class_docstr: Base class for pairs learners.
  
  Attributes
  ----------
  threshold_ : `float`
    If the distance metric between two points is lower than this threshold,
    points will be classified as similar, otherwise they will be
    classified as dissimilar.
class_docstr_tok: ["Base","class","for","pairs","learners",".","Attributes","--","--","--","--","--","threshold_",":","`","float","`","If","the","distance","metric","between","two","points","is","lower","than","this","threshold",",","points","will","be","classified","as","similar",",","otherwise","they","will","be","classified","as","dissimilar","."]
docstr: Ensure that calibration parameters have allowed values
docstr_tok: ["Ensure","that","calibration","parameters","have","allowed","values"]
code: def _validate_calibration_params(
      strategy="accuracy", min_rate=None, beta=1.0
  ):
      """Ensure that calibration parameters have allowed values"""
      if strategy not in ("accuracy", "f_beta", "max_tpr", "max_tnr"):
          raise ValueError(
              'Strategy can either be "accuracy", "f_beta" or '
              '"max_tpr" or "max_tnr". Got "{}" instead.'.format(
                  strategy
              )
          )
      if strategy == "max_tpr" or strategy == "max_tnr":
          if (
              min_rate is None
              or not isinstance(min_rate, (int, float))
              or not min_rate >= 0
              or not min_rate <= 1
          ):
              raise ValueError(
                  "Parameter min_rate must be a number in"
                  "[0, 1]. "
                  "Got {} instead.".format(min_rate)
              )
      if strategy == "f_beta":
          if beta is None or not isinstance(beta, (int, float)):
              raise ValueError(
                  "Parameter beta must be a real number. "
                  "Got {} instead.".format(type(beta))
              )
  
imports: from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.extmath import stable_cumsum
  from sklearn.utils.validation import _is_arraylike, check_is_fitted
  from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve
  import numpy
  from abc import ABCMeta, abstractmethod
  from ._util import ArrayIndexer, check_input, validate_vector
  import warnings
label_desc: Use image node_id 6 for calling the _PairsClassifierMixin obj's underlying member method code with example usage: obj._validate_calibration_params(strategy, min_rate, beta) without return types