"
- "Left: ML prediction for Stress Gauge Factor (original scale, MPa-1). "
- "Right: Literature Q&A via Hybrid RAG (BM25 + TF-IDF + optional dense) with MMR sentence selection. "
- "Answers cite short document codes (e.g., S71, S92)."
+ "Left tab: ML prediction for Stress Gauge Factor (kept identical to your deployed predictor). "
+ "Right tab: Literature Q&A via Hybrid RAG (BM25 + TF-IDF + optional dense) with MMR sentence selection. "
+ "Upload PDFs into papers/ in your Space repo."
"
papers/ — **{pdf_count} PDF(s)** indexed. "
- "Upload more PDFs and reload the Space to expand coverage. "
- "Answers cite short document codes such as S71, S92."
+ "Upload PDFs into the repository folder papers/ then reload the Space. "
+ "Answers cite (Doc.pdf, p.X). Toggle strict quotes or optional LLM paraphrasing."
)
with gr.Row():
top_k = gr.Slider(5, 12, value=8, step=1, label="Top-K chunks")
n_sentences = gr.Slider(2, 6, value=4, step=1, label="Answer length (sentences)")
- include_passages = gr.Checkbox(value=False, label="Include supporting passages", interactive=True)
-
+ include_passages = gr.Checkbox(value=False, label="Include supporting passages")
with gr.Accordion("Retriever weights (advanced)", open=False):
w_tfidf = gr.Slider(0.0, 1.0, value=W_TFIDF_DEFAULT, step=0.05, label="TF-IDF weight")
w_bm25 = gr.Slider(0.0, 1.0, value=W_BM25_DEFAULT, step=0.05, label="BM25 weight")
- w_emb = gr.Slider(0.0, 1.0, value=(0.0 if not USE_DENSE else 0.40), step=0.05, label="Dense weight (set 0 if disabled)")
-
- # Hidden states (unchanged)
- state_use_llm = gr.State(LLM_AVAILABLE)
- state_model_name = gr.State(os.getenv("OPENAI_MODEL", OPENAI_MODEL))
- state_temperature = gr.State(0.2)
- state_strict = gr.State(False)
-
+ w_emb = gr.Slider(0.0, 1.0, value=W_EMB_DEFAULT, step=0.05, label="Dense weight (set 0 if disabled)")
+ with gr.Accordion("LLM & Controls", open=False):
+ strict_quotes_only = gr.Checkbox(value=False, label="Strict quotes only (no paraphrasing)")
+ use_llm = gr.Checkbox(value=True, label="Use LLM to paraphrase selected sentences")
+ model_name = gr.Textbox(value=os.getenv("OPENAI_MODEL", OPENAI_MODEL),
+ label="LLM model", placeholder="e.g., gpt-5 or gpt-5-mini")
+ temperature = gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Temperature")
gr.ChatInterface(
fn=rag_chat_fn,
- additional_inputs=[
- top_k, n_sentences, include_passages,
- state_use_llm, state_model_name, state_temperature, state_strict,
- w_tfidf, w_bm25, w_emb
- ],
+ additional_inputs=[top_k, n_sentences, include_passages, use_llm, model_name,
+ temperature, strict_quotes_only, w_tfidf, w_bm25, w_emb],
title="Literature Q&A",
- description="Hybrid retrieval with diversity. Answers carry inline short-code citations (e.g., (S92), (S71))."
+ description="Hybrid retrieval with diversity. Answers carry inline (Doc, p.X) citations. Toggle strict/LLM modes."
)
- # ====== Evaluate (Gold vs Logs) ======
- with gr.Tab("📏 Evaluate (Gold vs Logs)", elem_id="eval-tab"):
- gr.Markdown("Upload your **gold.csv** and compute metrics against the app logs.")
- with gr.Row():
- gold_file = gr.File(label="gold.csv", file_types=[".csv"], interactive=True)
- k_slider = gr.Slider(3, 12, value=8, step=1, label="k for Hit/Recall/nDCG", elem_id="k-slider")
- with gr.Row():
- btn_eval = gr.Button("Compute Metrics", variant="primary")
- with gr.Row():
- out_perq = gr.File(label="Per-question metrics (CSV)", elem_id="perq-file")
- out_agg = gr.File(label="Aggregate metrics (JSON)", elem_id="agg-file")
- out_json = gr.JSON(label="Aggregate summary", elem_id="agg-json")
- out_log = gr.Markdown(label="Run log", elem_id="eval-log")
-
- def _run_eval_inproc(gold_path: str, k: int = 8):
- import json as _json
- out_dir = str(ARTIFACT_DIR)
- logs = str(LOG_PATH)
- cmd = [
- sys.executable, "rag_eval_metrics.py",
- "--gold_csv", gold_path,
- "--logs_jsonl", logs,
- "--k", str(k),
- "--out_dir", out_dir
- ]
- try:
- p = subprocess.run(cmd, capture_output=True, text=True, check=False)
- stdout = p.stdout or ""
- stderr = p.stderr or ""
- perq = ARTIFACT_DIR / "metrics_per_question.csv"
- agg = ARTIFACT_DIR / "metrics_aggregate.json"
- agg_json = {}
- if agg.exists():
- agg_json = _json.loads(agg.read_text(encoding="utf-8"))
- report = "```\n" + (stdout.strip() or "(no stdout)") + ("\n" + stderr.strip() if stderr else "") + "\n```"
- return (str(perq) if perq.exists() else None,
- str(agg) if agg.exists() else None,
- agg_json,
- report)
- except Exception as e:
- return (None, None, {}, f"**Eval error:** {e}")
-
- def _eval_wrapper(gf, k):
- from pathlib import Path as _Path
- if gf is None:
- default_gold = _Path("gold.csv")
- if not default_gold.exists():
- return None, None, {}, "**No gold.csv provided or found in repo root.**"
- gold_path = str(default_gold)
- else:
- gold_path = gf.name
- return _run_eval_inproc(gold_path, int(k))
-
- btn_eval.click(_eval_wrapper, inputs=[gold_file, k_slider],
- outputs=[out_perq, out_agg, out_json, out_log])
-
# ------------- Launch -------------
if __name__ == "__main__":
+ # queue() helps HF Spaces with concurrency; show_error suggests upload PDFs if none
demo.queue().launch()
-
- # After launch: export a simple list of PDFs as paper_list.csv
- import os as _os
- import pandas as _pd
- folder = "papers"
- files = sorted(_os.listdir(folder)) if _os.path.exists(folder) else []
- _pd.DataFrame({"doc": files}).to_csv("paper_list.csv", index=False)
- print("✅ Saved paper_list.csv with", len(files), "papers")
diff --git a/papers/Carbon fiber reinforced cement improved by using silane-treated carbon fibers.pdf b/literature_pdfs/Carbon fiber reinforced cement improved by using silane-treated carbon fibers.pdf
similarity index 100%
rename from papers/Carbon fiber reinforced cement improved by using silane-treated carbon fibers.pdf
rename to literature_pdfs/Carbon fiber reinforced cement improved by using silane-treated carbon fibers.pdf
diff --git a/papers/Silane-treated carbon fiber for reinforcing cement.pdf b/literature_pdfs/Silane-treated carbon fiber for reinforcing cement.pdf
similarity index 100%
rename from papers/Silane-treated carbon fiber for reinforcing cement.pdf
rename to literature_pdfs/Silane-treated carbon fiber for reinforcing cement.pdf
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-#!/usr/bin/env python3
-"""
-rag_eval_metrics.py
-
-Evaluate RAG retrieval quality by comparing app logs (JSONL) with a gold file (CSV).
-
-Extended to also evaluate answer quality using:
-- Lexical similarity: BLEU, ROUGE-1/2/L
-- Semantic similarity: BERTScore (Recall, F1)
-
-If nltk / rouge-score / bert-score are missing, the script still runs and
-returns NaN for these metrics instead of crashing.
-
-Also uses robust CSV reading to handle non-UTF8 encodings (cp1252/latin1).
-"""
-
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-from typing import Dict, List, Tuple, Any, Optional
-
-import pandas as pd
-import numpy as np
-
-# ----------------------------- Small Utils ----------------------------- #
-
-def filename_key(s: str) -> str:
- s = (s or "").strip().replace("\\", "/").split("/")[-1]
- return s.casefold()
-
-def re_split_sc(s: str) -> List[str]:
- import re
- return re.split(r"[;,]", s)
-
-def _pick_last_non_empty(hit_lists) -> List[dict]:
- """
- Robustly select the last non-empty hits list from a pandas Series or iterable.
-
- This fixes the KeyError that happens when using reversed() directly on a Series
- with a non-range index.
- """
- # Convert pandas Series or other iterables to a plain Python list
- try:
- values = list(hit_lists.tolist())
- except AttributeError:
- values = list(hit_lists)
-
- # Walk from last to first, return first non-empty list-like
- for lst in reversed(values):
- if isinstance(lst, (list, tuple)) and len(lst) > 0:
- return lst
-
- # If everything was empty / NaN
- return []
-
-def _read_csv_robust(path: Path) -> pd.DataFrame:
- """
- Try multiple encodings so we don't crash on Windows-1252 / Latin-1 CSVs.
- """
- encodings = ["utf-8", "utf-8-sig", "cp1252", "latin1"]
- last_err = None
- for enc in encodings:
- try:
- return pd.read_csv(path, encoding=enc)
- except UnicodeDecodeError as e:
- last_err = e
- continue
- # If all fail, re-raise the last error
- raise last_err if last_err is not None else ValueError(
- "Failed to read CSV with fallback encodings."
- )
-
-# ----------------------------- IO Helpers ----------------------------- #
-
-def read_logs(jsonl_path: Path) -> pd.DataFrame:
- """
- Read RAG JSONL logs and aggregate by question.
-
- Returns a DataFrame with columns:
- - question: original question text (last occurrence)
- - hits: list of dicts {doc, page} for retrieval
- - answer: final answer text logged for that question
- """
- rows = []
- if (not jsonl_path.exists()) or jsonl_path.stat().st_size == 0:
- return pd.DataFrame(columns=["question", "hits", "answer"])
-
- with open(jsonl_path, "r", encoding="utf-8") as f:
- for line in f:
- line = line.strip()
- if not line:
- continue
- try:
- rec = json.loads(line)
- except Exception:
- continue
-
- # Extract question
- q = (((rec.get("inputs") or {}).get("question")) or "").strip()
-
- # Extract retrieval hits (if present)
- retr = (rec.get("retrieval") or {})
- hits = retr.get("hits", [])
- norm_hits = []
- for h in hits or []:
- doc = (h.get("doc") or "").strip()
- page = str(h.get("page") or "").strip()
-
- # Normalize page to int or None
- try:
- page_int = int(page)
- except Exception:
- page_int = None
-
- norm_hits.append({"doc": doc, "page": page_int})
-
- # Extract final answer text (if present)
- out = (rec.get("output") or {})
- ans = ((out.get("final_answer") or "")).strip()
-
- rows.append({"question": q, "hits": norm_hits, "answer": ans})
-
- df = pd.DataFrame(rows)
- if df.empty:
- return pd.DataFrame(columns=["question", "hits", "answer"])
-
- # Group by normalized question text and keep last non-empty hits list and answer per question
- df = (
- df.groupby(df["question"].astype(str).str.casefold().str.strip(), as_index=False)
- .agg({
- "question": "last",
- "hits": _pick_last_non_empty,
- "answer": "last"
- })
- )
- return df
-
-def read_gold(csv_path: Path) -> Tuple[pd.DataFrame, Dict[str, str]]:
- """
- Read gold CSV with retrieval labels and optional reference answers.
-
- Returns:
- - gold_df: rows with columns ['question', 'doc', 'page', 'answer', ...]
- where 'question' is normalized (casefold+strip)
- - gold_answers: dict mapping normalized question -> reference answer text
- """
- df = _read_csv_robust(csv_path)
- cols = {c.lower().strip(): c for c in df.columns}
-
- # --- question column ---
- q_col = None
- for cand in ["question", "query", "q"]:
- if cand in cols:
- q_col = cols[cand]
- break
- if q_col is None:
- raise ValueError("Gold CSV must contain a 'question' column (case-insensitive).")
-
- # --- possible relevant_docs (list-in-cell) column ---
- rel_list_col = None
- for cand in ["relevant_docs", "relevant", "docs"]:
- if cand in cols:
- rel_list_col = cols[cand]
- break
-
- # --- single-doc-per-row column ---
- doc_col = None
- for cand in ["doc", "document", "file", "doc_name"]:
- if cand in cols:
- doc_col = cols[cand]
- break
-
- # --- optional page column ---
- page_col = None
- for cand in ["page", "page_num", "page_number"]:
- if cand in cols:
- page_col = cols[cand]
- break
-
- # --- optional answer column (for QA metrics) ---
- ans_col = None
- for cand in ["answer", "reference_answer", "gold_answer"]:
- if cand in cols:
- ans_col = cols[cand]
- break
-
- rows = []
-
- # Case 1: relevant_docs list column (no explicit doc_col)
- if rel_list_col and doc_col is None:
- for _, r in df.iterrows():
- q_raw = str(r[q_col]).strip()
- q_norm = q_raw.casefold().strip()
- ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""
-
- rel_val = str(r[rel_list_col]) if pd.notna(r[rel_list_col]) else ""
- if not rel_val:
- rows.append({
- "question_raw": q_raw,
- "question": q_norm,
- "doc": None,
- "page": np.nan,
- "answer": ans_raw
- })
- continue
-
- parts = [p.strip() for p in re_split_sc(rel_val)]
- for d in parts:
- rows.append({
- "question_raw": q_raw,
- "question": q_norm,
- "doc": filename_key(d),
- "page": np.nan,
- "answer": ans_raw
- })
-
- # Case 2: doc/page columns (one relevant doc per row)
- elif doc_col:
- for _, r in df.iterrows():
- q_raw = str(r[q_col]).strip()
- q_norm = q_raw.casefold().strip()
- ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""
-
- d = str(r[doc_col]).strip() if pd.notna(r[doc_col]) else ""
- p = r[page_col] if (page_col and pd.notna(r[page_col])) else np.nan
-
- try:
- p = int(p)
- except Exception:
- p = np.nan
-
- rows.append({
- "question_raw": q_raw,
- "question": q_norm,
- "doc": filename_key(d),
- "page": p,
- "answer": ans_raw
- })
-
- else:
- raise ValueError("Gold CSV must contain either a 'doc' column or a 'relevant_docs' column.")
-
- gold = pd.DataFrame(rows)
-
- # Keep only rows with a valid doc (when docs exist)
- gold["has_doc"] = gold["doc"].apply(lambda x: isinstance(x, str) and len(x) > 0)
- if gold["has_doc"].any():
- gold = gold[gold["has_doc"]].copy()
- gold.drop(columns=["has_doc"], inplace=True, errors="ignore")
-
- # Remove duplicates
- gold = gold.drop_duplicates(subset=["question", "doc", "page"])
-
- # Build question -> gold_answer map (normalized questions)
- gold_answers: Dict[str, str] = {}
- if "answer" in gold.columns:
- tmp = (
- gold[["question", "answer"]]
- .dropna(subset=["answer"])
- .drop_duplicates(subset=["question"])
- )
- gold_answers = dict(zip(tmp["question"], tmp["answer"]))
-
- return gold, gold_answers
-
-# ----------------------------- Retrieval Metric Core ----------------------------- #
-
-def dcg_at_k(relevances: List[int]) -> float:
- dcg = 0.0
- for i, rel in enumerate(relevances, start=1):
- if rel > 0:
- dcg += 1.0 / np.log2(i + 1.0)
- return float(dcg)
-
-def ndcg_at_k(relevances: List[int]) -> float:
- dcg = dcg_at_k(relevances)
- ideal = sorted(relevances, reverse=True)
- idcg = dcg_at_k(ideal)
- if idcg == 0.0:
- return 0.0
- return float(dcg / idcg)
-
-def compute_metrics_for_question(gold_docs, gold_pages, hits, k):
- top = hits[:k] if hits else []
- pred_docs = [filename_key(h.get("doc", "")) for h in top]
- pred_pairs = [(filename_key(h.get("doc", "")), h.get("page", None)) for h in top]
-
- # --- Doc-level metrics ---
- gold_doc_set = set([d for d in gold_docs if isinstance(d, str) and d])
-
- rel_bin_doc = [1 if d in gold_doc_set else 0 for d in pred_docs]
- hitk_doc = 1 if any(rel_bin_doc) else 0
- prec_doc = (sum(rel_bin_doc) / max(1, len(pred_docs))) if pred_docs else 0.0
- rec_doc = (sum(rel_bin_doc) / max(1, len(gold_doc_set))) if gold_doc_set else 0.0
- ndcg_doc = ndcg_at_k(rel_bin_doc)
-
- # --- Page-level metrics (only if gold has page labels) ---
- gold_pairs = set()
- for d, p in zip(gold_docs, gold_pages):
- if isinstance(d, str) and d and (p is not None) and (not (isinstance(p, float) and np.isnan(p))):
- try:
- p_int = int(p)
- except Exception:
- continue
- gold_pairs.add((d, p_int))
-
- if gold_pairs:
- rel_bin_page = []
- for (d, p) in pred_pairs:
- if p is None or not isinstance(p, int):
- rel_bin_page.append(0)
- else:
- rel_bin_page.append(1 if (d, p) in gold_pairs else 0)
-
- hitk_page = 1 if any(rel_bin_page) else 0
- prec_page = (sum(rel_bin_page) / max(1, len(pred_pairs))) if pred_pairs else 0.0
- rec_page = (sum(rel_bin_page) / max(1, len(gold_pairs))) if gold_pairs else 0.0
- ndcg_page = ndcg_at_k(rel_bin_page)
- else:
- hitk_page = prec_page = rec_page = ndcg_page = np.nan
-
- return {
- "hit@k_doc": hitk_doc,
- "precision@k_doc": prec_doc,
- "recall@k_doc": rec_doc,
- "ndcg@k_doc": ndcg_doc,
- "hit@k_page": hitk_page,
- "precision@k_page": prec_page,
- "recall@k_page": rec_page,
- "ndcg@k_page": ndcg_page,
- "n_gold_docs": int(len(gold_doc_set)),
- "n_gold_doc_pages": int(len(gold_pairs)),
- "n_pred": int(len(pred_docs))
- }
-
-# ---------------------- Answer Quality Metrics (with fallbacks) ---------------------- #
-
-# Try to import optional libraries; if missing, we fall back to NaN metrics
-try:
- from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
- HAVE_NLTK = True
-except Exception:
- sentence_bleu = None
- SmoothingFunction = None
- HAVE_NLTK = False
-
-try:
- from rouge_score import rouge_scorer
- HAVE_ROUGE = True
-except Exception:
- rouge_scorer = None
- HAVE_ROUGE = False
-
-try:
- from bert_score import score as bert_score
- HAVE_BERT = True
-except Exception:
- bert_score = None
- HAVE_BERT = False
-
-if HAVE_NLTK:
- _SMOOTH = SmoothingFunction().method1
-else:
- _SMOOTH = None
-
-if HAVE_ROUGE:
- _ROUGE_SCORER = rouge_scorer.RougeScorer(
- ["rouge1", "rouge2", "rougeL"], use_stemmer=True
- )
-else:
- _ROUGE_SCORER = None
-
-def _normalize_text_for_metrics(s: str) -> str:
- import re
- s = (s or "").strip().lower()
- # remove simple markdown markers
- s = re.sub(r"\*\*|\*", "", s)
- # drop inline citations like (Doc.pdf, p.X)
- s = re.sub(r"\([^)]*\)", " ", s)
- s = re.sub(r"\s+", " ", s)
- return s.strip()
-
-def compute_text_metrics(pred: str, ref: str) -> Dict[str, float]:
- """
- Compute lexical and semantic similarity metrics between prediction and reference:
- - BLEU
- - ROUGE-1/2/L (F-measure)
- - BERTScore Recall, F1
-
- If the required libraries (nltk, rouge-score, bert-score) are not installed,
- returns NaN for all metrics.
- """
- # If any of the libraries is missing, skip answer metrics
- if not (HAVE_NLTK and HAVE_ROUGE and HAVE_BERT):
- return {
- "bleu": np.nan,
- "rouge1": np.nan,
- "rouge2": np.nan,
- "rougeL": np.nan,
- "bert_recall": np.nan,
- "bert_f1": np.nan,
- }
-
- pred_n = _normalize_text_for_metrics(pred)
- ref_n = _normalize_text_for_metrics(ref)
-
- if not pred_n or not ref_n:
- return {
- "bleu": np.nan,
- "rouge1": np.nan,
- "rouge2": np.nan,
- "rougeL": np.nan,
- "bert_recall": np.nan,
- "bert_f1": np.nan,
- }
-
- pred_tokens = pred_n.split()
- ref_tokens = ref_n.split()
-
- # BLEU (sentence-level with smoothing)
- bleu = float(
- sentence_bleu([ref_tokens], pred_tokens, smoothing_function=_SMOOTH)
- )
-
- # ROUGE via rouge-score (F-measure)
- rs = _ROUGE_SCORER.score(ref_n, pred_n)
- rouge1 = float(rs["rouge1"].fmeasure)
- rouge2 = float(rs["rouge2"].fmeasure)
- rougeL = float(rs["rougeL"].fmeasure)
-
- # BERTScore (semantic similarity)
- P, R, F1 = bert_score([pred_n], [ref_n], lang="en", rescale_with_baseline=True)
- bert_recall = float(R.mean().item())
- bert_f1 = float(F1.mean().item())
-
- return {
- "bleu": bleu,
- "rouge1": rouge1,
- "rouge2": rouge2,
- "rougeL": rougeL,
- "bert_recall": bert_recall,
- "bert_f1": bert_f1,
- }
-
-# ----------------------------- Orchestration ----------------------------- #
-
-# === Dark blue and accent colors ===
-COLOR_TITLE = "\033[94m" # light blue for titles
-COLOR_TEXT = "\033[34m" # dark blue
-COLOR_ACCENT = "\033[36m" # cyan for metrics
-COLOR_RESET = "\033[0m"
-
-def _fmt(x: Any) -> str:
- try:
- return f"{float(x):.3f}"
- except Exception:
- return "-"
-
-def main():
- ap = argparse.ArgumentParser()
- ap.add_argument("--gold_csv", required=True, type=str)
- ap.add_argument("--logs_jsonl", required=True, type=str)
- ap.add_argument("--k", type=int, default=8)
- ap.add_argument("--out_dir", type=str, default="rag_artifacts")
- args = ap.parse_args()
-
- out_dir = Path(args.out_dir)
- out_dir.mkdir(parents=True, exist_ok=True)
-
- gold_path = Path(args.gold_csv)
- logs_path = Path(args.logs_jsonl)
-
- if not gold_path.exists():
- print(
- f"{COLOR_TEXT}❌ gold.csv not found at {gold_path}{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
- if not logs_path.exists() or logs_path.stat().st_size == 0:
- print(
- f"{COLOR_TEXT}❌ logs JSONL not found or empty at {logs_path}{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
-
- # Read gold (retrieval + QA answers)
- try:
- gold, gold_answers = read_gold(gold_path)
- except Exception as e:
- print(
- f"{COLOR_TEXT}❌ Failed to read gold: {e}{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
-
- # Read logs (with robust aggregation)
- try:
- logs = read_logs(logs_path)
- except Exception as e:
- print(
- f"{COLOR_TEXT}❌ Failed to read logs: {e}{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
-
- if gold.empty:
- print(
- f"{COLOR_TEXT}❌ Gold file contains no usable rows.{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
- if logs.empty:
- print(
- f"{COLOR_TEXT}❌ Logs file contains no usable entries.{COLOR_RESET}",
- file=sys.stderr,
- )
- sys.exit(0)
-
- # Build gold dict: normalized_question -> list of (doc, page)
- gdict: Dict[str, List[Tuple[str, Optional[int]]]] = {}
- for _, r in gold.iterrows():
- q = str(r["question"]).strip() # already normalized in read_gold
- d = r["doc"]
- p = r["page"] if "page" in r else np.nan
- gdict.setdefault(q, []).append((d, p))
-
- # Normalize log questions for join
- logs["q_norm"] = logs["question"].astype(str).str.casefold().str.strip()
-
- perq_rows = []
- not_in_logs, not_in_gold = [], []
-
- # For each gold question, compute metrics using logs
- for q_norm, pairs in gdict.items():
- row = logs[logs["q_norm"] == q_norm]
- gdocs = [d for (d, _) in pairs]
- gpages = [p for (_, p) in pairs]
-
- if row.empty:
- # No logs for this gold question → zero retrieval and no answer metrics
- not_in_logs.append(q_norm)
- base_metrics = {
- "hit@k_doc": 0,
- "precision@k_doc": 0.0,
- "recall@k_doc": 0.0,
- "ndcg@k_doc": 0.0,
- "hit@k_page": np.nan,
- "precision@k_page": np.nan,
- "recall@k_page": np.nan,
- "ndcg@k_page": np.nan,
- "n_gold_docs": int(len(set([d for d in gdocs if isinstance(d, str) and d]))),
- "n_gold_doc_pages": int(
- len(
- [
- (d, p)
- for (d, p) in zip(gdocs, gpages)
- if isinstance(d, str) and d and pd.notna(p)
- ]
- )
- ),
- "n_pred": 0,
- }
-
- txt_metrics = {
- "bleu": np.nan,
- "rouge1": np.nan,
- "rouge2": np.nan,
- "rougeL": np.nan,
- "bert_recall": np.nan,
- "bert_f1": np.nan,
- }
-
- perq_rows.append(
- {
- "question": q_norm,
- "covered_in_logs": 0,
- **base_metrics,
- **txt_metrics,
- }
- )
- continue
-
- # Use aggregated hits from read_logs
- hits = row.iloc[0]["hits"] or []
- base_metrics = compute_metrics_for_question(gdocs, gpages, hits, args.k)
-
- # Answer text: predicted vs. gold
- pred_answer = str(row.iloc[0].get("answer", "")).strip()
- gold_answer = str(gold_answers.get(q_norm, "")).strip()
-
- if gold_answer and pred_answer:
- txt_metrics = compute_text_metrics(pred_answer, gold_answer)
- else:
- txt_metrics = {
- "bleu": np.nan,
- "rouge1": np.nan,
- "rouge2": np.nan,
- "rougeL": np.nan,
- "bert_recall": np.nan,
- "bert_f1": np.nan,
- }
-
- perq_rows.append(
- {
- "question": q_norm,
- "covered_in_logs": 1,
- **base_metrics,
- **txt_metrics,
- }
- )
-
- # Any log questions not in gold
- gold_qs = set(gdict.keys())
- for qn in logs["q_norm"].tolist():
- if qn not in gold_qs:
- not_in_gold.append(qn)
-
- perq = pd.DataFrame(perq_rows)
- covered = perq[perq["covered_in_logs"] == 1].copy()
-
- agg = {
- "questions_total_gold": int(len(gdict)),
- "questions_covered_in_logs": int(covered.shape[0]),
- "questions_missing_in_logs": int(len(not_in_logs)),
- "questions_in_logs_not_in_gold": int(len(set(not_in_gold))),
- "k": int(args.k),
- "mean_hit@k_doc": float(covered["hit@k_doc"].mean()) if not covered.empty else 0.0,
- "mean_precision@k_doc": float(covered["precision@k_doc"].mean()) if not covered.empty else 0.0,
- "mean_recall@k_doc": float(covered["recall@k_doc"].mean()) if not covered.empty else 0.0,
- "mean_ndcg@k_doc": float(covered["ndcg@k_doc"].mean()) if not covered.empty else 0.0,
- "mean_hit@k_page": float(covered["hit@k_page"].dropna().mean())
- if covered["hit@k_page"].notna().any()
- else None,
- "mean_precision@k_page": float(covered["precision@k_page"].dropna().mean())
- if covered["precision@k_page"].notna().any()
- else None,
- "mean_recall@k_page": float(covered["recall@k_page"].dropna().mean())
- if covered["recall@k_page"].notna().any()
- else None,
- "mean_ndcg@k_page": float(covered["ndcg@k_page"].dropna().mean())
- if covered["ndcg@k_page"].notna().any()
- else None,
- "avg_gold_docs_per_q": float(perq["n_gold_docs"].mean()) if not perq.empty else 0.0,
- "avg_preds_per_q": float(perq["n_pred"].mean()) if not perq.empty else 0.0,
- "examples_missing_in_logs": list(not_in_logs[:10]),
- "examples_in_logs_not_in_gold": list(dict.fromkeys(not_in_gold))[:10],
- }
-
- # Aggregate answer-quality metrics (lexical + semantic)
- if "bleu" in covered.columns:
- agg["mean_bleu"] = float(covered["bleu"].mean(skipna=True))
- agg["mean_rouge1"] = float(covered["rouge1"].mean(skipna=True))
- agg["mean_rouge2"] = float(covered["rouge2"].mean(skipna=True))
- agg["mean_rougeL"] = float(covered["rougeL"].mean(skipna=True))
- agg["mean_bert_recall"] = float(covered["bert_recall"].mean(skipna=True))
- agg["mean_bert_f1"] = float(covered["bert_f1"].mean(skipna=True))
-
- perq_path = out_dir / "metrics_per_question.csv"
- agg_path = out_dir / "metrics_aggregate.json"
-
- perq.to_csv(perq_path, index=False)
- with open(agg_path, "w", encoding="utf-8") as f:
- json.dump(agg, f, ensure_ascii=False, indent=2)
-
- # === Console summary with color ===
- print(f"{COLOR_TITLE}RAG Evaluation Summary{COLOR_RESET}")
- print(f"{COLOR_TITLE}----------------------{COLOR_RESET}")
- print(f"{COLOR_TEXT}Gold questions: {COLOR_ACCENT}{agg['questions_total_gold']}{COLOR_RESET}")
- print(f"{COLOR_TEXT}Covered in logs: {COLOR_ACCENT}{agg['questions_covered_in_logs']}{COLOR_RESET}")
- print(f"{COLOR_TEXT}Missing in logs: {COLOR_ACCENT}{agg['questions_missing_in_logs']}{COLOR_RESET}")
- print(
- f"{COLOR_TEXT}In logs but not in gold: "
- f"{COLOR_ACCENT}{agg['questions_in_logs_not_in_gold']}{COLOR_RESET}"
- )
- print(f"{COLOR_TEXT}k = {COLOR_ACCENT}{agg['k']}{COLOR_RESET}\n")
-
- print(
- f"{COLOR_TEXT}Doc-level:{COLOR_RESET} "
- f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_doc'])} "
- f"Precision@k={_fmt(agg['mean_precision@k_doc'])} "
- f"Recall@k={_fmt(agg['mean_recall@k_doc'])} "
- f"nDCG@k={_fmt(agg['mean_ndcg@k_doc'])}{COLOR_RESET}"
- )
-
- if agg.get("mean_hit@k_page") is not None:
- print(
- f"{COLOR_TEXT}Page-level:{COLOR_RESET} "
- f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_page'])} "
- f"Precision@k={_fmt(agg['mean_precision@k_page'])} "
- f"Recall={_fmt(agg['mean_recall@k_page'])} "
- f"nDCG@k={_fmt(agg['mean_ndcg@k_page'])}{COLOR_RESET}"
- )
- else:
- print(f"{COLOR_TEXT}Page-level: (no page labels in gold){COLOR_RESET}")
-
- # Lexical metrics summary
- if "mean_bleu" in agg:
- print(
- f"{COLOR_TEXT}Lexical (answer quality):{COLOR_RESET} "
- f"{COLOR_ACCENT}BLEU={_fmt(agg.get('mean_bleu'))} "
- f"ROUGE-1={_fmt(agg.get('mean_rouge1'))} "
- f"ROUGE-2={_fmt(agg.get('mean_rouge2'))} "
- f"ROUGE-L={_fmt(agg.get('mean_rougeL'))}{COLOR_RESET}"
- )
-
- # Semantic metrics summary
- if "mean_bert_f1" in agg:
- print(
- f"{COLOR_TEXT}Semantic (BERTScore):{COLOR_RESET} "
- f"{COLOR_ACCENT}Recall={_fmt(agg.get('mean_bert_recall'))} "
- f"F1={_fmt(agg.get('mean_bert_f1'))}{COLOR_RESET}"
- )
-
- print()
- print(
- f"{COLOR_TEXT}Wrote per-question CSV → "
- f"{COLOR_ACCENT}{perq_path}{COLOR_RESET}"
- )
- print(
- f"{COLOR_TEXT}Wrote aggregate JSON → "
- f"{COLOR_ACCENT}{agg_path}{COLOR_RESET}"
- )
-
-if __name__ == "__main__":
- main()
diff --git a/requirements.txt b/requirements.txt
index 3fd8cc2b1c2388dd6c2d8415bfc67592b4ef07fe..ceda4050e2f066d149fbc87a3044d7b2ee593921 100644
Binary files a/requirements.txt and b/requirements.txt differ
diff --git a/stress_gf_xgb.joblib b/stress_gf_xgb.joblib
deleted file mode 100644
index dc0a827459f02880c5a944204229737de0190226..0000000000000000000000000000000000000000
--- a/stress_gf_xgb.joblib
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d1647acfa1a50c037437003f02b89ffeda2c82e4e7c852b87bce4c56449b521b
-size 1325590