def rule_based(self, issues):
"""
This method applies rule_based algorithms to predict labels
Args:
issues(list): a list of issue numbers
Return:
rule_based_predictions(list of lists): labels which satisfy rules
"""
DF = DataFetcher()
df_test = DF.fetch_issues(issues)
rule_based_predictions = []
for i in range(len(issues)):
# extract every issue's title
row = df_test.loc[i, 'title']
# apply rule-based algorithms
single_issue_predictions = []
if "feature request" in row.lower():
single_issue_predictions.append("Feature")
if "c++" in row.lower():
single_issue_predictions.append("C++")
tokens = self.tokenize(row)
for k, v in self.keywords.items():
for keyword in v:
if keyword in tokens:
single_issue_predictions.append(k)
rule_based_predictions.append(single_issue_predictions)
return rule_based_predictions
def ml_predict(self, issues, threshold=0.3):
"""
This method applies machine learning algorithms to predict labels
Args:
issues(list): a list of issue numbers
threshold(float): threshold of probability
Return:
ml_predictions(list of lists): predictions
"""
# step1: fetch data
DF = DataFetcher()
df_test = DF.fetch_issues(issues)
# step2: data cleaning
SP = SentenceParser()
SP.data = df_test
SP.clean_body('body', True, True)
SP.merge_column(['title', 'title', 'title', 'body'], 'train')
test_text = SP.process_text('train', True, False, True)
# step3: word embedding
test_data_tfidf = self.tv.transform(test_text).toarray()
le = LabelEncoder()
le.fit_transform(self.labels)
# step4: classification
probs = self.clf.predict_proba(test_data_tfidf)
# pick up top 2 predictions which exceeds threshold
best_n = np.argsort(probs, axis=1)[:, -2:]
ml_predictions = []
for i in range(len(best_n)):
# INFO:Predictor:issue:11919,Performance:0.47353076240017744,Question:0.2440056213336274
logging.info("issue:{}, {}:{}, {}:{}".format(
str(issues[i]), str(le.classes_[best_n[i][-1]]),
str(probs[i][best_n[i][-1]]), str(le.classes_[best_n[i][-2]]),
str(probs[i][best_n[i][-2]])))
single_issue_predictions = [
le.classes_[best_n[i][j]] for j in range(-1, -3, -1)
if probs[i][best_n[i][j]] > threshold
]
ml_predictions.append(single_issue_predictions)
return ml_predictions
class TestLabelBot(unittest.TestCase):
def setUp(self):
self.df = DataFetcher()
self.df.repo = "apache/incubator-mxnet"
self.df.github_user = "******"
self.df.github_oauth_token = "123"
def tearDown(self):
pass
def test_cleanstr(self):
new_string = self.df.cleanstr("a_b", "")
self.assertEqual(new_string, "ab")
def test_count_pages(self):
with patch('DataFetcher.requests.get') as mocked_get:
mocked_get.return_value.status_code = 200
mocked_get.return_value.json.return_value = [{
"body":
"issue's body",
"created_at":
"2018-07-28T18:27:17Z",
"comments":
"0",
"number":
11925,
"labels": [{
'name': 'Doc'
}],
"state":
"open",
"title":
"issue's title",
"html_url":
"https://github.com/apache/incubator-mxnet/issues/11925",
}, {
"body":
"issue's body",
"created_at":
"2018-07-28T18:27:17Z",
"comments":
"0",
"number":
11924,
"labels": [],
"state":
"closed",
"title":
"issue's title",
"html_url":
"https://github.com/apache/incubator-mxnet/issues/11925",
}]
page = self.df.count_pages('all')
self.assertEqual(page, 1)
def test_fetch_issues(self):
with patch('DataFetcher.requests.get') as mocked_get:
mocked_get.return_value.status_code = 200
mocked_get.return_value.json.return_value = {
"body":
"issue's body",
"created_at":
"2018-07-28T18:27:17Z",
"comments":
"0",
"number":
11925,
"labels": [{
'name': 'Feature'
}],
"state":
"open",
"title":
"issue's title",
"html_url":
"https://github.com/apache/incubator-mxnet/issues/11925",
}
data = self.df.fetch_issues([11925])
expected_data = [{
'id': "11925",
'title': "issue's title",
'body': "issue's body"
}]
assert_frame_equal(data, pd.DataFrame(expected_data))
def test_data2json(self):
with patch('DataFetcher.requests.get') as mocked_get:
mocked_get.return_value.status_code = 200
mocked_get.return_value.json.return_value = [{
"body":
"issue's body",
"created_at":
"2018-07-28T18:27:17Z",
"comments":
"0",
"number":
11925,
"labels": [{
'name': 'Feature'
}],
"state":
"open",
"title":
"issue's title",
"html_url":
"https://github.com/apache/incubator-mxnet/issues/11925",
}, {
"body":
"issue's body",
"created_at":
"2018-07-28T18:27:17Z",
"comments":
"0",
"number":
11924,
"labels": [],
"state":
"closed",
"title":
"issue's title",
"html_url":
"https://github.com/apache/incubator-mxnet/issues/11925",
}]
self.df.data2json('all', labels=["Feature"], other_labels=False)
expected_data = [{
'id': 11925,
'title': "issue's title",
'body': "issue's body",
'labels': 'Feature'
}]
self.assertEqual(expected_data, self.df.json_data)