def write_probabilities(self, train_data, test_data):
"""
Writes log probabilities to file. To be called only with a fitted model.
:param train_data: unsliced train data, including 'repo_name' and 'label'
:param test_data: unsliced test data, including 'repo_name' and 'label'
"""
train_data_trees = pd.DataFrame(data=self.select_features(train_data))
train_data_trees['blob_paths_updated'] = train_data_trees.apply(
lambda row: self.row_to_words(row), axis=1)
x_train = self.cV.transform(train_data_trees['blob_paths_updated'])
train_repo_names = train_data['repo_name']
train_labels = train_data['label']
Helper().write_probabilities(self.clf, x_train, train_repo_names,
train_labels,
'prob/prob_%s_train' % 'trees')
test_data_trees = pd.DataFrame(data=self.select_features(test_data))
test_data_trees['blob_paths_updated'] = test_data_trees.apply(
lambda row: self.row_to_words(row), axis=1)
x_test = self.cV.transform(test_data_trees['blob_paths_updated'])
test_repo_names = test_data['repo_name']
test_labels = test_data['label']
Helper().write_probabilities(self.clf, x_test, test_repo_names,
test_labels,
'prob/prob_%s_test' % 'trees')
def getReadmes(self, label, keyword):
names = self.repos_names_search % (label, label, keyword)
folder = self.readmes_repos_folder % label
with open(names, 'r') as file:
repos = file.readlines()
for repo in repos:
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, MD_README_FILE_NAME)
if os.path.exists(filename):
print 'exists'
continue
r = requests.get("https://api.github.com/repos/" + repo[:-1] +
"/readme",
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 200:
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, MD_README_FILE_NAME)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as file:
print "Writing to %s" % file.name
content = r.json()['content']
decoded = content.decode('base64')
file.write(decoded)
file.close()
else:
print r.headers
def confusion_matrix(self, y_test, y_pred):
"""
Saves confusion matrix to file
:param y_test: test labels
:param y_pred: predicted labels
"""
confusion_m = confusion_matrix(y_test, y_pred)
Helper().plot_confusion_matrix(self.input_type, confusion_m, normalize=True, classes=Labels.toArray(),
title='Confusion matrix for %s classifier' % self.input_type)
Helper().plot_confusion_matrix(self.input_type, confusion_m, normalize=False, classes=Labels.toArray(),
title='Confusion matrix for %s classifier' % self.input_type)
def write_proba(self, dataframe_train, dataframe_test):
X_train, Y_train = self.build_keyword_features(
dataframe_train), self.build_labels(dataframe_train)
X_test, Y_test = self.build_keyword_features(
dataframe_test), self.build_labels(dataframe_test)
Helper().write_probabilities(self.clf, X_train,
dataframe_train['repo_name'],
dataframe_train['label'],
'prob/prob_keyword_train')
Helper().write_probabilities(self.clf, X_test,
dataframe_test['repo_name'],
dataframe_test['label'],
'prob/prob_keyword_test')
def write_probabilities(self, train_data, test_data):
"""
Writes log probabilities to file
:param train_data: unsliced train data, including 'repo_name' and 'label'
:param test_data: unsliced test data, including 'repo_name' and 'label'
"""
X = self.select_features(train_data)
train_repo_names = train_data['repo_name']
train_labels = train_data['label']
Helper().write_probabilities(self.clf, X, train_repo_names, train_labels,
'prob/prob_%s_train' % self.input_type)
Y = self.select_features(test_data)
test_repo_names = test_data['repo_name']
test_labels = test_data['label']
Helper().write_probabilities(self.clf, Y, test_repo_names, test_labels, 'prob/prob_%s_test' % self.input_type)
def get_contents(self, label, keyword):
names = self.repos_names_search % (label, label, keyword)
folder = self.contents_repos_folder % label
with open(names, 'r') as file:
repos = file.readlines()
for repo in repos:
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, JSON_CONTENTS_FILE_NAME)
if os.path.exists(filename):
print filename, " exists"
continue
r = requests.get("https://api.github.com/repos/" + repo[:-1] +
"/contents",
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 200:
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as file:
print "Writing to %s" % file.name
contents = json.dumps((r.json()))
file.write(contents)
file.close()
else:
print r.headers
def update_existing(self):
file = open(self.output_file, 'r')
lines = file.readlines()
for line in lines:
repo_link = line.split(" ")[0]
assigned_label = line.split(" ")[1].rstrip('\n')
labelled_class = open("labelledd_%s" % assigned_label, 'a')
labelled_class.write(
Helper().build_repo_name_from_repo_link(repo_link) + '\n')
def getCommitActivity(self, label, keyword):
names = self.repos_names_search % (label, label, keyword)
folder = self.commit_activity_repos_folder % label
with open(names, 'r') as file:
repos = file.readlines()
print repos.__len__()
for repo in repos:
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, JSON_COMMIT_ACTIVITY_FILE_NAME)
if os.path.exists(filename):
print filename, " exists"
continue
r = requests.get("https://api.github.com/repos/" + repo[:-1] +
"/stats/commit_activity",
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 202:
while r.status_code == 202:
print "status code: ", r.status_code
r = requests.get("https://api.github.com/repos/" +
repo[:-1] + "/stats/commit_activity",
auth=HTTPBasicAuth(
self.username, self.password))
time.sleep(3)
if r.status_code == 200:
print "status code: ", r.status_code
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, JSON_COMMIT_ACTIVITY_FILE_NAME)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as file:
print "Writing to %s" % file.name
jsonContent = json.dumps((r.json()))
file.write(jsonContent)
file.close()
else:
print r.headers
def get_repos_additional_data(self, label):
names = self.additional_repos_names % label
folder = self.additional_repos_folder % label
with open(names, 'r') as file:
repos = file.readlines()
for repo in repos:
repo_name = Helper().build_repo_name_from_repo_link(repo)
r = requests.get("https://api.github.com/repos/" + repo_name,
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 200:
filename = Helper().build_path_from_folder_and_repo_link(
repo, folder, JSON_REPO_FILE_NAME)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as file:
print "Writing to %s" % file.name
jsonContent = json.dumps((r.json()))
file.write(jsonContent)
file.close()
else:
print r.headers
def get_all_commits_additional_data(self, label):
names = self.additional_repos_names % label
folder = self.additional_commits_repos_folder % label
query = {'per_page': 100}
with open(names, 'r') as file:
repos = file.readlines()
print repos.__len__()
for repo in repos:
filename = Helper().build_path_from_folder_and_repo_link(
repo, folder, JSON_COMMITS_FILE_NAME)
if os.path.exists(filename):
print filename, " exists"
continue
repo_name = Helper().build_repo_name_from_repo_link(repo)
print repo_name
r = requests.get("https://api.github.com/repos/" + repo_name +
"/commits",
params=query,
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 200:
print "status code: ", r.status_code
filename = Helper().build_path_from_folder_and_repo_link(
repo, folder, JSON_COMMITS_FILE_NAME)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
jsonCommits = r.json()
links = r.links
print "commits loaded:", len(jsonCommits)
while 'next' in links:
next_page_url = links['next']['url']
next_page_request = requests.get(next_page_url,
auth=HTTPBasicAuth(
self.username,
self.password))
if next_page_request.status_code == 200:
jsonCommits.extend(next_page_request.json())
links = next_page_request.links
print "commits loaded:", len(jsonCommits)
jsonCommitsList = []
for commit in jsonCommits:
author = commit['commit']['author']
committer = commit['commit']['author']
comment_count = commit['commit']['comment_count']
author_date = author['date']
committer_date = committer['date']
author_email = author['email']
committer_email = committer['email']
commit_date = {
'author_date': author_date,
'committer_date': committer_date,
'comment_count': comment_count,
'author_email': author_email,
'committer_email': committer_email
}
jsonCommitsList.append(commit_date)
with open(filename, 'w') as file:
print "Writing %d commits to %s" % (
jsonCommitsList.__len__(), file.name)
jsonContent = json.dumps(jsonCommitsList)
file.write(jsonContent)
file.close()
else:
print r.headers
print 'Successfully loaded commits for %d repos' % len(repos)
def get_commits_interval(self, label, keyword):
names = self.repos_names_search % (label, label, keyword)
folder = self.commits_interval_folder % label
with open(names, 'r') as file:
repos = file.readlines()
print repos.__len__()
for repo in repos:
filename = Helper().build_path_from_folder_and_repo_name(
repo, folder, JSON_COMMITS_INTERVAL)
if os.path.exists(filename):
print filename, " exists"
continue
r = requests.get("https://api.github.com/repos/" + repo[:-1] +
"/commits",
auth=HTTPBasicAuth(self.username, self.password))
if r.status_code == 200:
if 'last' not in r.links:
only_page_json_object = r.json()
first_commit = only_page_json_object[0]
last_commit = only_page_json_object[
len(only_page_json_object) - 1]
last_page = -1
else:
print r.links
first_page_json_object = r.json()
first_commit = first_page_json_object[0]
last_page_url = r.links['last']['url']
last_page = (int)(last_page_url.split('=')[1])
print last_page
if last_page != -1:
req = requests.get(last_page_url,
auth=HTTPBasicAuth(
self.username, self.password))
if req.status_code == 200:
last_page_json_object = req.json()
last_commit = last_page_json_object[
len(last_page_json_object) - 1]
else:
print 'Page request failed'
count = len(last_page_json_object)
if last_page != -1:
# multiply by results per page
count += (last_page - 1) * 30
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
json_interval_object = {"commits_count": count}
json_interval_object['first_commit'] = first_commit
json_interval_object['last_commit'] = last_commit
with open(filename, 'w') as file:
print "Writing commits interval and count %d to %s" % (
count, file.name)
jsonContent = json.dumps(json_interval_object)
file.write(jsonContent)
file.close()
else:
print r.headers
print 'Successfully loaded commits for %d repos' % len(repos)
repo_names = data['repo_name'] data = data.drop(labels='repo_name', axis=1) train_data, test_data = train_test_split(data, test_size=0.2) train_labels = train_data['label'] test_labels = test_data['label'] train_data = train_data.drop(labels='label', axis=1) test_data = test_data.drop(labels='label', axis=1) print data.shape print train_data.shape print test_data.shape forest_classifier = RandomForestClassifier(n_estimators=2000, max_depth=3) forest = forest_classifier.fit(train_data, train_labels) output = forest.predict(test_data) print mean_squared_error(output, test_labels) print accuracy_score(test_labels, output) score = precision_score(output, test_labels, average=None) # precision values high for hw and web, meaning that commit info is able to identify these classes? print score print np.mean(score) Helper().write_probabilities(forest, data, repo_names, 'prob/prob_commit_interval_data')
# precision scores much closer to one another and highly oscilating
# forest_classifier = RandomForestClassifier(n_estimators=1000, max_depth=10)
# below finds docs with high precision
# precision average is lower: 31-42 %, sometimes even oscillates 21 - 56
# forest_classifier = RandomForestClassifier(n_estimators=1000, max_depth=5)
forest_classifier = RandomForestClassifier(n_estimators=2000, max_depth=5)
forest = forest_classifier.fit(train_data, train_labels)
output = forest.predict(test_data)
print mean_squared_error(output, test_labels)
print accuracy_score(test_labels, output)
score = precision_score(test_labels, output, average=None)
print score
print np.mean(score)
Helper().write_probabilities(forest, data, repo_names,
'prob/prob_language_data')
# linear_svc = LinearSVC()
# rbf_svc = SVC(kernel='rbf')
# compare_performance_scores(linear_svc=linear_svc, rbf_svc=rbf_svc, data=data)
# tune_rbf_svc_hyperparameters(rbf_svc, data)
# rbf_svc = SVC(kernel='rbf', C=1, gamma=0.01)
# compare_performance_scores(linear_svc=linear_svc, rbf_svc=rbf_svc, data=data)
