def fit_predict(self, X, y):
    "Performs clustering on y and returns list of label lists\n\n        Builds a label graph using the provided graph builder's `transform` method\n        on `y` and then detects communities using the selected `method`.\n\n        Sets :code:`self.weights_` and :code:`self.graph_`.\n\n        Parameters\n        ----------\n        X : None\n            currently unused, left for scikit compatibility\n        y : scipy.sparse\n            label space of shape :code:`(n_samples, n_labels)`\n\n        Returns\n        -------\n        arrray of arrays of label indexes (numpy.ndarray)\n            label space division, each sublist represents labels that are in that community\n        "
    self._build_graph_instance(y)
    clusters = self.model.fit_predict(self.graph_, weights=self.weights_)
    return np.array([community for community in clusters if (len(community) > 0)])
Performs clustering on y and returns list of label lists

Builds a label graph using the provided graph builder's `transform` method
on `y` and then detects communities using the selected `method`.

Sets :code:`self.weights_` and :code:`self.graph_`.

Parameters
----------
X : None
    currently unused, left for scikit compatibility
y : scipy.sparse
    label space of shape :code:`(n_samples, n_labels)`

Returns
-------
arrray of arrays of label indexes (numpy.ndarray)
    label space division, each sublist represents labels that are in that community
def fit_predict(self, X, y):
    "Performs clustering on y and returns list of label lists\n\n        Builds a label graph using the provided graph builder's `transform` method\n        on `y` and then detects communities using the selected `method`.\n\n        Sets :code:`self.weights_` and :code:`self.graph_`.\n\n        Parameters\n        ----------\n        X : None\n            currently unused, left for scikit compatibility\n        y : scipy.sparse\n            label space of shape :code:`(n_samples, n_labels)`\n\n        Returns\n        -------\n        arrray of arrays of label indexes (numpy.ndarray)\n            label space division, each sublist represents labels that are in that community\n        "
    self._build_graph_instance(y)
    clusters = self.model.fit_predict(self.graph_, weights=self.weights_)
    return np.array([community for community in clusters if (len(community) > 0)])