add tutorial notebook for regression

dianna-ai · Dec 12, 2023 · 765dd67 · 765dd67
1 parent 2d83bdd
commit 765dd67
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Showing 4 changed files with 314 additions and 9 deletions.
diff --git a/dianna/methods/kernelshap_tabular.py b/dianna/methods/kernelshap_tabular.py
@@ -13,6 +13,7 @@ class KERNELSHAPTabular:
     def __init__(
         self,
         training_data: np.array,
+        mode: str = "classification",
         feature_names: List[int] = None,
         training_data_kmeans: Optional[int] = None,
     ) -> None:
@@ -26,6 +27,7 @@ def __init__(
 
         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
@@ -36,7 +38,7 @@ def __init__(
         else:
             self.training_data = training_data
         self.feature_names = feature_names
-
+        self.mode = mode
         self.explainer: KernelExplainer
 
     def explain(
@@ -76,4 +78,7 @@ def explain(
 
         saliency = self.explainer.shap_values(input_tabular, **explain_instance_kwargs)
 
+        if self.mode == 'regression':
+            return saliency[0]
+
         return saliency
diff --git a/dianna/methods/lime_tabular.py b/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):

diff --git a/tutorials/kernelshap_tabular_penguin.ipynb b/tutorials/kernelshap_tabular_penguin.ipynb
@@ -7,9 +7,7 @@
     "<img width=\"150\" alt=\"Logo_ER10\" src=\"https://user-images.githubusercontent.com/3244249/151994514-b584b984-a148-4ade-80ee-0f88b0aefa45.png\">\n",
     "\n",
     "### Model Interpretation using KernelSHAP for penguin dataset classifier\n",
-    "This notebook demonstrates the use of DIANNA with the SHAP Kernel explainer tabular method on the penguins dataset.\n",
-    "\n",
-    "https://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/model_agnostic/Census%20income%20classification%20with%20scikit-learn.html"
+    "This notebook demonstrates the use of DIANNA with the SHAP Kernel explainer tabular method on the penguins dataset."
    ]
   },
   {
@@ -348,7 +346,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 3. Applying LIME with DIANNA\n",
+    "#### 3. Applying KernelSHAP with DIANNA\n",
     "The simplest way to run DIANNA on image data is with `dianna.explain_tabular`.\n",
     "\n",
     "DIANNA requires input in numpy format, so the input data is converted into a numpy array.\n",
@@ -373,8 +371,8 @@
    ],
    "source": [
     "explanation = dianna.explain_tabular(run_model, input_tabular=data_instance, method='kernelshap',\n",
-    "                                     training_data = X_train, training_data_kmeans = 5,\n",
-    "                                     feature_names=input_features.columns)"
+    "                                     mode ='classification', training_data = X_train,\n",
+    "                                     training_data_kmeans = 5, feature_names=input_features.columns)"
    ]
   },
   {

diff --git a/tutorials/kernelshap_tabular_weather.ipynb b/tutorials/kernelshap_tabular_weather.ipynb