def main():
data = []
for verdict in ['spam', 'not_spam']:
for files in glob.glob(PATH + verdict + "/*")[:500]:
is_spam = True if verdict == 'spam' else False
with open(files, "r", encoding='utf-8', errors='ignore') as f:
for line in f:
if line.startswith("Subject:"):
subject = re.sub("^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
print("Spam" if classifier.classify("Get free laptops now!") > 0.5 else
"Not Spam")
classified = [(subject, is_spam, classifier.classify(subject))
for subject, is_spam in test_data]
count = Counter((is_spam, spam_probability > 0.5)
for _, is_spam, spam_probability in classified)
spammiest_hams, hammiest_spams = most_misclassified(classified)
print("Accuracy: ", accuracy(count))
print("Precision: ", precision(count))
print("Recall: ", recall(count))
print("\nTop 5 falsely classified as spam:\n\n", spammiest_hams)
print("\nTop 5 falsely classified as not spam:\n\n", hammiest_spams)
print("\nMost spammiest words: ", spammiest_word(classifier))
class TestClassifier(unittest.TestCase):
def setUp(self):
self.examples = {'university': ['''Abbottabad Public School , also commonly referred to as
APS and Railway Public School , is a private , all boys , boarding
school for , 7th to 12th grade students , located in Abbottabad ,
Pakistan .''']}
self.classifier = NaiveBayesClassifier(self.examples)
def test_create_vocabulary(self):
self.classifier.vocabulary.should.contain('private')
def test_vocabulary_size(self):
self.classifier.vocabulary_size.should.eql(28)
def test_subset_of_documents_with_target_value(self):
len(self.classifier.get_documents_with_target_value('university')).should.eql(1)
def test_text_of_documents(self):
documents = self.classifier.get_documents_with_target_value('university')
self.classifier.get_text(documents).should.contain('private')
def test_text_distinct_words(self):
documents = self.classifier.get_documents_with_target_value('university')
text = self.classifier.get_text(documents)
self.classifier.get_text_diff_words_count(text).should.eql(28)
def test_example_count(self):
self.classifier.get_example_count().should.eql(1)
def test_occurrences_of_word_count(self):
documents = self.classifier.get_documents_with_target_value('university')
text = self.classifier.get_text(documents)
self.classifier.occurrences_count(',', text).should.eql(7)
def test_learn(self):
self.classifier.learn()
def test_word_positions_in_doc(self):
documents = self.classifier.get_documents_with_target_value('university')
len(self.classifier.word_positions(documents[0])).should.eql(38)
def test_classify(self):
self.classifier.learn()
self.classifier.classify(self.examples['university'][0]).should.eql('university')
def spamFilterChecker():
print('Received the JAVA Request!')
# Get the text data from the JAVA Program.
req_data = abhishek_request.get_json()
text_to_be_classified = req_data['text_to_be_classified']
print(text_to_be_classified)
# ----------------------------------------------------------------------------
# Make a POST request to the plino Spam API.
# ----------------------------------------------------------------------------
data = []
for verdict in ['spam', 'not_spam']:
for files in glob.glob(PATH + verdict + "/*")[:500]:
is_spam = True if verdict == 'spam' else False
with open(files, "r", encoding='utf-8', errors='ignore') as f:
for line in f:
if line.startswith("Subject:"):
subject = re.sub("^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.80)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
json_response = ""
value = classifier.classify(text_to_be_classified)
if value < 0.9:
json_response = "{'email_class' : 'spam'}"
else:
json_response = "{'email_class' : 'ham'}"
print("====================================================")
print("POSSIBILITY OF HAM : ", value)
print(json_response)
print("====================================================")
return json_response
dir = os.path.realpath('..')
keyword = 'hillary'
trainingDataFile = '/home/cc/twitterSentiment/src/input/hillary.txt'
inpfile = open(trainingDataFile, "r")
lines = inpfile.read().split()
tweets = []
for tweet in tweets:
tweets.append(tweet)
time = 'daily'
classifierDumpFile = '/home/cc/twitterSentiment/src//input/naivebayes_model.pickle'
trainingRequired = 0
# instantiate the instance of classifier class
nb = NaiveBayesClassifier(tweets, keyword, time, \
trainingDataFile, classifierDumpFile, trainingRequired)
# run the classifier model on tweets
nb.classify()
htmlcode = nb.getHTML()
htmlfile = open('/var/www/html/index.html','w')
htmlfile.write(htmlcode)
htmlfile.close()
# time = 'lastweek'
# twitterData = get_twitter_data.TwitterData()
# tweets = twitterData.getTwitterData(keyword, time)
if not os.path.exists('./input/trump.txt'):
keyword = 'trump'
# time = 'lastweek'
# twitterData = get_twitter_data.TwitterData()
# tweets = twitterData.getTwitterData(keyword, time)
with open(fn, "r") as file:
for line in file:
if line.startswith("Subject:"):
# remove the leading "Subject: " and keep what's left
subject = re.sub(r"^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
# triplets (subject, actual is_spam, predicted spam probability)
classified = [(subject, is_spam, classifier.classify(subject)) for subject, is_spam in test_data]
# assume that spam_probability > 0.5 corresponds to spam prediction
# and count the combinations of (actual is_spam, predicted is_spam)
counts = Counter((is_spam, spam_probability > 0.5) for _, is_spam, spam_probability in classified)
# sort by spam_probability from smallest to largest
classified.sort(key=lambda row: row[2])
# the highest predicted spam probabilities among the non-spams
spammiest_hams = filter(lambda row: not row[1], classified)[-5:]
# the lowest predicted spam probabilities among the actual spams
hammiest_spams = filter(lambda row: row[1], classified)[:5]
print("Testing Probabilities")
assert naive_bayes.probability('scam', True) == 0.4
assert naive_bayes.probability('scam', False) == 0.6
print("passed")
print("Testing Conditional Probabilities")
assert naive_bayes.conditional_probability(('errors',True), given=('scam',True)) == 1.0
assert naive_bayes.conditional_probability(('links',False), given=('scam',True)) == 0.25
assert naive_bayes.conditional_probability(('errors',True), given=('scam',False)) == 0.16666666666666666
assert naive_bayes.conditional_probability(('links',False), given=('scam',False)) == 0.5
print("passed")
observed_features = {
'errors': True,
'links': False
}
print("Testing Likeihoods")
assert naive_bayes.likelihood(('scam',True), observed_features) == 0.1
assert round(naive_bayes.likelihood(('scam',False), observed_features),3) == 0.05
print("passed")
print("Testing Classification")
assert naive_bayes.classify(observed_features) == True
print('passed')
print('ALL TESTS PASSED')
with open(fn, 'r') as file:
for line in file:
if line.startswith("Subject:"):
# remove the leading "Subject: " and keep what's left
subject = re.sub(r"^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
# triplets (subject, actual is_spam, predicted spam probability)
classified = [(subject, is_spam, classifier.classify(subject))
for subject, is_spam in test_data]
# assume that spam_probability > 0.5 corresponds to spam prediction
# and count the combinations of (actual is_spam, predicted is_spam)
counts = Counter((is_spam, spam_probability > 0.5)
for _, is_spam, spam_probability in classified)
# sort by spam_probability from smallest to largest
classified.sort(key=lambda row: row[2])
# the highest predicted spam probabilities among the non-spams
spammiest_hams = filter(lambda row: not row[1], classified)[-5:]
# the lowest predicted spam probabilities among the actual spams
hammiest_spams = filter(lambda row: row[1], classified)[:5]