set out_dim dynamically

chebai/cli.py

@@ -1,7 +1,8 @@
-from typing import Dict, Set
+from typing import Dict, Set, Type
 
 from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
 
+from chebai.preprocessing.datasets import XYBaseDataModule
 from chebai.trainer.CustomTrainer import CustomTrainer
 
 
@@ -38,14 +39,35 @@ def add_arguments_to_parser(self, parser: LightningArgumentParser):
         Args:
             parser (LightningArgumentParser): Argument parser instance.
         """
+
+        def call_data_methods(data: Type[XYBaseDataModule]):
+            if data._num_of_labels is None:
+                data.prepare_data()
+                data.setup()
+            return data.num_of_labels
+
+        parser.link_arguments(
+            "data",
+            "model.init_args.out_dim",
+            apply_on="instantiate",
+            compute_fn=call_data_methods,
+        )
+
+        parser.link_arguments(
+            "data.feature_vector_size",
+            "model.init_args.input_dim",
+            apply_on="instantiate",
+        )
+
         for kind in ("train", "val", "test"):
             for average in ("micro-f1", "macro-f1", "balanced-accuracy"):
                 parser.link_arguments(
-                    "model.init_args.out_dim",
+                    "data.num_of_labels",
                     f"model.init_args.{kind}_metrics.init_args.metrics.{average}.init_args.num_labels",
+                    apply_on="instantiate",
                 )
         parser.link_arguments(
-            "model.init_args.out_dim", "trainer.callbacks.init_args.num_labels"
+            "data.num_of_labels", "trainer.callbacks.init_args.num_labels"
         )
 
     @staticmethod

chebai/models/base.py

@@ -36,6 +36,7 @@ def __init__(
         self,
         criterion: torch.nn.Module = None,
         out_dim: Optional[int] = None,
+        input_dim: Optional[int] = None,
         train_metrics: Optional[torch.nn.Module] = None,
         val_metrics: Optional[torch.nn.Module] = None,
         test_metrics: Optional[torch.nn.Module] = None,
@@ -57,7 +58,12 @@ def __init__(
                 *exclude_hyperparameter_logging,
             ]
         )
+
         self.out_dim = out_dim
+        self.input_dim = input_dim
+        assert out_dim is not None, "out_dim must be specified"
+        assert input_dim is not None, "input_dim must be specified"
+
         if optimizer_kwargs:
             self.optimizer_kwargs = optimizer_kwargs
         else:

chebai/preprocessing/datasets/base.py

@@ -119,6 +119,23 @@ def __init__(
             os.makedirs(os.path.join(self.processed_dir, self.fold_dir), exist_ok=True)
         self.save_hyperparameters()
 
+        self._num_of_labels = None
+        self._feature_vector_size = None
+        self._prepare_data_flag = 1
+        self._setup_data_flag = 1
+
+    @property
+    def num_of_labels(self):
+        assert self._num_of_labels is not None, "num of labels must be set"
+        return self._num_of_labels
+
+    @property
+    def feature_vector_size(self):
+        assert (
+            self._feature_vector_size is not None
+        ), "size of feature vector must be set"
+        return self._feature_vector_size
+
     @property
     def identifier(self) -> tuple:
         """Identifier for the dataset."""
@@ -390,7 +407,17 @@ def predict_dataloader(
         """
         return self.dataloader(self.prediction_kind, shuffle=False, **kwargs)
 
-    def setup(self, **kwargs):
+    def prepare_data(self, *args, **kwargs) -> None:
+        if self._prepare_data_flag != 1:
+            return
+
+        self._prepare_data_flag += 1
+        self._perform_data_preparation(*args, **kwargs)
+
+    def _perform_data_preparation(self, *args, **kwargs) -> None:
+        raise NotImplementedError
+
+    def setup(self, *args, **kwargs) -> None:
         """
         Setup the data module.
 
@@ -399,6 +426,11 @@ def setup(self, **kwargs):
         Args:
             **kwargs: Additional keyword arguments.
         """
+        if self._setup_data_flag != 1:
+            return
+
+        self._setup_data_flag += 1
+
         rank_zero_info(f"Check for processed data in {self.processed_dir}")
         rank_zero_info(f"Cross-validation enabled: {self.use_inner_cross_validation}")
         if any(
@@ -410,6 +442,21 @@ def setup(self, **kwargs):
         if not ("keep_reader" in kwargs and kwargs["keep_reader"]):
             self.reader.on_finish()
 
+        self._set_processed_data_props()
+
+    def _set_processed_data_props(self):
+
+        data_pt = torch.load(
+            os.path.join(self.processed_dir, self.processed_file_names_dict["data"]),
+            weights_only=False,
+        )
+
+        self._num_of_labels = len(data_pt[0]["labels"])
+        self._feature_vector_size = max(len(d["features"]) for d in data_pt)
+
+        print(f"Number of labels for loaded data: {self._num_of_labels}")
+        print(f"Feature vector size: {self._feature_vector_size}")
+
     def setup_processed(self):
         """
         Setup the processed data.
@@ -482,18 +529,6 @@ def raw_file_names_dict(self) -> dict:
         """
         raise NotImplementedError
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the number of labels.
-
-        This property should be implemented by subclasses to provide the number of labels.
-
-        Returns:
-            int: The number of labels. Returns -1 for seq2seq encoding.
-        """
-        raise NotImplementedError
-
 
 class MergedDataset(XYBaseDataModule):
     MERGED = []
@@ -531,7 +566,7 @@ def __init__(
         os.makedirs(self.processed_dir, exist_ok=True)
         super(pl.LightningDataModule, self).__init__(**kwargs)
 
-    def prepare_data(self):
+    def _perform_data_preparation(self):
         """
         Placeholder for data preparation logic.
         """
@@ -547,9 +582,15 @@ def setup(self, **kwargs):
         Args:
             **kwargs: Additional keyword arguments.
         """
+        if self._setup_data_flag != 1:
+            return
+
+        self._setup_data_flag += 1
         for s in self.subsets:
             s.setup(**kwargs)
 
+        self._set_processed_data_props()
+
     def dataloader(self, kind: str, **kwargs) -> DataLoader:
         """
         Creates a DataLoader for a specific subset.
@@ -623,13 +664,6 @@ def processed_file_names(self) -> List[str]:
         """
         return ["test.pt", "train.pt", "validation.pt"]
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the number of labels from the first subset.
-        """
-        return self.subsets[0].label_number
-
     @property
     def limits(self):
         """
@@ -725,7 +759,7 @@ def _validate_splits_file_path(splits_file_path: Optional[str]) -> Optional[str]
         return splits_file_path
 
     # ------------------------------ Phase: Prepare data -----------------------------------
-    def prepare_data(self, *args: Any, **kwargs: Any) -> None:
+    def _perform_data_preparation(self, *args: Any, **kwargs: Any) -> None:
         """
         Prepares the data for the dataset.
 

chebai/preprocessing/datasets/chebi.py

@@ -59,7 +59,7 @@ def download(self):
     def raw_file_names(self):
         return ["test.pkl", "train.pkl", "validation.pkl"]
 
-    def prepare_data(self, *args, **kwargs):
+    def _perform_data_preparation(self, *args, **kwargs):
         print("Check for raw data in", self.raw_dir)
         if any(
             not os.path.isfile(os.path.join(self.raw_dir, f))
@@ -88,10 +88,6 @@ def setup_processed(self):
                 os.path.join(self.processed_dir, f"{k}.pt"),
             )
 
-    @property
-    def label_number(self):
-        return 500
-
 
 class JCIData(JCIBase):
     READER = dr.OrdReader
@@ -158,7 +154,7 @@ def __init__(
             )
 
     # ------------------------------ Phase: Prepare data -----------------------------------
-    def prepare_data(self, *args: Any, **kwargs: Any) -> None:
+    def _perform_data_preparation(self, *args: Any, **kwargs: Any) -> None:
         """
         Prepares the data for the Chebi dataset.
 
@@ -179,7 +175,7 @@ def prepare_data(self, *args: Any, **kwargs: Any) -> None:
         Returns:
             None
         """
-        super().prepare_data(args, kwargs)
+        super()._perform_data_preparation(args, kwargs)
 
         if self.chebi_version_train is not None:
             if not os.path.isfile(
@@ -545,10 +541,6 @@ def raw_file_names_dict(self) -> dict:
 
 class JCIExtendedBase(_ChEBIDataExtractor):
 
-    @property
-    def label_number(self):
-        return 500
-
     @property
     def _name(self):
         return "JCI_extended"
@@ -573,16 +565,6 @@ class ChEBIOverX(_ChEBIDataExtractor):
     READER: dr.ChemDataReader = dr.ChemDataReader
     THRESHOLD: int = None
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the number of labels in the dataset.
-
-        Returns:
-            int: The number of labels.
-        """
-        return 854
-
     @property
     def _name(self) -> str:
         """
@@ -675,17 +657,6 @@ class ChEBIOver100(ChEBIOverX):
 
     THRESHOLD: int = 100
 
-    def label_number(self) -> int:
-        """
-        Returns the number of labels in the dataset.
-
-        Overrides the base class method to return the correct number of labels for this threshold.
-
-        Returns:
-            int: The number of labels.
-        """
-        return 854
-
 
 class ChEBIOver50(ChEBIOverX):
     """
@@ -699,17 +670,6 @@ class ChEBIOver50(ChEBIOverX):
 
     THRESHOLD: int = 50
 
-    def label_number(self) -> int:
-        """
-        Returns the number of labels in the dataset.
-
-        Overrides the base class method to return the correct number of labels for this threshold.
-
-        Returns:
-            int: The number of labels.
-        """
-        return 1332
-
 
 class ChEBIOver100DeepSMILES(ChEBIOverXDeepSMILES, ChEBIOver100):
     """

chebai/preprocessing/datasets/pubchem.py

@@ -179,7 +179,7 @@ def processed_file_names(self) -> List[str]:
         """
         return ["test.pt", "train.pt", "validation.pt"]
 
-    def prepare_data(self, *args, **kwargs):
+    def _perform_data_preparation(self, *args, **kwargs):
         """
         Checks for raw data and downloads if necessary.
         """
@@ -692,13 +692,6 @@ class PubchemChem(PubChem):
 
     READER: Type[dr.ChemDataReader] = dr.ChemDataReader
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the label number.
-        """
-        return -1
-
 
 class PubchemBPE(PubChem):
     """
@@ -712,13 +705,6 @@ class PubchemBPE(PubChem):
 
     READER: Type[dr.ChemBPEReader] = dr.ChemBPEReader
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the label number.
-        """
-        return -1
-
 
 class SWJChem(SWJPreChem):
     """
@@ -732,13 +718,6 @@ class SWJChem(SWJPreChem):
 
     READER: Type[dr.ChemDataUnlabeledReader] = dr.ChemDataUnlabeledReader
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the label number.
-        """
-        return -1
-
 
 class SWJBPE(SWJPreChem):
     """
@@ -752,13 +731,6 @@ class SWJBPE(SWJPreChem):
 
     READER: Type[dr.ChemBPEReader] = dr.ChemBPEReader
 
-    @property
-    def label_number(self) -> int:
-        """
-        Returns the label number.
-        """
-        return -1
-
 
 class PubChemTokens(PubChem):
     """