def send_feedback(self, X, feature_names, reward, truth):
""" Return outlier labels as part of the feedback loop.
Arguments:
- X: input data
- feature_names
- reward
- truth: outlier labels
"""
self.label = truth
self._labels.append(self.label)
self._labels = flatten(self._labels)
scores = performance(self._labels,
self._predictions,
roll_window=self.roll_window)
stats = outlier_stats(self._labels,
self._predictions,
roll_window=self.roll_window)
convert = flatten([scores, stats])
metric = []
for c in convert: # convert from np to native python type to jsonify
metric.append(np.asscalar(np.asarray(c)))
self.metric = metric
return
def send_feedback(self,X,feature_names,reward,truth):
""" Return outlier labels as part of the feedback loop.
Parameters
----------
X : array of the features sent in the original predict request
feature_names : array of feature names. May be None if not available.
reward (float): the reward
truth : array with correct value (optional)
"""
_ = super().send_feedback(X,feature_names,reward,truth)
# historical reconstruction errors and predictions
self._mse.append(self.mse)
self._mse = flatten(self._mse)
self._predictions.append(self.prediction)
self._predictions = flatten(self._predictions)
# target labels
self.label = truth
self._labels.append(self.label)
self._labels = flatten(self._labels)
# performance metrics
scores = performance(self._labels,self._predictions,roll_window=self.roll_window)
stats = outlier_stats(self._labels,self._predictions,roll_window=self.roll_window)
convert = flatten([scores,stats])
metric = []
for c in convert: # convert from np to native python type to jsonify
metric.append(np.asscalar(np.asarray(c)))
self.metric = metric
return []