code: from datetime import datetime
  import logging
  import os
  import time
  from typing import Callable, Tuple, Optional, Sequence
  
  import stopit
  from sklearn.base import TransformerMixin, is_classifier
  from sklearn.model_selection import ShuffleSplit, cross_validate, check_cv
  from sklearn.pipeline import Pipeline
  
  from gama.utilities.evaluation_library import Evaluation
  from gama.utilities.generic.stopwatch import Stopwatch
  import numpy as np
  from gama.utilities.metrics import Metric
  from gama.genetic_programming.components import Individual, PrimitiveNode, Fitness
  
  log = logging.getLogger(__name__)
  
  
  def primitive_node_to_sklearn(primitive_node: PrimitiveNode) -> object:
      hyperparameters = {
          terminal.output: terminal.value for terminal in primitive_node._terminals
      }
      return primitive_node._primitive.identifier(**hyperparameters)
  
  
  def compile_individual(
      individual: Individual,
      parameter_checks=None,
      preprocessing_steps: Sequence[Tuple[str, TransformerMixin]] = None,
  ) -> Pipeline:
      steps = [
          (str(i), primitive_node_to_sklearn(primitive))
          for i, primitive in enumerate(individual.primitives)
      ]
      if preprocessing_steps:
          steps = steps + list(reversed(preprocessing_steps))
      return Pipeline(list(reversed(steps)))
  
  
  def object_is_valid_pipeline(o):
      """ Determines if object behaves like a scikit-learn pipeline. """
      return (
          o is not None
          and hasattr(o, "fit")
          and hasattr(o, "predict")
          and hasattr(o, "steps")
      )
  
  
  def evaluate_pipeline(
      pipeline, x, y_train, timeout: float, metrics: Tuple[Metric], cv=5, subsample=None,
  ) -> Tuple:
      """ Score `pipeline` with k-fold CV according to `metrics` on (a subsample of) X, y
      Returns
      -------
      Tuple:
          prediction: np.ndarray if successful, None if not
          scores: tuple with one float per metric, each value is -inf on fail.
          estimators: list of fitted pipelines if successful, None if not
          error: None if successful, otherwise an Exception
      """
      if not object_is_valid_pipeline(pipeline):
          raise TypeError(f"Pipeline must not be None and requires fit, predict, steps.")
      if not timeout > 0:
          raise ValueError(f"`timeout` must be greater than 0, is {timeout}.")
  
      prediction, estimators = None, None
      # default score for e.g. timeout or failure
      scores = tuple([float("-inf")] * len(metrics))
  
      with stopit.ThreadingTimeout(timeout) as c_mgr:
          try:
              if isinstance(subsample, int) and subsample < len(y_train):
                  sampler = ShuffleSplit(n_splits=1, train_size=subsample, random_state=0)
                  idx, _ = next(sampler.split(x))
                  x, y_train = x.iloc[idx, :], y_train[idx]
  
              splitter = check_cv(cv, y_train, is_classifier(pipeline))
              result = cross_validate(
                  pipeline,
                  x,
                  y_train,
                  cv=splitter,
                  return_estimator=True,
                  scoring=[m.name for m in metrics],
                  error_score="raise",
              )
              scores = tuple([np.mean(result[f"test_{m.name}"]) for m in metrics])
              estimators = result["estimator"]
  
              for (estimator, (_, test)) in zip(estimators, splitter.split(x, y_train)):
                  if any([m.requires_probabilities for m in metrics]):
                      fold_pred = estimator.predict_proba(x.iloc[test, :])
                  else:
                      fold_pred = estimator.predict(x.iloc[test, :])
  
                  if prediction is None:
                      if fold_pred.ndim == 2:
                          prediction = np.empty(shape=(len(y_train), fold_pred.shape[1]))
                      else:
                          prediction = np.empty(shape=(len(y_train),))
                  prediction[test] = fold_pred
  
              # prediction, scores, estimators = cross_val_predict_score(
              #     pipeline, x, y_train, cv=cv, metrics=metrics
              # )
          except stopit.TimeoutException:
              # This exception is handled by the ThreadingTimeout context manager.
              raise
          except KeyboardInterrupt:
              raise
          except Exception as e:
              return prediction, scores, estimators, e
  
      if c_mgr.state == c_mgr.INTERRUPTED:
          # A TimeoutException was raised, but not by the context manager.
          # This indicates that the outer context manager (the ea) timed out.
          raise stopit.utils.TimeoutException()
  
      if not c_mgr:
          # For now we treat an eval timeout the same way as
          # e.g. NaN exceptions and use the default score.
          return prediction, scores, estimators, stopit.TimeoutException()
  
      return prediction, tuple(scores), estimators, None
  
  
  def evaluate_individual(
      individual: Individual,
      evaluate_pipeline: Callable,
      timeout: float = 1e6,
      deadline: Optional[float] = None,
      add_length_to_score: bool = True,
      **kwargs,
  ) -> Evaluation:
      """ Evaluate the pipeline specified by individual, and record
      Parameters
      ----------
      individual: Individual
          Blueprint for the pipeline to evaluate.
      evaluate_pipeline: Callable
          Function which takes the pipeline and produces validation predictions,
          scores, estimators and errors.
      timeout: float (default=1e6)
          Maximum time in seconds that the evaluation is allowed to take.
          Don't depend on high accuracy.
          A shorter timeout is imposed if `deadline` is in less than `timeout` seconds.
      deadline: float, optional
          A time in seconds since epoch.
          Cut off evaluation at `deadline` even if `timeout` seconds have not yet elapsed.
      add_length_to_score: bool (default=True)
          Add the length of the individual to the score result of the evaluation.
      **kwargs: Dict, optional (default=None)
          Passed to `evaluate_pipeline` function.
      Returns
      -------
      Evaluation
      """
      result = Evaluation(individual, pid=os.getpid())
      result.start_time = datetime.now()
  
      if deadline is not None:
          time_to_deadline = deadline - time.time()
          timeout = min(timeout, time_to_deadline)
  
      with Stopwatch() as wall_time, Stopwatch(time.process_time) as process_time:
          evaluation = evaluate_pipeline(individual.pipeline, timeout=timeout, **kwargs)
          result._predictions, result.score, result._estimators, error = evaluation
          if error is not None:
              result.error = f"{type(error)} {str(error)}"
      result.duration = wall_time.elapsed_time
  
      if add_length_to_score:
          result.score = result.score + (-len(individual.primitives),)
      individual.fitness = Fitness(
          result.score,
          result.start_time,
          wall_time.elapsed_time,
          process_time.elapsed_time,
      )
  
      return result
apis: sklearn.model_selection.ShuffleSplit sklearn.model_selection.cross_validate numpy.mean sklearn.base.is_classifier