def decision_path(self, X):
        """Return the decision path in the forest.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_timestamps)
            Univariate time series.

        Returns
        -------
        indicator : sparse csr array, shape = (n_samples, n_nodes)
            Return a node indicator matrix where non zero elements
            indicates that the samples goes through the nodes.

        n_nodes_ptr : array, shape = (n_estimators + 1,)
            The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
            gives the indicator value for the i-th estimator.

        """
        check_is_fitted(self)
        X = check_array(X, dtype='float64')
        X_new = self._pipeline['fe'].transform(X)
        return self._pipeline['rfc'].decision_path(X_new)
Return the decision path in the forest.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_timestamps)
            Univariate time series.

        Returns
        -------
        indicator : sparse csr array, shape = (n_samples, n_nodes)
            Return a node indicator matrix where non zero elements
            indicates that the samples goes through the nodes.

        n_nodes_ptr : array, shape = (n_estimators + 1,)
            The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
            gives the indicator value for the i-th estimator.

        
pyts/classification/time_series_forest.py