645 Implement SHAP for tabular data

dianna/methods/kernelshap_tabular.py

@@ -0,0 +1,84 @@
+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
+
+
+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,
+    ) -> 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
+        """
+        if training_data_kmeans:
+            self.training_data = shap.kmeans(training_data, training_data_kmeans)
+        else:
+            self.training_data = training_data
+        self.feature_names = feature_names
+        self.mode = mode
+        self.explainer: KernelExplainer
+
+    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:
+            explanation: An Explanation object 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, **explain_instance_kwargs)
+
+        if self.mode == 'regression':
+            return saliency[0]
+
+        return saliency

dianna/methods/lime_tabular.py

@@ -119,11 +119,11 @@ def explain(
             **explain_instance_kwargs,
         )
 
-        if self.mode == "regression":
+        if self.mode == 'regression':
             local_exp = sorted(explanation.local_exp[1])
             saliency = [i[1] for i in local_exp]
 
-        elif self.mode == "classification":
+        elif self.mode == 'classification':
             # extract scores from lime explainer
             saliency = []
             for i in range(self.top_labels):

tests/methods/test_shap_tabular.py

@@ -0,0 +1,35 @@
+"""Test LIME tabular method."""
+from unittest import TestCase
+import numpy as np
+import dianna
+from dianna.methods.kernelshap_tabular import KERNELSHAPTabular
+from tests.utils import run_model
+
+
+class LIMEOnTabular(TestCase):
+    """Suite of LIME tests for the tabular case."""
+
+    def test_shap_tabular_classification_correct_output_shape(self):
+        """Test whether the output of explainer has the correct shape."""
+        training_data = np.random.random((10, 2))
+        input_data = np.random.random(2)
+        feature_names = ["feature_1", "feature_2"]
+        explainer = KERNELSHAPTabular(training_data,
+                                      mode ='classification',
+                                      feature_names=feature_names,)
+        exp = explainer.explain(
+            run_model,
+            input_data,
+        )
+        assert len(exp[0]) == len(feature_names)
+
+    def test_shap_tabular_regression_correct_output_shape(self):
+        """Test whether the output of explainer has the correct length."""
+        training_data = np.random.random((10, 2))
+        input_data = np.random.random(2)
+        feature_names = ["feature_1", "feature_2"]
+        exp = dianna.explain_tabular(run_model, input_tabular=input_data, method='kernelshap',
+                                     mode ='regression', training_data = training_data,
+                                     training_data_kmeans = 2, feature_names=feature_names)
+
+        assert len(exp) == len(feature_names)