ensemble update

chebifier/__init__.py

@@ -2,10 +2,16 @@
 # even if multiple subpackages are imported later.
 
 from ._custom_cache import PerSmilesPerModelLRUCache, modelwise_smiles_lru_cache
-from .ensemble.base_ensemble import BaseEnsemble
+from .ensemble.voting_ensemble import (
+    MajorityVotingEnsemble,
+    VotingEnsemble,
+    WMVwithConfidenceEnsemble,
+)
 
 __all__ = [
-    "BaseEnsemble",
+    "VotingEnsemble",
+    "MajorityVotingEnsemble",
+    "WMVwithConfidenceEnsemble",
     "PerSmilesPerModelLRUCache",
     "modelwise_smiles_lru_cache",
 ]

chebifier/build_ensemble.py

@@ -0,0 +1,85 @@
+import os
+
+import torch
+
+from chebifier.predict import (
+    base_learner_cache_path,
+    collect_base_learner_predictions,
+    load_dense_predictions,
+    save_dense_predictions,
+)
+
+
+class EnsembleBuilder:
+    """
+    A class to build an ensemble model from base learners and validation data.
+
+    Attributes:
+        base_learners (dict[str, BasePredictor]): A dictionary of base learner models.
+        ensemble_model (BaseEnsemble): An instance of a BaseEnsemble model.
+        validation_data (list[Chem.Mol]): Validation data for calibration.
+        validation_labels (pd.DataFrame): Validation labels for calibration, one column per class.
+            The column names define the label set the base learner predictions are mapped onto.
+        prediction_cache_dir (str): Directory to cache predictions.
+    """
+
+    def __init__(
+        self,
+        base_learners,
+        ensemble_model,
+        validation_data,
+        validation_labels,
+        prediction_cache_dir,
+    ):
+        self.base_learners = base_learners
+        self.ensemble_model = ensemble_model
+        self.validation_data = validation_data
+        self.validation_labels = validation_labels
+        self.prediction_cache_dir = prediction_cache_dir
+        os.makedirs(self.prediction_cache_dir, exist_ok=True)
+
+    def build_ensemble(self):
+        """
+        Build an ensemble model from base learners and validation data.
+
+        Base learner predictions are cached to avoid recomputation.
+        """
+
+        # Step 1: Get predictions from base learners on validation data
+        validation_predictions = {}
+        classes = {}
+        # get cached predictions if available, otherwise compute and cache them
+        for model_name, model in self.base_learners.items():
+            cache_path = base_learner_cache_path(
+                self.prediction_cache_dir, model_name, "validation"
+            )
+            if os.path.exists(cache_path):
+                print(f"{model_name} validation predictions found in cache, loading...")
+                validation_predictions[model_name] = load_dense_predictions(cache_path)
+            else:
+                print(f"Computing {model_name} validation predictions...")
+                validation_predictions[model_name] = model.predict_dense(
+                    self.validation_data
+                )
+                save_dense_predictions(cache_path, *validation_predictions[model_name])
+
+        # Base learners may be trained on different label sets (e.g. ChEBI25 vs. ChEBI25_3_STAR),
+        # so their union does not match the labels we calibrate against. Map every base learner
+        # onto the label set of the validation data instead.
+        label_classes = [str(cls) for cls in self.validation_labels.columns]
+        validation_predictions, classes = collect_base_learner_predictions(
+            validation_predictions, classes=label_classes
+        )
+        validation_labels = torch.from_numpy(
+            self.validation_labels.to_numpy(dtype=bool)
+        )
+
+        print(
+            f"Collected validation predictions from {len(validation_predictions)} base learners with {len(classes)} unique classes. Calibrating ensemble model..."
+        )
+        # Step 2: Calibrate the ensemble model using validation predictions
+        self.ensemble_model.calibrate(
+            validation_predictions, self.validation_data, validation_labels
+        )
+
+        return self.ensemble_model