[Feature]: Generalize Prediction pipeline for Lightning CLI models

.gitignore

@@ -175,7 +175,7 @@ chebai.egg-info
 lightning_logs
 logs
 .isort.cfg
-/.vscode
+/.vscode/launch.json
 
 *.out
 *.err

.vscode/extensions.json

@@ -0,0 +1,11 @@
+{
+    "recommendations": [
+        "ms-python.python",
+        "ms-python.vscode-pylance",
+        "charliermarsh.ruff",
+        "usernamehw.errorlens"
+    ],
+    "unwantedRecommendations": [
+        "ms-python.vscode-python2"
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,16 @@
+{
+    "python.testing.unittestArgs": [
+        "-v",
+        "-s",
+        "./tests",
+        "-p",
+        "test*.py"
+    ],
+    "python.testing.pytestEnabled": false,
+    "python.testing.unittestEnabled": true,
+    "python.analysis.typeCheckingMode": "basic",
+    "editor.formatOnSave": true,
+    "[python]": {
+        "editor.defaultFormatter": "charliermarsh.ruff"
+    }
+}

README.md

@@ -63,7 +63,7 @@ python -m chebai fit --trainer=configs/training/default_trainer.yml --model=conf
 ```
 A command with additional options may look like this:
 ```
-python3 -m chebai fit --trainer=configs/training/default_trainer.yml --model=configs/model/electra.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --model.pretrained_checkpoint=electra_pretrained.ckpt --model.load_prefix=generator. --data=configs/data/chebi/chebi50.yml --model.criterion=configs/loss/bce.yml --data.init_args.batch_size=10 --trainer.logger.init_args.name=chebi50_bce_unweighted --data.init_args.num_workers=9 --model.pass_loss_kwargs=false --data.init_args.chebi_version=231 --data.init_args.data_limit=1000
+python3 -m chebai fit --trainer=configs/training/default_trainer.yml --model=configs/model/electra.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --model.pretrained_checkpoint=electra_pretrained.ckpt --model.load_prefix=generator. --data=configs/data/chebi/chebi50.yml --model.criterion=configs/loss/bce_weighted.yml --data.init_args.batch_size=10 --trainer.logger.init_args.name=chebi50_bce_weighted --data.init_args.num_workers=9 --model.pass_loss_kwargs=false --data.init_args.chebi_version=231 --data.init_args.data_limit=1000
 ```
 
 ### Fine-tuning for classification tasks, e.g. Toxicity prediction
@@ -78,11 +78,16 @@ python -m chebai fit --config=[path-to-your-esol-config] --trainer.callbacks=con
 
 ### Predicting classes given SMILES strings
 ```
-python3 -m chebai predict_from_file --model=[path-to-model-config] --checkpoint_path=[path-to-model] --input_path={path-to-file-containing-smiles] [--classes_path=[path-to-classes-file]] [--save_to=[path-to-output]]
+python3  chebai/result/prediction.py predict_from_file --checkpoint_path=[path-to-model] --smiles_file_path=[path-to-file-containing-smiles]  [--save_to=[path-to-output]]
 ```
-The input files should contain a list of line-separated SMILES strings. This generates a CSV file  that contains the
-one row for each SMILES string and one column for each class.
-The `classes_path` is the path to the dataset's `raw/classes.txt` file that contains the relationship between model output and ChEBI-IDs.
+
+* **`--checkpoint_path`**: Path to the Lightning checkpoint file (must end with `.ckpt`).
+
+* **`--smiles_file_path`**: Path to a text file containing one SMILES string per line.
+
+* **`--save_to`** *(optional)*: Predictions will be saved to the path as CSV file. The CSV will contain one row per SMILES string and one column per predicted class. Default path will be the current working directory with file name as `predictions.csv`.
+
+> **Note**: Newly created checkpoints after PR #148 must be used for this prediction pipeline. The list of ChEBI classes (classification labels) used during training is stored in new checkpoints, which are required.
 
 ## Evaluation
 
@@ -96,7 +101,7 @@ An example notebook is provided at `tutorials/eval_model_basic.ipynb`.
 Alternatively, you can evaluate the model via the CLI:
 
 ```bash
-python -m chebai test --trainer=configs/training/default_trainer.yml --trainer.devices=1 --trainer.num_nodes=1 --ckpt_path=[path-to-finetuned-model] --model=configs/model/electra.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --data=configs/data/chebi/chebi50.yml --data.init_args.batch_size=32 --data.init_args.num_workers=10 --data.init_args.chebi_version=[chebi-version] --model.pass_loss_kwargs=false --model.criterion=configs/loss/bce.yml --model.criterion.init_args.beta=0.99 --data.init_args.splits_file_path=[path-to-splits-file]
+python -m chebai test --trainer=configs/training/default_trainer.yml --trainer.devices=1 --trainer.num_nodes=1 --ckpt_path=[path-to-finetuned-model] --model=configs/model/electra.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --data=configs/data/chebi/chebi50.yml --data.init_args.batch_size=32 --data.init_args.num_workers=10 --data.init_args.chebi_version=[chebi-version] --model.pass_loss_kwargs=false --model.criterion=configs/loss/bce_weighted.yml --model.criterion.init_args.beta=0.99 --data.init_args.splits_file_path=[path-to-splits-file]
 ```
 
 > **Note**: It is recommended to use `devices=1` and `num_nodes=1` during testing; multi-device settings use a `DistributedSampler`, which may replicate some samples to maintain equal batch sizes, so using a single device ensures that each sample or batch is evaluated exactly once.

chebai/cli.py

@@ -59,6 +59,12 @@ def call_data_methods(data: Type[XYBaseDataModule]):
             apply_on="instantiate",
         )
 
+        parser.link_arguments(
+            "data.classes_txt_file_path",
+            "model.init_args.classes_txt_file_path",
+            apply_on="instantiate",
+        )
+
         for kind in ("train", "val", "test"):
             for average in (
                 "micro-f1",
@@ -111,8 +117,6 @@ def subcommands() -> Dict[str, Set[str]]:
             "fit": {"model", "train_dataloaders", "val_dataloaders", "datamodule"},
             "validate": {"model", "dataloaders", "datamodule"},
             "test": {"model", "dataloaders", "datamodule"},
-            "predict": {"model", "dataloaders", "datamodule"},
-            "predict_from_file": {"model"},
         }
 
 

chebai/models/base.py

@@ -40,15 +40,16 @@ def __init__(
         pass_loss_kwargs: bool = True,
         optimizer_kwargs: Optional[Dict[str, Any]] = None,
         exclude_hyperparameter_logging: Optional[Iterable[str]] = None,
+        classes_txt_file_path: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(**kwargs)
         # super().__init__()
         if exclude_hyperparameter_logging is None:
             exclude_hyperparameter_logging = tuple()
         self.criterion = criterion
-        assert out_dim is not None, "out_dim must be specified"
-        assert input_dim is not None, "input_dim must be specified"
+        assert out_dim is not None and out_dim > 0, "out_dim must be specified"
+        assert input_dim is not None and input_dim > 0, "input_dim must be specified"
         self.out_dim = out_dim
         self.input_dim = input_dim
         print(
@@ -62,6 +63,7 @@ def __init__(
                 "train_metrics",
                 "val_metrics",
                 "test_metrics",
+                "classes_txt_file_path",
                 *exclude_hyperparameter_logging,
             ]
         )
@@ -78,6 +80,23 @@ def __init__(
         self.test_metrics = test_metrics
         self.pass_loss_kwargs = pass_loss_kwargs
 
+        self.classes_txt_file_path = classes_txt_file_path
+
+        # During prediction `classes_txt_file_path` is set to None
+        if classes_txt_file_path is not None:
+            with open(classes_txt_file_path, "r") as f:
+                self.labels_list = [cls.strip() for cls in f.readlines()]
+            assert len(self.labels_list) > 0, "Class labels list is empty."
+            assert len(self.labels_list) == out_dim, (
+                f"Number of class labels ({len(self.labels_list)}) does not match "
+                f"the model output dimension ({out_dim})."
+            )
+
+    def on_save_checkpoint(self, checkpoint):
+        if self.classes_txt_file_path is not None:
+            # https://lightning.ai/docs/pytorch/stable/common/checkpointing_intermediate.html#modify-a-checkpoint-anywhere
+            checkpoint["classification_labels"] = self.labels_list
+
     def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         # avoid errors due to unexpected keys (e.g., if loading checkpoint from a bce model and using it with a
         # different loss)
@@ -100,7 +119,7 @@ def __init_subclass__(cls, **kwargs):
 
     def _get_prediction_and_labels(
         self, data: Dict[str, Any], labels: torch.Tensor, output: torch.Tensor
-    ) -> (torch.Tensor, torch.Tensor):
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Gets the predictions and labels from the model output.
 
@@ -151,7 +170,7 @@ def _process_for_loss(
         model_output: torch.Tensor,
         labels: torch.Tensor,
         loss_kwargs: Dict[str, Any],
-    ) -> (torch.Tensor, torch.Tensor, Dict[str, Any]):
+    ) -> tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
         """
         Processes the data for loss computation.
 
@@ -237,7 +256,7 @@ def predict_step(
         Returns:
             Dict[str, Union[torch.Tensor, Any]]: The result of the prediction step.
         """
-        return self._execute(batch, batch_idx, self.test_metrics, prefix="", log=False)
+        return self._execute(batch, batch_idx, log=False)
 
     def _execute(
         self,

chebai/preprocessing/datasets/base.py

@@ -340,18 +340,19 @@ def _load_data_from_file(self, path: str) -> List[Dict[str, Any]]:
             for d in tqdm.tqdm(self._load_dict(path), total=lines)
             if d["features"] is not None
         ]
-        # filter for missing features in resulting data, keep features length below token limit
-        data = [
-            val
-            for val in data
-            if val["features"] is not None
-            and (
-                self.n_token_limit is None or len(val["features"]) <= self.n_token_limit
-            )
-        ]
 
+        data = [val for val in data if self._filter_to_token_limit(val)]
         return data
 
+    def _filter_to_token_limit(self, data_instance: dict) -> bool:
+        # filter for missing features in resulting data, keep features length below token limit
+        if data_instance["features"] is not None and (
+            self.n_token_limit is None
+            or len(data_instance["features"]) <= self.n_token_limit
+        ):
+            return True
+        return False
+
     def train_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]]:
         """
         Returns the train DataLoader.
@@ -401,22 +402,84 @@ def test_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]
         Returns:
             Union[DataLoader, List[DataLoader]]: A DataLoader object for test data.
         """
+
         return self.dataloader("test", shuffle=False, **kwargs)
 
     def predict_dataloader(
-        self, *args, **kwargs
-    ) -> Union[DataLoader, List[DataLoader]]:
+        self,
+        smiles_list: List[str],
+        model_hparams: dict,
+        **kwargs,
+    ) -> tuple[DataLoader, list[int]]:
         """
         Returns the predict DataLoader.
 
         Args:
-            *args: Additional positional arguments (unused).
+            smiles_list (List[str]): List of SMILES strings to predict.
+            model_hparams (Optional[dict]): Model hyperparameters.
+                Some prediction pre-processing pipelines may require these.
             **kwargs: Additional keyword arguments, passed to dataloader().
 
         Returns:
-            Union[DataLoader, List[DataLoader]]: A DataLoader object for prediction data.
+            tuple[DataLoader, list[int]]: A DataLoader object for prediction data and a list of valid indices.
         """
-        return self.dataloader(self.prediction_kind, shuffle=False, **kwargs)
+
+        data, valid_indices = self._process_input_for_prediction(
+            smiles_list, model_hparams
+        )
+        return (
+            DataLoader(
+                data,
+                collate_fn=self.reader.collator,
+                batch_size=self.batch_size,
+                **kwargs,
+            ),
+            valid_indices,
+        )
+
+    def _process_input_for_prediction(
+        self, smiles_list: list[str], model_hparams: dict
+    ) -> tuple[list, list]:
+        """
+        Process input data for prediction.
+
+        Args:
+            smiles_list (List[str]): List of SMILES strings.
+            model_hparams (dict): Model hyperparameters.
+                Some prediction pre-processing pipelines may require these.
+
+        Returns:
+            tuple[list, list]: Processed input data and valid indices.
+        """
+        data, valid_indices = [], []
+        num_of_labels = int(model_hparams["out_dim"])
+        self._dummy_labels: list = list(range(1, num_of_labels + 1))
+
+        for idx, smiles in enumerate(smiles_list):
+            result = self._preprocess_smiles_for_pred(idx, smiles, model_hparams)
+            if result is None or result["features"] is None:
+                continue
+            if not self._filter_to_token_limit(result):
+                continue
+            data.append(result)
+            valid_indices.append(idx)
+
+        return data, valid_indices
+
+    def _preprocess_smiles_for_pred(
+        self, idx: int, smiles: str, model_hparams: Optional[dict] = None
+    ) -> dict:
+        """Preprocess prediction data."""
+        # Add dummy labels because the collate function requires them.
+        # Note: If labels are set to `None`, the collator will insert a `non_null_labels` entry into `loss_kwargs`,
+        # which later causes `_get_prediction_and_labels` method in the prediction pipeline to treat the data as empty.
+        return self.reader.to_data(
+            {
+                "id": f"smiles_{idx}",
+                "features": smiles,
+                "labels": self._dummy_labels,
+            }
+        )
 
     def prepare_data(self, *args, **kwargs) -> None:
         if self._prepare_data_flag != 1:
@@ -563,6 +626,19 @@ def raw_file_names_dict(self) -> dict:
         """
         raise NotImplementedError
 
+    @property
+    def classes_txt_file_path(self) -> str:
+        """
+        Returns the filename for the classes text file.
+
+        Returns:
+            str: The filename for the classes text file.
+        """
+        # This property also used in following places:
+        #   - chebai/result/prediction.py: to load class names for csv columns names
+        #   - chebai/cli.py: to link this property to `model.init_args.classes_txt_file_path`
+        return os.path.join(self.processed_dir_main, "classes.txt")
+
 
 class MergedDataset(XYBaseDataModule):
     MERGED = []
@@ -1189,7 +1265,8 @@ def _retrieve_splits_from_csv(self) -> None:
             print(f"Applying label filter from {self.apply_label_filter}...")
             with open(self.apply_label_filter, "r") as f:
                 label_filter = [line.strip() for line in f]
-            with open(os.path.join(self.processed_dir_main, "classes.txt"), "r") as cf:
+
+            with open(self.classes_txt_file_path, "r") as cf:
                 classes = [line.strip() for line in cf]
             # reorder labels
             old_labels = np.stack(df_data["labels"])

chebai/preprocessing/migration/migrate_checkpoints.py

@@ -0,0 +1,56 @@
+"""
+Docstring for chebai.preprocessing.migration.migrate_checkpoints
+
+This script migrates lightning checkpoints created before python-chebai
+version 1.2.1 to be compatible with the new version.
+
+The main change is the addition of a new key "classification_labels" in the checkpoint,
+which is required for the new version of python-chebai from version 1.2.1 onwards.
+
+For more details, see the pull request: https://github.com/ChEB-AI/python-chebai/pulls
+"""
+
+import sys
+
+import torch
+
+
+def add_class_labels_to_checkpoint(input_path, classes_file_path):
+    print(f"Loading checkpoint from {input_path}...")
+    print(f"Loading class labels from {classes_file_path}...")
+
+    with open(classes_file_path, "r") as f:
+        class_labels = [line.strip() for line in f.readlines()]
+
+    assert len(class_labels) > 0, "The classes file is empty."
+
+    # 1. Load the checkpoint
+    checkpoint = torch.load(
+        input_path, map_location=torch.device("cpu"), weights_only=False
+    )
+
+    if "classification_labels" in checkpoint:
+        print(
+            "Warning: 'classification_labels' key already exists in the checkpoint and will be overwritten."
+        )
+
+    # 2. Add your custom key/value pair
+    checkpoint["classification_labels"] = class_labels
+
+    # 3. Save the modified checkpoint
+    output_path = input_path.replace(".ckpt", "_modified.ckpt")
+    torch.save(checkpoint, output_path)
+    print(f"Successfully added classification_labels and saved to {output_path}")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("Usage: python migrate_checkpoints.py <input_checkpoint> <classes_file>")
+        sys.exit(1)
+
+    input_ckpt = sys.argv[1]
+    classes_file = sys.argv[2]
+
+    add_class_labels_to_checkpoint(
+        input_path=input_ckpt, classes_file_path=classes_file
+    )