regression

MaxError(feature=None)

Bases: SelectMixin, LazyMixin, Metric

Max error regression loss.

As defined by scikit-learn.

Initialize MaxError metric.

Parameters:

Name	Type	Description	Default
feature	str | None	The feature to calculate the metric for. If None, the metric expects a single feature in the data.	None

Source code in src/flowcean/sklearn/metrics/regression.py

def __init__(self, feature: str | None = None) -> None:
    """Initialize MaxError metric.

    Args:
        feature: The feature to calculate the metric for. If None, the
            metric expects a single feature in the data.
    """
    features = [feature] if feature is not None else None
    super().__init__(features=features)

MeanAbsoluteError(features=None, multioutput='raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute error (MAE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None
multioutput	Literal['raw_values', 'uniform_average']	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	'raw_values'

Source code in src/flowcean/sklearn/metrics/regression.py

def __init__(
    self,
    features: list[str] | None = None,
    multioutput: Literal[
        "raw_values",
        "uniform_average",
    ] = "raw_values",
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
        multioutput: Defines how to aggregate multiple output values.
            See [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html)
            for details.
    """
    super().__init__(features=features, multioutput=multioutput)

MeanAbsolutePercentageError(features=None, multioutput='raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute percentage error (MAPE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None
multioutput	Literal['raw_values', 'uniform_average']	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	'raw_values'

Source code in src/flowcean/sklearn/metrics/regression.py

def __init__(
    self,
    features: list[str] | None = None,
    multioutput: Literal[
        "raw_values",
        "uniform_average",
    ] = "raw_values",
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
        multioutput: Defines how to aggregate multiple output values.
            See [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html)
            for details.
    """
    super().__init__(features=features, multioutput=multioutput)

MeanSquaredError(features=None, multioutput='raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean squared error (MSE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None
multioutput	Literal['raw_values', 'uniform_average']	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	'raw_values'

Source code in src/flowcean/sklearn/metrics/regression.py

def __init__(
    self,
    features: list[str] | None = None,
    multioutput: Literal[
        "raw_values",
        "uniform_average",
    ] = "raw_values",
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
        multioutput: Defines how to aggregate multiple output values.
            See [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html)
            for details.
    """
    super().__init__(features=features, multioutput=multioutput)

R2Score(features=None, multioutput='raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

R^2 (coefficient of determination) regression score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None
multioutput	Literal['raw_values', 'uniform_average']	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	'raw_values'

Source code in src/flowcean/sklearn/metrics/regression.py

def __init__(
    self,
    features: list[str] | None = None,
    multioutput: Literal[
        "raw_values",
        "uniform_average",
    ] = "raw_values",
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
        multioutput: Defines how to aggregate multiple output values.
            See [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html)
            for details.
    """
    super().__init__(features=features, multioutput=multioutput)