class_docstr: An evaluator that uses a classification metrics.
class_docstr_tok: ["An","evaluator","that","uses","a","classification","metrics","."]
docstr: Initialize the evaluator.
  
  :param kwargs:
      keyword-based parameters passed to :meth:`Evaluator.__init__`.
docstr_tok: ["Initialize","the","evaluator",".",":","param","kwargs",":","keyword-based","parameters","passed","to",":","meth",":","`","Evaluator.__init__","`","."]
code: def __init__(self, **kwargs):
      """
      Initialize the evaluator.
  
      :param kwargs:
          keyword-based parameters passed to :meth:`Evaluator.__init__`.
      """
      super().__init__(
          filtered=False,
          requires_positive_mask=True,
          **kwargs,
      )
      self.all_scores = defaultdict(dict)
      self.all_positives = defaultdict(dict)
      self.metrics = tuple(
          classification_metric_resolver.make(metric_cls)
          for metric_cls in classification_metric_resolver.lookup_dict.values()
      )
  
imports: from __future__ import annotations
  import itertools
  from collections import defaultdict
  from typing import Iterable, Mapping, MutableMapping, NamedTuple, Optional, Tuple, cast
  import numpy
  import numpy
  import torch
  from .evaluator import Evaluator, MetricResults
  from ..constants import TARGET_TO_INDEX
  from ..metrics.classification import ClassificationMetric, classification_metric_resolver
  from ..typing import SIDE_BOTH, ExtendedTarget, MappedTriples, Target, normalize_target
label_desc: Use image node_id 1 to create a new ClassificationEvaluator object from inherited base classes: Evaluator with example: obj = ClassificationEvaluator()