Feat/module inputs validation

autointent/modules/abc/_base.py

@@ -133,7 +133,7 @@ def _validate_multilabel(self, data_is_multilabel: bool) -> None:
             logger.error(msg)
             raise WrongClassificationError(msg)
 
-    def _validate_oos(self, data_contains_oos: bool) -> None:
+    def _validate_oos(self, data_contains_oos: bool, raise_error: bool = True) -> None:
         if data_contains_oos != self.supports_oos:
             if self.supports_oos and not data_contains_oos:
                 msg = (
@@ -143,10 +143,18 @@ def _validate_oos(self, data_contains_oos: bool) -> None:
             elif not self.supports_oos and data_contains_oos:
                 msg = (
                     f'"{self.name}" is NOT designed to handle OOS samples, but your data '
-                    "contain it. So, using this method reduces the power of classification."
+                    "contains it. So, using this method reduces the power of classification."
                 )
+            if raise_error:
+                logger.error(msg)
+                raise ValueError(msg)
             logger.warning(msg)
 
+    def _validate_task(self, labels: ListOfGenericLabels) -> None:
+        self._n_classes, self._multilabel, self._oos = self._get_task_specs(labels)
+        self._validate_multilabel(self._multilabel)
+        self._validate_oos(self._oos)
+
     @staticmethod
     def _get_task_specs(labels: ListOfGenericLabels) -> tuple[int, bool, bool]:
         """

autointent/modules/abc/_decision.py

@@ -63,27 +63,18 @@ def get_assets(self) -> DecisionArtifact:
     def clear_cache(self) -> None:
         """Clear cache."""
 
-    def _validate_inputs(self, scores: npt.NDArray[Any], labels: ListOfGenericLabels) -> tuple[int, bool, bool]:
-        """
-        Sanity check if labels and scores are valid to be a training data for decision module.
-
-        :param scores: training scores
-        :param labels: training labels
-        :return: number of classes, indicator if it's a multi-label task,
-                    indicator if data contains oos samples
-        """
-        n_classes, multilabel, contains_oos_samples = super()._get_task_specs(labels)
-
-        if n_classes != scores.shape[1]:
+    def _validate_task(self, scores: npt.NDArray[Any], labels: ListOfGenericLabels) -> None:
+        self._n_classes, self._multilabel, self._oos = self._get_task_specs(labels)
+        self._validate_multilabel(self._multilabel)
+        self._validate_oos(self._oos, raise_error=False)
+        if self._n_classes != scores.shape[1]:
             msg = (
                 "There is a mismatch between provided labels and scores. "
-                f"Labels contains {n_classes} classes, but scores contain "
+                f"Labels contains {self._n_classes} classes, but scores contain "
                 f"probabilities for {scores.shape[1]} classes."
             )
             raise ValueError(msg)
 
-        return n_classes, multilabel, contains_oos_samples
-
 
 def get_decision_evaluation_data(
     context: Context,

autointent/modules/abc/_scoring.py

@@ -19,6 +19,8 @@ class ScoringModule(Module, ABC):
     using a scoring metric.
     """
 
+    supports_oos = False
+
     def score(
         self,
         context: Context,

autointent/modules/decision/_adaptive.py

@@ -97,9 +97,7 @@ def fit(
         """
         self.tags = tags
 
-        self._n_classes, multilabel, contains_oos = self._validate_inputs(scores, labels)
-        self._validate_multilabel(multilabel)
-        self._validate_oos(contains_oos)
+        self._validate_task(scores, labels)
 
         metrics_list = []
         for r in self.search_space:

autointent/modules/decision/_argmax.py

@@ -76,9 +76,7 @@ def fit(
         :param tags: Tags to fit
         :raises WrongClassificationError: If the classification is wrong.
         """
-        self._n_classes, multilabel, contains_oos = self._validate_inputs(scores, labels)
-        self._validate_multilabel(multilabel)
-        self._validate_oos(contains_oos)
+        self._validate_task(scores, labels)
 
     def predict(self, scores: npt.NDArray[Any]) -> list[int]:
         """

autointent/modules/decision/_jinoos.py

@@ -89,10 +89,7 @@ def fit(
         :param labels: Labels to fit
         :param tags: Tags to fit
         """
-        # TODO: use dev split instead of test split.
-        self._n_classes, multilabel, contains_oos = self._validate_inputs(scores, labels)
-        self._validate_multilabel(multilabel)
-        self._validate_oos(contains_oos)
+        self._validate_task(scores, labels)
 
         pred_classes, best_scores = _predict(scores)
 

autointent/modules/decision/_threshold.py

@@ -113,9 +113,7 @@ def fit(
         :param tags: Tags to fit
         """
         self.tags = tags
-        self._n_classes, self._multilabel, contains_oos = self._validate_inputs(scores, labels)
-        self._validate_multilabel(self._multilabel)
-        self._validate_oos(contains_oos)
+        self._validate_task(scores, labels)
 
         if not isinstance(self.thresh, float):
             if len(self.thresh) != self._n_classes:

autointent/modules/decision/_tunable.py

@@ -119,9 +119,7 @@ def fit(
         :param tags: Tags to fit
         """
         self.tags = tags
-        self._n_classes, self._multilabel, contains_oos = self._validate_inputs(scores, labels)
-        self._validate_multilabel(self._multilabel)
-        self._validate_oos(contains_oos)
+        self._validate_task(scores, labels)
 
         thresh_optimizer = ThreshOptimizer(
             n_classes=self._n_classes, multilabel=self._multilabel, n_trials=self.n_trials

autointent/modules/scoring/_description/description.py

@@ -29,6 +29,8 @@ class DescriptionScorer(ScoringModule):
     _n_classes: int
     _multilabel: bool
     _description_vectors: NDArray[Any]
+    supports_multiclass = True
+    supports_multilabel = True
 
     def __init__(
         self,
@@ -105,12 +107,7 @@ def fit(
         :param descriptions: List of intent descriptions.
         :raises ValueError: If descriptions contain None values or embeddings mismatch utterances.
         """
-        if isinstance(labels[0], list):
-            self._n_classes = len(labels[0])
-            self._multilabel = True
-        else:
-            self._n_classes = len(set(labels))
-            self._multilabel = False
+        self._validate_task(labels)
 
         if any(description is None for description in descriptions):
             error_text = (

autointent/modules/scoring/_dnnc/dnnc.py

@@ -70,6 +70,8 @@ class DNNCScorer(ScoringModule):
     _n_classes: int
     _vector_index: VectorIndex
     _cross_encoder: Ranker
+    supports_multilabel = False
+    supports_multiclass = True
 
     def __init__(  # noqa: PLR0913
         self,
@@ -155,7 +157,7 @@ def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         :param labels: List of labels corresponding to the utterances.
         :raises ValueError: If the vector index mismatches the provided utterances.
         """
-        self._n_classes = len(set(labels))
+        self._validate_task(labels)
 
         self._vector_index = VectorIndex(
             self.embedder_name,

autointent/modules/scoring/_knn/knn.py

@@ -52,7 +52,7 @@ class KNNScorer(ScoringModule):
     _n_classes: int
     _multilabel: bool
     supports_multilabel = True
-    supports_oos = False
+    supports_multiclass = True
 
     def __init__(
         self,
@@ -132,8 +132,7 @@ def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         :param labels: List of labels corresponding to the utterances.
         :raises ValueError: If the vector index mismatches the provided utterances.
         """
-        self._n_classes, self._multilabel, contains_oos = self._get_task_specs(labels)
-        self._validate_oos(contains_oos)
+        self._validate_task(labels)
 
         self._vector_index = VectorIndex(
             self.embedder_name,

autointent/modules/scoring/_linear.py

@@ -47,6 +47,8 @@ class LinearScorer(ScoringModule):
     _multilabel: bool
     _clf: LogisticRegressionCV | MultiOutputClassifier
     _embedder: Embedder
+    supports_multiclass = True
+    supports_multilabel = True
 
     def __init__(
         self,
@@ -125,7 +127,7 @@ def fit(
         :param labels: List of labels corresponding to the utterances.
         :raises ValueError: If the vector index mismatches the provided utterances.
         """
-        self._multilabel = isinstance(labels[0], list)
+        self._validate_task(labels)
 
         embedder = Embedder(
             device=self.embedder_device,

autointent/modules/scoring/_mlknn/mlknn.py

@@ -52,6 +52,8 @@ class MLKnnScorer(ScoringModule):
     _cond_prob_false: NDArray[Any]
     _features: NDArray[Any]
     _labels: NDArray[Any]
+    supports_multiclass = False
+    supports_multilabel = True
 
     def __init__(
         self,
@@ -135,11 +137,7 @@ def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         :raises TypeError: If the labels are not multi-label.
         :raises ValueError: If the vector index mismatches the provided utterances.
         """
-        if not isinstance(labels[0], list):
-            msg = "mlknn scorer support only multilabel input"
-            raise TypeError(msg)
-
-        self._n_classes = len(labels[0])
+        self._validate_task(labels)
 
         self._vector_index = VectorIndex(
             self.embedder_name,

autointent/modules/scoring/_sklearn/sklearn_scorer.py

@@ -9,7 +9,7 @@
 from typing_extensions import Self
 
 from autointent import Context, Embedder
-from autointent.custom_types import LabelType
+from autointent.custom_types import ListOfLabels
 from autointent.modules.abc import ScoringModule
 
 logger = logging.getLogger(__name__)
@@ -38,6 +38,8 @@ class SklearnScorer(ScoringModule):
     """
 
     name = "sklearn"
+    supports_multilabel = True
+    supports_multiclass = True
 
     def __init__(
         self,
@@ -101,7 +103,7 @@ def from_context(
     def fit(
         self,
         utterances: list[str],
-        labels: list[LabelType],
+        labels: ListOfLabels,
     ) -> None:
         """
         Train the chosen sklearn classifier.
@@ -110,7 +112,7 @@ def fit(
         :param labels: List of labels corresponding to the utterances.
         :raises ValueError: If the vector index mismatches the provided utterances.
         """
-        self._multilabel = isinstance(labels[0], list)
+        self._validate_task(labels)
 
         embedder = Embedder(
             device=self.embedder_device,