models.sklearn_wrap

models.sklearn_wrap#

Module: models.sklearn_wrap#

Inheritance diagram for ISLP.models.sklearn_wrap:

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Wrappers for statsmodels#

Classes#

sklearn_selected#

class ISLP.models.sklearn_wrap.sklearn_selected(model_type, strategy, model_args={}, scoring=None, cv=None)#

Bases: sklearn_sm

Parameters:
model_typeclass

A model type from statsmodels, e.g. sm.OLS or sm.GLM

strategyStrategy

A search strategy

model_argsdict (optional)

Arguments passed to the statsmodels model.

scoringstr or callable, default=None

A str (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y) which should return only a single value.

cv: int, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy.

Methods

fit(X, y)

First, select a model with design matrix determined from X and response y.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Compute predictions for design matrix X in selected model.

score(X, y[, sample_weight])

Score a statsmodel model with test design matrix X and test response y.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
__init__(model_type, strategy, model_args={}, scoring=None, cv=None)#
fit(X, y)#

First, select a model with design matrix determined from X and response y. Then, fit selected model.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)#

Compute predictions for design matrix X in selected model.

Parameters:
Xarray-like

Design matrix.

score(X, y, sample_weight=None)#

Score a statsmodel model with test design matrix X and test response y.

If model_type is OLS, use MSE. For a GLM this computes (average) deviance.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

sample_weightNone

Optional sample weights.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → sklearn_selected#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide 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 Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

sklearn_selection_path#

class ISLP.models.sklearn_wrap.sklearn_selection_path(model_type, strategy, model_args={}, scoring=None, cv=None)#

Bases: sklearn_sm

Parameters:
model_typeclass

A model type from statsmodels, e.g. sm.OLS or sm.GLM

strategyStrategy

A search strategy

model_argsdict (optional)

Arguments passed to the statsmodels model.

scoringstr or callable, default=None

A str (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y) which should return only a single value.

cv: int, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy.

Methods

fit(X, y)

First, select a model with design matrix determined from X and response y.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Compute predictions along selection path for design matrix X.

score(X, y[, sample_weight])

Score a statsmodel model with test design matrix X and test response y.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
__init__(model_type, strategy, model_args={}, scoring=None, cv=None)#
fit(X, y)#

First, select a model with design matrix determined from X and response y. Then, fit selected model.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)#

Compute predictions along selection path for design matrix X.

Parameters:
Xarray-like

Design matrix.

score(X, y, sample_weight=None)#

Score a statsmodel model with test design matrix X and test response y.

If model_type is OLS, use MSE. For a GLM this computes (average) deviance.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

sample_weightNone

Optional sample weights.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → sklearn_selection_path#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide 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 Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

sklearn_sm#

class ISLP.models.sklearn_wrap.sklearn_sm(model_type, model_spec=None, model_args={})#

Bases: BaseEstimator, RegressorMixin

Parameters:
model_type: class

A model type from statsmodels, e.g. sm.OLS or sm.GLM

model_spec: ModelSpec

Specify the design matrix.

model_args: dict (optional)

Arguments passed to the statsmodels model.

Notes

If model_str is present, then X and Y are presumed to be pandas objects that are placed into a dataframe before formula is evaluated. This affects fit and predict methods.

Methods

fit(X, y)

Fit a statsmodel model with design matrix determined from X and response y.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Compute predictions for design matrix X.

score(X, y[, sample_weight])

Score a statsmodel model with test design matrix X and test response y.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
__init__(model_type, model_spec=None, model_args={})#
fit(X, y)#

Fit a statsmodel model with design matrix determined from X and response y.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)#

Compute predictions for design matrix X.

Parameters:
Xarray-like

Design matrix.

score(X, y, sample_weight=None)#

Score a statsmodel model with test design matrix X and test response y.

If model_type is OLS, use MSE. For a GLM this computes (average) deviance.

Parameters:
Xarray-like

Design matrix.

yarray-like

Response vector.

sample_weightNone

Optional sample weights.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → sklearn_sm#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide 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 Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

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