98aeec9fcc311086cb6953dded42b399af97f189,pyod/models/iforest.py,IForest,fit,#IForest#Any#Any#Any#,9

Before Change

    def fit(self, X_train, y=None, sample_weight=None):
        self.X_train = X_train
        super().fit(X=X_train, y=y, sample_weight=sample_weight)
        return self

    def predict_proba(self, X_test, method="linear"):

After Change

        // invert scores. Outliers comes with higher scores
        self.decision_scores = self.detector_.decision_function(X_train) * -1
        self.threshold_ = scoreatpercentile(self.decision_scores,
                                            100 * (1 - self.contamination))
        self.y_pred = (self.decision_scores > self.threshold_).astype("int")

        return self

    def decision_function(self, X_test):

Instances