def test_allPredictionsWrong(self):
# Prepare the SUT
create_inverse_truth_and_prediction_file()
# Excercise the SUT
q = compute_quality_for_corpus(CORPUS_DIR)
# Assertions
self.assertEqual(q, 0.0)
def myfilter_test(corpus_dir):
'''Runs MyFilter test and than prints its prediction quality.'''
MyFilter.train(corpus_dir)
MyFilter.test(corpus_dir)
print ("MyFilter:")
print ( quality.compute_quality_for_corpus(corpus_dir) )
remove_prediction()
def test_allPredictionsCorrect(self):
# Prepare the SUT
create_identical_truth_and_prediction_file()
# Excercise the SUT
q = compute_quality_for_corpus(CORPUS_DIR)
# Assertions
self.assertEqual(q, 1.0)
def test_allPredictionsWrong(self):
# Prepare the SUT
# Create an artificial email classification dictionary
truth_dict = create_classification()
create_truth_and_prediction_file(truth_dict, invert_classes)
# Excercise the SUT
q = compute_quality_for_corpus(CORPUS_DIR)
# Assertions
self.assertEqual(q, 0.0)
def test_allPredictionsHam_for10SpamsAnd10Hams(self):
# Prepare the SUT
# Create an artificial email classification dictionary
truth_dict = create_classification(n_items=20, n_spams=10)
create_truth_and_prediction_file(truth_dict, hams_only)
# Since there are 10 spams and 10 hams in the corpus,
# and predictions are all ham, then the confusion matrix
# shall have TN = 10 and FN = 10, zero positives.
# The modified accuracy then is:
expected_q = 10 / (10 + 10)
# Excercise the SUT
q = compute_quality_for_corpus(CORPUS_DIR)
# Assertions
self.assertEqual(q, expected_q)
tests_done += 1
return (score, tests_done)
def test_word_filters(self, name, mail):
score = 0
tests_done = 0
for filt in self.word_filters:
result = filt.test(mail)
if result != -1:
score += result
tests_done += 1
return (score, tests_done)
if __name__ == '__main__':
import quality
train_dir = '1/'
test_dir = '2/'
# filt = atomfilters.XSpamStatusFilter()
# score = quality.test_atom_filter(filt, train_dir, test_dir)
# print("Score of {name} is {score:3.2f}".format(name=filt.__class__.__name__, score=score))
filt = MyFilter()
filt.train(train_dir)
filt.test(test_dir)
# utils.show_mismatched(test_dir)
score = quality.compute_quality_for_corpus(test_dir)
print("Score for {constant} is {score:3.2f}".format(
constant=POSITIVITY_THRESHOLD, score=score))
def quality_s(self):
return quality.compute_quality_for_corpus(self.path_to_corpus)
for b_line in pred:
b_list = b_line.strip().split()
b_dic[b_list[0]] = b_list[1]
if __name__ == '__main__':
train_corpus = '/Users/eygene/Desktop/spam-data-12-s75-h25/1'
test_corpus = '/Users/eygene/Desktop/spam-data-12-s75-h25/2'
filter = MyFilter()
filter.train(train_corpus)
filter.test(test_corpus)
print('1>2: ' ,compute_quality_for_corpus(test_corpus))
filter = MyFilter()
filter.train(test_corpus)
filter.test(train_corpus)
print('2->1: ' ,compute_quality_for_corpus(train_corpus))
filter = MyFilter()
filter.train(test_corpus)
filter.test(test_corpus)
print('2->2: ', compute_quality_for_corpus(test_corpus))
get_error(test_corpus)
filter = MyFilter()
filter.train(train_corpus)
filter.test(train_corpus)
print('1->1: ' ,compute_quality_for_corpus(train_corpus))
word_probability = (
spam_ham_dict.get(word) + self.alpha) / (
spam_ham_num_of_words +
self.alpha * self.num_of_all_words + self.alpha)
else:
word_probability = self.alpha / (
spam_ham_num_of_words +
self.alpha * self.num_of_all_words + self.alpha)
else:
word_probability = 1
self.average_word_probability += word_probability
email_probability *= word_probability
email_probability, underflow_overflow = underflow_overflow_exception_handler(
email_probability, underflow_overflow)
self.average_word_probability /= len(words)
return email_probability, underflow_overflow
if __name__ == "__main__":
my_filter1 = MyFilter()
my_filter1.train("spam-data-12-s75-h25/1/")
my_filter1.test("spam-data-12-s75-h25/2/")
print("Quality of the spam filter for first folder with emails:")
print(compute_quality_for_corpus("spam-data-12-s75-h25/2/"))
my_filter2 = MyFilter()
my_filter2.train("spam-data-12-s75-h25/2/")
my_filter2.test("spam-data-12-s75-h25/1/")
print("Quality of the spam filter for second folder with emails:")
print(compute_quality_for_corpus("spam-data-12-s75-h25/1/"))
import simplefilters
import quality
import os
nf = simplefilters.RandomFilter()
nf.train('C:\\Users\\Prabhath\\Desktop\\spam_filter\\1' + '\\!truth.txt')
nf.test('C:\\Users\\Prabhath\\Desktop\\spam_filter\\2')
print(quality.compute_quality_for_corpus('C:\\Users\\Prabhath\\Desktop\\spam_filter'))
os.remove("C:\\Users\\Prabhath\\Desktop\\spam_filter\\2\\!prediction.txt")
