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from __future__ import annotations
import argparse
import json
import os
from datetime import datetime
from pathlib import Path
from typing import Dict, List
import pandas as pd
from data.catalog_loader import load_catalog
from data.train_loader import load_train, save_label_resolution_report
from eval.metrics import recall_at_k, mrr_at_k
from recommenders.dummy_random import DummyRandomRecommender
from recommenders.bm25 import BM25Recommender
from recommenders.vector_recommender import VectorRecommender
from recommenders.hybrid_rrf import HybridRRFRecommender, HybridRerankRecommender
from recommenders.hybrid_rrf_lgbm import HybridRRFLGBMRecommender
from retrieval.vector_index import VectorIndex
from models.embedding_model import EmbeddingModel
from rerankers.cross_encoder import CrossEncoderReranker
from rerankers.lgbm_reranker import LGBMReranker
from retrieval.query_rewriter import rewrite_query
def split_examples(examples, val_ratio=0.2, seed=42):
import random
rnd = random.Random(seed)
shuffled = examples[:]
rnd.shuffle(shuffled)
cut = int(len(shuffled) * (1 - val_ratio))
return shuffled[:cut], shuffled[cut:]
def run_eval(catalog_path: str, train_path: str, recommender_name: str, out_dir: str, seed: int = 42):
df_catalog, catalog_by_id, id_by_url = load_catalog(catalog_path)
examples, label_report = load_train(train_path, id_by_url)
save_label_resolution_report(label_report, Path(out_dir) / "label_resolution_report.json")
train_split, val_split = split_examples(examples, val_ratio=0.2, seed=seed)
def make_recommender():
if recommender_name == "dummy_random":
return DummyRandomRecommender(df_catalog["assessment_id"].tolist(), seed=seed)
if recommender_name == "bm25":
return BM25Recommender(df_catalog)
if recommender_name == "vector":
# Expect doc_text present in df_catalog and provided index/ids/model via env/args; set below in main()
raise RuntimeError("Vector recommender should be constructed in main with index and ids.")
raise ValueError(f"Unknown recommender: {recommender_name}")
recommender = make_recommender()
def eval_split(split, split_name):
preds_list: List[List[str]] = []
gt_list: List[set] = []
rows = []
for ex in split:
preds_raw = recommender.recommend(ex.query, k=10)
preds = []
for pr in preds_raw:
if isinstance(pr, str):
preds.append(pr)
elif isinstance(pr, dict) and "assessment_id" in pr:
preds.append(pr["assessment_id"])
preds = preds[:10]
preds_list.append(preds)
gt_list.append(ex.relevant_ids)
hits = len(set(preds).intersection(ex.relevant_ids))
rows.append(
{
"query": ex.query,
"relevant_ids": list(ex.relevant_ids),
"predicted_ids": preds,
"hits": hits,
}
)
recall10 = sum(recall_at_k(g, p, 10) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
recall5 = sum(recall_at_k(g, p, 5) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
mrr10 = sum(mrr_at_k(g, p, 10) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
return recall10, recall5, mrr10, rows
train_r10, train_r5, train_mrr10, train_rows = eval_split(train_split, "train")
val_r10, val_r5, val_mrr10, val_rows = eval_split(val_split, "val")
Path(out_dir).mkdir(parents=True, exist_ok=True)
metrics = {
"recommender": recommender_name,
"label_match_pct": label_report.get("matched_pct"),
"train": {"recall@10": train_r10, "recall@5": train_r5, "mrr@10": train_mrr10, "n": len(train_split)},
"val": {"recall@10": val_r10, "recall@5": val_r5, "mrr@10": val_mrr10, "n": len(val_split)},
}
with open(Path(out_dir) / "metrics.json", "w") as f:
json.dump(metrics, f, indent=2)
pd.DataFrame(train_rows + val_rows).to_json(Path(out_dir) / "per_query_results.jsonl", orient="records", lines=True)
worst = sorted(val_rows, key=lambda r: r["hits"])[:10]
pd.DataFrame(worst).to_csv(Path(out_dir) / "worst_queries.csv", index=False)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--catalog", default="data/catalog.jsonl")
parser.add_argument("--train", required=True)
parser.add_argument("--recommender", default="dummy_random")
parser.add_argument("--out-dir", default=None)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--vector-index", type=str, help="Path to FAISS index (for recommender=vector/hybrid_rrf)")
parser.add_argument("--assessment-ids", type=str, help="Path to assessment_ids.json aligned with embeddings/index")
parser.add_argument("--model", type=str, default="sentence-transformers/all-MiniLM-L6-v2", help="Embedding model for vector recommender")
parser.add_argument("--topn-candidates", type=int, default=200, help="Top-N candidates to retrieve before fusion/rerank")
parser.add_argument("--rrf-k", type=int, default=60, help="RRF smoothing constant")
parser.add_argument("--reranker-model", type=str, default="cross-encoder/ms-marco-MiniLM-L-6-v2", help="Cross-encoder model for reranking")
parser.add_argument("--lgbm-model", type=str, help="Path to trained LGBM model (for hybrid_rrf_lgbm)")
parser.add_argument("--lgbm-features", type=str, help="Path to feature_schema.json for LGBM reranker")
parser.add_argument("--use-rewriter", action="store_true", help="Rewrite queries before retrieval/rerank.")
parser.add_argument("--vocab", type=str, help="Optional vocab JSON for rewriter boosts.")
args = parser.parse_args()
run_id = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
out_dir = args.out_dir or f"runs/{run_id}_{args.recommender}"
if args.recommender in {"vector", "hybrid_rrf", "hybrid_rrf_rerank", "hybrid_rrf_lgbm"}:
if not args.vector_index or not args.assessment_ids:
raise ValueError("Vector/hybrid recommender requires --vector-index and --assessment-ids")
df_catalog, _, id_by_url = load_catalog(args.catalog)
with open(args.assessment_ids) as f:
ids = json.load(f)
index = VectorIndex.load(args.vector_index)
embed_model = EmbeddingModel(args.model)
examples, label_report = load_train(args.train, id_by_url)
Path(out_dir).mkdir(parents=True, exist_ok=True)
save_label_resolution_report(label_report, Path(out_dir) / "label_resolution_report.json")
vocab = {}
if args.use_rewriter and args.vocab:
with open(args.vocab) as f:
vocab = json.load(f)
train_split, val_split = split_examples(examples, val_ratio=0.2, seed=args.seed)
vector_rec = VectorRecommender(embed_model, index, df_catalog, ids, k_candidates=args.topn_candidates)
if args.recommender == "vector":
recommender = vector_rec
elif args.recommender == "hybrid_rrf":
bm25_rec = BM25Recommender(df_catalog)
recommender = HybridRRFRecommender(bm25_rec, vector_rec, topn_candidates=args.topn_candidates, rrf_k=args.rrf_k)
elif args.recommender == "hybrid_rrf_rerank":
bm25_rec = BM25Recommender(df_catalog)
reranker = CrossEncoderReranker(model_name=args.reranker_model)
recommender = HybridRerankRecommender(
bm25_rec,
vector_rec,
reranker,
df_catalog,
topn_candidates=args.topn_candidates,
rrf_k=args.rrf_k,
)
else:
if not args.lgbm_model or not args.lgbm_features:
raise ValueError("hybrid_rrf_lgbm requires --lgbm-model and --lgbm-features")
bm25_rec = BM25Recommender(df_catalog)
feature_cols = json.load(open(args.lgbm_features))
if isinstance(feature_cols, dict) and "features" in feature_cols:
feature_cols = feature_cols["features"]
recommender = HybridRRFLGBMRecommender(
bm25_rec,
vector_rec,
lgbm_model_path=args.lgbm_model,
feature_cols=feature_cols,
catalog_df=df_catalog,
topn_candidates=args.topn_candidates,
rrf_k=args.rrf_k,
)
def eval_split(split, split_name):
preds_list = []
gt_list = []
rows = []
for ex in split:
retrieval_query = ex.query
rerank_query = ex.query
if args.use_rewriter:
rw = rewrite_query(ex.query, catalog_vocab=vocab)
retrieval_query = rw.retrieval_query
rerank_query = rw.rerank_query
if args.recommender == "hybrid_rrf_rerank":
preds_raw = recommender.recommend(retrieval_query, k=10, rerank_query=rerank_query)
else:
preds_raw = recommender.recommend(retrieval_query, k=10)
preds = []
for pr in preds_raw:
if isinstance(pr, str):
preds.append(pr)
elif isinstance(pr, dict) and "assessment_id" in pr:
preds.append(pr["assessment_id"])
preds = preds[:10]
preds_list.append(preds)
gt_list.append(ex.relevant_ids)
hits = len(set(preds).intersection(ex.relevant_ids))
rows.append(
{
"query": ex.query,
"relevant_ids": list(ex.relevant_ids),
"predicted_ids": preds,
"hits": hits,
}
)
recall10 = sum(recall_at_k(g, p, 10) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
recall5 = sum(recall_at_k(g, p, 5) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
mrr10 = sum(mrr_at_k(g, p, 10) for g, p in zip(gt_list, preds_list)) / len(gt_list) if gt_list else 0.0
return recall10, recall5, mrr10, rows
train_r10, train_r5, train_mrr10, train_rows = eval_split(train_split, "train")
val_r10, val_r5, val_mrr10, val_rows = eval_split(val_split, "val")
metrics = {
"recommender": args.recommender,
"label_match_pct": label_report.get("matched_pct"),
"train": {"recall@10": train_r10, "recall@5": train_r5, "mrr@10": train_mrr10, "n": len(train_split)},
"val": {"recall@10": val_r10, "recall@5": val_r5, "mrr@10": val_mrr10, "n": len(val_split)},
"config": {
"topn_candidates": args.topn_candidates,
"rrf_k": args.rrf_k,
"model": args.model,
"index": args.vector_index,
},
}
with open(Path(out_dir) / "metrics.json", "w") as f:
json.dump(metrics, f, indent=2)
pd.DataFrame(train_rows + val_rows).to_json(Path(out_dir) / "per_query_results.jsonl", orient="records", lines=True)
worst = sorted(val_rows, key=lambda r: r["hits"])[:10]
pd.DataFrame(worst).to_csv(Path(out_dir) / "worst_queries.csv", index=False)
print(f"Run saved to {out_dir}")
else:
run_eval(args.catalog, args.train, args.recommender, out_dir, seed=args.seed)
print(f"Run saved to {out_dir}")
if __name__ == "__main__":
main()
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