Fixed issue #53 related to the use of some transformer models without categorical columns

README.md

@@ -11,6 +11,7 @@
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/jrzaurin/pytorch-widedeep/issues)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
 
 # pytorch-widedeep
 

VERSION

-1.0.9
\ No newline at end of file
+1.0.10
\ No newline at end of file

pypi_README.md

@@ -6,6 +6,7 @@
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/jrzaurin/pytorch-widedeep/issues)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
 
 
 # pytorch-widedeep

pytorch_widedeep/models/transformers/ft_transformer.py

@@ -134,7 +134,7 @@ class FTTransformer(nn.Module):
     def __init__(
         self,
         column_idx: Dict[str, int],
-        embed_input: List[Tuple[str, int]],
+        embed_input: Optional[List[Tuple[str, int]]] = None,
         embed_dropout: float = 0.1,
         full_embed_dropout: bool = False,
         shared_embed: bool = False,
@@ -194,11 +194,6 @@ class FTTransformer(nn.Module):
         self.n_cont = len(continuous_cols) if continuous_cols is not None else 0
         self.n_feats = self.n_cat + self.n_cont
 
-        if self.n_cont and not self.n_cat and not self.embed_continuous:
-            raise ValueError(
-                "If only continuous features are used 'embed_continuous' must be set to 'True'"
-            )
-
         self.cat_and_cont_embed = CatAndContEmbeddings(
             input_dim,
             column_idx,

pytorch_widedeep/models/transformers/saint.py

@@ -120,7 +120,7 @@ class SAINT(nn.Module):
     def __init__(
         self,
         column_idx: Dict[str, int],
-        embed_input: List[Tuple[str, int]],
+        embed_input: Optional[List[Tuple[str, int]]] = None,
         embed_dropout: float = 0.1,
         full_embed_dropout: bool = False,
         shared_embed: bool = False,
@@ -173,11 +173,6 @@ class SAINT(nn.Module):
         self.n_cont = len(continuous_cols) if continuous_cols is not None else 0
         self.n_feats = self.n_cat + self.n_cont
 
-        if self.n_cont and not self.n_cat and not self.embed_continuous:
-            raise ValueError(
-                "If only continuous features are used 'embed_continuous' must be set to 'True'"
-            )
-
         self.cat_and_cont_embed = CatAndContEmbeddings(
             input_dim,
             column_idx,

pytorch_widedeep/models/transformers/tab_fastformer.py

@@ -182,11 +182,6 @@ class TabFastFormer(nn.Module):
         self.n_cont = len(continuous_cols) if continuous_cols is not None else 0
         self.n_feats = self.n_cat + self.n_cont
 
-        if self.n_cont and not self.n_cat and not self.embed_continuous:
-            raise ValueError(
-                "If only continuous features are used 'embed_continuous' must be set to 'True'"
-            )
-
         self.cat_and_cont_embed = CatAndContEmbeddings(
             input_dim,
             column_idx,

pytorch_widedeep/version.py

-__version__ = "1.0.9"
+__version__ = "1.0.10"

tests/test_model_components/test_mc_transformers.py

@@ -449,3 +449,34 @@ def test_ft_transformer_mlp(mlp_first_h, shoud_work):
     else:
         with pytest.raises(AssertionError):
             model = _build_model("fttransformer", params)  # noqa: F841
+
+
+###############################################################################
+# Test transformers with only continuous cols
+###############################################################################
+
+
+X_tab_only_cont = torch.from_numpy(
+    np.vstack([np.random.rand(10) for _ in range(4)]).transpose()
+)
+colnames_only_cont = list(string.ascii_lowercase)[:4]
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "fttransformer",
+        "saint",
+        "tabfastformer",
+    ],
+)
+def test_transformers_only_cont(model_name):
+    params = {
+        "column_idx": {k: v for v, k in enumerate(colnames_only_cont)},
+        "continuous_cols": colnames_only_cont,
+    }
+
+    model = _build_model(model_name, params)
+    out = model(X_tab_only_cont)
+
+    assert out.size(0) == 10 and out.size(1) == model.output_dim