def test_extract_events_odds(self):
instances = {'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100}
odds = Bayes.extract_events_odds(instances)
b = Bayes({'spam': 0.9, 'genuine': 0.1})
b.update_from_events('buy coffee for meeting'.split(), odds)
self.assertEqual(b.most_likely(0.8), 'genuine')
def test_extract_events_odds(self):
instances = {
'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100
}
odds = Bayes.extract_events_odds(instances)
b = Bayes({'spam': 0.9, 'genuine': 0.1})
b.update_from_events('buy coffee for meeting'.split(), odds)
self.assertEqual(b.most_likely(0.8), 'genuine')
print('')
print(' -- Spam Filter With Email Corpus -- ')
# Email corpus. A hundred spam emails to buy products and with the word
# "meeting" thrown around. Genuine emails are about meetings and buying
# milk.
instances = {
'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100
}
# Use str.split to extract features/events/words from the corpus and build
# the model.
model = Bayes.extract_events_odds(instances, str.split)
# Create a new Bayes instance with 10%/90% priors on emails being genuine.
b = Bayes({'spam': .9, 'genuine': .1})
# Update beliefs with features/events/words from an email.
b.update_from_events("buy coffee for meeting".split(), model)
# Print the email and if it's likely spam or not.
print("'buy coffee for meeting'", ':', b)
print('')
print(' -- Are You Cheating? -- ')
results = ['heads', 'heads', 'tails', 'heads', 'heads']
events_odds = {
'heads': {
'honest': .5,
'cheating': .9
# Print the email and if it's likely spam or not.
print(email[:15] + '...', b.most_likely())
print('')
print(' -- Spam Filter With Email Corpus -- ')
# Email corpus. A hundred spam emails to buy products and with the word
# "meeting" thrown around. Genuine emails are about meetings and buying
# milk.
instances = {'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100}
# Use str.split to extract features/events/words from the corpus and build
# the model.
model = Bayes.extract_events_odds(instances, str.split)
# Create a new Bayes instance with 10%/90% priors on emails being genuine.
b = Bayes({'spam': .9, 'genuine': .1})
# Update beliefs with features/events/words from an email.
b.update_from_events("buy coffee for meeting".split(), model)
# Print the email and if it's likely spam or not.
print("'buy coffee for meeting'", ':', b)
print('')
print(' -- Are You Cheating? -- ')
results = ['heads', 'heads', 'tails', 'heads', 'heads']
events_odds = {'heads': {'honest': .5, 'cheating': .9},
'tails': {'honest': .5, 'cheating': .1}}
b = Bayes({'cheating': .5, 'honest': .5})
b.update_from_events(results, events_odds)
