hidimstat.BasePerturbation#

class hidimstat.BasePerturbation(estimator, loss: callable = <function root_mean_squared_error>, n_permutations: int = 50, method: str = 'predict', n_jobs: int = 1)[source]#

Bases: BaseEstimator

__init__(estimator, loss: callable = <function root_mean_squared_error>, n_permutations: int = 50, method: str = 'predict', n_jobs: int = 1)[source]#

Base class for model-agnostic variable importance measures based on perturbation.

Parameters:
estimatorsklearn compatible estimator, optional

The estimator to use for the prediction.

losscallable, default=root_mean_squared_error

The function to compute the loss when comparing the perturbed model to the original model.

n_permutationsint, default=50

This parameter is relevant only for PermutationImportance or CPI. Specifies the number of times the variable group (residual for CPI) is permuted. For each permutation, the perturbed model’s loss is calculated and averaged over all permutations.

methodstr, default=”predict”

The method used for making predictions. This determines the predictions passed to the loss function. Supported methods are “predict”, “predict_proba”, “decision_function”, “transform”.

n_jobsint, default=1

The number of parallel jobs to run. Parallelization is done over the variables or groups of variables.

fit(X, y=None, groups=None)[source]#

Base fit method for perturbation-based methods. Identifies the groups.

Parameters:
X: array-like of shape (n_samples, n_features)

The input samples.

y: array-like of shape (n_samples,)

Not used, only present for consistency with the sklearn API.

groups: dict, optional

A dictionary where the keys are the group names and the values are the list of column names corresponding to each group. If None, the groups are identified based on the columns of X.

predict(X)[source]#

Compute the predictions after perturbation of the data for each group of variables.

Parameters:
X: array-like of shape (n_samples, n_features)

The input samples.

Returns:
out: array-like of shape (n_groups, n_permutations, n_samples)

The predictions after perturbation of the data for each group of variables.

score(X, y)[source]#

Compute the importance scores for each group of covariates.

Parameters:
X: array-like of shape (n_samples, n_features)

The input samples.

y: array-like of shape (n_samples,)

The target values.

Returns:
out_dict: dict

A dictionary containing the following keys: - ‘loss_reference’: the loss of the model with the original data. - ‘loss’: a dictionary containing the loss of the perturbed model for each group. - ‘importance’: the importance scores for each group.

get_metadata_routing()[source]#

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)[source]#

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_fit_request(*, groups: bool | None | str = '$UNCHANGED$') → BasePerturbation[source]#

Request metadata passed to the fit 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 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 Pipeline. Otherwise it has no effect.

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

Metadata routing for groups parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)[source]#

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.

Examples using hidimstat.BasePerturbation#

Variable Importance on diabetes dataset

Variable Importance on diabetes dataset

Measuring variable importance in classification

Measuring variable importance in classification