add missing docstrings

understandable-machine-intelligence-lab · Aug 30, 2023 · 4d1d25e · 4d1d25e
1 parent 6989748
commit 4d1d25e
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Showing 10 changed files with 144 additions and 11 deletions.
diff --git a/quantus/metrics/faithfulness/monotonicity_correlation.py b/quantus/metrics/faithfulness/monotonicity_correlation.py
@@ -399,7 +399,7 @@ def evaluate_batch(
         Parameters
         ----------
         model: ModelInterface
-            A ModelInterface that is subject to explanation.
+            A model that is subject to explanation.
         x_batch: np.ndarray
             The input to be evaluated on a batch-basis.
         y_batch: np.ndarray
@@ -412,19 +412,14 @@ def evaluate_batch(
         List[float]
             The evaluation results.
         """
-        # Asserts.
-        asserts.assert_features_in_step(
-            features_in_step=self.features_in_step,
-            input_shape=x_batch.shape[2:],
-        )
 
         # Evaluate explanations.
         return [
             self.evaluate_instance(
-                model=model,
-                x=x,
-                y=y,
-                a=a,
+                model,
+                x,
+                y,
+                a,
             )
             for x, y, a in zip(x_batch, y_batch, a_batch)
         ]
diff --git a/quantus/metrics/faithfulness/sufficiency.py b/quantus/metrics/faithfulness/sufficiency.py
@@ -309,7 +309,22 @@ def evaluate_batch(
         self, *, i_batch, a_sim_vector_batch, y_pred_classes, **_
     ) -> List[float]:
         """
+
         TODO: write meaningful docstring about what does it compute.
+        Parameters
+        ----------
+        i_batch:
+            The index of the current instance.
+        a_sim_vector_batch:
+            The custom input to be evaluated on an instance-basis.
+        y_pred_classes:
+            The class predictions of the complete input dataset.
+        _:
+            unused.
+
+        Returns
+        -------
+
         """
 
         return [

diff --git a/quantus/metrics/localisation/auc.py b/quantus/metrics/localisation/auc.py
@@ -286,5 +286,20 @@ def custom_preprocess(
     def evaluate_batch(
         self, *, a_batch: np.ndarray, s_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        s_batch:
+            A np.ndarray which contains segmentation masks that matches the input.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         # TODO: for performance reasons replace for-loop with vectorized dispatch.
         return [self.evaluate_instance(a, s) for a, s in zip(a_batch, s_batch)]
diff --git a/quantus/metrics/localisation/focus.py b/quantus/metrics/localisation/focus.py
@@ -386,4 +386,18 @@ def quadrant_bottom_right(self, a: np.ndarray) -> np.ndarray:
     def evaluate_batch(
         self, *, a_batch: np.ndarray, c_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        c_batch:
+            The custom input to be evaluated on an batch-basis.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [self.evaluate_instance(a, c) for a, c in zip(a_batch, c_batch)]
diff --git a/quantus/metrics/localisation/pointing_game.py b/quantus/metrics/localisation/pointing_game.py
@@ -309,4 +309,19 @@ def custom_preprocess(
     def evaluate_batch(
         self, *, a_batch: np.ndarray, s_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        s_batch:
+            A np.ndarray which contains segmentation masks that matches the input.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [self.evaluate_instance(a, s) for a, s in zip(a_batch, s_batch)]
diff --git a/quantus/metrics/localisation/relevance_mass_accuracy.py b/quantus/metrics/localisation/relevance_mass_accuracy.py
@@ -299,4 +299,18 @@ def custom_preprocess(
     def evaluate_batch(
         self, *, a_batch: np.ndarray, s_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        s_batch:
+            A np.ndarray which contains segmentation masks that matches the input.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [self.evaluate_instance(a, s) for a, s in zip(a_batch, s_batch)]
diff --git a/quantus/metrics/localisation/relevance_rank_accuracy.py b/quantus/metrics/localisation/relevance_rank_accuracy.py
@@ -308,5 +308,20 @@ def custom_preprocess(
     def evaluate_batch(
         self, *, a_batch: np.ndarray, s_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        s_batch:
+            A np.ndarray which contains segmentation masks that matches the input.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         # TODO: for performance reasons, this method should be vectorized.
         return [self.evaluate_instance(a, s) for a, s in zip(a_batch, s_batch)]
diff --git a/quantus/metrics/localisation/top_k_intersection.py b/quantus/metrics/localisation/top_k_intersection.py
@@ -318,4 +318,19 @@ def custom_preprocess(
     def evaluate_batch(
         self, *, a_batch: np.ndarray, s_batch: np.ndarray, **_
     ) -> List[float]:
+        """
+
+        Parameters
+        ----------
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        s_batch:
+            A np.ndarray which contains segmentation masks that matches the input.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [self.evaluate_instance(a, s) for a, s in zip(a_batch, s_batch)]
diff --git a/quantus/metrics/randomisation/random_logit.py b/quantus/metrics/randomisation/random_logit.py
@@ -333,7 +333,25 @@ def evaluate_batch(
         a_batch: np.ndarray,
         **_,
     ) -> List[float]:
-        # TODO: for performance reasons vectorize this for-loop
+        """
+
+        Parameters
+        ----------
+        model:
+            A model that is subject to explanation.
+        x_batch:
+            A np.ndarray which contains the input data that are explained.
+        y_batch:
+            A np.ndarray which contains the output labels that are explained.
+        a_batch:
+            A np.ndarray which contains pre-computed attributions i.e., explanations.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [
             self.evaluate_instance(model, x, y, a)
             for x, y, a in zip(x_batch, y_batch, a_batch)

diff --git a/quantus/metrics/robustness/continuity.py b/quantus/metrics/robustness/continuity.py
@@ -445,4 +445,21 @@ def evaluate_batch(
         y_batch: np.ndarray,
         **_,
     ) -> List[Dict[str, int]]:
+        """
+
+        Parameters
+        ----------
+        model:
+            A model that is subject to explanation.
+        x_batch:
+            A np.ndarray which contains the input data that are explained.
+        y_batch:
+            A np.ndarray which contains the output labels that are explained.
+        _:
+            unused.
+
+        Returns
+        -------
+
+        """
         return [self.evaluate_instance(model, x, y) for x, y in zip(x_batch, y_batch)]