feature-ensemble

README.md

@@ -1,2 +1,86 @@
 # python-chebifier
 An AI ensemble model for predicting chemical classes.
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/yourusername/python-chebifier.git
+cd python-chebifier
+
+# Install the package
+pip install -e .
+```
+
+## Usage
+
+### Command Line Interface
+
+The package provides a command-line interface (CLI) for making predictions using an ensemble model.
+
+```bash
+# Get help
+python -m chebifier.cli --help
+
+# Make predictions using a configuration file
+python -m chebifier.cli predict example_config.yml --smiles "CC(=O)OC1=CC=CC=C1C(=O)O" "C1=CC=C(C=C1)C(=O)O"
+
+# Make predictions using SMILES from a file
+python -m chebifier.cli predict example_config.yml --smiles-file smiles.txt
+```
+
+### Configuration File
+
+The CLI requires a YAML configuration file that defines the ensemble model. Here's an example:
+
+```yaml
+# Example configuration file for Chebifier ensemble model
+
+# Each key in the top-level dictionary is a model name
+model1:
+  # Required: type of model (must be one of the keys in MODEL_TYPES)
+  type: electra
+  # Required: name of the model
+  model_name: electra_model1
+  # Required: path to the checkpoint file
+  ckpt_path: /path/to/checkpoint1.ckpt
+  # Required: path to the target labels file
+  target_labels_path: /path/to/target_labels1.txt
+  # Optional: batch size for predictions (default is likely defined in the model)
+  batch_size: 32
+
+model2:
+  type: electra
+  model_name: electra_model2
+  ckpt_path: /path/to/checkpoint2.ckpt
+  target_labels_path: /path/to/target_labels2.txt
+  batch_size: 64
+```
+
+### Python API
+
+You can also use the package programmatically:
+
+```python
+from chebifier.ensemble.base_ensemble import BaseEnsemble
+import yaml
+
+# Load configuration from YAML file
+with open('configs/example_config.yml', 'r') as f:
+    config = yaml.safe_load(f)
+
+# Instantiate ensemble model
+ensemble = BaseEnsemble(config)
+
+# Make predictions
+smiles_list = ["CC(=O)OC1=CC=CC=C1C(=O)O", "C1=CC=C(C=C1)C(=O)O"]
+predictions = ensemble.predict_smiles_list(smiles_list)
+
+# Print results
+for smile, prediction in zip(smiles_list, predictions):
+    print(f"SMILES: {smile}")
+    if prediction:
+        print(f"Predicted classes: {prediction}")
+    else:
+        print("No predictions")
+```

chebifier/cli.py

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+
+
+
+import click
+import yaml
+import sys
+from chebifier.ensemble.base_ensemble import BaseEnsemble
+from chebifier.ensemble.weighted_majority_ensemble import WMVwithPPVNPVEnsemble, WMVwithF1Ensemble
+
+
+@click.group()
+def cli():
+    """Command line interface for Chebifier."""
+    pass
+
+ENSEMBLES = {
+    "mv": BaseEnsemble,
+    "wmv-ppvnpv": WMVwithPPVNPVEnsemble,
+    "wmv-f1": WMVwithF1Ensemble
+}
+
+@cli.command()
+@click.argument('config_file', type=click.Path(exists=True))
+@click.option('--smiles', '-s', multiple=True, help='SMILES strings to predict')
+@click.option('--smiles-file', '-f', type=click.Path(exists=True), help='File containing SMILES strings (one per line)')
+@click.option('--output', '-o', type=click.Path(), help='Output file to save predictions (optional)')
+@click.option('--ensemble-type', '-e', type=click.Choice(ENSEMBLES.keys()), default='mv', help='Type of ensemble to use (default: Majority Voting)')
+def predict(config_file, smiles, smiles_file, output, ensemble_type):
+    """Predict ChEBI classes for SMILES strings using an ensemble model.
+
+    CONFIG_FILE is the path to a YAML configuration file for the ensemble model.
+    """
+    # Load configuration from YAML file
+    with open(config_file, 'r') as f:
+        config = yaml.safe_load(f)
+
+    # Instantiate ensemble model
+    ensemble = ENSEMBLES[ensemble_type](config)
+
+    # Collect SMILES strings from arguments and/or file
+    smiles_list = list(smiles)
+    if smiles_file:
+        with open(smiles_file, 'r') as f:
+            smiles_list.extend([line.strip() for line in f if line.strip()])
+
+    if not smiles_list:
+        click.echo("No SMILES strings provided. Use --smiles or --smiles-file options.")
+        return
+
+    # Make predictions
+    predictions = ensemble.predict_smiles_list(smiles_list)
+
+    if output:
+        # save as json
+        import json
+        with open(output, 'w') as f:
+            json.dump({smiles: pred for smiles, pred in zip(smiles_list, predictions)}, f, indent=2)
+
+    else:
+        # Print results
+        for i, (smiles, prediction) in enumerate(zip(smiles_list, predictions)):
+            click.echo(f"Result for: {smiles}")
+            if prediction:
+                click.echo(f"  Predicted classes: {', '.join(map(str, prediction))}")
+            else:
+                click.echo("  No predictions")
+
+
+if __name__ == '__main__':
+    cli()

chebifier/ensemble/base_ensemble.py

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+import os
+from abc import ABC
+import torch
+import tqdm
+from rdkit import Chem
+
+from chebifier.prediction_models.base_predictor import BasePredictor
+from chebifier.prediction_models.chemlog_predictor import ChemLogPredictor
+from chebifier.prediction_models.electra_predictor import ElectraPredictor
+from chebifier.prediction_models.gnn_predictor import ResGatedPredictor
+
+MODEL_TYPES = {
+    "electra": ElectraPredictor,
+    "resgated": ResGatedPredictor,
+    "chemlog": ChemLogPredictor
+}
+
+class BaseEnsemble(ABC):
+
+    def __init__(self, model_configs: dict):
+        self.models = []
+        self.positive_prediction_threshold = 0.5
+        for model_name, model_config in model_configs.items():
+            model_cls = MODEL_TYPES[model_config["type"]]
+            model_instance = model_cls(**model_config)
+            assert isinstance(model_instance, BasePredictor)
+            self.models.append(model_instance)
+
+    def gather_predictions(self, smiles_list):
+        # get predictions from all models for the SMILES list
+        # order them by alphabetically by label class
+        model_predictions = []
+        predicted_classes = set()
+        for model in self.models:
+            model_predictions.append(model.predict_smiles_list(smiles_list))
+            for logits_for_smiles in model_predictions[-1]:
+                if logits_for_smiles is not None:
+                    for cls in logits_for_smiles:
+                        predicted_classes.add(cls)
+        print(f"Sorting predictions...")
+        predicted_classes = sorted(list(predicted_classes))
+        predicted_classes = {cls: i for i, cls in enumerate(predicted_classes)}
+        ordered_logits = torch.zeros(len(smiles_list), len(predicted_classes), len(self.models)) * torch.nan
+        for i, model_prediction in enumerate(model_predictions):
+            for j, logits_for_smiles in tqdm.tqdm(enumerate(model_prediction),
+                                                 total=len(model_prediction),
+                                                 desc=f"Sorting predictions for {self.models[i].model_name}"):
+                if logits_for_smiles is not None:
+                    for cls in logits_for_smiles:
+                        ordered_logits[j, predicted_classes[cls], i] = logits_for_smiles[cls]
+
+        return ordered_logits, predicted_classes
+
+
+    def consolidate_predictions(self, predictions, predicted_classes, classwise_weights, **kwargs):
+        """
+        Aggregates predictions from multiple models using weighted majority voting.
+        Optimized version using tensor operations instead of for loops.
+        """
+        num_smiles, num_classes, num_models = predictions.shape
+
+        # Create a mapping from class indices to class names for faster lookup
+        class_names = list(predicted_classes.keys())
+        class_indices = {predicted_classes[cls]: cls for cls in class_names}
+
+        # Get predictions for all classes
+        valid_predictions = ~torch.isnan(predictions)
+        valid_counts = valid_predictions.sum(dim=2)  # Sum over models dimension
+
+        # Skip classes with no valid predictions
+        has_valid_predictions = valid_counts > 0
+
+        # Calculate positive and negative predictions for all classes at once
+        positive_mask = (predictions > 0.5) & valid_predictions
+        negative_mask = (predictions < 0.5) & valid_predictions
+
+        confidence = 2 * torch.abs(predictions.nan_to_num() - self.positive_prediction_threshold)
+
+        # Extract positive and negative weights
+        pos_weights = classwise_weights[0]  # Shape: (num_classes, num_models)
+        neg_weights = classwise_weights[1]  # Shape: (num_classes, num_models)
+
+        # Calculate weighted predictions using broadcasting
+        # predictions shape: (num_smiles, num_classes, num_models)
+        # weights shape: (num_classes, num_models)
+        positive_weighted = positive_mask.float() * confidence * pos_weights.unsqueeze(0)
+        negative_weighted = negative_mask.float() * confidence * neg_weights.unsqueeze(0)
+
+        # Sum over models dimension
+        positive_sum = positive_weighted.sum(dim=2)  # Shape: (num_smiles, num_classes)
+        negative_sum = negative_weighted.sum(dim=2)  # Shape: (num_smiles, num_classes)
+
+        # Determine which classes to include for each SMILES
+        net_score = positive_sum - negative_sum  # Shape: (num_smiles, num_classes)
+        class_decisions = (net_score > 0) & has_valid_predictions  # Shape: (num_smiles, num_classes)
+
+        # Convert tensor decisions to result list using list comprehension for efficiency
+        result = [
+            [class_indices[idx.item()] for idx in torch.nonzero(class_decisions[i], as_tuple=True)[0]]
+            for i in range(num_smiles)
+        ]
+
+        return result
+
+
+    def calculate_classwise_weights(self, predicted_classes):
+        """No weights, simple majority voting"""
+        positive_weights = torch.ones(len(predicted_classes), len(self.models))
+        negative_weights = torch.ones(len(predicted_classes), len(self.models))
+
+        return positive_weights, negative_weights
+
+    def predict_smiles_list(self, smiles_list, load_preds_if_possible=True) -> list:
+        preds_file = f"predictions_by_model_{'_'.join(model.model_name for model in self.models)}.pt"
+        predicted_classes_file = f"predicted_classes_{'_'.join(model.model_name for model in self.models)}.txt"
+        if not load_preds_if_possible or not os.path.isfile(preds_file):
+            ordered_predictions = predicted_classes = self.gather_predictions(smiles_list)
+            # save predictions
+            torch.save(ordered_predictions, preds_file)
+            with open(predicted_classes_file, "w") as f:
+                for cls in predicted_classes:
+                    f.write(f"{cls}\n")
+        else:
+            print(f"Loading predictions from {preds_file} and label indexes from {predicted_classes_file}")
+            ordered_predictions = torch.load(preds_file)
+            with open(predicted_classes_file, "r") as f:
+                predicted_classes = {line.strip(): i for i, line in enumerate(f.readlines())}
+
+        classwise_weights = self.calculate_classwise_weights(predicted_classes)
+        aggregated_predictions = self.consolidate_predictions(ordered_predictions, predicted_classes, classwise_weights)
+        return aggregated_predictions

chebifier/ensemble/weighted_majority_ensemble.py

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+import torch
+
+from chebifier.ensemble.base_ensemble import BaseEnsemble
+
+
+
+class WMVwithPPVNPVEnsemble(BaseEnsemble):
+
+    def calculate_classwise_weights(self, predicted_classes):
+        """
+        Given the positions of predicted classes in the predictions tensor, assign weights to each class. The
+        result is two tensors of shape (num_predicted_classes, num_models). The weight for each class is the model_weight
+        (default: 1) multiplied by the class-specific positive / negative weight (default 1).
+        """
+        positive_weights = torch.ones(len(predicted_classes), len(self.models))
+        negative_weights = torch.ones(len(predicted_classes), len(self.models))
+        for j, model in enumerate(self.models):
+            positive_weights[:, j] *= model.model_weight
+            negative_weights[:, j] *= model.model_weight
+            if model.classwise_weights is None:
+                continue
+            for cls, weights in model.classwise_weights.items():
+                positive_weights[predicted_classes[cls], j] *= weights["PPV"]
+                negative_weights[predicted_classes[cls], j] *= weights["NPV"]
+
+        print(f"Calculated model weightings. The averages for positive / negative weights are:")
+        for i, model in enumerate(self.models):
+            print(f"{model.model_name}: {positive_weights[:, i].mean().item():.3f} / {negative_weights[:, i].mean().item():.3f}")
+
+        return positive_weights, negative_weights
+
+
+class WMVwithF1Ensemble(BaseEnsemble):
+
+    def calculate_classwise_weights(self, predicted_classes):
+        """
+        Given the positions of predicted classes in the predictions tensor, assign weights to each class. The
+        result is two tensors of shape (num_predicted_classes, num_models). The weight for each class is the model_weight
+        (default: 1) multiplied by the class-specific validation-f1 (default 1).
+        """
+        weights_by_cls = torch.ones(len(predicted_classes), len(self.models))
+        for j, model in enumerate(self.models):
+            weights_by_cls[:, j] *= model.model_weight
+            if model.classwise_weights is None:
+                continue
+            for cls, weights in model.classwise_weights.items():
+                f1 = 2 * weights["TP"] / (2 * weights["TP"] + weights["FP"] + weights["FN"])
+                weights_by_cls[predicted_classes[cls], j] *= f1
+
+        print(f"Calculated model weightings. The average weights are:")
+        for i, model in enumerate(self.models):
+            print(f"{model.model_name}: {weights_by_cls[:, i].mean().item():.3f}")
+
+        return weights_by_cls, weights_by_cls

chebifier/prediction_models/base_predictor.py

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+from abc import ABC
+import json
+
+class BasePredictor(ABC):
+
+    def __init__(self, model_name: str, model_weight: int = 1, classwise_weights_path: str = None, **kwargs):
+        self.model_name = model_name
+        self.model_weight = model_weight
+        if classwise_weights_path is not None:
+            self.classwise_weights = json.load(open(classwise_weights_path, encoding="utf-8"))
+        else:
+            self.classwise_weights = None
+
+
+    def predict_smiles_list(self, smiles_list: list[str]) -> dict:
+        raise NotImplementedError