docstr: Produce a filtered version of `parameters` that does not include hyperparameters that
  should be ignored during hashing, such as those pertaining to verbosity, seeds, and random
  states, as they have no effect on HyperparameterHunter experiment results
  
  Parameters
  ----------
  parameters: Dict
      Full dictionary of initial parameters to be filtered
  
  Returns
  -------
  parameters: Dict
      Filtered version of the given `parameters`
docstr_tok: ["Produce","a","filtered","version","of","`","parameters","`","that","does","not","include","hyperparameters","that","should","be","ignored","during","hashing",",","such","as","those","pertaining","to","verbosity",",","seeds",",","and","random","states",",","as","they","have","no","effect","on","HyperparameterHunter","experiment","results","Parameters","--","--","--","--","--","parameters",":","Dict","Full","dictionary","of","initial","parameters","to","be","filtered","Returns","--","--","--","-","parameters",":","Dict","Filtered","version","of","the","given","`","parameters","`"]
code: def _filter_parameters_to_hash(self, parameters):
      """Produce a filtered version of `parameters` that does not include hyperparameters that
      should be ignored during hashing, such as those pertaining to verbosity, seeds, and random
      states, as they have no effect on HyperparameterHunter experiment results
  
      Parameters
      ----------
      parameters: Dict
          Full dictionary of initial parameters to be filtered
  
      Returns
      -------
      parameters: Dict
          Filtered version of the given `parameters`"""
      reject = ["verbose", "verbosity", "silent"]
      reject += [
          "random_state",
          "random_seed",
          "seed",
          "n_jobs",
          "nthread",
      ]
  
      if self.is_task_keras:
          reject.append("build_fn")
  
      parameters["model_init_params"] = subdict(
          parameters["model_init_params"], drop=reject
      )
      parameters["model_extra_params"] = subdict(
          parameters["model_extra_params"], drop=reject
      )
      return parameters
  
imports: from hyperparameter_hunter.feature_engineering import FeatureEngineer, EngineerStep
  from hyperparameter_hunter.i_o.exceptions import EnvironmentInvalidError, EnvironmentInactiveError
  from hyperparameter_hunter.keys.hashing import make_hash_sha256
  from hyperparameter_hunter.compat.keras_helper import keras_callback_to_dict, keras_initializer_to_dict, parameterize_compiled_keras_model
  from hyperparameter_hunter.compat.keras_optimization_helper import initialize_dummy_model
  from hyperparameter_hunter.metrics import Metric
  from hyperparameter_hunter.sentinels import Sentinel
  from hyperparameter_hunter.settings import G
  from hyperparameter_hunter.utils.file_utils import write_json, read_json, add_to_json, make_dirs
  from hyperparameter_hunter.utils.file_utils import RetryMakeDirs
  from hyperparameter_hunter.utils.general_utils import subdict
  from hyperparameter_hunter.utils.boltons_utils import remap, default_enter
  from abc import ABCMeta, abstractmethod
  from copy import deepcopy
  from functools import partial
  from inspect import isclass, getsource
  from os import listdir
  import os.path
  import pandas
  import shelve
label_desc: Use image node_id 2 for calling the HyperparameterKeyMaker obj's underlying member method code with example usage: obj._filter_parameters_to_hash(parameters) and returns: parameters