Fix docstrings

Author: truff4ut

autointent/context/data_handler/_data_handler.py

@@ -77,63 +77,98 @@ def multilabel(self) -> bool:
 
     def train_utterances(self, idx: int | None = None) -> list[str]:
         """
-        Get the training utterances.
+        Retrieve training utterances from the dataset.
 
+        If a specific training split index is provided, retrieves utterances
+        from the indexed training split. Otherwise, retrieves utterances from
+        the primary training split.
+
+        :param idx: Optional index for a specific training split.
         :return: List of training utterances.
         """
         split = f"{Split.TRAIN}_{idx}" if idx is not None else Split.TRAIN
         return cast(list[str], self.dataset[split][self.dataset.utterance_feature])
 
     def train_labels(self, idx: int | None = None) -> list[LabelType]:
         """
-        Get the training labels.
+        Retrieve training labels from the dataset.
+
+        If a specific training split index is provided, retrieves labels
+        from the indexed training split. Otherwise, retrieves labels from
+        the primary training split.
 
+        :param idx: Optional index for a specific training split.
         :return: List of training labels.
         """
         split = f"{Split.TRAIN}_{idx}" if idx is not None else Split.TRAIN
         return cast(list[LabelType], self.dataset[split][self.dataset.label_feature])
 
     def validation_utterances(self, idx: int | None = None) -> list[str]:
         """
-        Get the validation utterances.
+        Retrieve validation utterances from the dataset.
 
+        If a specific validation split index is provided, retrieves utterances
+        from the indexed validation split. Otherwise, retrieves utterances from
+        the primary validation split.
+
+        :param idx: Optional index for a specific validation split.
         :return: List of validation utterances.
         """
         split = f"{Split.VALIDATION}_{idx}" if idx is not None else Split.VALIDATION
         return cast(list[str], self.dataset[split][self.dataset.utterance_feature])
 
     def validation_labels(self, idx: int | None = None) -> list[LabelType]:
         """
-        Get the validatio labels.
+        Retrieve validation labels from the dataset.
+
+        If a specific validation split index is provided, retrieves labels
+        from the indexed validation split. Otherwise, retrieves labels from
+        the primary validation split.
 
-        :return: List of validatio labels.
+        :param idx: Optional index for a specific validation split.
+        :return: List of validation labels.
         """
         split = f"{Split.VALIDATION}_{idx}" if idx is not None else Split.VALIDATION
         return cast(list[LabelType], self.dataset[split][self.dataset.label_feature])
 
     def test_utterances(self, idx: int | None = None) -> list[str]:
         """
-        Get the test utterances.
+        Retrieve test utterances from the dataset.
 
+        If a specific test split index is provided, retrieves utterances
+        from the indexed test split. Otherwise, retrieves utterances from
+        the primary test split.
+
+        :param idx: Optional index for a specific test split.
         :return: List of test utterances.
         """
         split = f"{Split.TEST}_{idx}" if idx is not None else Split.TEST
         return cast(list[str], self.dataset[split][self.dataset.utterance_feature])
 
     def test_labels(self, idx: int | None = None) -> list[LabelType]:
         """
-        Get the test labels.
+        Retrieve test labels from the dataset.
+
+        If a specific test split index is provided, retrieves labels
+        from the indexed test split. Otherwise, retrieves labels from
+        the primary test split.
 
+        :param idx: Optional index for a specific test split.
         :return: List of test labels.
         """
         split = f"{Split.TEST}_{idx}" if idx is not None else Split.TEST
         return cast(list[LabelType], self.dataset[split][self.dataset.label_feature])
 
     def oos_utterances(self, idx: int | None = None) -> list[str]:
         """
-        Get the out-of-scope utterances.
+        Retrieve out-of-scope (OOS) utterances from the dataset.
+
+        If the dataset contains out-of-scope samples, retrieves the utterances
+        from the specified OOS split index (if provided) or the primary OOS split.
+        Returns an empty list if no OOS samples are available in the dataset.
 
-        :return: List of out-of-scope utterances if available, otherwise an empty list.
+        :param idx: Optional index for a specific OOS split.
+        :return: List of out-of-scope utterances, or an empty list if unavailable.
         """
         if self.has_oos_samples():
             split = f"{Split.OOS}_{idx}" if idx is not None else Split.OOS

autointent/context/optimization_info/_optimization_info.py

@@ -178,7 +178,10 @@ def get_best_oos_scores(self, split: Literal["train", "validation", "test"]) ->
         """
         Retrieve the out-of-scope scores from the best scorer node.
 
-        :return: Out-of-scope scores as a numpy array.
+        :param split: The data split for which to retrieve the OOS scores.
+            Must be one of "train", "validation", or "test".
+        :return: A numpy array containing OOS scores for the specified split,
+            or `None` if no OOS scores are available.
         """
         best_scorer_artifact: ScorerArtifact = self._get_best_artifact(node_type=NodeType.scoring)  # type: ignore[assignment]
         if best_scorer_artifact.oos_scores is not None:
@@ -219,7 +222,7 @@ def get_inference_nodes_config(self) -> list[InferenceNodeConfig]:
             )
         return res
 
-    def get_best_module(self, node_type: str) -> "Module | None":
+    def _get_best_module(self, node_type: str) -> "Module | None":
         """
         Retrieve the best module for a specific node type.
 
@@ -237,5 +240,5 @@ def get_best_modules(self) -> dict[NodeTypeType, "Module"]:
 
         :return: Dictionary of the best modules for each node type.
         """
-        res = {nt: self.get_best_module(nt) for nt in NODE_TYPES}
+        res = {nt: self._get_best_module(nt) for nt in NODE_TYPES}
         return {nt: m for nt, m in res.items() if m is not None}  # type: ignore[misc]

autointent/modules/prediction/_base.py

@@ -50,6 +50,7 @@ def score(
         Calculate metric on test set and return metric value.
 
         :param context: Context to score
+        :param split: Target split
         :param metric_fn: Metric function
         :return: Score
         """

autointent/modules/retrieval/_vectordb.py

@@ -121,6 +121,7 @@ def score(
         Evaluate the retrieval model using a specified metric function.
 
         :param context: The context containing test data and labels.
+        :param split: Target split
         :param metric_fn: Function to compute the retrieval metric.
         :return: Computed metric score.
         """

autointent/modules/scoring/_base.py

@@ -31,6 +31,7 @@ def score(
         Evaluate the scorer on a test set and compute the specified metric.
 
         :param context: Context containing test set and other data.
+        :param split: Target split
         :param metric_fn: Function to compute the scoring metric.
         :return: Computed metric value for the test set.
         """