docstr: Instances of this class are cases which will be compared during evaluation
  Params are CatBoost params
  label is a string which will be used for plots and other visualisations
  ignored_features is a set of additional feature indices to ignore
docstr_tok: ["Instances","of","this","class","are","cases","which","will","be","compared","during","evaluation","Params","are","CatBoost","params","label","is","a","string","which","will","be","used","for","plots","and","other","visualisations","ignored_features","is","a","set","of","additional","feature","indices","to","ignore"]
code: def __init__(
      self,
      params,
      label=None,
      ignored_features=None,
      learning_rate=None,
  ):
      """
      Instances of this class are cases which will be compared during evaluation
      Params are CatBoost params
      label is a string which will be used for plots and other visualisations
      ignored_features is a set of additional feature indices to ignore
      """
      case_params = dict(params)
  
      if learning_rate is not None:
          case_params["learning_rate"] = learning_rate
  
      all_ignored_features = set()
      if "ignored_features" in case_params:
          all_ignored_features.update(
              set(case_params["ignored_features"])
          )
      if ignored_features is not None:
          all_ignored_features.update(ignored_features)
  
      case_params["ignored_features"] = list(all_ignored_features)
  
      self._label = label if label is not None else ""
      self._ignored_features = ignored_features
      self._ignored_features_str = FactorUtils.factors_to_ranges_string(
          self._ignored_features
      )
  
      self.__set_params(case_params)
  
file_path: catboost/catboost/python-package/catboost/eval/execution_case.py
imports: import json
  from ..None import CatBoostError
  from ..eval.factor_utils import FactorUtils
  from ..core import _NumpyAwareEncoder
label_desc: Use image node_id 1 to create a new ExecutionCase object  with example: obj = ExecutionCase(params, label, ignored_features, learning_rate)