes[1])
map_estimate2, label2 = bernouilli_classifier(testing_emails[x],
emails_classes[0],
emails_classes[1])
hypothetical_m_classifier.append(label)
hypothetical_b_classifier.append(label2)
map_m_estimate.append(map_estimate)
map_b_estimate.append(map_estimate2)
count_m_list = []
count_m_0 = len(hypothetical_m_classifier) - count_nonzero(
hypothetical_m_classifier)
count_m_1 = count_nonzero(hypothetical_m_classifier)
count_m_list.append(count_m_0)
count_m_list.append(count_m_1)
count_b_list = []
count_b_0 = len(hypothetical_b_classifier) - count_nonzero(
hypothetical_b_classifier)
count_b_1 = count_nonzero(hypothetical_b_classifier)
count_b_list.append(count_b_0)
count_b_list.append(count_b_1)
temp_actual = actual_labels[:]
for x in xrange(0, len(actual_labels)):
if (actual_labels[x] == 1):
temp_actual[x] = 0
elif (actual_labels[x] == -1):
temp_actual[x] = 1
for x in xrange(0, len(temp_actual)):
confusionMatrixM[temp_actual[x]][hypothetical_m_classifier[x]] += 1
confusionMatrixB[temp_actual[x]][hypothetical_b_classifier[x]] += 1
for x in xrange(0, 2):
for y in xrange(0, 2):
confusionMatrixM[x][y] = float(
confusionMatrixM[x][y] * 100) / count_m_list[x]
print count_m_list[x]
confusionMatrixB[x][y] = float(
confusionMatrixB[x][y] * 100) / count_b_list[x]
print confusionMatrixM
print confusionMatrixB
for x in xrange(0, len(hypothetical_m_classifier)):
if (hypothetical_m_classifier[x] == 1):
hypothetical_m_classifier[x] = -1
elif (hypothetical_m_classifier[x] == 0):
hypothetical_m_classifier[x] = 1
if (hypothetical_b_classifier[x] == 1):
hypothetical_b_classifier[x] = -1
elif (hypothetical_b_classifier[x] == 0):
hypothetical_b_classifier[x] = 1
error = list(array(actual_labels) - array(hypothetical_m_classifier))
error_b = list(array(actual_labels) - array(hypothetical_b_classifier))
error_value = float(count_nonzero(error)) / len(testing_emails)
error_b_value = float(count_nonzero(error_b)) / len(testing_emails)
sorted_m_positive = sorted(emails_classes[0].m_likelihood.items(),
key=operator.itemgetter(1),
reverse=True)
sorted_m_negative = sorted(emails_classes[1].m_likelihood.items(),
key=operator.itemgetter(1),
reverse=True)
sorted_b_positive = sorted(emails_classes[0].b_likelihood.items(),
key=operator.itemgetter(1),
reverse=True)
sorted_b_negative = sorted(emails_classes[1].b_likelihood.items(),
key=operator.itemgetter(1),
reverse=True)
for x in xrange(0, 20):
print "Word: ", sorted_m_negative[x][0], " Value: ", sorted_m_negative[
x][1]
print "Booyah"
for x in xrange(0, 20):
print "Word: ", sorted_b_positive[x][0], " Value: ", sorted_b_positive[
x][1]
print "Huzzah!"
for x in xrange(0, 20):
print "Word: ", sorted_b_negative[x][0], " Value: ", sorted_b_negative[
x][1]
print "The multinomial error is ", error_value
print "The bernouilli error is ", error_b_value
file = open("map_m_estimate.txt", "w")
print >> file, map_m_estimate
file.close()
file = open("map_b_estimate.txt", "w")
print >> file, map_b_estimate
file.close()
file = open("hypothetical_m_classifier.txt", "w")
print >> file, hypothetical_m_classifier
file.close()
file = open("hypothetical_b_classifier.txt", "w")
print >> file, hypothetical_b_classifier
file.close()
file = open("actual_labels.txt", "w")
print >> file, actual_labels
file.close()
def part2():
training_emails = readTrainingEmails()
spam_emails = []
reg_emails = []
for email in training_emails:
if(email.label == 0):
reg_emails.append(email)
else:
spam_emails.append(email)
emails_classes = []
emails_classes.append(emailClass(reg_emails))
emails_classes.append(emailClass(spam_emails))
multinomial(emails_classes[0])
multinomial(emails_classes[1])
bernouilli(emails_classes[0])
bernouilli(emails_classes[1])
file = open("multinomial_regular.txt", "w")
print>>file, emails_classes[0].m_likelihood
file.close()
file = open("multinomial_spam.txt", "w")
print>>file, emails_classes[1].m_likelihood
file.close()
file = open("bernouilli_regular.txt", "w")
print>>file, emails_classes[0].b_likelihood
file.close()
file = open("bernouilli_spam.txt", "w")
print>>file, emails_classes[1].b_likelihood
file.close()
actual_labels, testing_emails = readTestingEmails()
confusionMatrixM = zeros((2,2))
confusionMatrixB = zeros((2,2))
hypothetical_m_classifier = []
hypothetical_b_classifier = []
map_m_estimate = []
map_b_estimate = []
for x in xrange(0,len(testing_emails)):
map_estimate, label = multinomial_classifier(testing_emails[x], emails_classes[1], emails_classes[0])
map_estimate2, label2 = bernouilli_classifier(testing_emails[x], emails_classes[1], emails_classes[0])
hypothetical_m_classifier.append(label)
hypothetical_b_classifier.append(label2)
map_m_estimate.append(map_estimate)
map_b_estimate.append(map_estimate2)
count_m_list = []
count_m_0 = len(hypothetical_m_classifier) - count_nonzero(hypothetical_m_classifier)
count_m_1 = count_nonzero(hypothetical_m_classifier)
count_m_list.append(count_m_0)
count_m_list.append(count_m_1)
count_b_list = []
count_b_0 = len(hypothetical_b_classifier) - count_nonzero(hypothetical_b_classifier)
count_b_1 = count_nonzero(hypothetical_b_classifier)
count_b_list.append(count_b_0)
count_b_list.append(count_b_1)
for x in xrange(0,len(actual_labels)):
confusionMatrixM[actual_labels[x]][hypothetical_m_classifier[x]] += 1
confusionMatrixB[actual_labels[x]][hypothetical_b_classifier[x]] += 1
for x in xrange(0,2):
for y in xrange(0,2):
confusionMatrixM[x][y] = float(confusionMatrixM[x][y] * 100) / count_m_list[x]
print count_m_list[x]
confusionMatrixB[x][y] = float(confusionMatrixB[x][y] * 100) / count_b_list[x]
print confusionMatrixM
print confusionMatrixB
error = list(array(actual_labels) - array(hypothetical_m_classifier))
error_b = list(array(actual_labels) - array(hypothetical_b_classifier))
error_value = float(count_nonzero(error))/len(testing_emails)
error_b_value = float(count_nonzero(error_b))/len(testing_emails)
sorted_m_reg = sorted(emails_classes[0].m_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_m_spam = sorted(emails_classes[1].m_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_b_reg = sorted(emails_classes[0].b_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_b_spam = sorted(emails_classes[1].b_likelihood.items(), key=operator.itemgetter(1), reverse = True)
for x in xrange(1,20):
print "Word: ", sorted_m_reg[x][0], " Value: ", sorted_m_reg[x][1]
print "WAAAHOOO"
for x in xrange(0,20):
print "Word: ", sorted_m_spam[x][0], " Value: ", sorted_m_spam[x][1]
print "Booyah"
for x in xrange(0,20):
print "Word: ", sorted_b_reg[x][0], " Value: ", sorted_b_reg[x][1]
print "Huzzah!"
for x in xrange(0,20):
print "Word: ", sorted_b_spam[x][0], " Value: ", sorted_b_spam[x][1]
print "The multinomial error is ", error_value
print "The bernouilli error is ", error_b_value
file = open("map_m_estimate.txt", "w")
print>>file, map_m_estimate
file.close()
file = open("map_b_estimate.txt", "w")
print>>file, map_b_estimate
file.close()
file = open("hypothetical_m_classifier.txt", "w")
print>>file, hypothetical_m_classifier
file.close()
file = open("hypothetical_b_classifier.txt", "w")
print>>file, hypothetical_b_classifier
file.close()
file = open("actual_labels.txt", "w")
print>>file, actual_labels
file.close()
def main():
training_emails = readTrainingReviews()
neg_emails = []
pos_emails = []
for email in training_emails:
if(email.label == 1):
pos_emails.append(email)
else:
neg_emails.append(email)
emails_classes = []
emails_classes.append(emailClass(neg_emails))
emails_classes.append(emailClass(pos_emails))
multinomial(emails_classes[0])
multinomial(emails_classes[1])
bernouilli(emails_classes[0])
bernouilli(emails_classes[1])
file = open("multinomial_negative.txt", "w")
print>>file, emails_classes[0].m_likelihood
file.close()
file = open("multinomial_positive.txt", "w")
print>>file, emails_classes[1].m_likelihood
file.close()
file = open("bernouilli_negative.txt", "w")
print>>file, emails_classes[0].b_likelihood
file.close()
file = open("bernouilli_positive.txt", "w")
print>>file, emails_classes[1].b_likelihood
file.close()
actual_labels, testing_emails = readTestingReviews()
confusionMatrixM = zeros((2,2))
confusionMatrixB = zeros((2,2))
hypothetical_m_classifier = []
hypothetical_b_classifier = []
map_m_estimate = []
map_b_estimate = []
for x in xrange(0,len(testing_emails)):
map_estimate, label = multinomial_classifier(testing_emails[x], emails_classes[0], emails_classes[1])
map_estimate2, label2 = bernouilli_classifier(testing_emails[x], emails_classes[0], emails_classes[1])
hypothetical_m_classifier.append(label)
hypothetical_b_classifier.append(label2)
map_m_estimate.append(map_estimate)
map_b_estimate.append(map_estimate2)
count_m_list = []
count_m_0 = len(hypothetical_m_classifier) - count_nonzero(hypothetical_m_classifier)
count_m_1 = count_nonzero(hypothetical_m_classifier)
count_m_list.append(count_m_0)
count_m_list.append(count_m_1)
count_b_list = []
count_b_0 = len(hypothetical_b_classifier) - count_nonzero(hypothetical_b_classifier)
count_b_1 = count_nonzero(hypothetical_b_classifier)
count_b_list.append(count_b_0)
count_b_list.append(count_b_1)
temp_actual = actual_labels[:]
for x in xrange(0,len(actual_labels)):
if(actual_labels[x] == 1):
temp_actual[x] = 0
elif(actual_labels[x] == -1):
temp_actual[x] = 1
for x in xrange(0,len(temp_actual)):
confusionMatrixM[temp_actual[x]][hypothetical_m_classifier[x]] += 1
confusionMatrixB[temp_actual[x]][hypothetical_b_classifier[x]] += 1
for x in xrange(0,2):
for y in xrange(0,2):
confusionMatrixM[x][y] = float(confusionMatrixM[x][y] * 100) / count_m_list[x]
print count_m_list[x]
confusionMatrixB[x][y] = float(confusionMatrixB[x][y] * 100) / count_b_list[x]
print confusionMatrixM
print confusionMatrixB
for x in xrange(0,len(hypothetical_m_classifier)):
if(hypothetical_m_classifier[x] == 1):
hypothetical_m_classifier[x] = -1
elif(hypothetical_m_classifier[x] == 0):
hypothetical_m_classifier[x] = 1
if(hypothetical_b_classifier[x] == 1):
hypothetical_b_classifier[x] = -1
elif(hypothetical_b_classifier[x] == 0):
hypothetical_b_classifier[x] = 1
error = list(array(actual_labels) - array(hypothetical_m_classifier))
error_b = list(array(actual_labels) - array(hypothetical_b_classifier))
error_value = float(count_nonzero(error))/len(testing_emails)
error_b_value = float(count_nonzero(error_b))/len(testing_emails)
sorted_m_positive = sorted(emails_classes[0].m_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_m_negative = sorted(emails_classes[1].m_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_b_positive = sorted(emails_classes[0].b_likelihood.items(), key=operator.itemgetter(1), reverse = True)
sorted_b_negative = sorted(emails_classes[1].b_likelihood.items(), key=operator.itemgetter(1), reverse = True)
for x in xrange(0,20):
print "Word: ", sorted_m_negative[x][0], " Value: ", sorted_m_negative[x][1]
print "Booyah"
for x in xrange(0,20):
print "Word: ", sorted_b_positive[x][0], " Value: ", sorted_b_positive[x][1]
print "Huzzah!"
for x in xrange(0,20):
print "Word: ", sorted_b_negative[x][0], " Value: ", sorted_b_negative[x][1]
print "The multinomial error is ", error_value
print "The bernouilli error is ", error_b_value
file = open("map_m_estimate.txt", "w")
print>>file, map_m_estimate
file.close()
file = open("map_b_estimate.txt", "w")
print>>file, map_b_estimate
file.close()
file = open("hypothetical_m_classifier.txt", "w")
print>>file, hypothetical_m_classifier
file.close()
file = open("hypothetical_b_classifier.txt", "w")
print>>file, hypothetical_b_classifier
file.close()
file = open("actual_labels.txt", "w")
print>>file, actual_labels
file.close()
def part2():
training_emails = readTrainingEmails()
spam_emails = []
reg_emails = []
for email in training_emails:
if email.label == 0:
reg_emails.append(email)
else:
spam_emails.append(email)
emails_classes = []
emails_classes.append(emailClass(reg_emails))
emails_classes.append(emailClass(spam_emails))
multinomial(emails_classes[0])
multinomial(emails_classes[1])
bernouilli(emails_classes[0])
bernouilli(emails_classes[1])
file = open("multinomial_regular.txt", "w")
print >> file, emails_classes[0].m_likelihood
file.close()
file = open("multinomial_spam.txt", "w")
print >> file, emails_classes[1].m_likelihood
file.close()
file = open("bernouilli_regular.txt", "w")
print >> file, emails_classes[0].b_likelihood
file.close()
file = open("bernouilli_spam.txt", "w")
print >> file, emails_classes[1].b_likelihood
file.close()
actual_labels, testing_emails = readTestingEmails()
confusionMatrixM = zeros((2, 2))
confusionMatrixB = zeros((2, 2))
hypothetical_m_classifier = []
hypothetical_b_classifier = []
map_m_estimate = []
map_b_estimate = []
for x in xrange(0, len(testing_emails)):
map_estimate, label = multinomial_classifier(testing_emails[x], emails_classes[1], emails_classes[0])
map_estimate2, label2 = bernouilli_classifier(testing_emails[x], emails_classes[1], emails_classes[0])
hypothetical_m_classifier.append(label)
hypothetical_b_classifier.append(label2)
map_m_estimate.append(map_estimate)
map_b_estimate.append(map_estimate2)
count_m_list = []
count_m_0 = len(hypothetical_m_classifier) - count_nonzero(hypothetical_m_classifier)
count_m_1 = count_nonzero(hypothetical_m_classifier)
count_m_list.append(count_m_0)
count_m_list.append(count_m_1)
count_b_list = []
count_b_0 = len(hypothetical_b_classifier) - count_nonzero(hypothetical_b_classifier)
count_b_1 = count_nonzero(hypothetical_b_classifier)
count_b_list.append(count_b_0)
count_b_list.append(count_b_1)
for x in xrange(0, len(actual_labels)):
confusionMatrixM[actual_labels[x]][hypothetical_m_classifier[x]] += 1
confusionMatrixB[actual_labels[x]][hypothetical_b_classifier[x]] += 1
for x in xrange(0, 2):
for y in xrange(0, 2):
confusionMatrixM[x][y] = float(confusionMatrixM[x][y] * 100) / count_m_list[x]
print count_m_list[x]
confusionMatrixB[x][y] = float(confusionMatrixB[x][y] * 100) / count_b_list[x]
print confusionMatrixM
print confusionMatrixB
error = list(array(actual_labels) - array(hypothetical_m_classifier))
error_b = list(array(actual_labels) - array(hypothetical_b_classifier))
error_value = float(count_nonzero(error)) / len(testing_emails)
error_b_value = float(count_nonzero(error_b)) / len(testing_emails)
sorted_m_reg = sorted(emails_classes[0].m_likelihood.items(), key=operator.itemgetter(1), reverse=True)
sorted_m_spam = sorted(emails_classes[1].m_likelihood.items(), key=operator.itemgetter(1), reverse=True)
sorted_b_reg = sorted(emails_classes[0].b_likelihood.items(), key=operator.itemgetter(1), reverse=True)
sorted_b_spam = sorted(emails_classes[1].b_likelihood.items(), key=operator.itemgetter(1), reverse=True)
for x in xrange(1, 20):
print "Word: ", sorted_m_reg[x][0], " Value: ", sorted_m_reg[x][1]
print "WAAAHOOO"
for x in xrange(0, 20):
print "Word: ", sorted_m_spam[x][0], " Value: ", sorted_m_spam[x][1]
print "Booyah"
for x in xrange(0, 20):
print "Word: ", sorted_b_reg[x][0], " Value: ", sorted_b_reg[x][1]
print "Huzzah!"
for x in xrange(0, 20):
print "Word: ", sorted_b_spam[x][0], " Value: ", sorted_b_spam[x][1]
print "The multinomial error is ", error_value
print "The bernouilli error is ", error_b_value
file = open("map_m_estimate.txt", "w")
print >> file, map_m_estimate
file.close()
file = open("map_b_estimate.txt", "w")
print >> file, map_b_estimate
file.close()
file = open("hypothetical_m_classifier.txt", "w")
print >> file, hypothetical_m_classifier
file.close()
file = open("hypothetical_b_classifier.txt", "w")
print >> file, hypothetical_b_classifier
file.close()
file = open("actual_labels.txt", "w")
print >> file, actual_labels
file.close()
