from typing import List
from typing import Optional
from typing import Union
import numpy as np
import shap
from shap import KernelExplainer
from dianna import utils
[docs]
class KERNELSHAPTabular:
"""Wrapper around the SHAP Kernel explainer for tabular data."""
def __init__(
self,
training_data: np.array,
mode: str = "classification",
feature_names: List[int] = None,
training_data_kmeans: Optional[int] = None,
silent: bool = False,
) -> None:
"""Initializer of KERNELSHAPTabular.
Training data must be provided for the explainer to estimate the expected
values.
More information can be found in the API guide:
https://github.com/shap/shap/blob/master/shap/explainers/_kernel.py
Arguments:
training_data (np.array): training data, which should be numpy 2d array
mode (str, optional): "classification" or "regression"
feature_names (list(str), optional): list of names corresponding to the columns
in the training data.
training_data_kmeans(int, optional): summarize the whole training set with
weighted kmeans
silent (bool, optional): whether to print progress messages
"""
if training_data_kmeans:
self.training_data = shap.kmeans(training_data,
training_data_kmeans)
else:
self.training_data = training_data
[docs]
self.feature_names = feature_names
[docs]
self.explainer: KernelExplainer
[docs]
def explain(
self,
model_or_function: Union[str, callable],
input_tabular: np.array,
link: str = "identity",
**kwargs,
) -> np.array:
"""Run the KernelSHAP explainer.
Args:
model_or_function (callable or str): The function that runs the model to be explained
or the path to a ONNX model on disk.
input_tabular (np.ndarray): Data to be explained.
link (str): A generalized linear model link to connect the feature importance values
to the model. Must be either "identity" or "logit".
kwargs: These parameters are passed on
Other keyword arguments: see the documentation for KernelExplainer:
https://github.com/shap/shap/blob/master/shap/explainers/_kernel.py
Returns:
An array (np.ndarray) containing the KernelExplainer explanations for each class.
"""
init_instance_kwargs = utils.get_kwargs_applicable_to_function(
KernelExplainer, kwargs)
self.explainer = KernelExplainer(model_or_function, self.training_data,
link, **init_instance_kwargs)
explain_instance_kwargs = utils.get_kwargs_applicable_to_function(
self.explainer.shap_values, kwargs)
saliency = self.explainer.shap_values(input_tabular, silent=self.silent, **explain_instance_kwargs)
if self.mode == 'regression':
saliency = saliency[0]
return np.array(saliency)