transforms

transforms#

Module: transforms#

Inheritance diagram for ISLP.transforms:

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Transformers#

This module defines some sklearn transformers useful for defining flexible regression models for single features, as well as interactions between sets of columns.

  • Poly: for orthogonalized polynomial regression

  • Interaction: computing pairwise product for interactions

  • BSpline: arbitrary degree B-splines

  • NaturalSpline: natural cubic splines

Classes#

BSpline#

class ISLP.transforms.BSpline(degree=3, intercept=False, lower_bound=None, upper_bound=None, internal_knots=None, df=None, ext=0)#

Bases: TransformerMixin, BaseEstimator

Parameters:
degreeint, default=3

Degree of polynomial.

interceptbool, default=False

If False, a column of basis is dropped so that by adding an intercept column design stays full rank.

lower_boundfloat, default=None

Lower boundary not. Will be set to minimal value if not supplied.

upper_boundfloat, default=None

Upper boundary not. Will be set to maximal value if not supplied.

internal_knotsarray-like (optional)

Optional internal knots of B-spline. Will be set to appropriate quantiles based on df.

dfint, default=None

Degrees of freedom for spline. Defaults to degree + intercept.

extint

How B-splines are to be extended beyond the boundary using scipy.interpolate.splev.

Methods

fit(X[, y])

Compute knots for B-spline representation.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Construct design for B-splines using features X.
__init__(degree=3, intercept=False, lower_bound=None, upper_bound=None, internal_knots=None, df=None, ext=0)#
fit(X, y=None)#

Compute knots for B-spline representation.

Parameters:
Xarray-like

Single feature on which B-spline will be evaluated.

yNone

Ignored. This parameter exists only for compatibility with Pipeline.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

transform(X)#

Construct design for B-splines using features X.

Parameters:
Xarray-like

X on which splines will be evaluated.

Returns:
XSnp.ndarray

Evaluated splines.

Interaction#

class ISLP.transforms.Interaction(variables, columns, column_names)#

Bases: TransformerMixin, BaseEstimator

Form the tensor product interaction from a group of columns.

Parameters:
variablessequence

Variables in the interactions.

columnsdict

Mapping from variable names to columns.

column_namesdict

Mapping from variable names to lists of column names.

Methods

fit(X[, y])

Nothing to be computed for the fit.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Construct columns representing interactions of relevant variables.
__init__(variables, columns, column_names)#
fit(X, y=None)#

Nothing to be computed for the fit.

Parameters:
Xarray-like
ydefault=None

Ignored. This parameter exists only for compatibility with Pipeline.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

transform(X)#

Construct columns representing interactions of relevant variables.

Parameters:
Xarray-like

X on which features will be evaluated.

Returns:
XInp.ndarray

Evaluated interaction features.

NaturalSpline#

class ISLP.transforms.NaturalSpline(intercept=False, lower_bound=None, upper_bound=None, internal_knots=None, df=None, ext=0)#

Bases: TransformerMixin, BaseEstimator

Natural cubic spline.

Parameters:
interceptbool, default=False

If False, a column of basis is dropped so that by adding an intercept column design stays full rank.

lower_boundfloat, default=None

Lower boundary not. Will be set to minimal value if not supplied.

upper_boundfloat, default=None

Upper boundary not. Will be set to maximal value if not supplied.

internal_knotsarray-like (optional)

Optional internal knots of B-spline. Will be set to appropriate quantiles based on df.

dfint, default=None

Degrees of freedom for spline. Defaults to 3 + intercept.

extint, default=0

How B-splines are to be extended beyond the boundary using scipy.interpolate.splev.

Methods

fit(X[, y])

Compute knots for natural spline representation.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Construct design for natural cubic splines for features X.
__init__(intercept=False, lower_bound=None, upper_bound=None, internal_knots=None, df=None, ext=0)#
fit(X, y=None)#

Compute knots for natural spline representation.

Parameters:
Xarray-like

Single feature on which B-spline will be evaluated.

yNone

Ignored. This parameter exists only for compatibility with Pipeline.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

transform(X)#

Construct design for natural cubic splines for features X.

Parameters:
Xarray-like

X on which natural cubic splines will be evaluated.

Returns:
XNnp.ndarray

Evaluated natural cubic spline features.

Poly#

class ISLP.transforms.Poly(degree=1, intercept=False, raw=False)#

Bases: TransformerMixin, BaseEstimator

Parameters:
degreeint, default=1

Degree of polynomial.

interceptbool, default=False

Include a column for intercept?

rawbool, default=False

If False, perform a QR decomposition on the resulting matrix of powers of centered and / or scaled features.

Methods

fit(X[, y])

Construct parameters for orthogonal polynomials in the feature X.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Construct parameters for orthogonal polynomials in the feature X.
__init__(degree=1, intercept=False, raw=False)#
fit(X, y=None)#

Construct parameters for orthogonal polynomials in the feature X.

Parameters:
Xarray-like of shape (n_samples,)

Features used in fitting svm. Assumed to have at least 2 columns.

ydefault=None

Ignored. This parameter exists only for compatibility with Pipeline.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

transform(X)#

Construct parameters for orthogonal polynomials in the feature X.

Parameters:
Xarray-like

X on which features will be evaluated.

Returns:
XPnp.ndarray

Evaluated polynomial features.