ec396411be11d514a44a18813278f3a41c73ac5f,deslib/des/des_knn.py,DESKNN,__init__,#DESKNN#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,70

Before Change

                 more_diverse=True,
                 metric="DF"):

        metric = metric.upper()
        super(DESKNN, self).__init__(pool_classifiers, k, DFP=DFP, with_IH=with_IH, safe_k=safe_k, IH_rate=IH_rate)

        self.name = "Dynamic Ensemble Selection-KNN (DES-KNN)"
        self.N = int(self.n_classifiers * pct_accuracy)
        self.J = int(np.ceil(self.n_classifiers * pct_diversity))
        self.metric = metric

        self._validate_parameters()

        // Set up the diversity metric
        self.more_diverse = more_diverse
        if metric == "DF":
            self.diversity_func = negative_double_fault
        elif metric == "Q":
            self.diversity_func = Q_statistic
        else:
            self.diversity_func = ratio_errors

    def estimate_competence(self, query, predictions=None):
        estimate the competence level of each base classifier :math:`c_{i}` for
        the classification of the query sample.

After Change

                 metric="DF",
                 knn_classifier="knn"):

        metric = metric.upper()
        super(DESKNN, self).__init__(pool_classifiers, k, DFP=DFP, with_IH=with_IH, safe_k=safe_k,
                                     IH_rate=IH_rate, knn_classifier=knn_classifier)

        self.name = "Dynamic Ensemble Selection-KNN (DES-KNN)"
        self.N = int(self.n_classifiers * pct_accuracy)
        self.J = int(np.ceil(self.n_classifiers * pct_diversity))
        self.metric = metric

Instances