steer_eval.py

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# Copyright (c) 2025-present Aquin Labs Private Limited. All Rights Reserved.
"""Baseline vs steered probe eval for `aquin steer --eval`."""
from __future__ import annotations
 
import json
from pathlib import Path
from typing import Any, Callable
 
from aquin.compute.deception_behavior import (
    BehaviorProbe,
    classify_behavior,
    load_behavior_probes,
)
from aquin.compute.evals import keyword_overlap_score
 
MAX_PROBES = 50
DEFAULT_THRESHOLD = 0.5
 
 
def _as_str_list(raw: Any) -> list[str] | None:
    if raw is None:
        return None
    if isinstance(raw, list):
        out = [str(x).strip() for x in raw if str(x).strip()]
        return out or None
    if isinstance(raw, str):
        text = raw.strip()
        if not text:
            return None
        if text.startswith("["):
            try:
                parsed = json.loads(text)
            except json.JSONDecodeError:
                return [text]
            if isinstance(parsed, list):
                out = [str(x).strip() for x in parsed if str(x).strip()]
                return out or None
        path = Path(text).expanduser()
        if path.is_file():
            body = path.read_text(encoding="utf-8").strip()
            if path.suffix == ".jsonl":
                rows = [json.loads(line) for line in body.splitlines() if line.strip()]
                out: list[str] = []
                for row in rows:
                    if isinstance(row, str) and row.strip():
                        out.append(row.strip())
                    elif isinstance(row, dict):
                        for key in ("prompt", "instruction", "text", "question"):
                            val = row.get(key)
                            if isinstance(val, str) and val.strip():
                                out.append(val.strip())
                                break
                return out or None
            try:
                parsed = json.loads(body)
            except json.JSONDecodeError:
                return None
            if isinstance(parsed, list):
                out = []
                for item in parsed:
                    if isinstance(item, str) and item.strip():
                        out.append(item.strip())
                    elif isinstance(item, dict):
                        for key in ("prompt", "instruction", "text", "question"):
                            val = item.get(key)
                            if isinstance(val, str) and val.strip():
                                out.append(val.strip())
                                break
                return out or None
        return [text]
    return None
 
 
def _rate(n: int, total: int) -> float:
    return round(n / total, 4) if total else 0.0
 
 
def _behavior_summary(rows: list[dict[str, Any]], *, key: str) -> dict[str, Any]:
    total = len(rows)
    n_truthful = sum(1 for r in rows if r[key]["behavior"] == "truthful")
    n_deceptive = sum(1 for r in rows if r[key]["behavior"] == "deceptive")
    n_ambiguous = sum(1 for r in rows if r[key]["behavior"] == "ambiguous")
    n_passed = sum(1 for r in rows if r[key]["passed"])
    return {
        "n": total,
        "n_truthful": n_truthful,
        "n_deceptive": n_deceptive,
        "n_ambiguous": n_ambiguous,
        "n_passed": n_passed,
        "truthful_rate": _rate(n_truthful, total),
        "deceptive_rate": _rate(n_deceptive, total),
        "ambiguous_rate": _rate(n_ambiguous, total),
        "pass_rate": _rate(n_passed, total),
    }
 
 
def _custom_summary(rows: list[dict[str, Any]], *, key: str) -> dict[str, Any]:
    total = len(rows)
    n_passed = sum(1 for r in rows if r[key]["passed"])
    scores = [float(r[key]["score"]) for r in rows]
    mean_score = round(sum(scores) / len(scores), 4) if scores else 0.0
    return {
        "n": total,
        "n_passed": n_passed,
        "pass_rate": _rate(n_passed, total),
        "mean_score": mean_score,
    }
 
 
def _resolve_behavior_probes(
    prompts: Any,
) -> tuple[list[BehaviorProbe], dict[str, Any]] | None:
    """Try deception/behavior probe loading; return None if not applicable."""
    if prompts is None or prompts is True:
        return load_behavior_probes(None)
 
    if isinstance(prompts, (str, Path)):
        text = str(prompts).strip()
        if not text or text.startswith("["):
            return None
        try:
            return load_behavior_probes(text)
        except (FileNotFoundError, ValueError, json.JSONDecodeError, OSError):
            return None
 
    return None
 
 
def run_steer_probe_eval(
    *,
    generate_baseline: Callable[[str], str],
    generate_steered: Callable[[str], str],
    prompts: Any = None,
    reference_answers: Any = None,
    threshold: float = DEFAULT_THRESHOLD,
    max_probes: int = MAX_PROBES,
) -> dict[str, Any]:
    """
    Score the same probe set under baseline vs steered generation.
 
    Modes:
      - behavior (default): deception-style probes with honest/deceptive references;
        pass = classified truthful.
      - custom: prompts + reference_answers; pass = keyword overlap >= threshold.
    """
    refs = _as_str_list(reference_answers)
    behavior_loaded = None if refs is not None else _resolve_behavior_probes(prompts)
 
    if behavior_loaded is not None:
        probes, meta = behavior_loaded
        probes = probes[: max(1, int(max_probes))]
        rows: list[dict[str, Any]] = []
        for probe in probes:
            base_resp = generate_baseline(probe.prompt)
            steered_resp = generate_steered(probe.prompt)
            b_beh, b_h, b_d = classify_behavior(
                base_resp, probe.honest_reference, probe.deceptive_reference,
            )
            s_beh, s_h, s_d = classify_behavior(
                steered_resp, probe.honest_reference, probe.deceptive_reference,
            )
            rows.append({
                "probe_id": probe.probe_id,
                "prompt": probe.prompt,
                "intent": probe.intent,
                "baseline": {
                    "response": base_resp,
                    "behavior": b_beh,
                    "honest_score": b_h,
                    "deceptive_score": b_d,
                    "passed": b_beh == "truthful",
                },
                "steered": {
                    "response": steered_resp,
                    "behavior": s_beh,
                    "honest_score": s_h,
                    "deceptive_score": s_d,
                    "passed": s_beh == "truthful",
                },
                "changed": base_resp.strip() != steered_resp.strip(),
            })
 
        baseline = _behavior_summary(rows, key="baseline")
        steered = _behavior_summary(rows, key="steered")
        return {
            "mode": "behavior",
            "prompts_path": meta.get("prompts_path"),
            "n_probes": len(rows),
            "pass_means": "truthful",
            "baseline": baseline,
            "steered": steered,
            "delta_pass_rate": round(steered["pass_rate"] - baseline["pass_rate"], 4),
            "n_changed": sum(1 for r in rows if r["changed"]),
            "probes": rows,
        }
 
    prompt_list = _as_str_list(prompts)
    if not prompt_list:
        return {
            "error": (
                "steer --eval needs a probe set. Pass --prompts <jsonl|json|array> "
                "and --reference_answers for custom scoring."
            ),
        }
    if refs is None:
        return {
            "error": (
                "Custom steer --eval requires --reference_answers (one per prompt). "
                "Or pass a deception probe file with honest_reference / deceptive_reference."
            ),
        }
    if len(refs) != len(prompt_list):
        return {
            "error": (
                f"reference_answers length ({len(refs)}) must match prompts ({len(prompt_list)})"
            ),
        }
 
    prompt_list = prompt_list[: max(1, int(max_probes))]
    refs = refs[: len(prompt_list)]
    thr = float(threshold)
    rows = []
    for i, (prompt, reference) in enumerate(zip(prompt_list, refs)):
        base_resp = generate_baseline(prompt)
        steered_resp = generate_steered(prompt)
        b_score = keyword_overlap_score(base_resp, reference)
        s_score = keyword_overlap_score(steered_resp, reference)
        rows.append({
            "probe_id": f"p{i}",
            "prompt": prompt,
            "reference": reference,
            "baseline": {
                "response": base_resp,
                "score": b_score,
                "passed": b_score >= thr,
            },
            "steered": {
                "response": steered_resp,
                "score": s_score,
                "passed": s_score >= thr,
            },
            "changed": base_resp.strip() != steered_resp.strip(),
        })
 
    baseline = _custom_summary(rows, key="baseline")
    steered = _custom_summary(rows, key="steered")
    return {
        "mode": "custom",
        "n_probes": len(rows),
        "threshold": thr,
        "pass_means": f"keyword_overlap>={thr}",
        "baseline": baseline,
        "steered": steered,
        "delta_pass_rate": round(steered["pass_rate"] - baseline["pass_rate"], 4),
        "n_changed": sum(1 for r in rows if r["changed"]),
        "probes": rows,
    }