188ca433f68c85b00a4efb7ed0491ca9c57412fe,pythonds/des/meta_des.py,METADES,__init__,#METADES#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,71

Before Change

                                      with_IH=with_IH, safe_k=safe_k, IH_rate=IH_rate, aknn=aknn)
        mode.lower()
        assert Hc > 0.5
        assert gamma > 0
        assert mode in ["selection", "hybrid", "weighting"]
        self.name = "META-des"
        self.version = mode
        self.kp = kp

After Change

        self.meta_training_target = []
        self.n_meta_features = (self.k * 2) + self.Kp + 2

        self.bins = np.linspace(0.1, 1, 10)

    def fit(self, X, y):
        Prepare the DS model by setting the KNN algorithm and
        pre-processing the information required to apply the DS

Instances