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Predictors

AbstractPredictor

Bases: ABC

Abstract base class for all predictors.

Methods

fit(X, Y, kwargs) Fit the model to the data. predict(X, kwargs) Predict using the model. save(file_path) Save the model to a file. load(file_path) Load the model from a file. get_configuration_space(cs) Get the configuration space for the predictor. get_from_configuration(configuration) Get a predictor instance from a configuration.

Source code in asf/predictors/abstract_predictor.py 
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class AbstractPredictor(ABC):
    """
    Abstract base class for all predictors.

    Methods
    -------
    fit(X, Y, **kwargs)
        Fit the model to the data.
    predict(X, **kwargs)
        Predict using the model.
    save(file_path)
        Save the model to a file.
    load(file_path)
        Load the model from a file.
    get_configuration_space(cs)
        Get the configuration space for the predictor.
    get_from_configuration(configuration)
        Get a predictor instance from a configuration.
    """

    def __init__(self):
        """
        Initialize the predictor.
        """
        pass

    @abstractmethod
    def fit(self, X: Any, Y: Any, **kwargs: Any) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : Any
            Training data.
        Y : Any
            Target values.
        kwargs : Any
            Additional arguments for fitting the model.
        """
        pass

    @abstractmethod
    def predict(self, X: Any, **kwargs: Any) -> Any:
        """
        Predict using the model.

        Parameters
        ----------
        X : Any
            Data to predict on.
        kwargs : Any
            Additional arguments for prediction.

        Returns
        -------
        Any
            Predicted values.
        """
        pass

    @abstractmethod
    def save(self, file_path: str) -> None:
        """
        Save the model to a file.

        Parameters
        ----------
        file_path : str
            Path to the file where the model will be saved.
        """
        pass

    @abstractmethod
    def load(self, file_path: str) -> None:
        """
        Load the model from a file.

        Parameters
        ----------
        file_path : str
            Path to the file from which the model will be loaded.
        """
        pass

__init__()

Initialize the predictor.

Source code in asf/predictors/abstract_predictor.py 
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def __init__(self):
    """
    Initialize the predictor.
    """
    pass

fit(X, Y, **kwargs) abstractmethod

Fit the model to the data.

Parameters

X : Any Training data. Y : Any Target values. kwargs : Any Additional arguments for fitting the model.

Source code in asf/predictors/abstract_predictor.py 
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@abstractmethod
def fit(self, X: Any, Y: Any, **kwargs: Any) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : Any
        Training data.
    Y : Any
        Target values.
    kwargs : Any
        Additional arguments for fitting the model.
    """
    pass

load(file_path) abstractmethod

Load the model from a file.

Parameters

file_path : str Path to the file from which the model will be loaded.

Source code in asf/predictors/abstract_predictor.py 
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@abstractmethod
def load(self, file_path: str) -> None:
    """
    Load the model from a file.

    Parameters
    ----------
    file_path : str
        Path to the file from which the model will be loaded.
    """
    pass

predict(X, **kwargs) abstractmethod

Predict using the model.

Parameters

X : Any Data to predict on. kwargs : Any Additional arguments for prediction.

Returns

Any Predicted values.

Source code in asf/predictors/abstract_predictor.py 
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@abstractmethod
def predict(self, X: Any, **kwargs: Any) -> Any:
    """
    Predict using the model.

    Parameters
    ----------
    X : Any
        Data to predict on.
    kwargs : Any
        Additional arguments for prediction.

    Returns
    -------
    Any
        Predicted values.
    """
    pass

save(file_path) abstractmethod

Save the model to a file.

Parameters

file_path : str Path to the file where the model will be saved.

Source code in asf/predictors/abstract_predictor.py 
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@abstractmethod
def save(self, file_path: str) -> None:
    """
    Save the model to a file.

    Parameters
    ----------
    file_path : str
        Path to the file where the model will be saved.
    """
    pass

EPMRandomForest

Bases: ForestRegressor, AbstractPredictor

Implementation of random forest as described in the paper "Algorithm runtime prediction: Methods & evaluation" by Hutter, Xu, Hoos, and Leyton-Brown (2014).

This class extends ForestRegressor and AbstractPredictor to provide a random forest implementation with additional functionality for runtime prediction.

Parameters

n_estimators : int, optional The number of trees in the forest. Default is 100. log : bool, optional Whether to apply logarithmic transformation to the tree values. Default is False. cross_trees_variance : bool, optional Whether to compute variance across trees. Default is False. criterion : str, optional The function to measure the quality of a split. Default is "squared_error". splitter : str, optional The strategy used to choose the split at each node. Default is "random". max_depth : int, optional The maximum depth of the tree. Default is None. min_samples_split : int, optional The minimum number of samples required to split an internal node. Default is 2. min_samples_leaf : int, optional The minimum number of samples required to be at a leaf node. Default is 1. min_weight_fraction_leaf : float, optional The minimum weighted fraction of the sum total of weights required to be at a leaf node. Default is 0.0. max_features : float, optional The number of features to consider when looking for the best split. Default is 1.0. max_leaf_nodes : int, optional Grow trees with max_leaf_nodes in best-first fashion. Default is None. min_impurity_decrease : float, optional A node will be split if this split induces a decrease of the impurity greater than or equal to this value. Default is 0.0. bootstrap : bool, optional Whether bootstrap samples are used when building trees. Default is False. oob_score : bool, optional Whether to use out-of-bag samples to estimate the generalization score. Default is False. n_jobs : int, optional The number of jobs to run in parallel. Default is None. random_state : int, optional Controls the randomness of the estimator. Default is None. verbose : int, optional Controls the verbosity when fitting and predicting. Default is 0. warm_start : bool, optional When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble. Default is False. ccp_alpha : float, optional Complexity parameter used for Minimal Cost-Complexity Pruning. Default is 0.0. max_samples : int or float, optional If bootstrap is True, the number of samples to draw from X to train each base estimator. Default is None. monotonic_cst : array-like, optional Constraints for monotonicity of features. Default is None.

Source code in asf/predictors/epm_random_forest.py 
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class EPMRandomForest(ForestRegressor, AbstractPredictor):
    """
    Implementation of random forest as described in the paper
    "Algorithm runtime prediction: Methods & evaluation" by Hutter, Xu, Hoos, and Leyton-Brown (2014).

    This class extends `ForestRegressor` and `AbstractPredictor` to provide
    a random forest implementation with additional functionality for runtime prediction.

    Parameters
    ----------
    n_estimators : int, optional
        The number of trees in the forest. Default is 100.
    log : bool, optional
        Whether to apply logarithmic transformation to the tree values. Default is False.
    cross_trees_variance : bool, optional
        Whether to compute variance across trees. Default is False.
    criterion : str, optional
        The function to measure the quality of a split. Default is "squared_error".
    splitter : str, optional
        The strategy used to choose the split at each node. Default is "random".
    max_depth : int, optional
        The maximum depth of the tree. Default is None.
    min_samples_split : int, optional
        The minimum number of samples required to split an internal node. Default is 2.
    min_samples_leaf : int, optional
        The minimum number of samples required to be at a leaf node. Default is 1.
    min_weight_fraction_leaf : float, optional
        The minimum weighted fraction of the sum total of weights required to be at a leaf node. Default is 0.0.
    max_features : float, optional
        The number of features to consider when looking for the best split. Default is 1.0.
    max_leaf_nodes : int, optional
        Grow trees with max_leaf_nodes in best-first fashion. Default is None.
    min_impurity_decrease : float, optional
        A node will be split if this split induces a decrease of the impurity greater than or equal to this value. Default is 0.0.
    bootstrap : bool, optional
        Whether bootstrap samples are used when building trees. Default is False.
    oob_score : bool, optional
        Whether to use out-of-bag samples to estimate the generalization score. Default is False.
    n_jobs : int, optional
        The number of jobs to run in parallel. Default is None.
    random_state : int, optional
        Controls the randomness of the estimator. Default is None.
    verbose : int, optional
        Controls the verbosity when fitting and predicting. Default is 0.
    warm_start : bool, optional
        When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble. Default is False.
    ccp_alpha : float, optional
        Complexity parameter used for Minimal Cost-Complexity Pruning. Default is 0.0.
    max_samples : int or float, optional
        If bootstrap is True, the number of samples to draw from X to train each base estimator. Default is None.
    monotonic_cst : array-like, optional
        Constraints for monotonicity of features. Default is None.
    """

    def __init__(
        self,
        n_estimators: int = 100,
        *,
        log: bool = False,
        return_var: bool = False,
        criterion: str = "squared_error",
        splitter: str = "random",
        max_depth: int = None,
        min_samples_split: int = 2,
        min_samples_leaf: int = 1,
        min_weight_fraction_leaf: float = 0.0,
        max_features: float = 1.0,
        max_leaf_nodes: int = None,
        min_impurity_decrease: float = 0.0,
        bootstrap: bool = False,
        oob_score: bool = False,
        n_jobs: int = None,
        random_state: int = None,
        verbose: int = 0,
        warm_start: bool = False,
        ccp_alpha: float = 0.0,
        max_samples: int | float = None,
        monotonic_cst: np.ndarray = None,
    ) -> None:
        super().__init__(
            DecisionTreeRegressor(),
            n_estimators,
            estimator_params=(
                "criterion",
                "max_depth",
                "min_samples_split",
                "min_samples_leaf",
                "min_weight_fraction_leaf",
                "max_features",
                "max_leaf_nodes",
                "min_impurity_decrease",
                "random_state",
                "ccp_alpha",
                "monotonic_cst",
            ),
            bootstrap=bootstrap,
            oob_score=oob_score,
            n_jobs=n_jobs,
            random_state=random_state,
            verbose=verbose,
            warm_start=warm_start,
            max_samples=max_samples,
        )
        self.criterion = criterion
        self.max_depth = max_depth
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.min_weight_fraction_leaf = min_weight_fraction_leaf
        self.max_features = max_features
        self.max_leaf_nodes = max_leaf_nodes
        self.min_impurity_decrease = min_impurity_decrease
        self.ccp_alpha = ccp_alpha
        self.monotonic_cst = monotonic_cst
        self.splitter = splitter
        self.log = log
        self.return_var = return_var

    def fit(
        self, X: np.ndarray, y: np.ndarray, sample_weight: np.ndarray = None
    ) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : np.ndarray
            Training data of shape (n_samples, n_features).
        y : np.ndarray
            Target values of shape (n_samples,).
        sample_weight : np.ndarray, optional
            Sample weights. Default is None.

        Raises
        ------
        AssertionError
            If sample weights are provided, as they are not supported.
        """
        assert sample_weight is None, "Sample weights are not supported"
        super().fit(X=X, y=y, sample_weight=sample_weight)

        self.trainX = X
        self.trainY = y
        if self.log:
            for tree, samples_idx in zip(self.estimators_, self.estimators_samples_):
                curX = X[samples_idx]
                curY = y[samples_idx]
                preds = tree.apply(curX)
                for k in np.unique(preds):
                    tree.tree_.value[k, 0, 0] = np.log(np.exp(curY[preds == k]).mean())

    def predict(self, X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
        """
        Predict using the model.

        Parameters
        ----------
        X : np.ndarray
            Data to predict on of shape (n_samples, n_features).

        Returns
        -------
        tuple[np.ndarray, np.ndarray]
            A tuple containing:
            - Predicted means of shape (n_samples, 1).
            - Predicted variances of shape (n_samples, 1).
        """
        preds = []
        for tree, samples_idx in zip(self.estimators_, self.estimators_samples_):
            preds.append(tree.predict(X))
        preds = np.array(preds).T

        means = preds.mean(axis=1)
        vars = preds.var(axis=1)

        if self.return_var:
            return means, vars
        else:
            return means

    def save(self, file_path: str) -> None:
        """
        Save the model to a file.

        Parameters
        ----------
        file_path : str
            Path to the file where the model will be saved.
        """
        import joblib

        joblib.dump(self, file_path)

    def load(self, file_path: str) -> "EPMRandomForest":
        """
        Load the model from a file.

        Parameters
        ----------
        file_path : str
            Path to the file from which the model will be loaded.

        Returns
        -------
        EPMRandomForest
            The loaded model.
        """
        import joblib

        return joblib.load(file_path)

fit(X, y, sample_weight=None)

Fit the model to the data.

Parameters

X : np.ndarray Training data of shape (n_samples, n_features). y : np.ndarray Target values of shape (n_samples,). sample_weight : np.ndarray, optional Sample weights. Default is None.

Raises

AssertionError If sample weights are provided, as they are not supported.

Source code in asf/predictors/epm_random_forest.py 
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def fit(
    self, X: np.ndarray, y: np.ndarray, sample_weight: np.ndarray = None
) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : np.ndarray
        Training data of shape (n_samples, n_features).
    y : np.ndarray
        Target values of shape (n_samples,).
    sample_weight : np.ndarray, optional
        Sample weights. Default is None.

    Raises
    ------
    AssertionError
        If sample weights are provided, as they are not supported.
    """
    assert sample_weight is None, "Sample weights are not supported"
    super().fit(X=X, y=y, sample_weight=sample_weight)

    self.trainX = X
    self.trainY = y
    if self.log:
        for tree, samples_idx in zip(self.estimators_, self.estimators_samples_):
            curX = X[samples_idx]
            curY = y[samples_idx]
            preds = tree.apply(curX)
            for k in np.unique(preds):
                tree.tree_.value[k, 0, 0] = np.log(np.exp(curY[preds == k]).mean())

load(file_path)

Load the model from a file.

Parameters

file_path : str Path to the file from which the model will be loaded.

Returns

EPMRandomForest The loaded model.

Source code in asf/predictors/epm_random_forest.py 
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def load(self, file_path: str) -> "EPMRandomForest":
    """
    Load the model from a file.

    Parameters
    ----------
    file_path : str
        Path to the file from which the model will be loaded.

    Returns
    -------
    EPMRandomForest
        The loaded model.
    """
    import joblib

    return joblib.load(file_path)

predict(X)

Predict using the model.

Parameters

X : np.ndarray Data to predict on of shape (n_samples, n_features).

Returns

tuple[np.ndarray, np.ndarray] A tuple containing: - Predicted means of shape (n_samples, 1). - Predicted variances of shape (n_samples, 1).

Source code in asf/predictors/epm_random_forest.py 
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def predict(self, X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    """
    Predict using the model.

    Parameters
    ----------
    X : np.ndarray
        Data to predict on of shape (n_samples, n_features).

    Returns
    -------
    tuple[np.ndarray, np.ndarray]
        A tuple containing:
        - Predicted means of shape (n_samples, 1).
        - Predicted variances of shape (n_samples, 1).
    """
    preds = []
    for tree, samples_idx in zip(self.estimators_, self.estimators_samples_):
        preds.append(tree.predict(X))
    preds = np.array(preds).T

    means = preds.mean(axis=1)
    vars = preds.var(axis=1)

    if self.return_var:
        return means, vars
    else:
        return means

save(file_path)

Save the model to a file.

Parameters

file_path : str Path to the file where the model will be saved.

Source code in asf/predictors/epm_random_forest.py 
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def save(self, file_path: str) -> None:
    """
    Save the model to a file.

    Parameters
    ----------
    file_path : str
        Path to the file where the model will be saved.
    """
    import joblib

    joblib.dump(self, file_path)

LinearClassifierWrapper

Bases: SklearnWrapper

A wrapper for the SGDClassifier from scikit-learn, providing additional functionality for configuration space generation and parameter extraction.

Source code in asf/predictors/linear_model.py 
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class LinearClassifierWrapper(SklearnWrapper):
    """
    A wrapper for the SGDClassifier from scikit-learn, providing additional functionality
    for configuration space generation and parameter extraction.
    """

    PREFIX = "linear_classifier"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the LinearClassifierWrapper.

        Parameters
        ----------
        init_params : dict, optional
            A dictionary of initialization parameters for the SGDClassifier.
        """
        super().__init__(SGDClassifier, init_params or {})

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the Linear Classifier.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the Linear Classifier parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{LinearClassifierWrapper.PREFIX}"
            else:
                prefix = LinearClassifierWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="Linear Classifier")

            alpha = Float(
                f"{prefix}:alpha",
                (1e-5, 1),
                log=True,
                default=1e-3,
            )
            eta0 = Float(
                f"{prefix}:eta0",
                (1e-5, 1),
                log=True,
                default=1e-2,
            )

            params = [
                alpha,
                eta0,
            ]

            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create a partial function to initialize LinearClassifierWrapper with parameters from a configuration.

            Parameters
            ----------
            configuration : dict
                A dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to include in the initialization.

            Returns
            -------
            partial
                A partial function to initialize LinearClassifierWrapper.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{LinearClassifierWrapper.PREFIX}"
            else:
                prefix = LinearClassifierWrapper.PREFIX

            linear_classifier_params = {
                "alpha": configuration[f"{prefix}:alpha"],
                "eta0": configuration[f"{prefix}:eta0"],
                **kwargs,
            }

            return partial(
                LinearClassifierWrapper, init_params=linear_classifier_params
            )

__init__(init_params=None)

Initialize the LinearClassifierWrapper.

Parameters

init_params : dict, optional A dictionary of initialization parameters for the SGDClassifier.

Source code in asf/predictors/linear_model.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the LinearClassifierWrapper.

    Parameters
    ----------
    init_params : dict, optional
        A dictionary of initialization parameters for the SGDClassifier.
    """
    super().__init__(SGDClassifier, init_params or {})

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the Linear Classifier.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the Linear Classifier parameters.

Source code in asf/predictors/linear_model.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the Linear Classifier.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the Linear Classifier parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{LinearClassifierWrapper.PREFIX}"
    else:
        prefix = LinearClassifierWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="Linear Classifier")

    alpha = Float(
        f"{prefix}:alpha",
        (1e-5, 1),
        log=True,
        default=1e-3,
    )
    eta0 = Float(
        f"{prefix}:eta0",
        (1e-5, 1),
        log=True,
        default=1e-2,
    )

    params = [
        alpha,
        eta0,
    ]

    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create a partial function to initialize LinearClassifierWrapper with parameters from a configuration.

Parameters

configuration : dict A dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to include in the initialization.

Returns

partial A partial function to initialize LinearClassifierWrapper.

Source code in asf/predictors/linear_model.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create a partial function to initialize LinearClassifierWrapper with parameters from a configuration.

    Parameters
    ----------
    configuration : dict
        A dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to include in the initialization.

    Returns
    -------
    partial
        A partial function to initialize LinearClassifierWrapper.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{LinearClassifierWrapper.PREFIX}"
    else:
        prefix = LinearClassifierWrapper.PREFIX

    linear_classifier_params = {
        "alpha": configuration[f"{prefix}:alpha"],
        "eta0": configuration[f"{prefix}:eta0"],
        **kwargs,
    }

    return partial(
        LinearClassifierWrapper, init_params=linear_classifier_params
    )

LinearRegressorWrapper

Bases: SklearnWrapper

A wrapper for the SGDRegressor from scikit-learn, providing additional functionality for configuration space generation and parameter extraction.

Source code in asf/predictors/linear_model.py 
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class LinearRegressorWrapper(SklearnWrapper):
    """
    A wrapper for the SGDRegressor from scikit-learn, providing additional functionality
    for configuration space generation and parameter extraction.
    """

    PREFIX = "linear_regressor"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the LinearRegressorWrapper.

        Parameters
        ----------
        init_params : dict, optional
            A dictionary of initialization parameters for the SGDRegressor.
        """
        super().__init__(SGDRegressor, init_params or {})

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the Linear Regressor.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the Linear Regressor parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{LinearRegressorWrapper.PREFIX}"
            else:
                prefix = LinearRegressorWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="Linear Regressor")

            alpha = Float(
                f"{prefix}:alpha",
                (1e-5, 1),
                log=True,
                default=1e-3,
            )
            eta0 = Float(
                f"{prefix}:eta0",
                (1e-5, 1),
                log=True,
                default=1e-2,
            )

            params = [alpha, eta0]

            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create a partial function to initialize LinearRegressorWrapper with parameters from a configuration.

            Parameters
            ----------
            configuration : dict
                A dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to include in the initialization.

            Returns
            -------
            partial
                A partial function to initialize LinearRegressorWrapper.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{LinearRegressorWrapper.PREFIX}"
            else:
                prefix = LinearRegressorWrapper.PREFIX

            linear_regressor_params = {
                "alpha": configuration[f"{prefix}:alpha"],
                "eta0": configuration[f"{prefix}:eta0"],
                **kwargs,
            }

            return partial(LinearRegressorWrapper, init_params=linear_regressor_params)

__init__(init_params=None)

Initialize the LinearRegressorWrapper.

Parameters

init_params : dict, optional A dictionary of initialization parameters for the SGDRegressor.

Source code in asf/predictors/linear_model.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the LinearRegressorWrapper.

    Parameters
    ----------
    init_params : dict, optional
        A dictionary of initialization parameters for the SGDRegressor.
    """
    super().__init__(SGDRegressor, init_params or {})

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the Linear Regressor.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the Linear Regressor parameters.

Source code in asf/predictors/linear_model.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the Linear Regressor.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the Linear Regressor parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{LinearRegressorWrapper.PREFIX}"
    else:
        prefix = LinearRegressorWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="Linear Regressor")

    alpha = Float(
        f"{prefix}:alpha",
        (1e-5, 1),
        log=True,
        default=1e-3,
    )
    eta0 = Float(
        f"{prefix}:eta0",
        (1e-5, 1),
        log=True,
        default=1e-2,
    )

    params = [alpha, eta0]

    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create a partial function to initialize LinearRegressorWrapper with parameters from a configuration.

Parameters

configuration : dict A dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to include in the initialization.

Returns

partial A partial function to initialize LinearRegressorWrapper.

Source code in asf/predictors/linear_model.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create a partial function to initialize LinearRegressorWrapper with parameters from a configuration.

    Parameters
    ----------
    configuration : dict
        A dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to include in the initialization.

    Returns
    -------
    partial
        A partial function to initialize LinearRegressorWrapper.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{LinearRegressorWrapper.PREFIX}"
    else:
        prefix = LinearRegressorWrapper.PREFIX

    linear_regressor_params = {
        "alpha": configuration[f"{prefix}:alpha"],
        "eta0": configuration[f"{prefix}:eta0"],
        **kwargs,
    }

    return partial(LinearRegressorWrapper, init_params=linear_regressor_params)

MLPClassifierWrapper

Bases: SklearnWrapper

A wrapper for the MLPClassifier from scikit-learn, providing additional functionality for configuration space and parameter handling.

Source code in asf/predictors/mlp.py 
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class MLPClassifierWrapper(SklearnWrapper):
    """
    A wrapper for the MLPClassifier from scikit-learn, providing additional functionality
    for configuration space and parameter handling.
    """

    PREFIX = "mlp_classifier"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the MLPClassifierWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Initial parameters for the MLPClassifier.
        """
        super().__init__(MLPClassifier, init_params or {})

    def fit(
        self, X: Any, Y: Any, sample_weight: Optional[Any] = None, **kwargs: Any
    ) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : array-like
            Training data.
        Y : array-like
            Target values.
        sample_weight : array-like, optional
            Sample weights. Not supported for MLPClassifier.
        kwargs : dict
            Additional arguments for the fit method.
        """
        assert sample_weight is None, (
            "Sample weights are not supported for MLPClassifier"
        )
        self.model_class.fit(X, Y, **kwargs)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the MLP Classifier.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the MLP Classifier parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{MLPClassifierWrapper.PREFIX}"
            else:
                prefix = MLPClassifierWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="MLP Classifier")

            depth = Integer(f"{prefix}:depth", (1, 3), default=3, log=False)

            width = Integer(f"{prefix}:width", (16, 1024), default=64, log=True)

            batch_size = Integer(
                f"{prefix}:batch_size",
                (256, 1024),
                default=32,
                log=True,
            )  # MODIFIED from HPOBENCH

            alpha = Float(
                f"{prefix}:alpha",
                (10**-8, 1),
                default=10**-3,
                log=True,
            )

            learning_rate_init = Float(
                f"{prefix}:learning_rate_init",
                (10**-5, 1),
                default=10**-3,
                log=True,
            )

            params = [depth, width, batch_size, alpha, learning_rate_init]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: ConfigurationSpace, pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create an MLPClassifierWrapper instance from a configuration.

            Parameters
            ----------
            configuration : ConfigurationSpace
                The configuration containing the parameters.
            additional_params : dict, optional
                Additional parameters to override the default configuration.

            Returns
            -------
            partial
                A partial function to create an MLPClassifierWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{MLPClassifierWrapper.PREFIX}"
            else:
                prefix = MLPClassifierWrapper.PREFIX

            hidden_layers = [configuration[f"{prefix}:width"]] * configuration[
                f"{prefix}:depth"
            ]

            if "activation" not in kwargs:
                kwargs["activation"] = "relu"
            if "solver" not in kwargs:
                kwargs["solver"] = "adam"

            mlp_params = {
                "hidden_layer_sizes": hidden_layers,
                "batch_size": configuration[f"{prefix}:batch_size"],
                "alpha": configuration[f"{prefix}:alpha"],
                "learning_rate_init": configuration[f"{prefix}:learning_rate_init"],
                **kwargs,
            }

            return partial(MLPClassifierWrapper, init_params=mlp_params)

__init__(init_params=None)

Initialize the MLPClassifierWrapper.

Parameters

init_params : dict, optional Initial parameters for the MLPClassifier.

Source code in asf/predictors/mlp.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the MLPClassifierWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Initial parameters for the MLPClassifier.
    """
    super().__init__(MLPClassifier, init_params or {})

fit(X, Y, sample_weight=None, **kwargs)

Fit the model to the data.

Parameters

X : array-like Training data. Y : array-like Target values. sample_weight : array-like, optional Sample weights. Not supported for MLPClassifier. kwargs : dict Additional arguments for the fit method.

Source code in asf/predictors/mlp.py 
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def fit(
    self, X: Any, Y: Any, sample_weight: Optional[Any] = None, **kwargs: Any
) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : array-like
        Training data.
    Y : array-like
        Target values.
    sample_weight : array-like, optional
        Sample weights. Not supported for MLPClassifier.
    kwargs : dict
        Additional arguments for the fit method.
    """
    assert sample_weight is None, (
        "Sample weights are not supported for MLPClassifier"
    )
    self.model_class.fit(X, Y, **kwargs)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the MLP Classifier.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the MLP Classifier parameters.

Source code in asf/predictors/mlp.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the MLP Classifier.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the MLP Classifier parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{MLPClassifierWrapper.PREFIX}"
    else:
        prefix = MLPClassifierWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="MLP Classifier")

    depth = Integer(f"{prefix}:depth", (1, 3), default=3, log=False)

    width = Integer(f"{prefix}:width", (16, 1024), default=64, log=True)

    batch_size = Integer(
        f"{prefix}:batch_size",
        (256, 1024),
        default=32,
        log=True,
    )  # MODIFIED from HPOBENCH

    alpha = Float(
        f"{prefix}:alpha",
        (10**-8, 1),
        default=10**-3,
        log=True,
    )

    learning_rate_init = Float(
        f"{prefix}:learning_rate_init",
        (10**-5, 1),
        default=10**-3,
        log=True,
    )

    params = [depth, width, batch_size, alpha, learning_rate_init]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an MLPClassifierWrapper instance from a configuration.

Parameters

configuration : ConfigurationSpace The configuration containing the parameters. additional_params : dict, optional Additional parameters to override the default configuration.

Returns

partial A partial function to create an MLPClassifierWrapper instance.

Source code in asf/predictors/mlp.py 
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@staticmethod
def get_from_configuration(
    configuration: ConfigurationSpace, pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create an MLPClassifierWrapper instance from a configuration.

    Parameters
    ----------
    configuration : ConfigurationSpace
        The configuration containing the parameters.
    additional_params : dict, optional
        Additional parameters to override the default configuration.

    Returns
    -------
    partial
        A partial function to create an MLPClassifierWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{MLPClassifierWrapper.PREFIX}"
    else:
        prefix = MLPClassifierWrapper.PREFIX

    hidden_layers = [configuration[f"{prefix}:width"]] * configuration[
        f"{prefix}:depth"
    ]

    if "activation" not in kwargs:
        kwargs["activation"] = "relu"
    if "solver" not in kwargs:
        kwargs["solver"] = "adam"

    mlp_params = {
        "hidden_layer_sizes": hidden_layers,
        "batch_size": configuration[f"{prefix}:batch_size"],
        "alpha": configuration[f"{prefix}:alpha"],
        "learning_rate_init": configuration[f"{prefix}:learning_rate_init"],
        **kwargs,
    }

    return partial(MLPClassifierWrapper, init_params=mlp_params)

MLPRegressorWrapper

Bases: SklearnWrapper

A wrapper for the MLPRegressor from scikit-learn, providing additional functionality for configuration space and parameter handling.

Source code in asf/predictors/mlp.py 
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class MLPRegressorWrapper(SklearnWrapper):
    """
    A wrapper for the MLPRegressor from scikit-learn, providing additional functionality
    for configuration space and parameter handling.
    """

    PREFIX = "mlp_regressor"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the MLPRegressorWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Initial parameters for the MLPRegressor.
        """
        super().__init__(MLPRegressor, init_params or {})

    def fit(
        self, X: Any, Y: Any, sample_weight: Optional[Any] = None, **kwargs: Any
    ) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : array-like
            Training data.
        Y : array-like
            Target values.
        sample_weight : array-like, optional
            Sample weights. Not supported for MLPRegressor.
        kwargs : dict
            Additional arguments for the fit method.
        """
        assert sample_weight is None, (
            "Sample weights are not supported for MLPRegressor"
        )
        self.model_class.fit(X, Y, **kwargs)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the MLP Regressor.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the MLP Regressor parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{MLPRegressorWrapper.PREFIX}"
            else:
                prefix = MLPRegressorWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="MLP Regressor")

            depth = Integer(f"{prefix}:depth", (1, 3), default=3, log=False)

            width = Integer(f"{prefix}:width", (16, 1024), default=64, log=True)

            batch_size = Integer(
                f"{prefix}:batch_size",
                (256, 1024),
                default=256,
                log=True,
            )

            alpha = Float(
                f"{prefix}:alpha",
                (10**-8, 1),
                default=10**-3,
                log=True,
            )

            learning_rate_init = Float(
                f"{prefix}:learning_rate_init",
                (10**-5, 1),
                default=10**-3,
                log=True,
            )

            params = [depth, width, batch_size, alpha, learning_rate_init]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: ConfigurationSpace, pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create an MLPRegressorWrapper instance from a configuration.

            Parameters
            ----------
            configuration : ConfigurationSpace
                The configuration containing the parameters.
            additional_params : dict, optional
                Additional parameters to override the default configuration.

            Returns
            -------
            partial
                A partial function to create an MLPRegressorWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{MLPRegressorWrapper.PREFIX}"
            else:
                prefix = MLPRegressorWrapper.PREFIX

            hidden_layers = [configuration[f"{prefix}:width"]] * configuration[
                f"{prefix}:depth"
            ]

            if "activation" not in kwargs:
                kwargs["activation"] = "relu"
            if "solver" not in kwargs:
                kwargs["solver"] = "adam"

            mlp_params = {
                "hidden_layer_sizes": hidden_layers,
                "batch_size": configuration[f"{prefix}:batch_size"],
                "alpha": configuration[f"{prefix}:alpha"],
                "learning_rate_init": configuration[f"{prefix}:learning_rate_init"],
                **kwargs,
            }

            return partial(MLPRegressorWrapper, init_params=mlp_params)

__init__(init_params=None)

Initialize the MLPRegressorWrapper.

Parameters

init_params : dict, optional Initial parameters for the MLPRegressor.

Source code in asf/predictors/mlp.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the MLPRegressorWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Initial parameters for the MLPRegressor.
    """
    super().__init__(MLPRegressor, init_params or {})

fit(X, Y, sample_weight=None, **kwargs)

Fit the model to the data.

Parameters

X : array-like Training data. Y : array-like Target values. sample_weight : array-like, optional Sample weights. Not supported for MLPRegressor. kwargs : dict Additional arguments for the fit method.

Source code in asf/predictors/mlp.py 
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def fit(
    self, X: Any, Y: Any, sample_weight: Optional[Any] = None, **kwargs: Any
) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : array-like
        Training data.
    Y : array-like
        Target values.
    sample_weight : array-like, optional
        Sample weights. Not supported for MLPRegressor.
    kwargs : dict
        Additional arguments for the fit method.
    """
    assert sample_weight is None, (
        "Sample weights are not supported for MLPRegressor"
    )
    self.model_class.fit(X, Y, **kwargs)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the MLP Regressor.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the MLP Regressor parameters.

Source code in asf/predictors/mlp.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the MLP Regressor.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the MLP Regressor parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{MLPRegressorWrapper.PREFIX}"
    else:
        prefix = MLPRegressorWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="MLP Regressor")

    depth = Integer(f"{prefix}:depth", (1, 3), default=3, log=False)

    width = Integer(f"{prefix}:width", (16, 1024), default=64, log=True)

    batch_size = Integer(
        f"{prefix}:batch_size",
        (256, 1024),
        default=256,
        log=True,
    )

    alpha = Float(
        f"{prefix}:alpha",
        (10**-8, 1),
        default=10**-3,
        log=True,
    )

    learning_rate_init = Float(
        f"{prefix}:learning_rate_init",
        (10**-5, 1),
        default=10**-3,
        log=True,
    )

    params = [depth, width, batch_size, alpha, learning_rate_init]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an MLPRegressorWrapper instance from a configuration.

Parameters

configuration : ConfigurationSpace The configuration containing the parameters. additional_params : dict, optional Additional parameters to override the default configuration.

Returns

partial A partial function to create an MLPRegressorWrapper instance.

Source code in asf/predictors/mlp.py 
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@staticmethod
def get_from_configuration(
    configuration: ConfigurationSpace, pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create an MLPRegressorWrapper instance from a configuration.

    Parameters
    ----------
    configuration : ConfigurationSpace
        The configuration containing the parameters.
    additional_params : dict, optional
        Additional parameters to override the default configuration.

    Returns
    -------
    partial
        A partial function to create an MLPRegressorWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{MLPRegressorWrapper.PREFIX}"
    else:
        prefix = MLPRegressorWrapper.PREFIX

    hidden_layers = [configuration[f"{prefix}:width"]] * configuration[
        f"{prefix}:depth"
    ]

    if "activation" not in kwargs:
        kwargs["activation"] = "relu"
    if "solver" not in kwargs:
        kwargs["solver"] = "adam"

    mlp_params = {
        "hidden_layer_sizes": hidden_layers,
        "batch_size": configuration[f"{prefix}:batch_size"],
        "alpha": configuration[f"{prefix}:alpha"],
        "learning_rate_init": configuration[f"{prefix}:learning_rate_init"],
        **kwargs,
    }

    return partial(MLPRegressorWrapper, init_params=mlp_params)

RandomForestClassifierWrapper

Bases: SklearnWrapper

A wrapper for the RandomForestClassifier from scikit-learn, providing additional functionality for configuration space management.

Source code in asf/predictors/random_forest.py 
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class RandomForestClassifierWrapper(SklearnWrapper):
    """
    A wrapper for the RandomForestClassifier from scikit-learn, providing
    additional functionality for configuration space management.
    """

    PREFIX = "rf_classifier"

    def __init__(self, init_params: Dict[str, Any] = {}):
        """
        Initialize the RandomForestClassifierWrapper.

        Parameters
        ----------
        init_params : dict, optional
            A dictionary of initialization parameters for the RandomForestClassifier.
        """
        super().__init__(RandomForestClassifier, init_params)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the Random Forest Classifier.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the Random Forest Classifier parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{RandomForestClassifierWrapper.PREFIX}:"
            else:
                prefix = RandomForestClassifierWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="RandomForest")

            n_estimators = Integer(
                f"{prefix}n_estimators",
                (16, 128),
                log=True,
                default=116,
            )
            min_samples_split = Integer(
                f"{prefix}min_samples_split",
                (2, 20),
                log=False,
                default=2,
            )
            min_samples_leaf = Integer(
                f"{prefix}min_samples_leaf",
                (1, 20),
                log=False,
                default=2,
            )
            max_features = Float(
                f"{prefix}max_features",
                (0.1, 1.0),
                log=False,
                default=0.17055852159745608,
            )
            bootstrap = Categorical(
                f"{prefix}bootstrap",
                items=[True, False],
                default=False,
            )

            params = [
                n_estimators,
                min_samples_split,
                min_samples_leaf,
                max_features,
                bootstrap,
            ]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create a RandomForestClassifierWrapper instance from a configuration.

            Parameters
            ----------
            configuration : dict
                A dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to override or extend the configuration.

            Returns
            -------
            partial
                A partial function to create a RandomForestClassifierWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{RandomForestClassifierWrapper.PREFIX}:"
            else:
                prefix = RandomForestClassifierWrapper.PREFIX

            rf_params = {
                "n_estimators": configuration[f"{prefix}n_estimators"],
                "min_samples_split": configuration[f"{prefix}min_samples_split"],
                "min_samples_leaf": configuration[f"{prefix}min_samples_leaf"],
                "max_features": configuration[f"{prefix}max_features"],
                "bootstrap": configuration[f"{prefix}bootstrap"],
                **kwargs,
            }

            return partial(RandomForestClassifierWrapper, init_params=rf_params)

__init__(init_params={})

Initialize the RandomForestClassifierWrapper.

Parameters

init_params : dict, optional A dictionary of initialization parameters for the RandomForestClassifier.

Source code in asf/predictors/random_forest.py 
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def __init__(self, init_params: Dict[str, Any] = {}):
    """
    Initialize the RandomForestClassifierWrapper.

    Parameters
    ----------
    init_params : dict, optional
        A dictionary of initialization parameters for the RandomForestClassifier.
    """
    super().__init__(RandomForestClassifier, init_params)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the Random Forest Classifier.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the Random Forest Classifier parameters.

Source code in asf/predictors/random_forest.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the Random Forest Classifier.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the Random Forest Classifier parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{RandomForestClassifierWrapper.PREFIX}:"
    else:
        prefix = RandomForestClassifierWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="RandomForest")

    n_estimators = Integer(
        f"{prefix}n_estimators",
        (16, 128),
        log=True,
        default=116,
    )
    min_samples_split = Integer(
        f"{prefix}min_samples_split",
        (2, 20),
        log=False,
        default=2,
    )
    min_samples_leaf = Integer(
        f"{prefix}min_samples_leaf",
        (1, 20),
        log=False,
        default=2,
    )
    max_features = Float(
        f"{prefix}max_features",
        (0.1, 1.0),
        log=False,
        default=0.17055852159745608,
    )
    bootstrap = Categorical(
        f"{prefix}bootstrap",
        items=[True, False],
        default=False,
    )

    params = [
        n_estimators,
        min_samples_split,
        min_samples_leaf,
        max_features,
        bootstrap,
    ]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create a RandomForestClassifierWrapper instance from a configuration.

Parameters

configuration : dict A dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to override or extend the configuration.

Returns

partial A partial function to create a RandomForestClassifierWrapper instance.

Source code in asf/predictors/random_forest.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create a RandomForestClassifierWrapper instance from a configuration.

    Parameters
    ----------
    configuration : dict
        A dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to override or extend the configuration.

    Returns
    -------
    partial
        A partial function to create a RandomForestClassifierWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{RandomForestClassifierWrapper.PREFIX}:"
    else:
        prefix = RandomForestClassifierWrapper.PREFIX

    rf_params = {
        "n_estimators": configuration[f"{prefix}n_estimators"],
        "min_samples_split": configuration[f"{prefix}min_samples_split"],
        "min_samples_leaf": configuration[f"{prefix}min_samples_leaf"],
        "max_features": configuration[f"{prefix}max_features"],
        "bootstrap": configuration[f"{prefix}bootstrap"],
        **kwargs,
    }

    return partial(RandomForestClassifierWrapper, init_params=rf_params)

RandomForestRegressorWrapper

Bases: SklearnWrapper

A wrapper for the RandomForestRegressor from scikit-learn, providing additional functionality for configuration space management.

Source code in asf/predictors/random_forest.py 
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class RandomForestRegressorWrapper(SklearnWrapper):
    """
    A wrapper for the RandomForestRegressor from scikit-learn, providing
    additional functionality for configuration space management.
    """

    PREFIX = "rf_regressor"

    def __init__(self, init_params: Dict[str, Any] = {}):
        """
        Initialize the RandomForestRegressorWrapper.

        Parameters
        ----------
        init_params : dict, optional
            A dictionary of initialization parameters for the RandomForestRegressor.
        """
        super().__init__(RandomForestRegressor, init_params)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the Random Forest Regressor.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the Random Forest Regressor parameters.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{RandomForestRegressorWrapper.PREFIX}:"
            else:
                prefix = RandomForestRegressorWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="RandomForestRegressor")

            n_estimators = Integer(
                f"{prefix}n_estimators",
                (16, 128),
                log=True,
                default=116,
            )
            min_samples_split = Integer(
                f"{prefix}min_samples_split",
                (2, 20),
                log=False,
                default=2,
            )
            min_samples_leaf = Integer(
                f"{prefix}min_samples_leaf",
                (1, 20),
                log=False,
                default=2,
            )
            max_features = Float(
                f"{prefix}max_features",
                (0.1, 1.0),
                log=False,
                default=0.17055852159745608,
            )
            bootstrap = Categorical(
                f"{prefix}bootstrap",
                items=[True, False],
                default=False,
            )
            params = [
                n_estimators,
                min_samples_split,
                min_samples_leaf,
                max_features,
                bootstrap,
            ]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create a RandomForestRegressorWrapper instance from a configuration.

            Parameters
            ----------
            configuration : dict
                A dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to override or extend the configuration.

            Returns
            -------
            partial
                A partial function to create a RandomForestRegressorWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{RandomForestRegressorWrapper.PREFIX}:"
            else:
                prefix = RandomForestRegressorWrapper.PREFIX

            rf_params = {
                "n_estimators": configuration[f"{prefix}n_estimators"],
                "min_samples_split": configuration[f"{prefix}min_samples_split"],
                "min_samples_leaf": configuration[f"{prefix}min_samples_leaf"],
                "max_features": configuration[f"{prefix}max_features"],
                "bootstrap": configuration[f"{prefix}bootstrap"],
                **kwargs,
            }

            return partial(RandomForestRegressorWrapper, init_params=rf_params)

__init__(init_params={})

Initialize the RandomForestRegressorWrapper.

Parameters

init_params : dict, optional A dictionary of initialization parameters for the RandomForestRegressor.

Source code in asf/predictors/random_forest.py 
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def __init__(self, init_params: Dict[str, Any] = {}):
    """
    Initialize the RandomForestRegressorWrapper.

    Parameters
    ----------
    init_params : dict, optional
        A dictionary of initialization parameters for the RandomForestRegressor.
    """
    super().__init__(RandomForestRegressor, init_params)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the Random Forest Regressor.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the Random Forest Regressor parameters.

Source code in asf/predictors/random_forest.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the Random Forest Regressor.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the Random Forest Regressor parameters.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{RandomForestRegressorWrapper.PREFIX}:"
    else:
        prefix = RandomForestRegressorWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="RandomForestRegressor")

    n_estimators = Integer(
        f"{prefix}n_estimators",
        (16, 128),
        log=True,
        default=116,
    )
    min_samples_split = Integer(
        f"{prefix}min_samples_split",
        (2, 20),
        log=False,
        default=2,
    )
    min_samples_leaf = Integer(
        f"{prefix}min_samples_leaf",
        (1, 20),
        log=False,
        default=2,
    )
    max_features = Float(
        f"{prefix}max_features",
        (0.1, 1.0),
        log=False,
        default=0.17055852159745608,
    )
    bootstrap = Categorical(
        f"{prefix}bootstrap",
        items=[True, False],
        default=False,
    )
    params = [
        n_estimators,
        min_samples_split,
        min_samples_leaf,
        max_features,
        bootstrap,
    ]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create a RandomForestRegressorWrapper instance from a configuration.

Parameters

configuration : dict A dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to override or extend the configuration.

Returns

partial A partial function to create a RandomForestRegressorWrapper instance.

Source code in asf/predictors/random_forest.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create a RandomForestRegressorWrapper instance from a configuration.

    Parameters
    ----------
    configuration : dict
        A dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to override or extend the configuration.

    Returns
    -------
    partial
        A partial function to create a RandomForestRegressorWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{RandomForestRegressorWrapper.PREFIX}:"
    else:
        prefix = RandomForestRegressorWrapper.PREFIX

    rf_params = {
        "n_estimators": configuration[f"{prefix}n_estimators"],
        "min_samples_split": configuration[f"{prefix}min_samples_split"],
        "min_samples_leaf": configuration[f"{prefix}min_samples_leaf"],
        "max_features": configuration[f"{prefix}max_features"],
        "bootstrap": configuration[f"{prefix}bootstrap"],
        **kwargs,
    }

    return partial(RandomForestRegressorWrapper, init_params=rf_params)

RankingMLP

Bases: AbstractPredictor

A placeholder for RankingMLP when PyTorch is not available. This class raises an ImportError if any method is called.

Source code in asf/predictors/ranking_mlp.py 
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class RankingMLP(AbstractPredictor):
    """
    A placeholder for RankingMLP when PyTorch is not available.
    This class raises an ImportError if any method is called.
    """

    def __init__(self, *args, **kwargs):
        raise ImportError(
            "PyTorch is not available. Please install it to use RankingMLP."
        )

SVMClassifierWrapper

Bases: SklearnWrapper

A wrapper for the Scikit-learn SVC (Support Vector Classifier) model. Provides methods to define a configuration space and create an instance of the classifier from a configuration.

Attributes

PREFIX : str Prefix used for parameter names in the configuration space.

Source code in asf/predictors/svm.py 
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class SVMClassifierWrapper(SklearnWrapper):
    """
    A wrapper for the Scikit-learn SVC (Support Vector Classifier) model.
    Provides methods to define a configuration space and create an instance
    of the classifier from a configuration.

    Attributes
    ----------
    PREFIX : str
        Prefix used for parameter names in the configuration space.
    """

    PREFIX = "svm_classifier"

    def __init__(self, init_params: Dict[str, Any] = {}):
        """
        Initialize the SVMClassifierWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Dictionary of parameters to initialize the SVC model.
        """
        super().__init__(SVC, init_params)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Define the configuration space for the SVM classifier.

            Returns
            -------
            ConfigurationSpace
                The configuration space containing hyperparameters for the SVM classifier.
            """
            if cs is None:
                cs = ConfigurationSpace(name="SVM")

            if pre_prefix != "":
                prefix = f"{pre_prefix}:{SVMClassifierWrapper.PREFIX}"
            else:
                prefix = SVMClassifierWrapper.PREFIX

            kernel = Categorical(
                f"{prefix}:kernel",
                items=["linear", "rbf", "poly", "sigmoid"],
                default="rbf",
            )
            degree = Integer(f"{prefix}:degree", (1, 128), log=True, default=1)
            coef0 = Float(
                f"{prefix}:coef0",
                (-0.5, 0.5),
                log=False,
                default=0.49070634552851977,
            )
            tol = Float(
                f"{prefix}:tol",
                (1e-4, 1e-2),
                log=True,
                default=0.0002154969698207585,
            )
            gamma = Categorical(
                f"{prefix}:gamma",
                items=["scale", "auto"],
                default="scale",
            )
            C = Float(
                f"{prefix}:C",
                (1.0, 20),
                log=True,
                default=3.2333262862494365,
            )
            shrinking = Categorical(
                f"{prefix}:shrinking",
                items=[True, False],
                default=True,
            )

            params = [kernel, degree, coef0, tol, gamma, C, shrinking]

            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create an SVMClassifierWrapper instance from a configuration.

            Parameters
            ----------
            configuration : dict
                Dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to include in the model initialization.

            Returns
            -------
            partial
                A partial function to create an SVMClassifierWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{SVMClassifierWrapper.PREFIX}"
            else:
                prefix = SVMClassifierWrapper.PREFIX

            svm_params = {
                "kernel": configuration[f"{prefix}:kernel"],
                "degree": configuration[f"{prefix}:degree"],
                "coef0": configuration[f"{prefix}:coef0"],
                "tol": configuration[f"{prefix}:tol"],
                "gamma": configuration[f"{prefix}:gamma"],
                "C": configuration[f"{prefix}:C"],
                "shrinking": configuration[f"{prefix}:shrinking"],
                **kwargs,
            }

            return partial(SVMClassifierWrapper, init_params=svm_params)

__init__(init_params={})

Initialize the SVMClassifierWrapper.

Parameters

init_params : dict, optional Dictionary of parameters to initialize the SVC model.

Source code in asf/predictors/svm.py 
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def __init__(self, init_params: Dict[str, Any] = {}):
    """
    Initialize the SVMClassifierWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Dictionary of parameters to initialize the SVC model.
    """
    super().__init__(SVC, init_params)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Define the configuration space for the SVM classifier.

Returns

ConfigurationSpace The configuration space containing hyperparameters for the SVM classifier.

Source code in asf/predictors/svm.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Define the configuration space for the SVM classifier.

    Returns
    -------
    ConfigurationSpace
        The configuration space containing hyperparameters for the SVM classifier.
    """
    if cs is None:
        cs = ConfigurationSpace(name="SVM")

    if pre_prefix != "":
        prefix = f"{pre_prefix}:{SVMClassifierWrapper.PREFIX}"
    else:
        prefix = SVMClassifierWrapper.PREFIX

    kernel = Categorical(
        f"{prefix}:kernel",
        items=["linear", "rbf", "poly", "sigmoid"],
        default="rbf",
    )
    degree = Integer(f"{prefix}:degree", (1, 128), log=True, default=1)
    coef0 = Float(
        f"{prefix}:coef0",
        (-0.5, 0.5),
        log=False,
        default=0.49070634552851977,
    )
    tol = Float(
        f"{prefix}:tol",
        (1e-4, 1e-2),
        log=True,
        default=0.0002154969698207585,
    )
    gamma = Categorical(
        f"{prefix}:gamma",
        items=["scale", "auto"],
        default="scale",
    )
    C = Float(
        f"{prefix}:C",
        (1.0, 20),
        log=True,
        default=3.2333262862494365,
    )
    shrinking = Categorical(
        f"{prefix}:shrinking",
        items=[True, False],
        default=True,
    )

    params = [kernel, degree, coef0, tol, gamma, C, shrinking]

    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an SVMClassifierWrapper instance from a configuration.

Parameters

configuration : dict Dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to include in the model initialization.

Returns

partial A partial function to create an SVMClassifierWrapper instance.

Source code in asf/predictors/svm.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create an SVMClassifierWrapper instance from a configuration.

    Parameters
    ----------
    configuration : dict
        Dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to include in the model initialization.

    Returns
    -------
    partial
        A partial function to create an SVMClassifierWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{SVMClassifierWrapper.PREFIX}"
    else:
        prefix = SVMClassifierWrapper.PREFIX

    svm_params = {
        "kernel": configuration[f"{prefix}:kernel"],
        "degree": configuration[f"{prefix}:degree"],
        "coef0": configuration[f"{prefix}:coef0"],
        "tol": configuration[f"{prefix}:tol"],
        "gamma": configuration[f"{prefix}:gamma"],
        "C": configuration[f"{prefix}:C"],
        "shrinking": configuration[f"{prefix}:shrinking"],
        **kwargs,
    }

    return partial(SVMClassifierWrapper, init_params=svm_params)

SVMRegressorWrapper

Bases: SklearnWrapper

A wrapper for the Scikit-learn SVR (Support Vector Regressor) model. Provides methods to define a configuration space and create an instance of the regressor from a configuration.

Attributes

PREFIX : str Prefix used for parameter names in the configuration space.

Source code in asf/predictors/svm.py 
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class SVMRegressorWrapper(SklearnWrapper):
    """
    A wrapper for the Scikit-learn SVR (Support Vector Regressor) model.
    Provides methods to define a configuration space and create an instance
    of the regressor from a configuration.

    Attributes
    ----------
    PREFIX : str
        Prefix used for parameter names in the configuration space.
    """

    PREFIX = "svm_regressor"

    def __init__(self, init_params: Dict[str, Any] = {}):
        """
        Initialize the SVMRegressorWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Dictionary of parameters to initialize the SVR model.
        """
        super().__init__(SVR, init_params)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Define the configuration space for the SVM regressor.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new
                ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space containing hyperparameters for the SVM regressor.
            """

            if pre_prefix != "":
                prefix = f"{pre_prefix}:{SVMRegressorWrapper.PREFIX}"
            else:
                prefix = SVMRegressorWrapper.PREFIX

            if cs is None:
                cs = ConfigurationSpace(name="SVM Regressor")

            kernel = Categorical(
                f"{prefix}:kernel",
                items=["linear", "rbf", "poly", "sigmoid"],
                default="rbf",
            )
            degree = Integer(f"{prefix}:degree", (1, 128), log=True, default=1)
            coef0 = Float(
                f"{prefix}:coef0",
                (-0.5, 0.5),
                log=False,
                default=0.0,
            )
            tol = Float(
                f"{prefix}:tol",
                (1e-4, 1e-2),
                log=True,
                default=0.001,
            )
            gamma = Categorical(
                f"{prefix}:gamma",
                items=["scale", "auto"],
                default="scale",
            )
            C = Float(f"{prefix}:C", (1.0, 20), log=True, default=1.0)
            shrinking = Categorical(
                f"{prefix}:shrinking",
                items=[True, False],
                default=True,
            )
            params = [kernel, degree, coef0, tol, gamma, C, shrinking]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
        ) -> partial:
            """
            Create an SVMRegressorWrapper instance from a configuration.

            Parameters
            ----------
            configuration : dict
                Dictionary containing the configuration parameters.
            additional_params : dict, optional
                Additional parameters to include in the model initialization.

            Returns
            -------
            partial
                A partial function to create an SVMRegressorWrapper instance.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{SVMRegressorWrapper.PREFIX}"
            else:
                prefix = SVMRegressorWrapper.PREFIX

            svr_params = {
                "kernel": configuration[f"{prefix}:kernel"],
                "degree": configuration[f"{prefix}:degree"],
                "coef0": configuration[f"{prefix}:coef0"],
                "tol": configuration[f"{prefix}:tol"],
                "gamma": configuration[f"{prefix}:gamma"],
                "C": configuration[f"{prefix}:C"],
                "shrinking": configuration[f"{prefix}:shrinking"],
                **kwargs,
            }

            return partial(SVMRegressorWrapper, init_params=svr_params)

__init__(init_params={})

Initialize the SVMRegressorWrapper.

Parameters

init_params : dict, optional Dictionary of parameters to initialize the SVR model.

Source code in asf/predictors/svm.py 
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def __init__(self, init_params: Dict[str, Any] = {}):
    """
    Initialize the SVMRegressorWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Dictionary of parameters to initialize the SVR model.
    """
    super().__init__(SVR, init_params)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Define the configuration space for the SVM regressor.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space containing hyperparameters for the SVM regressor.

Source code in asf/predictors/svm.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Define the configuration space for the SVM regressor.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new
        ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space containing hyperparameters for the SVM regressor.
    """

    if pre_prefix != "":
        prefix = f"{pre_prefix}:{SVMRegressorWrapper.PREFIX}"
    else:
        prefix = SVMRegressorWrapper.PREFIX

    if cs is None:
        cs = ConfigurationSpace(name="SVM Regressor")

    kernel = Categorical(
        f"{prefix}:kernel",
        items=["linear", "rbf", "poly", "sigmoid"],
        default="rbf",
    )
    degree = Integer(f"{prefix}:degree", (1, 128), log=True, default=1)
    coef0 = Float(
        f"{prefix}:coef0",
        (-0.5, 0.5),
        log=False,
        default=0.0,
    )
    tol = Float(
        f"{prefix}:tol",
        (1e-4, 1e-2),
        log=True,
        default=0.001,
    )
    gamma = Categorical(
        f"{prefix}:gamma",
        items=["scale", "auto"],
        default="scale",
    )
    C = Float(f"{prefix}:C", (1.0, 20), log=True, default=1.0)
    shrinking = Categorical(
        f"{prefix}:shrinking",
        items=[True, False],
        default=True,
    )
    params = [kernel, degree, coef0, tol, gamma, C, shrinking]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an SVMRegressorWrapper instance from a configuration.

Parameters

configuration : dict Dictionary containing the configuration parameters. additional_params : dict, optional Additional parameters to include in the model initialization.

Returns

partial A partial function to create an SVMRegressorWrapper instance.

Source code in asf/predictors/svm.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any], pre_prefix: str = "", **kwargs
) -> partial:
    """
    Create an SVMRegressorWrapper instance from a configuration.

    Parameters
    ----------
    configuration : dict
        Dictionary containing the configuration parameters.
    additional_params : dict, optional
        Additional parameters to include in the model initialization.

    Returns
    -------
    partial
        A partial function to create an SVMRegressorWrapper instance.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{SVMRegressorWrapper.PREFIX}"
    else:
        prefix = SVMRegressorWrapper.PREFIX

    svr_params = {
        "kernel": configuration[f"{prefix}:kernel"],
        "degree": configuration[f"{prefix}:degree"],
        "coef0": configuration[f"{prefix}:coef0"],
        "tol": configuration[f"{prefix}:tol"],
        "gamma": configuration[f"{prefix}:gamma"],
        "C": configuration[f"{prefix}:C"],
        "shrinking": configuration[f"{prefix}:shrinking"],
        **kwargs,
    }

    return partial(SVMRegressorWrapper, init_params=svr_params)

SklearnWrapper

Bases: AbstractPredictor

A generic wrapper for scikit-learn models.

This class allows scikit-learn models to be used with the ASF framework.

Methods

fit(X, Y, sample_weight=None, kwargs) Fit the model to the data. predict(X, kwargs) Predict using the model. save(file_path) Save the model to a file. load(file_path) Load the model from a file.

Source code in asf/predictors/sklearn_wrapper.py 
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class SklearnWrapper(AbstractPredictor):
    """
    A generic wrapper for scikit-learn models.

    This class allows scikit-learn models to be used with the ASF framework.

    Methods
    -------
    fit(X, Y, sample_weight=None, **kwargs)
        Fit the model to the data.
    predict(X, **kwargs)
        Predict using the model.
    save(file_path)
        Save the model to a file.
    load(file_path)
        Load the model from a file.
    """

    def __init__(self, model_class: ClassifierMixin, init_params: Dict[str, Any] = {}):
        """
        Initialize the wrapper with a scikit-learn model.

        Parameters
        ----------
        model_class : ClassifierMixin
            A scikit-learn model class.
        init_params : dict, optional
            Initialization parameters for the scikit-learn model (default is an empty dictionary).
        """
        self.model_class = model_class(**init_params)

    def fit(
        self,
        X: np.ndarray,
        Y: np.ndarray,
        sample_weight: np.ndarray = None,
        **kwargs: Any,
    ) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : np.ndarray
            Training data of shape (n_samples, n_features).
        Y : np.ndarray
            Target values of shape (n_samples,).
        sample_weight : np.ndarray, optional
            Sample weights of shape (n_samples,) (default is None).
        **kwargs : Any
            Additional keyword arguments for the scikit-learn model's `fit` method.
        """
        self.model_class.fit(X, Y, sample_weight=sample_weight, **kwargs)

    def predict(self, X: np.ndarray, **kwargs: Any) -> np.ndarray:
        """
        Predict using the model.

        Parameters
        ----------
        X : np.ndarray
            Data to predict on of shape (n_samples, n_features).
        **kwargs : Any
            Additional keyword arguments for the scikit-learn model's `predict` method.

        Returns
        -------
        np.ndarray
            Predicted values of shape (n_samples,).
        """
        return self.model_class.predict(X, **kwargs)

    def save(self, file_path: str) -> None:
        """
        Save the model to a file.

        Parameters
        ----------
        file_path : str
            Path to the file where the model will be saved.
        """
        import joblib

        joblib.dump(self, file_path)

    def load(self, file_path: str) -> "SklearnWrapper":
        """
        Load the model from a file.

        Parameters
        ----------
        file_path : str
            Path to the file from which the model will be loaded.

        Returns
        -------
        SklearnWrapper
            The loaded model.
        """
        import joblib

        return joblib.load(file_path)

__init__(model_class, init_params={})

Initialize the wrapper with a scikit-learn model.

Parameters

model_class : ClassifierMixin A scikit-learn model class. init_params : dict, optional Initialization parameters for the scikit-learn model (default is an empty dictionary).

Source code in asf/predictors/sklearn_wrapper.py 
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def __init__(self, model_class: ClassifierMixin, init_params: Dict[str, Any] = {}):
    """
    Initialize the wrapper with a scikit-learn model.

    Parameters
    ----------
    model_class : ClassifierMixin
        A scikit-learn model class.
    init_params : dict, optional
        Initialization parameters for the scikit-learn model (default is an empty dictionary).
    """
    self.model_class = model_class(**init_params)

fit(X, Y, sample_weight=None, **kwargs)

Fit the model to the data.

Parameters

X : np.ndarray Training data of shape (n_samples, n_features). Y : np.ndarray Target values of shape (n_samples,). sample_weight : np.ndarray, optional Sample weights of shape (n_samples,) (default is None). **kwargs : Any Additional keyword arguments for the scikit-learn model's fit method.

Source code in asf/predictors/sklearn_wrapper.py 
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def fit(
    self,
    X: np.ndarray,
    Y: np.ndarray,
    sample_weight: np.ndarray = None,
    **kwargs: Any,
) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : np.ndarray
        Training data of shape (n_samples, n_features).
    Y : np.ndarray
        Target values of shape (n_samples,).
    sample_weight : np.ndarray, optional
        Sample weights of shape (n_samples,) (default is None).
    **kwargs : Any
        Additional keyword arguments for the scikit-learn model's `fit` method.
    """
    self.model_class.fit(X, Y, sample_weight=sample_weight, **kwargs)

load(file_path)

Load the model from a file.

Parameters

file_path : str Path to the file from which the model will be loaded.

Returns

SklearnWrapper The loaded model.

Source code in asf/predictors/sklearn_wrapper.py 
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def load(self, file_path: str) -> "SklearnWrapper":
    """
    Load the model from a file.

    Parameters
    ----------
    file_path : str
        Path to the file from which the model will be loaded.

    Returns
    -------
    SklearnWrapper
        The loaded model.
    """
    import joblib

    return joblib.load(file_path)

predict(X, **kwargs)

Predict using the model.

Parameters

X : np.ndarray Data to predict on of shape (n_samples, n_features). **kwargs : Any Additional keyword arguments for the scikit-learn model's predict method.

Returns

np.ndarray Predicted values of shape (n_samples,).

Source code in asf/predictors/sklearn_wrapper.py 
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def predict(self, X: np.ndarray, **kwargs: Any) -> np.ndarray:
    """
    Predict using the model.

    Parameters
    ----------
    X : np.ndarray
        Data to predict on of shape (n_samples, n_features).
    **kwargs : Any
        Additional keyword arguments for the scikit-learn model's `predict` method.

    Returns
    -------
    np.ndarray
        Predicted values of shape (n_samples,).
    """
    return self.model_class.predict(X, **kwargs)

save(file_path)

Save the model to a file.

Parameters

file_path : str Path to the file where the model will be saved.

Source code in asf/predictors/sklearn_wrapper.py 
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def save(self, file_path: str) -> None:
    """
    Save the model to a file.

    Parameters
    ----------
    file_path : str
        Path to the file where the model will be saved.
    """
    import joblib

    joblib.dump(self, file_path)

XGBoostClassifierWrapper

Bases: SklearnWrapper

Wrapper for the XGBoost classifier to integrate with the ASF framework.

Source code in asf/predictors/xgboost.py 
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class XGBoostClassifierWrapper(SklearnWrapper):
    """
    Wrapper for the XGBoost classifier to integrate with the ASF framework.
    """

    PREFIX: str = "xgb_classifier"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the XGBoostClassifierWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Initialization parameters for the XGBoost classifier.
        """
        if not XGB_AVAILABLE:
            raise ImportError(
                "XGBoost is not installed. Please install it using pip install asf-lib[xgb]."
            )
        super().__init__(XGBClassifier, init_params or {})

    def fit(
        self,
        X: np.ndarray,
        Y: np.ndarray,
        sample_weight: np.ndarray = None,
        **kwargs: Any,
    ) -> None:
        """
        Fit the model to the data.

        Parameters
        ----------
        X : np.ndarray
            Training data of shape (n_samples, n_features).
        Y : np.ndarray
            Target values of shape (n_samples,).
        sample_weight : np.ndarray, optional
            Sample weights of shape (n_samples,) (default is None).
        **kwargs : Any
            Additional keyword arguments for the scikit-learn model's `fit` method.
        """
        if Y.dtype == bool:
            self.bool_labels = True
        else:
            self.bool_labels = False

        self.model_class.fit(X, Y, sample_weight=sample_weight, **kwargs)

    def predict(self, X: np.ndarray, **kwargs: Any) -> np.ndarray:
        """
        Predict using the model.

        Parameters
        ----------
        X : np.ndarray
            Data to predict on of shape (n_samples, n_features).
        **kwargs : Any
            Additional keyword arguments for the scikit-learn model's `predict` method.

        Returns
        -------
        np.ndarray
            Predicted values of shape (n_samples,).
        """
        if self.bool_labels:
            return self.model_class.predict(X, **kwargs).astype(bool)
        return self.model_class.predict(X, **kwargs)

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the XGBoost classifier.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the XGBoost parameters.
            """
            if cs is None:
                cs = ConfigurationSpace(name="XGBoost")

            if pre_prefix != "":
                prefix = f"{pre_prefix}:{XGBoostClassifierWrapper.PREFIX}"
            else:
                prefix = XGBoostClassifierWrapper.PREFIX

            booster = Constant(f"{prefix}:booster", "gbtree")
            max_depth = Integer(
                f"{prefix}:max_depth",
                (1, 20),
                log=False,
                default=13,
            )
            min_child_weight = Integer(
                f"{prefix}:min_child_weight",
                (1, 100),
                log=True,
                default=39,
            )
            colsample_bytree = Float(
                f"{prefix}:colsample_bytree",
                (0.0, 1.0),
                log=False,
                default=0.2545374925231651,
            )
            colsample_bylevel = Float(
                f"{prefix}:colsample_bylevel",
                (0.0, 1.0),
                log=False,
                default=0.6909224923784677,
            )
            lambda_param = Float(
                f"{prefix}:lambda",
                (0.001, 1000),
                log=True,
                default=31.393252465064943,
            )
            alpha = Float(
                f"{prefix}:alpha",
                (0.001, 1000),
                log=True,
                default=0.24167936088332426,
            )
            learning_rate = Float(
                f"{prefix}:learning_rate",
                (0.001, 0.1),
                log=True,
                default=0.008237525103357958,
            )

            params = [
                booster,
                max_depth,
                min_child_weight,
                colsample_bytree,
                colsample_bylevel,
                lambda_param,
                alpha,
                learning_rate,
            ]

            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any],
            pre_prefix: str = "",
            **kwargs: Any,
        ) -> Callable[..., "XGBoostClassifierWrapper"]:
            """
            Create an XGBoostClassifierWrapper from a configuration.

            Parameters
            ----------
            configuration : dict
                The configuration dictionary.
            additional_params : dict, optional
                Additional parameters to include in the configuration.

            Returns
            -------
            Callable[..., XGBoostClassifierWrapper]
                A callable that initializes the wrapper with the given configuration.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{XGBoostClassifierWrapper.PREFIX}"
            else:
                prefix = XGBoostClassifierWrapper.PREFIX

            xgb_params = {
                "booster": configuration[f"{prefix}:booster"],
                "max_depth": configuration[f"{prefix}:max_depth"],
                "min_child_weight": configuration[f"{prefix}:min_child_weight"],
                "colsample_bytree": configuration[f"{prefix}:colsample_bytree"],
                "colsample_bylevel": configuration[f"{prefix}:colsample_bylevel"],
                "lambda": configuration[f"{prefix}:lambda"],
                "alpha": configuration[f"{prefix}:alpha"],
                "learning_rate": configuration[f"{prefix}:learning_rate"],
                **kwargs,
            }

            return partial(XGBoostClassifierWrapper, init_params=xgb_params)

__init__(init_params=None)

Initialize the XGBoostClassifierWrapper.

Parameters

init_params : dict, optional Initialization parameters for the XGBoost classifier.

Source code in asf/predictors/xgboost.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the XGBoostClassifierWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Initialization parameters for the XGBoost classifier.
    """
    if not XGB_AVAILABLE:
        raise ImportError(
            "XGBoost is not installed. Please install it using pip install asf-lib[xgb]."
        )
    super().__init__(XGBClassifier, init_params or {})

fit(X, Y, sample_weight=None, **kwargs)

Fit the model to the data.

Parameters

X : np.ndarray Training data of shape (n_samples, n_features). Y : np.ndarray Target values of shape (n_samples,). sample_weight : np.ndarray, optional Sample weights of shape (n_samples,) (default is None). **kwargs : Any Additional keyword arguments for the scikit-learn model's fit method.

Source code in asf/predictors/xgboost.py 
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def fit(
    self,
    X: np.ndarray,
    Y: np.ndarray,
    sample_weight: np.ndarray = None,
    **kwargs: Any,
) -> None:
    """
    Fit the model to the data.

    Parameters
    ----------
    X : np.ndarray
        Training data of shape (n_samples, n_features).
    Y : np.ndarray
        Target values of shape (n_samples,).
    sample_weight : np.ndarray, optional
        Sample weights of shape (n_samples,) (default is None).
    **kwargs : Any
        Additional keyword arguments for the scikit-learn model's `fit` method.
    """
    if Y.dtype == bool:
        self.bool_labels = True
    else:
        self.bool_labels = False

    self.model_class.fit(X, Y, sample_weight=sample_weight, **kwargs)

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the XGBoost classifier.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the XGBoost parameters.

Source code in asf/predictors/xgboost.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the XGBoost classifier.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the XGBoost parameters.
    """
    if cs is None:
        cs = ConfigurationSpace(name="XGBoost")

    if pre_prefix != "":
        prefix = f"{pre_prefix}:{XGBoostClassifierWrapper.PREFIX}"
    else:
        prefix = XGBoostClassifierWrapper.PREFIX

    booster = Constant(f"{prefix}:booster", "gbtree")
    max_depth = Integer(
        f"{prefix}:max_depth",
        (1, 20),
        log=False,
        default=13,
    )
    min_child_weight = Integer(
        f"{prefix}:min_child_weight",
        (1, 100),
        log=True,
        default=39,
    )
    colsample_bytree = Float(
        f"{prefix}:colsample_bytree",
        (0.0, 1.0),
        log=False,
        default=0.2545374925231651,
    )
    colsample_bylevel = Float(
        f"{prefix}:colsample_bylevel",
        (0.0, 1.0),
        log=False,
        default=0.6909224923784677,
    )
    lambda_param = Float(
        f"{prefix}:lambda",
        (0.001, 1000),
        log=True,
        default=31.393252465064943,
    )
    alpha = Float(
        f"{prefix}:alpha",
        (0.001, 1000),
        log=True,
        default=0.24167936088332426,
    )
    learning_rate = Float(
        f"{prefix}:learning_rate",
        (0.001, 0.1),
        log=True,
        default=0.008237525103357958,
    )

    params = [
        booster,
        max_depth,
        min_child_weight,
        colsample_bytree,
        colsample_bylevel,
        lambda_param,
        alpha,
        learning_rate,
    ]

    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an XGBoostClassifierWrapper from a configuration.

Parameters

configuration : dict The configuration dictionary. additional_params : dict, optional Additional parameters to include in the configuration.

Returns

Callable[..., XGBoostClassifierWrapper] A callable that initializes the wrapper with the given configuration.

Source code in asf/predictors/xgboost.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any],
    pre_prefix: str = "",
    **kwargs: Any,
) -> Callable[..., "XGBoostClassifierWrapper"]:
    """
    Create an XGBoostClassifierWrapper from a configuration.

    Parameters
    ----------
    configuration : dict
        The configuration dictionary.
    additional_params : dict, optional
        Additional parameters to include in the configuration.

    Returns
    -------
    Callable[..., XGBoostClassifierWrapper]
        A callable that initializes the wrapper with the given configuration.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{XGBoostClassifierWrapper.PREFIX}"
    else:
        prefix = XGBoostClassifierWrapper.PREFIX

    xgb_params = {
        "booster": configuration[f"{prefix}:booster"],
        "max_depth": configuration[f"{prefix}:max_depth"],
        "min_child_weight": configuration[f"{prefix}:min_child_weight"],
        "colsample_bytree": configuration[f"{prefix}:colsample_bytree"],
        "colsample_bylevel": configuration[f"{prefix}:colsample_bylevel"],
        "lambda": configuration[f"{prefix}:lambda"],
        "alpha": configuration[f"{prefix}:alpha"],
        "learning_rate": configuration[f"{prefix}:learning_rate"],
        **kwargs,
    }

    return partial(XGBoostClassifierWrapper, init_params=xgb_params)

predict(X, **kwargs)

Predict using the model.

Parameters

X : np.ndarray Data to predict on of shape (n_samples, n_features). **kwargs : Any Additional keyword arguments for the scikit-learn model's predict method.

Returns

np.ndarray Predicted values of shape (n_samples,).

Source code in asf/predictors/xgboost.py 
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def predict(self, X: np.ndarray, **kwargs: Any) -> np.ndarray:
    """
    Predict using the model.

    Parameters
    ----------
    X : np.ndarray
        Data to predict on of shape (n_samples, n_features).
    **kwargs : Any
        Additional keyword arguments for the scikit-learn model's `predict` method.

    Returns
    -------
    np.ndarray
        Predicted values of shape (n_samples,).
    """
    if self.bool_labels:
        return self.model_class.predict(X, **kwargs).astype(bool)
    return self.model_class.predict(X, **kwargs)

XGBoostRegressorWrapper

Bases: SklearnWrapper

Wrapper for the XGBoost regressor to integrate with the ASF framework.

Source code in asf/predictors/xgboost.py 
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class XGBoostRegressorWrapper(SklearnWrapper):
    """
    Wrapper for the XGBoost regressor to integrate with the ASF framework.
    """

    PREFIX: str = "xgb_regressor"

    def __init__(self, init_params: Optional[Dict[str, Any]] = None):
        """
        Initialize the XGBoostRegressorWrapper.

        Parameters
        ----------
        init_params : dict, optional
            Initialization parameters for the XGBoost regressor.
        """
        super().__init__(XGBRegressor, init_params or {})

    if CONFIGSPACE_AVAILABLE:

        @staticmethod
        def get_configuration_space(
            cs: Optional[ConfigurationSpace] = None,
            pre_prefix: str = "",
            parent_param: Optional[Hyperparameter] = None,
            parent_value: Optional[str] = None,
        ) -> ConfigurationSpace:
            """
            Get the configuration space for the XGBoost regressor.

            Parameters
            ----------
            cs : ConfigurationSpace, optional
                The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

            Returns
            -------
            ConfigurationSpace
                The configuration space with the XGBoost parameters.
            """
            if cs is None:
                cs = ConfigurationSpace(name="XGBoostRegressor")

            if pre_prefix != "":
                prefix = f"{pre_prefix}:{XGBoostRegressorWrapper.PREFIX}"
            else:
                prefix = XGBoostRegressorWrapper.PREFIX

            booster = Constant(f"{prefix}:booster", "gbtree")
            max_depth = Integer(
                f"{prefix}:max_depth",
                (1, 20),
                log=False,
                default=13,
            )
            min_child_weight = Integer(
                f"{prefix}:min_child_weight",
                (1, 100),
                log=True,
                default=39,
            )
            colsample_bytree = Float(
                f"{prefix}:colsample_bytree",
                (0.0, 1.0),
                log=False,
                default=0.2545374925231651,
            )
            colsample_bylevel = Float(
                f"{prefix}:colsample_bylevel",
                (0.0, 1.0),
                log=False,
                default=0.6909224923784677,
            )
            lambda_param = Float(
                f"{prefix}:lambda",
                (0.001, 1000),
                log=True,
                default=31.393252465064943,
            )
            alpha = Float(
                f"{prefix}:alpha",
                (0.001, 1000),
                log=True,
                default=0.24167936088332426,
            )
            learning_rate = Float(
                f"{prefix}:learning_rate",
                (0.001, 0.1),
                log=True,
                default=0.008237525103357958,
            )

            params = [
                booster,
                max_depth,
                min_child_weight,
                colsample_bytree,
                colsample_bylevel,
                lambda_param,
                alpha,
                learning_rate,
            ]
            if parent_param is not None:
                conditions = [
                    EqualsCondition(
                        child=param,
                        parent=parent_param,
                        value=parent_value,
                    )
                    for param in params
                ]
            else:
                conditions = []

            cs.add(params + conditions)

            return cs

        @staticmethod
        def get_from_configuration(
            configuration: Dict[str, Any],
            pre_prefix: str = "",
            **kwargs,
        ) -> Callable[..., "XGBoostRegressorWrapper"]:
            """
            Create an XGBoostRegressorWrapper from a configuration.

            Parameters
            ----------
            configuration : dict
                The configuration dictionary.
            additional_params : dict, optional
                Additional parameters to include in the configuration.

            Returns
            -------
            Callable[..., XGBoostRegressorWrapper]
                A callable that initializes the wrapper with the given configuration.
            """
            if pre_prefix != "":
                prefix = f"{pre_prefix}:{XGBoostRegressorWrapper.PREFIX}"
            else:
                prefix = XGBoostRegressorWrapper.PREFIX

            xgb_params = {
                "booster": configuration[f"{prefix}:booster"],
                "max_depth": configuration[f"{prefix}:max_depth"],
                "min_child_weight": configuration[f"{prefix}:min_child_weight"],
                "colsample_bytree": configuration[f"{prefix}:colsample_bytree"],
                "colsample_bylevel": configuration[f"{prefix}:colsample_bylevel"],
                "lambda": configuration[f"{prefix}:lambda"],
                "alpha": configuration[f"{prefix}:alpha"],
                "learning_rate": configuration[f"{prefix}:learning_rate"],
                **kwargs,
            }

            return partial(XGBoostRegressorWrapper, init_params=xgb_params)

__init__(init_params=None)

Initialize the XGBoostRegressorWrapper.

Parameters

init_params : dict, optional Initialization parameters for the XGBoost regressor.

Source code in asf/predictors/xgboost.py 
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def __init__(self, init_params: Optional[Dict[str, Any]] = None):
    """
    Initialize the XGBoostRegressorWrapper.

    Parameters
    ----------
    init_params : dict, optional
        Initialization parameters for the XGBoost regressor.
    """
    super().__init__(XGBRegressor, init_params or {})

get_configuration_space(cs=None, pre_prefix='', parent_param=None, parent_value=None) staticmethod

Get the configuration space for the XGBoost regressor.

Parameters

cs : ConfigurationSpace, optional The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

Returns

ConfigurationSpace The configuration space with the XGBoost parameters.

Source code in asf/predictors/xgboost.py 
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@staticmethod
def get_configuration_space(
    cs: Optional[ConfigurationSpace] = None,
    pre_prefix: str = "",
    parent_param: Optional[Hyperparameter] = None,
    parent_value: Optional[str] = None,
) -> ConfigurationSpace:
    """
    Get the configuration space for the XGBoost regressor.

    Parameters
    ----------
    cs : ConfigurationSpace, optional
        The configuration space to add the parameters to. If None, a new ConfigurationSpace will be created.

    Returns
    -------
    ConfigurationSpace
        The configuration space with the XGBoost parameters.
    """
    if cs is None:
        cs = ConfigurationSpace(name="XGBoostRegressor")

    if pre_prefix != "":
        prefix = f"{pre_prefix}:{XGBoostRegressorWrapper.PREFIX}"
    else:
        prefix = XGBoostRegressorWrapper.PREFIX

    booster = Constant(f"{prefix}:booster", "gbtree")
    max_depth = Integer(
        f"{prefix}:max_depth",
        (1, 20),
        log=False,
        default=13,
    )
    min_child_weight = Integer(
        f"{prefix}:min_child_weight",
        (1, 100),
        log=True,
        default=39,
    )
    colsample_bytree = Float(
        f"{prefix}:colsample_bytree",
        (0.0, 1.0),
        log=False,
        default=0.2545374925231651,
    )
    colsample_bylevel = Float(
        f"{prefix}:colsample_bylevel",
        (0.0, 1.0),
        log=False,
        default=0.6909224923784677,
    )
    lambda_param = Float(
        f"{prefix}:lambda",
        (0.001, 1000),
        log=True,
        default=31.393252465064943,
    )
    alpha = Float(
        f"{prefix}:alpha",
        (0.001, 1000),
        log=True,
        default=0.24167936088332426,
    )
    learning_rate = Float(
        f"{prefix}:learning_rate",
        (0.001, 0.1),
        log=True,
        default=0.008237525103357958,
    )

    params = [
        booster,
        max_depth,
        min_child_weight,
        colsample_bytree,
        colsample_bylevel,
        lambda_param,
        alpha,
        learning_rate,
    ]
    if parent_param is not None:
        conditions = [
            EqualsCondition(
                child=param,
                parent=parent_param,
                value=parent_value,
            )
            for param in params
        ]
    else:
        conditions = []

    cs.add(params + conditions)

    return cs

get_from_configuration(configuration, pre_prefix='', **kwargs) staticmethod

Create an XGBoostRegressorWrapper from a configuration.

Parameters

configuration : dict The configuration dictionary. additional_params : dict, optional Additional parameters to include in the configuration.

Returns

Callable[..., XGBoostRegressorWrapper] A callable that initializes the wrapper with the given configuration.

Source code in asf/predictors/xgboost.py 
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@staticmethod
def get_from_configuration(
    configuration: Dict[str, Any],
    pre_prefix: str = "",
    **kwargs,
) -> Callable[..., "XGBoostRegressorWrapper"]:
    """
    Create an XGBoostRegressorWrapper from a configuration.

    Parameters
    ----------
    configuration : dict
        The configuration dictionary.
    additional_params : dict, optional
        Additional parameters to include in the configuration.

    Returns
    -------
    Callable[..., XGBoostRegressorWrapper]
        A callable that initializes the wrapper with the given configuration.
    """
    if pre_prefix != "":
        prefix = f"{pre_prefix}:{XGBoostRegressorWrapper.PREFIX}"
    else:
        prefix = XGBoostRegressorWrapper.PREFIX

    xgb_params = {
        "booster": configuration[f"{prefix}:booster"],
        "max_depth": configuration[f"{prefix}:max_depth"],
        "min_child_weight": configuration[f"{prefix}:min_child_weight"],
        "colsample_bytree": configuration[f"{prefix}:colsample_bytree"],
        "colsample_bylevel": configuration[f"{prefix}:colsample_bylevel"],
        "lambda": configuration[f"{prefix}:lambda"],
        "alpha": configuration[f"{prefix}:alpha"],
        "learning_rate": configuration[f"{prefix}:learning_rate"],
        **kwargs,
    }

    return partial(XGBoostRegressorWrapper, init_params=xgb_params)