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6da8289 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | """
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)
}
"""
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