text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  import typing as tp
  import numpy as np
  from nevergrad.common import testing
  from . import mlfunctionlib
  
  
  @testing.parametrized(
      decision_tree_depth=(
          dict(regressor="decision_tree_depth", data_dimension=1, dataset="artificial"),
          dict(depth=3),
          0.00116792703,
      ),
      decision_tree_artificial=(
          dict(regressor="decision_tree", data_dimension=12, dataset="artificial"),
          dict(depth=3, criterion="mse", min_samples_split=0.001),
          0.19138713337,
      ),
      mlp_artificial=(
          dict(regressor="mlp", data_dimension=2, dataset="artificial"),
          dict(activation="relu", solver="adam", alpha=0.01, learning_rate="constant"),
          0.01809799097751899,
      ),
      choosing_regressor_perceptron=(
          dict(regressor="any", data_dimension=2, dataset="artificial"),
          dict(
              activation="relu",
              solver="adam",
              alpha=0.01,
              learning_rate="constant",
              depth=3,
              criterion="mse",
              min_samples_split=0.001,
              regressor="decision_tree",
          ),
          0.0046690423,
      ),
      choosing_regressor_mlp=(
          dict(regressor="any", data_dimension=2, dataset="artificial"),
          dict(
              activation="relu",
              solver="adam",
              alpha=0.01,
              learning_rate="constant",
              depth=3,
              criterion="mse",
              min_samples_split=0.001,
              regressor="mlp",
          ),
          0.005673646,
      ),
      decision_tree_boston=(
          dict(regressor="decision_tree", data_dimension=None, dataset="boston"),
          dict(depth=5, criterion="mse", min_samples_split=0.001),
          24.985272503,
      ),
      decision_tree_diabetes=(
          dict(regressor="decision_tree", data_dimension=None, dataset="diabetes"),
          dict(depth=5, criterion="mse", min_samples_split=0.001),
          5057.790249631,
      ),
      decision_tree_cos=(
          dict(regressor="decision_tree", data_dimension=3, dataset="artificialcos"),
          dict(depth=5, criterion="mse", min_samples_split=0.001),
          0.00040148525,
      ),
      decision_tree_square=(
          dict(regressor="decision_tree", data_dimension=3, dataset="artificialsquare"),
          dict(depth=5, criterion="mse", min_samples_split=0.001),
          0.0019345073,
      ),
  )
  def test_mltuning_values(
      cls_params: tp.Dict[str, tp.Any], func_params: tp.Dict[str, tp.Any], expected: float
  ) -> None:
  
      np.random.seed(12)
      func = mlfunctionlib.MLTuning(**cls_params)
      if any(params.get("regressor", "") == "mlp" for params in (func_params, cls_params)):
          # make it way faster
          func.num_data = 12
          func._cross_val_num = 2
      outputs = [func(**func_params) for _ in range(2)]
      assert outputs[0] == outputs[1]  # function is deterministic once initialized
      np.testing.assert_almost_equal(outputs[0], expected, decimal=8)
      func.evaluation_function(func.parametrization)
  