text: # Copyright (c) 2019-2020, NVIDIA CORPORATION.
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from cugraph.community import ecg_wrapper
  from cugraph.utilities import check_nx_graph
  from cugraph.utilities import df_score_to_dictionary
  
  
  def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
      """
      Compute the Ensemble Clustering for Graphs (ECG) partition of the input
      graph. ECG runs truncated Louvain on an ensemble of permutations of the
      input graph, then uses the ensemble partitions to determine weights for
      the input graph. The final result is found by running full Louvain on
      the input graph using the determined weights.
  
      See https://arxiv.org/abs/1809.05578 for further information.
  
      Parameters
      ----------
      input_graph : cugraph.Graph or NetworkX Graph
          The graph descriptor should contain the connectivity information
          and weights. The adjacency list will be computed if not already
          present.
  
      min_weight : floating point
          The minimum value to assign as an edgeweight in the ECG algorithm.
          It should be a value in the range [0,1] usually left as the default
          value of .05
  
      ensemble_size : integer
          The number of graph permutations to use for the ensemble.
          The default value is 16, larger values may produce higher quality
          partitions for some graphs.
  
      weight : str
          This parameter is here for NetworkX compatibility and
          represents which NetworkX data column represents Edge weights.
          Default is None
  
      Returns
      -------
      parts : cudf.DataFrame or python dictionary
          GPU data frame of size V containing two columns, the vertex id and
          the partition id it is assigned to.
  
          df[vertex] : cudf.Series
              Contains the vertex identifiers
          df[partition] : cudf.Series
              Contains the partition assigned to the vertices
  
      Examples
      --------
      >>> M = cudf.read_csv('datasets/karate.csv', delimiter = ' ',
                            dtype=['int32', 'int32', 'float32'],
                            header=None)
      >>> G = cugraph.Graph()
      >>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
      >>> parts = cugraph.ecg(G)
  
      """
  
      input_graph, isNx = check_nx_graph(input_graph, weight)
  
      parts = ecg_wrapper.ecg(input_graph, min_weight, ensemble_size)
  
      if input_graph.renumbered:
          parts = input_graph.unrenumber(parts, "vertex")
  
      if isNx is True:
          return df_score_to_dictionary(parts, 'partition')
      else:
          return parts
  