def run_self_CFS(project):
X, y = load_data(project)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.40,
random_state=18)
loc = X_test.CountLineCode
df_smote = pd.concat([X_train, y_train], axis=1)
df_smote = apply_smote(df_smote)
df_smote, cols = apply_cfs(df_smote)
y_train = df_smote.Bugs
X_train = df_smote.drop('Bugs', axis=1)
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predicted = clf.predict(X_test[cols[:-1]])
abcd = metrices.measures(y_test, predicted, loc)
pf = abcd.get_pf()
recall = abcd.calculate_recall()
precision = abcd.calculate_precision()
f1 = abcd.calculate_f1_score()
g_score = abcd.get_g_score()
pci_20 = abcd.get_pci_20()
ifa = abcd.get_ifa()
try:
auc = roc_auc_score(y_test, predicted)
except:
auc = 0
print(classification_report(y_test, predicted))
return recall, precision, pf, f1, g_score, auc, pci_20, ifa
def run_self_k(project, metric):
precision = []
recall = []
pf = []
f1 = []
g_score = []
auc = []
pci_20 = []
ifa = []
importance = []
X, y = load_both_data(project, metric)
for _ in range(5):
skf = StratifiedKFold(n_splits=5)
for train_index, test_index in skf.split(X, y):
X_train, X_test = X.loc[train_index], X.loc[test_index]
y_train, y_test = y.loc[train_index], y.loc[test_index]
if metric == 'process':
loc = X_test['file_la'] + X_test['file_lt']
elif metric == 'product':
loc = X_test.CountLineCode
else:
loc = X_test['file_la'] + X_test['file_lt']
df_smote = pd.concat([X_train, y_train], axis=1)
df_smote = apply_smote(df_smote)
y_train = df_smote.Bugs
X_train = df_smote.drop('Bugs', axis=1)
clf = SVC()
clf.fit(X_train, y_train)
importance = 0
predicted = clf.predict(X_test)
abcd = metrices.measures(y_test, predicted, loc)
pf.append(abcd.get_pf())
recall.append(abcd.calculate_recall())
precision.append(abcd.calculate_precision())
f1.append(abcd.calculate_f1_score())
g_score.append(abcd.get_g_score())
pci_20.append(abcd.get_pci_20())
ifa.append(abcd.get_ifa())
try:
auc.append(roc_auc_score(y_test, predicted))
except:
auc.append(0)
# print(classification_report(y_test, predicted))
return recall, precision, pf, f1, g_score, auc, pci_20, ifa, importance
def run_self_release(project,metric):
precision = []
recall = []
pf = []
f1 = []
g_score = []
auc = []
pci_20 = []
ifa = []
X_train,y_train,test_df = load_data_commit_level(project,metric)
df_smote = pd.concat([X_train,y_train],axis = 1)
df_smote = apply_smote(df_smote)
y_train = df_smote.Bugs
X_train = df_smote.drop('Bugs',axis = 1)
clf = RandomForestClassifier()
clf.fit(X_train,y_train)
importance = clf.feature_importances_
unique_commits_list = np.array_split(test_df.commit_hash.unique(), 5)
for i in range(len(unique_commits_list)):
test_df_subset = test_df[test_df.commit_hash.isin(unique_commits_list[i])]
y_test = test_df_subset.Bugs
X_test = test_df_subset.drop(['Bugs','commit_hash'],axis = 1)
if metric == 'process':
loc = X_test['file_la'] + X_test['file_lt']
elif metric == 'product':
loc = X_test.CountLineCode
else:
loc = X_test['file_la'] + X_test['file_lt']
predicted = clf.predict(X_test)
abcd = metrices.measures(y_test,predicted,loc)
pf.append(abcd.get_pf())
recall.append(abcd.calculate_recall())
precision.append(abcd.calculate_precision())
f1.append(abcd.calculate_f1_score())
g_score.append(abcd.get_g_score())
pci_20.append(abcd.get_pci_20())
ifa.append(abcd.get_ifa())
try:
auc.append(roc_auc_score(y_test, predicted))
except:
auc.append(0)
print(classification_report(y_test, predicted))
# print(recall,precision,pf,f1,g_score,auc,pci_20)
return recall,precision,pf,f1,g_score,auc,pci_20,ifa,importance
def run_self_k(project,projects,metric):
precision = []
recall = []
pf = []
f1 = []
g_score = []
auc = []
pci_20 = []
ifa = []
importance = []
X_train,y_train = load_both_data(project,metric)
df_smote = pd.concat([X_train,y_train],axis = 1)
df_smote = apply_smote(df_smote)
y_train = df_smote.Bugs
X_train = df_smote.drop('Bugs',axis = 1)
clf = RandomForestClassifier()
clf.fit(X_train,y_train)
importance = clf.feature_importances_
for _project in projects:
try:
X_test,y_test = load_both_data(_project,metric)
if metric == 'process':
loc = X_test['file_la'] + X_test['file_lt']
elif metric == 'product':
loc = X_test.CountLineCode
else:
loc = X_test['file_la'] + X_test['file_lt']
predicted = clf.predict(X_test)
abcd = metrices.measures(y_test,predicted,loc)
pf.append(abcd.get_pf())
recall.append(abcd.calculate_recall())
precision.append(abcd.calculate_precision())
f1.append(abcd.calculate_f1_score())
g_score.append(abcd.get_g_score())
pci_20.append(abcd.get_pci_20())
ifa.append(abcd.get_ifa())
try:
auc.append(roc_auc_score(y_test, predicted))
except:
auc.append(0)
except Exception as e:
print('error in test',_project,e)
continue
return recall,precision,pf,f1,g_score,auc,pci_20,ifa,importance
def run_self(project, projects):
precision_list = []
recall_list = []
pf_list = []
f1_list = []
g_list = []
auc_list = []
pci_20_list = []
ifa_list = []
featue_importance = []
X_train, y_train = load_data(project)
df_smote = pd.concat([X_train, y_train], axis=1)
df_smote = apply_smote(df_smote)
y_train = df_smote.Bugs
X_train = df_smote.drop('Bugs', axis=1)
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
importance = 0
for project in projects:
try:
X_test, y_test = load_data(project)
loc = X_test.CountLineCode
predicted = clf.predict(X_test)
abcd = metrices.measures(y_test, predicted, loc)
pf_list.append(abcd.get_pf())
recall_list.append(abcd.calculate_recall())
precision_list.append(abcd.calculate_precision())
f1_list.append(abcd.calculate_f1_score())
g_list.append(abcd.get_g_score())
pci_20_list.append(abcd.get_pci_20())
ifa_list.append(abcd.get_ifa())
try:
auc_list.append(roc_auc_score(y_test, predicted))
except:
precision_list.append(0)
except:
continue
return np.nanmedian(recall_list), np.nanmedian(
precision_list), np.nanmedian(pf_list), np.nanmedian(
f1_list), np.nanmedian(g_list), np.nanmedian(
auc_list), np.nanmedian(pci_20_list), np.nanmedian(
ifa_list), importance