def make_patterns(args, cur_work_dir):
print('Compute patterns...')
result = subprocess.run(['make', 'patterns'], stdout=subprocess.PIPE, encoding='utf-8', cwd=cur_work_dir)
print(result.returncode)
if result.returncode != 0:
print(result.stderr)
exit(3)
else:
print(result.stdout)
if args.dataset_file:
dataset_file_path = Path(cur_work_dir, args.dataset_file)
if not dataset_file_path.parent.exists():
dataset_file_path.parent.mkdir(parents=True)
shutil.copy(Path(Config.dataset_file()), dataset_file_path)
else:
dataset_file_path = Path(Config.dataset_file())
print('dataset was saved to {}'.format(str(dataset_file_path.absolute())))
def preprocess_file(self, scale_ncss=True, scale=False, **kwargs):
df = pd.read_csv(Config.dataset_file())
df = df[~df["filename"].str.lower().str.contains("test")]
config = Config.get_patterns_config()
if self.do_rename_columns:
p_codes = \
[x['code'] for x in config['patterns']] \
+ ['lines' + x['code'] for x in config['patterns']]
m_codes = [x['code'] for x in config['metrics']]
keys = p_codes + m_codes
vals = \
[x['name'] for x in config['patterns']] \
+ ['lines' + x['name'] for x in config['patterns']] \
+ [x['name'] for x in config['metrics']]
replace_dict = dict(zip(keys, vals))
df = df.rename(replace_dict)
df.columns = vals
print('Columns renamed:' + df.head())
df = df.dropna().drop_duplicates(
subset=df.columns.difference(['filename']))
df = df[(df.ncss > 20) & (df.ncss < 100) &
(df.npath_method_avg < 100000.00)].copy().reset_index()
df.drop('filename', axis=1, inplace=True)
df.drop('index', axis=1, inplace=True)
self.target = df[['M4']].values[:, 0]
if scale_ncss:
new = pd.DataFrame(df[self.only_patterns].values /
df['M2'].values.reshape((-1, 1)),
columns=self.only_patterns)
self.target /= df['M2'].values.reshape(-1)
else:
new = df[self.only_patterns].copy()
if scale:
self.input = pd.DataFrame(StandardScaler().fit_transform(
new.values),
columns=new.columns,
index=new.index).values
else:
self.input = new.values
self.feature_order = list(new.columns)