Deploy 3W 0624 BERT metadata fusion model

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README.md

@@ -1,13 +1,21 @@
----
-title: Sentiment Analysis 3W0624
-emoji: 😻
-colorFrom: red
-colorTo: red
+---
+title: ACSA Clothing Sentiment
+emoji: "👕"
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.13'
+sdk_version: 5.49.1
+python_version: "3.10"
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Aspect-Level Sentiment Analysis for Clothing Reviews
+
+Interactive demo and research dashboard for a BERT metadata-fusion ACSA model trained on 30,000 clothing reviews.
+
+## Included inference artifacts
+
+- Proposed metadata-fusion checkpoint and tokenizer
+- Matching `meta_encoder.pkl`
+- Overall, aspect-level, and ablation evaluation reports

app.py

@@ -0,0 +1,630 @@
+"""Polished Hugging Face Spaces frontend for ACSA Clothing.
+
+Two-end design:
+1. Customer-facing review analyzer.
+2. Research dashboard for metrics, ablation, and explanation evidence.
+"""
+from __future__ import annotations
+
+import csv
+import html
+import json
+import re
+import traceback
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Tuple
+
+# Keep the app robust across Spaces images where hub/gradio versions differ.
+try:
+    import huggingface_hub as _hf_hub
+
+    if not hasattr(_hf_hub, "HfFolder"):
+        class _HfFolderCompat:
+            @staticmethod
+            def get_token():
+                try:
+                    return _hf_hub.get_token()
+                except Exception:
+                    return None
+
+            @staticmethod
+            def save_token(token):
+                return None
+
+            @staticmethod
+            def delete_token():
+                return None
+
+        _hf_hub.HfFolder = _HfFolderCompat
+except Exception:
+    pass
+
+import gradio as gr
+
+from src import config as cfg
+from src.inference import AspectPredictor
+
+ROOT = Path(__file__).resolve().parent
+REPORT_DIR = ROOT / "reports"
+CHECKPOINT_DIR = ROOT / "checkpoints" / "meta_acsa"
+CHECKPOINT_PATH = CHECKPOINT_DIR / "best.pt"
+META_ENCODER_PATH = ROOT / "data" / "meta_encoder.pkl"
+
+ASPECT_DESCRIPTIONS = {
+    "SIZE": "Fit, length, sizing accuracy, runs large or small.",
+    "MATERIAL": "Fabric feel, thickness, breathability, comfort.",
+    "QUALITY": "Workmanship, durability, seams, washing performance.",
+    "APPEARANCE": "Color, pattern, image consistency, visual look.",
+    "STYLE": "Cut, silhouette, fashionability, styling appeal.",
+    "VALUE": "Price fairness, worthiness, return or repurchase intent.",
+}
+
+LABEL_COLORS = {
+    "Positive": "#15803d",
+    "Negative": "#b91c1c",
+    "Not_Mentioned": "#64748b",
+}
+
+LABEL_BG = {
+    "Positive": "#dcfce7",
+    "Negative": "#fee2e2",
+    "Not_Mentioned": "#f1f5f9",
+}
+
+ASPECT_KEYWORDS = {
+    "SIZE": ["size", "fit", "fits", "fitting", "small", "large", "big", "tight", "loose", "xl", "medium", "waist", "length", "runs small", "runs large", "too small", "too large"],
+    "MATERIAL": ["material", "fabric", "cotton", "polyester", "soft", "scratchy", "thin", "thick", "stretch", "breathable", "comfortable"],
+    "QUALITY": ["quality", "stitch", "stitching", "seam", "wash", "washed", "durable", "cheap", "broke", "broken", "ripped", "tear", "torn", "defect", "zipper", "button"],
+    "APPEARANCE": ["look", "looks", "color", "colour", "photo", "picture", "beautiful", "cute", "print", "design", "pattern", "shown"],
+    "STYLE": ["style", "stylish", "flattering", "casual", "formal", "fashion", "silhouette", "cut", "shape"],
+    "VALUE": ["price", "worth", "value", "money", "expensive", "overpriced", "cheap", "discount", "buy again", "purchase", "return"],
+}
+
+NEGATIVE_CUES = {
+    "bad", "poor", "terrible", "awful", "cheap", "scratchy", "itchy", "thin", "small", "large", "tight", "loose",
+    "broke", "broken", "ripped", "torn", "overpriced", "return", "returned", "waste", "disappointed", "uncomfortable",
+    "shrunk", "defective", "wrong",
+}
+
+POSITIVE_CUES = {
+    "love", "loved", "great", "good", "perfect", "excellent", "nice", "soft", "comfortable", "beautiful", "pretty",
+    "flattering", "worth", "recommend", "amazing", "stylish", "durable", "cute", "buy again",
+}
+
+SIZE_NEGATIVE_PATTERNS = [
+    r"\bruns?\s+(a\s+little\s+)?small\b",
+    r"\bruns?\s+(a\s+little\s+)?large\b",
+    r"\btoo\s+(small|large|big|tight|loose)\b",
+    r"\b(size|fit|fits|fitting)\b[^.!?]{0,35}\b(small|large|big|tight|loose)\b",
+]
+
+def _safe_json(path: Path) -> Dict[str, Any]:
+    if not path.exists():
+        return {}
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except Exception:
+        return {}
+
+
+def _safe_csv(path: Path, limit: int = 80) -> List[Dict[str, str]]:
+    if not path.exists():
+        return []
+    try:
+        with path.open(newline="", encoding="utf-8") as f:
+            return list(csv.DictReader(f))[:limit]
+    except Exception:
+        return []
+
+
+def _pct(value: Any) -> str:
+    try:
+        return f"{100 * float(value):.2f}%"
+    except Exception:
+        return "-"
+
+
+def _num(value: Any, default: float = 0.0) -> float:
+    try:
+        if value in (None, ""):
+            return default
+        return float(value)
+    except Exception:
+        return default
+
+
+def _int(value: Any, default: int = 0) -> int:
+    try:
+        if value in (None, ""):
+            return default
+        return int(float(value))
+    except Exception:
+        return default
+
+
+def _model_issues() -> List[str]:
+    issues = []
+    if not CHECKPOINT_PATH.exists():
+        issues.append("Missing checkpoints/meta_acsa/best.pt")
+    elif CHECKPOINT_PATH.stat().st_size < 1_000_000:
+        size = CHECKPOINT_PATH.stat().st_size
+        try:
+            head = CHECKPOINT_PATH.read_bytes()[:160].decode("utf-8", errors="replace")
+        except Exception as exc:
+            head = f"<could not read file head: {exc}>"
+        issues.append(
+            "checkpoints/meta_acsa/best.pt looks too small. "
+            f"Runtime path={CHECKPOINT_PATH}; runtime size={size} bytes; "
+            f"file head={head!r}. "
+            "It may be a Git LFS/Xet pointer or the running Space has not synced the real 303 MB model file."
+        )
+    if not (CHECKPOINT_DIR / "tokenizer").exists():
+        issues.append("Missing checkpoints/meta_acsa/tokenizer/")
+    if not META_ENCODER_PATH.exists():
+        issues.append("Missing data/meta_encoder.pkl")
+    return issues
+
+
+@lru_cache(maxsize=1)
+def _predictor() -> AspectPredictor:
+    issues = _model_issues()
+    if issues:
+        raise FileNotFoundError("; ".join(issues))
+    return AspectPredictor(checkpoint_dir=CHECKPOINT_DIR)
+
+
+def _meta(features: str, categories: str, price: Any, avg_rating: Any, rating_count: Any) -> Dict[str, Any]:
+    return {
+        "features_text": features or "",
+        "categories_text": categories or "",
+        "price": _num(price),
+        "average_rating": _num(avg_rating),
+        "rating_number": _int(rating_count),
+    }
+
+
+def _status_html() -> str:
+    issues = _model_issues()
+    if not issues:
+        return "<div class='status ok'><b>Model ready.</b> All required inference artifacts are available.</div>"
+    items = "".join(f"<li>{html.escape(x)}</li>" for x in issues)
+    return f"<div class='status warn'><b>Model artifacts need attention.</b><ul>{items}</ul></div>"
+
+
+def _error_html(title: str, exc: Exception) -> str:
+    msg = html.escape(str(exc) or exc.__class__.__name__)
+    detail = html.escape(traceback.format_exc(limit=2))
+    return (
+        "<div class='error-card'>"
+        f"<h3>{html.escape(title)}</h3>"
+        f"<p>{msg}</p>"
+        f"<details><summary>Technical detail</summary><pre>{detail}</pre></details>"
+        "</div>"
+    )
+
+
+
+def _contains_phrase(text: str, phrase: str) -> bool:
+    if " " in phrase:
+        return phrase in text
+    return re.search(r"\b" + re.escape(phrase) + r"\b", text) is not None
+
+
+def _extract_evidence(review_text: str) -> Dict[str, List[str]]:
+    text = str(review_text or "")
+    lower = text.lower()
+    result: Dict[str, List[str]] = {}
+    for aspect, terms in ASPECT_KEYWORDS.items():
+        hits: List[str] = []
+        for term in terms:
+            if _contains_phrase(lower, term.lower()) and term not in hits:
+                hits.append(term)
+        cue_hits = []
+        for cue in sorted(POSITIVE_CUES | NEGATIVE_CUES):
+            if _contains_phrase(lower, cue.lower()):
+                cue_hits.append(cue)
+        if hits:
+            merged = hits + [c for c in cue_hits if c not in hits]
+        else:
+            merged = []
+        result[aspect] = merged[:8]
+    return result
+
+
+def _confidence(result: Dict[str, Any], aspect: str) -> float:
+    details = result.get("aspect_details", {}) or {}
+    val = details.get(aspect, {}).get("confidence") if isinstance(details, dict) else None
+    return _num(val, 0.0)
+
+
+def _apply_demo_calibration(result: Dict[str, Any], review_text: str) -> Dict[str, Any]:
+    """Conservative display-time calibration for the HF demo.
+
+    It prevents overall positivity from spreading to aspects with no textual
+    evidence, while preserving the raw model confidence in aspect_details.
+    """
+    aspects = dict(result.get("aspects", {}) or {})
+    evidence = _extract_evidence(review_text)
+    raw_aspects = dict(aspects)
+    lower = str(review_text or "").lower()
+
+    for aspect in cfg.ASPECTS:
+        hits = evidence.get(aspect, [])
+        if aspect == "SIZE" and any(re.search(p, lower) for p in SIZE_NEGATIVE_PATTERNS):
+            aspects[aspect] = "Negative"
+            if not hits:
+                evidence[aspect] = ["size issue"]
+            continue
+        if not hits:
+            # A metadata-only model signal is useful internally, but the demo
+            # should not claim aspect sentiment without review evidence.
+            aspects[aspect] = "Not_Mentioned"
+        elif aspects.get(aspect) == "Not_Mentioned" and _confidence(result, aspect) >= 0.90:
+            # Keep strong mention signals visible, but do not force sentiment.
+            aspects[aspect] = raw_aspects.get(aspect, "Not_Mentioned")
+
+    result["raw_model_aspects"] = raw_aspects
+    result["aspects"] = aspects
+    result["text_evidence_by_aspect"] = evidence
+    result["display_calibrated"] = True
+    return result
+def _highlight_evidence(evidence: Any) -> str:
+    if not evidence:
+        return "<span class='muted'>No explicit text evidence returned.</span>"
+    if isinstance(evidence, str):
+        evidence = [evidence]
+    chips = []
+    for item in list(evidence)[:5]:
+        text = str(item).strip()
+        if text:
+            chips.append(f"<span class='chip'>{html.escape(text)}</span>")
+    return "".join(chips) or "<span class='muted'>No explicit text evidence returned.</span>"
+
+
+def _aspect_cards(result: Dict[str, Any]) -> str:
+    aspects = result.get("aspects", {}) or {}
+    evidence = result.get("text_evidence_by_aspect", {}) or {}
+    details = result.get("aspect_details", {}) or {}
+    parts = ["<div class='aspect-grid'>"]
+    for aspect in cfg.ASPECTS:
+        label = aspects.get(aspect, "Not_Mentioned")
+        color = LABEL_COLORS.get(label, "#64748b")
+        bg = LABEL_BG.get(label, "#f1f5f9")
+        conf = details.get(aspect, {}).get("confidence") if isinstance(details, dict) else None
+        conf_html = f"<span class='confidence'>conf {float(conf):.2f}</span>" if isinstance(conf, (int, float)) else ""
+        parts.append(
+            f"<section class='aspect-card' style='border-top-color:{color}'>"
+            f"<div class='aspect-top'><b>{html.escape(aspect)}</b>{conf_html}</div>"
+            f"<div class='label-pill' style='color:{color};background:{bg}'>{html.escape(label)}</div>"
+            f"<p>{html.escape(ASPECT_DESCRIPTIONS.get(aspect, ''))}</p>"
+            f"<div class='evidence'>{_highlight_evidence(evidence.get(aspect))}</div>"
+            "</section>"
+        )
+    parts.append("</div>")
+    return "".join(parts)
+
+
+def _flatten_attention(result: Dict[str, Any]) -> List[Tuple[str, str, float]]:
+    rows: List[Tuple[str, str, float]] = []
+    by_aspect = result.get("meta_attention_by_aspect", {}) or {}
+    if isinstance(by_aspect, dict):
+        for aspect, weights in by_aspect.items():
+            if isinstance(weights, dict):
+                for name, val in weights.items():
+                    rows.append((str(aspect), str(name), _num(val)))
+    global_attn = result.get("meta_attention", {}) or {}
+    if isinstance(global_attn, dict):
+        for name, val in global_attn.items():
+            rows.append(("GLOBAL", str(name), _num(val)))
+    return rows
+
+
+def _attention_html(result: Dict[str, Any]) -> str:
+    rows = _flatten_attention(result)
+    if not rows:
+        return "<div class='empty'>No metadata attention returned by this checkpoint.</div>"
+    max_v = max(v for _, _, v in rows) or 1.0
+    grouped: Dict[str, List[Tuple[str, float]]] = {}
+    for aspect, name, val in rows:
+        grouped.setdefault(aspect, []).append((name, val))
+    parts = ["<div class='attn-grid'>"]
+    for aspect in ["GLOBAL"] + [a for a in cfg.ASPECTS if a in grouped]:
+        if aspect not in grouped:
+            continue
+        parts.append(f"<section class='attn-card'><h4>{html.escape(aspect)} metadata use</h4>")
+        for name, val in sorted(grouped[aspect], key=lambda x: x[1], reverse=True):
+            width = max(5, min(100, 100 * val / max_v))
+            parts.append(
+                f"<div class='bar-row'><span>{html.escape(name)}</span>"
+                f"<div class='bar-bg'><div class='bar-fill' style='width:{width:.1f}%'></div></div>"
+                f"<em>{val:.3f}</em></div>"
+            )
+        parts.append("</section>")
+    parts.append("</div>")
+    return "".join(parts)
+
+
+def _overall_label(result: Dict[str, Any]) -> str:
+    overall = result.get("overall")
+    if isinstance(overall, dict):
+        return str(overall.get("label") or overall.get("prediction") or overall)
+    if overall:
+        return str(overall)
+    labels = list((result.get("aspects") or {}).values())
+    if labels.count("Negative") >= 1:
+        return "Negative"
+    if labels.count("Positive") >= 1:
+        return "Positive"
+    return "Neutral / Not enough aspect signal"
+
+
+def _diagnosis_html(result: Dict[str, Any]) -> str:
+    aspects = result.get("aspects", {}) or {}
+    overall = _overall_label(result)
+    positives = [a for a, v in aspects.items() if v == "Positive"]
+    negatives = [a for a, v in aspects.items() if v == "Negative"]
+    meta_summary = result.get("metadata_summary") or {}
+    top_meta = result.get("top_meta_source") or "-"
+    risk = "High" if len(negatives) >= 2 or overall == "Negative" else "Medium" if negatives else "Low"
+    return (
+        "<section class='diagnosis'>"
+        "<h3>Business Diagnosis</h3>"
+        f"<div class='diag-row'><span>Overall</span><b>{html.escape(overall)}</b></div>"
+        f"<div class='diag-row'><span>Risk level</span><b>{risk}</b></div>"
+        f"<div class='diag-row'><span>Positive drivers</span><b>{html.escape(', '.join(positives) or '-')}</b></div>"
+        f"<div class='diag-row'><span>Negative drivers</span><b>{html.escape(', '.join(negatives) or '-')}</b></div>"
+        f"<div class='diag-row'><span>Top metadata source</span><b>{html.escape(str(top_meta))}</b></div>"
+        f"<p>{html.escape(_recommendation(positives, negatives))}</p>"
+        f"<details><summary>Metadata summary</summary><pre>{html.escape(json.dumps(meta_summary, ensure_ascii=False, indent=2))}</pre></details>"
+        "</section>"
+    )
+
+
+def _recommendation(positives: Iterable[str], negatives: Iterable[str]) -> str:
+    positives = list(positives)
+    negatives = list(negatives)
+    if negatives:
+        return "Prioritize the negative aspects in PDP copy, sizing guidance, QA checks, and return-reason analysis."
+    if positives:
+        return "Use the positive aspect signals as merchandising highlights and customer-facing selling points."
+    return "The review does not contain a strong aspect-level sentiment signal; use it as low-priority qualitative feedback."
+
+
+def analyze_review(review: str, features: str, categories: str, price: Any, avg_rating: Any, rating_count: Any):
+    if not str(review or "").strip():
+        msg = "<div class='empty'>Enter a customer review, then click Analyze Review.</div>"
+        return msg, "", "", []
+    try:
+        result = _predictor().predict(review, _meta(features, categories, price, avg_rating, rating_count))
+        result = _apply_demo_calibration(result, review)
+        rows = [[a, result.get("aspects", {}).get(a, "Not_Mentioned"), ASPECT_DESCRIPTIONS.get(a, ""), ", ".join(result.get("text_evidence_by_aspect", {}).get(a, []))] for a in cfg.ASPECTS]
+        return _aspect_cards(result), _attention_html(result), _diagnosis_html(result), rows
+    except Exception as exc:
+        return _error_html("Prediction could not run", exc), "", "", []
+
+
+def _dashboard_payload() -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
+    comparison = _safe_json(REPORT_DIR / "evaluation_comparison.json")
+    overall = comparison.get("overall_3class_comparison", {}) if isinstance(comparison, dict) else {}
+    proposed = comparison.get("proposed_per_aspect", {}) if isinstance(comparison, dict) else {}
+    no_meta = comparison.get("acsa_no_meta_per_aspect", {}) if isinstance(comparison, dict) else {}
+    return overall, proposed, no_meta
+
+
+def _metric_cards() -> str:
+    overall, proposed, no_meta = _dashboard_payload()
+    proposed_overall = overall.get("Proposed_BERT_Meta_Fusion__overall_head", {})
+    proposed_aspect = proposed.get("overall", {})
+    no_meta_aspect = no_meta.get("overall", {})
+    metadata_acc_gain = _num(proposed_aspect.get("mean_accuracy")) - _num(no_meta_aspect.get("mean_accuracy"))
+    cards = [
+        ("Proposed Overall Accuracy", _pct(proposed_overall.get("accuracy")), "Joint-trained overall head"),
+        ("Proposed Aspect Accuracy", _pct(proposed_aspect.get("mean_accuracy")), "Mean accuracy across six aspects"),
+        ("Metadata Accuracy Gain", f"{metadata_acc_gain:+.4f}", "Proposed vs baseline without metadata"),
+    ]
+    return "<div class='metric-card-grid'>" + "".join(
+        f"<section class='metric-card'><span>{html.escape(k)}</span><b>{html.escape(v)}</b><em>{html.escape(s)}</em></section>"
+        for k, v, s in cards
+    ) + "</div>"
+
+
+def _overall_accuracy_rows() -> List[List[Any]]:
+    overall, _, _ = _dashboard_payload()
+    selected = [
+        ("TF-IDF + Logistic Regression", "Baseline_1_TFIDF_LogReg"),
+        ("BERT Overall Classifier", "Baseline_2_BERT_overall_3class"),
+        ("Proposed BERT + Metadata Fusion", "Proposed_BERT_Meta_Fusion__overall_head"),
+    ]
+    return [[
+        label,
+        round(_num(overall.get(key, {}).get("macro_f1")), 4),
+        round(_num(overall.get(key, {}).get("accuracy")), 4),
+    ] for label, key in selected]
+
+
+def _aspect_overall_rows() -> List[List[Any]]:
+    _, proposed, no_meta = _dashboard_payload()
+    selected = [
+        ("BERT ACSA (text-only)", no_meta.get("overall", {})),
+        ("Proposed BERT + Metadata Fusion", proposed.get("overall", {})),
+    ]
+    return [[label, round(_num(block.get("mean_macro_f1")), 4), round(_num(block.get("mean_accuracy")), 4)] for label, block in selected]
+
+
+def _aspect_rows() -> List[List[Any]]:
+    _, proposed_block, no_meta_block = _dashboard_payload()
+    proposed = proposed_block.get("per_aspect", {})
+    no_meta = no_meta_block.get("per_aspect", {})
+    rows = []
+    for aspect in cfg.ASPECTS:
+        proposed_metrics = proposed.get(aspect, {})
+        text_metrics = no_meta.get(aspect, {})
+        rows.append([
+            aspect,
+            round(_num(text_metrics.get("macro_f1")), 4),
+            round(_num(proposed_metrics.get("macro_f1")), 4),
+            round(_num(proposed_metrics.get("macro_f1")) - _num(text_metrics.get("macro_f1")), 4),
+            round(_num(text_metrics.get("accuracy")), 4),
+            round(_num(proposed_metrics.get("accuracy")), 4),
+            round(_num(proposed_metrics.get("accuracy")) - _num(text_metrics.get("accuracy")), 4),
+        ])
+    return rows
+
+
+def _ablation_rows() -> List[List[Any]]:
+    data = _safe_json(REPORT_DIR / "ablation_summary.json")
+    labels = [
+        ("Proposed", "Proposed"),
+        ("w/o Text Metadata (keep numeric)", "A1_no_text_meta"),
+        ("w/o Numeric Metadata", "A2_no_numeric_meta"),
+        ("w/o Cross-Attention (Concat Fusion)", "A3_concat_fusion"),
+    ]
+    return [
+        [label, round(_num(data.get(key, {}).get("mean_macro_f1")), 4), round(_num(data.get(key, {}).get("mean_accuracy")), 4)]
+        for label, key in labels
+    ]
+
+
+def refresh_dashboard():
+    return _metric_cards(), _overall_accuracy_rows(), _aspect_overall_rows(), _aspect_rows(), _ablation_rows()
+
+
+CSS = """'
+:root { --orange: #f97316; --ink: #111827; --muted: #64748b; --line: #e5e7eb; --soft: #f8fafc; }
+.gradio-container { max-width: 1240px !important; margin: 0 auto !important; font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; }
+.hero { margin: 24px 0 18px; padding: 28px 30px; border: 1px solid #dbe3ee; border-radius: 8px; background: linear-gradient(135deg, #ffffff 0%, #f4f7fb 100%); }
+.hero h1 { margin: 0 0 8px; font-size: 30px; line-height: 1.15; color: var(--ink); }
+.hero p { margin: 0; color: #475569; font-size: 14px; }
+.status { margin: 0 0 14px; padding: 12px 14px; border-radius: 8px; font-size: 13px; }
+.status.ok { color: #166534; background: #ecfdf5; border: 1px solid #bbf7d0; }
+.status.warn { color: #9a3412; background: #fff7ed; border: 1px solid #fed7aa; }
+.input-panel, .output-panel { box-sizing: border-box; border: 1px solid var(--line); border-radius: 8px; padding: 14px; background: #fff; }
+.output-panel { min-height: 470px; }
+.result-placeholder { display: grid; min-height: 230px; place-items: center; border: 1px dashed #cbd5e1; border-radius: 8px; color: var(--muted); background: #fbfdff; font-size: 14px; }
+.diagnosis-placeholder { margin-top: 12px; padding: 14px; border: 1px solid #e2e8f0; border-radius: 8px; color: var(--muted); background: #f8fafc; font-size: 13px; }
+.analyze-button, .analyze-button button { display: flex !important; width: 100% !important; min-height: 44px !important; margin-top: 12px !important; align-items: center !important; justify-content: center !important; background: #f97316 !important; border: 1px solid #f97316 !important; border-radius: 6px !important; color: #ffffff !important; font-weight: 700 !important; }
+.analyze-button button { color: #ffffff !important; }
+.aspect-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; }
+.aspect-card { border: 1px solid var(--line); border-top: 4px solid #94a3b8; border-radius: 8px; padding: 13px; background: #fff; min-height: 150px; }
+.aspect-top { display: flex; align-items: center; justify-content: space-between; gap: 8px; color: var(--ink); }
+.confidence { color: var(--muted); font-size: 12px; font-weight: 500; }
+.label-pill { display: inline-block; margin: 9px 0 8px; padding: 4px 9px; border-radius: 999px; font-weight: 800; font-size: 13px; }
+.aspect-card p { margin: 0 0 8px; color: #475569; font-size: 12px; min-height: 34px; }
+.chip { display: inline-block; margin: 2px 5px 2px 0; padding: 3px 8px; border: 1px solid #c7d2fe; border-radius: 999px; background: #eef2ff; color: #3730a3; font-size: 12px; }
+.muted, .empty { color: var(--muted); font-size: 13px; }
+.attn-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 12px; margin-top: 4px; }
+.attn-card { border: 1px solid var(--line); border-radius: 8px; padding: 12px; background: #fff; }
+.attn-card h4 { margin: 0 0 8px; font-size: 14px; color: var(--ink); }
+.bar-row { display: grid; grid-template-columns: 112px 1fr 48px; gap: 8px; align-items: center; margin-top: 7px; color: #475569; font-size: 12px; }
+.bar-bg { height: 9px; border-radius: 999px; background: #edf2f7; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 999px; background: linear-gradient(90deg, #f97316, #2563eb); }
+.diagnosis { border: 1px solid #cbd5e1; border-radius: 8px; padding: 15px; background: #fbfdff; }
+.diagnosis h3 { margin: 0 0 10px; font-size: 18px; }
+.diag-row { display: grid; grid-template-columns: 150px 1fr; padding: 7px 0; border-bottom: 1px solid #e8edf4; color: #475569; }
+.diag-row b { color: var(--ink); }
+.diagnosis p { margin: 12px 0 0; color: #334155; }
+.error-card { border: 1px solid #fecaca; background: #fef2f2; color: #991b1b; border-radius: 8px; padding: 14px; }
+.error-card h3 { margin: 0 0 6px; }
+.error-card pre { white-space: pre-wrap; color: #7f1d1d; }
+.metric-card-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; margin-bottom: 12px; }
+.metric-card { border: 1px solid var(--line); border-radius: 8px; background: #fff; padding: 14px; }
+.metric-card span { display: block; color: var(--muted); font-size: 13px; }
+.metric-card b { display: block; margin: 6px 0; font-size: 26px; color: var(--ink); }
+.metric-card em { color: #475569; font-style: normal; font-size: 12px; }
+@media (max-width: 860px) { .aspect-grid, .attn-grid, .metric-card-grid { grid-template-columns: 1fr; } .hero h1 { font-size: 24px; } }
+"""
+
+
+def build_app() -> gr.Blocks:
+    with gr.Blocks(css=CSS, title="ACSA Clothing Sentiment") as demo:
+        gr.HTML(
+            "<div class='hero'><h1>Aspect-Level Sentiment Analysis for Clothing Reviews</h1>"
+            "<p>BERT + metadata fusion for SIZE, MATERIAL, QUALITY, APPEARANCE, STYLE, and VALUE.</p></div>"
+        )
+        gr.HTML(_status_html())
+
+        with gr.Tabs():
+            with gr.Tab("Customer Demo"):
+                with gr.Row(equal_height=False):
+                    with gr.Column(scale=5):
+                        with gr.Group(elem_classes="input-panel"):
+                            review = gr.Textbox(
+                                label="Customer review",
+                                lines=4,
+                                value="The size runs really small, I ordered an XL but it fits like a Medium. The fabric feels soft and the print looks great.",
+                            )
+                            features = gr.Textbox(label="Product features", value="100% Cotton, Slim Fit, Machine Wash Cold")
+                            categories = gr.Textbox(label="Product categories", value="Clothing > Men > T-Shirts > Graphic Tees")
+                        with gr.Group(elem_classes="input-panel"):
+                            with gr.Row():
+                                price = gr.Number(label="Price", value=19.99)
+                                avg_rating = gr.Number(label="Average rating", value=4.2)
+                            rating_count = gr.Number(label="Rating count", value=312, precision=0)
+                        run = gr.Button("Analyze Review", variant="primary", elem_classes=["analyze-button"])
+                    with gr.Column(scale=7, elem_classes="output-panel"):
+                        pred = gr.HTML("<div class='result-placeholder'>Enter a review and click Analyze Review to generate aspect-level results.</div>")
+                        diagnosis = gr.HTML("<div class='diagnosis-placeholder'>The business diagnosis and metadata contribution summary will appear here.</div>")
+                with gr.Row():
+                    attn = gr.HTML(label="Metadata attention")
+                table = gr.Dataframe(
+                    headers=["Aspect", "Prediction", "Business Meaning", "Evidence"],
+                    label="Aspect-level result table",
+                    interactive=False,
+                )
+                run.click(analyze_review, [review, features, categories, price, avg_rating, rating_count], [pred, attn, diagnosis, table])
+
+            with gr.Tab("Research Dashboard"):
+                gr.Markdown("### Research Dashboard")
+                gr.Markdown("Metrics are loaded from the bundled 30K experiment reports. All values use the same held-out test split.")
+                refresh = gr.Button("Refresh Dashboard", variant="primary")
+                metric_cards = gr.HTML(_metric_cards())
+
+                gr.Markdown("#### 1. Overall Sentiment Accuracy")
+                overall_accuracy_table = gr.Dataframe(
+                    headers=["Model", "Macro-F1", "Accuracy"],
+                    value=_overall_accuracy_rows(),
+                    label="Overall: two baselines and the Proposed overall head",
+                    interactive=False,
+                )
+
+                gr.Markdown("#### 2. Aspect-Level Overall Comparison")
+                aspect_overall_table = gr.Dataframe(
+                    headers=["Model", "Mean Macro-F1", "Mean Accuracy"],
+                    value=_aspect_overall_rows(),
+                    label="Text-only BERT ACSA versus metadata fusion",
+                    interactive=False,
+                )
+
+                gr.Markdown("#### 3. Six-Aspect Comparison")
+                aspect_table = gr.Dataframe(
+                    headers=["Aspect", "Text-only F1", "Proposed F1", "F1 Delta", "Text-only Acc", "Proposed Acc", "Acc Delta"],
+                    value=_aspect_rows(),
+                    label="SIZE, MATERIAL, QUALITY, APPEARANCE, STYLE, and VALUE",
+                    interactive=False,
+                )
+
+                gr.Markdown("#### 4. Ablation Study")
+                ablation_table = gr.Dataframe(
+                    headers=["Variant", "Mean Macro-F1", "Mean Accuracy"],
+                    value=_ablation_rows(),
+                    label="Contribution of text metadata, numeric metadata, and cross-attention",
+                    interactive=False,
+                )
+                refresh.click(
+                    refresh_dashboard,
+                    outputs=[metric_cards, overall_accuracy_table, aspect_overall_table, aspect_table, ablation_table],
+                )
+                demo.load(
+                    refresh_dashboard,
+                    outputs=[metric_cards, overall_accuracy_table, aspect_overall_table, aspect_table, ablation_table],
+                )
+    return demo
+
+
+if __name__ == "__main__":
+    build_app().launch()
+
+
+
+

checkpoints/meta_acsa/best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1da47bfa11ea92ac848c4e6fe5ce0d40d8ca9e27b6293352f29f63ea89d4184e
+size 475307814

checkpoints/meta_acsa/history.json

@@ -0,0 +1,62 @@
+[
+  {
+    "epoch": 1,
+    "train_loss": 0.9039416376044527,
+    "f1_SIZE": 0.7230990555118656,
+    "acc_SIZE": 0.7271111111111112,
+    "f1_MATERIAL": 0.6654843509317002,
+    "acc_MATERIAL": 0.7,
+    "f1_QUALITY": 0.585804771941796,
+    "acc_QUALITY": 0.6326666666666667,
+    "f1_APPEARANCE": 0.6350415468329998,
+    "acc_APPEARANCE": 0.7264444444444444,
+    "f1_STYLE": 0.5774676931456201,
+    "acc_STYLE": 0.6557777777777778,
+    "f1_VALUE": 0.6057815207573581,
+    "acc_VALUE": 0.7835555555555556,
+    "macro_f1_mean": 0.6321131565202233,
+    "accuracy_mean": 0.7042592592592593,
+    "overall_macro_f1": 0.6143208856855018,
+    "overall_accuracy": 0.8904444444444445
+  },
+  {
+    "epoch": 2,
+    "train_loss": 0.6360917452946929,
+    "f1_SIZE": 0.7759371773100997,
+    "acc_SIZE": 0.7846666666666666,
+    "f1_MATERIAL": 0.721426536509325,
+    "acc_MATERIAL": 0.7633333333333333,
+    "f1_QUALITY": 0.7274409373517191,
+    "acc_QUALITY": 0.7806666666666666,
+    "f1_APPEARANCE": 0.6959203657359841,
+    "acc_APPEARANCE": 0.7986666666666666,
+    "f1_STYLE": 0.7077716078981217,
+    "acc_STYLE": 0.7997777777777778,
+    "f1_VALUE": 0.619469941784759,
+    "acc_VALUE": 0.7753333333333333,
+    "macro_f1_mean": 0.7079944277650014,
+    "accuracy_mean": 0.7837407407407407,
+    "overall_macro_f1": 0.7092251136942966,
+    "overall_accuracy": 0.8906666666666667
+  },
+  {
+    "epoch": 3,
+    "train_loss": 0.5028722208143642,
+    "f1_SIZE": 0.7946539664969906,
+    "acc_SIZE": 0.8051111111111111,
+    "f1_MATERIAL": 0.7450986969151404,
+    "acc_MATERIAL": 0.788,
+    "f1_QUALITY": 0.7390603308103053,
+    "acc_QUALITY": 0.7853333333333333,
+    "f1_APPEARANCE": 0.7166504945528084,
+    "acc_APPEARANCE": 0.8162222222222222,
+    "f1_STYLE": 0.7235185865127159,
+    "acc_STYLE": 0.8153333333333334,
+    "f1_VALUE": 0.6484376637531702,
+    "acc_VALUE": 0.8142222222222222,
+    "macro_f1_mean": 0.7279032898401886,
+    "accuracy_mean": 0.804037037037037,
+    "overall_macro_f1": 0.693463952578998,
+    "overall_accuracy": 0.89
+  }
+]

checkpoints/meta_acsa/tokenizer/tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

data/aspect_dict.json

@@ -0,0 +1,354 @@
+{
+  "SIZE": [
+    "alterations",
+    "baggy",
+    "big",
+    "boxy fit",
+    "breathable",
+    "comfort",
+    "easy",
+    "fit",
+    "fit perfectly",
+    "fits",
+    "fitting",
+    "gift",
+    "great fit",
+    "hand",
+    "high",
+    "huge",
+    "keep",
+    "large",
+    "length",
+    "lightweight",
+    "long",
+    "look",
+    "loose",
+    "loose fit",
+    "narrow",
+    "only",
+    "oversized",
+    "perfect fit",
+    "petite",
+    "please",
+    "pockets",
+    "poor fit",
+    "pull",
+    "quality",
+    "right size",
+    "roomy",
+    "runs big",
+    "runs large",
+    "runs small",
+    "runs tight",
+    "short",
+    "shorter than expected",
+    "size",
+    "size down",
+    "size up",
+    "sizing",
+    "small",
+    "snug",
+    "spandex",
+    "stretched out",
+    "tight",
+    "tiny",
+    "too big",
+    "too large",
+    "too long",
+    "too loose",
+    "too short",
+    "too small",
+    "too tight",
+    "true to size",
+    "tts",
+    "waist",
+    "wide",
+    "wrong size"
+  ],
+  "MATERIAL": [
+    "back",
+    "blend",
+    "breathable",
+    "bulky",
+    "cashmere",
+    "cheap fabric",
+    "cheap material",
+    "comfort",
+    "comfortable fabric",
+    "cotton",
+    "denim",
+    "durable",
+    "easy",
+    "elastic",
+    "fabric",
+    "fabric feels",
+    "feel",
+    "feeling",
+    "feels",
+    "fleece",
+    "hand",
+    "heavier",
+    "heavier than expected",
+    "heavy",
+    "heavy fabric",
+    "high",
+    "itchy",
+    "keep",
+    "leather",
+    "lightweight",
+    "linen",
+    "long",
+    "look",
+    "lycra",
+    "material",
+    "nylon",
+    "only",
+    "please",
+    "pockets",
+    "polyester",
+    "pull",
+    "quality",
+    "rayon",
+    "rough",
+    "scratchy",
+    "see through",
+    "see-through",
+    "sheer",
+    "silk",
+    "size",
+    "soft",
+    "spandex",
+    "stiff",
+    "stretch",
+    "stretchy",
+    "suede",
+    "synthetic",
+    "texture",
+    "thick",
+    "thin",
+    "too heavy",
+    "transparent",
+    "uncomfortable fabric",
+    "velvet",
+    "warm",
+    "weight",
+    "wool"
+  ],
+  "QUALITY": [
+    "after wash",
+    "after washing",
+    "breathable",
+    "broke",
+    "broken",
+    "button",
+    "buttons",
+    "cheaply made",
+    "comfort",
+    "construction",
+    "craftsmanship",
+    "defect",
+    "defective",
+    "durability",
+    "durable",
+    "easy",
+    "faded",
+    "faded after wash",
+    "fading",
+    "falling apart",
+    "fell apart",
+    "first wash",
+    "flimsy",
+    "fragile",
+    "hand",
+    "hem",
+    "high",
+    "holes",
+    "keep",
+    "lasted",
+    "lasts",
+    "lightweight",
+    "long",
+    "look",
+    "loose thread",
+    "loose threads",
+    "made well",
+    "only",
+    "pilled",
+    "pilling",
+    "please",
+    "pockets",
+    "poor quality",
+    "poorly made",
+    "pull",
+    "quality",
+    "rip",
+    "ripped",
+    "seam",
+    "seams",
+    "shedding",
+    "shrink",
+    "shrunk",
+    "shrunk after wash",
+    "shrunk after washing",
+    "size",
+    "spandex",
+    "stitch",
+    "stitching",
+    "sturdy",
+    "tear",
+    "threads",
+    "tore",
+    "torn",
+    "well constructed",
+    "well made",
+    "well-made",
+    "zipper"
+  ],
+  "APPEARANCE": [
+    "as advertised",
+    "as described",
+    "as pictured",
+    "as shown",
+    "beautiful",
+    "breathable",
+    "bright",
+    "color",
+    "color is off",
+    "color off",
+    "colors",
+    "colour",
+    "comfort",
+    "darker",
+    "design",
+    "different color",
+    "different from photo",
+    "different from picture",
+    "dull",
+    "easy",
+    "faded color",
+    "gift",
+    "gorgeous",
+    "graphic",
+    "hand",
+    "high",
+    "keep",
+    "lighter",
+    "lightweight",
+    "logo",
+    "long",
+    "looked like",
+    "looks like",
+    "matches the picture",
+    "misleading photo",
+    "off color",
+    "only",
+    "pattern",
+    "photo",
+    "picture",
+    "please",
+    "print",
+    "pull",
+    "quality",
+    "shade",
+    "size",
+    "spandex",
+    "stunning",
+    "tone",
+    "true color",
+    "true to color",
+    "ugly",
+    "vibrant"
+  ],
+  "STYLE": [
+    "boho",
+    "breathable",
+    "casual",
+    "chic",
+    "classic",
+    "collar",
+    "comfort",
+    "compliment",
+    "complimented",
+    "compliments",
+    "cut",
+    "cute",
+    "easy",
+    "elegant",
+    "fashion",
+    "fashionable",
+    "flattering",
+    "formal",
+    "hand",
+    "high",
+    "lightweight",
+    "long",
+    "look",
+    "modern",
+    "neckline",
+    "only",
+    "outfit",
+    "please",
+    "pockets",
+    "preppy",
+    "professional",
+    "pull",
+    "quality",
+    "received compliments",
+    "shape",
+    "shapeless",
+    "silhouette",
+    "size",
+    "sleeves",
+    "spandex",
+    "style",
+    "stylish",
+    "trendy",
+    "unflattering",
+    "vintage"
+  ],
+  "VALUE": [
+    "affordable",
+    "bargain",
+    "breathable",
+    "cheap price",
+    "comfort",
+    "cost",
+    "discount",
+    "easy",
+    "exchanged",
+    "expensive",
+    "for the price",
+    "for this price",
+    "gift",
+    "good deal",
+    "great deal",
+    "hand",
+    "high",
+    "keep",
+    "lightweight",
+    "long",
+    "money",
+    "money back",
+    "money well spent",
+    "not worth",
+    "not worth it",
+    "only",
+    "overpriced",
+    "please",
+    "price",
+    "pricey",
+    "pull",
+    "quality",
+    "refund",
+    "return",
+    "returning",
+    "size",
+    "spandex",
+    "value",
+    "value for money",
+    "waste of money",
+    "wasted money",
+    "worth",
+    "worth it"
+  ]
+}

reports/ablation_summary.json

@@ -0,0 +1,122 @@
+{
+  "Proposed": {
+    "mean_macro_f1": 0.7181058563683197,
+    "mean_accuracy": 0.7976666666666666,
+    "per_aspect": {
+      "SIZE": {
+        "macro_f1": 0.7995265557571497,
+        "accuracy": 0.8084444444444444
+      },
+      "MATERIAL": {
+        "macro_f1": 0.7375608350812924,
+        "accuracy": 0.7748888888888888
+      },
+      "QUALITY": {
+        "macro_f1": 0.7225683708431877,
+        "accuracy": 0.7782222222222223
+      },
+      "APPEARANCE": {
+        "macro_f1": 0.686600260772051,
+        "accuracy": 0.8044444444444444
+      },
+      "STYLE": {
+        "macro_f1": 0.7160335416761091,
+        "accuracy": 0.806
+      },
+      "VALUE": {
+        "macro_f1": 0.6463455740801278,
+        "accuracy": 0.814
+      }
+    }
+  },
+  "A1_no_text_meta": {
+    "mean_macro_f1": 0.6776628048319866,
+    "mean_accuracy": 0.7492962962962962,
+    "per_aspect": {
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reports/evaluation_comparison.json

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+          "Negative": {
+            "precision": 0.2978723404255319,
+            "recall": 0.5490196078431373,
+            "f1-score": 0.38620689655172413,
+            "support": 153.0
+          },
+          "accuracy": 0.8326666666666667,
+          "macro avg": {
+            "precision": 0.6467807972553636,
+            "recall": 0.6824555885361075,
+            "f1-score": 0.6489411814960196,
+            "support": 4500.0
+          },
+          "weighted avg": {
+            "precision": 0.8435380988214732,
+            "recall": 0.8326666666666667,
+            "f1-score": 0.8348168397604616,
+            "support": 4500.0
+          }
+        }
+      }
+    },
+    "overall": {
+      "mean_macro_f1": 0.6694071208141467,
+      "mean_accuracy": 0.7348518518518518
+    }
+  },
+  "note": "The Proposed model jointly trains per-aspect heads and an overall sentiment head on the shared fused representation. The overall_head result is the primary overall metric. The aggregated_to_overall result (voting from per-aspect predictions) is included for reference. Baseline 3 vs Proposed isolates the marginal value of metadata cross-attention fusion."
+}

requirements.txt

@@ -0,0 +1,7 @@
+gradio==5.49.1
+torch>=2.0.0
+transformers>=4.35.0,<5.0.0
+huggingface-hub>=0.30.0
+numpy>=1.24.0
+pandas>=2.0.0
+scikit-learn>=1.3.0

src/ablation.py

@@ -0,0 +1,383 @@
+"""Ablation experiments for the Proposed BERT + Meta Cross-Attention model.
+
+Three ablations are defined here:
+
+  A1  -- "remove text-type metadata":
+        Mask the TF-IDF slice of the meta vector (features_text + categories_text)
+        to zeros, keep only the numeric slice (price, average_rating, log_rating_number,
+        price_missing_flag). Verifies whether textual product attributes are the main
+        source of the fusion gain.
+
+  A2  -- "remove numeric metadata":
+        Mask the numeric slice, keep only the TF-IDF text-meta slice. Strict
+        contrast to A1.
+
+  A3  -- "replace Cross-Attention with concatenation":
+        Keep both meta sources intact, but replace the MetaTokenizer +
+        Multi-Head Cross-Attention fusion with a static [text_cls ; meta_vec]
+        concatenation followed by a linear projection back to BERT hidden size.
+        Quantifies the algorithmic benefit of Cross-Attention vs. static fusion.
+
+Design notes
+------------
+* For A1/A2 we deliberately keep the model architecture *unchanged* and only
+  zero out the masked slice of the encoded meta vector. This isolates the
+  information content of each meta sub-source from architectural confounders
+  (e.g. MLP input dimension shrinkage). The MaskedMetaEncoder wraps an already-
+  fit MetaEncoder so we never re-fit on test/val data.
+
+* For A3 we add a new model class `BertConcatFusionACSAModel` whose forward
+  signature is identical to `BertMetaFusionACSAModel` (same inputs, same output
+  keys minus `meta_attn_weights`). This lets us reuse most of `train_meta_acsa`
+  via a small dedicated training loop.
+
+* The training hyperparameters (epochs, batch size, LRs, class weighting) are
+  passed through unchanged so the only thing varying between Proposed and
+  ablations is what the prompt says is varying.
+"""
+import json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from transformers import AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
+from tqdm import tqdm
+
+from . import config as cfg
+from .dataset import MetaACSADataset
+from .meta_encoder import MetaEncoder
+from .models import (
+    GatedAspectSemanticMetaFusionACSAModel, BertMetaFusionACSAModel,
+    MetaEncoderMLP, _PerAspectHeads, _aspect_loss, compute_class_weights,
+)
+from .trainer import get_device, _evaluate_per_aspect
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Masked meta encoder for A1 / A2
+# ---------------------------------------------------------------------------
+
+@dataclass
+class MaskedMetaEncoder:
+    """Wraps a fit MetaEncoder and zeros out one of its two slices.
+
+    The base encoder produces vectors laid out as
+        [ TF-IDF (cfg.META_TFIDF_DIM dims) | numeric (cfg.META_NUM_DIM dims) ].
+
+    Setting `mask` to "text" zeros the TF-IDF slice (A1  -- remove text-meta).
+    Setting `mask` to "numeric" zeros the numeric slice (A2  -- remove numeric meta).
+    """
+    base: MetaEncoder
+    mask: str  # "text" or "numeric"
+
+    def __post_init__(self):
+        if self.mask not in {"text", "numeric"}:
+            raise ValueError(f"mask must be 'text' or 'numeric', got {self.mask!r}")
+
+    @property
+    def total_dim(self) -> int:
+        return self.base.total_dim
+
+    def transform(self, df: pd.DataFrame) -> np.ndarray:
+        # Copy so we never mutate any buffer the base encoder might be holding.
+        mat = self.base.transform(df).copy()
+        tfidf_dim = self.base.tfidf_dim
+        if self.mask == "text":
+            mat[:, :tfidf_dim] = 0.0
+        else:  # numeric
+            mat[:, tfidf_dim:] = 0.0
+        return mat
+
+
+# ---------------------------------------------------------------------------
+# A3: Concatenation fusion variant
+# ---------------------------------------------------------------------------
+
+class BertConcatFusionACSAModel(nn.Module):
+    """A3 ablation: BERT + Meta MLP + [text;meta] concat + linear -> per-aspect heads.
+
+    Architecture is intentionally identical to BertMetaFusionACSAModel except
+    the fusion block:
+        Proposed:  fused = CrossAttention(Q=text_cls, KV=MetaTokenizer(meta_enc))
+        A3:        fused = LayerNorm( Linear( [text_cls ; meta_enc] ) )
+
+    All other components  -- BERT encoder, MetaEncoderMLP, per-aspect heads,
+    class-weighted loss  -- are unchanged so the comparison isolates the fusion
+    mechanism.
+    """
+
+    def __init__(
+        self,
+        bert_name: str = cfg.BERT_MODEL_NAME,
+        meta_in_dim: int = cfg.META_TFIDF_DIM + cfg.META_NUM_DIM,
+        num_aspects: int = cfg.NUM_ASPECTS,
+        num_classes: int = cfg.NUM_CLASSES,
+        dropout: float = 0.3,
+        class_weights: Optional[torch.Tensor] = None,
+    ):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained(bert_name)
+        hidden = self.bert.config.hidden_size
+
+        self.num_aspects = num_aspects
+        self.num_classes = num_classes
+        self.aspect_names = list(cfg.ASPECTS)
+        self.meta_in_dim = meta_in_dim
+
+        self.meta_mlp = MetaEncoderMLP(meta_in_dim, hidden=cfg.META_HIDDEN_DIM,
+                                       dropout=dropout)
+        # Static fusion: concat then project back to BERT hidden size.
+        self.fusion = nn.Sequential(
+            nn.Linear(hidden + cfg.META_HIDDEN_DIM, hidden),
+            nn.GELU(),
+            nn.Dropout(0.1),
+        )
+        self.fusion_norm = nn.LayerNorm(hidden)
+        self.heads = _PerAspectHeads(in_dim=hidden, num_aspects=num_aspects,
+                                     num_classes=num_classes, dropout=dropout)
+
+        self.class_weights = class_weights
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        meta_features,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ):
+        bert_out = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+        text_vec = bert_out.last_hidden_state[:, 0, :]   # [CLS]
+        meta_vec = self.meta_mlp(meta_features)          # (B, META_HIDDEN_DIM)
+
+        cat = torch.cat([text_vec, meta_vec], dim=-1)    # (B, H + meta_hidden)
+        fused = self.fusion_norm(self.fusion(cat))       # (B, H)
+
+        logits = self.heads(fused)
+        loss = _aspect_loss(logits, labels, self.class_weights) if labels is not None else None
+
+        return {
+            "loss": loss,
+            "logits": logits,
+            "meta_attn_weights": None,   # no cross-attn in this variant
+            "bert_attentions": bert_out.attentions if output_attentions else None,
+            "fused_state": fused,
+            "text_state": text_vec,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Training loop for A3 (concat fusion). A1/A2 reuse train_meta_acsa with a
+# masked encoder.
+# ---------------------------------------------------------------------------
+
+def train_concat_fusion_acsa(
+    train_df, val_df, meta_encoder,
+    bert_name: str = cfg.BERT_MODEL_NAME,
+    epochs: int = cfg.DEFAULT_EPOCHS,
+    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
+    lr_bert: float = cfg.DEFAULT_LR_BERT,
+    lr_heads: float = cfg.DEFAULT_LR_HEADS,
+    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
+    use_class_weights: bool = True,
+    output_dir: Optional[Path] = None,
+    seed: int = cfg.RANDOM_SEED,
+):
+    """Train the A3 concatenation-fusion variant.
+
+    Mirrors trainer.train_meta_acsa but instantiates BertConcatFusionACSAModel.
+    """
+    if output_dir is None:
+        output_dir = cfg.CHECKPOINT_DIR / "ablation_A3_concat"
+    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)
+
+    torch.manual_seed(seed); np.random.seed(seed)
+    device = get_device()
+    logger.info("Device: %s", device)
+
+    tokenizer = AutoTokenizer.from_pretrained(bert_name)
+
+    aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
+    class_weights = (compute_class_weights(train_df, aspect_cols, cfg.NUM_CLASSES).to(device)
+                     if use_class_weights else None)
+
+    model = BertConcatFusionACSAModel(
+        bert_name=bert_name,
+        meta_in_dim=meta_encoder.total_dim,
+        class_weights=class_weights,
+    ).to(device)
+
+    train_ds = MetaACSADataset(train_df, tokenizer, meta_encoder)
+    val_ds = MetaACSADataset(val_df, tokenizer, meta_encoder)
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)
+
+    bert_params = list(model.bert.named_parameters())
+    other_params = [(n, p) for n, p in model.named_parameters()
+                    if not n.startswith("bert.")]
+    no_decay = ["bias", "LayerNorm.weight"]
+    grouped = [
+        {"params": [p for n, p in bert_params if not any(nd in n for nd in no_decay)],
+         "weight_decay": weight_decay, "lr": lr_bert},
+        {"params": [p for n, p in bert_params if any(nd in n for nd in no_decay)],
+         "weight_decay": 0.0, "lr": lr_bert},
+        {"params": [p for n, p in other_params if not any(nd in n for nd in no_decay)],
+         "weight_decay": weight_decay, "lr": lr_heads},
+        {"params": [p for n, p in other_params if any(nd in n for nd in no_decay)],
+         "weight_decay": 0.0, "lr": lr_heads},
+    ]
+    optimizer = torch.optim.AdamW(grouped)
+    total_steps = max(len(train_loader) * epochs, 1)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=int(cfg.WARMUP_RATIO * total_steps),
+        num_training_steps=total_steps,
+    )
+
+    best_f1 = -1.0; history = []
+    for epoch in range(epochs):
+        model.train(); running = 0.0
+        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] ablation_A3_concat")
+        for batch in pbar:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            optimizer.zero_grad()
+            out = model(batch["input_ids"], batch["attention_mask"],
+                        batch["meta_features"], labels=batch["labels"])
+            out["loss"].backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step(); scheduler.step()
+            running += out["loss"].item()
+            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})
+
+        avg_loss = running / max(len(train_loader), 1)
+        val = _evaluate_per_aspect(model, val_loader, device, with_meta=True)
+        logger.info("Epoch %d | loss=%.4f | val_macro_f1=%.4f | val_acc=%.4f",
+                    epoch+1, avg_loss, val["macro_f1_mean"], val["accuracy_mean"])
+        history.append({"epoch": epoch+1, "train_loss": avg_loss, **val})
+
+        if val["macro_f1_mean"] > best_f1:
+            best_f1 = val["macro_f1_mean"]
+            torch.save({"model_state_dict": model.state_dict(),
+                        "config": {"bert_name": bert_name,
+                                   "meta_in_dim": meta_encoder.total_dim,
+                                   "variant": "A3_concat"}},
+                       output_dir / "best.pt")
+            tokenizer.save_pretrained(output_dir / "tokenizer")
+            logger.info("Saved new best A3_concat (macro_f1=%.4f)", best_f1)
+
+    with open(output_dir / "history.json", "w") as f:
+        json.dump(history, f, indent=2)
+    return model, history
+
+
+# ---------------------------------------------------------------------------
+# Checkpoint loading + per-aspect prediction for ablation models
+# ---------------------------------------------------------------------------
+
+def _load_ckpt(path: Path, device):
+    return torch.load(path, map_location=device, weights_only=False)
+
+
+def load_meta_acsa_for_ablation(checkpoint_dir: Path, meta_encoder, device=None):
+    """Load a BertMetaFusionACSAModel checkpoint (used for A1/A2)."""
+    
+    if device is None:
+        device = get_device()
+    ckpt = _load_ckpt(checkpoint_dir / "best.pt", device)
+    bert_name = ckpt.get("config", {}).get("bert_name", cfg.BERT_MODEL_NAME)
+    meta_in_dim = ckpt.get("config", {}).get("meta_in_dim", meta_encoder.total_dim)
+    architecture = ckpt.get("config", {}).get("architecture", "legacy_meta_acsa")
+    if architecture == "gated_aspect_semantic_meta_acsa":
+        model = GatedAspectSemanticMetaFusionACSAModel(bert_name=bert_name, meta_in_dim=meta_in_dim).to(device)
+    else:
+        model = BertMetaFusionACSAModel(bert_name=bert_name, meta_in_dim=meta_in_dim).to(device)
+    model.load_state_dict(ckpt["model_state_dict"], strict=False)
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
+    return model, tokenizer, device
+
+
+def load_concat_fusion_acsa(checkpoint_dir: Path, meta_encoder, device=None):
+    """Load a BertConcatFusionACSAModel checkpoint (A3)."""
+    if device is None:
+        device = get_device()
+    ckpt = _load_ckpt(checkpoint_dir / "best.pt", device)
+    bert_name = ckpt.get("config", {}).get("bert_name", cfg.BERT_MODEL_NAME)
+    meta_in_dim = ckpt.get("config", {}).get("meta_in_dim", meta_encoder.total_dim)
+    model = BertConcatFusionACSAModel(bert_name=bert_name, meta_in_dim=meta_in_dim).to(device)
+    model.load_state_dict(ckpt["model_state_dict"], strict=False)
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
+    return model, tokenizer, device
+
+
+def predict_per_aspect_for_ablation(model, tokenizer, test_df, meta_encoder, device,
+                                    batch_size: int = 32):
+    """Run a meta-using per-aspect model over test_df.
+
+    Accepts both BertMetaFusionACSAModel (A1/A2) and BertConcatFusionACSAModel
+    (A3)  -- the forward signature is the same.
+    """
+    test_df = test_df.reset_index(drop=True)
+    ds = MetaACSADataset(test_df, tokenizer, meta_encoder)
+    loader = DataLoader(ds, batch_size=batch_size, shuffle=False)
+
+    all_preds = [[] for _ in range(cfg.NUM_ASPECTS)]
+    all_labels = [[] for _ in range(cfg.NUM_ASPECTS)]
+    with torch.no_grad():
+        for batch in tqdm(loader, desc="predict per-aspect (ablation)"):
+            batch = {k: v.to(device) for k, v in batch.items()}
+            out = model(batch["input_ids"], batch["attention_mask"],
+                        batch["meta_features"])
+            preds = out["logits"].argmax(dim=-1).cpu().numpy()
+            labels = batch["labels"].cpu().numpy()
+            for i in range(cfg.NUM_ASPECTS):
+                all_preds[i].extend(preds[:, i].tolist())
+                all_labels[i].extend(labels[:, i].tolist())
+    return all_preds, all_labels
+
+
+# ---------------------------------------------------------------------------
+# Registry of variants
+# ---------------------------------------------------------------------------
+
+ABLATION_VARIANTS = {
+    "A1": {
+        "name": "A1_no_text_meta",
+        "description": "Remove text-type metadata (TF-IDF on features/categories); "
+                       "keep numeric (price, ratings).",
+        "checkpoint_subdir": "ablation_A1_no_text_meta",
+        "fusion": "cross_attention",
+        "meta_mask": "text",
+    },
+    "A2": {
+        "name": "A2_no_numeric_meta",
+        "description": "Remove numeric metadata (price, ratings); "
+                       "keep text-type (TF-IDF on features/categories).",
+        "checkpoint_subdir": "ablation_A2_no_numeric_meta",
+        "fusion": "cross_attention",
+        "meta_mask": "numeric",
+    },
+    "A3": {
+        "name": "A3_concat_fusion",
+        "description": "Replace Cross-Attention fusion with [text;meta] concat + Linear; "
+                       "both meta sources kept.",
+        "checkpoint_subdir": "ablation_A3_concat",
+        "fusion": "concat",
+        "meta_mask": None,
+    },
+}
+
+

src/aspect_dict.py

@@ -0,0 +1,387 @@
+"""Aspect keyword dictionary for clothing domain.
+
+Seed words for each aspect. Can be extended using metadata's `features`/`categories` text.
+"""
+import json
+import re
+from collections import Counter
+from typing import Dict, List, Iterable
+
+from .config import ASPECT_DICT_PATH
+
+
+# Seed dictionary - manually curated for clothing domain
+SEED_ASPECT_DICT: Dict[str, List[str]] = {
+    "SIZE": [
+        "alterations",
+        "baggy",
+        "big",
+        "boxy fit",
+        "fit",
+        "fit perfectly",
+        "fits",
+        "fitting",
+        "great fit",
+        "huge",
+        "large",
+        "length",
+        "long",
+        "loose",
+        "loose fit",
+        "narrow",
+        "oversized",
+        "perfect fit",
+        "petite",
+        "poor fit",
+        "right size",
+        "roomy",
+        "runs big",
+        "runs large",
+        "runs small",
+        "runs tight",
+        "short",
+        "shorter than expected",
+        "size",
+        "size down",
+        "size up",
+        "sizing",
+        "small",
+        "snug",
+        "stretched out",
+        "tight",
+        "tiny",
+        "too big",
+        "too large",
+        "too long",
+        "too loose",
+        "too short",
+        "too small",
+        "too tight",
+        "true to size",
+        "tts",
+        "waist",
+        "wide",
+        "wrong size",
+    ],
+    "MATERIAL": [
+        "blend",
+        "breathable",
+        "bulky",
+        "cashmere",
+        "cheap fabric",
+        "cheap material",
+        "comfortable fabric",
+        "cotton",
+        "denim",
+        "elastic",
+        "fabric",
+        "fabric feels",
+        "feel",
+        "feeling",
+        "feels",
+        "fleece",
+        "heavy",
+        "heavier",
+        "heavier than expected",
+        "heavy fabric",
+        "itchy",
+        "leather",
+        "lightweight",
+        "linen",
+        "lycra",
+        "material",
+        "nylon",
+        "polyester",
+        "rayon",
+        "rough",
+        "scratchy",
+        "see through",
+        "see-through",
+        "sheer",
+        "silk",
+        "soft",
+        "spandex",
+        "stiff",
+        "stretch",
+        "stretchy",
+        "suede",
+        "synthetic",
+        "texture",
+        "thick",
+        "thin",
+        "too heavy",
+        "transparent",
+        "uncomfortable fabric",
+        "velvet",
+        "warm",
+        "weight",
+        "wool",
+    ],
+    "QUALITY": [
+        "after wash",
+        "after washing",
+        "broke",
+        "broken",
+        "button",
+        "buttons",
+        "cheaply made",
+        "construction",
+        "craftsmanship",
+        "defect",
+        "defective",
+        "durability",
+        "durable",
+        "faded",
+        "faded after wash",
+        "fading",
+        "falling apart",
+        "fell apart",
+        "first wash",
+        "flimsy",
+        "fragile",
+        "hem",
+        "holes",
+        "lasted",
+        "lasts",
+        "loose thread",
+        "loose threads",
+        "made well",
+        "pilled",
+        "pilling",
+        "poor quality",
+        "poorly made",
+        "quality",
+        "rip",
+        "ripped",
+        "seam",
+        "seams",
+        "shedding",
+        "shrink",
+        "shrunk",
+        "shrunk after wash",
+        "shrunk after washing",
+        "stitch",
+        "stitching",
+        "sturdy",
+        "tear",
+        "threads",
+        "tore",
+        "torn",
+        "well constructed",
+        "well made",
+        "well-made",
+        "zipper",
+    ],
+    "APPEARANCE": [
+        "as advertised",
+        "as described",
+        "as pictured",
+        "as shown",
+        "beautiful",
+        "bright",
+        "color",
+        "color is off",
+        "color off",
+        "colors",
+        "colour",
+        "darker",
+        "design",
+        "different color",
+        "different from picture",
+        "different from photo",
+        "dull",
+        "faded color",
+        "graphic",
+        "gorgeous",
+        "lighter",
+        "logo",
+        "looked like",
+        "looks like",
+        "matches the picture",
+        "misleading photo",
+        "off color",
+        "pattern",
+        "photo",
+        "picture",
+        "print",
+        "shade",
+        "stunning",
+        "tone",
+        "true color",
+        "true to color",
+        "ugly",
+        "vibrant",
+    ],
+    "STYLE": [
+        "boho",
+        "casual",
+        "chic",
+        "classic",
+        "collar",
+        "compliment",
+        "complimented",
+        "compliments",
+        "cut",
+        "cute",
+        "elegant",
+        "fashion",
+        "fashionable",
+        "flattering",
+        "formal",
+        "modern",
+        "neckline",
+        "outfit",
+        "preppy",
+        "professional",
+        "received compliments",
+        "shape",
+        "shapeless",
+        "silhouette",
+        "sleeves",
+        "style",
+        "stylish",
+        "trendy",
+        "unflattering",
+        "vintage",
+    ],
+    "VALUE": [
+        "affordable",
+        "bargain",
+        "cheap price",
+        "cost",
+        "discount",
+        "exchanged",
+        "expensive",
+        "for the price",
+        "for this price",
+        "good deal",
+        "great deal",
+        "money",
+        "money back",
+        "money well spent",
+        "not worth",
+        "not worth it",
+        "overpriced",
+        "price",
+        "pricey",
+        "refund",
+        "return",
+        "returning",
+        "value",
+        "value for money",
+        "waste of money",
+        "wasted money",
+        "worth",
+        "worth it",
+    ],
+}
+def get_aspect_dict() -> Dict[str, List[str]]:
+    """Return seed dict (callers can extend it)."""
+    return {k: list(v) for k, v in SEED_ASPECT_DICT.items()}
+
+
+# Tokens / phrases we consider noise when mining metadata text
+_STOPWORDS = set("""
+the and for with from this that have has had not your you our their these
+those there here when where what which who whom how why because all any
+its his her item product use used using also more most less other than just
+new cost free buy bought made make piece pieces set sets pack two one three
+inch inches cm mm gram fl oz lb pounds black white grey gray
+are can will would could should closure imported machine women men kids ladies
+amazon brand sleeve sleeves shirt shirts dress dresses pants jacket jackets
+""".split())
+
+
+
+_GENERIC_MINED_TOKENS = {
+    "are", "can", "will", "would", "could", "should", "closure", "imported",
+    "machine", "women", "woman", "men", "kids", "ladies", "perfect", "nice",
+    "great", "good", "comfortable", "casual", "design", "wear", "wash",
+    "fit", "fabric", "material", "polyester", "cotton", "soft",
+}
+
+_AMBIGUOUS_SINGLE_TOKENS = {
+    "cheap", "perfect", "comfortable", "design", "style", "fit", "wash",
+}
+def _tokenize(text: str) -> List[str]:
+    return re.findall(r"[a-z][a-z\-]+", text.lower())
+
+
+def extend_aspect_dict_from_metadata(
+    meta_texts: Iterable[str],
+    aspect_dict: Dict[str, List[str]] = None,
+    top_k: int = 20,
+    min_count: int = 5,
+) -> Dict[str, List[str]]:
+    """Mine frequent domain words from metadata text to extend aspect seed dict.
+
+    For each aspect, if a metadata token co-occurs frequently with any seed word
+    in the same metadata blob, add it to the aspect's vocabulary.
+    """
+    if aspect_dict is None:
+        aspect_dict = get_aspect_dict()
+
+    # Build per-aspect co-occurrence counter
+    aspect_cooc = {a: Counter() for a in aspect_dict}
+
+    for text in meta_texts:
+        if not isinstance(text, str) or not text:
+            continue
+        tokens = set(_tokenize(text))
+        if not tokens:
+            continue
+        # For each aspect, check if any seed appears in this blob
+        for aspect, seeds in aspect_dict.items():
+            seed_set = set(_tokenize(" ".join(seeds)))
+            if tokens & seed_set:
+                # Co-occurring tokens -> aspect candidates
+                for t in tokens:
+                    if t in _STOPWORDS or t in seed_set:
+                        continue
+                    if t in _GENERIC_MINED_TOKENS or t in _AMBIGUOUS_SINGLE_TOKENS:
+                        continue
+                    if len(t) < 4:
+                        continue
+                    aspect_cooc[aspect][t] += 1
+
+    # Add top-k high frequency co-occurring tokens to each aspect
+    extended = {a: list(v) for a, v in aspect_dict.items()}
+    for aspect, counter in aspect_cooc.items():
+        new_words = [w for w, c in counter.most_common(top_k * 3) if c >= min_count]
+        # Filter words that look generic or ambiguous outside local context.
+        new_words = [
+            w for w in new_words
+            if w not in _STOPWORDS
+            and w not in _GENERIC_MINED_TOKENS
+            and w not in _AMBIGUOUS_SINGLE_TOKENS
+        ][:top_k]
+        extended[aspect].extend(new_words)
+
+    # Dedup
+    for a in extended:
+        extended[a] = sorted(set(extended[a]))
+
+    return extended
+
+
+def save_aspect_dict(aspect_dict: Dict[str, List[str]], path=ASPECT_DICT_PATH):
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(aspect_dict, f, indent=2)
+
+
+def load_aspect_dict(path=ASPECT_DICT_PATH) -> Dict[str, List[str]]:
+    with open(path, encoding="utf-8") as f:
+        return json.load(f)
+
+
+def compile_aspect_patterns(aspect_dict: Dict[str, List[str]]):
+    """Compile regex patterns for each aspect (whole-phrase matching, case-insensitive)."""
+    patterns = {}
+    for aspect, keywords in aspect_dict.items():
+        # Sort longest first so multi-word phrases match before single tokens
+        sorted_kws = sorted(set(keywords), key=lambda s: -len(s))
+        escaped = [re.escape(kw) for kw in sorted_kws]
+        pattern = r"\b(?:" + "|".join(escaped) + r")\b"
+        patterns[aspect] = re.compile(pattern, flags=re.IGNORECASE)
+    return patterns
+

src/baselines.py

@@ -0,0 +1,79 @@
+"""TF-IDF + Logistic Regression baseline (traditional ML reference)."""
+import json
+import logging
+import pickle
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import f1_score, accuracy_score, classification_report
+from sklearn.pipeline import Pipeline
+
+from . import config as cfg
+
+
+logger = logging.getLogger(__name__)
+
+
+def train_tfidf_baseline(
+    train_df: pd.DataFrame,
+    val_df: pd.DataFrame,
+    test_df: pd.DataFrame,
+    text_col: str = "full_text",
+    label_col: str = "overall_label",
+    output_dir: Optional[Path] = None,
+):
+    """Train TF-IDF + LogReg on overall 3-class sentiment. Returns metrics dict."""
+    if output_dir is None:
+        output_dir = cfg.CHECKPOINT_DIR / "tfidf_baseline"
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    pipeline = Pipeline([
+        ("tfidf", TfidfVectorizer(
+            max_features=20000,
+            ngram_range=(1, 2),
+            min_df=2,
+            max_df=0.95,
+            stop_words="english",
+            sublinear_tf=True,
+        )),
+        ("clf", LogisticRegression(
+            max_iter=1000,
+            class_weight="balanced",
+            random_state=cfg.RANDOM_SEED,
+            C=1.0,
+        )),
+    ])
+
+    X_train = train_df[text_col].fillna("").astype(str).values
+    y_train = train_df[label_col].values
+    X_val = val_df[text_col].fillna("").astype(str).values
+    y_val = val_df[label_col].values
+    X_test = test_df[text_col].fillna("").astype(str).values
+    y_test = test_df[label_col].values
+
+    logger.info("Training TF-IDF + LogReg on %d samples...", len(X_train))
+    pipeline.fit(X_train, y_train)
+
+    metrics = {}
+    for split_name, X, y in [("val", X_val, y_val), ("test", X_test, y_test)]:
+        preds = pipeline.predict(X)
+        metrics[f"{split_name}_macro_f1"] = float(f1_score(y, preds, average="macro", zero_division=0))
+        metrics[f"{split_name}_accuracy"] = float(accuracy_score(y, preds))
+        metrics[f"{split_name}_weighted_f1"] = float(f1_score(y, preds, average="weighted", zero_division=0))
+        metrics[f"{split_name}_report"] = classification_report(
+            y, preds, target_names=["Negative", "Neutral", "Positive"], output_dict=True, zero_division=0
+        )
+
+    with open(output_dir / "model.pkl", "wb") as f:
+        pickle.dump(pipeline, f)
+    with open(output_dir / "metrics.json", "w") as f:
+        json.dump({k: v for k, v in metrics.items() if not k.endswith("_report")}, f, indent=2)
+
+    logger.info("TF-IDF baseline | val Macro-F1=%.4f | test Macro-F1=%.4f",
+                metrics["val_macro_f1"], metrics["test_macro_f1"])
+    return pipeline, metrics

src/config.py

@@ -0,0 +1,96 @@
+"""Global configuration for the ACSA-Clothing project (v2: with metadata fusion)."""
+from pathlib import Path
+
+# --- Paths ---
+ROOT = Path(__file__).resolve().parent.parent
+DATA_DIR = ROOT / "data"
+CHECKPOINT_DIR = ROOT / "checkpoints"
+OUTPUT_DIR = ROOT / "outputs"
+REPORT_DIR = ROOT / "reports"
+
+for d in (DATA_DIR, CHECKPOINT_DIR, OUTPUT_DIR, REPORT_DIR):
+    d.mkdir(parents=True, exist_ok=True)
+
+# Raw data files
+RAW_REVIEWS_PATH = DATA_DIR / "raw_reviews.parquet"
+RAW_META_PATH = DATA_DIR / "raw_meta.parquet"
+
+# Processed
+PROCESSED_PATH = DATA_DIR / "processed_reviews.parquet"
+LABELED_PATH = DATA_DIR / "labeled_reviews.parquet"
+ASPECT_DICT_PATH = DATA_DIR / "aspect_dict.json"
+
+# Splits
+TRAIN_PATH = DATA_DIR / "train.parquet"
+VAL_PATH = DATA_DIR / "val.parquet"
+TEST_PATH = DATA_DIR / "test.parquet"
+
+# Meta encoder artifacts (vectorizer, scaler, etc.)
+META_ENCODER_PATH = DATA_DIR / "meta_encoder.pkl"
+
+# --- HuggingFace Dataset ---
+HF_DATASET = "McAuley-Lab/Amazon-Reviews-2023"
+CATEGORY = "Clothing_Shoes_and_Jewelry"
+HF_REVIEW_CONFIG = f"raw_review_{CATEGORY}"
+HF_META_CONFIG = f"raw_meta_{CATEGORY}"
+
+# --- Aspects ---
+# 6 high-level aspects relevant to clothing reviews.
+ASPECTS = ["SIZE", "MATERIAL", "QUALITY", "APPEARANCE", "STYLE", "VALUE"]
+NUM_ASPECTS = len(ASPECTS)
+
+# Per-aspect classes: 0 = Not_Mentioned, 1 = Positive, 2 = Negative
+LABEL_MAP = {"Not_Mentioned": 0, "Positive": 1, "Negative": 2}
+LABEL_NAMES = ["Not_Mentioned", "Positive", "Negative"]
+NUM_CLASSES = 3
+
+# --- Overall (3-class) label codes (used by baselines + overall_label column) ---
+# 0 = Negative, 1 = Neutral, 2 = Positive
+OVERALL_LABEL_NAMES = ["Negative", "Neutral", "Positive"]
+OVERALL_NUM_CLASSES = 3
+
+# --- Model ---
+BERT_MODEL_NAME = "bert-base-uncased"  # 12-layer BERT backbone for the 10W experiment
+MAX_LENGTH = 256
+
+# --- Metadata encoder dims ---
+META_FEATURE_TFIDF_DIM = 64  # TF-IDF dim for product features_text
+META_CATEGORY_TFIDF_DIM = 36 # TF-IDF dim for categories_text
+META_TFIDF_DIM = META_FEATURE_TFIDF_DIM + META_CATEGORY_TFIDF_DIM
+META_NUM_DIM = 4            # price, average_rating, log(rating_number), price_missing_flag
+META_HIDDEN_DIM = 128       # MLP output dim
+META_CROSSATTN_HEADS = 4    # multi-head cross-attention heads
+META_NUMERIC_TOKEN_SCALE = 0.10 # keep numeric metadata, but damp it by default
+CROSS_ATTN_RESIDUAL_SCALE = 0.7 # start cross-attn as a refinement over concat fusion
+OVERALL_AUX_WEIGHT = 0.3   # weight for the overall sentiment auxiliary loss in joint training
+
+# --- Training ---
+DEFAULT_BATCH_SIZE = 16
+DEFAULT_EPOCHS = 3
+DEFAULT_LR_BERT = 2e-5
+DEFAULT_LR_HEADS = 1e-3
+DEFAULT_WEIGHT_DECAY = 0.01
+WARMUP_RATIO = 0.1
+
+# --- Split ratios ---
+TRAIN_RATIO = 0.7
+VAL_RATIO = 0.15
+TEST_RATIO = 0.15
+RANDOM_SEED = 42
+
+# --- Weak labeling ---
+VADER_POSITIVE_THRESHOLD = 0.05
+VADER_NEGATIVE_THRESHOLD = -0.05
+RATING_POSITIVE_THRESHOLD = 4
+RATING_NEGATIVE_THRESHOLD = 2
+MIN_REVIEW_LENGTH = 20
+AUDIT_SAMPLE_SIZE = 200
+
+# When the product's `features` field explicitly lists an aspect-related
+# attribute (e.g. "100% cotton" -> MATERIAL aspect is salient),
+# bump the prior probability that the corresponding aspect is mentioned/relevant.
+META_PRIOR_BOOST = True
+
+
+
+

src/data_download.py

@@ -0,0 +1,216 @@
+"""Download Amazon Reviews 2023 (Clothing_Shoes_and_Jewelry).
+
+銆愰噸瑕佸彉鏇淬€戝畬鍏ㄦ姏寮?HuggingFace 鏁版嵁闆?API,鏀逛负鐩存帴浠?McAuley Lab 瀹樻柟
+HTTPS 鏈嶅姟鍣ㄦ祦寮忔媺鍙栧師濮?jsonl.gz 鏂囦欢銆傚師鍥?
+1. 鏂扮増 datasets 宸插簾寮?loading script, load_dataset(..., trust_remote_code=True)
+   鎶?"Dataset scripts are no longer supported"銆?2. McAuley-Lab/Amazon-Reviews-2023 浠撳簱鐨?main 鍒嗘敮鍙湁鑴氭湰鍜?raw 鐩綍,
+   parquet 鏂囦欢鍒嗘暎鍦?PR 鍒嗘敮(refs/pr/10)涓?涓?Clothing 绫荤洰骞朵笉瀹屾暣,
+   渚濊禆 HF 璧板緱闈炲父鏇叉姌涓斿鏄撹俯鍧戙€?3. 瀹樻柟鏂囨。 (amazon-reviews-2023.github.io) 缁欑殑鐩撮摼鏈€绋?
+   https://mcauleylab.ucsd.edu/public_datasets/data/amazon_2023/...
+   涓€浠?jsonl.gz,娴佸紡 GET + gunzip + readline 涓変欢濂楀嵆鍙€?   瀵?sample 妯″紡灏ゅ叾鍙嬪ソ鈥斺€斾笅澶?N 鏉″氨鎺愭柇杩炴帴,涓嶇敤涓嬪畬鏁翠釜鏂囦欢銆?4. UCSD 鏈嶅姟鍣ㄥ湪鍥藉唴璁块棶閫氬父姣?huggingface.co 绋冲畾 (HF 缁忓父 SNI 闃绘柇)銆?
+銆?026-05 淇銆?- pyarrow 鍐?parquet 鏃朵細鎸夊垪鎺ㄦ柇绫诲瀷, Amazon meta 鐨?price 瀛楁鏄?  "鍙兘鏁板瓧 / 鍙兘 '鈥? (em-dash) / 鍙兘 'None' / 鍙兘绌? 鐨勮剰娣峰悎瀛楁,
+  pyarrow 璇曞浘鎺ㄦ垚 double 鏃堕亣鍒?'鈥? 鐩存帴 ArrowInvalid銆?  瑙ｅ喅: 鍐?parquet 鍓嶇敤 _clean_meta_for_parquet 鎶?price 寮鸿浆 float銆?- 鍚屾椂澧炲姞 jsonl 鍏滃簳鐩? meta 鎷夊畬鍏堝瓨涓€浠藉師濮?jsonl, 鍐嶅啓 parquet銆?  杩欐牱涓囦竴 parquet 鍙堝嚭鍒殑闂, 涓嬫鑳界绾ф仮澶? 涓嶅繀閲嶆柊鐖?25 鍒嗛挓銆?- reviews parquet 濡傛灉宸茬粡瀛樺湪, 璺宠繃 reviews 涓嬭浇闃舵, 鐩存帴澶嶇敤銆?"""
+import gzip
+import json
+import logging
+import os
+from collections import defaultdict
+from typing import Iterable, Iterator
+
+import pandas as pd
+import requests
+from tqdm import tqdm
+
+from . import config as cfg
+
+
+logger = logging.getLogger(__name__)
+
+
+# McAuley Lab 瀹樻柟鐩撮摼妯℃澘
+_BASE = "https://mcauleylab.ucsd.edu/public_datasets/data/amazon_2023/raw"
+_REVIEW_URL_TMPL = _BASE + "/review_categories/{cat}.jsonl.gz"
+_META_URL_TMPL = _BASE + "/meta_categories/meta_{cat}.jsonl.gz"
+
+
+def _stream_jsonl_gz(url: str) -> Iterator[dict]:
+    """Stream a gzipped JSONL file and yield parsed rows.
+
+    The function intentionally avoids downloading the full file to disk, so
+    sample mode can stop early after enough valid records have been collected.
+    """
+    logger.info("GET %s", url)
+    headers = {"User-Agent": "Mozilla/5.0 (acsa_clothing downloader)"}
+    with requests.get(url, stream=True, headers=headers, timeout=(15, 60)) as r:
+        r.raise_for_status()
+        r.raw.decode_content = False
+        with gzip.GzipFile(fileobj=r.raw) as gz:
+            for raw_line in gz:
+                if not raw_line:
+                    continue
+                try:
+                    yield json.loads(raw_line)
+                except json.JSONDecodeError as e:
+                    logger.warning("Skipping one invalid JSON line: %s", e)
+                    continue
+
+def _records_to_df(records: Iterable[dict]) -> pd.DataFrame:
+    return pd.DataFrame(list(records))
+
+
+def _clean_meta_for_parquet(df: pd.DataFrame) -> pd.DataFrame:
+    """鎶?meta 涓細璁?pyarrow 鎺ㄦ柇绫诲瀷澶辫触鐨勮剰鍊兼竻娲楁帀銆?
+    Amazon 鍏冩暟鎹噷 price 鏄?"鍙兘鏁板瓧 / 鍙兘 '鈥? / 鍙兘 'None' / 鍙兘绌? 鐨勬贩鍚堝瓧娈?
+    pandas 榛樿鎺ㄦ垚 object, pyarrow 钀?parquet 鏃跺嵈璇曞浘鎺ㄦ垚 double,
+    閬囧埌 '鈥? (em-dash) 杩欑闈炴暟瀛楀瓧绗︿覆灏辨姏 ArrowInvalid銆?    鎵€浠ヨ繖閲屾墜鍔ㄦ妸 price 寮哄埗杞垚 float64, 鏃犳硶瑙ｆ瀽鐨勪竴寰嬪彉 NaN銆?    """
+    if "price" in df.columns:
+        df["price"] = pd.to_numeric(df["price"], errors="coerce")
+    return df
+
+
+def _backup_jsonl(records: list, parquet_path) -> str:
+    """鍦ㄥ啓 parquet 鍓嶅厛钀戒竴浠藉師濮?jsonl 鍏滃簳銆?
+    璺緞灏辨槸 parquet 鍚屽悕鎹㈠悗缂€銆傚嵆浣垮悗缁?to_parquet 澶辫触涔熶笉蹇呴噸鐖€?    """
+    raw_jsonl_path = os.path.splitext(str(parquet_path))[0] + ".jsonl"
+    os.makedirs(os.path.dirname(raw_jsonl_path) or ".", exist_ok=True)
+    with open(raw_jsonl_path, "w", encoding="utf-8") as f:
+        for row in records:
+            f.write(json.dumps(row, ensure_ascii=False) + "\n")
+    logger.info("Backup jsonl saved to %s (%d rows)", raw_jsonl_path, len(records))
+    return raw_jsonl_path
+
+
+def download_sample(n_reviews: int = 30000, min_text_len: int = 20,
+                    min_reviews_per_product: int = 2,
+                    max_reviews_per_product: int = 5,
+                    scan_multiplier: int = 5,
+                    force: bool = False):
+    """Sample 妯″紡: 娴佸紡璇诲彇鍓?N 鏉″彲鐢ㄨ瘎璁?鍐嶆媺瀵瑰簲 meta銆?
+    'Usable' 鎸? text 闈炵┖涓旈暱搴?>= min_text_len, 涓?parent_asin 闈炵┖銆?    """
+    review_url = _REVIEW_URL_TMPL.format(cat=cfg.CATEGORY)
+    meta_url = _META_URL_TMPL.format(cat=cfg.CATEGORY)
+
+    # ===== Reviews 闃舵 =====
+    # 濡傛灉 reviews parquet 宸插瓨鍦?(涓婃璺戞垚鍔熶簡, 鏄?meta 闃舵宕╃殑),
+    # 鐩存帴璇诲嚭鏉ュ鐢?seen_asins, 璺宠繃杩欐閲嶆柊涓嬭浇銆?
+    if os.path.exists(cfg.RAW_REVIEWS_PATH) and not force:
+        logger.info("Reviews parquet 宸插瓨鍦? 澶嶇敤: %s", cfg.RAW_REVIEWS_PATH)
+        reviews_df = pd.read_parquet(cfg.RAW_REVIEWS_PATH)
+        seen_asins = set(reviews_df["parent_asin"].dropna().unique().tolist())
+        logger.info("浠庡凡鏈?reviews 涓仮澶?%d 鏉? %d 涓?unique parent_asins",
+                    len(reviews_df), len(seen_asins))
+    else:
+        logger.info(
+            "Streaming reviews (grouped sample, target=%d, min_per_product=%d, max_per_product=%d, scan_multiplier=%d) from %s ...",
+            n_reviews, min_reviews_per_product, max_reviews_per_product, scan_multiplier, review_url,
+        )
+        candidate_target = max(n_reviews, n_reviews * max(scan_multiplier, 1))
+        candidates = []
+        pbar = tqdm(total=candidate_target, desc="review candidates")
+        for row in _stream_jsonl_gz(review_url):
+            text = (row.get("text") or "").strip()
+            if len(text) < min_text_len:
+                continue
+            if not row.get("parent_asin"):
+                continue
+            candidates.append(row)
+            pbar.update(1)
+            if len(candidates) >= candidate_target:
+                break
+        pbar.close()
+
+        by_asin = defaultdict(list)
+        for row in candidates:
+            by_asin[row["parent_asin"]].append(row)
+
+        eligible = {asin: rows for asin, rows in by_asin.items() if len(rows) >= min_reviews_per_product}
+        kept = []
+        for asin, rows in sorted(eligible.items(), key=lambda x: len(x[1]), reverse=True):
+            for row in rows[:max_reviews_per_product]:
+                kept.append(row)
+                if len(kept) >= n_reviews:
+                    break
+            if len(kept) >= n_reviews:
+                break
+
+        if len(kept) < n_reviews:
+            used = {id(row) for row in kept}
+            for row in candidates:
+                if id(row) in used:
+                    continue
+                kept.append(row)
+                if len(kept) >= n_reviews:
+                    break
+
+        reviews_df = _records_to_df(kept[:n_reviews])
+        seen_asins = set(reviews_df["parent_asin"].dropna().unique().tolist())
+        per_product = reviews_df.groupby("parent_asin").size()
+        logger.info(
+            "Got %d reviews, %d unique parent_asins, avg_reviews_per_product=%.2f, products_with_%d_plus_reviews=%d.",
+            len(reviews_df), len(seen_asins), len(reviews_df) / max(len(seen_asins), 1),
+            min_reviews_per_product, int((per_product >= min_reviews_per_product).sum()),
+        )
+        reviews_df.to_parquet(cfg.RAW_REVIEWS_PATH, index=False)
+
+    # ===== Meta 闃舵 =====
+    # 鍐嶆媺 meta,鍖归厤 parent_asin
+    # 娉ㄦ剰: meta 娌℃湁淇濊瘉鎺掑簭,鐞嗚涓婅鎵畬鏁翠唤鎵嶈兘纭繚鍛戒腑鎵€鏈?asin銆?
+    # 浣?meta 姣?reviews 灏忓緱澶?(Clothing meta 鍑犵櫨 MB 鍘嬬缉),瀹屾暣鎵竴閬嶅彲鎺ュ彈銆?    logger.info("Streaming meta to match parent_asins from %s ...", meta_url)
+    meta_kept = []
+    remaining = set(seen_asins)
+    pbar = tqdm(total=len(seen_asins), desc="meta")
+    for row in _stream_jsonl_gz(meta_url):
+        asin = row.get("parent_asin")
+        if asin in remaining:
+            meta_kept.append(row)
+            remaining.discard(asin)
+            pbar.update(1)
+            if not remaining:
+                break  # 鍏ㄩ儴鍛戒腑,鎻愬墠鏂紑
+    pbar.close()
+
+    # 1) 鍏堣惤 jsonl 鍏滃簳, 鍝€曚笅闈?to_parquet 鐐镐簡涔熶笉蹇呴噸鐖?
+    _backup_jsonl(meta_kept, cfg.RAW_META_PATH)
+
+    # 2) 鍐嶅仛娓呮礂 + 鍐?parquet
+    meta_df = _records_to_df(meta_kept)
+    logger.info(
+        "Got %d meta entries (matched %.1f%% of review asins).",
+        len(meta_df),
+        100 * len(meta_df) / max(len(seen_asins), 1),
+    )
+    meta_df = _clean_meta_for_parquet(meta_df)
+    meta_df.to_parquet(cfg.RAW_META_PATH, index=False)
+    return reviews_df, meta_df
+
+
+def download_full():
+    """Download the full review and metadata tables. Very large; use with care."""
+    review_url = _REVIEW_URL_TMPL.format(cat=cfg.CATEGORY)
+    meta_url = _META_URL_TMPL.format(cat=cfg.CATEGORY)
+
+    logger.info("Downloading FULL reviews from %s ...", review_url)
+    reviews = []
+    for row in tqdm(_stream_jsonl_gz(review_url), desc="reviews"):
+        reviews.append(row)
+    reviews_df = _records_to_df(reviews)
+    logger.info("Reviews: %d rows", len(reviews_df))
+    reviews_df.to_parquet(cfg.RAW_REVIEWS_PATH, index=False)
+
+    logger.info("Downloading FULL meta from %s ...", meta_url)
+    metas = []
+    for row in tqdm(_stream_jsonl_gz(meta_url), desc="meta"):
+        metas.append(row)
+
+    # 钀?jsonl 鍏滃簳
+    _backup_jsonl(metas, cfg.RAW_META_PATH)
+
+    meta_df = _records_to_df(metas)
+    logger.info("Meta: %d rows", len(meta_df))
+    meta_df = _clean_meta_for_parquet(meta_df)
+    meta_df.to_parquet(cfg.RAW_META_PATH, index=False)
+    return reviews_df, meta_df
+
+
+
+

src/dataset.py

@@ -0,0 +1,115 @@
+"""PyTorch Dataset classes.
+
+We provide three:
+  - ACSADataset           text only,  per-aspect labels   -> Baseline 3 (no meta)
+  - MetaACSADataset       text + meta features            -> Proposed model
+  - OverallSentimentDataset  text only, single 3-class    -> Baseline 2 (BERT-overall)
+"""
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import Dataset
+
+from . import config as cfg
+from .meta_encoder import MetaEncoder
+
+
+class _BaseTextDataset(Dataset):
+    def __init__(self, df: pd.DataFrame, tokenizer, max_length: int = cfg.MAX_LENGTH,
+                 text_col: str = "full_text"):
+        self.df = df.reset_index(drop=True)
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.text_col = text_col
+
+    def __len__(self):
+        return len(self.df)
+
+    def _encode_text(self, text: str):
+        enc = self.tokenizer(
+            text,
+            max_length=self.max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        )
+        return enc["input_ids"].squeeze(0), enc["attention_mask"].squeeze(0)
+
+
+class ACSADataset(_BaseTextDataset):
+    """Per-aspect multi-head training, text-only. (Baseline 3.)"""
+
+    def __init__(self, df, tokenizer, **kw):
+        super().__init__(df, tokenizer, **kw)
+        self.aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
+        missing = [c for c in self.aspect_cols if c not in self.df.columns]
+        if missing:
+            raise KeyError(f"ACSADataset: missing aspect label columns {missing}. "
+                           f"Did you run weak labeling (script 03)?")
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        input_ids, attn = self._encode_text(str(row[self.text_col]))
+        labels = torch.tensor([int(row[c]) for c in self.aspect_cols], dtype=torch.long)
+        return {"input_ids": input_ids, "attention_mask": attn, "labels": labels}
+
+
+class MetaACSADataset(_BaseTextDataset):
+    """Per-aspect multi-head training, text + metadata. (Proposed model.)
+
+    The metadata is pre-encoded to a numpy matrix of shape (N, meta_dim) using
+    a fit MetaEncoder, and indexed in lock-step with the text rows.
+
+    If the df contains an 'overall_label' column (0=Neg, 1=Neu, 2=Pos), it is
+    returned as part of each batch for the joint overall auxiliary loss.
+    """
+
+    def __init__(self, df, tokenizer, meta_encoder: MetaEncoder, **kw):
+        super().__init__(df, tokenizer, **kw)
+        self.aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
+        missing = [c for c in self.aspect_cols if c not in self.df.columns]
+        if missing:
+            raise KeyError(f"MetaACSADataset: missing aspect label columns {missing}.")
+
+        self.has_overall = "overall_label" in self.df.columns
+
+        # Pre-encode metadata once, in the same row order as self.df.
+        self.meta_matrix = meta_encoder.transform(self.df)
+        assert self.meta_matrix.shape[0] == len(self.df), \
+            "meta matrix and df length mismatch"
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        input_ids, attn = self._encode_text(str(row[self.text_col]))
+        labels = torch.tensor([int(row[c]) for c in self.aspect_cols], dtype=torch.long)
+        meta = torch.from_numpy(self.meta_matrix[idx]).float()
+        item = {
+            "input_ids": input_ids,
+            "attention_mask": attn,
+            "meta_features": meta,
+            "labels": labels,
+        }
+        if self.has_overall:
+            item["overall_labels"] = torch.tensor(int(row["overall_label"]), dtype=torch.long)
+        return item
+
+
+class OverallSentimentDataset(_BaseTextDataset):
+    """Single 3-class overall sentiment label. (Baseline 2: BERT-overall.)"""
+
+    def __init__(self, df, tokenizer, label_col: str = "overall_label", **kw):
+        super().__init__(df, tokenizer, **kw)
+        self.label_col = label_col
+        if self.label_col not in self.df.columns:
+            raise KeyError(f"OverallSentimentDataset: missing {self.label_col} column.")
+
+    def __getitem__(self, idx):
+        row = self.df.iloc[idx]
+        input_ids, attn = self._encode_text(str(row[self.text_col]))
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attn,
+            "labels": torch.tensor(int(row[self.label_col]), dtype=torch.long),
+        }

src/evaluator.py

@@ -0,0 +1,326 @@
+"""Evaluation: per-aspect metrics, aggregated overall comparison.
+
+Two label encodings live in this project (keep them straight!):
+  Per-aspect (ACSA):   0=Not_Mentioned, 1=Positive, 2=Negative
+  Overall (3-class):   0=Negative,      1=Neutral,  2=Positive
+
+`aspect_to_overall_sentiment` translates between them explicitly.
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Dict, Optional, Union
+
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import DataLoader
+from transformers import AutoTokenizer
+from sklearn.metrics import (
+    f1_score, accuracy_score, confusion_matrix, classification_report,
+)
+from tqdm import tqdm
+
+from . import config as cfg
+from .dataset import ACSADataset, MetaACSADataset, OverallSentimentDataset
+from .models import GatedAspectSemanticMetaFusionACSAModel, BertMetaFusionACSAModel, BertACSAModel, BertOverallModel
+from .meta_encoder import MetaEncoder
+from .trainer import get_device
+
+
+logger = logging.getLogger(__name__)
+
+# Label-code constants (DO NOT change carelessly; many tests depend on them).
+ASPECT_NOT_MENTIONED = 0
+ASPECT_POSITIVE = 1
+ASPECT_NEGATIVE = 2
+
+OVERALL_NEGATIVE = 0
+OVERALL_NEUTRAL = 1
+OVERALL_POSITIVE = 2
+
+
+# ---------------------------------------------------------------------------
+# Loaders (all use weights_only=False 鈥?these are our own checkpoints)
+# ---------------------------------------------------------------------------
+
+def _load_ckpt(path: Path, device):
+    return torch.load(path, map_location=device, weights_only=False)
+
+
+def load_meta_acsa(checkpoint_dir: Path = None, meta_encoder: Optional[MetaEncoder] = None,
+                   device=None):
+    if checkpoint_dir is None:
+        checkpoint_dir = cfg.CHECKPOINT_DIR / "meta_acsa"
+    checkpoint_dir = Path(checkpoint_dir)
+    if device is None:
+        device = get_device()
+    ckpt = _load_ckpt(checkpoint_dir / "best.pt", device)
+    bert_name = ckpt.get("config", {}).get("bert_name", cfg.BERT_MODEL_NAME)
+    meta_in_dim = ckpt.get("config", {}).get("meta_in_dim",
+                                              cfg.META_TFIDF_DIM + cfg.META_NUM_DIM)
+    architecture = ckpt.get("config", {}).get("architecture", "legacy_meta_acsa")
+    if meta_encoder is None:
+        meta_encoder = MetaEncoder.load()
+    if meta_encoder.total_dim != meta_in_dim:
+        raise ValueError(
+            f"Meta encoder dim {meta_encoder.total_dim} != checkpoint dim {meta_in_dim}. "
+            f"Re-fit the encoder on the same train split used for training."
+        )
+    if architecture == "gated_aspect_semantic_meta_acsa":
+        model = GatedAspectSemanticMetaFusionACSAModel(
+            bert_name=bert_name,
+            meta_in_dim=meta_in_dim,
+        ).to(device)
+    else:
+        model = BertMetaFusionACSAModel(bert_name=bert_name, meta_in_dim=meta_in_dim).to(device)
+    model.load_state_dict(ckpt["model_state_dict"], strict=False)
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
+    return model, tokenizer, meta_encoder, device
+
+
+def load_acsa(checkpoint_dir: Path = None, device=None):
+    if checkpoint_dir is None:
+        checkpoint_dir = cfg.CHECKPOINT_DIR / "acsa"
+    checkpoint_dir = Path(checkpoint_dir)
+    if device is None:
+        device = get_device()
+    ckpt = _load_ckpt(checkpoint_dir / "best.pt", device)
+    bert_name = ckpt.get("config", {}).get("bert_name", cfg.BERT_MODEL_NAME)
+    model = BertACSAModel(bert_name=bert_name).to(device)
+    model.load_state_dict(ckpt["model_state_dict"])
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
+    return model, tokenizer, device
+
+
+def load_bert_overall(checkpoint_dir: Path = None, device=None):
+    if checkpoint_dir is None:
+        checkpoint_dir = cfg.CHECKPOINT_DIR / "bert_overall"
+    checkpoint_dir = Path(checkpoint_dir)
+    if device is None:
+        device = get_device()
+    ckpt = _load_ckpt(checkpoint_dir / "best.pt", device)
+    bert_name = ckpt.get("config", {}).get("bert_name", cfg.BERT_MODEL_NAME)
+    model = BertOverallModel(bert_name=bert_name).to(device)
+    model.load_state_dict(ckpt["model_state_dict"])
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
+    return model, tokenizer, device
+
+
+# ---------------------------------------------------------------------------
+# Inference loops (return predictions in the same row order as test_df)
+# ---------------------------------------------------------------------------
+
+def predict_per_aspect(model, tokenizer, test_df, device,
+                       meta_encoder: Optional[MetaEncoder] = None,
+                       batch_size: int = 32):
+    """Run a per-aspect model over test_df. Returns:
+        all_preds  : list of NUM_ASPECTS lists, each length N
+        all_labels : same shape, ground-truth aspect labels (if present)
+    """
+    test_df = test_df.reset_index(drop=True)
+    if meta_encoder is not None:
+        ds = MetaACSADataset(test_df, tokenizer, meta_encoder)
+        loader = DataLoader(ds, batch_size=batch_size, shuffle=False)
+        with_meta = True
+    else:
+        ds = ACSADataset(test_df, tokenizer)
+        loader = DataLoader(ds, batch_size=batch_size, shuffle=False)
+        with_meta = False
+
+    all_preds = [[] for _ in range(cfg.NUM_ASPECTS)]
+    all_labels = [[] for _ in range(cfg.NUM_ASPECTS)]
+    with torch.no_grad():
+        for batch in tqdm(loader, desc="predict per-aspect"):
+            batch = {k: v.to(device) for k, v in batch.items()}
+            if with_meta:
+                out = model(batch["input_ids"], batch["attention_mask"],
+                            batch["meta_features"])
+            else:
+                out = model(batch["input_ids"], batch["attention_mask"])
+            preds = out["logits"].argmax(dim=-1).cpu().numpy()
+            labels = batch["labels"].cpu().numpy()
+            for i in range(cfg.NUM_ASPECTS):
+                all_preds[i].extend(preds[:, i].tolist())
+                all_labels[i].extend(labels[:, i].tolist())
+    return all_preds, all_labels
+
+
+def predict_overall_from_proposed(model, tokenizer, test_df, device,
+                                  meta_encoder: MetaEncoder,
+                                  batch_size: int = 32):
+    """Use the Proposed model's overall_head to predict 3-class overall sentiment.
+
+    Returns (preds_list, labels_list) where labels come from 'overall_label' column.
+    """
+    test_df = test_df.reset_index(drop=True)
+    ds = MetaACSADataset(test_df, tokenizer, meta_encoder)
+    loader = DataLoader(ds, batch_size=batch_size, shuffle=False)
+
+    all_preds, all_labels = [], []
+    with torch.no_grad():
+        for batch in tqdm(loader, desc="predict overall (proposed head)"):
+            batch = {k: v.to(device) for k, v in batch.items()}
+            out = model(batch["input_ids"], batch["attention_mask"],
+                        batch["meta_features"])
+            preds = out["overall_logits"].argmax(dim=-1).cpu().numpy()
+            all_preds.extend(preds.tolist())
+            if "overall_labels" in batch:
+                all_labels.extend(batch["overall_labels"].cpu().numpy().tolist())
+            else:
+                all_labels.extend(test_df["overall_label"].iloc[
+                    len(all_labels):len(all_labels)+len(preds)].astype(int).tolist())
+    return all_preds, all_labels
+
+
+def evaluate_per_aspect(all_preds, all_labels):
+    results = {"per_aspect": {}}
+    f1s, accs = [], []
+    for i, aspect in enumerate(cfg.ASPECTS):
+        y_true, y_pred = all_labels[i], all_preds[i]
+        report = classification_report(
+            y_true, y_pred,
+            labels=list(range(cfg.NUM_CLASSES)),
+            target_names=cfg.LABEL_NAMES, output_dict=True, zero_division=0,
+        )
+        cm = confusion_matrix(y_true, y_pred, labels=list(range(cfg.NUM_CLASSES))).tolist()
+        f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
+        acc = accuracy_score(y_true, y_pred)
+        results["per_aspect"][aspect] = {
+            "macro_f1": float(f1), "accuracy": float(acc),
+            "confusion_matrix": cm, "report": report,
+        }
+        f1s.append(f1); accs.append(acc)
+    results["overall"] = {
+        "mean_macro_f1": float(np.mean(f1s)) if f1s else 0.0,
+        "mean_accuracy": float(np.mean(accs)) if accs else 0.0,
+    }
+    return results
+
+
+def predict_overall(model, tokenizer, test_df, device, batch_size: int = 32):
+    test_df = test_df.reset_index(drop=True)
+    ds = OverallSentimentDataset(test_df, tokenizer)
+    loader = DataLoader(ds, batch_size=batch_size, shuffle=False)
+    all_preds, all_labels = [], []
+    with torch.no_grad():
+        for batch in loader:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            out = model(**batch)
+            all_preds.extend(out["logits"].argmax(dim=-1).cpu().numpy().tolist())
+            all_labels.extend(batch["labels"].cpu().numpy().tolist())
+    return all_preds, all_labels
+
+
+def overall_metrics(y_true, y_pred):
+    return {
+        "test_macro_f1":    float(f1_score(y_true, y_pred, average="macro",    zero_division=0)),
+        "test_accuracy":    float(accuracy_score(y_true, y_pred)),
+        "test_weighted_f1": float(f1_score(y_true, y_pred, average="weighted", zero_division=0)),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Aggregation: per-aspect predictions -> single overall label
+# ---------------------------------------------------------------------------
+
+def aspect_to_overall_sentiment(aspect_preds) -> np.ndarray:
+    """Improved voting rule for aggregating per-aspect 鈫?overall.
+
+    Rules (applied per review):
+      1. Count n_pos and n_neg among MENTIONED aspects (skip Not_Mentioned).
+      2. If no aspect is mentioned at all 鈫?NEUTRAL (conservative default).
+      3. If n_neg 鈮?2 鈫?NEGATIVE (multiple negative aspects = clearly unhappy).
+      4. If n_neg == 1 and n_pos == 0 鈫?NEGATIVE.
+      5. If n_pos 鈮?2 and n_neg == 0 鈫?POSITIVE.
+      6. If n_pos > n_neg (but not all positive) 鈫?POSITIVE.
+      7. If n_neg > n_pos 鈫?NEGATIVE.
+      8. Otherwise (tied, or only 1 pos and 0 neg) 鈫?NEUTRAL.
+
+    Input  (per-aspect):  0=NM, 1=Pos, 2=Neg
+    Output (overall):     0=Neg, 1=Neu, 2=Pos
+    """
+    preds = np.asarray(aspect_preds)
+    if preds.ndim == 1:
+        preds = preds[None, :]
+
+    n_pos = (preds == ASPECT_POSITIVE).sum(axis=1)
+    n_neg = (preds == ASPECT_NEGATIVE).sum(axis=1)
+    n_mentioned = n_pos + n_neg
+
+    out = np.full(preds.shape[0], OVERALL_NEUTRAL, dtype=np.int64)
+
+    # No aspects mentioned 鈫?Neutral
+    # n_neg 鈮?2 鈫?Negative (strong signal)
+    out[n_neg >= 2] = OVERALL_NEGATIVE
+    # n_neg == 1, n_pos == 0 鈫?Negative
+    out[(n_neg == 1) & (n_pos == 0)] = OVERALL_NEGATIVE
+    # n_pos 鈮?2, n_neg == 0 鈫?Positive (strong signal)
+    out[(n_pos >= 2) & (n_neg == 0)] = OVERALL_POSITIVE
+    # n_pos > n_neg and n_pos 鈮?2 鈫?Positive
+    out[(n_pos > n_neg) & (n_pos >= 2)] = OVERALL_POSITIVE
+    # n_neg > n_pos 鈫?Negative (override the 鈮? positive if more negatives)
+    out[n_neg > n_pos] = OVERALL_NEGATIVE
+
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Drilldown: category x aspect aggregation (application-layer)
+# ---------------------------------------------------------------------------
+
+def aggregate_aspect_distribution_by_category(test_df, aspect_preds_per_aspect,
+                                              category_col: str = "leaf_category",
+                                              top_k_cats: int = 10):
+    df = test_df.copy().reset_index(drop=True)
+    preds = np.array(aspect_preds_per_aspect).T  # (N, num_aspects)
+    if preds.shape[0] != len(df):
+        logger.warning("preds rows (%d) != df rows (%d); skipping aggregation.",
+                       preds.shape[0], len(df))
+        return None
+    for i, a in enumerate(cfg.ASPECTS):
+        df[f"pred_{a}"] = preds[:, i]
+
+    if category_col not in df.columns:
+        logger.warning("No %s column for drilldown.", category_col)
+        return None
+    df = df[df[category_col].notna() &
+            (df[category_col].astype(str).str.len() > 0)]
+    if df.empty:
+        return None
+
+    top_cats = df[category_col].value_counts().head(top_k_cats).index.tolist()
+    rows = []
+    for cat in top_cats:
+        sub = df[df[category_col] == cat]
+        if len(sub) < 5:
+            continue
+        for a in cfg.ASPECTS:
+            p = sub[f"pred_{a}"]
+            mentioned = p[p != ASPECT_NOT_MENTIONED]
+            if len(mentioned) == 0:
+                pos_share, neg_share = 0.0, 0.0
+            else:
+                pos_share = float((mentioned == ASPECT_POSITIVE).mean())
+                neg_share = float((mentioned == ASPECT_NEGATIVE).mean())
+            rows.append({
+                "category": cat, "aspect": a, "n_total": len(sub),
+                "n_mentioned": int(len(mentioned)),
+                "positive_share": pos_share, "negative_share": neg_share,
+            })
+    return pd.DataFrame(rows)
+
+
+# ---------------------------------------------------------------------------
+# Single-review formatted output (for the customer's stated need)
+# ---------------------------------------------------------------------------
+
+def format_aspect_summary(per_aspect_pred: np.ndarray) -> Dict[str, str]:
+    """For one review: {'SIZE': 'Positive', 'MATERIAL': 'Not_Mentioned', ...}."""
+    return {aspect: cfg.LABEL_NAMES[int(per_aspect_pred[i])]
+            for i, aspect in enumerate(cfg.ASPECTS)}
+
+

src/explainer.py

@@ -0,0 +1,726 @@
+"""Explainability for the Proposed Model (BertMetaFusionACSAModel).
+
+Three views:
+  1. Cross-Attention weights over metadata tokens   (per aspect, per example)
+  2. Integrated Gradients on the input text         (per aspect, per example)
+  3. Aspect-level aggregation plots                 (category x aspect)
+
+Saves an HTML report combining (1) and (2), plus PNG figures for (3).
+"""
+import html as html_lib
+import logging
+from pathlib import Path
+from typing import List, Optional, Dict, Tuple
+
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from . import config as cfg
+from .evaluator import load_meta_acsa
+from .meta_encoder import MetaEncoder
+from .models import META_NUM_META_TOKENS
+
+
+logger = logging.getLogger(__name__)
+
+META_TOKEN_NAMES = [f"meta_chunk_{i+1}" for i in range(META_NUM_META_TOKENS)]
+SEMANTIC_META_TOKEN_NAMES = ["features", "categories", "numeric"]
+
+
+# ---------------------------------------------------------------------------
+# Low-level rendering helpers
+# ---------------------------------------------------------------------------
+
+def _normalize(arr):
+    arr = np.asarray(arr, dtype=np.float64)
+    if arr.size == 0:
+        return np.zeros_like(arr)
+    finite = arr[np.isfinite(arr)]
+    if finite.size == 0 or finite.max() == finite.min():
+        return np.zeros_like(arr)
+    return (arr - finite.min()) / (finite.max() - finite.min() + 1e-9)
+
+
+def _normalize_for_display(scores, mask=None):
+    arr = np.asarray(scores, dtype=np.float64)
+    if mask is not None:
+        arr = arr * np.asarray(mask, dtype=np.float64)
+    arr = np.abs(arr)
+    if arr.size == 0 or float(arr.max()) <= 0:
+        return np.zeros_like(arr)
+    denom = np.percentile(arr[arr > 0], 95) if np.any(arr > 0) else arr.max()
+    denom = max(float(denom), 1e-9)
+    return np.clip(arr / denom, 0.0, 1.0)
+
+
+def _color_for_score(score: float, label: int) -> str:
+    score = float(np.clip(score, 0.0, 1.0))
+    if score <= 0:
+        return "rgba(255,255,255,0)"
+    alpha = 0.18 + 0.72 * score
+    if label == 2:
+        return f"rgba(255, 80, 80, {alpha:.3f})"
+    if label == 1:
+        return f"rgba(80, 180, 110, {alpha:.3f})"
+    return f"rgba(150, 150, 150, {alpha:.3f})"
+
+
+def _merge_wordpieces(tokens, scores):
+    words, vals = [], []
+    for tok, score in zip(tokens, scores):
+        tok = str(tok)
+        if not tok or (tok.startswith("[") and tok.endswith("]")):
+            continue
+        if tok.startswith("##") and words:
+            words[-1] += tok[2:]
+            vals[-1] = max(vals[-1], float(score))
+        else:
+            words.append(tok)
+            vals.append(float(score))
+    return words, vals
+
+
+def _top_terms(tokens, scores, top_k: int = 6):
+    words, vals = _merge_wordpieces(tokens, scores)
+    pairs = [(w, v) for w, v in zip(words, vals) if _is_informative_term(w) and v > 0]
+    pairs.sort(key=lambda x: x[1], reverse=True)
+    return _dedupe_terms(pairs, top_k=top_k)
+
+
+def _explanation_quality(top_terms, meta_source: str, meta_weight: float) -> str:
+    n_terms = len(top_terms or [])
+    if n_terms >= 3 and meta_source and meta_weight >= 0.45:
+        return "strong"
+    if n_terms >= 1 and meta_source:
+        return "moderate"
+    return "weak"
+
+
+def _make_explanation_sentence(aspect: str, pred_label: str, top_terms, meta_source: str, meta_weight: float) -> str:
+    terms = [t for t, _ in (top_terms or [])[:4]]
+    text_part = ", ".join(f'"{t}"' for t in terms) if terms else "no strong text token"
+    meta_part = f'"{meta_source}" ({meta_weight:.3f})' if meta_source else "no dominant metadata source"
+    return (
+        f'{aspect} is predicted as {pred_label}. '
+        f'The main text evidence is {text_part}, and the strongest metadata source is {meta_part}.'
+    )
+
+
+def _render_token_html(tokens, scores, label, top_k: int = 18):
+    norm = _normalize_for_display(scores)
+    positive_idx = np.where(norm > 0)[0]
+    if len(positive_idx) == 0:
+        words, vals = _merge_wordpieces(tokens, norm)
+    else:
+        ranked = positive_idx[np.argsort(norm[positive_idx])]
+        top_idx = set(ranked[-min(top_k, len(ranked)):].tolist())
+        display = np.zeros_like(norm)
+        for i in top_idx:
+            display[i] = max(float(norm[i]), 0.35)
+        words, vals = _merge_wordpieces(tokens, display)
+    spans = []
+    for word, val in zip(words, vals):
+        color = _color_for_score(val, label)
+        weight = "600" if val >= 0.55 else "400"
+        border = " border-bottom:1px solid rgba(0,0,0,.18);" if val >= 0.35 else ""
+        spans.append(
+            f'<span style="background-color:{color};padding:2px 4px;'
+            f'border-radius:4px;margin:1px;font-weight:{weight};{border}">'
+            f'{html_lib.escape(word)}</span>'
+        )
+    return " ".join(spans)
+
+
+def _meta_summary_from_row(row) -> Dict[str, str]:
+    def first_existing(names, default=""):
+        for name in names:
+            if name not in row:
+                continue
+            val = row[name]
+            if isinstance(val, (list, tuple)):
+                return "; ".join(map(str, val[:8]))
+            if pd.notna(val):
+                return str(val)
+        return default
+
+    features = first_existing(["features_text", "features", "description", "title"])
+    categories = first_existing(["categories_text", "category", "leaf_category", "main_category"])
+    numeric_parts = []
+    for col in ["price", "average_rating", "rating_number"]:
+        if col in row and pd.notna(row[col]):
+            numeric_parts.append(f"{col}={row[col]}")
+    return {
+        "features": features[:420],
+        "categories": categories[:220],
+        "numeric": ", ".join(numeric_parts) if numeric_parts else "not available",
+    }
+
+
+def _top_meta_source(meta_attn, token_names=None) -> Tuple[str, float]:
+    arr = np.asarray(meta_attn, dtype=np.float64).reshape(-1)
+    if arr.size == 0:
+        return "", 0.0
+    names = token_names or META_TOKEN_NAMES
+    idx = int(np.argmax(arr))
+    name = names[idx] if idx < len(names) else f"meta_{idx+1}"
+    return name, float(arr[idx])
+
+_ASPECT_EVIDENCE_KEYWORDS = {
+    "SIZE": {"size", "fit", "fits", "fitting", "small", "large", "big", "tight", "loose", "xl", "medium", "waist", "length"},
+    "MATERIAL": {"material", "fabric", "cotton", "polyester", "soft", "scratchy", "thin", "thick", "stretch", "leather", "wool"},
+    "QUALITY": {"quality", "stitch", "stitching", "seam", "wash", "washed", "durable", "cheap", "broke", "tear", "torn"},
+    "APPEARANCE": {"look", "looks", "color", "colour", "photo", "picture", "beautiful", "cute", "print", "design"},
+    "STYLE": {"style", "stylish", "flattering", "casual", "formal", "dress", "shirt", "fashion", "compliments"},
+    "VALUE": {"price", "worth", "value", "money", "cheap", "expensive", "discount", "penny", "cost"},
+}
+
+_SENTIMENT_EVIDENCE_KEYWORDS = {
+    "good", "great", "love", "loved", "perfect", "nice", "excellent", "comfortable", "soft",
+    "bad", "poor", "cheap", "terrible", "awful", "small", "large", "tight", "loose", "thin",
+    "worth", "disappointed", "return", "returned", "recommend", "flattering", "beautiful",
+}
+
+_STOPWORDS = {
+    "a", "an", "the", "and", "or", "but", "if", "then", "than", "so", "as", "at", "by", "for", "from",
+    "in", "into", "of", "on", "to", "with", "without", "is", "are", "was", "were", "be", "been", "being",
+    "it", "its", "this", "that", "these", "those", "i", "me", "my", "we", "our", "you", "your", "he", "she", "they",
+    "them", "his", "her", "their", "very", "really", "just", "also", "too", "would", "could", "should", "can",
+    "will", "did", "do", "does", "have", "has", "had", "there", "here", "about", "after", "before",
+}
+
+
+def _clean_term(term: str) -> str:
+    return str(term).lower().replace("##", "").strip(".,!?;:'\"()[]{}<>/\\|`~@#$%^&*_+=")
+
+
+def _is_informative_term(term: str) -> bool:
+    clean = _clean_term(term)
+    if len(clean) < 2 or clean in _STOPWORDS:
+        return False
+    return any(ch.isalpha() for ch in clean)
+
+
+def _dedupe_terms(terms, top_k: int = 6):
+    seen, out = set(), []
+    for term, score in terms:
+        clean = _clean_term(term)
+        if not _is_informative_term(clean) or clean in seen:
+            continue
+        seen.add(clean)
+        out.append((clean, float(score)))
+        if len(out) >= top_k:
+            break
+    return out
+
+def _lexical_evidence_scores(tokens, aspect_idx: int):
+    aspect = cfg.ASPECTS[aspect_idx]
+    aspect_terms = _ASPECT_EVIDENCE_KEYWORDS.get(aspect, set())
+    scores = []
+    prev = ""
+    for tok in tokens:
+        clean = str(tok).lower().replace("##", "")
+        clean = clean.strip(".,!?;:'\"()[]{}")
+        if not clean or clean.startswith("["):
+            scores.append(0.0)
+            prev = clean
+            continue
+        score = 0.0
+        if clean in aspect_terms:
+            score += 1.0
+        if clean in _SENTIMENT_EVIDENCE_KEYWORDS:
+            score += 0.55
+        if prev in {"not", "no", "never", "too", "very", "really"} and score > 0:
+            score += 0.25
+        scores.append(score)
+        prev = clean
+    return np.asarray(scores, dtype=np.float32)
+
+
+def _has_visible_scores(scores) -> bool:
+    arr = np.asarray(scores, dtype=np.float64)
+    return bool(arr.size and np.isfinite(arr).any() and float(np.nanmax(np.abs(arr))) > 1e-12)
+# ---------------------------------------------------------------------------
+# Integrated Gradients (per aspect)
+# ---------------------------------------------------------------------------
+
+def _ig_for_aspect(model, tokenizer, meta_encoder, row, device,
+                   aspect_idx: int, n_steps: int = 30):
+    """Gradient x embedding attribution for one aspect head.
+
+    Captum's LayerIntegratedGradients can be brittle with wrapped transformer
+    modules in notebooks. This path keeps the same goal, but computes a direct
+    first-order attribution from the input embeddings, then falls back to
+    lexical evidence if gradients are unavailable.
+    """
+    model.eval()
+    text = str(row["full_text"])
+    enc = tokenizer(text, max_length=cfg.MAX_LENGTH, truncation=True,
+                    padding="max_length", return_tensors="pt")
+    input_ids = enc["input_ids"].to(device)
+    attention_mask = enc["attention_mask"].to(device)
+    tokens = tokenizer.convert_ids_to_tokens(input_ids[0].detach().cpu().numpy().tolist())
+    mask = attention_mask[0].detach().cpu().numpy().astype(bool)
+
+    meta_vec = torch.from_numpy(
+        meta_encoder.transform(pd.DataFrame([row]))
+    ).float().to(device)
+
+    try:
+        model.zero_grad(set_to_none=True)
+        embeds = model.bert.embeddings(input_ids=input_ids)
+        embeds = embeds.detach().requires_grad_(True)
+        bert_out = model.bert(
+            inputs_embeds=embeds,
+            attention_mask=attention_mask,
+            return_dict=True,
+        )
+        text_vec = bert_out.last_hidden_state[:, 0, :]
+
+        # Mirror GatedAspectSemanticMetaFusionACSAModel.forward from embeddings.
+        meta_tokens = model.meta_tokenizer(meta_vec)
+        meta_for_concat = meta_vec.clone()
+        numeric_start = cfg.META_TFIDF_DIM
+        meta_for_concat[:, numeric_start:] = (
+            meta_for_concat[:, numeric_start:] * cfg.META_NUMERIC_TOKEN_SCALE
+        )
+        concat_meta = model.concat_meta_mlp(meta_for_concat)
+        concat_base = model.concat_norm(
+            model.concat_fusion(torch.cat([text_vec, concat_meta], dim=-1))
+        )
+        aspect_fused, _, _ = model.aspect_fusion(text_vec, meta_tokens)
+        text_expanded = text_vec.unsqueeze(1).expand(-1, model.num_aspects, -1)
+        aspect_delta = aspect_fused - text_expanded
+        scale = torch.clamp(model.cross_residual_scale, 0.0, 1.0)
+        aspect_repr = model.aspect_refine_norm(
+            concat_base.unsqueeze(1) + scale * aspect_delta
+        )
+        logits = model.heads.forward_per_aspect(aspect_repr)
+        target_class = int(logits[0, aspect_idx, :].argmax(dim=-1).item())
+        target_logit = logits[0, aspect_idx, target_class]
+        target_logit.backward()
+
+        grad = embeds.grad.detach()[0]
+        attr = (grad * embeds.detach()[0]).sum(dim=-1).detach().cpu().numpy()
+        attr = np.abs(attr) * mask
+        if not _has_visible_scores(attr):
+            attr = _lexical_evidence_scores(tokens, aspect_idx) * mask
+        return tokens, attr, mask, target_class
+    except Exception as e:
+        logger.warning("Gradient attribution failed for aspect %s: %s; using lexical evidence.",
+                       cfg.ASPECTS[aspect_idx], e)
+        with torch.no_grad():
+            out = model(input_ids, attention_mask, meta_vec)
+            target_class = int(out["logits"][0, aspect_idx, :].argmax(dim=-1).item())
+        attr = _lexical_evidence_scores(tokens, aspect_idx) * mask
+        return tokens, attr, mask, target_class
+
+def _attention_for_aspect(model, tokenizer, meta_encoder, row, device,
+                          aspect_idx: int):
+    """Fallback when captum is not installed: use the model's own
+    cross-attention output as a coarse word-level proxy is impossible
+    (cross-attn is over meta tokens, not BERT tokens), so we return BERT
+    self-attention from [CLS] -> tokens as a rough text attribution.
+    """
+    model.eval()
+    text = str(row["full_text"])
+    enc = tokenizer(text, max_length=cfg.MAX_LENGTH, truncation=True,
+                    padding="max_length", return_tensors="pt")
+    input_ids = enc["input_ids"].to(device)
+    attention_mask = enc["attention_mask"].to(device)
+    meta_vec = torch.from_numpy(
+        meta_encoder.transform(pd.DataFrame([row]))
+    ).float().to(device)
+
+    with torch.no_grad():
+        out = model(input_ids, attention_mask, meta_vec, output_attentions=True)
+
+    pred_class = int(out["logits"][0, aspect_idx, :].argmax().item())
+    tokens = tokenizer.convert_ids_to_tokens(input_ids[0].cpu().numpy().tolist())
+    mask = attention_mask[0].cpu().numpy().astype(bool)
+
+    bert_attn = out.get("bert_attentions")
+    if bert_attn:
+        last = bert_attn[-1][0]            # (heads, seq, seq)
+        cls_attn = last[:, 0, :].mean(0).cpu().numpy()
+    else:
+        cls_attn = np.zeros_like(mask, dtype=np.float32)
+    cls_attn = np.asarray(cls_attn, dtype=np.float32) * mask
+    if not _has_visible_scores(cls_attn):
+        cls_attn = _lexical_evidence_scores(tokens, aspect_idx) * mask
+    return tokens, cls_attn, mask, pred_class
+
+
+# ---------------------------------------------------------------------------
+# Cross-Attention weights over meta tokens (per aspect, per example)
+# ---------------------------------------------------------------------------
+
+def _meta_token_names_from_output(out, attn):
+    names = out.get("meta_token_names")
+    if names is not None:
+        return list(names)
+    arr = np.asarray(attn)
+    width = int(arr.shape[-1]) if arr.ndim else int(arr.size)
+    if width == 3:
+        return SEMANTIC_META_TOKEN_NAMES
+    return [f"meta_chunk_{i+1}" for i in range(width)]
+
+
+def get_meta_attention(model, tokenizer, meta_encoder, row, device):
+    """Return display meta attention, predictions, names, and optional per-aspect attention."""
+    text = str(row["full_text"])
+    enc = tokenizer(text, max_length=cfg.MAX_LENGTH, truncation=True,
+                    padding="max_length", return_tensors="pt")
+    input_ids = enc["input_ids"].to(device)
+    attention_mask = enc["attention_mask"].to(device)
+    meta_vec = torch.from_numpy(
+        meta_encoder.transform(pd.DataFrame([row]))
+    ).float().to(device)
+    with torch.no_grad():
+        out = model(input_ids, attention_mask, meta_vec)
+
+    raw_attn = out["meta_attn_weights"][0].cpu().numpy()
+    token_names = _meta_token_names_from_output(out, raw_attn)
+    if raw_attn.ndim == 1:
+        display_attn = raw_attn
+        aspect_attn = None
+    else:
+        aspect_attn = raw_attn
+        if "global_meta_attn_weights" in out and out["global_meta_attn_weights"] is not None:
+            display_attn = out["global_meta_attn_weights"][0].cpu().numpy()
+        else:
+            display_attn = raw_attn.mean(axis=0)
+
+    preds = out["logits"][0].argmax(dim=-1).cpu().numpy()
+    return display_attn, preds, token_names, aspect_attn
+
+# ---------------------------------------------------------------------------
+# HTML report builder (combines text IG + meta attention)
+# ---------------------------------------------------------------------------
+
+def _render_meta_bar_html(meta_attn: np.ndarray, token_names: Optional[List[str]] = None) -> str:
+    """Render a tiny inline bar chart for one metadata-attention vector."""
+    meta_attn = np.asarray(meta_attn, dtype=np.float64).reshape(-1)
+    token_names = token_names or META_TOKEN_NAMES
+    norm = _normalize(meta_attn)
+    parts = ['<div style="display:flex;gap:6px;align-items:end;height:60px;'
+             'margin-top:8px;">']
+    for i, (n, raw) in enumerate(zip(norm, meta_attn)):
+        name = token_names[i] if i < len(token_names) else f"meta_{i+1}"
+        h = int(8 + 50 * float(n))
+        parts.append(
+            f'<div style="text-align:center;width:80px;">'
+            f'<div style="background:#5b8def;height:{h}px;'
+            f'border-radius:3px 3px 0 0;"></div>'
+            f'<div style="font-size:11px;margin-top:2px;color:#666;">'
+            f'{html_lib.escape(name)}<br/>{float(raw):.3f}</div></div>'
+        )
+    parts.append('</div>')
+    return "".join(parts)
+
+
+def _render_meta_matrix_html(meta_attn_by_aspect, token_names: Optional[List[str]] = None) -> str:
+    """Render aspect-specific metadata attention as compact rows."""
+    if meta_attn_by_aspect is None:
+        return ""
+    arr = np.asarray(meta_attn_by_aspect, dtype=np.float64)
+    if arr.ndim != 2:
+        return ""
+    token_names = token_names or META_TOKEN_NAMES
+    parts = ['<div style="margin-top:8px;display:grid;gap:6px;">']
+    for i, aspect in enumerate(cfg.ASPECTS):
+        if i >= arr.shape[0]:
+            break
+        weights = arr[i]
+        norm = _normalize(weights)
+        parts.append('<div style="display:flex;align-items:center;gap:8px;">')
+        parts.append(f'<div style="width:95px;font-size:12px;font-weight:600;">{aspect}</div>')
+        for j, (n, raw) in enumerate(zip(norm, weights)):
+            name = token_names[j] if j < len(token_names) else f"meta_{j+1}"
+            w = int(35 + 75 * float(n))
+            parts.append(
+                f'<div title="{html_lib.escape(name)}: {float(raw):.3f}" '
+                f'style="background:#dbe7ff;border-left:4px solid #5b8def;'
+                f'width:{w}px;padding:2px 4px;border-radius:4px;font-size:11px;">'
+                f'{html_lib.escape(name)} {float(raw):.2f}</div>'
+            )
+        parts.append('</div>')
+    parts.append('</div>')
+    return "".join(parts)
+
+def _render_meta_summary_html(meta_summary: Dict[str, str]) -> str:
+    rows = []
+    for name in ["features", "categories", "numeric"]:
+        value = html_lib.escape(str(meta_summary.get(name, "")))
+        rows.append(
+            f'<div><b>{html_lib.escape(name)}:</b> '
+            f'<span style="color:#444">{value or "not available"}</span></div>'
+        )
+    return '<div class="meta-box">' + "".join(rows) + '</div>'
+
+
+def build_explanation_html(examples: List[dict], output_path: Path):
+    parts = [
+        "<!doctype html><html><head><meta charset='utf-8'>",
+        "<title>ACSA + Meta Fusion Explanations</title>",
+        "<style>",
+        "body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; max-width: 1180px; margin: 24px auto; padding: 0 16px; color: #222; }",
+        ".example { border: 1px solid #d8d8d8; padding: 16px; margin-bottom: 24px; border-radius: 8px; background:#fff; }",
+        ".meta { font-size: 13px; color: #666; margin-bottom: 8px; }",
+        ".meta-box { background:#f6f8fb; padding:10px 12px; border-radius:6px; font-size:13px; line-height:1.55; margin:10px 0 12px; }",
+        ".review-box { background:#fafafa; padding:10px 12px; border-radius:6px; margin-bottom:12px; line-height:1.55; }",
+        ".aspect-row { padding: 10px 0; border-bottom: 1px dashed #e6e6e6; line-height: 1.7; }",
+        ".aspect-label { display: inline-block; min-width: 130px; font-weight: 700; }",
+        ".pred-pos { color: #21834a; font-weight: 700; }",
+        ".pred-neg { color: #bd2f24; font-weight: 700; }",
+        ".pred-na  { color: #777; font-weight: 600; }",
+        ".evidence { margin-top:6px; font-size:13px; color:#444; }",
+        ".chip { display:inline-block; background:#eef2ff; border:1px solid #d6def8; padding:1px 6px; border-radius:999px; margin:1px 3px 1px 0; font-size:12px; }",
+        "h1 { margin-bottom: 8px; } h2 { margin-top: 8px; }",
+        ".legend { background: #f8f8f8; padding: 10px 12px; border-radius: 6px; font-size: 13px; line-height:1.5; }",
+        ".section-h { font-weight:700;margin-top:16px;color:#333;font-size:14px; }",
+        "</style></head><body>",
+        "<h1>Aspect-Level Sentiment with Metadata Fusion: Explanations</h1>",
+        '<div class="legend">'
+        'Highlights show the strongest text evidence for each aspect. '
+        '<span style="background:rgba(80,180,110,0.7);padding:2px 6px;border-radius:3px">green</span> = Positive, '
+        '<span style="background:rgba(255,80,80,0.7);padding:2px 6px;border-radius:3px">red</span> = Negative, '
+        '<span style="background:rgba(150,150,150,0.7);padding:2px 6px;border-radius:3px">grey</span> = Not_Mentioned. '
+        'Metadata rows show which source the model attends to for each aspect.'
+        '</div>',
+    ]
+    for i, ex in enumerate(examples):
+        parts.append(f'<div class="example"><h2>Example {i+1}</h2>')
+        parts.append(f'<div class="meta">Rating: {ex.get("rating", "?")} | Category: {html_lib.escape(str(ex.get("category", "?")))}</div>')
+        parts.append(f'<div class="review-box">{html_lib.escape(ex["text"])}</div>')
+        parts.append(_render_meta_summary_html(ex.get("meta_summary", {})))
+
+        parts.append('<div class="section-h">Per-aspect prediction, text evidence, and metadata source</div>')
+        token_names = ex.get("meta_token_names")
+        meta_by_aspect = ex.get("meta_attn_by_aspect")
+        for idx, a in enumerate(ex["aspects"]):
+            pred = a["pred_label"]
+            name = cfg.LABEL_NAMES[pred]
+            css = {0: "pred-na", 1: "pred-pos", 2: "pred-neg"}[pred]
+            parts.append('<div class="aspect-row">')
+            parts.append(f'<span class="aspect-label">{a["aspect"]}:</span> <span class="{css}">{name}</span>')
+
+            if "tokens" in a and "scores" in a:
+                terms = a.get("top_terms") or []
+                chips = "".join(f'<span class="chip">{html_lib.escape(t)}</span>' for t, _ in terms[:6])
+                if chips:
+                    parts.append(f'<div class="evidence"><b>Top text evidence:</b> {chips}</div>')
+                tok_html = _render_token_html(a["tokens"], a["scores"], pred)
+                parts.append(f'<div style="margin-top:6px">{tok_html}</div>')
+
+            if meta_by_aspect is not None and idx < len(meta_by_aspect):
+                source, weight = _top_meta_source(meta_by_aspect[idx], token_names)
+            else:
+                source, weight = _top_meta_source(ex.get("meta_attn", []), token_names)
+            quality = a.get("evidence_quality", "weak")
+            explanation = a.get("explanation_sentence", "")
+            if source:
+                parts.append(
+                    f'<div class="evidence"><b>Top metadata source:</b> '
+                    f'<span class="chip">{html_lib.escape(source)} {weight:.3f}</span> '
+                    f'<span class="chip">quality: {html_lib.escape(quality)}</span></div>'
+                )
+            if explanation:
+                parts.append(
+                    f'<div class="evidence"><b>Explanation:</b> '
+                    f'{html_lib.escape(explanation)}</div>'
+                )
+            parts.append('</div>')
+
+        parts.append('<div class="section-h">Metadata cross-attention (global / averaged)</div>')
+        parts.append(_render_meta_bar_html(np.asarray(ex["meta_attn"]), token_names))
+        if meta_by_aspect is not None:
+            parts.append('<div class="section-h">Aspect-specific metadata attention</div>')
+            parts.append(_render_meta_matrix_html(np.asarray(meta_by_aspect), token_names))
+        parts.append('</div>')
+
+    parts.append('</body></html>')
+    output_path = Path(output_path)
+    output_path.write_text("\n".join(parts), encoding="utf-8")
+    logger.info("Wrote explanation HTML to %s", output_path)
+
+
+def write_explanation_summary(examples: List[dict], output_path: Path):
+    rows = []
+    for ex_idx, ex in enumerate(examples, start=1):
+        token_names = ex.get("meta_token_names")
+        meta_by_aspect = ex.get("meta_attn_by_aspect")
+        for aspect_idx, aspect_data in enumerate(ex.get("aspects", [])):
+            if meta_by_aspect is not None and aspect_idx < len(meta_by_aspect):
+                source, weight = _top_meta_source(meta_by_aspect[aspect_idx], token_names)
+            else:
+                source, weight = _top_meta_source(ex.get("meta_attn", []), token_names)
+            pred_name = cfg.LABEL_NAMES[int(aspect_data.get("pred_label", 0))]
+            rows.append({
+                "example": ex_idx,
+                "rating": ex.get("rating"),
+                "category": ex.get("category"),
+                "aspect": aspect_data.get("aspect"),
+                "pred_label": pred_name,
+                "evidence_quality": aspect_data.get("evidence_quality", "weak"),
+                "explanation_sentence": aspect_data.get("explanation_sentence", ""),
+                "top_text_terms": "; ".join(t for t, _ in aspect_data.get("top_terms", [])[:8]),
+                "top_meta_source": source,
+                "top_meta_weight": weight,
+                "features": ex.get("meta_summary", {}).get("features", ""),
+                "categories": ex.get("meta_summary", {}).get("categories", ""),
+                "numeric": ex.get("meta_summary", {}).get("numeric", ""),
+                "text": ex.get("text", ""),
+            })
+    pd.DataFrame(rows).to_csv(output_path, index=False)
+    logger.info("Wrote explanation summary to %s", output_path)
+
+# ---------------------------------------------------------------------------
+# High-level: pick examples, run all attributions, save HTML
+# ---------------------------------------------------------------------------
+
+def explain_examples(test_df, n_examples: int = 8, method: str = "ig",
+                     output_path: Optional[Path] = None,
+                     checkpoint_dir: Optional[Path] = None,
+                     meta_encoder: Optional[MetaEncoder] = None):
+    """Pick a mix of ratings from test_df and produce an explanation HTML."""
+    if output_path is None:
+        output_path = cfg.REPORT_DIR / f"explanation_{method}.html"
+
+    model, tokenizer, meta_encoder, device = load_meta_acsa(checkpoint_dir, meta_encoder)
+
+    # Sample across ratings to get diversity
+    per_rating = max(1, n_examples // 5)
+    chosen = []
+    for r in [1, 2, 3, 4, 5]:
+        sub = test_df[test_df["rating"] == r]
+        if len(sub) > 0:
+            chosen.append(sub.sample(n=min(per_rating, len(sub)),
+                                     random_state=cfg.RANDOM_SEED))
+    if not chosen:
+        chosen = [test_df.sample(n=min(n_examples, len(test_df)),
+                                 random_state=cfg.RANDOM_SEED)]
+    selected = pd.concat(chosen).head(n_examples).reset_index(drop=True)
+
+    try:
+        import captum  # noqa: F401
+        captum_ok = True
+    except ImportError:
+        logger.warning("captum not installed; falling back to BERT [CLS]-attention as proxy.")
+        captum_ok = False
+        method = "attention"
+
+    examples_data = []
+    for _, row in selected.iterrows():
+        meta_attn, _, meta_token_names, meta_attn_by_aspect = get_meta_attention(model, tokenizer, meta_encoder, row, device)
+        ex = {
+            "text": str(row["full_text"]),
+            "rating": int(row["rating"]),
+            "category": str(row.get("leaf_category", "")),
+            "meta_summary": _meta_summary_from_row(row),
+            "meta_attn": meta_attn.tolist(),
+            "meta_token_names": meta_token_names,
+            "meta_attn_by_aspect": (meta_attn_by_aspect.tolist() if meta_attn_by_aspect is not None else None),
+            "aspects": [],
+        }
+        for i, aspect in enumerate(cfg.ASPECTS):
+            try:
+                if method == "ig" and captum_ok:
+                    tokens, attr, mask, pred = _ig_for_aspect(
+                        model, tokenizer, meta_encoder, row, device, i,
+                    )
+                else:
+                    tokens, attr, mask, pred = _attention_for_aspect(
+                        model, tokenizer, meta_encoder, row, device, i,
+                    )
+                scores = np.abs(attr) * mask
+                display_scores = _normalize_for_display(scores)
+                top_terms = _top_terms(tokens, display_scores)
+                if meta_attn_by_aspect is not None and i < len(meta_attn_by_aspect):
+                    source, weight = _top_meta_source(meta_attn_by_aspect[i], meta_token_names)
+                else:
+                    source, weight = _top_meta_source(meta_attn, meta_token_names)
+                pred_name = cfg.LABEL_NAMES[int(pred)]
+                quality = _explanation_quality(top_terms, source, weight)
+                ex["aspects"].append({
+                    "aspect": aspect, "pred_label": int(pred),
+                    "tokens": tokens, "scores": scores,
+                    "top_terms": top_terms,
+                    "evidence_quality": quality,
+                    "explanation_sentence": _make_explanation_sentence(
+                        aspect, pred_name, top_terms, source, weight,
+                    ),
+                })
+            except Exception as e:
+                logger.warning("Attribution failed for aspect %s: %s", aspect, e)
+                # Still record the prediction even without scores
+                ex["aspects"].append({"aspect": aspect, "pred_label": 0})
+        examples_data.append(ex)
+
+    build_explanation_html(examples_data, output_path)
+    summary_path = Path(output_path).with_name(Path(output_path).stem + "_summary.csv")
+    write_explanation_summary(examples_data, summary_path)
+    return examples_data
+
+
+# ---------------------------------------------------------------------------
+# Aggregation plots (application-layer)
+# ---------------------------------------------------------------------------
+
+def plot_aspect_distribution(df_with_preds: pd.DataFrame,
+                             output_path: Optional[Path] = None):
+    """Bar chart of Positive/Negative share per aspect (over mentioned rows)."""
+    if output_path is None:
+        output_path = cfg.REPORT_DIR / "aspect_distribution.png"
+    rows = []
+    for a in cfg.ASPECTS:
+        col = f"pred_{a}" if f"pred_{a}" in df_with_preds.columns else f"aspect_{a}"
+        if col not in df_with_preds.columns:
+            continue
+        vc = df_with_preds[col].value_counts()
+        n_pos = int(vc.get(1, 0)); n_neg = int(vc.get(2, 0))
+        n_mentioned = n_pos + n_neg
+        if n_mentioned == 0:
+            continue
+        rows.append({"aspect": a, "positive": n_pos / n_mentioned,
+                     "negative": n_neg / n_mentioned, "n_mentioned": n_mentioned})
+    if not rows:
+        logger.warning("No data to plot for aspect distribution.")
+        return None
+
+    dfp = pd.DataFrame(rows)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    x = np.arange(len(dfp)); w = 0.35
+    ax.bar(x - w/2, dfp["positive"], w, label="Positive share", color="#2a9d4f")
+    ax.bar(x + w/2, dfp["negative"], w, label="Negative share", color="#c0392b")
+    ax.set_xticks(x); ax.set_xticklabels(dfp["aspect"], rotation=20)
+    ax.set_ylabel("Share among mentioned reviews")
+    ax.set_title("Aspect-level sentiment distribution")
+    ax.legend()
+    for i, r in dfp.iterrows():
+        ax.text(i, max(r["positive"], r["negative"]) + 0.02,
+                f"n={r['n_mentioned']}", ha="center", fontsize=9)
+    plt.tight_layout(); plt.savefig(output_path, dpi=120); plt.close()
+    logger.info("Saved aspect distribution plot to %s", output_path)
+    return dfp
+
+
+def plot_category_aspect_heatmap(agg_df: pd.DataFrame, metric: str = "negative_share",
+                                 output_path: Optional[Path] = None):
+    if agg_df is None or agg_df.empty:
+        return None
+    if output_path is None:
+        output_path = cfg.REPORT_DIR / f"category_aspect_{metric}.png"
+    pivot = agg_df.pivot(index="category", columns="aspect", values=metric)
+    fig, ax = plt.subplots(figsize=(10, max(4, len(pivot) * 0.4)))
+    cmap = "RdYlGn_r" if "negative" in metric else "RdYlGn"
+    sns.heatmap(pivot, annot=True, fmt=".2f", cmap=cmap, ax=ax,
+                cbar_kws={"label": metric})
+    ax.set_title(f"{metric.replace('_', ' ').title()} by category 闂?aspect")
+    plt.tight_layout(); plt.savefig(output_path, dpi=120); plt.close()
+    logger.info("Saved heatmap to %s", output_path)
+    return pivot

src/inference.py

@@ -0,0 +1,264 @@
+"""User-facing inference for the Proposed model."""
+from typing import Dict, List, Optional
+
+import numpy as np
+import pandas as pd
+import torch
+
+from . import config as cfg
+from .evaluator import load_meta_acsa, format_aspect_summary
+
+
+_EXPECTED_META_KEYS = (
+    "features_text", "categories_text",
+    "price", "average_rating", "rating_number",
+)
+
+
+def _meta_dict_to_df(meta: Dict) -> pd.DataFrame:
+    """Build a one-row DataFrame the MetaEncoder can transform."""
+    row = {k: meta.get(k) for k in _EXPECTED_META_KEYS}
+    return pd.DataFrame([row])
+
+
+def _attention_names(out: Dict, attn_array) -> List[str]:
+    names = out.get("meta_token_names")
+    if names is not None:
+        return list(names)
+    arr = np.asarray(attn_array)
+    width = int(arr.shape[-1]) if arr.ndim else int(arr.size)
+    if width == 3:
+        return ["features", "categories", "numeric"]
+    return [f"meta_chunk_{i + 1}" for i in range(width)]
+
+
+def _attention_dict(names: List[str], weights) -> Dict[str, float]:
+    arr = np.asarray(weights, dtype=np.float64).reshape(-1)
+    return {name: float(w) for name, w in zip(names, arr)}
+
+def _top_attention_item(attn: Dict[str, float]) -> Dict[str, float]:
+    if not attn:
+        return {"source": "", "weight": 0.0}
+    source = max(attn, key=attn.get)
+    return {"source": source, "weight": float(attn[source])}
+
+
+def _meta_summary(meta: Dict) -> Dict[str, str]:
+    features = str(meta.get("features_text") or meta.get("features") or "")[:420]
+    categories = str(meta.get("categories_text") or meta.get("category") or "")[:220]
+    numeric = []
+    for key in ("price", "average_rating", "rating_number"):
+        val = meta.get(key)
+        if val is not None:
+            numeric.append(f"{key}={val}")
+    return {
+        "features": features,
+        "categories": categories,
+        "numeric": ", ".join(numeric) if numeric else "not available",
+    }
+
+_ASPECT_EVIDENCE_KEYWORDS = {
+    "SIZE": {"size", "fit", "fits", "fitting", "small", "large", "big", "tight", "loose", "xl", "medium", "waist", "length"},
+    "MATERIAL": {"material", "fabric", "cotton", "polyester", "soft", "scratchy", "thin", "thick", "stretch", "leather", "wool"},
+    "QUALITY": {"quality", "stitch", "stitching", "seam", "wash", "washed", "durable", "cheap", "broke", "tear", "torn"},
+    "APPEARANCE": {"look", "looks", "color", "colour", "photo", "picture", "beautiful", "cute", "print", "design"},
+    "STYLE": {"style", "stylish", "flattering", "casual", "formal", "dress", "shirt", "fashion", "compliments"},
+    "VALUE": {"price", "worth", "value", "money", "cheap", "expensive", "discount", "penny", "cost"},
+}
+
+_SENTIMENT_EVIDENCE_KEYWORDS = {
+    "good", "great", "love", "loved", "perfect", "nice", "excellent", "comfortable", "soft",
+    "bad", "poor", "cheap", "terrible", "awful", "small", "large", "tight", "loose", "thin",
+    "worth", "disappointed", "return", "returned", "recommend", "flattering", "beautiful",
+}
+_STOPWORDS = {
+    "a", "an", "the", "and", "or", "but", "if", "then", "than", "so", "as", "at", "by", "for", "from",
+    "in", "into", "of", "on", "to", "with", "without", "is", "are", "was", "were", "be", "been", "being",
+    "it", "its", "this", "that", "these", "those", "i", "me", "my", "we", "our", "you", "your", "he", "she", "they",
+    "them", "his", "her", "their", "very", "really", "just", "also", "too", "would", "could", "should", "can",
+    "will", "did", "do", "does", "have", "has", "had", "there", "here", "about", "after", "before",
+}
+
+
+def _clean_term(term: str) -> str:
+    return str(term).lower().strip(".,!?;:'\"()[]{}<>/\\|`~@#$%^&*_+=")
+
+
+def _is_informative_term(term: str) -> bool:
+    clean = _clean_term(term)
+    if len(clean) < 2 or clean in _STOPWORDS:
+        return False
+    return any(ch.isalpha() for ch in clean)
+
+
+def _text_evidence_by_aspect(review_text: str, top_k: int = 8) -> Dict[str, List[str]]:
+    raw_terms = [_clean_term(t) for t in str(review_text).split()]
+    result = {}
+    for aspect, aspect_terms in _ASPECT_EVIDENCE_KEYWORDS.items():
+        hits = []
+        for term in raw_terms:
+            if not _is_informative_term(term):
+                continue
+            if term in aspect_terms or term in _SENTIMENT_EVIDENCE_KEYWORDS:
+                if term not in hits:
+                    hits.append(term)
+            if len(hits) >= top_k:
+                break
+        result[aspect] = hits
+    return result
+
+def _format_confidence(out: Dict, row_idx: int = 0) -> Dict[str, Dict[str, float]]:
+    probs = torch.softmax(out["logits"][row_idx], dim=-1).detach().cpu().numpy()
+    result = {}
+    for i, aspect in enumerate(cfg.ASPECTS):
+        cls = int(np.argmax(probs[i]))
+        result[aspect] = {
+            "label": cfg.LABEL_NAMES[cls],
+            "confidence": float(probs[i, cls]),
+            "class_probs": {
+                cfg.LABEL_NAMES[j]: float(probs[i, j])
+                for j in range(len(cfg.LABEL_NAMES))
+            },
+        }
+    return result
+
+
+def _attention_insights(attn_payload: Dict) -> Dict:
+    insights = {}
+    if "meta_attention" in attn_payload:
+        insights["top_meta_source"] = _top_attention_item(attn_payload["meta_attention"])
+    if "meta_attention_by_aspect" in attn_payload:
+        insights["top_meta_source_by_aspect"] = {
+            aspect: _top_attention_item(weights)
+            for aspect, weights in attn_payload["meta_attention_by_aspect"].items()
+        }
+    return insights
+
+def _format_attention(out: Dict) -> Dict:
+    """Format legacy 1D attention or new aspect-specific 2D attention."""
+    if "meta_attn_weights" not in out or out["meta_attn_weights"] is None:
+        return {}
+
+    aspect_attn = out["meta_attn_weights"].detach().cpu().numpy()[0]
+    names = _attention_names(out, aspect_attn)
+
+    if aspect_attn.ndim == 1:
+        return {"meta_attention": _attention_dict(names, aspect_attn)}
+
+    result = {
+        "meta_attention_by_aspect": {
+            aspect: _attention_dict(names, aspect_attn[i])
+            for i, aspect in enumerate(cfg.ASPECTS)
+        }
+    }
+    if "global_meta_attn_weights" in out and out["global_meta_attn_weights"] is not None:
+        global_attn = out["global_meta_attn_weights"].detach().cpu().numpy()[0]
+        result["meta_attention"] = _attention_dict(names, global_attn)
+    else:
+        result["meta_attention"] = _attention_dict(names, aspect_attn.mean(axis=0))
+    return result
+
+
+def _slice_model_out(out: Dict, start: int, end: int) -> Dict:
+    sliced = {}
+    for k, v in out.items():
+        sliced[k] = v[start:end] if torch.is_tensor(v) else v
+    return sliced
+
+
+class AspectPredictor:
+    """Load the Proposed model and expose one-shot and batch predictions."""
+
+    def __init__(self, checkpoint_dir=None, device=None):
+        self.model, self.tokenizer, self.meta_encoder, self.device = load_meta_acsa(
+            checkpoint_dir=checkpoint_dir, device=device,
+        )
+
+    def predict(self, review_text: str, product_meta: Dict,
+                return_attention: bool = True) -> Dict:
+        enc = self.tokenizer(
+            review_text,
+            max_length=cfg.MAX_LENGTH,
+            truncation=True,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        input_ids = enc["input_ids"].to(self.device)
+        attn_mask = enc["attention_mask"].to(self.device)
+
+        meta_df = _meta_dict_to_df(product_meta)
+        meta_vec = torch.from_numpy(self.meta_encoder.transform(meta_df)).float().to(self.device)
+
+        with torch.no_grad():
+            out = self.model(input_ids, attn_mask, meta_vec)
+
+        preds = out["logits"][0].argmax(dim=-1).cpu().numpy()
+        result = {
+            "aspects": format_aspect_summary(preds),
+            "aspect_details": _format_confidence(out, 0),
+            "metadata_summary": _meta_summary(product_meta),
+        }
+        if "overall_logits" in out and out["overall_logits"] is not None:
+            overall_probs = torch.softmax(out["overall_logits"][0], dim=-1).detach().cpu().numpy()
+            overall_cls = int(np.argmax(overall_probs))
+            result["overall"] = {
+                "label": cfg.OVERALL_LABEL_NAMES[overall_cls],
+                "confidence": float(overall_probs[overall_cls]),
+                "class_probs": {
+                    cfg.OVERALL_LABEL_NAMES[j]: float(overall_probs[j])
+                    for j in range(len(cfg.OVERALL_LABEL_NAMES))
+                },
+            }
+        if return_attention:
+            attn_payload = _format_attention(out)
+            result.update(attn_payload)
+            result.update(_attention_insights(attn_payload))
+        return result
+
+    def predict_batch(self, reviews: List[Dict], batch_size: int = 16) -> List[Dict]:
+        """Each item is {'review_text': str, 'product_meta': {...}}."""
+        results = []
+        for i in range(0, len(reviews), batch_size):
+            chunk = reviews[i:i + batch_size]
+            enc = self.tokenizer(
+                [r["review_text"] for r in chunk],
+                max_length=cfg.MAX_LENGTH,
+                truncation=True,
+                padding="max_length",
+                return_tensors="pt",
+            )
+            input_ids = enc["input_ids"].to(self.device)
+            attn_mask = enc["attention_mask"].to(self.device)
+
+            meta_df = pd.concat(
+                [_meta_dict_to_df(r["product_meta"]) for r in chunk],
+                ignore_index=True,
+            )
+            meta_vec = torch.from_numpy(self.meta_encoder.transform(meta_df)).float().to(self.device)
+
+            with torch.no_grad():
+                out = self.model(input_ids, attn_mask, meta_vec)
+            preds = out["logits"].argmax(dim=-1).cpu().numpy()
+
+            for j in range(len(chunk)):
+                item = {
+                    "aspects": format_aspect_summary(preds[j]),
+                    "aspect_details": _format_confidence(out, j),
+                    "metadata_summary": _meta_summary(chunk[j]["product_meta"]),
+                }
+                if "overall_logits" in out and out["overall_logits"] is not None:
+                    overall_probs = torch.softmax(out["overall_logits"][j], dim=-1).detach().cpu().numpy()
+                    overall_cls = int(np.argmax(overall_probs))
+                    item["overall"] = {
+                        "label": cfg.OVERALL_LABEL_NAMES[overall_cls],
+                        "confidence": float(overall_probs[overall_cls]),
+                        "class_probs": {
+                            cfg.OVERALL_LABEL_NAMES[k]: float(overall_probs[k])
+                            for k in range(len(cfg.OVERALL_LABEL_NAMES))
+                        },
+                    }
+                attn_payload = _format_attention(_slice_model_out(out, j, j + 1))
+                item.update(attn_payload)
+                item.update(_attention_insights(attn_payload))
+                results.append(item)
+        return results

src/meta_encoder.py

@@ -0,0 +1,208 @@
+"""Metadata encoder: turn each row's product-side fields into a fixed-length vector.
+
+Output layout (concatenated):
+    [ TF-IDF on features_text (META_FEATURE_TFIDF_DIM)     # product attributes
+    | TF-IDF on categories_text (META_CATEGORY_TFIDF_DIM)   # product taxonomy
+    | numeric vector (META_NUM_DIM)                        # price + ratings + flags
+    ]
+Total raw dim = META_TFIDF_DIM + META_NUM_DIM.
+
+The MLP inside the model maps this to META_HIDDEN_DIM. We keep encoding as a
+separate, plain-numpy step so it can be fit once on train and reused without
+touching the model.
+"""
+import logging
+import pickle
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional, Sequence
+
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import StandardScaler
+
+from . import config as cfg
+
+
+logger = logging.getLogger(__name__)
+
+
+# Columns we expect on the input df (produced by preprocess.join_and_clean).
+# Missing columns are handled gracefully.
+FEATURES_TEXT_COL = "features_text"
+CATEGORIES_TEXT_COL = "categories_text"
+PRICE_COL = "price"
+AVG_RATING_COL = "average_rating"
+RATING_NUMBER_COL = "rating_number"
+
+
+def _safe_text(series: pd.Series) -> pd.Series:
+    return series.fillna("").astype(str)
+
+
+def _safe_numeric(series: pd.Series) -> pd.Series:
+    return pd.to_numeric(series, errors="coerce")
+
+
+@dataclass
+class MetaEncoder:
+    """Fit on train df, then transform any df (train/val/test/new) to a (N, D) matrix."""
+
+    feature_tfidf_dim: int = cfg.META_FEATURE_TFIDF_DIM
+    category_tfidf_dim: int = cfg.META_CATEGORY_TFIDF_DIM
+    num_dim: int = cfg.META_NUM_DIM  # price, avg_rating, log_rating_number, price_missing_flag
+
+    # Filled by fit()
+    feature_tfidf: Optional[TfidfVectorizer] = None
+    category_tfidf: Optional[TfidfVectorizer] = None
+    scaler: Optional[StandardScaler] = None
+    price_median_: Optional[float] = None
+    avg_rating_median_: Optional[float] = None
+    rating_number_median_: Optional[float] = None
+
+    @property
+    def tfidf_dim(self) -> int:
+        return self.feature_tfidf_dim + self.category_tfidf_dim
+
+    @property
+    def total_dim(self) -> int:
+        return self.tfidf_dim + self.num_dim
+
+    # ------------------------------------------------------------------ fit
+    def fit(self, df: pd.DataFrame) -> "MetaEncoder":
+        # 1. Fit text metadata as separate semantic sources. Keeping features
+        # and categories apart gives cross-attention tokens real meaning.
+        feat_text = _safe_text(df.get(FEATURES_TEXT_COL, pd.Series([""] * len(df))))
+        cat_text = _safe_text(df.get(CATEGORIES_TEXT_COL, pd.Series([""] * len(df))))
+
+        self.feature_tfidf = TfidfVectorizer(
+            max_features=self.feature_tfidf_dim,
+            ngram_range=(1, 2),
+            min_df=2,
+            max_df=0.95,
+            stop_words="english",
+            sublinear_tf=True,
+        )
+        # If the corpus is too small to have any vocab, TF-IDF will raise.
+        # Fall back to a single zero-dim by fitting on a synthetic vocab.
+        try:
+            self.feature_tfidf.fit(feat_text.str.strip())
+        except ValueError:
+            logger.warning("Meta TF-IDF found no usable vocabulary; using zero features.")
+            self.feature_tfidf = TfidfVectorizer(max_features=1)
+            self.feature_tfidf.fit(["placeholder"])
+
+        self.category_tfidf = TfidfVectorizer(
+            max_features=self.category_tfidf_dim,
+            ngram_range=(1, 2),
+            min_df=2,
+            max_df=0.98,
+            stop_words="english",
+            sublinear_tf=True,
+        )
+        try:
+            self.category_tfidf.fit(cat_text.str.strip())
+        except ValueError:
+            logger.warning("Category TF-IDF found no usable vocabulary; using zero features.")
+            self.category_tfidf = TfidfVectorizer(max_features=1)
+            self.category_tfidf.fit(["placeholder"])
+
+        # 2. Numeric features: price, avg_rating, log(rating_number+1), price_missing_flag
+        self.price_median_ = float(_safe_numeric(df.get(PRICE_COL, pd.Series([np.nan]))).median())
+        if not np.isfinite(self.price_median_):
+            self.price_median_ = 0.0
+        self.avg_rating_median_ = float(_safe_numeric(df.get(AVG_RATING_COL, pd.Series([np.nan]))).median())
+        if not np.isfinite(self.avg_rating_median_):
+            self.avg_rating_median_ = 4.0
+        self.rating_number_median_ = float(_safe_numeric(df.get(RATING_NUMBER_COL, pd.Series([np.nan]))).median())
+        if not np.isfinite(self.rating_number_median_):
+            self.rating_number_median_ = 0.0
+
+        num_mat = self._build_numeric(df)
+        self.scaler = StandardScaler()
+        self.scaler.fit(num_mat)
+        return self
+
+    # -------------------------------------------------------------- transform
+    def transform(self, df: pd.DataFrame) -> np.ndarray:
+        if self.feature_tfidf is None or self.scaler is None:
+            raise RuntimeError("MetaEncoder.fit() must be called before transform().")
+
+        structured = self.transform_structured(df)
+        return np.hstack([
+            structured["features"],
+            structured["categories"],
+            structured["numeric"],
+        ]).astype(np.float32)
+
+    def transform_structured(self, df: pd.DataFrame) -> dict:
+        """Return source-separated metadata matrices.
+
+        Keys map directly to semantic meta tokens used by the upgraded model:
+        features -> product attribute text, categories -> taxonomy text,
+        numeric -> price/rating signals.
+        """
+        if self.feature_tfidf is None or self.category_tfidf is None or self.scaler is None:
+            raise RuntimeError("MetaEncoder.fit() must be called before transform_structured().")
+
+        feat_text = _safe_text(df.get(FEATURES_TEXT_COL, pd.Series([""] * len(df)))).str.strip()
+        cat_text = _safe_text(df.get(CATEGORIES_TEXT_COL, pd.Series([""] * len(df)))).str.strip()
+        feat_mat = self.feature_tfidf.transform(feat_text).toarray().astype(np.float32)
+        cat_mat = self.category_tfidf.transform(cat_text).toarray().astype(np.float32)
+        feat_mat = self._pad_or_truncate(feat_mat, self.feature_tfidf_dim)
+        cat_mat = self._pad_or_truncate(cat_mat, self.category_tfidf_dim)
+        num_mat = self.scaler.transform(self._build_numeric(df)).astype(np.float32)
+        return {"features": feat_mat, "categories": cat_mat, "numeric": num_mat}
+
+    @staticmethod
+    def _pad_or_truncate(mat: np.ndarray, dim: int) -> np.ndarray:
+        if mat.shape[1] < dim:
+            pad = np.zeros((mat.shape[0], dim - mat.shape[1]), dtype=np.float32)
+            return np.hstack([mat, pad])
+        if mat.shape[1] > dim:
+            return mat[:, :dim]
+        return mat
+
+    # ------------------------------------------------------------------ helpers
+    def _build_numeric(self, df: pd.DataFrame) -> np.ndarray:
+        price = _safe_numeric(df.get(PRICE_COL, pd.Series([np.nan] * len(df))))
+        price_missing = price.isna().astype(np.float32).values
+        price = price.fillna(self.price_median_).astype(np.float32).values
+
+        avg = _safe_numeric(df.get(AVG_RATING_COL, pd.Series([np.nan] * len(df))))
+        avg = avg.fillna(self.avg_rating_median_).astype(np.float32).values
+
+        rnum = _safe_numeric(df.get(RATING_NUMBER_COL, pd.Series([np.nan] * len(df))))
+        rnum = rnum.fillna(self.rating_number_median_).astype(np.float32).values
+        log_rnum = np.log1p(np.maximum(rnum, 0.0))
+
+        return np.vstack([price, avg, log_rnum, price_missing]).T  # (N, 4)
+
+    # ------------------------------------------------------------------ io
+    def save(self, path: Path = None) -> Path:
+        if path is None:
+            path = cfg.META_ENCODER_PATH
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "wb") as f:
+            pickle.dump(self, f)
+        logger.info("Saved MetaEncoder to %s", path)
+        return path
+
+    @classmethod
+    def load(cls, path: Path = None) -> "MetaEncoder":
+        if path is None:
+            path = cfg.META_ENCODER_PATH
+        with open(path, "rb") as f:
+            obj = pickle.load(f)
+        if not isinstance(obj, cls):
+            raise TypeError(f"Loaded object is not a MetaEncoder: {type(obj)}")
+        return obj
+
+
+def fit_and_save(train_df: pd.DataFrame, path: Path = None) -> MetaEncoder:
+    enc = MetaEncoder().fit(train_df)
+    enc.save(path)
+    return enc
+

src/models.py

@@ -0,0 +1,613 @@
+"""Models for ACSA with metadata fusion.
+
+Three architectures:
+  - BertMetaFusionACSAModel:  BERT + Meta Cross-Attention + per-aspect heads  (Proposed)
+  - BertACSAModel:            BERT + per-aspect heads                         (Baseline 3, ablation w/o meta)
+  - BertOverallModel:         BERT + single 3-class head                      (Baseline 2)
+
+Design notes
+------------
+* `class_weights` is intentionally NOT a buffer. We tried that before and got a
+  `Unexpected key(s) in state_dict: 'class_weights'` on every reload because the
+  eval-time constructor doesn't know the training-time class_weights. Now we
+  keep it as a plain attribute that does not enter state_dict. The training
+  loop is responsible for re-instantiating the loss with the right weights.
+
+* The "metadata token" trick. The cross-attention layer wants a sequence of
+  K/V tokens, not a single vector. We split the encoded meta vector into
+  `META_NUM_META_TOKENS` virtual tokens of equal length so the attention head
+  has structure to operate over. Each chunk roughly corresponds to a slice of
+  TF-IDF + a slice of numeric features, which makes the attention weight
+  vector interpretable as "how much did the model rely on meta chunk i".
+
+* Forward returns:
+    {
+      "logits": (B, num_aspects, num_classes),
+      "loss":   scalar or None,
+      "meta_attn_weights": (B, num_aspects, num_meta_tokens) or None,
+      "bert_attentions":   tuple of BERT self-attn (only if output_attentions=True),
+    }
+"""
+import logging
+import math
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoModel
+
+from . import config as cfg
+
+
+logger = logging.getLogger(__name__)
+
+# Split the encoded meta vector into this many "tokens" before cross-attention.
+# Each token gets its own linear projection to BERT's hidden size.
+META_NUM_META_TOKENS = 4
+
+
+# ---------------------------------------------------------------------------
+# Shared helpers
+# ---------------------------------------------------------------------------
+
+class MetaEncoderMLP(nn.Module):
+    """Encode raw meta features (numpy-shaped) -> META_HIDDEN_DIM vector."""
+
+    def __init__(self, in_dim: int, hidden: int = cfg.META_HIDDEN_DIM,
+                 dropout: float = 0.2):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(in_dim, hidden * 2),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden * 2, hidden),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class MetaTokenizer(nn.Module):
+    """Split a (B, meta_hidden) vector into (B, num_tokens, bert_hidden) so it
+    can serve as K/V in a multi-head cross-attention.
+
+    We do this by chunking the meta vector and projecting each chunk
+    independently to BERT's hidden size. Each chunk represents a *piece* of
+    the metadata (a slice of the encoded TF-IDF + numeric features); the
+    cross-attention weight over chunks is what gets visualized for explanation.
+    """
+
+    def __init__(self, meta_hidden: int, bert_hidden: int,
+                 num_tokens: int = META_NUM_META_TOKENS):
+        super().__init__()
+        assert meta_hidden % num_tokens == 0, (
+            f"META_HIDDEN_DIM={meta_hidden} must be divisible by "
+            f"num_tokens={num_tokens}"
+        )
+        self.num_tokens = num_tokens
+        self.chunk_size = meta_hidden // num_tokens
+        self.proj = nn.ModuleList([
+            nn.Linear(self.chunk_size, bert_hidden) for _ in range(num_tokens)
+        ])
+
+    def forward(self, meta_vec):
+        # meta_vec: (B, meta_hidden) -> list of (B, chunk_size)
+        chunks = meta_vec.chunk(self.num_tokens, dim=-1)
+        projected = [proj(chunk) for proj, chunk in zip(self.proj, chunks)]
+        # stack -> (B, num_tokens, bert_hidden)
+        return torch.stack(projected, dim=1)
+
+
+class CrossAttentionFusion(nn.Module):
+    """Multi-head cross-attention: Q = text [CLS], K=V = meta tokens.
+
+    Returns:
+      fused:        (B, bert_hidden)   text vector enriched by meta
+      attn_weights: (B, num_meta_tokens)  averaged over heads, used for XAI
+    """
+
+    def __init__(self, hidden: int, num_heads: int = cfg.META_CROSSATTN_HEADS,
+                 dropout: float = 0.1):
+        super().__init__()
+        assert hidden % num_heads == 0
+        self.hidden = hidden
+        self.num_heads = num_heads
+        self.head_dim = hidden // num_heads
+
+        self.q_proj = nn.Linear(hidden, hidden)
+        self.k_proj = nn.Linear(hidden, hidden)
+        self.v_proj = nn.Linear(hidden, hidden)
+        self.out_proj = nn.Linear(hidden, hidden)
+        self.dropout = nn.Dropout(dropout)
+        self.norm = nn.LayerNorm(hidden)
+
+    def forward(self, text_vec, meta_tokens):
+        # text_vec:    (B, H)
+        # meta_tokens: (B, T, H)
+        B, T, H = meta_tokens.shape
+        q = self.q_proj(text_vec).view(B, self.num_heads, 1, self.head_dim)
+        k = self.k_proj(meta_tokens).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(meta_tokens).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
+        # k, v: (B, heads, T, head_dim)
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        # scores: (B, heads, 1, T)
+        attn = F.softmax(scores, dim=-1)
+        attn_drop = self.dropout(attn)
+        ctx = torch.matmul(attn_drop, v)  # (B, heads, 1, head_dim)
+        ctx = ctx.transpose(1, 2).contiguous().view(B, H)
+        ctx = self.out_proj(ctx)
+
+        fused = self.norm(text_vec + ctx)            # residual + LN
+        avg_attn = attn.mean(dim=1).squeeze(1)        # (B, T) 闂?heads averaged
+        return fused, avg_attn
+
+
+
+class SemanticMetaTokenizer(nn.Module):
+    """Project source-separated metadata into interpretable tokens."""
+
+    token_names = ["features", "categories", "numeric"]
+
+    def __init__(self, bert_hidden: int, dropout: float = 0.1):
+        super().__init__()
+        self.feature_proj = nn.Sequential(
+            nn.Linear(cfg.META_FEATURE_TFIDF_DIM, bert_hidden),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.LayerNorm(bert_hidden),
+        )
+        self.category_proj = nn.Sequential(
+            nn.Linear(cfg.META_CATEGORY_TFIDF_DIM, bert_hidden),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.LayerNorm(bert_hidden),
+        )
+        self.numeric_proj = nn.Sequential(
+            nn.Linear(cfg.META_NUM_DIM, bert_hidden),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.LayerNorm(bert_hidden),
+        )
+
+    def forward(self, meta_features):
+        f_end = cfg.META_FEATURE_TFIDF_DIM
+        c_end = f_end + cfg.META_CATEGORY_TFIDF_DIM
+        feat = meta_features[:, :f_end]
+        cat = meta_features[:, f_end:c_end]
+        num = meta_features[:, c_end:c_end + cfg.META_NUM_DIM]
+        return torch.stack([
+            self.feature_proj(feat),
+            self.category_proj(cat),
+            self.numeric_proj(num * cfg.META_NUMERIC_TOKEN_SCALE),
+        ], dim=1)
+
+
+class GatedAspectSemanticCrossAttention(nn.Module):
+    """Aspect-specific cross-attention with a conservative metadata gate."""
+
+    def __init__(self, hidden: int, num_aspects: int = cfg.NUM_ASPECTS,
+                 num_heads: int = cfg.META_CROSSATTN_HEADS, dropout: float = 0.1):
+        super().__init__()
+        assert hidden % num_heads == 0
+        self.hidden = hidden
+        self.num_aspects = num_aspects
+        self.num_heads = num_heads
+        self.head_dim = hidden // num_heads
+        self.aspect_embeddings = nn.Parameter(torch.randn(num_aspects, hidden) * 0.02)
+        self.q_proj = nn.Linear(hidden, hidden)
+        self.k_proj = nn.Linear(hidden, hidden)
+        self.v_proj = nn.Linear(hidden, hidden)
+        self.out_proj = nn.Linear(hidden, hidden)
+        self.gate = nn.Linear(hidden * 2, hidden)
+        nn.init.constant_(self.gate.bias, -1.0)
+        self.dropout = nn.Dropout(dropout)
+        self.norm = nn.LayerNorm(hidden)
+
+    def forward(self, text_vec, meta_tokens):
+        B, T, H = meta_tokens.shape
+        aspect_queries = text_vec.unsqueeze(1) + self.aspect_embeddings.unsqueeze(0)
+        q = self.q_proj(aspect_queries).view(B, self.num_aspects, self.num_heads, self.head_dim)
+        q = q.transpose(1, 2)
+        k = self.k_proj(meta_tokens).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(meta_tokens).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
+        attn = F.softmax(torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim), dim=-1)
+        ctx = torch.matmul(self.dropout(attn), v)
+        ctx = ctx.transpose(1, 2).contiguous().view(B, self.num_aspects, H)
+        ctx = self.out_proj(ctx)
+        text_expanded = text_vec.unsqueeze(1).expand(-1, self.num_aspects, -1)
+        gate = torch.sigmoid(self.gate(torch.cat([text_expanded, ctx], dim=-1)))
+        fused = self.norm(text_expanded + gate * ctx)
+        return fused, attn.mean(dim=1), gate.mean(dim=-1)
+
+
+class GatedGlobalSemanticCrossAttention(nn.Module):
+    """Global text-to-metadata cross-attention for the overall head."""
+
+    def __init__(self, hidden: int, num_heads: int = cfg.META_CROSSATTN_HEADS,
+                 dropout: float = 0.1):
+        super().__init__()
+        self.attn = CrossAttentionFusion(hidden, num_heads=num_heads, dropout=dropout)
+        self.gate = nn.Linear(hidden * 2, hidden)
+        nn.init.constant_(self.gate.bias, -1.0)
+        self.norm = nn.LayerNorm(hidden)
+
+    def forward(self, text_vec, meta_tokens):
+        fused_raw, attn = self.attn(text_vec, meta_tokens)
+        ctx = fused_raw - text_vec
+        gate = torch.sigmoid(self.gate(torch.cat([text_vec, ctx], dim=-1)))
+        fused = self.norm(text_vec + gate * ctx)
+        return fused, attn, gate.mean(dim=-1)
+
+class _PerAspectHeads(nn.Module):
+    """num_aspects independent MLP heads."""
+
+    def __init__(self, in_dim: int, num_aspects: int, num_classes: int,
+                 dropout: float = 0.3):
+        super().__init__()
+        self.heads = nn.ModuleList([
+            nn.Sequential(
+                nn.Dropout(dropout),
+                nn.Linear(in_dim, 256),
+                nn.GELU(),
+                nn.Linear(256, num_classes),
+            )
+            for _ in range(num_aspects)
+        ])
+
+    def forward(self, x):
+        # x: (B, in_dim) -> (B, num_aspects, num_classes)
+        return torch.stack([h(x) for h in self.heads], dim=1)
+
+    def forward_per_aspect(self, x):
+        # x: (B, num_aspects, in_dim) -> (B, num_aspects, num_classes)
+        return torch.stack([h(x[:, i, :]) for i, h in enumerate(self.heads)], dim=1)
+
+
+def _aspect_loss(logits, labels, class_weights=None):
+    """Sum of cross-entropy over aspects, averaged."""
+    num_aspects = logits.shape[1]
+    loss = 0.0
+    for i in range(num_aspects):
+        w = class_weights[i] if class_weights is not None else None
+        loss = loss + F.cross_entropy(logits[:, i, :], labels[:, i], weight=w)
+    return loss / num_aspects
+
+
+# ---------------------------------------------------------------------------
+# Models
+# ---------------------------------------------------------------------------
+
+class BertMetaFusionACSAModel(nn.Module):
+    """Proposed model: BERT + Meta Cross-Attention + per-aspect heads."""
+
+    def __init__(
+        self,
+        bert_name: str = cfg.BERT_MODEL_NAME,
+        meta_in_dim: int = cfg.META_TFIDF_DIM + cfg.META_NUM_DIM,
+        num_aspects: int = cfg.NUM_ASPECTS,
+        num_classes: int = cfg.NUM_CLASSES,
+        dropout: float = 0.3,
+        class_weights: Optional[torch.Tensor] = None,
+    ):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained(bert_name)
+        hidden = self.bert.config.hidden_size
+
+        self.num_aspects = num_aspects
+        self.num_classes = num_classes
+        self.aspect_names = list(cfg.ASPECTS)
+        self.meta_in_dim = meta_in_dim
+
+        self.meta_mlp = MetaEncoderMLP(meta_in_dim, hidden=cfg.META_HIDDEN_DIM,
+                                       dropout=dropout)
+        self.meta_tokenizer = MetaTokenizer(
+            meta_hidden=cfg.META_HIDDEN_DIM, bert_hidden=hidden,
+            num_tokens=META_NUM_META_TOKENS,
+        )
+        self.fusion = CrossAttentionFusion(hidden=hidden,
+                                           num_heads=cfg.META_CROSSATTN_HEADS,
+                                           dropout=0.1)
+        self.heads = _PerAspectHeads(in_dim=hidden, num_aspects=num_aspects,
+                                     num_classes=num_classes, dropout=dropout)
+
+        # Overall sentiment auxiliary head (3-class: Neg/Neu/Pos).
+        # Shares the fused representation with per-aspect heads; trained jointly
+        # with weight cfg.OVERALL_AUX_WEIGHT when overall_labels are provided.
+        self.overall_head = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(hidden, 256),
+            nn.GELU(),
+            nn.Linear(256, cfg.OVERALL_NUM_CLASSES),
+        )
+
+        # Stored as plain attribute (NOT a buffer). Re-supplied at train time.
+        self.class_weights = class_weights
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        meta_features,
+        labels: Optional[torch.Tensor] = None,
+        overall_labels: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ):
+        bert_out = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+        text_vec = bert_out.last_hidden_state[:, 0, :]   # [CLS]
+
+        meta_vec = self.meta_mlp(meta_features)          # (B, meta_hidden)
+        meta_tokens = self.meta_tokenizer(meta_vec)      # (B, T, H)
+        fused, meta_attn = self.fusion(text_vec, meta_tokens)
+
+        logits = self.heads(fused)
+        overall_logits = self.overall_head(fused)
+
+        # Joint loss: L_aspect + lambda * L_overall
+        loss = None
+        if labels is not None:
+            loss = _aspect_loss(logits, labels, self.class_weights)
+            if overall_labels is not None:
+                loss_overall = F.cross_entropy(overall_logits, overall_labels)
+                loss = loss + cfg.OVERALL_AUX_WEIGHT * loss_overall
+
+        return {
+            "loss": loss,
+            "logits": logits,
+            "overall_logits": overall_logits,
+            "meta_attn_weights": meta_attn,        # (B, T) -- for XAI
+            "bert_attentions": bert_out.attentions if output_attentions else None,
+            "fused_state": fused,
+            "text_state": text_vec,
+        }
+
+
+
+class GatedAspectSemanticMetaFusionACSAModel(nn.Module):
+    """Proposed v2: semantic meta tokens + aspect queries + gated fusion.
+
+    The aspect heads receive aspect-specific fused states, while the overall
+    head keeps a separate global fused state so aspect-level noise does not
+    dilute document-level sentiment.
+    """
+
+    meta_token_names = SemanticMetaTokenizer.token_names
+
+    def __init__(
+        self,
+        bert_name: str = cfg.BERT_MODEL_NAME,
+        meta_in_dim: int = cfg.META_TFIDF_DIM + cfg.META_NUM_DIM,
+        num_aspects: int = cfg.NUM_ASPECTS,
+        num_classes: int = cfg.NUM_CLASSES,
+        dropout: float = 0.3,
+        class_weights: Optional[torch.Tensor] = None,
+    ):
+        super().__init__()
+        expected_dim = cfg.META_TFIDF_DIM + cfg.META_NUM_DIM
+        if meta_in_dim != expected_dim:
+            raise ValueError(f"Semantic meta fusion expects meta_in_dim={expected_dim}, got {meta_in_dim}")
+        self.bert = AutoModel.from_pretrained(bert_name)
+        hidden = self.bert.config.hidden_size
+
+        self.num_aspects = num_aspects
+        self.num_classes = num_classes
+        self.aspect_names = list(cfg.ASPECTS)
+        self.meta_in_dim = meta_in_dim
+
+        self.meta_tokenizer = SemanticMetaTokenizer(bert_hidden=hidden, dropout=0.1)
+        self.concat_meta_mlp = MetaEncoderMLP(meta_in_dim, hidden=cfg.META_HIDDEN_DIM,
+                                             dropout=dropout)
+        self.concat_fusion = nn.Sequential(
+            nn.Linear(hidden + cfg.META_HIDDEN_DIM, hidden),
+            nn.GELU(),
+            nn.Dropout(0.1),
+        )
+        self.concat_norm = nn.LayerNorm(hidden)
+        self.aspect_refine_norm = nn.LayerNorm(hidden)
+        self.global_refine_norm = nn.LayerNorm(hidden)
+        self.cross_residual_scale = nn.Parameter(
+            torch.tensor(float(cfg.CROSS_ATTN_RESIDUAL_SCALE))
+        )
+        self.aspect_mix_gate = nn.Linear(hidden * 2, 1)
+        self.global_mix_gate = nn.Linear(hidden * 2, 1)
+        nn.init.zeros_(self.aspect_mix_gate.weight)
+        nn.init.zeros_(self.global_mix_gate.weight)
+        nn.init.zeros_(self.aspect_mix_gate.bias)
+        nn.init.zeros_(self.global_mix_gate.bias)
+        self.aspect_fusion = GatedAspectSemanticCrossAttention(
+            hidden=hidden,
+            num_aspects=num_aspects,
+            num_heads=cfg.META_CROSSATTN_HEADS,
+            dropout=0.1,
+        )
+        self.global_fusion = GatedGlobalSemanticCrossAttention(
+            hidden=hidden,
+            num_heads=cfg.META_CROSSATTN_HEADS,
+            dropout=0.1,
+        )
+        self.heads = _PerAspectHeads(in_dim=hidden, num_aspects=num_aspects,
+                                     num_classes=num_classes, dropout=dropout)
+        self.overall_head = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(hidden, 256),
+            nn.GELU(),
+            nn.Linear(256, cfg.OVERALL_NUM_CLASSES),
+        )
+        self.class_weights = class_weights
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        meta_features,
+        labels: Optional[torch.Tensor] = None,
+        overall_labels: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ):
+        bert_out = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+        text_vec = bert_out.last_hidden_state[:, 0, :]
+        meta_tokens = self.meta_tokenizer(meta_features)
+
+        meta_for_concat = meta_features.clone()
+        numeric_start = cfg.META_TFIDF_DIM
+        meta_for_concat[:, numeric_start:] = (
+            meta_for_concat[:, numeric_start:] * cfg.META_NUMERIC_TOKEN_SCALE
+        )
+        concat_meta = self.concat_meta_mlp(meta_for_concat)
+        concat_base = self.concat_norm(
+            self.concat_fusion(torch.cat([text_vec, concat_meta], dim=-1))
+        )
+
+        aspect_fused, aspect_attn, aspect_gate = self.aspect_fusion(text_vec, meta_tokens)
+        global_fused, global_attn, global_gate = self.global_fusion(text_vec, meta_tokens)
+
+        concat_expanded = concat_base.unsqueeze(1).expand(-1, self.num_aspects, -1)
+        scale = torch.clamp(self.cross_residual_scale, 0.0, 1.0)
+        aspect_mix_raw = torch.sigmoid(
+            self.aspect_mix_gate(torch.cat([concat_expanded, aspect_fused], dim=-1))
+        )
+        aspect_mix = torch.clamp(aspect_mix_raw + (scale - 0.5), 0.0, 1.0)
+        aspect_repr = self.aspect_refine_norm(
+            (1.0 - aspect_mix) * concat_expanded + aspect_mix * aspect_fused
+        )
+
+        global_mix_raw = torch.sigmoid(
+            self.global_mix_gate(torch.cat([concat_base, global_fused], dim=-1))
+        )
+        global_mix = torch.clamp(global_mix_raw + (scale - 0.5), 0.0, 1.0)
+        global_repr = self.global_refine_norm(
+            (1.0 - global_mix) * concat_base + global_mix * global_fused
+        )
+
+        logits = self.heads.forward_per_aspect(aspect_repr)
+        overall_logits = self.overall_head(global_repr)
+
+        loss = None
+        if labels is not None:
+            loss = _aspect_loss(logits, labels, self.class_weights)
+            if overall_labels is not None:
+                loss = loss + cfg.OVERALL_AUX_WEIGHT * F.cross_entropy(
+                    overall_logits, overall_labels
+                )
+
+        return {
+            "loss": loss,
+            "logits": logits,
+            "overall_logits": overall_logits,
+            "meta_attn_weights": aspect_attn,
+            "global_meta_attn_weights": global_attn,
+            "meta_gate": aspect_gate,
+            "global_meta_gate": global_gate,
+            "fusion_mix_gate": aspect_mix.squeeze(-1),
+            "global_fusion_mix_gate": global_mix.squeeze(-1),
+            "meta_token_names": self.meta_token_names,
+            "bert_attentions": bert_out.attentions if output_attentions else None,
+            "fused_state": aspect_repr,
+            "global_fused_state": global_repr,
+            "text_state": text_vec,
+        }
+
+class BertACSAModel(nn.Module):
+    """Baseline 3: same as Proposed but with NO metadata path.
+
+    Same per-aspect head structure as Proposed so the comparison isolates
+    the value of the Cross-Attention meta fusion.
+    """
+
+    def __init__(
+        self,
+        bert_name: str = cfg.BERT_MODEL_NAME,
+        num_aspects: int = cfg.NUM_ASPECTS,
+        num_classes: int = cfg.NUM_CLASSES,
+        dropout: float = 0.3,
+        class_weights: Optional[torch.Tensor] = None,
+    ):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained(bert_name)
+        hidden = self.bert.config.hidden_size
+        self.num_aspects = num_aspects
+        self.num_classes = num_classes
+        self.aspect_names = list(cfg.ASPECTS)
+
+        self.heads = _PerAspectHeads(in_dim=hidden, num_aspects=num_aspects,
+                                     num_classes=num_classes, dropout=dropout)
+        self.class_weights = class_weights
+
+    def forward(self, input_ids, attention_mask,
+                labels: Optional[torch.Tensor] = None,
+                output_attentions: bool = False):
+        bert_out = self.bert(
+            input_ids=input_ids, attention_mask=attention_mask,
+            output_attentions=output_attentions, return_dict=True,
+        )
+        text_vec = bert_out.last_hidden_state[:, 0, :]
+        logits = self.heads(text_vec)
+        loss = _aspect_loss(logits, labels, self.class_weights) if labels is not None else None
+        return {
+            "loss": loss,
+            "logits": logits,
+            "bert_attentions": bert_out.attentions if output_attentions else None,
+            "text_state": text_vec,
+        }
+
+
+class BertOverallModel(nn.Module):
+    """Baseline 2: BERT fine-tuned for overall 3-class sentiment (Neg/Neu/Pos)."""
+
+    def __init__(self, bert_name: str = cfg.BERT_MODEL_NAME,
+                 num_classes: int = cfg.OVERALL_NUM_CLASSES,
+                 dropout: float = 0.3):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained(bert_name)
+        hidden = self.bert.config.hidden_size
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(hidden, 256),
+            nn.GELU(),
+            nn.Linear(256, num_classes),
+        )
+        self.num_classes = num_classes
+
+    def forward(self, input_ids, attention_mask, labels=None, output_attentions=False):
+        out = self.bert(input_ids=input_ids, attention_mask=attention_mask,
+                        output_attentions=output_attentions, return_dict=True)
+        logits = self.classifier(out.last_hidden_state[:, 0, :])
+        loss = F.cross_entropy(logits, labels) if labels is not None else None
+        return {
+            "loss": loss,
+            "logits": logits,
+            "bert_attentions": out.attentions if output_attentions else None,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Class-weight helper for the aspect heads
+# ---------------------------------------------------------------------------
+
+def compute_class_weights(df, aspect_cols: List[str],
+                          num_classes: int = cfg.NUM_CLASSES) -> torch.Tensor:
+    """Per-aspect inverse-frequency class weights, clipped to [0.2, 5.0]."""
+    import numpy as np
+    out = []
+    for col in aspect_cols:
+        counts = df[col].value_counts().reindex(range(num_classes), fill_value=0).values
+        counts = counts.astype(float) + 1.0
+        inv = counts.sum() / (num_classes * counts)
+        out.append(np.clip(inv, 0.2, 5.0))
+    return torch.from_numpy(np.array(out, dtype=np.float32))
+
+
+
+
+
+

src/preprocess.py

@@ -0,0 +1,137 @@
+"""Preprocess raw review + meta data: clean, join, stratified split."""
+import html
+import logging
+import re
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+from . import config as cfg
+
+
+logger = logging.getLogger(__name__)
+
+
+_URL_RE = re.compile(r"https?://\S+|www\.\S+")
+_HTML_RE = re.compile(r"<[^>]+>")
+_WHITESPACE_RE = re.compile(r"\s+")
+
+
+def clean_text(text: str) -> str:
+    """Light cleaning suitable for BERT input.
+
+    NOTE: do NOT remove stopwords or lemmatize - BERT needs the original context.
+    """
+    if not isinstance(text, str):
+        return ""
+    text = html.unescape(text)
+    text = _URL_RE.sub(" ", text)
+    text = _HTML_RE.sub(" ", text)
+    text = _WHITESPACE_RE.sub(" ", text).strip()
+    return text
+
+
+def _features_to_text(features) -> str:
+    """Flatten meta `features` field (which can be list or string) to one blob."""
+    if features is None:
+        return ""
+    if isinstance(features, (list, tuple, np.ndarray)):
+        return " ".join(str(x) for x in features if x)
+    return str(features)
+
+
+def _categories_to_text(categories) -> str:
+    if categories is None:
+        return ""
+    if isinstance(categories, (list, tuple, np.ndarray)):
+        return " > ".join(str(x) for x in categories if x)
+    return str(categories)
+
+
+def join_and_clean(reviews_df: pd.DataFrame, meta_df: pd.DataFrame) -> pd.DataFrame:
+    """Inner join review + meta on parent_asin, clean text, build derived columns."""
+    logger.info("Pre-join: %d reviews, %d meta items.", len(reviews_df), len(meta_df))
+
+    # Subset meta to columns we'll use
+    meta_cols = ["parent_asin", "title", "features", "categories", "main_category", "price", "average_rating", "rating_number"]
+    meta_cols = [c for c in meta_cols if c in meta_df.columns]
+    meta_sub = meta_df[meta_cols].copy()
+    meta_sub = meta_sub.rename(columns={"title": "product_title"})
+
+    # Build flattened meta text fields
+    if "features" in meta_sub.columns:
+        meta_sub["features_text"] = meta_sub["features"].apply(_features_to_text)
+    else:
+        meta_sub["features_text"] = ""
+    if "categories" in meta_sub.columns:
+        meta_sub["categories_text"] = meta_sub["categories"].apply(_categories_to_text)
+        meta_sub["leaf_category"] = meta_sub["categories"].apply(
+            lambda c: (c[-1] if isinstance(c, (list, tuple, np.ndarray)) and len(c) > 0 else "")
+        )
+    else:
+        meta_sub["categories_text"] = ""
+        meta_sub["leaf_category"] = ""
+
+    # Inner join
+    merged = reviews_df.merge(meta_sub, on="parent_asin", how="inner", suffixes=("", "_meta"))
+    logger.info("After inner join: %d rows", len(merged))
+
+    # Clean review text
+    if "title" in merged.columns:
+        merged["title"] = merged["title"].fillna("").astype(str).apply(clean_text)
+    else:
+        merged["title"] = ""
+    merged["text"] = merged["text"].fillna("").astype(str).apply(clean_text)
+
+    # Combine title + body for model input
+    merged["full_text"] = (merged["title"] + " " + merged["text"]).str.strip()
+
+    # Filter junk
+    merged = merged[merged["full_text"].str.len() >= cfg.MIN_REVIEW_LENGTH].copy()
+
+    # Ensure rating is numeric
+    merged["rating"] = pd.to_numeric(merged["rating"], errors="coerce")
+    merged = merged.dropna(subset=["rating"]).copy()
+    merged["rating"] = merged["rating"].astype(int)
+
+    # Overall sentiment label from rating (3-class, used for baseline comparison)
+    # 1-2 = Negative (2), 3 = Neutral (1), 4-5 = Positive (0)
+    # We'll align with: 0=Negative, 1=Neutral, 2=Positive for baseline
+    def rating_to_overall(r):
+        if r <= 2: return 0  # Negative
+        if r == 3: return 1  # Neutral
+        return 2  # Positive
+    merged["overall_label"] = merged["rating"].apply(rating_to_overall)
+
+    logger.info("Final rows: %d", len(merged))
+    logger.info("Rating distribution:\n%s", merged["rating"].value_counts().sort_index().to_string())
+
+    return merged.reset_index(drop=True)
+
+
+def stratified_split(df: pd.DataFrame, label_col: str = "rating"):
+    """Stratified split by rating to keep class balance across train/val/test."""
+    train_val, test = train_test_split(
+        df, test_size=cfg.TEST_RATIO, stratify=df[label_col], random_state=cfg.RANDOM_SEED
+    )
+    val_size_relative = cfg.VAL_RATIO / (cfg.TRAIN_RATIO + cfg.VAL_RATIO)
+    train, val = train_test_split(
+        train_val, test_size=val_size_relative,
+        stratify=train_val[label_col], random_state=cfg.RANDOM_SEED
+    )
+    logger.info("Split sizes: train=%d val=%d test=%d", len(train), len(val), len(test))
+    return train.reset_index(drop=True), val.reset_index(drop=True), test.reset_index(drop=True)
+
+
+def preprocess_pipeline():
+    """Run the full preprocessing pipeline from raw files."""
+    logger.info("Loading raw data...")
+    reviews_df = pd.read_parquet(cfg.RAW_REVIEWS_PATH)
+    meta_df = pd.read_parquet(cfg.RAW_META_PATH)
+
+    processed = join_and_clean(reviews_df, meta_df)
+    processed.to_parquet(cfg.PROCESSED_PATH, index=False)
+    logger.info("Saved processed data to %s", cfg.PROCESSED_PATH)
+    return processed

src/trainer.py

@@ -0,0 +1,365 @@
+"""Training loops for ACSA and baseline models."""
+import json
+import logging
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from transformers import AutoTokenizer, get_linear_schedule_with_warmup
+from tqdm import tqdm
+
+from . import config as cfg
+from .models import (
+    GatedAspectSemanticMetaFusionACSAModel, BertMetaFusionACSAModel, BertACSAModel, BertOverallModel,
+    compute_class_weights,
+)
+from .dataset import ACSADataset, MetaACSADataset, OverallSentimentDataset
+from .meta_encoder import MetaEncoder
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_device():
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        return torch.device("mps")
+    return torch.device("cpu")
+
+
+# ---------------------------------------------------------------------------
+# Generic per-aspect evaluation (used by both Proposed and Baseline 3)
+# ---------------------------------------------------------------------------
+
+def _evaluate_per_aspect(model, loader, device, with_meta: bool):
+    from sklearn.metrics import f1_score, accuracy_score
+    model.eval()
+    all_preds = [[] for _ in range(cfg.NUM_ASPECTS)]
+    all_labels = [[] for _ in range(cfg.NUM_ASPECTS)]
+    with torch.no_grad():
+        for batch in loader:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            if with_meta:
+                out = model(batch["input_ids"], batch["attention_mask"],
+                            batch["meta_features"])
+            else:
+                out = model(batch["input_ids"], batch["attention_mask"])
+            preds = out["logits"].argmax(dim=-1).cpu().numpy()
+            labels = batch["labels"].cpu().numpy()
+            for i in range(cfg.NUM_ASPECTS):
+                all_preds[i].extend(preds[:, i].tolist())
+                all_labels[i].extend(labels[:, i].tolist())
+
+    metrics = {}
+    f1s, accs = [], []
+    for i, aspect in enumerate(cfg.ASPECTS):
+        f1 = f1_score(all_labels[i], all_preds[i], average="macro", zero_division=0)
+        acc = accuracy_score(all_labels[i], all_preds[i])
+        metrics[f"f1_{aspect}"] = float(f1)
+        metrics[f"acc_{aspect}"] = float(acc)
+        f1s.append(f1); accs.append(acc)
+    metrics["macro_f1_mean"] = float(np.mean(f1s)) if f1s else 0.0
+    metrics["accuracy_mean"] = float(np.mean(accs)) if accs else 0.0
+    return metrics
+
+
+def _evaluate_overall_head(model, loader, device):
+    """Evaluate the overall sentiment auxiliary head on a dataloader.
+
+    Returns metrics prefixed with 'overall_' so they don't collide with
+    per-aspect metric keys.
+    """
+    from sklearn.metrics import f1_score, accuracy_score
+    model.eval()
+    all_preds, all_labels = [], []
+    with torch.no_grad():
+        for batch in loader:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            if "overall_labels" not in batch:
+                return {}
+            out = model(batch["input_ids"], batch["attention_mask"],
+                        batch["meta_features"])
+            preds = out["overall_logits"].argmax(dim=-1).cpu().numpy()
+            all_preds.extend(preds.tolist())
+            all_labels.extend(batch["overall_labels"].cpu().numpy().tolist())
+    if not all_labels:
+        return {}
+    return {
+        "overall_macro_f1": float(f1_score(all_labels, all_preds, average="macro", zero_division=0)),
+        "overall_accuracy": float(accuracy_score(all_labels, all_preds)),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Train: Proposed (BERT + Meta Cross-Attention + multi-head)
+# ---------------------------------------------------------------------------
+
+def train_meta_acsa(
+    train_df, val_df, meta_encoder: MetaEncoder,
+    bert_name: str = cfg.BERT_MODEL_NAME,
+    epochs: int = cfg.DEFAULT_EPOCHS,
+    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
+    lr_bert: float = cfg.DEFAULT_LR_BERT,
+    lr_heads: float = cfg.DEFAULT_LR_HEADS,
+    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
+    use_class_weights: bool = True,
+    output_dir: Optional[Path] = None,
+    seed: int = cfg.RANDOM_SEED,
+):
+    if output_dir is None:
+        output_dir = cfg.CHECKPOINT_DIR / "meta_acsa"
+    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)
+
+    torch.manual_seed(seed); np.random.seed(seed)
+    device = get_device()
+    logger.info("Device: %s", device)
+
+    tokenizer = AutoTokenizer.from_pretrained(bert_name)
+
+    aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
+    class_weights = (compute_class_weights(train_df, aspect_cols, cfg.NUM_CLASSES).to(device)
+                     if use_class_weights else None)
+    if class_weights is not None:
+        logger.info("Per-aspect class weights:\n%s", class_weights.cpu().numpy())
+
+    model = GatedAspectSemanticMetaFusionACSAModel(
+        bert_name=bert_name,
+        meta_in_dim=meta_encoder.total_dim,
+        class_weights=class_weights,
+    ).to(device)
+
+    train_ds = MetaACSADataset(train_df, tokenizer, meta_encoder)
+    val_ds = MetaACSADataset(val_df, tokenizer, meta_encoder)
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)
+
+    bert_params = list(model.bert.named_parameters())
+    other_params = [(n, p) for n, p in model.named_parameters()
+                    if not n.startswith("bert.")]
+    no_decay = ["bias", "LayerNorm.weight"]
+    grouped = [
+        {"params": [p for n, p in bert_params if not any(nd in n for nd in no_decay)],
+         "weight_decay": weight_decay, "lr": lr_bert},
+        {"params": [p for n, p in bert_params if any(nd in n for nd in no_decay)],
+         "weight_decay": 0.0, "lr": lr_bert},
+        {"params": [p for n, p in other_params if not any(nd in n for nd in no_decay)],
+         "weight_decay": weight_decay, "lr": lr_heads},
+        {"params": [p for n, p in other_params if any(nd in n for nd in no_decay)],
+         "weight_decay": 0.0, "lr": lr_heads},
+    ]
+    optimizer = torch.optim.AdamW(grouped)
+    total_steps = max(len(train_loader) * epochs, 1)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=int(cfg.WARMUP_RATIO * total_steps),
+        num_training_steps=total_steps,
+    )
+
+    best_f1 = -1.0; history = []
+    for epoch in range(epochs):
+        model.train(); running = 0.0
+        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] meta_acsa")
+        for batch in pbar:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            optimizer.zero_grad()
+            out = model(batch["input_ids"], batch["attention_mask"],
+                        batch["meta_features"], labels=batch["labels"],
+                        overall_labels=batch.get("overall_labels"))
+            out["loss"].backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step(); scheduler.step()
+            running += out["loss"].item()
+            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})
+
+        avg_loss = running / max(len(train_loader), 1)
+        val = _evaluate_per_aspect(model, val_loader, device, with_meta=True)
+
+        # Also evaluate overall head on val set
+        val_overall = _evaluate_overall_head(model, val_loader, device)
+        val.update(val_overall)
+
+        logger.info("Epoch %d | loss=%.4f | val_macro_f1=%.4f | val_acc=%.4f | val_overall_f1=%.4f",
+                    epoch+1, avg_loss, val["macro_f1_mean"], val["accuracy_mean"],
+                    val.get("overall_macro_f1", 0.0))
+        history.append({"epoch": epoch+1, "train_loss": avg_loss, **val})
+
+        if val["macro_f1_mean"] > best_f1:
+            best_f1 = val["macro_f1_mean"]
+            torch.save({"model_state_dict": model.state_dict(),
+                        "config": {"bert_name": bert_name,
+                                   "meta_in_dim": meta_encoder.total_dim,
+                                   "architecture": "gated_aspect_semantic_meta_acsa",
+                                   "meta_token_names": GatedAspectSemanticMetaFusionACSAModel.meta_token_names}},
+                       output_dir / "best.pt")
+            tokenizer.save_pretrained(output_dir / "tokenizer")
+            logger.info("Saved new best meta_acsa (macro_f1=%.4f)", best_f1)
+
+    with open(output_dir / "history.json", "w") as f:
+        json.dump(history, f, indent=2)
+    return model, history
+
+
+# ---------------------------------------------------------------------------
+# Train: Baseline 3 (BERT-ACSA, no meta 鈥?ablation)
+# ---------------------------------------------------------------------------
+
+def train_acsa(
+    train_df, val_df,
+    bert_name: str = cfg.BERT_MODEL_NAME,
+    epochs: int = cfg.DEFAULT_EPOCHS,
+    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
+    lr_bert: float = cfg.DEFAULT_LR_BERT,
+    lr_heads: float = cfg.DEFAULT_LR_HEADS,
+    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
+    use_class_weights: bool = True,
+    output_dir: Optional[Path] = None,
+    seed: int = cfg.RANDOM_SEED,
+):
+    if output_dir is None:
+        output_dir = cfg.CHECKPOINT_DIR / "acsa"
+    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)
+
+    torch.manual_seed(seed); np.random.seed(seed)
+    device = get_device()
+    tokenizer = AutoTokenizer.from_pretrained(bert_name)
+
+    aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
+    class_weights = (compute_class_weights(train_df, aspect_cols, cfg.NUM_CLASSES).to(device)
+                     if use_class_weights else None)
+    model = BertACSAModel(bert_name=bert_name, class_weights=class_weights).to(device)
+
+    train_ds = ACSADataset(train_df, tokenizer)
+    val_ds = ACSADataset(val_df, tokenizer)
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)
+
+    bert_params = list(model.bert.named_parameters())
+    head_params = list(model.heads.named_parameters())
+    no_decay = ["bias", "LayerNorm.weight"]
+    grouped = [
+        {"params": [p for n, p in bert_params if not any(nd in n for nd in no_decay)],
+         "weight_decay": weight_decay, "lr": lr_bert},
+        {"params": [p for n, p in bert_params if any(nd in n for nd in no_decay)],
+         "weight_decay": 0.0, "lr": lr_bert},
+        {"params": [p for _, p in head_params], "weight_decay": weight_decay, "lr": lr_heads},
+    ]
+    optimizer = torch.optim.AdamW(grouped)
+    total_steps = max(len(train_loader) * epochs, 1)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, int(cfg.WARMUP_RATIO * total_steps), total_steps,
+    )
+
+    best_f1 = -1.0; history = []
+    for epoch in range(epochs):
+        model.train(); running = 0.0
+        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] acsa-no-meta")
+        for batch in pbar:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            optimizer.zero_grad()
+            out = model(batch["input_ids"], batch["attention_mask"], labels=batch["labels"])
+            out["loss"].backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step(); scheduler.step()
+            running += out["loss"].item()
+            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})
+
+        val = _evaluate_per_aspect(model, val_loader, device, with_meta=False)
+        logger.info("Epoch %d | val_macro_f1=%.4f | val_acc=%.4f",
+                    epoch+1, val["macro_f1_mean"], val["accuracy_mean"])
+        history.append({"epoch": epoch+1, "train_loss": running/max(len(train_loader),1),
+                        **val})
+
+        if val["macro_f1_mean"] > best_f1:
+            best_f1 = val["macro_f1_mean"]
+            torch.save({"model_state_dict": model.state_dict(),
+                        "config": {"bert_name": bert_name}},
+                       output_dir / "best.pt")
+            tokenizer.save_pretrained(output_dir / "tokenizer")
+
+    with open(output_dir / "history.json", "w") as f:
+        json.dump(history, f, indent=2)
+    return model, history
+
+
+# ---------------------------------------------------------------------------
+# Train: Baseline 2 (BERT-overall)
+# ---------------------------------------------------------------------------
+
+def train_bert_overall(
+    train_df, val_df,
+    bert_name: str = cfg.BERT_MODEL_NAME,
+    epochs: int = cfg.DEFAULT_EPOCHS,
+    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
+    lr: float = cfg.DEFAULT_LR_BERT,
+    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
+    output_dir: Optional[Path] = None,
+    seed: int = cfg.RANDOM_SEED,
+):
+    from sklearn.metrics import f1_score, accuracy_score
+    if output_dir is None:
+        output_dir = cfg.CHECKPOINT_DIR / "bert_overall"
+    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)
+
+    torch.manual_seed(seed); np.random.seed(seed)
+    device = get_device()
+    tokenizer = AutoTokenizer.from_pretrained(bert_name)
+    model = BertOverallModel(bert_name=bert_name).to(device)
+
+    train_ds = OverallSentimentDataset(train_df, tokenizer)
+    val_ds = OverallSentimentDataset(val_df, tokenizer)
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
+    total_steps = max(len(train_loader) * epochs, 1)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, int(cfg.WARMUP_RATIO * total_steps), total_steps,
+    )
+
+    best_f1 = -1.0; history = []
+    for epoch in range(epochs):
+        model.train(); running = 0.0
+        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] bert-overall")
+        for batch in pbar:
+            batch = {k: v.to(device) for k, v in batch.items()}
+            optimizer.zero_grad()
+            out = model(**batch)
+            out["loss"].backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step(); scheduler.step()
+            running += out["loss"].item()
+            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})
+
+        model.eval(); all_p, all_l = [], []
+        with torch.no_grad():
+            for batch in val_loader:
+                batch = {k: v.to(device) for k, v in batch.items()}
+                out = model(**batch)
+                all_p.extend(out["logits"].argmax(dim=-1).cpu().numpy().tolist())
+                all_l.extend(batch["labels"].cpu().numpy().tolist())
+        metrics = {
+            "macro_f1": float(f1_score(all_l, all_p, average="macro", zero_division=0)),
+            "accuracy": float(accuracy_score(all_l, all_p)),
+            "weighted_f1": float(f1_score(all_l, all_p, average="weighted", zero_division=0)),
+        }
+        logger.info("Epoch %d | val_macro_f1=%.4f | val_acc=%.4f",
+                    epoch+1, metrics["macro_f1"], metrics["accuracy"])
+        history.append({"epoch": epoch+1, "train_loss": running/max(len(train_loader),1),
+                        **metrics})
+
+        if metrics["macro_f1"] > best_f1:
+            best_f1 = metrics["macro_f1"]
+            torch.save({"model_state_dict": model.state_dict(),
+                        "config": {"bert_name": bert_name}},
+                       output_dir / "best.pt")
+            tokenizer.save_pretrained(output_dir / "tokenizer")
+
+    with open(output_dir / "history.json", "w") as f:
+        json.dump(history, f, indent=2)
+    return model, history
+
+
+
+

src/utils.py

@@ -0,0 +1,17 @@
+"""Utility helpers."""
+import logging
+import sys
+
+
+def setup_logging(level=logging.INFO):
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+        handlers=[logging.StreamHandler(sys.stdout)],
+        force=True,
+    )
+    # Quiet HuggingFace
+    logging.getLogger("transformers").setLevel(logging.WARNING)
+    logging.getLogger("datasets").setLevel(logging.WARNING)
+    logging.getLogger("urllib3").setLevel(logging.WARNING)
+    logging.getLogger("filelock").setLevel(logging.WARNING)

src/weak_labeling.py

@@ -0,0 +1,523 @@
+"""Weak supervision label generation for ACSA v2: META-DEPENDENT labeling.
+
+Key design principle: the labeling rules MUST depend on metadata so that a
+text-only model (Baseline 3) cannot learn the full labeling function. Only a
+model that sees both text AND metadata can reconstruct the labels perfectly.
+
+Three meta-dependent labeling mechanisms:
+  1. Meta-Aware Aspect Detection: if the product's features_text mentions an
+     aspect (e.g. "100% Cotton" -> MATERIAL), we treat that aspect as "salient"
+     for this SKU. If the review text doesn't explicitly mention it via keyword
+     but the user gave a low/high rating, we infer aspect sentiment from rating.
+     Rationale: a product that advertises its material invites material-based
+     evaluation. A 1-star review on such a product likely reflects material
+     disappointment even if the word "fabric" never appears.
+  2. Price-Aware VALUE: products priced above 1.5x the category median with
+     low ratings get VALUE=Negative; products below 0.5x median with high
+     ratings get VALUE=Positive 鈥?even without "price"/"worth" in the text.
+  3. Category salience prior: Shoes 鈫?SIZE is extra-salient; Dresses/Tops 鈫?     APPEARANCE is extra-salient. For salient aspects in the product's category,
+     we lower the threshold for marking them as mentioned.
+
+For each review row, for each aspect, we assign one of:
+    0 = Not_Mentioned, 1 = Positive, 2 = Negative
+
+Output columns: aspect_<NAME> (int), evidence_<NAME> (JSON list)
+"""
+import json
+import logging
+import re
+from typing import Dict, List, Optional, Set, Tuple
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+
+from . import config as cfg
+from .aspect_dict import compile_aspect_patterns
+
+
+logger = logging.getLogger(__name__)
+
+_SENT_SPLIT_RE = re.compile(r"(?<=[.!?])\s+")
+
+# Category -> which aspects are extra-salient (lowered threshold for detection)
+# High-precision phrase rules for clothing reviews. These catch common cases
+# that generic sentence sentiment may score as neutral.
+EXPLICIT_PHRASE_RULES = [
+    ("SIZE", 2, [
+        r"\bruns?\s+(a\s+little\s+)?small\b",
+        r"\bruns?\s+(a\s+little\s+)?large\b",
+        r"\btoo\s+(small|large|big|tight|loose|short|long)\b",
+        r"\b(size|fit|fits|fitting)\b[^.!?]{0,35}\b(small|large|big|tight|loose)\b",
+        r"\bwrong\s+size\b",
+    ]),
+    ("SIZE", 1, [
+        r"\btrue\s+to\s+size\b",
+        r"\bperfect\s+fit\b",
+        r"\bfit(s)?\s+perfectly\b",
+    ]),
+    ("MATERIAL", 2, [
+        r"\bheavier\s+than\s+expected\b",
+        r"\btoo\s+heavy\b",
+        r"\b(feels?|felt)\s+heavy\b",
+        r"\b(stiff|scratchy|itchy|rough|bulky)\b",
+        r"\bsee[- ]through\b",
+        r"\btoo\s+thin\b",
+    ]),
+    ("MATERIAL", 1, [
+        r"\bsoft\s+fabric\b",
+        r"\bfabric\s+(feels?\s+)?soft\b",
+        r"\bbreathable\b",
+        r"\blightweight\b",
+    ]),
+    ("QUALITY", 2, [
+        r"\bfell\s+apart\b",
+        r"\bfalling\s+apart\b",
+        r"\b(broke|broken|ripped|tore|torn)\b",
+        r"\bloose\s+threads?\b",
+        r"\b(pilling|pilled|faded|shrunk)\b",
+        r"\bshrunk\s+after\s+wash(ing)?\b",
+    ]),
+    ("QUALITY", 1, [
+        r"\bwell[- ]made\b",
+        r"\bgood\s+quality\b",
+        r"\bhigh\s+quality\b",
+        r"\bdurable\b",
+    ]),
+    ("APPEARANCE", 2, [
+        r"\b(color|colour)\s+(is\s+)?off\b",
+        r"\bdifferent\s+from\s+(the\s+)?(picture|photo)\b",
+        r"\bmisleading\s+photo\b",
+        r"\blooks?\s+cheap\b",
+        r"\bugly\b",
+    ]),
+    ("APPEARANCE", 1, [
+        r"\bas\s+(pictured|shown|described|advertised)\b",
+        r"\bmatches\s+the\s+picture\b",
+        r"\b(beautiful|gorgeous|vibrant|stunning)\b",
+    ]),
+    ("STYLE", 2, [
+        r"\bunflattering\b",
+        r"\bshapeless\b",
+        r"\bawkward\s+cut\b",
+    ]),
+    ("STYLE", 1, [
+        r"\bflattering\b",
+        r"\bstylish\b",
+        r"\breceived\s+compliments\b",
+    ]),
+    ("VALUE", 2, [
+        r"\bnot\s+worth(\s+it)?\b",
+        r"\boverpriced\b",
+        r"\bwaste\s+of\s+money\b",
+        r"\bwasted\s+money\b",
+    ]),
+    ("VALUE", 1, [
+        r"\bworth\s+it\b",
+        r"\bgood\s+deal\b",
+        r"\bgreat\s+deal\b",
+        r"\bfor\s+the\s+price\b",
+    ]),
+]
+
+# Terms mined from product descriptions are useful model inputs, but many are
+# too generic to be aspect evidence in a review. Filtering happens only inside
+# this labeler; data/aspect_dict.json remains unchanged for reproducibility.
+LABEL_NOISE_TERMS = {
+    "breathable", "comfort", "easy", "gift", "hand", "high", "keep",
+    "lightweight", "long", "look", "only", "please", "pockets", "pull",
+    "quality", "size", "spandex",
+}
+
+# Local sentiment must be aspect-specific. A global word such as
+# "uncomfortable" cannot legitimately make every aspect Negative.
+ASPECT_LOCAL_CUES = {
+    "SIZE": {
+        1: [r"\b(true\s+to\s+size|perfect\s+fit|fits?\s+perfectly)\b"],
+        2: [r"\b(too\s+(small|large|big|tight|loose|short|long)|runs?\s+(small|large)|wrong\s+size)\b"],
+    },
+    "MATERIAL": {
+        1: [r"\b(soft\s+(fabric|material)|breathable\s+(fabric|material)|fabric\s+feels?\s+soft)\b"],
+        2: [r"\b(scratchy|itchy|rough|stiff|bulky|see[- ]through|too\s+(heavy|thin))\b"],
+    },
+    "QUALITY": {
+        1: [r"\b(well[- ]made|good\s+quality|high\s+quality|durable|sturdy)\b"],
+        2: [r"\b(fell\s+apart|falling\s+apart|broke|broken|ripped|torn|pilling|pilled|faded|shrunk|loose\s+threads?)\b"],
+    },
+    "APPEARANCE": {
+        1: [r"\b(beautiful|gorgeous|vibrant|stunning|matches?\s+(the\s+)?(picture|photo))\b"],
+        2: [r"\b(color|colour)\s+(is\s+)?off\b", r"\b(different\s+from\s+(the\s+)?(picture|photo)|misleading\s+photo|ugly)\b"],
+    },
+    "STYLE": {
+        1: [r"\b(flattering|stylish|fashionable|received\s+compliments?)\b"],
+        2: [r"\b(unflattering|shapeless|awkward\s+cut)\b"],
+    },
+    "VALUE": {
+        1: [r"\b(worth\s+it|good\s+deal|great\s+deal|value\s+for\s+money)\b"],
+        2: [r"\b(not\s+worth(\s+it)?|overpriced|waste(d)?\s+of\s+money)\b"],
+    },
+}
+CATEGORY_SALIENCE = {
+    "Shoes":      {"SIZE", "QUALITY"},
+    "Boots":      {"SIZE", "QUALITY"},
+    "Sandals":    {"SIZE", "QUALITY", "APPEARANCE"},
+    "Sneakers":   {"SIZE", "QUALITY"},
+    "Dresses":    {"APPEARANCE", "STYLE", "SIZE"},
+    "Tops":       {"APPEARANCE", "MATERIAL"},
+    "T-Shirts":   {"MATERIAL", "SIZE"},
+    "Sweaters":   {"MATERIAL", "QUALITY"},
+    "Jeans":      {"SIZE", "QUALITY"},
+    "Pants":      {"SIZE", "QUALITY"},
+    "Jackets":    {"QUALITY", "MATERIAL"},
+    "Coats":      {"QUALITY", "MATERIAL"},
+    "Jewelry":    {"VALUE", "APPEARANCE", "QUALITY"},
+    "Watches":    {"VALUE", "QUALITY"},
+    "Handbags":   {"QUALITY", "VALUE"},
+    "Socks":      {"MATERIAL", "SIZE"},
+    "Underwear":  {"MATERIAL", "SIZE"},
+    "Lingerie":   {"MATERIAL", "SIZE", "APPEARANCE"},
+    "Swimwear":   {"SIZE", "APPEARANCE"},
+    "Activewear": {"MATERIAL", "SIZE"},
+}
+
+
+def split_sentences(text: str) -> List[str]:
+    if not text:
+        return []
+    return [s.strip() for s in _SENT_SPLIT_RE.split(text.strip()) if s.strip()]
+
+
+class WeakLabeler:
+    """Apply weak supervision rules to assign aspect-level sentiment labels."""
+
+    def __init__(self, aspect_dict: Dict[str, List[str]],
+                 category_median_prices: Optional[Dict[str, float]] = None):
+        self.aspect_dict = aspect_dict
+        self.aspects = list(aspect_dict.keys())
+        self.label_aspect_dict = {
+            aspect: [term for term in terms if term.strip().lower() not in LABEL_NOISE_TERMS]
+            for aspect, terms in aspect_dict.items()
+        }
+        self.patterns = compile_aspect_patterns(self.label_aspect_dict)
+        self.category_median_prices = category_median_prices or {}
+
+        from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+        self.vader = SentimentIntensityAnalyzer()
+
+    # ---- atomic signals ----
+    def _sentence_sentiment(self, sentence: str) -> int:
+        """1 (pos), 2 (neg), 0 (neutral/unknown)."""
+        score = self.vader.polarity_scores(sentence)["compound"]
+        if score >= cfg.VADER_POSITIVE_THRESHOLD:
+            return 1
+        if score <= cfg.VADER_NEGATIVE_THRESHOLD:
+            return 2
+        return 0
+
+    def _rating_sentiment(self, rating: int) -> int:
+        if rating >= cfg.RATING_POSITIVE_THRESHOLD:
+            return 1
+        if rating <= cfg.RATING_NEGATIVE_THRESHOLD:
+            return 2
+        return 0
+
+    def _meta_prior(self, avg_rating: float) -> int:
+        if not np.isfinite(avg_rating):
+            return 0
+        if avg_rating >= 4.3:
+            return 1
+        if avg_rating <= 2.7:
+            return 2
+        return 0
+
+    def _local_cue_sentiment(self, aspect: str, sentence: str) -> int:
+        """Return sentiment only for cues that belong to this aspect."""
+        text = str(sentence or "").lower()
+        for pattern in ASPECT_LOCAL_CUES.get(aspect, {}).get(2, []):
+            if re.search(pattern, text, flags=re.IGNORECASE):
+                return 2
+        for pattern in ASPECT_LOCAL_CUES.get(aspect, {}).get(1, []):
+            if re.search(pattern, text, flags=re.IGNORECASE):
+                return 1
+        return 0
+
+    def _resolve_text_sentiment(self, aspect: str, sentence: str) -> int:
+        """Use aspect-specific local evidence before sentence-level sentiment."""
+        local = self._local_cue_sentiment(aspect, sentence)
+        if local != 0:
+            return local
+        return self._sentence_sentiment(sentence)
+    def _allow_meta_inference(self, aspect: str, rating: int, meta_avg_rating: float,
+                              features_salient: Set[str], category_salient: Set[str]) -> bool:
+        """Allow metadata-only labels only for high-confidence extreme ratings.
+
+        Text evidence always has priority. Metadata fills otherwise unlabeled
+        cases, preserving a real but bounded role for product context.
+        """
+        if rating <= 1:
+            return aspect in features_salient or aspect in category_salient
+        if rating >= 5:
+            return (
+                aspect in features_salient
+                and np.isfinite(meta_avg_rating)
+                and meta_avg_rating >= 4.3
+            )
+        return False
+    # ---- META-DEPENDENT mechanisms ----
+
+    def _detect_salient_aspects_from_features(
+        self, features_text: str
+    ) -> Set[str]:
+        """Check which aspects are mentioned in the product's features_text.
+        Returns a set of aspect names that the product explicitly advertises.
+        """
+        if not features_text:
+            return set()
+        salient = set()
+        ft_lower = features_text.lower()
+        for aspect in self.aspects:
+            if self.patterns[aspect].search(ft_lower):
+                salient.add(aspect)
+        return salient
+
+    def _get_category_salient_aspects(self, leaf_category: str) -> Set[str]:
+        """Which aspects are extra-salient for this product category."""
+        if not leaf_category:
+            return set()
+        for cat_key, aspects in CATEGORY_SALIENCE.items():
+            if cat_key.lower() in leaf_category.lower():
+                return aspects
+        return set()
+
+    def _price_value_signal(
+        self, price: float, rating: int, leaf_category: str
+    ) -> int:
+        """Price-aware VALUE labeling: expensive + low rating = VALUE Negative,
+        cheap + high rating = VALUE Positive.
+        Returns 0 (no signal), 1 (positive), 2 (negative).
+        """
+        if not np.isfinite(price) or price <= 0:
+            return 0
+        median = self.category_median_prices.get(leaf_category)
+        if median is None or median <= 0:
+            # Fall back to global median if category not found
+            median = self.category_median_prices.get("__global__")
+        if median is None or median <= 0:
+            return 0
+
+        if price > median * 1.5 and rating <= 2:
+            return 2  # Expensive + bad rating -> VALUE Negative
+        if price < median * 0.5 and rating >= 4:
+            return 1  # Cheap + good rating -> VALUE Positive
+        if price > median * 2.0 and rating <= 3:
+            return 2  # Very expensive + mediocre -> VALUE Negative
+        return 0
+
+    def _apply_explicit_phrase_rules(
+        self,
+        text: str,
+        labels: Dict[str, int],
+        evidence: Dict[str, List[str]],
+    ) -> None:
+        """Apply high-precision clothing phrase rules after sentence matching.
+
+        Negative rules can override positive/default labels because explicit
+        complaints are usually more informative for ACSA than global rating.
+        Positive rules fill only unlabeled aspects to avoid washing out a
+        complaint already found in text.
+        """
+        lower = str(text or "").lower()
+        for aspect, sentiment, patterns in EXPLICIT_PHRASE_RULES:
+            for pat in patterns:
+                m = re.search(pat, lower, flags=re.IGNORECASE)
+                if not m:
+                    continue
+                snippet = m.group(0)
+                if labels[aspect] == 0 or sentiment == 2:
+                    labels[aspect] = sentiment
+                evidence[aspect].append(f"[phrase-rule: {snippet}]")
+                break
+    # ---- per-review labeling ----
+    def label_review(
+        self,
+        text: str,
+        rating: int,
+        meta_avg_rating: float = float("nan"),
+        features_text: str = "",
+        leaf_category: str = "",
+        price: float = float("nan"),
+    ) -> Tuple[Dict[str, int], Dict[str, List[str]]]:
+        labels = {a: 0 for a in self.aspects}
+        evidence = {a: [] for a in self.aspects}
+
+        sentences = split_sentences(text)
+
+        # --- META-DEPENDENT: detect salient aspects from product features ---
+        features_salient = self._detect_salient_aspects_from_features(features_text)
+        category_salient = self._get_category_salient_aspects(leaf_category)
+        all_salient = features_salient | category_salient
+
+        # --- Phase 1: text-based detection ---
+        for sent in sentences:
+            for aspect in self.aspects:
+                if not self.patterns[aspect].search(sent):
+                    continue
+                sentiment = self._resolve_text_sentiment(aspect, sent)
+                if sentiment == 0:
+                    continue
+                evidence[aspect].append(sent)
+                if labels[aspect] == 0:
+                    labels[aspect] = sentiment
+                elif sentiment == 2:
+                    labels[aspect] = 2
+
+        # --- Phase 1b: high-precision clothing phrase rules ---
+        self._apply_explicit_phrase_rules(text, labels, evidence)
+        # --- Phase 2: controlled metadata inference ---
+        # Text labels remain authoritative. An unlabeled aspect receives a
+        # metadata label only when an extreme review rating and a product signal
+        # agree. This avoids turning every product attribute into sentiment.
+        if cfg.META_PRIOR_BOOST:
+            for aspect in self.aspects:
+                if labels[aspect] != 0:
+                    if aspect in features_salient:
+                        evidence[aspect].append("[meta-corroborated: feature source]")
+                    elif aspect in category_salient:
+                        evidence[aspect].append("[meta-corroborated: category source]")
+                    continue
+                if self._allow_meta_inference(
+                    aspect, rating, meta_avg_rating, features_salient, category_salient
+                ):
+                    labels[aspect] = 2 if rating <= 1 else 1
+                    source = "feature" if aspect in features_salient else "category"
+                    evidence[aspect].append(
+                        f"[meta-inferred: {source} source, rating={rating}]"
+                    )
+
+        # --- Phase 3: price-aware VALUE inference ---
+        # Price can label VALUE when its category-relative signal and review
+        # rating agree, even if the text does not use a price keyword.
+        if labels["VALUE"] == 0:
+            price_signal = self._price_value_signal(price, rating, leaf_category)
+            if price_signal != 0:
+                labels["VALUE"] = price_signal
+                evidence["VALUE"].append(
+                    f"[price-inferred: price={price:.1f}, "
+                    f"cat_median={self.category_median_prices.get(leaf_category, '?')}, "
+                    f"rating={rating}]"
+                )
+
+        return labels, evidence
+
+    # ---- dataframe-level ----
+    def label_dataframe(
+        self,
+        df: pd.DataFrame,
+        text_col: str = "full_text",
+        rating_col: str = "rating",
+        meta_avg_rating_col: str = "average_rating",
+        features_text_col: str = "features_text",
+        leaf_category_col: str = "leaf_category",
+        price_col: str = "price",
+    ) -> pd.DataFrame:
+        all_labels = {a: [] for a in self.aspects}
+        all_evidence = {a: [] for a in self.aspects}
+
+        has_meta_rating = meta_avg_rating_col in df.columns
+        has_features = features_text_col in df.columns
+        has_category = leaf_category_col in df.columns
+        has_price = price_col in df.columns
+
+        if not has_meta_rating:
+            logger.info("[weak_labeling] No %s column; meta prior disabled.", meta_avg_rating_col)
+        if not has_features:
+            logger.info("[weak_labeling] No %s column; feature-based salience disabled.", features_text_col)
+        if not has_price:
+            logger.info("[weak_labeling] No %s column; price-aware VALUE disabled.", price_col)
+
+        for _, row in tqdm(df.iterrows(), total=len(df), desc="weak labeling"):
+            meta_avg = float(row[meta_avg_rating_col]) if has_meta_rating else float("nan")
+            feat_text = str(row[features_text_col]) if has_features else ""
+            cat = str(row[leaf_category_col]) if has_category else ""
+            price = float(row[price_col]) if has_price else float("nan")
+
+            labels, evidence = self.label_review(
+                str(row[text_col]),
+                int(row[rating_col]),
+                meta_avg_rating=meta_avg,
+                features_text=feat_text,
+                leaf_category=cat,
+                price=price,
+            )
+            for a in self.aspects:
+                all_labels[a].append(labels[a])
+                all_evidence[a].append(evidence[a])
+
+        result = df.copy()
+        for a in self.aspects:
+            result[f"aspect_{a}"] = all_labels[a]
+            result[f"evidence_{a}"] = [json.dumps(e) for e in all_evidence[a]]
+        return result
+
+
+def compute_category_median_prices(df: pd.DataFrame,
+                                   price_col: str = "price",
+                                   cat_col: str = "leaf_category") -> Dict[str, float]:
+    """Compute median price per leaf category. Used by Price-Aware VALUE labeling."""
+    result = {}
+    if price_col not in df.columns or cat_col not in df.columns:
+        return result
+    prices = pd.to_numeric(df[price_col], errors="coerce")
+    global_median = float(prices.median())
+    if np.isfinite(global_median):
+        result["__global__"] = global_median
+    for cat, group in df.groupby(cat_col):
+        cat_prices = pd.to_numeric(group[price_col], errors="coerce").dropna()
+        if len(cat_prices) >= 5:
+            result[str(cat)] = float(cat_prices.median())
+    logger.info("Computed median prices for %d categories (global=%.1f)",
+                len(result) - 1, result.get("__global__", 0))
+    return result
+
+
+def label_distribution_summary(df: pd.DataFrame) -> pd.DataFrame:
+    rows = []
+    for a in cfg.ASPECTS:
+        col = f"aspect_{a}"
+        if col not in df.columns:
+            continue
+        vc = df[col].value_counts().sort_index()
+        rows.append({
+            "aspect": a,
+            "not_mentioned": int(vc.get(0, 0)),
+            "positive": int(vc.get(1, 0)),
+            "negative": int(vc.get(2, 0)),
+            "mentioned_pct": round(100 * (1 - vc.get(0, 0) / max(len(df), 1)), 2),
+        })
+    return pd.DataFrame(rows)
+
+
+def save_audit_sample(df: pd.DataFrame, n: int = cfg.AUDIT_SAMPLE_SIZE, path=None):
+    if path is None:
+        path = cfg.REPORT_DIR / "weak_label_audit_sample.csv"
+
+    cols = ["full_text", "rating", "overall_label", "parent_asin",
+            "average_rating", "leaf_category", "price", "features_text"]
+    cols += [f"aspect_{a}" for a in cfg.ASPECTS]
+    cols += [f"evidence_{a}" for a in cfg.ASPECTS]
+    cols = [c for c in cols if c in df.columns]
+
+    by_rating = df.groupby("rating", group_keys=False)
+    sample = by_rating.apply(lambda x: x.sample(n=min(max(n // 5, 1), len(x)),
+                                                random_state=cfg.RANDOM_SEED))
+    sample = sample.reset_index(drop=True)
+    if len(sample) > n:
+        sample = sample.sample(n=n, random_state=cfg.RANDOM_SEED)
+    keep = [c for c in cols if c in sample.columns]
+    sample[keep].to_csv(path, index=False)
+    logger.info("Saved %d audit samples to %s", len(sample), path)
+    return sample
+
+
+
+