def __calculate_features(self,
data,
feature_file_name,
window=DEFAULT_WINDOW):
"""
Caculate time features and save as libsvm format.
:param data: the time series to detect of
:param feature_file_name: the file to use
:param window: the length of window
"""
features = []
for index in data:
if is_standard_time_series(index["data"], window):
temp = []
temp.append(
feature_service.extract_features(index["data"], window))
temp.append(index["flag"])
features.append(temp)
try:
ret_code, ret_data = self.__save_libsvm_format(
features, feature_file_name)
except Exception as ex:
ret_code = CAL_FEATURE_ERR
ret_data = str(ex)
return ret_code, ret_data
def __calculate_features(self, data, window=180):
"""
Caculate time features.
:param data: the time series to detect of
:param window: the length of window
"""
features = []
for index in data:
if is_standard_time_series(index["data"], window):
temp = []
temp.append(feature_service.extract_features(index["data"], window))
temp.append(index["flag"])
features.append(temp)
return features
def predict(self, X, window=180, model_name=DEFAULT_MODEL):
"""
:param X: the time series to detect of
:type X: pandas.Series
:param window: the length of window
:param model_name: Use a xgboost model to predict a particular sample is an outlier or not.
:return 1 denotes normal, 0 denotes abnormal.
"""
if is_standard_time_series(X, window):
ts_features = []
features = [10]
features.extend(feature_service.extract_features(X, window))
ts_features.append(features)
res_pred = xgb.DMatrix(np.array(ts_features))
bst = xgb.Booster({'nthread': 4})
bst.load_model(model_name)
xgb_ret = bst.predict(res_pred)
if xgb_ret[0] < self.threshold:
value = 0
else:
value = 1
return [value, xgb_ret[0]]
else:
return [0, 0]
def predict(self, X, window=DEFAULT_WINDOW, model_name=DEFAULT_MODEL):
"""
Predict if a particular sample is an outlier or not.
:param X: the time series to detect of
:param type X: pandas.Series
:param window: the length of window
:param type window: int
:param model_name: the model to use
:param type model_name: string
:return 1 denotes normal, 0 denotes abnormal
"""
if is_standard_time_series(X):
ts_features = feature_service.extract_features(X, window)
ts_features = np.array([ts_features])
load_model = pickle.load(open(model_name, "rb"))
gbdt_ret = load_model.predict_proba(ts_features)[:, 1]
if gbdt_ret[0] < self.threshold:
value = 0
else:
value = 1
return [value, gbdt_ret[0]]
else:
return [0, 0]
