# ClassicalRetrievalEvaluator

**Use when**: You want to evaluate retrieval performance with classical IR metrics (MAP, NDCG, Precision, Recall, Top-K Accuracy).

**Fields**:

1. retrieved\_chunks (dict\[str, float]) — The dictionary of retrieved documents/chunks containing the chunk id and its score.
2. ground\_truth\_chunk\_ids (list\[str]) — The list of reference chunk ids marking relevant chunks.

#### Example Usage

```python
import asyncio

from gllm_evals.evaluator.classical_retrieval_evaluator import ClassicalRetrievalEvaluator
from gllm_evals.types import RetrievalData


async def main():
    """Main function."""
    data = RetrievalData(
        retrieved_chunks={
            "chunk1": 9.0,
            "chunk2": 0.0,
            "chunk3": 0.3,
            "chunk4": 0.1,
            "chunk5": 0.2,
            "chunk6": 0.4,
            "chunk7": 0.5,
            "chunk8": 0.6,
            "chunk9": 0.7,
            "chunk10": 0.8,
        },
        ground_truth_chunk_ids=["chunk9", "chunk3", "chunk2"],
    )
    evaluator = ClassicalRetrievalEvaluator(k=[5, 10])
    results = await evaluator.evaluate(data)
    print(results)


if __name__ == "__main__":
    asyncio.run(main())
```

#### Example Output

```json
{
    "classical_retrieval_evals": {
        "global_explanation": "The following metrics failed to meet expectations:\n1. Map@5 is 0.1111111111111111 (should be 1)\n2. Precision@5 is 0.2 (should be 1)\n3. Ndcg@5 is 0.23463936301137822 (should be 1)\n4. Precision@10 is 0.3 (should be 1)\n5. Map@10 is 0.30634920634920637 (should be 1)\n6. Recall@5 is 0.3333333333333333 (should be 1)\n7. Ndcg@10 is 0.5267175784514114 (should be 1)",
        "precision@5": {
            "score": 0.2,
            "explanation": None
        },
        "precision@10": {
            "score": 0.3,
            "explanation": None
        },
        "recall@5": {
            "score": 0.3333333333333333,
            "explanation": None
        },
        "recall@10": {
            "score": 1.0,
            "explanation": None
        },
        "ndcg@5": {
            "score": 0.23463936301137822,
            "explanation": None
        },
        "ndcg@10": {
            "score": 0.5267175784514114,
            "explanation": None
        },
        "map@5": {
            "score": 0.1111111111111111,
            "explanation": None
        },
        "map@10": {
            "score": 0.30634920634920637,
            "explanation": None
        },
        "top_k_accuracy@5": {
            "score": 1.0,
            "explanation": None
        },
        "top_k_accuracy@10": {
            "score": 1.0,
            "explanation": None
        },
    }
}

```