def __init__(self, train_data, beta=1):
self.counts = Counter()
spam = 0
total = 0
spam_prior = Counter()
ham_prior = Counter()
for row in util.get_rows(train_data):
if row["spam"]:
spam += 1
del row["spam"]
spam_prior.update(row)
else:
del row["spam"]
ham_prior.update(row)
total += 1
ham = total - spam
prior = ((ham + beta - 1) / (total + 2 * beta - 2), (spam + beta - 1) / (total + 2 * beta - 2))
for k, v in spam_prior.items():
spam_prior[k] = (v + beta - 1) / (spam + 2 * beta - 2)
for k, v in ham_prior.items():
ham_prior[k] = (v + beta - 1) / (ham + 2 * beta - 2)
self.weights = {}
self.weights["base"] = math.log(prior[1] / prior[0])
for k in set(spam_prior) | set(ham_prior):
self.weights["base"] += math.log((1 - spam_prior[k]) / (1 - ham_prior[k]))
self.weights[k] = math.log(spam_prior[k] / ham_prior[k]) - math.log(
(1 - spam_prior[k]) / (1 - ham_prior[k])
)
def main(train, test, beta, model):
classifier = nb.Classifier(train, beta)
util.dump_model(classifier, model)
count = correct = 0
for row in util.get_rows(test):
prob, is_spam = classifier.classify(row)
print(round(prob, 4))
if round(prob) == is_spam:
correct += 1
count += 1
print('\nAccuracy: {:.2%}'.format(correct/count))