predictive_imputer.py 文件源码

def transform(self, X):
        check_is_fitted(self, ['statistics_', 'estimators_', 'gamma_'])
        X = check_array(X, copy=True, dtype=np.float64, force_all_finite=False)
        if X.shape[1] != self.statistics_.shape[1]:
            raise ValueError("X has %d features per sample, expected %d"
                             % (X.shape[1], self.statistics_.shape[1]))

        X_nan = np.isnan(X)
        imputed = self.initial_imputer.fit_transform(X)

        if len(self.estimators_) > 1:
            for i, estimator_ in enumerate(self.estimators_):
                X_s = np.delete(imputed, i, 1)
                y_nan = X_nan[:, i]

                X_unk = X_s[y_nan]
                if len(X_unk) > 0:
                    X[y_nan, i] = estimator_.predict(X_unk)

        else:
            estimator_ = self.estimators_[0]
            X[X_nan] = estimator_.inverse_transform(estimator_.transform(imputed))[X_nan]

        return X