facet.inspection.shap.sklearn.RegressorShapCalculator#

class facet.inspection.shap.sklearn.RegressorShapCalculator(model, *, output_names, explainer_factory, interaction_values, n_jobs=None, shared_memory=None, pre_dispatch=None, verbose=None)[source]#

Calculates SHAP (interaction) values for regression models.

Bases

LearnerShapCalculator [~T_Regressor]

Generic types

~T_Regressor(bound= RegressorMixin)

Metaclasses

ABCMeta

Parameters

model (RegressorShapCalculator) – the model for which to calculate SHAP values
output_names (List[str]) – the names of the outputs of the regressor
explainer_factory (ExplainerFactory[RegressorShapCalculator]) – 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[int, str, 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.

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 target.
`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

RegressorShapCalculator

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

MULTI_OUTPUT_INDEX_NAME = 'target'#: Multi-output SHAP values are determined by target.

property input_names: Optional[List[str]]#

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

Return type: Optional[List[str]]

property is_fitted: bool#

[see superclass]

Return type: bool

property main_effects: pandas.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
Return type: DataFrame

property output_names: List[str]#

The names of the outputs explained by this SHAP calculator.

Return type: List[str]

property shap_interaction_values: pandas.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
Return type: DataFrame

property shap_values: pandas.DataFrame#

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

Return type: DataFrame

facet.inspection.shap.sklearn.LearnerShapCalculator

facet.selection