from sklearn.datasets import fetch_20newsgroups
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

train_emails = fetch_20newsgroups(categories = ['rec.sport.baseball', 'rec.sport.hockey'], subset = 'train',shuffle =True,random_state =108)
#print(emails.target_names)
#print(emails.target[5])
test_emails = fetch_20newsgroups(categories = ['rec.sport.baseball', 'rec.sport.hockey'], subset = 'test',shuffle =True,random_state =108)

counter = CountVectorizer()
counter.fit(test_emails.data + train_emails.data)

train_counts = counter.transform(train_emails.data)
test_counts = counter.transform(test_emails.data)

classifier = MultinomialNB()
classifier.fit(train_counts,train_emails.target)
print(classifier.score(test_counts,test_emails.target))
