out_dim for sum of embeddings constructor

Author: spirinamayya

CHANGELOG.md

@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added 
 - `SimilarityModuleBase`, `DistanceSimilarityModule`, similarity module to `TransformerTorchBackbone`, parameters to transformer-based models `similarity_module_type`, `similarity_module_kwargs` ([#272](https://github.com/MobileTeleSystems/RecTools/pull/272))
+- `out_dim` property to `IdEmbeddingsItemNet`, `CatFeaturesItemNet` and `SumOfEmbeddingsConstructor` ([#276](https://github.com/MobileTeleSystems/RecTools/pull/276))
 
 ## [0.12.0] - 24.02.2025
 

rectools/models/nn/item_net.py

@@ -449,6 +449,11 @@ def forward(self, items: torch.Tensor) -> torch.Tensor:
         """
         raise NotImplementedError()
 
+    @property
+    def out_dim(self) -> int:
+        """Return item net constructor output dimension."""
+        raise NotImplementedError()
+
 
 class SumOfEmbeddingsConstructor(ItemNetConstructorBase):
     """
@@ -482,3 +487,8 @@ def forward(self, items: torch.Tensor) -> torch.Tensor:
             item_emb = self.item_net_blocks[idx_block](items)
             item_embs.append(item_emb)
         return torch.sum(torch.stack(item_embs, dim=0), dim=0)
+
+    @property
+    def out_dim(self) -> int:
+        """Return item net constructor output dimension."""
+        return self.item_net_blocks[0].out_dim

tests/models/nn/test_item_net.py

@@ -306,9 +306,9 @@ def test_out_dim(self, dataset_item_features: Dataset, n_factors: int) -> None:
         cat_item_embeddings = CatFeaturesItemNet.from_dataset(
             dataset_item_features, n_factors=n_factors, dropout_rate=0.5
         )
-        if cat_item_embeddings is not None:
-            out_dim = cat_item_embeddings.out_dim
-            assert out_dim == n_factors
+        assert isinstance(cat_item_embeddings, CatFeaturesItemNet)
+        out_dim = cat_item_embeddings.out_dim
+        assert out_dim == n_factors
 
 
 @pytest.mark.filterwarnings("ignore::DeprecationWarning")
@@ -500,3 +500,20 @@ def test_raise_when_no_item_net_blocks(
             SumOfEmbeddingsConstructor.from_dataset(
                 ds, n_factors=10, dropout_rate=0.5, item_net_block_types=item_net_block_types
             )
+
+    @pytest.mark.parametrize(
+        "item_net_block_types,n_factors",
+        (
+            ((IdEmbeddingsItemNet,), 8),
+            ((IdEmbeddingsItemNet, CatFeaturesItemNet), 16),
+            ((CatFeaturesItemNet,), 16),
+        ),
+    )
+    def test_out_dim(
+        self, dataset_item_features: Dataset, item_net_block_types: tp.Sequence[tp.Type[ItemNetBase]], n_factors: int
+    ) -> None:
+        item_net = SumOfEmbeddingsConstructor.from_dataset(
+            dataset_item_features, n_factors=n_factors, dropout_rate=0.5, item_net_block_types=item_net_block_types
+        )
+        out_dim = item_net.out_dim
+        assert out_dim == n_factors