class_docstr: Callable objective function to be used with optuna.
class_docstr_tok: ["Callable","objective","function","to","be","used","with","optuna","."]
code: def evaluate_model(
      self,
      model: tuple[
          PreProcessing, InProcessing, PostProcessing, Threshold
      ],
      X: np.ndarray,
      y: np.ndarray,
      s: np.ndarray = None,
      number: int = 0,
      type: str = "validation",
  ) -> dict:
      if model[0].used_in_inference:
          X, y, s = model[0].transform(X.copy(), y.copy(), s.copy())
      y_pred = model[1].predict_proba(X, s)
      if "predictions" in self.artifacts:
          y_pred.to_frame().to_parquet(
              self.artifacts_folder
              / str(number)
              / f"{type}_scores.parquet"
          )
      if type == "validation":
          model[2].fit(X, y_pred, y, s)
      y_pred = model[2].transform(X, y_pred, s)
      if sorted(y_pred.unique().tolist()) != [0, 1]:
          if type == "validation":
              model[3].fit(X, y_pred, y, s)
          y_pred = model[3].transform(X, y_pred, s)
      if "predictions" in self.artifacts:
          y_pred.to_frame().to_parquet(
              self.artifacts_folder
              / str(number)
              / f"{type}_bin.parquet"
          )
      results = evaluate_performance(y, y_pred)
  
      if s is not None:
          results.update(evaluate_fairness(y, y_pred, s, True))
  
      return results
  
imports: import dataclasses
  import logging
  import pickle
  import time
  from pathlib import Path
  from typing import Optional, Union
  import numpy
  import pandas
  from hpt.suggest import suggest_callable_hyperparams
  from omegaconf import DictConfig, OmegaConf
  from optuna.trial import BaseTrial
  from ..evaluation import evaluate_fairness, evaluate_performance
  from ..methods import InProcessing, PostProcessing, PreProcessing
  from ..methods.postprocessing.threshold import Threshold
label_desc: Use image node_id 7 for calling the ObjectiveFunction obj's underlying member method code with example usage: obj.evaluate_model(model, X, y, s, number, type) and returns: results