src/evaluation/evaluator.py

"""
Evaluator for Agentic Document AI benchmark.

This module should be implemented to compute ANLS scores by comparing
predictions against gold standard answers.

TODO: Implement the evaluation logic to compute:
- Overall ANLS score
- ANLS by evidence type (single, multi-doc same, multi-doc different)
- Agent steps (sum of iterations from predictions)
- Cost estimation (if available)
"""

import json
from typing import Dict, List


def load_predictions(predictions_path: str) -> List[Dict]:
    """Load predictions from JSONL file."""
    predictions = []
    with open(predictions_path, "r") as f:
        for line in f:
            line = line.strip()
            if line:
                predictions.append(json.loads(line))
    return predictions


def load_gold_standard(gold_path: str) -> Dict:
    """
    Load gold standard answers.

    TODO: Implement based on your gold standard format.
    The gold standard should contain:
    - question IDs
    - correct answers
    - evidence type classification
    - citation information
    """
    # Placeholder implementation
    raise NotImplementedError("Please implement gold standard loading")


def compute_anls(prediction: str, gold: str) -> float:
    """
    Compute ANLS (Average Normalized Levenshtein Similarity) between prediction and gold answer.

    ANLS = 1 - (Levenshtein distance / max(len(prediction), len(gold)))
    If normalized Levenshtein distance > threshold (typically 0.5), ANLS = 0

    TODO: Implement ANLS calculation.
    Consider using python-Levenshtein library for efficiency.
    """
    # Placeholder implementation
    raise NotImplementedError("Please implement ANLS calculation")


def classify_evidence_type(question_id: str, gold_data: Dict) -> str:
    """
    Classify question by evidence type.

    Returns one of: "single_evidence", "multi_evidence_same_doc", "multi_evidence_multi_doc"

    TODO: Implement based on your gold standard metadata.
    """
    # Placeholder implementation
    raise NotImplementedError("Please implement evidence type classification")


def evaluate_predictions(predictions_path: str, gold_path: str = None) -> Dict:
    """
    Evaluate predictions against gold standard.

    Args:
        predictions_path: Path to JSONL file with predictions
        gold_path: Path to gold standard file (optional, can be hardcoded)

    Returns:
        Dictionary with evaluation results in the format:
        {
            "model_name": str,
            "results": {
                "overall": {"anls": float},
                "single_evidence": {"anls": float},
                "multi_evidence_same_doc": {"anls": float},
                "multi_evidence_multi_doc": {"anls": float}
            },
            "metadata": {
                "agent_steps": int,
                "cost_usd": float,
                "model_type": str
            },
            "submitted_by": str,
            "submission_date": str,
            "num_predictions": int
        }

    TODO: Implement full evaluation pipeline.
    """
    predictions = load_predictions(predictions_path)

    # Placeholder return - replace with actual evaluation
    return {
        "results": {
            "overall": {"anls": 0.50},
            "single_evidence": {"anls": 0.50},
            "multi_evidence_same_doc": {"anls": 0.50},
            "multi_evidence_multi_doc": {"anls": 0.50},
        },
        "metadata": {
            "agent_steps": sum(p.get("iterations", 0) for p in predictions),
            "cost_usd": 0.0,  # TODO: Implement cost calculation
        },
        "num_predictions": len(predictions),
    }


# Example implementation structure (commented out):
"""
def evaluate_predictions(predictions_path: str, gold_path: str = "path/to/gold.json") -> Dict:
    predictions = load_predictions(predictions_path)
    gold_data = load_gold_standard(gold_path)

    # Group by evidence type
    by_type = {
        "single_evidence": [],
        "multi_evidence_same_doc": [],
        "multi_evidence_multi_doc": []
    }

    all_anls = []
    total_iterations = 0

    for pred in predictions:
        question_id = pred["id"]
        pred_answer = pred["answer"][0] if pred["answer"] else ""

        # Get gold answer
        if question_id not in gold_data:
            continue
        gold_answer = gold_data[question_id]["answer"]

        # Compute ANLS
        anls_score = compute_anls(pred_answer, gold_answer)
        all_anls.append(anls_score)

        # Classify and group
        evidence_type = classify_evidence_type(question_id, gold_data)
        by_type[evidence_type].append(anls_score)

        # Track iterations
        total_iterations += pred.get("iterations", 0)

    # Compute averages
    results = {
        "overall": {"anls": sum(all_anls) / len(all_anls) if all_anls else 0.0},
        "single_evidence": {"anls": sum(by_type["single_evidence"]) / len(by_type["single_evidence"]) if by_type["single_evidence"] else 0.0},
        "multi_evidence_same_doc": {"anls": sum(by_type["multi_evidence_same_doc"]) / len(by_type["multi_evidence_same_doc"]) if by_type["multi_evidence_same_doc"] else 0.0},
        "multi_evidence_multi_doc": {"anls": sum(by_type["multi_evidence_multi_doc"]) / len(by_type["multi_evidence_multi_doc"]) if by_type["multi_evidence_multi_doc"] else 0.0}
    }

    return {
        "results": results,
        "metadata": {
            "agent_steps": total_iterations,
            "cost_usd": 0.0,  # Calculate based on model pricing if available
        },
        "num_predictions": len(predictions)
    }
"""