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: Initialises PartitioningReporter.
  
  Parameters
  ----------
  partitionings:
       List of partitioning strategies.
docstr_tok: ["Initialises","PartitioningReporter",".","Parameters","--","--","--","--","--","partitionings",":","List","of","partitioning","strategies","."]
code: def __init__(self, partitionings):
      """Initialises PartitioningReporter.
  
      Parameters
      ----------
      partitionings:
           List of partitioning strategies.
      """
      self.partitionings = partitionings
  
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 1 to create a new PartitioningReporter object  with example: obj = PartitioningReporter(partitionings)