def test_process(self):
sample_docs = [\
"hello this is virus mail",\
"hi this is from friend",\
"how about buy this virus",\
"facebook friend contact to you",\
"I love you baby virus",\
"what a nice day how about you"\
]
docs_label =\
['spam','real','spam','real','spam','real']
nlp_eng = nlp("eng")
# extract vocabulary from docs
voca = nlp_eng.extract_vocabulary(sample_docs)
self.tlog(voca)
assert len(voca) == 12
# convert docs to bag of word vector using vocabulary
docs_vector = []
for doc in sample_docs:
docs_vector.append(nlp_eng.bag_of_word2vector(voca, doc))
self.tlog(docs_vector)
# training NaiveBayes
nbayes = NaiveBayes(docs_vector, docs_label)
nbayes.fit()
# test case 1
tc1 = "this is virus mail"
tc1_vec = nlp_eng.bag_of_word2vector(voca, tc1)
self.tlog(tc1)
self.tlog(tc1_vec)
r1 = autotest.eval_predict_one(nbayes, tc1_vec, 'spam', self.logging)
assert r1 == True
# test case 2
tc2 = "I love you love"
tc2_vec = nlp_eng.bag_of_word2vector(voca, tc2)
self.tlog(tc2)
self.tlog(tc2_vec)
r2 = autotest.eval_predict_one(nbayes, tc2_vec, 'spam', self.logging)
assert r2 == True
def test_process(self):
sample_docs = [
"hello this is virus mail",
"hi this is from friend",
"how about buy this virus",
"facebook friend contact to you",
"I love you baby virus",
"what a nice day how about you",
]
docs_label = ["spam", "real", "spam", "real", "spam", "real"]
nlp_eng = nlp("eng")
# extract vocabulary from docs
voca = nlp_eng.extract_vocabulary(sample_docs)
self.tlog(voca)
assert len(voca) == 12
# convert docs to bag of word vector using vocabulary
docs_vector = []
for doc in sample_docs:
docs_vector.append(nlp_eng.bag_of_word2vector(voca, doc))
self.tlog(docs_vector)
# training NaiveBayes
nbayes = NaiveBayes(docs_vector, docs_label)
nbayes.fit()
# test case 1
tc1 = "this is virus mail"
tc1_vec = nlp_eng.bag_of_word2vector(voca, tc1)
self.tlog(tc1)
self.tlog(tc1_vec)
r1 = autotest.eval_predict_one(nbayes, tc1_vec, "spam", self.logging)
assert r1 == True
# test case 2
tc2 = "I love you love"
tc2_vec = nlp_eng.bag_of_word2vector(voca, tc2)
self.tlog(tc2)
self.tlog(tc2_vec)
r2 = autotest.eval_predict_one(nbayes, tc2_vec, "spam", self.logging)
assert r2 == True
def test_process(self):
nlp_eng = nlp("eng")
email_data_file = "sample_data/email/email.tsv"
emailmat_train, emaillabel_train, voca, emailmat_test, emaillabel_test = fs.tsv_loader_with_nlp(
email_data_file, 0.3, nlp_eng
)
self.tlog(voca)
email_nbayes = NaiveBayes(emailmat_train, emaillabel_train)
email_nbayes.fit()
error_rate = autotest.eval_predict(email_nbayes, emailmat_test, emaillabel_test, self.logging)
self.tlog("spam-mail predict (with NaiveBayes) error rate : " + str(error_rate))
assert error_rate <= 0.1
def test_process(self):
nlp_eng = nlp("eng_lower")
email_data_file = "sample_data/email/email.tsv"
emailmat_train, emaillabel_train, voca, emailmat_test, emaillabel_test \
= fs.tsv_loader_with_nlp(email_data_file, 0.4, nlp_eng)
self.tlog(voca)
email_nbayes = NaiveBayes(emailmat_train, emaillabel_train)
email_nbayes.fit()
error_rate = autotest.eval_predict(email_nbayes, emailmat_test,
emaillabel_test, self.logging)
self.tlog("spam-mail predict (with NaiveBayes) error rate : " +
str(error_rate))
assert error_rate <= 0.1