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wassnameandClaudypoo
7f873ffb42
Canonical home (moralmaps.metrics) for the continuous coherence-gated selectivity sel_gated = (on - 0.1*off)*coh^2 plus the behavioral si_flips cross-check, imported (not re-forked) by steering-lite and j-steer so the definition can't silently diverge. README Measurement section defines both. 4 unit tests. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
61 lines
2.9 KiB
Python
61 lines
2.9 KiB
Python
"""moralmaps: moral and value instruments for LLMs.
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One answer-token reader, two reducer families:
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- nominal MFV vignettes: answer = foundation category; `evaluate` reports a
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7-way profile plus label-match metrics.
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- ordinal Likert questionnaires: answer = scale point; `administer` reports
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E, C, agree-vs-disagree log-odds, entropy, and pmass diagnostics.
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High-level usage:
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from moralmaps import evaluate
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B").cuda()
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rep = evaluate(model, tok, name="classic")
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print(rep["table"]) # per-foundation
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print(rep["top1_acc"]) # argmax accuracy vs label
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print(rep["mean_nll_T"]) # temperature-scaled soft NLL vs label dist
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Lower-level: see `guided_rollout_forced_choice` in `moralmaps.guided`.
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"""
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from .data import load_vignettes, load_all_vignettes, CONFIGS, ConfigName
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from .eval import evaluate, CONDITIONS, EvalResult, EvalRow, EvalInfo
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from .guided import guided_rollout_forced_choice, _DEFAULT_FORCED_FOUNDATIONS
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from .instrument import Instrument, InstrItem, per_item_categorical, reduce_nominal, reduce_ordinal
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from .instruments import get as get_instrument, INSTRUMENTS, build_instrument
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from .read import read_items, resolve_answer_ids, build_user_content
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from .readouts import expected_score, logit_contrast, logodds_agree, entropy
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from .metrics import gated_selectivity, si_flips, clr_per_row, OFF_WEIGHT
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from .administer import administer
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def __getattr__(name: str):
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# `maps` pulls matplotlib; load it lazily so `import moralmaps` stays headless and fast for the
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# numeric-only consumers (steering-lite). `moralmaps.maps.plot_*` still works -- first access
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# triggers the import here.
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if name == "maps":
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import importlib
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return importlib.import_module(".maps", __name__)
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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# The front door. Other symbols above stay importable (e.g. read_items for item subsets,
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# per_item_categorical, build_instrument) but are helper internals, kept out of `import *`.
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__all__ = [
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# entrypoints
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"evaluate", "administer", "get_instrument", "read_items",
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# ordinal readouts (pure functions of the raw answer-token logprobs)
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"expected_score", "logit_contrast", "logodds_agree", "entropy",
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# headline steering metrics (shared canonical defs; imported by steering-lite + j-steer)
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"gated_selectivity", "si_flips", "clr_per_row", "OFF_WEIGHT",
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# types consumers build / subset
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"Instrument", "InstrItem", "EvalResult", "EvalRow", "EvalInfo", "reduce_nominal", "reduce_ordinal",
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# data API
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"load_vignettes", "load_all_vignettes", "CONFIGS", "ConfigName", "CONDITIONS",
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# lower-level rollout + lazy plotting
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"guided_rollout_forced_choice", "maps",
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]
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