Check input features of Model match Schema

transformers4rec/torch/features/tabular.py

@@ -163,7 +163,8 @@ def from_schema(  # type: ignore
             None,
             None,
         )
-
+        processed_features = []
+
         if continuous_tags:
             if continuous_soft_embeddings:
                 maybe_continuous_module = cls.SOFT_EMBEDDING_MODULE_CLASS.from_schema(
@@ -175,14 +176,29 @@ def from_schema(  # type: ignore
                 maybe_continuous_module = cls.CONTINUOUS_MODULE_CLASS.from_schema(
                     schema, tags=continuous_tags, **kwargs
                 )
+            processed_features.extend(schema.select_by_tag(continuous_tags).column_names)
         if categorical_tags:
             maybe_categorical_module = cls.EMBEDDING_MODULE_CLASS.from_schema(
                 schema, tags=categorical_tags, **kwargs
             )
+            processed_features.extend(schema.select_by_tag(categorical_tags).column_names)
         if pretrained_embeddings_tags:
             maybe_pretrained_module = cls.PRETRAINED_EMBEDDING_MODULE_CLASS.from_schema(
                 schema, tags=pretrained_embeddings_tags, **kwargs
             )
+            processed_features.extend(schema.select_by_tag(pretrained_embeddings_tags).column_names)
+
+        unprocessed_features = set(schema.column_names).difference(set(processed_features))
+        if unprocessed_features:
+            raise ValueError(
+                "Schema provided to `TabularFeatures` includes features "
+                "without any of the following tags: "
+                f"continuous ({continuous_tags}), categorical ({categorical_tags}), "
+                f"or pretrained embeddings ({pretrained_embeddings_tags}). "
+                "Please ensure all columns have one of these tags "
+                "or are excluded from the schema. "
+                f"\nUnproceesed features: {unprocessed_features} "
+
 
         output = cls(
             continuous_module=maybe_continuous_module,

transformers4rec/torch/model/base.py

@@ -538,6 +538,21 @@ def __init__(
         self.top_k = top_k
 
     def forward(self, inputs: TabularData, targets=None, training=False, testing=False, **kwargs):
+        model_expected_features = set(self.input_schema.column_names)
+        call_input_features = set(inputs.keys())
+        if not (training or testing) and model_expected_features != call_input_features:
+            raise ValueError(
+                "Model forward called with different set of features "
+                "compared with the input schema it was configured with "
+                "Please check that the inputs passed to the model are only  "
+                "those required by the model."
+                f"\nModel expected features:\n\t{model_expected_features}"
+                f"\nCall input features:\n\t{call_input_features}"
+                f"\nFeatures expected by model input schema only:"
+                f"\n\t{model_expected_features.difference(call_input_features)}"
+                f"\nFeatures provided in inputs only:"
+                f"\n\t{call_input_features.difference(model_expected_features)}"
+            )
         # Convert inputs to float32 which is the default type, expected by PyTorch
         for name, val in inputs.items():
             if torch.is_floating_point(val):