facet.inspection.shap.sklearn.ClassifierShapCalculator

class facet.inspection.shap.sklearn.ClassifierShapCalculator(model, *, explainer_factory, interaction_values, n_jobs=None, shared_memory=None, pre_dispatch=None, verbose=None)

Calculates SHAP (interaction) values for classification models.

Bases:

LearnerShapCalculator [~T_Classifier]

Generic types:

~T_Classifier(bound= ClassifierMixin)

Metaclasses:

ABCMeta

Parameters:

• model (ClassifierShapCalculator) – the model for which to calculate SHAP values

• explainer_factory (ExplainerFactory[ClassifierShapCalculator]) – the explainer factory used to create the SHAP explainer for this calculator

• interaction_values (bool) – if True, calculate SHAP interaction values, otherwise calculate SHAP values

• n_jobs (Optional[int]) – number of jobs to use in parallel; if None, use joblib default (default: None)

• shared_memory (Optional[bool]) – if True, use threads in the parallel runs; if False or None, use multiprocessing (default: None)

• pre_dispatch (Union[str, int, None]) – number of batches to pre-dispatch; if None, use joblib default (default: None)

• verbose (Optional[int]) – verbosity level used in the parallel computation; if None, use joblib default (default: None)

Method summary

fit	Calculate the SHAP values.
validate_features	Check that the given feature matrix is valid for this calculator.
validate_learner	The type of the None singleton.

Attribute summary

IDX_FEATURE	Name for the feature index (= column index) of the resulting SHAP data frame.
MULTI_OUTPUT_INDEX_NAME	Multi-output SHAP values are determined by class.
input_names	The names of the inputs explained by this SHAP calculator, or None if no names are defined.
is_fitted	[see superclass]
main_effects	The main effects per observation and featuren (i.e., the diagonals of the interaction matrices), with shape \((n_\mathrm{observations}, n_\mathrm{outputs} \cdot n_\mathrm{features})\).
output_names	The names of the outputs explained by this SHAP calculator.
shap_interaction_values	The SHAP interaction values per observation and feature pair, with shape \((n_\mathrm{observations} \cdot n_\mathrm{features}, n_\mathrm{outputs} \cdot n_\mathrm{features})\)
shap_values	The SHAP values per observation and feature, with shape \((n_\mathrm{observations}, n_\mathrm{outputs} \cdot n_\mathrm{features})\)
n_jobs	Number of jobs to use in parallel; if None, use joblib default.
shared_memory	If True, use threads in the parallel runs; if False or None, use multiprocessing.
pre_dispatch	Number of batches to pre-dispatch; if None, use joblib default.
verbose	Verbosity level used in the parallel computation; if None, use joblib default.
model	The model for which to calculate SHAP values.
explainer_factory	The explainer factory used to create the SHAP explainer for this calculator.
shap_	The SHAP values for all observations this calculator has been fitted to.
feature_index_	The names of the features for which SHAP values were calculated.

Definitions

fit(__X, **fit_params)

Calculate the SHAP values.

Parameters:

• __X (DataFrame) – the observations for which to calculate SHAP values

• fit_params (Any) – additional fit parameters (unused)

Return type:

ClassifierShapCalculator

Returns:

self

Raises:

ValueError – if the observations are not a valid feature matrix for this calculator

validate_features(features)

Check that the given feature matrix is valid for this calculator.

Parameters:

features (DataFrame) – the feature matrix to validate

Raises:

ValueError – if the feature matrix is not compatible with this calculator

Return type:

None

static validate_learner(learner)

The type of the None singleton.

Return type:

None

IDX_FEATURE = 'feature'

Name for the feature index (= column index) of the resulting SHAP data frame.

MULTI_OUTPUT_INDEX_NAME = 'class'

Multi-output SHAP values are determined by class.

explainer_factory: ExplainerFactory[TypeVar(T_Classifier, bound= ClassifierMixin)]

The explainer factory used to create the SHAP explainer for this calculator.

feature_index_: Optional[Index]

The names of the features for which SHAP values were calculated.

property input_names: list[str] | None

The names of the inputs explained by this SHAP calculator, or None if no names are defined.

property is_fitted: bool

[see superclass]

property main_effects: DataFrame

The main effects per observation and featuren (i.e., the diagonals of the interaction matrices), with shape \((n_\mathrm{observations}, n_\mathrm{outputs} \cdot n_\mathrm{features})\).

Raises:

AttributeError – this SHAP calculator does not support interaction values

model: TypeVar(T_Classifier, bound= ClassifierMixin)

The model for which to calculate SHAP values.

n_jobs: Optional[int]

Number of jobs to use in parallel; if None, use joblib default.

property output_names: list[str]

The names of the outputs explained by this SHAP calculator.

pre_dispatch: Union[str, int, None]

Number of batches to pre-dispatch; if None, use joblib default.

shap_: Optional[DataFrame]

The SHAP values for all observations this calculator has been fitted to.

property shap_interaction_values: DataFrame

The SHAP interaction values per observation and feature pair, with shape \((n_\mathrm{observations} \cdot n_\mathrm{features}, n_\mathrm{outputs} \cdot n_\mathrm{features})\)

Raises:

AttributeError – this SHAP calculator does not support interaction values

property shap_values: DataFrame

The SHAP values per observation and feature, with shape \((n_\mathrm{observations}, n_\mathrm{outputs} \cdot n_\mathrm{features})\)

shared_memory: Optional[bool]

If True, use threads in the parallel runs; if False or None, use multiprocessing.

verbose: Optional[int]

Verbosity level used in the parallel computation; if None, use joblib default.