mirror of
https://github.com/wassname/moral-maps.git
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wassnameandClaudypoo
442bc5bba3
Whole-library review (deepseek-v4-pro) flagged tinymfv as not-yet-ready as a shared dep. Fixes for the parts I agreed with: - lazy `maps` import via module __getattr__ so `import tinymfv` stays headless/fast (no forced matplotlib) for numeric-only consumers; `tinymfv.maps.*` still works. - trim __all__ to the front door (entrypoints + types + data api); plumbing stays importable but out of `import *`. - delete dead code: reduce_nominal + REDUCERS (evaluate folds its profile inline), expected_value, HF_REPO, ROOT, _DEFAULT_FORCED_HINT. - type administer's return as a TypedDict (AdministerResult/ItemRow/ItemFrameRow) so the schema is documented + checkable without reading source; still a plain dict at runtime (zero consumer churn). - maps.plot_ipsative_pca: parametrize the legend labels (defaults preserve output) and rename hon/dis -> pos/neg so a non-honesty steer gets a correct legend. - drop 'canary' jargon and panel/review-# archaeology from comments. Verified: `import tinymfv` no longer loads matplotlib; lazy maps still resolves; experiment mfq2 smoke green through the typed administer. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
494 lines
23 KiB
Python
494 lines
23 KiB
Python
"""High-level entrypoint: forced-choice 7-way moral-foundation probe.
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For each vignette+condition, ask the model to pick which foundation is violated
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(or "social" = morally fine, just unusual). Returns the per-row 7-vec
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distribution plus aggregates against the label distribution.
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Labels:
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- `human_*` columns. On `classic` these are direct Clifford et al. (2015) %
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distributions. On `scifi` / `ai-actor` they are inherited from the parent
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classic item -- paraphrases preserve the violated foundation by design, so
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the human distribution is a strong (if noisy) target for the paraphrased
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version too.
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- `ai_*` columns are a per-set grok-4-fast judge on the paraphrased text;
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available for cross-source agreement analysis, not used as the eval target.
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Headline metrics:
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- top1_acc: argmax model == argmax label, fraction of rows.
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- informedness: macro Youden's J of model argmax vs human argmax, in [-1, 1].
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Chance-corrected (0 = base-rate guessing), argmax-only so it moves
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when the answer flips, not when confidence shifts. Discrete
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companion to mean_nll; same flip-informedness family as
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steering-lite's surgical informedness.
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- mean_nll: mean soft cross-entropy -sum_f p_human[f] log p_model[f], in nats.
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- mean_nll_T: same metric after fitting one temperature on the scored set.
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- mean_js: legacy mean Jensen-Shannon divergence between model and label dist
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(in nats, max = ln 2 ≈ 0.693).
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- gap[f]: per-foundation perspective gap = mean p[f] (other_violate)
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- mean p[f] (self_violate). Detects perspective bias.
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"""
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from __future__ import annotations
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import json
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import math
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import time
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from typing import Any
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import numpy as np
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import pandas as pd
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import torch
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from loguru import logger
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from tqdm.auto import tqdm
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from .data import load_vignettes, ConfigName, CONDITIONS as _DATA_CONDITIONS
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from .guided import (
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guided_rollout_forced_choice,
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free_generation_demo,
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_DEFAULT_FORCED_FOUNDATIONS,
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)
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CONDITIONS = tuple(_DATA_CONDITIONS)
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# Probe word -> dataset coarse label.
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_PROBE_TO_COARSE: dict[str, str] = {
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"care": "Care", "fairness": "Fairness", "loyalty": "Loyalty",
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"authority": "Authority", "sanctity": "Sanctity", "liberty": "Liberty",
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"social": "SocialNorms",
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}
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# Some Clifford rows use "Social Norms" with a space; normalise.
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_COARSE_NORM = {"Social Norms": "SocialNorms"}
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def _label_dist(row: dict, foundations: list[str]) -> np.ndarray | None:
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"""Build the 7-vec human label distribution for a vignette.
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Order matches `foundations` (probe-word order: care, fairness, ..., social).
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Reads `human_*` -- on `classic` these are direct Clifford et al. (2015) %
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distributions; on `scifi` and `ai-actor` they are inherited from the
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parent classic item (paraphrases preserve the violated foundation by
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design). Returns None if any column missing or row sums to 0.
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"""
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coarse = [_PROBE_TO_COARSE[f] for f in foundations]
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vals = [row.get(f"human_{c}") for c in coarse]
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if any(v is None for v in vals):
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return None
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arr = np.array(vals, dtype=float)
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s = float(arr.sum())
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if s <= 0:
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return None
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return arr / s
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def _js_divergence(p: np.ndarray, q: np.ndarray) -> float:
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"""Jensen-Shannon divergence in nats. Symmetric, bounded by ln 2."""
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p = p + 1e-12; q = q + 1e-12
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p = p / p.sum(); q = q / q.sum()
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m = 0.5 * (p + q)
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kl_pm = float((p * np.log(p / m)).sum())
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kl_qm = float((q * np.log(q / m)).sum())
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return 0.5 * kl_pm + 0.5 * kl_qm
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def _soft_nll(p_human: np.ndarray, p_model: np.ndarray) -> float:
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"""Soft cross-entropy: -sum_f p_human[f] log p_model[f], in nats.
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Standard quantity for matching a predicted distribution to a soft-labelled
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target. Unbounded; sensitive to confident-wrong rows.
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"""
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p_model = np.clip(p_model, 1e-12, 1.0)
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return float(-(p_human * np.log(p_model)).sum())
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def _informedness(y_true: np.ndarray, y_pred: np.ndarray, n_classes: int) -> float:
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"""Macro bookmaker informedness (multi-class Youden's J), in [-1, 1].
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Discrete companion to `mean_nll`: it reads only the argmax answer (which
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foundation the model would pick), so it moves when the *answer flips*, not
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when confidence shifts on an already-decided row. Less sensitive than the
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nats signal but better aligned with qualitative "the model changed its
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mind" reads, and the same flip-informedness family as `steering-lite`'s
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surgical informedness (there it anchors on a base model; here on the human
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argmax label). See https://github.com/wassname/steering-lite
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Per class f, one-vs-rest: J_f = TPR_f - FPR_f, where
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TPR_f = #(true==f & pred==f) / #(true==f) (sensitivity)
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FPR_f = #(true!=f & pred==f) / #(true!=f) (1 - specificity)
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Macro-averaged over classes present in `y_true`. 0 = chance (base-rate
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guessing), 1 = perfect; chance-corrected, unlike top1_acc which a model
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can inflate by always picking the majority foundation.
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Reference: Powers 2011, "Evaluation: from precision, recall and F-measure
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to ROC, informedness, markedness and correlation".
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"""
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js = []
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for f in range(n_classes):
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pos = y_true == f
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n_pos = int(pos.sum())
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if n_pos == 0:
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continue # class absent from labels -> undefined TPR, skip
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neg = ~pos
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tpr = float((y_pred[pos] == f).mean())
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fpr = float((y_pred[neg] == f).mean()) if neg.any() else 0.0
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js.append(tpr - fpr)
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return float(np.mean(js)) if js else float("nan")
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def _fit_temperature(
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scores: np.ndarray, # [N, K] pre-softmax scores (sum of fwd+rev logprobs / 2)
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p_human: np.ndarray, # [N, K] target distributions, rows sum to 1
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grid: np.ndarray | None = None,
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) -> float:
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"""Fit one temperature `T>0` minimizing mean soft-NLL of `p_human` under
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softmax(scores/T). Coarse-then-fine 1D search; cheap, deterministic, no
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autograd. Returns T*.
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Forced-choice probes are typically far overconfident (T*>>1); we search
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on a log-grid `[0.1, 50]` then refine around the minimum.
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"""
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if grid is None:
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grid = np.logspace(np.log10(0.1), np.log10(50.0), 41)
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def mean_nll_at(T: float) -> float:
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s = scores / T
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s = s - s.max(axis=1, keepdims=True)
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logp = s - np.log(np.exp(s).sum(axis=1, keepdims=True))
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return float(-(p_human * logp).sum(axis=1).mean())
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coarse = np.array([mean_nll_at(float(T)) for T in grid])
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i = int(np.argmin(coarse))
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lo = grid[max(0, i - 1)]; hi = grid[min(len(grid) - 1, i + 1)]
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fine = np.linspace(lo, hi, 41)
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fine_vals = np.array([mean_nll_at(float(T)) for T in fine])
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return float(fine[int(np.argmin(fine_vals))])
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def evaluate(
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model,
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tokenizer,
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name: ConfigName = "classic",
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vignettes: list[dict] | None = None,
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*,
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n_vignettes: int | None = None,
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conditions: tuple[str, ...] = ("other_violate",),
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max_think_tokens: int = 64,
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n_samples: int = 1,
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temperature: float = 0.0,
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top_p: float = 1.0,
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skip_special_tokens: bool = False,
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batch_size: int = 8,
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device: str | None = None,
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return_per_row: bool = False,
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verbose: int = 1,
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) -> dict[str, Any]:
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"""Run forced-choice 7-way probe per (vignette, condition).
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Args:
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model, tokenizer: HuggingFace causal LM + matching tokenizer with chat template.
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name: dataset config (`classic` / `scifi` / `ai-actor`).
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vignettes: optional pre-loaded list (overrides `name`).
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n_vignettes: optional slice — keep only the first N (after loading).
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conditions: which condition strings to score. Default =
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("other_violate",) to match Clifford 2015 classic, which is
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other-violation only. Pass ("other_violate", "self_violate")
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for both framings (doubles cost; useful for ablations).
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max_think_tokens: think budget per (row, frame). Two frames per row.
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n_samples: rollouts per direction. At N>1 we sample N think traces per
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frame and Bayesian-model-average their answer logprobs (logsumexp_n
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lp_samples - log N), then average fwd+rev as today. Requires
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`temperature > 0`. At N=1 the call is greedy (current behaviour).
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temperature: Phase-1 sampling temperature. 0 = greedy. Must be > 0 when
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n_samples > 1.
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top_p: nucleus-sampling threshold for Phase 1 (ignored when greedy).
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skip_special_tokens: passed to `tok.decode` when building `gen_text`
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for each result. Default False = return the full raw stream
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(including `</think>`, chat-template markers, etc.). Set True if
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you want the stripped text.
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batch_size: rows per forced-choice call (KV cache = batch * 2 * max_think_tokens).
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return_per_row: if True, include the per-row 7-vec p + think text in the result.
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verbose: log level (int). 0 = silent (sweeps/loops). 1 (default) = terse:
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one-line aux-stats dict + the free-reasoning DEMO B generation only,
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collapsed to 64 chars (no prompt), bracketed by blank lines so it
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stands apart from the steer demos. 2 = full: also the first row's FULL
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forced-choice trace (special tokens shown), the model-vs-human profile
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table, and the complete DEMO B (prompt + generation + SHOULD note).
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Returns:
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Dict with `table`, `profile`, `mean_js`, `mean_nll`, `mean_nll_T`,
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`median_nll_T`, `T`, `top1_acc`, `mean_pmass_allowed`, `mean_nll_json`, and `info`.
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With `return_per_row=True`, also includes `per_row` with per-row
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`p`, `score` (debiased logp per foundation), `pmass_allowed`,
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`nll_json`, `gen_text` / `gen_text_rev` (full decoded gen, no stripping),
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and `top1` / `margin`.
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"""
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if vignettes is None:
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vignettes = load_vignettes(name)
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if n_vignettes is not None:
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vignettes = vignettes[:n_vignettes]
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "left"
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if device is None:
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device = next(model.parameters()).device.type
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foundations = list(_DEFAULT_FORCED_FOUNDATIONS)
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t0 = time.time()
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per_row: list[dict] = []
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total_calls = len(vignettes) * len(conditions)
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with tqdm(total=total_calls, desc=f"forced-choice {name}", mininterval=60, maxinterval=120) as pbar:
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for cond in conditions:
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for i in range(0, len(vignettes), batch_size):
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chunk = vignettes[i: i + batch_size]
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user_prompts = [r[cond] for r in chunk]
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results = guided_rollout_forced_choice(
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model, tokenizer, user_prompts,
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foundations=foundations,
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max_think_tokens=max_think_tokens,
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n_samples=n_samples,
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temperature=temperature,
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top_p=top_p,
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skip_special_tokens=skip_special_tokens,
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verbose=(verbose >= 2) and i == 0 and cond == conditions[0],
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)
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for src, res in zip(chunk, results):
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p_vec = np.array([res.p[f] for f in foundations], dtype=float)
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score_vec = np.array([res.score[f] for f in foundations], dtype=float)
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label = _label_dist(src, foundations)
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coarse = _COARSE_NORM.get(src["foundation_coarse"], src["foundation_coarse"])
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per_row.append({
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"id": src["id"],
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"condition": cond,
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"foundation_coarse": coarse,
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"p": p_vec,
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"score": score_vec, # pre-softmax BMA'd + fwd/rev-averaged logprobs, for temperature fit
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"label": label, # may be None on unlabeled rows
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"top1": res.top1,
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"margin": res.margin,
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"pmass_allowed": res.pmass_allowed,
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"nll_json": res.nll_json,
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"think_tokens": res.think_tokens, # list[int], length N
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"think_tokens_rev": res.think_tokens_rev, # list[int], length N
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"emitted_close": res.emitted_close, # list[bool], length N
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"emitted_close_rev": res.emitted_close_rev, # list[bool], length N
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"gen_text": res.gen_text, # list[str], length N
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"gen_text_rev": res.gen_text_rev, # list[str], length N
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"lp_fwd_samples": res.lp_fwd_samples, # [N, K]
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"lp_rev_samples": res.lp_rev_samples, # [N, K]
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})
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pbar.update(len(chunk))
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elapsed = time.time() - t0
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n_rows = len(per_row)
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n_labeled = sum(1 for r in per_row if r["label"] is not None)
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# Tokens-per-second: per row, sum N samples × (fwd + rev) think lengths.
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total_gen_tokens = sum(
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sum(r["think_tokens"]) + sum(r["think_tokens_rev"])
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for r in per_row
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)
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tps = total_gen_tokens / elapsed if elapsed > 0 else 0.0
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logger.info(
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f"{name}: {n_rows} rows in {elapsed:.1f}s ({n_rows/elapsed:.1f} rows/s, "
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f"~{tps:.0f} tok/s); {n_labeled}/{n_rows} have label dist"
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)
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# Per-sample think-token distribution across all (row × frame × sample).
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# If most samples are well below max_think_tokens, the cap can be lowered.
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nt = sorted(t for r in per_row for t in r["think_tokens"] + r["think_tokens_rev"])
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n_closed = sum(sum(r["emitted_close"]) + sum(r["emitted_close_rev"]) for r in per_row)
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if nt:
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n = len(nt)
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def _q(p): return nt[min(n - 1, int(p * n))]
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logger.info(
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f" think_tokens: median={_q(0.5)} p75={_q(0.75)} p90={_q(0.9)} "
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f"p99={_q(0.99)} max={nt[-1]} emitted_close={n_closed}/{n}"
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)
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# === per-foundation aggregates ===
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rows = []
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for fi, fname in enumerate(foundations):
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coarse = _PROBE_TO_COARSE[fname]
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ov = [r["p"][fi] for r in per_row if r["condition"] == "other_violate"]
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sv = [r["p"][fi] for r in per_row if r["condition"] == "self_violate"]
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# Cross-vignette Pearson with label[fi] on labeled rows.
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labeled = [r for r in per_row if r["label"] is not None and r["condition"] == "other_violate"]
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if len(labeled) >= 5:
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x = np.array([r["p"][fi] for r in labeled])
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y = np.array([r["label"][fi] for r in labeled])
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if x.std() > 0 and y.std() > 0:
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pr = float(np.corrcoef(x, y)[0, 1])
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else:
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pr = float("nan")
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else:
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pr = float("nan")
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rows.append({
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"foundation": coarse,
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"n": len(ov),
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"mean_p_other": float(np.mean(ov)) if ov else float("nan"),
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"mean_p_self": float(np.mean(sv)) if sv else float("nan"),
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"gap": (float(np.mean(ov)) - float(np.mean(sv))) if ov and sv else float("nan"),
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"pearson_label": pr,
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})
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table = pd.DataFrame(rows)
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# === headline scalars (need labels) ===
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labeled_rows = [r for r in per_row if r["label"] is not None]
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if labeled_rows:
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js_vals = np.array([_js_divergence(r["p"], r["label"]) for r in labeled_rows])
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mean_js = float(js_vals.mean())
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median_js = float(np.median(js_vals))
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y_pred = np.array([np.argmax(r["p"]) for r in labeled_rows])
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y_true = np.array([np.argmax(r["label"]) for r in labeled_rows])
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top1_acc = float(np.mean(y_pred == y_true))
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# Chance-corrected, argmax-only flip metric (see _informedness).
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informedness = _informedness(y_true, y_pred, len(foundations))
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# Soft NLL at T=1 (raw, overconfident-dominated).
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nll_raw = np.array([_soft_nll(r["label"], r["p"]) for r in labeled_rows])
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mean_nll = float(nll_raw.mean())
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median_nll = float(np.median(nll_raw))
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# Temperature scaling: fit one T* on `classic` (or whatever set we're on)
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# to minimise mean soft NLL. T cancels for steering deltas; only matters
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# for absolute model-vs-human comparison.
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scores = np.stack([r["score"] for r in labeled_rows])
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humans = np.stack([r["label"] for r in labeled_rows])
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T = _fit_temperature(scores, humans)
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s_scaled = scores / T
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s_scaled = s_scaled - s_scaled.max(axis=1, keepdims=True)
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p_scaled = np.exp(s_scaled)
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p_scaled = p_scaled / p_scaled.sum(axis=1, keepdims=True)
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nll_T = -(humans * np.log(np.clip(p_scaled, 1e-12, 1.0))).sum(axis=1)
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mean_nll_T = float(nll_T.mean())
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median_nll_T = float(np.median(nll_T))
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# Mean profile across vignettes (model and human), each a 7-vec on the
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# simplex. The natural object to compare moral character across models.
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model_profile = np.stack([r["p"] for r in labeled_rows]).mean(axis=0)
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human_profile = humans.mean(axis=0)
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profile = pd.DataFrame({
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"foundation": [_PROBE_TO_COARSE[f] for f in foundations],
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"human": human_profile,
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"model": model_profile,
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"model_T": p_scaled.mean(axis=0),
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})
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else:
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mean_js = median_js = top1_acc = informedness = None
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mean_nll = median_nll = mean_nll_T = median_nll_T = None
|
||
T = None
|
||
profile = None
|
||
|
||
mean_pmass_allowed = (
|
||
float(np.mean([r["pmass_allowed"] for r in per_row]))
|
||
if per_row else None
|
||
)
|
||
mean_nll_json = (
|
||
float(np.mean([r["nll_json"] for r in per_row]))
|
||
if per_row else None
|
||
)
|
||
|
||
# --- verbose readout: level 1 (default) = one-line aux stats + a 64-char free-form
|
||
# generation, bracketed by blank lines; level 2 = the full first-row trace, profile
|
||
# table, and complete DEMO B. Inline so a reader confirms format-following without
|
||
# opening a separate file (token-efficient-logging). ---
|
||
demos: dict[str, Any] | None = None
|
||
if verbose and per_row:
|
||
r0 = per_row[0]
|
||
# one-line quantitative readout, kept at every verbose level (it IS the signal)
|
||
aux = {k: (round(v, 4) if isinstance(v, float) else v) for k, v in {
|
||
"top1_acc": top1_acc, "mean_js": mean_js, "mean_nll_T": mean_nll_T,
|
||
"T": T, "informedness": informedness, "mean_pmass_allowed": mean_pmass_allowed,
|
||
}.items() if v is not None}
|
||
logger.info("aux stats: " + json.dumps(aux))
|
||
if verbose >= 2:
|
||
# full first-row score dump + profile table (DEMO A trace already printed
|
||
# above in the rollout, first batch).
|
||
logger.info(
|
||
f"first row [{name}] id={r0['id']} cond={r0['condition']} scored p "
|
||
"(fwd+rev BMA, renormalized over the 7 foundations):\n"
|
||
"SHOULD: mass concentrates on the violated foundation; if it is flat or "
|
||
"pmass_allowed~0 the model did not answer in-format and the row is noise.\n"
|
||
+ " ".join(f"{f}={p:.3f}" for f, p in zip(foundations, r0["p"]))
|
||
+ f"\n top1={r0['top1']} pmass_allowed={r0['pmass_allowed']:.3f} nll_json={r0['nll_json']:.3f}"
|
||
)
|
||
if profile is not None:
|
||
logger.info(
|
||
"profile (mean p over vignettes; model vs human on the same 7-simplex):\n"
|
||
+ profile.to_string(index=False, float_format=lambda v: f"{v:.3f}")
|
||
)
|
||
|
||
# DEMO B: free reasoning on a single vignette (bs=1, one time). The forced
|
||
# readout (DEMO A) prefills the answer slot so it shows no real reasoning; this
|
||
# lets the chain-of-thought run to completion. bs=1 frees the batch memory, so
|
||
# spend a generous think budget (scaled by dropped batch, floored, capped).
|
||
demo_budget = min(2048, max(512, max_think_tokens * batch_size))
|
||
demo_prompt, demo_gen = free_generation_demo(
|
||
model, tokenizer, vignettes[0][conditions[0]],
|
||
foundations=foundations, max_think_tokens=demo_budget,
|
||
temperature=temperature, top_p=top_p,
|
||
)
|
||
if verbose >= 2:
|
||
logger.info(
|
||
f"\n--- DEMO B: free reasoning (bs=1, think budget={demo_budget}, "
|
||
f"temp={temperature}) [{name}] id={r0['id']} ---\n"
|
||
f"{demo_prompt}{demo_gen}\n"
|
||
"SHOULD: a real chain-of-thought that ends in a moral-foundation choice. "
|
||
"If it is empty or degenerate the model is not reasoning at this budget; "
|
||
"if it answers a different foundation than DEMO A's top1, the readout and "
|
||
"free reasoning disagree (worth noting).\n--- end DEMO B ---\n"
|
||
)
|
||
else: # terse default: generation only, whitespace-collapsed to 64 chars, bracketed
|
||
gen64 = " ".join(demo_gen.split())[:64]
|
||
logger.info(f"\nfree-form [{name}] id={r0['id']}: {gen64!r}\n")
|
||
demos = {
|
||
"forced_think": per_row[0]["gen_text"][0], # DEMO A think (degenerate at low budget)
|
||
"forced_top1": per_row[0]["top1"],
|
||
"free_prompt": demo_prompt,
|
||
"free_gen": demo_gen,
|
||
"free_think_budget": demo_budget,
|
||
}
|
||
|
||
info = {
|
||
"name": name,
|
||
"n_rows": n_rows,
|
||
"n_labeled": n_labeled,
|
||
"elapsed_s": elapsed,
|
||
"median_js": median_js,
|
||
"max_js": math.log(2),
|
||
"mean_nll": mean_nll,
|
||
"median_nll": median_nll,
|
||
"median_nll_T": median_nll_T,
|
||
# Macro bookmaker informedness (Youden's J) of model argmax vs human
|
||
# argmax, in [-1, 1]. Discrete answer-flip metric; 0 = chance,
|
||
# chance-corrected unlike top1_acc. See _informedness.
|
||
"informedness": informedness,
|
||
# Mean pmass_format: average prob mass on the K foundation answer
|
||
# tokens at the JSON answer slot, across rows × framings. In [0, 1].
|
||
# Direct coherence check for forced-choice: drops when the model
|
||
# emits non-foundation tokens (gibberish, refusal, format collapse),
|
||
# independent of which foundation is picked. Higher = more
|
||
# "in-format"; a sharp drop after steering signals coherence loss.
|
||
"mean_pmass_allowed": mean_pmass_allowed,
|
||
# Mean NLL in nats/token over the assistant prefill content. Perplexity
|
||
# is exp(mean_nll_json).
|
||
"mean_nll_json": mean_nll_json,
|
||
}
|
||
|
||
out: dict[str, Any] = {
|
||
"table": table,
|
||
"profile": profile, # 7-row DataFrame: foundation, human, model, model_T
|
||
"mean_js": mean_js,
|
||
"mean_nll": mean_nll,
|
||
"mean_nll_T": mean_nll_T, # temperature-scaled soft cross-entropy in nats
|
||
"median_nll_T": median_nll_T,
|
||
"T": T, # fitted temperature (>1 = model is overconfident)
|
||
"top1_acc": top1_acc,
|
||
"informedness": informedness, # macro Youden's J, model vs human argmax, in [-1, 1]
|
||
"mean_pmass_allowed": mean_pmass_allowed,
|
||
"mean_nll_json": mean_nll_json,
|
||
"info": info,
|
||
"demos": demos, # DEMO A (forced think + top1) + DEMO B (free reasoning); None if not verbose
|
||
}
|
||
if return_per_row:
|
||
out["per_row"] = per_row
|
||
return out
|
||
|