add dummy labels

Author: sfluegel05

chebifier/prediction_models/electra_predictor.py

@@ -63,7 +63,7 @@ def explain_smiles(self, smiles) -> dict:
         from chebai.preprocessing.reader import EMBEDDING_OFFSET
 
         reader = self.reader_cls()
-        token_dict = reader.to_data(dict(features=smiles, labels=None))
+        token_dict = reader.to_data(dict(features=smiles, labels=[1, 2]))  # dummy label
         tokens = np.array(token_dict["features"]).astype(int).tolist()
         result = self.calculate_results([token_dict])
 

chebifier/prediction_models/gnn_predictor.py

@@ -77,7 +77,9 @@ def init_model(self, ckpt_path: str, **kwargs) -> "ResGatedGraphPred":
     def read_smiles(self, smiles):
         from chebai_graph.preprocessing.datasets.chebi import GraphPropAsPerNodeType
 
-        d = self.dataset.READER().to_data(dict(features=smiles, labels=None))
+        d = self.dataset.READER().to_data(
+            dict(features=smiles, labels=[1, 2])
+        )  # dummy label
         property_data = d
         # TODO merge props into base should not be a method of a dataset (or at least static)
         for property in self.dataset.properties:

chebifier/prediction_models/nn_predictor.py

@@ -50,7 +50,7 @@ def batchify(self, batch):
             yield cache
 
     def read_smiles(self, smiles):
-        d = self.reader.to_data(dict(features=smiles, labels=None))
+        d = self.reader.to_data(dict(features=smiles, labels=[1, 2]))  # dummy label
         return d
 
     @modelwise_smiles_lru_cache.batch_decorator