lm_polygraph.utils.cir_model module

This module contains the CenteredIsotonicRegression class. Copied with minor modifications from https://github.com/mathijs02/cir-model/blob/main/src/cir_model/cir_model.py

class lm_polygraph.utils.cir_model.CenteredIsotonicRegression(non_centered_points: List[float | int] = [0, 1], **kwargs: Any)

Bases: IsotonicRegression

Centered Isotonic Regression (CIR) model. CIR is described in [1] and is similar to Isotonic Regression (IR). CIR takes as an additional constraint, compared to IR, that the resulting function needs to be strictly monotonic: ranges of constant function values are prevented as much as possible. The CenteredIsotonicRegression class inherits all methods and attributes from the scikit-learn implementation IsotonicRegression and it is therefore compatible with the other components of the scikit-learn library, like for example pipelines.

Parameters

This class takes the same parameters and has the same attributes as IsotonicRegression from scikit-learn.[2]_ For full documentation of IsotonicRegression, see: https://scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html

CenteredIsotonicRegression takes one additional parameter:

non_centered_pointslist, default: [0, 1]

A list of y values that should not be collapsed in the CIR algorithm. In the original CIR algorithm, y values of 0 and 1 are treated differently by not collapsing them. This is because CIR is typically used for a binary target variable. The default behaviour can be overruled by passing a list of values for non_centered_points. An empty list means that no points are treated differently.

References

[1]

Centered Isotonic Regression: Point and Interval Estimation for Dose-Response Studies, Assaf P. Oron & Nancy Flournoy, Statistics in Biopharmaceutical Research, Volume 9, Issue 3, 258-267, 2017

[2]

Scikit-learn: Machine Learning in Python, Fabian Pedregosa et al., JMLR 12, 2825-2830, 2011

Examples

>>> from cir_model import CenteredIsotonicRegression
>>> x = [1, 2, 3, 4]
>>> y = [1, 21, 41, 34]
>>> model = CenteredIsotonicRegression().fit(x, y)
>>> model.transform(x)
array([ 1. , 21. , 32. , 37.5])

fit(X: ndarray | List, y: ndarray | List, sample_weight: ndarray | List | None = None) → CenteredIsotonicRegression

Fit the model using X, y and optionally sample_weight as training data. This method takes the same parameters and returns the same objects as fit from IsotonicRegression. For full documentation of IsotonicRegression, see: https://scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression.fit

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → CenteredIsotonicRegression

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide 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.

New in version 1.3.

Parameters

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

Metadata routing for sample_weight parameter in fit.

Returns

selfobject

The updated object.

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

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

New in version 1.3.

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.