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65dfa4b | 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 | """Retrieval evaluation using annotated ground truth from eval_set.json.
Loads the annotation file, computes Hit Rate@k and MRR for each retrieval
method separately using the pooled_from data and relevant_chunk_ids, and
produces a comparison table.
Usage:
python scripts/retrieval_eval.py
python scripts/retrieval_eval.py --k 5
"""
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.config import PROJECT_ROOT
from src.evaluation.metrics import mrr, precision_at_k, recall_at_k, ndcg_at_k
EVAL_SET_PATH = PROJECT_ROOT / "data" / "eval_set.json"
def load_eval_set() -> list[dict]:
if not EVAL_SET_PATH.exists():
print(f"Eval set not found: {EVAL_SET_PATH}")
print("Run: python scripts/write_questions.py && python scripts/annotate.py")
sys.exit(1)
with open(EVAL_SET_PATH, encoding="utf-8") as f:
data = json.load(f)
annotated = [e for e in data if e.get("relevant_chunk_ids")]
if not annotated:
print("No annotated entries found. Run scripts/annotate.py first.")
sys.exit(1)
return annotated
def evaluate_method(
entries: list[dict],
method_key: str,
k_values: list[int],
) -> dict:
"""Compute retrieval metrics for one method across all entries."""
all_metrics: dict[str, list[float]] = {}
for entry in entries:
pooled = entry.get("pooled_from", {})
retrieved_ids = [str(x) for x in pooled.get(method_key, [])]
relevant_ids = set(str(x) for x in entry.get("relevant_chunk_ids", []))
if not relevant_ids:
continue
entry_mrr = mrr(retrieved_ids, relevant_ids)
all_metrics.setdefault("mrr", []).append(entry_mrr)
for k in k_values:
hit = 1.0 if any(rid in relevant_ids for rid in retrieved_ids[:k]) else 0.0
all_metrics.setdefault(f"hit_rate@{k}", []).append(hit)
all_metrics.setdefault(f"precision@{k}", []).append(
precision_at_k(retrieved_ids, relevant_ids, k)
)
all_metrics.setdefault(f"recall@{k}", []).append(
recall_at_k(retrieved_ids, relevant_ids, k)
)
all_metrics.setdefault(f"ndcg@{k}", []).append(
ndcg_at_k(retrieved_ids, relevant_ids, k)
)
# Average
return {
key: sum(vals) / len(vals) if vals else 0.0
for key, vals in all_metrics.items()
}
def print_table(results: dict[str, dict], k_values: list[int]) -> None:
"""Print a comparison table."""
methods = list(results.keys())
# Collect metric columns in order
columns = ["mrr"]
for k in k_values:
columns.extend([f"hit_rate@{k}", f"precision@{k}", f"recall@{k}", f"ndcg@{k}"])
# Header
header = f"{'Method':<15}"
for col in columns:
header += f" {col:>12}"
print(header)
print("-" * len(header))
# Rows
for method in methods:
row = f"{method:<15}"
for col in columns:
val = results[method].get(col, 0.0)
row += f" {val:>12.3f}"
print(row)
def main():
parser = argparse.ArgumentParser(description="Retrieval evaluation from annotations")
parser.add_argument(
"--k", type=int, nargs="+", default=[1, 3, 5, 10],
help="k values for metrics (default: 1 3 5 10)",
)
args = parser.parse_args()
entries = load_eval_set()
print(f"\n=== Retrieval Evaluation ({len(entries)} annotated questions) ===\n")
method_keys = ["bm25_top10", "vector_top10", "hybrid_top10"]
results = {}
for mk in method_keys:
label = mk.replace("_top10", "")
results[label] = evaluate_method(entries, mk, args.k)
print_table(results, args.k)
print()
if __name__ == "__main__":
main()
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