scripts/eval/intent_elicitation.py

"""Task 3 — Intent Elicitation: recover what the designer intended.

Given N-1 views of a patent (as text descriptions, no images), produce a
structured schema describing the patent's design intent. Score against the
gold drawing description using text embedding cosine similarity.

Two sub-tasks:
  MC   — select the correct component description from a pool of candidates
  Free — fill in the structured schema; score vs. gold via cosine similarity

The structured schema forces outputs into comparable format regardless of
writing style, reducing noise in the free-form comparison.

Schema:
  {
    "viewpoint_set":   "what views the patent shows and why",
    "primary_view":    "which view is the reference/primary",
    "component_vocab": ["list", "of", "component", "names"],
    "spatial_layout":  "how components relate to each other",
    "design_intent":   "what the designer is communicating about the object"
  }

Usage:
    python scripts/eval/intent_elicitation.py \
        --enriched data/enriched/enriched_2022.parquet \
        --n 18 --out results/intent_elicitation_results.json
"""

import argparse
import json
import re
import time
from pathlib import Path

import numpy as np
import pandas as pd

import sys
sys.path.insert(0, str(Path(__file__).parent))
from provider import chat, get_client, get_model


# ── Prompts ───────────────────────────────────────────────────────────────────

FREE_FORM_PROMPT = """You are analyzing a US design patent.

Patent title: {title}

The patent has {n_figs} figures described as follows (some viewpoints may be redacted):
{drawing_description}

Based on this, fill in the design intent schema below as specifically as possible:

{{
  "viewpoint_set":   "...",
  "primary_view":    "...",
  "component_vocab": ["...", "..."],
  "spatial_layout":  "...",
  "design_intent":   "..."
}}

Rules:
- viewpoint_set: name which projection types appear and what each reveals
- primary_view: which single view best communicates the overall form
- component_vocab: list the distinct named parts of this design
- spatial_layout: describe how parts are arranged relative to each other
- design_intent: what visual/functional story is the designer communicating?

Reply with the JSON only."""


MC_PROMPT = """You are analyzing a US design patent to identify its design intent.

Patent title: {title}

Drawing description (N-1 views shown, one view's label is redacted):
{drawing_description}

Which of the following best describes the OVERALL design intent of this patent?

{choices}

Reply with just the letter (A-D)."""


# ── Scoring ───────────────────────────────────────────────────────────────────

def schema_to_text(schema: dict) -> str:
    """Flatten schema to a single text string for embedding."""
    parts = []
    for k, v in schema.items():
        if isinstance(v, list):
            parts.append(f"{k}: {', '.join(str(x) for x in v)}")
        else:
            parts.append(f"{k}: {v}")
    return " | ".join(parts)


def cosine_sim(a: np.ndarray, b: np.ndarray) -> float:
    a = a / (np.linalg.norm(a) + 1e-8)
    b = b / (np.linalg.norm(b) + 1e-8)
    return float(np.dot(a, b))


def embed_text(text: str, client) -> np.ndarray | None:
    """Get text embedding via the chat model's implicit representation.
    Falls back to a bag-of-words overlap score if embedding API unavailable.
    """
    # Use the model to score text similarity via a direct comparison prompt
    # (proper embedding would use a dedicated embedding model)
    return None  # placeholder — see scoring below


def token_overlap_score(pred: str, gold: str) -> float:
    """Token-level F1 between predicted and gold text (no embedding needed)."""
    pred_tokens = set(re.findall(r"\w+", pred.lower()))
    gold_tokens = set(re.findall(r"\w+", gold.lower()))
    if not gold_tokens:
        return 0.0
    precision = len(pred_tokens & gold_tokens) / max(len(pred_tokens), 1)
    recall = len(pred_tokens & gold_tokens) / len(gold_tokens)
    if precision + recall == 0:
        return 0.0
    return 2 * precision * recall / (precision + recall)


# ── Main eval ─────────────────────────────────────────────────────────────────

def run(enriched_path: str, n: int, out_path: str, seed: int = 42):
    import random
    client = get_client()

    df = pd.read_parquet(enriched_path)

    # Parse viewpoints and find eligible patents
    def parse_vp(desc, fig_num):
        m = re.compile(
            rf"FIG\.\s*{fig_num+1}\s+is\s+(?:a\s+|an\s+)?(.{{5,80}}?)\s*(?:view|thereof|;|\n|$)",
            re.I
        ).search(desc or "")
        return m.group(1).strip().lower() if m else ""

    df["vp"] = df.apply(lambda r: parse_vp(r.get("drawing_description", ""), r["figure_number"]), axis=1)

    eligible = df.groupby("patent_id").filter(
        lambda g: g["vp"].str.contains("perspective").any() and
                  (g["vp"].str.contains("front elev") | g["vp"].isin(["front elevation","front plan"])).any() and
                  len(g) >= 3
    )["patent_id"].unique().tolist()

    rng = random.Random(seed)
    rng.shuffle(eligible)
    eval_pids = eligible[:n]
    print(f"Evaluating {len(eval_pids)} patents | model: {get_model()}")

    results = []
    free_scores, mc_correct, mc_total = [], 0, 0

    for pid in eval_pids:
        group = df[df["patent_id"] == pid].sort_values("figure_number")
        title = str(group["patent_title"].iloc[0])
        draw_desc = str(group["drawing_description"].iloc[0])
        n_figs = int(group["n_figures_in_patent"].iloc[0]) if "n_figures_in_patent" in group.columns else len(group)

        # ── Task 3a: Free-form intent elicitation ─────────────────────────────
        # Input: drawing_description (text) + patent title
        # Output: structured schema
        # Score: token F1 against gold drawing_description
        prompt = FREE_FORM_PROMPT.format(
            title=title,
            n_figs=n_figs,
            drawing_description=draw_desc[:1000],  # truncate for context
        )
        msgs = [{"role": "user", "content": prompt}]
        response = chat(client, msgs, max_tokens=400)

        predicted_schema = None
        try:
            m = re.search(r"\{.*\}", response, re.DOTALL)
            if m:
                predicted_schema = json.loads(m.group())
        except Exception:
            pass

        pred_text = schema_to_text(predicted_schema) if predicted_schema else response
        score = token_overlap_score(pred_text, draw_desc)
        free_scores.append(score)
        time.sleep(0.5)

        # ── Task 3b: MC intent selection ──────────────────────────────────────
        # Build 4 candidates: gold description + 3 distractors from other patents
        other_pids = [p for p in eligible if p != pid]
        rng.shuffle(other_pids)
        distractor_descs = []
        for dpid in other_pids[:3]:
            dg = df[df["patent_id"] == dpid]
            if not dg.empty:
                distractor_descs.append(str(dg["drawing_description"].iloc[0])[:200])

        correct_pos = rng.randint(0, 3)
        choices_list = distractor_descs[:3]
        choices_list.insert(correct_pos, draw_desc[:200])
        letters = "ABCD"
        choices_str = "\n".join(f"({letters[i]}) {c[:150]}" for i, c in enumerate(choices_list))

        mc_prompt = MC_PROMPT.format(
            title=title,
            drawing_description=draw_desc[:500],
            choices=choices_str,
        )
        mc_msgs = [{"role": "user", "content": mc_prompt}]
        mc_response = chat(client, mc_msgs, max_tokens=5)
        chosen_letter = re.search(r"\b([ABCD])\b", mc_response.upper())
        chosen_idx = letters.index(chosen_letter.group()) if chosen_letter else -1
        mc_ok = chosen_idx == correct_pos
        mc_correct += int(mc_ok)
        mc_total += 1
        time.sleep(0.5)

        result = {
            "patent_id": pid,
            "patent_title": title,
            "free_form_score": float(score),
            "predicted_schema": predicted_schema,
            "mc_correct": mc_ok,
            "mc_chosen": chosen_idx,
            "mc_correct_pos": correct_pos,
        }
        results.append(result)
        print(
            f"[{pid}] {title[:35]} | "
            f"free F1={score:.2f} | "
            f"MC={'✓' if mc_ok else '✗'} | "
            f"running MC: {mc_correct}/{mc_total} ({mc_correct/mc_total:.1%})"
        )

    avg_free = sum(free_scores) / max(len(free_scores), 1)
    mc_acc = mc_correct / max(mc_total, 1)

    print(f"\n=== INTENT ELICITATION RESULTS ===")
    print(f"Free-form token F1:  {avg_free:.3f}  (higher = describes drawing better)")
    print(f"MC accuracy:         {mc_acc:.1%}  (chance = 25.0%)")

    output = {
        "model": get_model(),
        "summary": {
            "free_form_avg_f1": avg_free,
            "mc_acc": mc_acc,
            "mc_chance": 0.25,
            "n": len(results),
        },
        "results": results,
    }
    Path(out_path).parent.mkdir(parents=True, exist_ok=True)
    with open(out_path, "w") as f:
        json.dump(output, f, indent=2)
    print(f"Results → {out_path}")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--enriched", default="data/enriched/enriched_2022.parquet")
    parser.add_argument("--n",        type=int, default=18)
    parser.add_argument("--out",      default="results/intent_elicitation_results.json")
    args = parser.parse_args()
    run(args.enriched, args.n, args.out)


if __name__ == "__main__":
    main()