mirror of
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TheTomandClaude Opus 4.7
64c19e76d5
Audit follow-up to the earlier skip-axis fix (728cd84). The stub axis
result dataclasses still carry score=100, so without these changes:
- json_report still wrote 'axes.gtm.score: 100.0' for a skipped axis,
visible to anyone parsing the JSON or running 'refract compare'
- 'refract compare' showed 100.0 in the Traj/KLD column for skipped
reports, which made cross-report comparison falsely flatter the
skipped run
Now: when CompositeScore.gtm_score is None we write axes.gtm.score = null
in the JSON. 'refract compare' reads 'skipped' or score=None and prints
'skip' in that column instead of the stub 100.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-Authored-By: tturney1@gmail.com <tturney1@gmail.com>
475 lines
18 KiB
Python
475 lines
18 KiB
Python
"""REFRACT v0.1 report card formatter.
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Produces:
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- text() : ANSI-coloured human-readable report card with bar charts.
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- json() : machine-readable dict suitable for ML pipelines.
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No external dependencies: plain ANSI escapes for colour. Set NO_COLOR=1 in
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the env to suppress them.
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"""
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from __future__ import annotations
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import datetime as _dt
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import json as _json
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import os
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from dataclasses import asdict
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from typing import Optional
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from .axes.gtm import GTMResult
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from .axes.kld import KLDResult
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from .axes.plad import PLADResult
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from .axes.rniah import RNIAHResult
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from .axes.trajectory import TrajectoryResult
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from .score import CompositeScore, MIN_FLOOR, band, interpret_pattern
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def _use_color() -> bool:
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return not os.environ.get("NO_COLOR")
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def _c(code: str, s: str) -> str:
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if not _use_color():
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return s
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return f"\033[{code}m{s}\033[0m"
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def _wrap_lines(text: str, indent: str = "", width: int = 68) -> list[str]:
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"""Word-wrap a paragraph to ``width`` chars with a leading ``indent``.
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Used by the v0.2.0 Diagnosis block so multi-sentence interpretations
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don't overflow the report card on narrow terminals.
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"""
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import textwrap
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return textwrap.wrap(
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text, width=width,
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initial_indent=indent, subsequent_indent=indent,
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) or [indent.rstrip()]
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def _band_color(b: str) -> str:
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return {
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"EXCELLENT": "32", # green
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"PASS": "32",
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"DEGRADED": "33", # yellow
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"FAIL": "31", # red
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}.get(b, "0")
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# v0.1.4: layman-readable one-line interpretation per band.
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_BAND_PROSE: dict[str, str] = {
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"EXCELLENT": "Indistinguishable from the reference. Safe to deploy.",
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"PASS": "Minor drift; safe to deploy in most uses.",
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"DEGRADED": "Visible drift. Audit on your workload before deploying.",
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"FAIL": "Material quality loss. Treat as broken.",
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}
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# v0.1.4: short, human description per axis. Used to label what each
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# axis actually measures so layman-readers can map "Axis A score is bad"
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# to a real-world consequence without reading the paper.
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_AXIS_PROSE: dict[str, str] = {
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"gtm": "Token-level agreement with the fp16 reference.",
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"trajectory": "Token-level agreement with the fp16 reference.",
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"kld": "Distribution-level divergence from the fp16 reference.",
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"rniah": "Long-context retrieval quality vs the reference.",
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"plad": "Robustness to small prompt changes vs the reference.",
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}
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def _axis_label(name: str) -> str:
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"""Map internal axis key → display label in the report card."""
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return {
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"gtm": "Axis A GTM ",
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"trajectory": "Axis A Trajectory",
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"kld": "Axis B KLD ",
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"rniah": "Axis C R-NIAH ",
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"plad": "Axis D PLAD ",
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}.get(name, name)
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def _axis_line(name: str, score: Optional[float], bar_width: int = 40) -> str:
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"""One report line per axis: label, score, bar, band, prose.
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When ``score is None`` the axis was skipped via ``--skip-gtm`` /
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``--skip-kld``; render it explicitly as ``n/a`` so a reader doesn't
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interpret a missing axis as a perfect 100.
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"""
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if score is None:
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empty_bar = " " * bar_width
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skipped = _c("33", "skipped ")
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return (
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f" {_axis_label(name)}: n/a "
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f"{empty_bar} {skipped} "
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f"{_AXIS_PROSE.get(name, '')}"
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)
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b = band(score)
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band_str = _c(_band_color(b), f"{b:<9}")
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return (
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f" {_axis_label(name)}: {score:6.2f} "
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f"{_bar(score, bar_width)} {band_str} "
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f"{_AXIS_PROSE.get(name, '')}"
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)
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def _bar(score: float, width: int = 40) -> str:
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"""ANSI bar of length ``width`` representing 0–100."""
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fill = int(round(width * max(0.0, min(score, 100.0)) / 100.0))
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bar = "#" * fill + "-" * (width - fill)
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color = _band_color(band(score))
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return _c(color, f"[{bar}]")
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def text_report(
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*,
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model: str,
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reference_label: str,
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candidate_label: str,
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composite: CompositeScore,
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gtm: GTMResult,
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kld: KLDResult,
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rniah: Optional[RNIAHResult] = None,
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plad: Optional[PLADResult] = None,
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extras: Optional[dict] = None,
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) -> str:
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"""Render the report card as a human-readable string."""
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lines: list[str] = []
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bar_width = 40
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# Header
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lines.append("=" * 72)
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lines.append(_c("1", " REFRACT v0.2 — Reference-anchored Robust Acid-test"))
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lines.append(_c("2",
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" Scoring: 0 = broken, 100 = matches the fp16 reference. "
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"Higher is better."))
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lines.append("=" * 72)
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lines.append(f" model : {model}")
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lines.append(f" reference : {reference_label}")
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lines.append(f" candidate : {candidate_label}")
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lines.append(f" timestamp : {_dt.datetime.now().isoformat(timespec='seconds')}")
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lines.append("-" * 72)
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# Floor
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if composite.floor_score is not None:
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floor_ok = composite.floor_ok
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tag = _c("32", "OK") if floor_ok else _c("31", "FAIL")
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lines.append(
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f" Noise floor (ref vs ref): "
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f"{composite.floor_score:6.2f} (min {MIN_FLOOR}) [{tag}]"
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)
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else:
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lines.append(
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_c("33", " Noise floor: NOT MEASURED — pass --measure-floor to verify."),
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)
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lines.append("-" * 72)
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# Composite
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band_str = _c(_band_color(composite.band), composite.band)
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# Count axes that contributed to the harmonic mean so the gloss is honest
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# about what produced the number. Skipped axes (--skip-gtm / --skip-kld)
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# contribute None to composite_score and are excluded here too.
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n_axes = (
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(1 if composite.gtm_score is not None else 0)
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+ (1 if composite.kld_score is not None else 0)
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+ (1 if composite.rniah_score is not None else 0)
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+ (1 if composite.plad_score is not None else 0)
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)
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lines.append(
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_c("1", f" REFRACT score : {composite.composite:6.2f} "
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f"{_bar(composite.composite, bar_width)} {band_str}")
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)
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lines.append(f" (harmonic mean of {n_axes} axes; any single low axis "
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f"drops the score hard)")
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# v0.1.4: layman-readable interpretation. The headline tells techies a
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# number; this line tells everyone else what the number means.
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lines.append(f" → { _BAND_PROSE.get(composite.band, '') }")
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lines.append("")
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# v0.1.4: per-axis lines now carry per-axis bands and a short prose
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# description of what that axis measures. So a "DEGRADED" composite
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# comes with a per-axis breakdown showing which surface degraded.
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axis_a_key = "trajectory" if isinstance(gtm, TrajectoryResult) else "gtm"
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lines.append(_axis_line(axis_a_key, composite.gtm_score, bar_width))
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lines.append(_axis_line("kld", composite.kld_score, bar_width))
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if composite.rniah_score is not None:
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lines.append(_axis_line("rniah", composite.rniah_score, bar_width))
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if composite.plad_score is not None:
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lines.append(_axis_line("plad", composite.plad_score, bar_width))
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lines.append("-" * 72)
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# v0.2.0: pattern-matched plain-English diagnosis. Tells the user what
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# the per-axis pattern means in human terms (e.g. "decode distribution
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# broken but reasoning intact") and what to consider next. Distinct from
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# the layman summary line under the composite, which only describes the
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# severity band.
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diagnosis = interpret_pattern(
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gtm_score=composite.gtm_score,
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kld_score=composite.kld_score,
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rniah_score=composite.rniah_score,
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plad_score=composite.plad_score,
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)
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if diagnosis:
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lines.append(_c("1", " Diagnosis"))
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for d in diagnosis:
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for chunk in _wrap_lines(d, indent=" ", width=68):
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lines.append(chunk)
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lines.append("-" * 72)
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# GTM / Trajectory diagnostics — suppress if axis was skipped, since
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# the diagnostic block on a stub result reads as "0 prompts, all matched"
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# which is misleading.
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if composite.gtm_score is not None:
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lines.append(" GTM diagnostics")
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lines.append(f" prompts : {gtm.n_prompts}")
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lines.append(f" tokens decoded each : {gtm.n_tokens_each}")
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lines.append(f" full match rate : {gtm.full_match_rate*100:5.1f} %")
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if gtm.median_first_divergence is not None:
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lines.append(
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f" median first divergence : token {gtm.median_first_divergence}"
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)
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else:
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lines.append(" median first divergence : (all matched)")
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lines.append(
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f" mean prefix agreement : {gtm.mean_prefix_agreement_length:5.1f} tokens"
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)
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lines.append(
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f" mean cand / ref length : {gtm.mean_cand_length:5.1f} / "
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f"{gtm.mean_ref_length:5.1f} tokens"
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)
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if gtm.notes:
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for n in gtm.notes:
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lines.append(_c("33", f" NOTE: {n}"))
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else:
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lines.append(_c("33",
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" GTM/Trajectory: SKIPPED via --skip-gtm; not measured."))
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# KLD diagnostics
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if composite.kld_score is not None:
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lines.append("")
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lines.append(" KLD diagnostics")
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lines.append(f" chunks x ctx : {kld.chunks} x {kld.ctx}")
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lines.append(f" mean KLD (nats) : {kld.mean_kld:.6f}")
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if kld.ppl is not None:
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lines.append(f" candidate PPL : {kld.ppl:.4f}")
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if kld.rms_dp_pct is not None:
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lines.append(f" RMS Δp (vs reference) : {kld.rms_dp_pct:.2f} %")
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if kld.same_topp_pct is not None:
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lines.append(f" same top-p (vs reference) : {kld.same_topp_pct:.2f} %")
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else:
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lines.append("")
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lines.append(_c("33",
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" KLD: SKIPPED via --skip-kld; not measured."))
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# R-NIAH diagnostics
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if rniah is not None:
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lines.append("")
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lines.append(" R-NIAH diagnostics")
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lines.append(f" needle keyword : {rniah.password_keyword}")
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lines.append(f" cells run : {rniah.n_cells}")
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if rniah.skipped_cells:
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lines.append(
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f" cells skipped (length>ctx) : {len(rniah.skipped_cells)}"
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)
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if rniah.cells:
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lines.append(" per-cell (length, pos) → base_acc / cand_acc / degradation:")
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for c in rniah.cells:
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lines.append(
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f" ({c.length:>5}, {c.position:.2f}) → "
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f"{c.base_acc:.2f} / {c.cand_acc:.2f} / {c.degradation:.2f}"
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)
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if rniah.notes:
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for n in rniah.notes:
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lines.append(_c("33", f" NOTE: {n}"))
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# PLAD diagnostics
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if plad is not None:
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lines.append("")
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lines.append(" PLAD diagnostics")
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lines.append(f" prompts × perturbations : "
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f"{plad.n_prompts} × {plad.n_perturbations}")
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import math as _math
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for pert, score in plad.per_perturbation_score.items():
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if not isinstance(score, (int, float)) or _math.isnan(score):
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# Skipped perturbation (no eligible word, no synonym match).
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# Display as "skipped" rather than "nan FAIL" which would
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# misread as a failure rather than an inapplicable test.
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ptag = _c("33", f"{'skipped':>6} {'n/a':<9}")
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else:
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ptag = _c(_band_color(band(score)), f"{score:6.2f} {band(score):<9}")
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lines.append(f" {pert:<10} : {ptag}")
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if plad.notes:
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for n in plad.notes:
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lines.append(_c("33", f" NOTE: {n}"))
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if composite.notes:
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lines.append("-" * 72)
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for n in composite.notes:
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lines.append(_c("33", f" NOTE: {n}"))
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if extras:
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lines.append("-" * 72)
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for k, v in extras.items():
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lines.append(f" {k}: {v}")
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lines.append("=" * 72)
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return "\n".join(lines)
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def json_report(
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*,
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model: str,
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reference_label: str,
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candidate_label: str,
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composite: CompositeScore,
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gtm: GTMResult,
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kld: KLDResult,
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rniah: Optional[RNIAHResult] = None,
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plad: Optional[PLADResult] = None,
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include_per_prompt: bool = True,
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extras: Optional[dict] = None,
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) -> dict:
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"""Return a JSON-serialisable dict twin of the text report."""
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gtm_dict = asdict(gtm)
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if not include_per_prompt:
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gtm_dict.pop("per_prompt", None)
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composite_dict = asdict(composite)
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# Flatten the composite scalar to top-level so consumers can read
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# `d['composite']` as a number directly. Keep the full breakdown under
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# `composite_detail` for diagnostics.
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composite_scalar = composite_dict.pop("composite")
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composite_band = composite_dict.pop("band")
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def _band_or_skipped(s):
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return band(s) if s is not None else "skipped"
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# When an axis was skipped, the underlying result dataclass still has a
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# stub score=100. Null out the JSON 'score' field so downstream readers
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# (compare, leaderboards, paper tables) don't pick up the stub as real.
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if composite.gtm_score is None:
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gtm_dict["score"] = None
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kld_dict = asdict(kld)
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if composite.kld_score is None:
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kld_dict["score"] = None
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axes_block: dict = {
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"gtm": {
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**gtm_dict,
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"band": _band_or_skipped(composite.gtm_score),
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"skipped": composite.gtm_score is None,
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"description": _AXIS_PROSE["gtm"],
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},
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"kld": {
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**kld_dict,
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"band": _band_or_skipped(composite.kld_score),
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"skipped": composite.kld_score is None,
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"description": _AXIS_PROSE["kld"],
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},
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}
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if rniah is not None and composite.rniah_score is not None:
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rn_dict = asdict(rniah)
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# v0.3.1: confidence guard. If base_acc averages below 0.2 across
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# cells, the model isn't engaging the retrieval task at all; an
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# R-NIAH score of 100 is then a noise-floor reading rather than
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# real signal.
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cells = rniah.cells
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base_avg = (sum(c.base_acc for c in cells) / len(cells)) if cells else 0.0
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rn_dict["confidence"] = "low" if base_avg < 0.2 else "ok"
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rn_dict["base_acc_avg"] = base_avg
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axes_block["rniah"] = {
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**rn_dict,
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"band": band(composite.rniah_score),
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"description": _AXIS_PROSE["rniah"],
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}
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if plad is not None and composite.plad_score is not None:
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pl_dict = asdict(plad)
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# v0.3.1: confidence guard. Per-perturbation scores that are NaN
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# indicate the perturbation never fired (typo on prompts with no
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# ≥4-char words, paraphrase with no synonym matches). Mark them
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# as "skipped" so a reader doesn't read NaN as FAIL.
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import math as _math
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skipped = [k for k, v in plad.per_perturbation_score.items()
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if not isinstance(v, (int, float)) or _math.isnan(v)]
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pl_dict["skipped_perturbations"] = skipped
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pl_dict["confidence"] = (
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"partial" if skipped else "ok"
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)
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axes_block["plad"] = {
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**pl_dict,
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"band": band(composite.plad_score),
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"description": _AXIS_PROSE["plad"],
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}
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# v0.3.1: framework version + environment metadata so cross-person
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# report comparison is reproducible. Backend metadata (llama.cpp commit,
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# mlx-lm version, etc.) is also captured if the active backend can
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# supply it.
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try:
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from . import __version__ as _fv
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except Exception:
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_fv = "unknown"
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env_meta: dict = {}
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try:
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from .runner import get_active_backend
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bk = get_active_backend()
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if bk is not None:
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from pathlib import Path as _P
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env_meta = bk.model_metadata(model=_P(model)) if model else {"backend": bk.name}
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except Exception:
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pass
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# v0.3.2: capture sanitized repro command so the HTML report shows the
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# actual `refract score ...` invocation that produced the result rather
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# than whatever script re-rendered the JSON. Strip /Users/<name>/ to ~/
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# to avoid leaking personal paths in shared reports. Gated on "argv
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# looks like a refract CLI run" so regen/test scripts don't pollute
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# the field.
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try:
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import sys as _sys, shlex as _shlex, os as _os
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argv = _sys.argv
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looks_like_refract = any(
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"refract.cli" in a or a.endswith("/refract") or a == "refract"
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for a in argv
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)
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if looks_like_refract:
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home = _os.path.expanduser("~")
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parts = []
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for a in argv:
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if home and a.startswith(home + "/"):
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a = "~" + a[len(home):]
|
||
parts.append(_shlex.quote(a))
|
||
repro_cmd = " ".join(parts)
|
||
else:
|
||
repro_cmd = ""
|
||
except Exception:
|
||
repro_cmd = ""
|
||
|
||
return {
|
||
"schema": "refract.report.v0.3.1",
|
||
"framework_version": _fv,
|
||
"environment": env_meta,
|
||
"repro_command": repro_cmd,
|
||
"timestamp": _dt.datetime.now().isoformat(timespec="seconds"),
|
||
# v0.2.0: be explicit about score direction so machine consumers
|
||
# (and humans converting from PPL where lower is better) don't
|
||
# invert the comparison.
|
||
"score_direction": "higher_is_better",
|
||
"score_range": [0, 100],
|
||
"model": model,
|
||
"reference": reference_label,
|
||
"candidate": candidate_label,
|
||
"composite": composite_scalar,
|
||
"band": composite_band,
|
||
# v0.1.4: layman-readable one-liner alongside the numeric score
|
||
# so non-techies can read the report without grepping the paper.
|
||
"summary": _BAND_PROSE.get(composite_band, ""),
|
||
# v0.2.0: pattern-matched plain-English diagnosis of the per-axis
|
||
# band combination. Empty list means all axes were intact.
|
||
"diagnosis": interpret_pattern(
|
||
gtm_score=composite.gtm_score,
|
||
kld_score=composite.kld_score,
|
||
rniah_score=composite.rniah_score,
|
||
plad_score=composite.plad_score,
|
||
),
|
||
"composite_detail": composite_dict,
|
||
"axes": axes_block,
|
||
"extras": extras or {},
|
||
}
|
||
|
||
|
||
def to_json_string(report: dict) -> str:
|
||
return _json.dumps(report, indent=2, default=str)
|