332759f41664989156b7a2994e8c21ebc9e7f798,sklearn/impute/_iterative.py,IterativeImputer,transform,#IterativeImputer#Any#,644

Before Change

        check_is_fitted(self)

        if self.add_indicator:
            X_trans_indicator = self.indicator_.transform(X)

        X, Xt, mask_missing_values = self._initial_imputation(X)

        if self.n_iter_ == 0 or np.all(mask_missing_values):
            return Xt

        imputations_per_round = len(self.imputation_sequence_) // self.n_iter_
        i_rnd = 0
        if self.verbose > 0:
            print("[IterativeImputer] Completing matrix with shape %s"
                  % (X.shape,))
        start_t = time()
        for it, estimator_triplet in enumerate(self.imputation_sequence_):
            Xt, _ = self._impute_one_feature(
                Xt,
                mask_missing_values,
                estimator_triplet.feat_idx,
                estimator_triplet.neighbor_feat_idx,
                estimator=estimator_triplet.estimator,
                fit_mode=False
            )
            if not (it + 1) % imputations_per_round:
                if self.verbose > 1:
                    print("[IterativeImputer] Ending imputation round "
                          "%d/%d, elapsed time %0.2f"
                          % (i_rnd + 1, self.n_iter_, time() - start_t))
                i_rnd += 1

        Xt[~mask_missing_values] = X[~mask_missing_values]

        if self.add_indicator:
            Xt = np.hstack((Xt, X_trans_indicator))
        return Xt

    def fit(self, X, y=None):
        Fits the imputer on X and return self.

After Change

        Xt[~mask_missing_values] = X[~mask_missing_values]

        return super()._concatenate_indicator(Xt, X_indicator)

    def fit(self, X, y=None):
        Fits the imputer on X and return self.

Instances