Fix/warp-kos loss for LightFMWrapperModel (#175)

- Fixed `NotImplementedError` for LightFM with warp-kos loss

CHANGELOG.md

@@ -15,6 +15,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Fixed
 - `display()` method in `MetricsApp` ([#169](https://github.com/MobileTeleSystems/RecTools/pull/169))
 
+### Fixed
+- Allow warp-kos loss for LightFMWrapperModel ([#175](https://github.com/MobileTeleSystems/RecTools/pull/175))
+
 ## [0.7.0] - 29.07.2024
 
 ### Added

rectools/models/lightfm.py

@@ -77,12 +77,13 @@ def _fit(self, dataset: Dataset) -> None:  # type: ignore
         ui_coo = dataset.get_user_item_matrix(include_weights=True).tocoo(copy=False)
         user_features = self._prepare_features(dataset.get_hot_user_features(), dataset.n_hot_users)
         item_features = self._prepare_features(dataset.get_hot_item_features(), dataset.n_hot_items)
+        sample_weight = None if self._model.loss == "warp-kos" else ui_coo
 
         self.model.fit(
             ui_coo,
             user_features=user_features,
             item_features=item_features,
-            sample_weight=ui_coo,
+            sample_weight=sample_weight,
             epochs=self.n_epochs,
             num_threads=self.n_threads,
             verbose=self.verbose > 0,

tests/models/test_lightfm.py

@@ -222,6 +222,16 @@ def test_with_weights(self, interactions_df: pd.DataFrame) -> None:
             actual,
         )
 
+    def test_with_warp_kos(self, dataset: Dataset) -> None:
+        base_model = DeterministicLightFM(no_components=2, loss="warp-kos")
+        try:
+            LightFMWrapperModel(model=base_model, epochs=10).fit(dataset)
+        except NotImplementedError:
+            pytest.fail("Should not raise NotImplementedError")
+        except ValueError:
+            # LightFM raises ValueError with the dataset
+            pass
+
     def test_get_vectors(self, dataset_with_features: Dataset) -> None:
         base_model = LightFM(no_components=2, loss="logistic")
         model = LightFMWrapperModel(model=base_model).fit(dataset_with_features)