#39;, &#39;n_features_&#39;, &#39;feature_placeholders&#39;])
        X = check_array(X)

        if X.shape[1] != self.n_features_:
            raise ValueError(&#34;X.shape[1] = %d should be equal to %d, the number of features at training time.&#34;
                             &#34; Please reshape your data.&#34;
                             % (X.shape[1], self.n_features_))

        df = pd.DataFrame(X, columns=self.feature_placeholders)
        selected_rules = self.rules_without_feature_names_

      