Module panama.ml.benchmark.regression

Classes

class BenchmarkMeanRegressor

A benchmark regressor that predicts the mean of the training targets

Initializes the regressor with the mean strategy

Expand source code 
class BenchmarkMeanRegressor(DummyRegressor):
    """A benchmark regressor that predicts the mean of the training targets"""

    def __init__(self):
        """Initializes the regressor with the mean strategy"""
        super().__init__(strategy="mean")

Ancestors

  • sklearn.dummy.DummyRegressor
  • sklearn.base.MultiOutputMixin
  • sklearn.base.RegressorMixin
  • sklearn.base.BaseEstimator
  • sklearn.utils._metadata_requests._MetadataRequester

Methods

def set_fit_request(self: BenchmarkMeanRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.

Returns

self : object
The updated object.
def set_predict_request(self: BenchmarkMeanRegressor, *, return_std: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

return_std : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for return_std parameter in predict.

Returns

self : object
The updated object.
def set_score_request(self: BenchmarkMeanRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

Returns

self : object
The updated object.
class BenchmarkMedianRegressor

A benchmark regressor that predicts the median of the training targets

Initializes the regressor with the median strategy

Expand source code 
class BenchmarkMedianRegressor(DummyRegressor):
    """A benchmark regressor that predicts the median of the training targets"""

    def __init__(self):
        """Initializes the regressor with the median strategy"""
        super().__init__(strategy="median")

Ancestors

  • sklearn.dummy.DummyRegressor
  • sklearn.base.MultiOutputMixin
  • sklearn.base.RegressorMixin
  • sklearn.base.BaseEstimator
  • sklearn.utils._metadata_requests._MetadataRequester

Methods

def set_fit_request(self: BenchmarkMedianRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.

Returns

self : object
The updated object.
def set_predict_request(self: BenchmarkMedianRegressor, *, return_std: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

return_std : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for return_std parameter in predict.

Returns

self : object
The updated object.
def set_score_request(self: BenchmarkMedianRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

Returns

self : object
The updated object.
class BenchmarkQuantileRegressor (q: float = 0.5)

A benchmark regressor that predicts a quantile of the training targets

Initializes the regressor with the quantile strategy and the specified quantile.

Args

q
The quantile to predict. Defaults to 0.5 (median).
Expand source code 
class BenchmarkQuantileRegressor(DummyRegressor):
    """A benchmark regressor that predicts a quantile of the training targets"""

    def __init__(self, q: float = 0.5):
        """Initializes the regressor with the `quantile` strategy and the specified quantile.

        Args:
            q: The quantile to predict. Defaults to 0.5 (median).
        """
        super().__init__(strategy="quantile", quantile=q)

Ancestors

  • sklearn.dummy.DummyRegressor
  • sklearn.base.MultiOutputMixin
  • sklearn.base.RegressorMixin
  • sklearn.base.BaseEstimator
  • sklearn.utils._metadata_requests._MetadataRequester

Methods

def set_fit_request(self: BenchmarkQuantileRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.

Returns

self : object
The updated object.
def set_predict_request(self: BenchmarkQuantileRegressor, *, return_std: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

return_std : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for return_std parameter in predict.

Returns

self : object
The updated object.
def set_score_request(self: BenchmarkQuantileRegressor, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see :func:sklearn.set_config). Please see :ref:User Guide <metadata_routing> on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version: 1.3

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a :class:pipeline.Pipeline. Otherwise it has no effect.

Parameters

sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.

Returns

self : object
The updated object.