shap.explainers.GPUTree
- class shap.explainers.GPUTree(model, data=None, model_output='raw', feature_perturbation='interventional', feature_names=None, approximate=False, **deprecated_options)
Experimental GPU accelerated version of TreeExplainer. Currently requires source build with cuda available and ‘CUDA_PATH’ environment variable defined.
- Parameters
- modelmodel object
The tree based machine learning model that we want to explain. XGBoost, LightGBM, CatBoost, Pyspark and most tree-based scikit-learn models are supported.
- datanumpy.array or pandas.DataFrame
The background dataset to use for integrating out features. This argument is optional when feature_perturbation=”tree_path_dependent”, since in that case we can use the number of training samples that went down each tree path as our background dataset (this is recorded in the model object).
- feature_perturbation“interventional” (default) or “tree_path_dependent” (default when data=None)
Since SHAP values rely on conditional expectations we need to decide how to handle correlated (or otherwise dependent) input features. The “interventional” approach breaks the dependencies between features according to the rules dictated by casual inference (Janzing et al. 2019). Note that the “interventional” option requires a background dataset and its runtime scales linearly with the size of the background dataset you use. Anywhere from 100 to 1000 random background samples are good sizes to use. The “tree_path_dependent” approach is to just follow the trees and use the number of training examples that went down each leaf to represent the background distribution. This approach does not require a background dataset and so is used by default when no background dataset is provided.
- model_output“raw”, “probability”, “log_loss”, or model method name
What output of the model should be explained. If “raw” then we explain the raw output of the trees, which varies by model. For regression models “raw” is the standard output, for binary classification in XGBoost this is the log odds ratio. If model_output is the name of a supported prediction method on the model object then we explain the output of that model method name. For example model_output=”predict_proba” explains the result of calling model.predict_proba. If “probability” then we explain the output of the model transformed into probability space (note that this means the SHAP values now sum to the probability output of the model). If “logloss” then we explain the log base e of the model loss function, so that the SHAP values sum up to the log loss of the model for each sample. This is helpful for breaking down model performance by feature. Currently the probability and logloss options are only supported when feature_dependence=”independent”.
Examples
See GPUTree explainer examples
- __init__(model, data=None, model_output='raw', feature_perturbation='interventional', feature_names=None, approximate=False, **deprecated_options)
Build a new Tree explainer for the passed model.
- Parameters
- modelmodel object
The tree based machine learning model that we want to explain. XGBoost, LightGBM, CatBoost, Pyspark and most tree-based scikit-learn models are supported.
- datanumpy.array or pandas.DataFrame
The background dataset to use for integrating out features. This argument is optional when feature_perturbation=”tree_path_dependent”, since in that case we can use the number of training samples that went down each tree path as our background dataset (this is recorded in the model object).
- feature_perturbation“interventional” (default) or “tree_path_dependent” (default when data=None)
Since SHAP values rely on conditional expectations we need to decide how to handle correlated (or otherwise dependent) input features. The “interventional” approach breaks the dependencies between features according to the rules dictated by causal inference (Janzing et al. 2019). Note that the “interventional” option requires a background dataset and its runtime scales linearly with the size of the background dataset you use. Anywhere from 100 to 1000 random background samples are good sizes to use. The “tree_path_dependent” approach is to just follow the trees and use the number of training examples that went down each leaf to represent the background distribution. This approach does not require a background dataset and so is used by default when no background dataset is provided.
- model_output“raw”, “probability”, “log_loss”, or model method name
What output of the model should be explained. If “raw” then we explain the raw output of the trees, which varies by model. For regression models “raw” is the standard output, for binary classification in XGBoost this is the log odds ratio. If model_output is the name of a supported prediction method on the model object then we explain the output of that model method name. For example model_output=”predict_proba” explains the result of calling model.predict_proba. If “probability” then we explain the output of the model transformed into probability space (note that this means the SHAP values now sum to the probability output of the model). If “logloss” then we explain the log base e of the model loss function, so that the SHAP values sum up to the log loss of the model for each sample. This is helpful for breaking down model performance by feature. Currently the probability and logloss options are only supported when feature_dependence=”independent”.
Examples
Methods
__init__
(model[, data, model_output, ...])Build a new Tree explainer for the passed model.
assert_additivity
(phi, model_output)explain_row
(*row_args, max_evals, ...)Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes, main_effects).
load
(in_file[, model_loader, masker_loader, ...])Load an Explainer from the given file stream.
save
(out_file[, model_saver, masker_saver])Write the explainer to the given file stream.
shap_interaction_values
(X[, y, tree_limit])Estimate the SHAP interaction values for a set of samples.
shap_values
(X[, y, tree_limit, approximate, ...])Estimate the SHAP values for a set of samples.
supports_model_with_masker
(model, masker)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),
- classmethod load(in_file, model_loader=<bound method Model.load of <class 'shap.models._model.Model'>>, masker_loader=<bound method Serializable.load of <class 'shap.maskers._masker.Masker'>>, instantiate=True)
Load an Explainer from the given file stream.
- Parameters
- in_fileThe file stream to load objects from.
- save(out_file, model_saver='.save', masker_saver='.save')
Write the explainer to the given file stream.
- shap_interaction_values(X, y=None, tree_limit=None)
Estimate the SHAP interaction values for a set of samples.
- Parameters
- Xnumpy.array, pandas.DataFrame or catboost.Pool (for catboost)
A matrix of samples (# samples x # features) on which to explain the model’s output.
- ynumpy.array
An array of label values for each sample. Used when explaining loss functions (not yet supported).
- tree_limitNone (default) or int
Limit the number of trees used by the model. By default None means no use the limit of the original model, and -1 means no limit.
- Returns
- array or list
For models with a single output this returns a tensor of SHAP values (# samples x # features x # features). The matrix (# features x # features) for each sample sums to the difference between the model output for that sample and the expected value of the model output (which is stored in the expected_value attribute of the explainer). Each row of this matrix sums to the SHAP value for that feature for that sample. The diagonal entries of the matrix represent the “main effect” of that feature on the prediction and the symmetric off-diagonal entries represent the interaction effects between all pairs of features for that sample. For models with vector outputs this returns a list of tensors, one for each output.
- shap_values(X, y=None, tree_limit=None, approximate=False, check_additivity=True, from_call=False)
Estimate the SHAP values for a set of samples.
- Parameters
- Xnumpy.array, pandas.DataFrame or catboost.Pool (for catboost)
A matrix of samples (# samples x # features) on which to explain the model’s output.
- ynumpy.array
An array of label values for each sample. Used when explaining loss functions.
- tree_limitNone (default) or int
Limit the number of trees used by the model. By default None means no use the limit of the original model, and -1 means no limit.
- approximatebool
Not supported.
- check_additivitybool
Run a validation check that the sum of the SHAP values equals the output of the model. This check takes only a small amount of time, and will catch potential unforeseen errors. Note that this check only runs right now when explaining the margin of the model.
- Returns
- array or list
For models with a single output this returns a matrix of SHAP values (# samples x # features). Each row sums to the difference between the model output for that sample and the expected value of the model output (which is stored in the expected_value attribute of the explainer when it is constant). For models with vector outputs this returns a list of such matrices, one for each output.
- static supports_model_with_masker(model, masker)
Determines if this explainer can handle the given model.
This is an abstract static method meant to be implemented by each subclass.