class_docstr: Assesses the quality of partitioning according to chosen metrics and report it.
  
  Available metrics: edge cut, edge imbalance, vertex imbalance, time, memory usage.
  
  Parameters
  ----------
  partitionings:
          Data splits to be compared.
  
  Example
  -------
  
  >>>>quality = PartitioningReporter(partitionings)
  >>>>report = quality.report(visualize=False)
class_docstr_tok: ["Assesses","the","quality","of","partitioning","according","to","chosen","metrics","and","report","it",".","Available","metrics",":","edge","cut",",","edge","imbalance",",","vertex","imbalance",",","time",",","memory","usage",".","Parameters","--","--","--","--","--","partitionings",":","Data","splits","to","be","compared",".","Example","--","--","--","-",">",">",">",">","quality","=","PartitioningReporter","(","partitionings",")",">",">",">",">","report","=","quality.report","(","visualize=False",")"]
docstr: Counts number of edges in each partition that estimates the size of partition.
  
  Parameters
  ---------
  partitions:
       Partitions in one partitioning.
  
  Returns
  -------
  info: list
       List of counts, e.g. for 2 partitions the list will be of size two with the edge count: (10,12);
       for 3 partitions: (7,8,7).
docstr_tok: ["Counts","number","of","edges","in","each","partition","that","estimates","the","size","of","partition",".","Parameters","--","--","--","--","-","partitions",":","Partitions","in","one","partitioning",".","Returns","--","--","--","-","info",":","list","List","of","counts",",","e.g",".","for","2","partitions","the","list","will","be","of","size","two","with","the","edge","count",":","(","10,12",")",";","for","3","partitions",":","(","7,8,7",")","."]
code: def get_edges_count(self, partitions):
      """Counts number of edges in each partition that estimates the size of partition.
  
      Parameters
      ---------
      partitions:
           Partitions in one partitioning.
  
      Returns
      -------
      info: list
           List of counts, e.g. for 2 partitions the list will be of size two with the edge count: (10,12);
           for 3 partitions: (7,8,7).
      """
      info = []
      for partition in partitions:
          edges = partition.get_data_size()
          info.append(edges)
  
      return info
  
file_path: AmpliGraph/ampligraph/datasets/partitioning_reporter.py
imports: import copy
  import logging
  import matplotlib.pyplot
  import numpy
  from matplotlib.pyplot import cm
  from .datasets import load_fb15k_237
  from .graph_partitioner import RandomVerticesGraphPartitioner
label_desc: Use image node_id 7 for calling the PartitioningReporter obj's underlying member method code with example usage: obj.get_edges_count(partitions) and returns: info