Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
"""Tests for post-hoc E2 analysis (analysis.py) and the tail-band metric.
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Encodes the work-order's verified numbers: the operational g* on a synthetic E2 set, the
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stationary reducer, and the band-wise survival ordering on a grounded Zipf tail.
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"""
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import numpy as np
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import pandas as pd
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import pytest
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from inheritance.analysis import reduce_to_stationary, critical_grounding
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from inheritance.metrics import tail_band_metrics
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def _H_eq(n, m, Hs):
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return Hs * m * (2 * n + m - 1) / (n + 2 * n * m + m * m)
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def _synthetic_E2(n=200, Hs=0.95, reps=100, seed=1):
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gs = [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
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rng = np.random.default_rng(seed)
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rows = []
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for g in gs:
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m = 0 if g == 0 else int(round(g * n / (1 - g)))
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Htrue = 0.10 if g == 0 else _H_eq(n, m, Hs) # g=0: finite-time artifact
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for s in range(reps):
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rows.append({"g": g, "seed": s, "heterozygosity": Htrue + rng.normal(0, 0.004)})
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return pd.DataFrame(rows)
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def test_critical_grounding_matches_known_crossing():
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stat = _synthetic_E2()
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r = critical_grounding(stat, H_star=0.95, frac=0.95, seed=7)
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assert r["status"] == "ok"
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assert 0.03 < r["g_star"] < 0.07 # ~0.047 for frac=0.95
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assert r["ci_low"] <= r["g_star"] <= r["ci_high"]
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r90 = critical_grounding(stat, H_star=0.95, frac=0.90, seed=7)
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assert r90["g_star"] < r["g_star"] # lower bar -> smaller g*
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def test_reduce_to_stationary_recovers_plateau():
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# constant plateau + noise -> mean ~ plateau
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rng = np.random.default_rng(0)
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rows = []
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for g, plateau in [(0.02, 0.843), (0.05, 0.908)]:
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for s in range(20):
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for t in range(300):
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v = (0.95 if t < 50 else plateau) + rng.normal(0, 0.003)
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rows.append({"g": g, "seed": s, "generation": t, "heterozygosity": v})
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red = reduce_to_stationary(pd.DataFrame(rows), replicate_col="seed", last_frac=0.33)
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means = red.groupby("g")["heterozygosity"].mean()
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assert means[0.02] == pytest.approx(0.843, abs=0.01)
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assert means[0.05] == pytest.approx(0.908, abs=0.01)
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def test_tail_band_deep_below_shallow():
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# grounded dynamics on a Zipf tail: deepest band <= shallowest band, replicate-avg
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K = 1000
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s = 1.1
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w = 1.0 / np.arange(1, K + 1) ** s
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p_star = w / w.sum()
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tail_mask = p_star < 1e-3
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n, m = 200, 10
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FA = np.zeros(4)
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for r in range(20):
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rng = np.random.default_rng(1000 + r)
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p = p_star.copy()
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for _ in range(600):
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c = rng.multinomial(n, p) + rng.multinomial(m, p_star)
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p = c / c.sum()
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fa, _ = tail_band_metrics(p, p_star, tail_mask, n_bands=4)
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FA += fa
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FA /= 20
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assert FA[0] <= FA[-1] # deepest no better than shallowest
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assert FA[-1] > FA[0] # and strictly worse on average
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