Fix tabular with no categorical fields (#1144)

CHANGELOG.md

@@ -70,6 +70,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug where the `labels` property would return `None` when using `ObjectDetectionData.from_fiftyone` ([#1136](https://github.com/PyTorchLightning/lightning-flash/pull/1136))
 
+- Fixed a bug where `TabularData` would not work correctly with no categorical variables ([#1144](https://github.com/PyTorchLightning/lightning-flash/pull/1144))
+
 ### Removed
 
 - Removed the `Seq2SeqData` base class (use `TranslationData` or `SummarizationData` directly) ([#1128](https://github.com/PyTorchLightning/lightning-flash/pull/1128))

flash/tabular/input.py

@@ -120,8 +120,8 @@ def preprocess(
         num_vars = _to_num_vars_numpy(df, parameters["numerical_fields"])
 
         num_samples = len(df)
-        cat_vars = np.stack(cat_vars, 1) if len(cat_vars) else np.zeros((num_samples, 0))
-        num_vars = np.stack(num_vars, 1) if len(num_vars) else np.zeros((num_samples, 0))
+        cat_vars = np.stack(cat_vars, 1) if len(cat_vars) else np.zeros((num_samples, 0), dtype=np.int64)
+        num_vars = np.stack(num_vars, 1) if len(num_vars) else np.zeros((num_samples, 0), dtype=np.float32)
 
         return cat_vars, num_vars
 

tests/tabular/classification/test_data_model_integration.py

@@ -32,16 +32,25 @@
 
 @pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 @pytest.mark.parametrize(
-    "backbone", ["tabnet", "tabtransformer", "fttransformer", "autoint", "node", "category_embedding"]
+    "backbone,fields",
+    [
+        ("tabnet", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("tabtransformer", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("fttransformer", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("autoint", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("node", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("category_embedding", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        # No categorical / numerical fields
+        ("tabnet", {"categorical_fields": ["category"]}),
+        ("tabnet", {"numerical_fields": ["scalar_a", "scalar_b"]}),
+    ],
 )
-def test_classification(backbone, tmpdir):
-
+def test_classification(backbone, fields, tmpdir):
     train_data_frame = TEST_DF_1.copy()
     val_data_frame = TEST_DF_1.copy()
     test_data_frame = TEST_DF_1.copy()
     data = TabularClassificationData.from_data_frame(
-        categorical_fields=["category"],
-        numerical_fields=["scalar_a", "scalar_b"],
+        **fields,
         target_fields="label",
         train_data_frame=train_data_frame,
         val_data_frame=val_data_frame,

tests/tabular/regression/test_data_model_integration.py

@@ -32,16 +32,25 @@
 
 @pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 @pytest.mark.parametrize(
-    "backbone", ["tabnet", "tabtransformer", "fttransformer", "autoint", "node", "category_embedding"]
+    "backbone,fields",
+    [
+        ("tabnet", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("tabtransformer", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("fttransformer", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("autoint", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("node", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        ("category_embedding", {"categorical_fields": ["category"], "numerical_fields": ["scalar_a", "scalar_b"]}),
+        # No categorical / numerical fields
+        ("tabnet", {"categorical_fields": ["category"]}),
+        ("tabnet", {"numerical_fields": ["scalar_a", "scalar_b"]}),
+    ],
 )
-def test_regression(backbone, tmpdir):
-
+def test_regression(backbone, fields, tmpdir):
     train_data_frame = TEST_DF_1.copy()
     val_data_frame = TEST_DF_1.copy()
     test_data_frame = TEST_DF_1.copy()
     data = TabularRegressionData.from_data_frame(
-        categorical_fields=["category"],
-        numerical_fields=["scalar_a", "scalar_b"],
+        **fields,
         target_field="label",
         train_data_frame=train_data_frame,
         val_data_frame=val_data_frame,