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
62 lines
3 KiB
Python
62 lines
3 KiB
Python
"""Tests for E12 model speciation — the BDM construction, the snowball, and the isolation falsifiers."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from inheritance.speciation import _bdm_point, _nk_point, run_speciation
|
|
|
|
|
|
def test_bdm_parents_carry_no_incompatibility():
|
|
# The BDM construction's defining property: each derived allele is benign on its OWN parent's
|
|
# background (disjoint substitutions), so a parent's fitness is purely additive even at rho=1.
|
|
rng = np.random.default_rng(0)
|
|
r = _bdm_point(L=20, d=10, rho=1.0, s=5.0, beta=1.0, rate=0.5, n_off=200, rng=rng)
|
|
assert r["parent_fitness"] == pytest.approx(1.0 * (10 // 2)) # beta * (d/2), no penalty
|
|
|
|
|
|
def test_bdm_snowball_is_superlinear_in_divergence():
|
|
# Orr-Turelli: # incompatibilities ~ (d/2)^2, so doubling divergence ~quadruples them.
|
|
def mean_ndmi(d, reps=40):
|
|
return np.mean([_bdm_point(24, d, 0.5, 1.0, 1.0, 0.5, 50,
|
|
np.random.default_rng(i))["n_dmi"] for i in range(reps)])
|
|
ratio = mean_ndmi(12) / max(mean_ndmi(6), 1e-9)
|
|
assert ratio > 3.0 # ~4x (quadratic), well above linear (2x)
|
|
|
|
|
|
def test_bdm_no_epistasis_means_no_isolation():
|
|
rng = np.random.default_rng(1)
|
|
r = _bdm_point(L=20, d=20, rho=0.0, s=1.0, beta=1.0, rate=0.5, n_off=300, rng=rng)
|
|
assert r["n_dmi"] == 0 and r["isolation"] == 0.0 # no BDMIs -> hybrids always viable
|
|
|
|
|
|
def test_bdm_isolation_rises_with_epistasis_density():
|
|
# At fixed high divergence, denser epistasis (rho) -> more reproductive isolation.
|
|
def iso(rho):
|
|
return np.mean([_bdm_point(20, 20, rho, 1.0, 1.0, 0.5, 300,
|
|
np.random.default_rng(i))["isolation"] for i in range(8)])
|
|
assert iso(0.5) > iso(0.1)
|
|
|
|
|
|
def test_nk_additive_landscape_has_no_isolation():
|
|
# K=0 is a single-peak additive landscape: parents hill-climb to the same optimum (divergence 0),
|
|
# and recombination cannot produce outbreeding depression.
|
|
r = _nk_point(L=12, K=0, landscape_seed=3, rate=0.5, n_pairs=20, n_off=50,
|
|
rng=np.random.default_rng(0))
|
|
assert r["divergence"] == pytest.approx(0.0) and r["outbreeding_depression"] == pytest.approx(0.0)
|
|
|
|
|
|
def test_nk_ruggedness_increases_outbreeding_depression():
|
|
def od(K):
|
|
return _nk_point(12, K, 3, 0.5, 30, 60, np.random.default_rng(0))["outbreeding_depression"]
|
|
assert od(8) > od(0) # rugged landscapes punish recombination
|
|
|
|
|
|
def test_run_speciation_is_deterministic():
|
|
cfg = {"seed": 7, "n_replicates": 3,
|
|
"speciation": {"landscape": "bdm", "L": 12, "rho": [0.3], "divergences": [0, 4, 8],
|
|
"s": 1.0, "beta": 1.0, "recomb_rate": 0.5, "n_offspring": 80}}
|
|
a = run_speciation(cfg, 7)
|
|
b = run_speciation(cfg, 7)
|
|
assert a.equals(b) # pure function of the resolved config + seed
|