Class BayesianOptimizer

modAL/models/learners.py:305–429

This class is an abstract model of a Bayesian optimizer algorithm. Args: estimator: The estimator to be used in the Bayesian optimization. (For instance, a GaussianProcessRegressor.) query_strategy: Function providing the query strategy for Bayesian optimization

class BayesianOptimizer(ActiveLearner):
    """
    This class is an abstract model of a Bayesian optimizer algorithm.

    Args:
        estimator: The estimator to be used in the Bayesian optimization. (For instance, a
            GaussianProcessRegressor.)
        query_strategy: Function providing the query strategy for Bayesian optimization,
            for instance, modAL.acquisitions.max_EI.
        X_training: Initial training samples, if available.
        y_training: Initial training labels corresponding to initial training samples.
        bootstrap_init: If initial training data is available, bootstrapping can be done during the first training.
            Useful when building Committee models with bagging.
        **fit_kwargs: keyword arguments.

    Attributes:
        estimator: The estimator to be used in the Bayesian optimization.
        query_strategy: Function providing the query strategy for Bayesian optimization.
        X_training: If the model hasn't been fitted yet it is None, otherwise it contains the samples
            which the model has been trained on.
        y_training: The labels corresponding to X_training.
        X_max: argmax of the function so far.
        y_max: Max of the function so far.

    Examples:

        >>> import numpy as np
        >>> from functools import partial
        >>> from sklearn.gaussian_process import GaussianProcessRegressor
        >>> from sklearn.gaussian_process.kernels import Matern
        >>> from modAL.models import BayesianOptimizer
        >>> from modAL.acquisition import optimizer_PI, optimizer_EI, optimizer_UCB, max_PI, max_EI, max_UCB
        >>>
        >>> # generating the data
        >>> X = np.linspace(0, 20, 1000).reshape(-1, 1)
        >>> y = np.sin(X)/2 - ((10 - X)**2)/50 + 2
        >>>
        >>> # assembling initial training set
        >>> X_initial, y_initial = X[150].reshape(1, -1), y[150].reshape(1, -1)
        >>>
        >>> # defining the kernel for the Gaussian process
        >>> kernel = Matern(length_scale=1.0)
        >>>
        >>> tr = 0.1
        >>> PI_tr = partial(optimizer_PI, tradeoff=tr)
        >>> PI_tr.__name__ = 'PI, tradeoff = %1.1f' % tr
        >>> max_PI_tr = partial(max_PI, tradeoff=tr)
        >>>
        >>> acquisitions = zip(
        ...     [PI_tr, optimizer_EI, optimizer_UCB],
        ...     [max_PI_tr, max_EI, max_UCB],
        ... )
        >>>
        >>> for acquisition, query_strategy in acquisitions:
        ...     # initializing the optimizer
        ...     optimizer = BayesianOptimizer(
        ...         estimator=GaussianProcessRegressor(kernel=kernel),
        ...         X_training=X_initial, y_training=y_initial,