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wassname
81340e3272
scripts/diag_axis.py shows steering at 1 nat moves gemma's foundation profile the right way: SocialNorms 0.68->0.42, Care 0.21->0.33, coherence 0.72->0.88. Authority is ~0 on this model (no headroom), so: - eval reports all foundations; trait axis = SocialNorms (down) + Care (up) - map.html plots Care vs SocialNorms - add gen_alpha=1.5: over-steer generation into the incoherent regime so the heal (Q1) has work to do (at 1 nat coherence improved, nothing to heal) - results.py groups on coherence/socialnorms/care Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
"""`just results`: group results.tsv into comparable arms and print a markdown table.
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Grouping key is `reg` (the regulariser under test, U2); argv last so each row is
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copy-paste reproducible. Edit GROUP when the knob under test changes.
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"""
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from pathlib import Path
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import polars as pl
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from tabulate import tabulate
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RESULTS_TSV = Path(__file__).resolve().parents[1] / "results.tsv"
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GROUP = ["reg"] # all-else-equal grouping; the arm under test
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if not RESULTS_TSV.exists():
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raise SystemExit(f"no {RESULTS_TSV.name} yet; run something first")
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df = pl.read_csv(RESULTS_TSV, separator="\t")
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agg = (
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df.group_by(GROUP)
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.agg(
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pl.col("coherence").mean().round(3),
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pl.col("socialnorms").mean().round(3), # trait axis: lower = more trait
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pl.col("care").mean().round(3),
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pl.col("care").std().round(3).alias("care_sd"),
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pl.len().alias("n"),
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pl.col("seed").cast(pl.Utf8).sort().str.join(",").alias("seeds"),
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pl.col("argv").first(),
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)
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.sort("care", descending=True)
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)
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print(tabulate(agg.to_pandas(), headers="keys", tablefmt="pipe", floatfmt="+.3f"))
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