add rise tabular

dianna/__init__.py

@@ -131,7 +131,7 @@ def explain_text(model_or_function: Union[Callable,
 def explain_tabular(model_or_function: Union[Callable, str],
                     input_tabular: np.ndarray,
                     method: str,
-                    labels=(1, ),
+                    labels=None,
                     **kwargs) -> np.ndarray:
     """Explain tabular (input_text) given a model and a chosen method.
 

dianna/methods/kernelshap_tabular.py

@@ -34,7 +34,8 @@ def __init__(
                                                  weighted kmeans
         """
         if training_data_kmeans:
-            self.training_data = shap.kmeans(training_data, training_data_kmeans)
+            self.training_data = shap.kmeans(training_data,
+                                             training_data_kmeans)
         else:
             self.training_data = training_data
         self.feature_names = feature_names
@@ -65,17 +66,15 @@ def explain(
             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
-        )
+            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
-        )
+            self.explainer.shap_values, kwargs)
 
-        saliency = self.explainer.shap_values(input_tabular, **explain_instance_kwargs)
+        saliency = self.explainer.shap_values(input_tabular,
+                                              **explain_instance_kwargs)
 
         if self.mode == 'regression':
             saliency = saliency[0]

dianna/methods/lime_tabular.py

@@ -1,6 +1,8 @@
 """LIME tabular explainer."""
+import sys
 from typing import Iterable
 from typing import List
+from typing import Optional
 from typing import Union
 import numpy as np
 from lime.lime_tabular import LimeTabularExplainer
@@ -58,12 +60,10 @@ def __init__(
         """
         self.mode = mode
         init_instance_kwargs = utils.get_kwargs_applicable_to_function(
-            LimeTabularExplainer, kwargs
-        )
+            LimeTabularExplainer, kwargs)
 
         # temporary solution for setting num_features and top_labels
         self.num_features = len(feature_names)
-        self.top_labels = len(class_names)
 
         self.explainer = LimeTabularExplainer(
             training_data,
@@ -83,7 +83,7 @@ def explain(
         self,
         model_or_function: Union[str, callable],
         input_tabular: np.array,
-        labels: Iterable[int] = (1,),
+        labels: Optional[Iterable[int]] = None,
         num_samples: int = 5000,
         **kwargs,
     ) -> np.array:
@@ -93,7 +93,7 @@ def explain(
             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.
-            labels (Iterable(int), optional): Indices of classes to be explained.
+            labels (Iterable(int)): Indices of classes to be explained.
             num_samples (int, optional): Number of samples
             kwargs: These parameters are passed on
 
@@ -105,15 +105,14 @@ def explain(
         """
         # run the explanation.
         explain_instance_kwargs = utils.get_kwargs_applicable_to_function(
-            self.explainer.explain_instance, kwargs
-        )
+            self.explainer.explain_instance, kwargs)
         runner = utils.get_function(model_or_function)
 
         explanation = self.explainer.explain_instance(
             input_tabular,
             runner,
             labels=labels,
-            top_labels=self.top_labels,
+            top_labels=sys.maxsize,
             num_features=self.num_features,
             num_samples=num_samples,
             **explain_instance_kwargs,
@@ -126,10 +125,13 @@ def explain(
         elif self.mode == 'classification':
             # extract scores from lime explainer
             saliency = []
-            for i in range(self.top_labels):
+            for i in range(len(explanation.local_exp.items())):
                 local_exp = sorted(explanation.local_exp[i])
                 # shape of local_exp [(index, saliency)]
                 selected_saliency = [x[1] for x in local_exp]
                 saliency.append(selected_saliency[:])
 
+        else:
+            raise ValueError(f'Unsupported mode "{self.mode}"')
+
         return np.array(saliency)

dianna/methods/lime_timeseries.py

@@ -81,7 +81,7 @@ def explain(
         # wrap up the input model or function using the runner
         runner = utils.get_function(
             model_or_function, preprocess_function=self.preprocess_function)
-        masks = generate_time_series_masks(input_timeseries,
+        masks = generate_time_series_masks(input_timeseries.shape,
                                            num_samples,
                                            p_keep=0.1)
         # NOTE: Required by `lime_base` explainer since the first instance must be the original data

dianna/methods/rise_tabular.py

@@ -0,0 +1,111 @@
+"""RISE tabular explainer."""
+from typing import Iterable
+from typing import List
+from typing import Optional
+from typing import Union
+import numpy as np
+from dianna import utils
+from dianna.utils.maskers import generate_tabular_masks
+from dianna.utils.maskers import mask_data_tabular
+from dianna.utils.predict import make_predictions
+from dianna.utils.rise_utils import normalize
+
+
+class RISETabular:
+    """RISE explainer for tabular data."""
+
+    def __init__(
+        self,
+        training_data: np.array,
+        mode: str = "classification",
+        feature_names: List[str] = None,
+        categorical_features: List[int] = None,
+        n_masks: int = 1000,
+        feature_res: int = 8,
+        p_keep: float = 0.5,
+        preprocess_function: Optional[callable] = None,
+        class_names=None,
+        keep_masks: bool = False,
+        keep_masked: bool = False,
+        keep_predictions: bool = False,
+    ) -> np.ndarray:
+        """RISE initializer.
+
+        Args:
+            n_masks: Number of masks to generate.
+            feature_res: Resolution of features in masks.
+            p_keep: Fraction of input data to keep in each mask (Default: auto-tune this value).
+            preprocess_function: Function to preprocess input data with
+            categorical_features: list of categorical features
+            class_names: Names of the classes
+            feature_names: Names of the features
+            mode: Either classification of regression
+            training_data: Training data used for imputation of masked features
+            keep_masks: keep masks in memory for the user to inspect
+            keep_masked: keep masked data in memory for the user to inspect
+            keep_predictions: keep model predictions in memory for the user to inspect
+        """
+        self.training_data = training_data
+        self.n_masks = n_masks
+        self.feature_res = feature_res
+        self.p_keep = p_keep
+        self.preprocess_function = preprocess_function
+        self.masks = None
+        self.masked = None
+        self.predictions = None
+        self.keep_masks = keep_masks
+        self.keep_masked = keep_masked
+        self.keep_predictions = keep_predictions
+        self.mode = mode
+
+    def explain(
+        self,
+        model_or_function: Union[str, callable],
+        input_tabular: np.array,
+        labels: Optional[Iterable[int]] = None,
+        mask_type: Optional[Union[str, callable]] = 'most_frequent',
+        batch_size: Optional[int] = 100,
+    ) -> np.array:
+        """Run the RISE explainer.
+
+        Args:
+            model_or_function: The function that runs the model to be explained
+                                                 or the path to a ONNX model on disk.
+            input_tabular: Data to be explained.
+            labels: Indices of classes to be explained.
+            num_samples: Number of samples
+            mask_type: Imputation strategy for masked features
+            batch_size: Number of samples to process by the model per batch
+
+        Returns:
+            explanation: An Explanation object containing the LIME explanations for each class.
+        """
+        # run the explanation.
+        runner = utils.get_function(model_or_function)
+
+        masks = np.stack(
+            list(
+                generate_tabular_masks(input_tabular.shape,
+                                       number_of_masks=self.n_masks,
+                                       p_keep=self.p_keep)))
+        self.masks = masks if self.keep_masks else None
+
+        masked = mask_data_tabular(input_tabular,
+                                   masks,
+                                   self.training_data,
+                                   mask_type=mask_type)
+        self.masked = masked if self.keep_masked else None
+        predictions = make_predictions(masked, runner, batch_size)
+        self.predictions = predictions if self.keep_predictions else None
+        n_labels = predictions.shape[1]
+
+        masks_reshaped = masks.reshape(self.n_masks, -1)
+
+        saliency = predictions.T.dot(masks_reshaped).reshape(
+            n_labels, *input_tabular.shape)
+
+        if self.mode == 'regression':
+            return saliency[0]
+
+        selected_saliency = saliency if labels is None else saliency[labels]
+        return normalize(selected_saliency, self.n_masks, self.p_keep)

dianna/methods/rise_timeseries.py

@@ -68,7 +68,7 @@ def explain(self,
         runner = utils.get_function(
             model_or_function, preprocess_function=self.preprocess_function)
 
-        masks = generate_time_series_masks(input_timeseries,
+        masks = generate_time_series_masks(input_timeseries.shape,
                                            number_of_masks=self.n_masks,
                                            feature_res=self.feature_res,
                                            p_keep=self.p_keep)