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)
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docstr: Calculates edge imbalance of partitions.
  
  Parameters
  ----------
  avg_size: int
      Average size of partition.
  max_size: int
      Maximum size of partition.
  
  Returns
  -------
  edge_imb: float
      Edge imbalance
docstr_tok: ["Calculates","edge","imbalance","of","partitions",".","Parameters","--","--","--","--","--","avg_size",":","int","Average","size","of","partition",".","max_size",":","int","Maximum","size","of","partition",".","Returns","--","--","--","-","edge_imb",":","float","Edge","imbalance"]
code: def get_edge_imbalance(self, avg_size, max_size):
      """Calculates edge imbalance of partitions.
  
      Parameters
      ----------
      avg_size: int
          Average size of partition.
      max_size: int
          Maximum size of partition.
  
      Returns
      -------
      edge_imb: float
          Edge imbalance
      """
  
      edge_imb = max_size / avg_size - 1
      return edge_imb
  
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 3 for calling the PartitioningReporter obj's underlying member method code with example usage: obj.get_edge_imbalance(avg_size, max_size) and returns: edge_imb