def fit(self, X, **kwargs):
self._classes = 2
X = self.normalizer.fit_transform(X)
self.detector_.fit(X)
self.decision_scores_ = invert_order(
self.detector_.decision_function(X))
self._process_decision_scores()
def decision_function(self, X):
"""Predict raw anomaly scores of X using the fitted detector.
After invert_order(): the higher score, the more probability of x that is predicted as abnormal
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
# check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
return invert_order(self.model_.score_samples(X))
def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.
The anomaly score of an input sample is computed based on different
detector algorithms. For consistency, outliers are assigned with
larger anomaly scores.
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
# invert outlier scores. Outliers comes with higher outlier scores
return invert_order(self.model_.decision_function(X))
def decision_function(self, X):
"""Predict raw anomaly scores of X using the fitted detector.
The anomaly score of an input sample is computed based on the fitted
detector. For consistency, outliers are assigned with
larger anomaly scores. so use invert_order
After invert_order(): the higher score, the more probability of x that is predicted as abnormal
Parameters
----------
X : numpy array of shape (n_samples, n_features)
The input samples. Sparse matrices are accepted only
if they are supported by the base estimator.
Returns
-------
anomaly_scores : numpy array of shape (n_samples,)
The anomaly score of the input samples.
"""
# check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
return invert_order(logsumexp(self.model_._estimate_weighted_log_prob(X), axis=1))