tree.py 文件源码

def _validate_X_predict(self, X, check_input):
        """Validate X whenever one tries to predict, apply, predict_proba"""
        if self.tree_ is None:
            raise NotFittedError("Estimator not fitted, "
                                 "call `fit` before exploiting the model.")

        if check_input:
            X = check_array(X, dtype=DTYPE, accept_sparse="csr")
            if issparse(X) and (X.indices.dtype != np.intc or
                                X.indptr.dtype != np.intc):
                raise ValueError("No support for np.int64 index based "
                                 "sparse matrices")

        n_features = X.shape[1]
        if self.n_features_ != n_features:
            raise ValueError("Number of features of the model must "
                             "match the input. Model n_features is %s and "
                             "input n_features is %s "
                             % (self.n_features_, n_features))

        return X