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ElasticNetRegression

Bases: Regressor

Elastic net regression.

Parameters:

Name Type Description Default
alpha float

Controls the regularization of the model. The higher the value, the more regularized it becomes.

 1.0
lasso_ratio float

Number between 0 and 1 that controls the ratio between Lasso and Ridge regularization. If 0, only Ridge regularization is used. If 1, only Lasso regularization is used.

 0.5

Raises:

Type Description
OutOfBoundsError

If alpha is negative or lasso_ratio is not between 0 and 1.
Source code in src/safeds/ml/classical/regression/_elastic_net_regression.py 
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class ElasticNetRegression(Regressor):
    """Elastic net regression.

    Parameters
    ----------
    alpha : float
        Controls the regularization of the model. The higher the value, the more regularized it becomes.
    lasso_ratio: float
        Number between 0 and 1 that controls the ratio between Lasso and Ridge regularization. If 0, only Ridge
        regularization is used. If 1, only Lasso regularization is used.

    Raises
    ------
    OutOfBoundsError
        If `alpha` is negative or `lasso_ratio` is not between 0 and 1.
    """

    def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
        # Validation
        if alpha < 0:
            raise OutOfBoundsError(alpha, name="alpha", lower_bound=ClosedBound(0))
        if alpha == 0:
            warn(
                (
                    "Setting alpha to zero makes this model equivalent to LinearRegression. You should use "
                    "LinearRegression instead for better numerical stability."
                ),
                UserWarning,
                stacklevel=2,
            )
        if lasso_ratio < 0 or lasso_ratio > 1:
            raise OutOfBoundsError(
                lasso_ratio,
                name="lasso_ratio",
                lower_bound=ClosedBound(0),
                upper_bound=ClosedBound(1),
            )
        elif lasso_ratio == 0:
            warnings.warn(
                (
                    "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
                    " Use RidgeRegression instead for better numerical stability."
                ),
                stacklevel=2,
            )
        elif lasso_ratio == 1:
            warnings.warn(
                (
                    "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
                    " Use LassoRegression instead for better numerical stability."
                ),
                stacklevel=2,
            )

        # Hyperparameters
        self._alpha = alpha
        self._lasso_ratio = lasso_ratio

        # Internal state
        self._wrapped_regressor: sk_ElasticNet | None = None
        self._feature_names: list[str] | None = None
        self._target_name: str | None = None

    @property
    def alpha(self) -> float:
        """
        Get the regularization of the model.

        Returns
        -------
        result: float
            The regularization of the model.
        """
        return self._alpha

    @property
    def lasso_ratio(self) -> float:
        """
        Get the ratio between Lasso and Ridge regularization.

        Returns
        -------
        result: float
            The ratio between Lasso and Ridge regularization.
        """
        return self._lasso_ratio

    def fit(self, training_set: TaggedTable) -> ElasticNetRegression:
        """
        Create a copy of this regressor and fit it with the given training data.

        This regressor is not modified.

        Parameters
        ----------
        training_set : TaggedTable
            The training data containing the feature and target vectors.

        Returns
        -------
        fitted_regressor : ElasticNetRegression
            The fitted regressor.

        Raises
        ------
        LearningError
            If the training data contains invalid values or if the training failed.
        UntaggedTableError
            If the table is untagged.
        NonNumericColumnError
            If the training data contains non-numerical values.
        MissingValuesColumnError
            If the training data contains missing values.
        DatasetMissesDataError
            If the training data contains no rows.
        """
        wrapped_regressor = self._get_sklearn_regressor()
        fit(wrapped_regressor, training_set)

        result = ElasticNetRegression(alpha=self._alpha, lasso_ratio=self._lasso_ratio)
        result._wrapped_regressor = wrapped_regressor
        result._feature_names = training_set.features.column_names
        result._target_name = training_set.target.name

        return result

    def predict(self, dataset: Table) -> TaggedTable:
        """
        Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

        Parameters
        ----------
        dataset : Table
            The dataset containing the feature vectors.

        Returns
        -------
        table : TaggedTable
            A dataset containing the given feature vectors and the predicted target vector.

        Raises
        ------
        ModelNotFittedError
            If the model has not been fitted yet.
        DatasetContainsTargetError
            If the dataset contains the target column already.
        DatasetMissesFeaturesError
            If the dataset misses feature columns.
        PredictionError
            If predicting with the given dataset failed.
        NonNumericColumnError
            If the dataset contains non-numerical values.
        MissingValuesColumnError
            If the dataset contains missing values.
        DatasetMissesDataError
            If the dataset contains no rows.
        """
        return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)

    def is_fitted(self) -> bool:
        """
        Check if the regressor is fitted.

        Returns
        -------
        is_fitted : bool
            Whether the regressor is fitted.
        """
        return self._wrapped_regressor is not None

    def _get_sklearn_regressor(self) -> RegressorMixin:
        """
        Return a new wrapped Regressor from sklearn.

        Returns
        -------
        wrapped_regressor: RegressorMixin
            The sklearn Regressor.
        """
        return sk_ElasticNet(alpha=self._alpha, l1_ratio=self._lasso_ratio)

alpha: float property

Get the regularization of the model.

Returns:

Name Type Description
result float

The regularization of the model.

lasso_ratio: float property

Get the ratio between Lasso and Ridge regularization.

Returns:

Name Type Description
result float

The ratio between Lasso and Ridge regularization.

__init__(*, alpha=1.0, lasso_ratio=0.5)

Source code in src/safeds/ml/classical/regression/_elastic_net_regression.py 
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def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
    # Validation
    if alpha < 0:
        raise OutOfBoundsError(alpha, name="alpha", lower_bound=ClosedBound(0))
    if alpha == 0:
        warn(
            (
                "Setting alpha to zero makes this model equivalent to LinearRegression. You should use "
                "LinearRegression instead for better numerical stability."
            ),
            UserWarning,
            stacklevel=2,
        )
    if lasso_ratio < 0 or lasso_ratio > 1:
        raise OutOfBoundsError(
            lasso_ratio,
            name="lasso_ratio",
            lower_bound=ClosedBound(0),
            upper_bound=ClosedBound(1),
        )
    elif lasso_ratio == 0:
        warnings.warn(
            (
                "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
                " Use RidgeRegression instead for better numerical stability."
            ),
            stacklevel=2,
        )
    elif lasso_ratio == 1:
        warnings.warn(
            (
                "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
                " Use LassoRegression instead for better numerical stability."
            ),
            stacklevel=2,
        )

    # Hyperparameters
    self._alpha = alpha
    self._lasso_ratio = lasso_ratio

    # Internal state
    self._wrapped_regressor: sk_ElasticNet | None = None
    self._feature_names: list[str] | None = None
    self._target_name: str | None = None

fit(training_set)

Create a copy of this regressor and fit it with the given training data.

This regressor is not modified.

Parameters:

Name Type Description Default
training_set TaggedTable

The training data containing the feature and target vectors.

 required

Returns:

Name Type Description
fitted_regressor ElasticNetRegression

The fitted regressor.

Raises:

Type Description
LearningError

If the training data contains invalid values or if the training failed.
UntaggedTableError

If the table is untagged.
NonNumericColumnError

If the training data contains non-numerical values.
MissingValuesColumnError

If the training data contains missing values.
DatasetMissesDataError

If the training data contains no rows.
Source code in src/safeds/ml/classical/regression/_elastic_net_regression.py 
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def fit(self, training_set: TaggedTable) -> ElasticNetRegression:
    """
    Create a copy of this regressor and fit it with the given training data.

    This regressor is not modified.

    Parameters
    ----------
    training_set : TaggedTable
        The training data containing the feature and target vectors.

    Returns
    -------
    fitted_regressor : ElasticNetRegression
        The fitted regressor.

    Raises
    ------
    LearningError
        If the training data contains invalid values or if the training failed.
    UntaggedTableError
        If the table is untagged.
    NonNumericColumnError
        If the training data contains non-numerical values.
    MissingValuesColumnError
        If the training data contains missing values.
    DatasetMissesDataError
        If the training data contains no rows.
    """
    wrapped_regressor = self._get_sklearn_regressor()
    fit(wrapped_regressor, training_set)

    result = ElasticNetRegression(alpha=self._alpha, lasso_ratio=self._lasso_ratio)
    result._wrapped_regressor = wrapped_regressor
    result._feature_names = training_set.features.column_names
    result._target_name = training_set.target.name

    return result

is_fitted()

Check if the regressor is fitted.

Returns:

Name Type Description
is_fitted bool

Whether the regressor is fitted.
Source code in src/safeds/ml/classical/regression/_elastic_net_regression.py 
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def is_fitted(self) -> bool:
    """
    Check if the regressor is fitted.

    Returns
    -------
    is_fitted : bool
        Whether the regressor is fitted.
    """
    return self._wrapped_regressor is not None

predict(dataset)

Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

Parameters:

Name Type Description Default
dataset Table

The dataset containing the feature vectors.

 required

Returns:

Name Type Description
table TaggedTable

A dataset containing the given feature vectors and the predicted target vector.

Raises:

Type Description
ModelNotFittedError

If the model has not been fitted yet.
DatasetContainsTargetError

If the dataset contains the target column already.
DatasetMissesFeaturesError

If the dataset misses feature columns.
PredictionError

If predicting with the given dataset failed.
NonNumericColumnError

If the dataset contains non-numerical values.
MissingValuesColumnError

If the dataset contains missing values.
DatasetMissesDataError

If the dataset contains no rows.
Source code in src/safeds/ml/classical/regression/_elastic_net_regression.py 
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def predict(self, dataset: Table) -> TaggedTable:
    """
    Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

    Parameters
    ----------
    dataset : Table
        The dataset containing the feature vectors.

    Returns
    -------
    table : TaggedTable
        A dataset containing the given feature vectors and the predicted target vector.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    DatasetContainsTargetError
        If the dataset contains the target column already.
    DatasetMissesFeaturesError
        If the dataset misses feature columns.
    PredictionError
        If predicting with the given dataset failed.
    NonNumericColumnError
        If the dataset contains non-numerical values.
    MissingValuesColumnError
        If the dataset contains missing values.
    DatasetMissesDataError
        If the dataset contains no rows.
    """
    return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)