update numpy typing (#188)

Author: Samoed

autointent/modules/decision/_adaptive.py

@@ -148,7 +148,7 @@ def multilabel_predict(scores: npt.NDArray[Any], r: float, tags: list[Tag] | Non
     res = (scores >= thresh[:, None]).astype(int)
     if tags:
         res = apply_tags(res, scores, tags)
-    y_pred: list[MultiLabel] = res.tolist()  # type: ignore[assignment]
+    y_pred: list[MultiLabel] = res.tolist()
     return [lab if sum(lab) > 0 else None for lab in y_pred]
 
 

autointent/modules/decision/_jinoos.py

@@ -113,7 +113,7 @@ def predict(self, scores: npt.NDArray[Any]) -> list[int | None]:
         if scores.shape[1] != self._n_classes:
             raise MismatchNumClassesError
         pred_classes, best_scores = _predict(scores)
-        y_pred: list[int] = _detect_oos(pred_classes, best_scores, self._thresh).tolist()  # type: ignore[assignment]
+        y_pred: list[int] = _detect_oos(pred_classes, best_scores, self._thresh).tolist()
         return [lab if lab != -1 else None for lab in y_pred]
 
     @staticmethod

autointent/modules/decision/_threshold.py

@@ -169,7 +169,7 @@ def multiclass_predict(scores: npt.NDArray[Any], thresh: float | npt.NDArray[Any
         thresh_selected = thresh[pred_classes]
         pred_classes[best_scores < thresh_selected] = -1  # out of scope
 
-    y_pred: list[int] = pred_classes.tolist()  # type: ignore[assignment]
+    y_pred: list[int] = pred_classes.tolist()
     return [lab if lab != -1 else None for lab in y_pred]
 
 
@@ -191,5 +191,5 @@ def multilabel_predict(
     res = (scores >= thresh).astype(int) if isinstance(thresh, float) else (scores >= thresh[None, :]).astype(int)
     if tags:
         res = apply_tags(res, scores, tags)
-    y_pred: list[MultiLabel] = res.tolist()  # type: ignore[assignment]
+    y_pred: list[MultiLabel] = res.tolist()
     return [lab if sum(lab) > 0 else None for lab in y_pred]

autointent/modules/scoring/_dnnc/dnnc.py

@@ -180,7 +180,7 @@ def _get_cross_encoder_scores(self, utterances: list[str], candidates: list[list
 
         flattened_cross_encoder_scores: npt.NDArray[np.float64] = self._cross_encoder.predict(flattened_text_pairs)
         return [
-            flattened_cross_encoder_scores[i : i + self.k].tolist()  # type: ignore[misc]
+            flattened_cross_encoder_scores[i : i + self.k].tolist()
             for i in range(0, len(flattened_cross_encoder_scores), self.k)
         ]