
from kladml.evaluation.base import BaseEvaluator
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import numpy as np

class ClassificationEvaluator(BaseEvaluator):
    """Evaluator for classification tasks."""
    
    def compute_metrics(self, predictions, targets) -> dict[str, float]:
        """Compute accuracy, precision, recall, f1."""
        accuracy = accuracy_score(targets, predictions)
        precision, recall, f1, _ = precision_recall_fscore_support(
            targets, predictions, average='weighted', zero_division=0
        )
        return {
            "accuracy": float(accuracy),
            "precision": float(precision),
            "recall": float(recall),
            "f1": float(f1)
        }

    def save_plots(self, predictions, targets) -> None:
        """Save confusion matrix."""
        # TODO: Implement confusion matrix plot
        pass

    def generate_report(self) -> str:
        return f"# Classification Report\n\nAccuracy: {self.metrics.get('accuracy', 0):.2f}"

    # Abstract methods from BaseEvaluator that depend on specific model/data loading logic
    # kept abstract or simple default?
    # For now, we assume user implements load_model/load_data or we provide a generic one.
    # But BaseEvaluator is abstract.
    # We should make this concrete enough or leave it as a reusable base for classification.
    
    def load_model(self):
        # Placeholder or use self.model_path to load via torch.load / registry
        pass
        
    def load_data(self):
        # Placeholder
        pass
        
    def inference(self, model, data):
        # Placeholder
        return [], []
