shap.Explainer¶

class
shap.
Explainer
(model, masker, link=CPUDispatcher(<function identity>), algorithm='auto', output_names=None)¶ 
__init__
(model, masker, link=CPUDispatcher(<function identity>), algorithm='auto', output_names=None)¶ Uses Shapley values to explain any machine learning model or python function.
This is the primary explainer interface for the SHAP library. It takes any combination of a model and masker and returns a callable subclass object that implements the particular estimation algorithm that was chosen.
 Parameters
 modelobject or function
User supplied function or model object that takes a dataset of samples and computes the output of the model for those samples.
 maskerfunction, numpy.array, pandas.DataFrame, tokenizer, or a list of these for each model input
The function used to “mask” out hidden features of the form masked_args = masker(*model_args, mask=mask). It takes input in the same form as the model, but for just a single sample with a binary mask, then returns an iterable of masked samples. These masked samples will then be evaluated using the model function and the outputs averaged. slice() model(*masker(*args, mask=mask)).mean() As a shortcut for the standard masking using by SHAP you can pass a background data matrix instead of a function and that matrix will be used for masking. Domain specific masking functions are available in shap such as shap.ImageMasker for images and shap.TokenMasker for text. In addition to determining how to replace hidden features, the masker can also constrain the rules of the cooperative game used to explain the model. For example shap.TabularMasker(data, hclustering=”correlation”) will enforce a hierarchial clustering of coalitions for the game (in this special case the attributions are known as the Owen values).
 linkfunction
The link function used to map between the output units of the model and the SHAP value units. By default it is shap.links.identity, but shap.links.logit can be useful so that expectations are computed in probability units while explanations remain in the (more naturally additive) logodds units. For more details on how link functions work see any overview of link functions for generalized linear models.
 algorithm“auto”, “permutation”, “partition”, “tree”, “kernel”, “sampling”, “linear”, “deep”, or “gradient”
The algorithm used to estimate the Shapley values. There are many different algorithms that can be used to estimate the Shapley values (and the related value for constrained games), each of these algorithms have various tradeoffs and are preferrable in different situations. By default the “auto” options attempts to make the best choice given the passed model and masker, but this choice can always be overriden by passing the name of a specific algorithm. The type of algorithm used will determine what type of subclass object is returned by this constructor, and you can also build those subclasses directly if you prefer or need more fine grained control over their options.
 output_namesNone or list of strings
The names of the model outputs. For example if the model is an image classifier, then output_names would be the names of all the output classes. This parameter is optional. When output_names is None then the Explanation objects produced by this explainer will not have any output_names, which could effect downstream plots.
Methods
__init__
(model, masker[, link, algorithm, …])Uses Shapley values to explain any machine learning model or python function.
explain_row
(*row_args, max_evals, …)Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes, main_effects).
supports_model
(model)Determines if this explainer can handle the given model.

explain_row
(*row_args, max_evals, main_effects, error_bounds, outputs, silent, **kwargs)¶ Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes, main_effects).
This is an abstract method meant to be implemented by each subclass.
 Returns
 tuple
A tuple of (row_values, row_expected_values, row_mask_shapes), where row_values is an array of the attribution values for each sample, row_expected_values is an array (or single value) representing the expected value of the model for each sample (which is the same for all samples unless there are fixed inputs present, like labels when explaining the loss), and row_mask_shapes is a list of all the input shapes (since the row_values is always flattened),

static
supports_model
(model)¶ Determines if this explainer can handle the given model.
This is an abstract static method meant to be implemented by each subclass.
