hyperactive.opt.gfo._pattern_search

Complexity

Total Complexity

3

Size/Duplication

Total Lines	148
Duplicated Lines	97.3 %

Importance

Changes

0

3 Methods

Rating	Name	Duplication	Size	Complexity
A	PatternSearch._get_gfo_class()	11	11	1
A	PatternSearch.__init__()	27	27	1
A	PatternSearch.get_test_params()	26	26	1

from hyperactive.opt._adapters._gfo import _BaseGFOadapter


class PatternSearch(_BaseGFOadapter):

This code seems to be duplicated in your project.

    """Pattern search optimizer.

    Parameters
    ----------
    search_space : dict[str, list]
        The search space to explore. A dictionary with parameter
        names as keys and a numpy array as values.
    initialize : dict[str, int]
        The method to generate initial positions. A dictionary with
        the following key literals and the corresponding value type:
        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
    constraints : list[callable]
        A list of constraints, where each constraint is a callable.
        The callable returns `True` or `False` dependend on the input parameters.
    random_state : None, int
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    rand_rest_p : float
        The probability of a random iteration during the the search process.
    n_positions : int
        Number of positions that the pattern consists of.
    pattern_size : float
        The initial size of the patterns in percentage of the size of the search space in the corresponding dimension.
    reduction : float
        The factor that reduces the size of the pattern if no better position is found.
    n_iter : int, default=100
        The number of iterations to run the optimizer.
    verbose : bool, default=False
        If True, print the progress of the optimization process.
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later via ``set_params``.

    Examples
    --------
    Basic usage of PatternSearch with a scikit-learn experiment:

    1. defining the experiment to optimize:
    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.svm import SVC
    >>>
    >>> X, y = load_iris(return_X_y=True)
    >>>
    >>> sklearn_exp = SklearnCvExperiment(
    ...     estimator=SVC(),
    ...     X=X,
    ...     y=y,
    ... )

    2. setting up the patternSearch optimizer:
    >>> from hyperactive.opt import PatternSearch
    >>> import numpy as np
    >>>
    >>> config = {
    ...     "search_space": {
    ...         "C": [0.01, 0.1, 1, 10],
    ...         "gamma": [0.0001, 0.01, 0.1, 1, 10],
    ...     },
    ...     "n_iter": 100,
    ... }
    >>> optimizer = PatternSearch(experiment=sklearn_exp, **config)

    3. running the optimization:
    >>> best_params = optimizer.run()

    Best parameters can also be accessed via:
    >>> best_params = optimizer.best_params_
    """

    _tags = {
        "info:name": "Pattern Search",
        "info:local_vs_global": "local",
        "info:explore_vs_exploit": "explore",
        "info:compute": "middle",
    }

    def __init__(
        self,
        search_space=None,
        initialize=None,
        constraints=None,
        random_state=None,
        rand_rest_p=0.1,
        n_positions=4,
        pattern_size=0.25,
        reduction=0.9,
        n_iter=100,
        verbose=False,
        experiment=None,
    ):
        self.random_state = random_state
        self.rand_rest_p = rand_rest_p
        self.n_positions = n_positions
        self.pattern_size = pattern_size
        self.reduction = reduction
        self.search_space = search_space
        self.initialize = initialize
        self.constraints = constraints
        self.n_iter = n_iter
        self.experiment = experiment
        self.verbose = verbose

        super().__init__()

    def _get_gfo_class(self):
        """Get the GFO class to use.

        Returns
        -------
        class
            The GFO class to use. One of the concrete GFO classes
        """
        from gradient_free_optimizers import PatternSearch

        return PatternSearch

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Get the test parameters for the optimizer.

        Returns
        -------
        dict with str keys
            The test parameters dictionary.
        """
        import numpy as np

        params = super().get_test_params()
        experiment = params[0]["experiment"]
        more_params = {
            "experiment": experiment,
            "n_positions": 3,
            "pattern_size": 0.5,
            "reduction": 0.999,
            "search_space": {
                "C": [0.01, 0.1, 1, 10],
                "gamma": [0.0001, 0.01, 0.1, 1, 10],
            },
            "n_iter": 100,
        }
        params.append(more_params)
        return params