4. bags.py

from spam_data import training_spam_docs, training_doc_tokens, training_labels, test_labels, test_spam_docs, training_docs, test_docs
from sklearn.naive_bayes import MultinomialNB

def create_features_dictionary(document_tokens):
  features_dictionary = {}
  index = 0
  for token in document_tokens:
    if token not in features_dictionary:
      features_dictionary[token] = index
      index += 1
  return features_dictionary

def tokens_to_bow_vector(document_tokens, features_dictionary):
  bow_vector = [0] * len(features_dictionary)
  for token in document_tokens:
    if token in features_dictionary:
      feature_index = features_dictionary[token]
      bow_vector[feature_index] += 1
  return bow_vector

# Define bow_sms_dictionary:
bow_sms_dictionary = create_features_dictionary(training_doc_tokens)

# Define training_vectors:
training_vectors = [tokens_to_bow_vector(training_doc, bow_sms_dictionary) for training_doc in training_spam_docs]

# Define test_vectors:
test_vectors = [tokens_to_bow_vector(test_doc, bow_sms_dictionary) for test_doc in test_spam_docs]


spam_classifier = MultinomialNB()

def spam_or_not(label):
  return "spam" if label else "not spam"

# Uncomment the code below when you're done:
spam_classifier.fit(training_vectors, training_labels)

predictions = spam_classifier.score(test_vectors, test_labels)

print("The predictions for the test data were {0}% accurate.\n\nFor example, '{1}' was classified as {2}.\n\nMeanwhile, '{3}' was classified as {4}.".format(predictions * 100, test_docs[0], spam_or_not(test_labels[0]), test_docs[10], spam_or_not(test_labels[10])))