class_docstr: Accumulator which maintains summary statistics describing an anomaly
  detection algorithm's performance. Can be used to compute many different
  time series anomaly detection metrics.
code: def f1(self, score_type: ScoreType = ScoreType.RevisedPointAdjusted):
      if isinstance(score_type, tuple) and len(score_type) == 2:
          prec_score_type, rec_score_type = score_type
      else:
          prec_score_type = rec_score_type = score_type
      p = self.precision(prec_score_type)
      r = self.recall(rec_score_type)
      return 0.0 if p == 0 or r == 0 else 2 * p * r / (p + r)
  
imports: from bisect import bisect_left
  from enum import Enum
  from functools import partial
  from typing import Tuple, Union
  import numpy
  import pandas
  from merlion.evaluate.base import EvaluatorBase, EvaluatorConfig
  from merlion.utils import TimeSeries, UnivariateTimeSeries
  from merlion.utils.misc import call_with_accepted_kwargs
label_desc: Use image node_id 5 for calling the TSADScoreAccumulator obj's underlying member method code with example usage: obj.f1(score_type) and returns: unknown