figures: publication-ready — unified, lettered, codename-free
paper/pnas/make_figs.py re-plots every panel directly from the committed results artifacts into six single-file figures (figs/fig1..fig6.pdf): no experiment codenames or suptitles (interpretation moved to captions), bold panel letters, plain-language axis labels and legend entries, one consistent style (8pt, no top/right spines). Panels: fig1 A-B (grounding equilibrium + MNIST montage with its baked-in title cropped), fig2 A-B (blending cancellation + Fisher-Muller), fig3 A-D (outbreeding, directed recombination, mating breadth champion + diversity), fig4 A-C (society ablation trajectories), fig5 A-F (speciation: analytic curve + cliff, MLP decomposition + conflict sweep, LLM coherence + duration null), fig6 A-D (seed-replicated merging, 7B-hard routing vs averaging, predictive-test scatter, predictor comparison). build.py now places the single PDFs; captions rewritten per lettered panel; in-text panel refs updated (5B->5C-D, 5C->5E-F); stale stacked copies removed. Document 20pp -> 18pp. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
This commit is contained in:
parent
6f1f8bf172
commit
96902e87f0
26 changed files with 469 additions and 96 deletions
381
paper/pnas/make_figs.py
Normal file
381
paper/pnas/make_figs.py
Normal file
|
|
@ -0,0 +1,381 @@
|
|||
"""Publication figures for the PNAS draft — unified, lettered, codename-free.
|
||||
|
||||
Re-plots every panel directly from the committed results artifacts into six single-file figures
|
||||
(figs/fig1.pdf .. fig6.pdf): no experiment codenames, no suptitles, no per-panel headline titles
|
||||
(interpretation lives in the captions), bold panel letters, one consistent style. The per-experiment
|
||||
figures under results/ remain the exploratory versions; these are the manuscript's.
|
||||
|
||||
Usage: python paper/pnas/make_figs.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
sys.path.insert(0, str(ROOT / "figures"))
|
||||
sys.path.insert(0, str(ROOT / "src"))
|
||||
import os
|
||||
os.chdir(ROOT) # load_bundle uses repo-relative paths
|
||||
from _figlib import load_bundle, mean_ci # noqa: E402
|
||||
|
||||
OUT = ROOT / "paper" / "pnas" / "figs"
|
||||
|
||||
plt.rcParams.update({
|
||||
"font.size": 8, "axes.labelsize": 8.5, "legend.fontsize": 7, "legend.frameon": False,
|
||||
"lines.markersize": 3.6, "axes.spines.top": False, "axes.spines.right": False,
|
||||
})
|
||||
|
||||
|
||||
def letter(ax, s, x=-0.14):
|
||||
ax.text(x, 1.02, s, transform=ax.transAxes, fontsize=12, fontweight="bold", va="bottom")
|
||||
|
||||
|
||||
def save(fig, name):
|
||||
OUT.mkdir(exist_ok=True)
|
||||
fig.savefig(OUT / f"{name}.pdf", bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print("wrote", OUT / f"{name}.pdf")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 1: grounding + MNIST
|
||||
def fig1():
|
||||
from knowledge.analysis import critical_grounding, reduce_to_stationary
|
||||
from knowledge.metrics import heterozygosity
|
||||
from knowledge.truth import make_true_distribution
|
||||
|
||||
df, cfg = load_bundle("results/E2")
|
||||
n = cfg["dynamics"]["n"]
|
||||
td = make_true_distribution(cfg["truth"]["K"], 1, "zipf", cfg["truth"]["tail_frac"],
|
||||
cfg["truth"]["zipf_s"], 0, tail_threshold=cfg["truth"]["tail_threshold"])
|
||||
H_star = heterozygosity(td.p_star)
|
||||
last = int(cfg["generations"] * 0.8)
|
||||
stat = df[df["generation"] >= last]
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(10.6, 3.5), gridspec_kw={"width_ratios": [1, 1.35]})
|
||||
|
||||
ax = axes[0]
|
||||
st = reduce_to_stationary(stat, value_col="heterozygosity", replicate_col="replicate", last_frac=1.0)
|
||||
gg, Hm, Hci = mean_ci(stat, "g", "heterozygosity")
|
||||
nz = gg > 0
|
||||
ax.errorbar(gg[nz], Hm[nz], yerr=Hci[nz], fmt="o", color="#1f77b4", capsize=2, label="simulation")
|
||||
ax.plot(gg[~nz], Hm[~nz], "o", mfc="white", mec="#1f77b4")
|
||||
m_of_g = stat.groupby("g")["m"].first().to_numpy()
|
||||
m_grid = np.linspace(0, m_of_g.max(), 400)
|
||||
|
||||
def H_eq(m):
|
||||
m = np.asarray(m, float)
|
||||
return np.where(m <= 0, 0.0, H_star * m * (2 * n + m - 1) / (n + 2 * n * m + m * m))
|
||||
|
||||
ax.plot(m_grid / (n + m_grid), H_eq(m_grid), "k--", lw=1, label="exact equilibrium")
|
||||
ax.axhline(H_star, ls=":", color="gray", lw=1, label="source diversity $H^*$")
|
||||
r = critical_grounding(st, H_star=H_star, frac=0.95, seed=7)
|
||||
ax.axvspan(r["ci_low"], r["ci_high"], color="#d62728", alpha=0.15)
|
||||
ax.axvline(r["g_star"], color="#d62728", lw=1.1,
|
||||
label=f"95%-retention threshold $g\\approx{r['g_star']:.3f}$")
|
||||
ax.set(xlabel="grounding fraction $g = m/(n+m)$", ylabel="stationary diversity $H$")
|
||||
ax.legend()
|
||||
letter(ax, "A")
|
||||
|
||||
ax = axes[1]
|
||||
from PIL import Image
|
||||
im = np.asarray(Image.open("results/mnist_collapse/mnist_montage.png"))
|
||||
crop = int(im.shape[0] * 0.085) # remove the baked-in title band
|
||||
ax.imshow(im[crop:], interpolation="bilinear")
|
||||
ax.set_axis_off()
|
||||
letter(ax, "B", x=-0.02)
|
||||
save(fig, "fig1")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 2: blending vs union + Fisher–Muller
|
||||
def fig2():
|
||||
fig, axes = plt.subplots(1, 2, figsize=(10.6, 3.5))
|
||||
|
||||
df, _ = load_bundle("results/E4")
|
||||
r0 = df[(df["g"] == 0.0) & (df["rho"] == 0.0)]
|
||||
mx = r0.groupby("K_T")["surviving_max"].agg(["mean", "sem"])
|
||||
mn = r0.groupby("K_T")["surviving_mean"].agg(["mean", "sem"])
|
||||
ax = axes[0]
|
||||
ax.errorbar(mx.index, mx["mean"], yerr=1.96 * mx["sem"], fmt="-o", color="#1f77b4",
|
||||
capsize=2, label="union operator (strongest source)")
|
||||
ax.errorbar(mn.index, mn["mean"], yerr=1.96 * mn["sem"], fmt="--s", color="#d62728",
|
||||
capsize=2, label="output-mean (blending)")
|
||||
ax.set(xlabel="number of parents", ylabel="rare capabilities surviving in the child",
|
||||
xticks=sorted(r0["K_T"].unique()))
|
||||
ax.legend()
|
||||
letter(ax, "A")
|
||||
|
||||
df8, cfg8 = load_bundle("results/E8")
|
||||
L = cfg8["society"]["L"]
|
||||
d0 = df8[df8["rho"] == 0.0]
|
||||
ax = axes[1]
|
||||
for col, c, lab in [("best_parent", "#7f7f7f", "best single parent"),
|
||||
("average", "#1f77b4", "blended average"),
|
||||
("sexual", "#d62728", "recombined offspring")]:
|
||||
k, m, ci = mean_ci(d0, "K_T", col)
|
||||
ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=2, label=lab)
|
||||
ax.axhline(L, ls=":", color="green", lw=1, label="optimum")
|
||||
ax.set(xlabel="number of parents", ylabel="offspring capability")
|
||||
ax.legend()
|
||||
letter(ax, "B")
|
||||
save(fig, "fig2")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 3: rugged landscapes
|
||||
def fig3():
|
||||
fig, axes = plt.subplots(2, 2, figsize=(10.6, 6.8))
|
||||
|
||||
df9, _ = load_bundle("results/E9")
|
||||
Ks = sorted(df9["K"].unique())
|
||||
colors = plt.cm.viridis(np.linspace(0, 0.85, len(Ks)))
|
||||
bp = df9.groupby("K")["best_parent"].mean()
|
||||
ax = axes[0, 0]
|
||||
for K, c in zip(Ks, colors):
|
||||
s = df9[df9["K"] == K].groupby("rate")["mean_offspring"].mean() - bp[K]
|
||||
ax.plot(s.index, s.values, "-o", color=c, label=f"$K$={K}")
|
||||
ax.axhline(0, ls=":", color="gray", lw=1)
|
||||
ax.set(xlabel="recombination rate", ylabel="mean offspring − best parent")
|
||||
ax.legend(title="ruggedness", ncol=2)
|
||||
letter(ax, "A")
|
||||
|
||||
df10, _ = load_bundle("results/E10")
|
||||
ax = axes[0, 1]
|
||||
for col, c, lab in [("global_opt", "green", "global optimum"),
|
||||
("directed_sex", "#d62728", "screened recombination (directed)"),
|
||||
("best_parent", "#7f7f7f", "best single parent"),
|
||||
("random_sex", "#1f77b4", "blind recombination")]:
|
||||
k, m, ci = mean_ci(df10, "K", col)
|
||||
if col == "global_opt":
|
||||
ax.plot(k, m, ":", color=c, label=lab)
|
||||
else:
|
||||
ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=2, label=lab)
|
||||
ax.set(xlabel="landscape ruggedness $K$", ylabel="offspring capability")
|
||||
ax.legend()
|
||||
letter(ax, "B")
|
||||
|
||||
df14, _ = load_bundle("results/E14")
|
||||
last = df14[df14["generation"] == df14["generation"].max()].copy()
|
||||
last["best_n"] = last["best_fitness"] / last["global_opt"]
|
||||
K14 = sorted(last["K"].unique())
|
||||
cmap = plt.get_cmap("viridis")
|
||||
c14 = {K: cmap(i / max(1, len(K14) - 1)) for i, K in enumerate(K14)}
|
||||
for ax, col, ylab, L in [(axes[1, 0], "best_n", "best fitness / optimum", "C"),
|
||||
(axes[1, 1], "diversity", "population diversity", "D")]:
|
||||
for K in K14:
|
||||
g = last[last["K"] == K].groupby("breadth")[col].agg(["mean", "sem"]).reset_index()
|
||||
ax.errorbar(g["breadth"], g["mean"], yerr=1.96 * g["sem"].fillna(0), fmt="-o",
|
||||
color=c14[K], capsize=2, label=f"$K$={K}")
|
||||
ax.set_xscale("log")
|
||||
ax.set(xlabel="mate-pool breadth (monogamous → panmictic)", ylabel=ylab)
|
||||
ax.legend(title="ruggedness")
|
||||
letter(ax, L)
|
||||
save(fig, "fig3")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 4: the society
|
||||
def fig4():
|
||||
df, _ = load_bundle("results/E11")
|
||||
arms = [("full", "#2ca02c", "full system"),
|
||||
("no_sex", "#ff7f0e", "no recombination"),
|
||||
("no_diversity", "#9467bd", "no diversity preservation"),
|
||||
("no_grounding", "#d62728", "no grounded evaluation")]
|
||||
arms = [a for a in arms if a[0] in set(df["arm"].unique())]
|
||||
g_opt = df["global_opt"].mean()
|
||||
|
||||
fig, axes = plt.subplots(1, 3, figsize=(11.4, 3.2))
|
||||
panels = [("best_fitness", "best real fitness", "A"),
|
||||
("diversity", "population diversity", "B"),
|
||||
("conformity_true_gap", "conformity − true fitness", "C")]
|
||||
for ax, (col, ylab, L) in zip(axes, panels):
|
||||
for name, c, lab in arms:
|
||||
sub = df[df["arm"] == name]
|
||||
g, m, ci = mean_ci(sub, "generation", col)
|
||||
ax.plot(g, m, "-", color=c, lw=1.6, label=lab)
|
||||
ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.15)
|
||||
if col == "best_fitness":
|
||||
ax.axhline(g_opt, ls=":", color="gray", lw=1, label="global optimum")
|
||||
ax.legend()
|
||||
ax.set(xlabel="generation", ylabel=ylab)
|
||||
letter(ax, L)
|
||||
save(fig, "fig4")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 5: speciation, three tiers
|
||||
def fig5():
|
||||
fig, axes = plt.subplots(2, 3, figsize=(11.4, 6.6))
|
||||
|
||||
bdm, _ = load_bundle("results/E12")
|
||||
rhos = sorted(bdm["rho"].unique())
|
||||
colors = plt.cm.viridis(np.linspace(0.15, 0.85, len(rhos)))
|
||||
|
||||
def agg(df, keys, value):
|
||||
g = df.groupby(keys)[value].agg(["mean", "std", "count"]).reset_index()
|
||||
g["se"] = g["std"] / np.sqrt(g["count"].clip(lower=1))
|
||||
return g
|
||||
|
||||
ax = axes[0, 0]
|
||||
par = agg(bdm, "divergence", "parent_fitness")
|
||||
ax.plot(par["divergence"], par["mean"], "k--", lw=1.3, label="parents")
|
||||
for rho, c in zip(rhos, colors):
|
||||
g = agg(bdm[bdm["rho"] == rho], "divergence", "offspring_fitness")
|
||||
ax.plot(g["divergence"], g["mean"], "-o", color=c, label=f"hybrid, density {rho:g}")
|
||||
ax.fill_between(g["divergence"], g["mean"] - g["se"], g["mean"] + g["se"], color=c, alpha=0.15)
|
||||
ax.axhline(0, color="#999", lw=0.7, ls=":")
|
||||
ax.set(xlabel="parental divergence (substitutions)", ylabel="fitness")
|
||||
ax.legend()
|
||||
letter(ax, "A")
|
||||
|
||||
ax = axes[0, 1]
|
||||
for rho, c in zip(rhos, colors):
|
||||
g = agg(bdm[bdm["rho"] == rho], "divergence", "isolation")
|
||||
ax.plot(g["divergence"], g["mean"], "-o", color=c, label=f"{rho:g}")
|
||||
ax.set(xlabel="parental divergence (substitutions)", ylabel="P(hybrid inviable)", ylim=(-0.02, 1.02))
|
||||
ax.legend(title="incompatibility density")
|
||||
letter(ax, "B")
|
||||
|
||||
dec, _ = load_bundle("results/speciation_real")
|
||||
order = [c for c in ["shared", "independent", "conflict"] if c in set(dec["condition"])]
|
||||
g = dec.groupby("condition").agg(naive=("barrier_naive", "mean"),
|
||||
res=("residual_scale", "mean")).reindex(order)
|
||||
ax = axes[0, 2]
|
||||
x = np.arange(len(order)); w = 0.38
|
||||
ax.bar(x - w / 2, g["naive"], w, color="#9ecae1", label="before alignment")
|
||||
ax.bar(x + w / 2, g["res"], w, color="#d62728", label="after alignment (residual)")
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(["same task,\nshared start", "same task,\ndifferent start", "conflicting\ntasks"])
|
||||
ax.set(ylabel="merge error barrier")
|
||||
ax.legend()
|
||||
letter(ax, "C")
|
||||
|
||||
cliff, _ = load_bundle("results/speciation_real_cliff")
|
||||
cg = cliff.groupby("conflict_frac").agg(res=("residual_scale", "mean"),
|
||||
hyb=("acc_merge_scale", "mean")).reset_index()
|
||||
ax = axes[1, 0]
|
||||
ax.plot(cg["conflict_frac"], cg["res"], "-o", color="#d62728", label="residual barrier")
|
||||
ax2 = ax.twinx()
|
||||
ax2.plot(cg["conflict_frac"], cg["hyb"], "-s", color="#2c7fb8", label="merged-model accuracy")
|
||||
ax2.set_ylabel("merged accuracy", color="#2c7fb8")
|
||||
ax2.tick_params(axis="y", labelcolor="#2c7fb8")
|
||||
ax2.spines["right"].set_visible(True)
|
||||
ax.set(xlabel="fraction of classes in conflict", ylabel="residual barrier")
|
||||
l1, la1 = ax.get_legend_handles_labels(); l2, la2 = ax2.get_legend_handles_labels()
|
||||
ax.legend(l1 + l2, la1 + la2, loc="center left")
|
||||
letter(ax, "D")
|
||||
|
||||
rep, _ = load_bundle("results/llm_speciation")
|
||||
|
||||
def series(df, mode, model, metric):
|
||||
sub = df[(df["mode"] == mode) & (df["model"] == model) & (df["metric"] == metric)]
|
||||
g = sub.groupby("x")["accuracy"].mean().reset_index()
|
||||
return g["x"], g["accuracy"]
|
||||
|
||||
ax = axes[1, 1]
|
||||
x_, y_ = series(rep, "conflict", "parent_a", "ambig_asc")
|
||||
ax.plot(x_, y_, "--o", color="#9ecae1", label="parent A, own convention")
|
||||
x_, y_ = series(rep, "conflict", "parent_b", "ambig_desc")
|
||||
ax.plot(x_, y_, "--o", color="#a1d99b", label="parent B, own convention")
|
||||
x_, y_ = series(rep, "conflict", "merge_soup", "coherence")
|
||||
ax.plot(x_, y_, "-s", color="#d62728", label="merge, best convention")
|
||||
ax.set(xlabel="fraction of training in conflict", ylabel="accuracy, shared prompts")
|
||||
ax.legend()
|
||||
letter(ax, "E")
|
||||
|
||||
ax = axes[1, 2]
|
||||
x_, y_ = series(rep, "duration", "merge_soup", "mean_private")
|
||||
ax.plot(x_, y_, "-o", color="#d62728", label="merged model")
|
||||
x_, y_ = series(rep, "duration", "parent_a", "strings")
|
||||
ax.plot(x_, y_, "--o", color="#9ecae1", label="parent A, own task")
|
||||
x_, y_ = series(rep, "duration", "parent_b", "arith")
|
||||
ax.plot(x_, y_, "--o", color="#a1d99b", label="parent B, own task")
|
||||
ax.set(xlabel="specialist training (epochs)", ylabel="accuracy", ylim=(0, 1.02))
|
||||
ax.legend()
|
||||
letter(ax, "F")
|
||||
save(fig, "fig5")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- fig 6: the language-model tier
|
||||
def fig6():
|
||||
import pandas as pd
|
||||
from scipy.stats import spearmanr
|
||||
|
||||
fig, axes = plt.subplots(2, 2, figsize=(10.6, 6.6))
|
||||
|
||||
dfm, _ = load_bundle("results/llm_merge_seeds")
|
||||
specs = sorted(m for m in dfm["model"].unique() if m.startswith("spec_"))
|
||||
rows = []
|
||||
for s, sub in dfm.groupby("seed"):
|
||||
ov = {m: sub[(sub["model"] == m) & (sub["metric"] == "overall")]["accuracy"].mean() for m in specs}
|
||||
b = sub[sub["model"] == max(ov, key=ov.get)].copy(); b["model"] = "best_specialist"
|
||||
rows.append(b)
|
||||
dfm = pd.concat([dfm] + rows, ignore_index=True)
|
||||
models = ["base", "best_specialist", "merge_soup", "merge_ties"]
|
||||
labels = ["base", "best\nspecialist", "merged\n(average)", "merged\n(interference-aware)"]
|
||||
ax = axes[0, 0]
|
||||
x = np.arange(len(models))
|
||||
for off, metric, c, lab in ((-0.19, "overall", "#2c7fb8", "overall"),
|
||||
(0.19, "worst_family", "#d62728", "worst task family")):
|
||||
vals, errs = [], []
|
||||
for m in models:
|
||||
v = dfm[(dfm["model"] == m) & (dfm["metric"] == metric)].groupby("seed")["accuracy"].mean()
|
||||
vals.append(v.mean()); errs.append(1.96 * v.std(ddof=1) / max(1, np.sqrt(len(v))))
|
||||
ax.bar(x + off, vals, 0.36, yerr=errs, capsize=2, color=c, label=lab)
|
||||
ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7)
|
||||
ax.set(ylabel="verifier accuracy")
|
||||
ax.legend()
|
||||
letter(ax, "A")
|
||||
|
||||
df7, _ = load_bundle("results/llm_moe_hard_hpc")
|
||||
|
||||
def acc7(model, metric):
|
||||
r = df7[(df7["model"] == model) & (df7["metric"] == metric)]["accuracy"]
|
||||
return float(r.iloc[0]) if len(r) else np.nan
|
||||
specs7 = sorted(m for m in df7["model"].unique() if m.startswith("spec_"))
|
||||
best7 = max(specs7, key=lambda m: acc7(m, "overall"))
|
||||
models7 = [best7, "merge_soup", "merge_ties", "moe_oracle"]
|
||||
labels7 = ["best\nspecialist", "merged\n(average)", "merged\n(interference-aware)", "routed\n(kept separate)"]
|
||||
ax = axes[0, 1]
|
||||
x = np.arange(len(models7))
|
||||
for off, metric, c, lab in ((-0.19, "overall", "#2c7fb8", "overall"),
|
||||
(0.19, "worst_family", "#d62728", "worst task family")):
|
||||
ax.bar(x + off, [acc7(m, metric) for m in models7], 0.36, color=c, label=lab)
|
||||
ax.set_xticks(x); ax.set_xticklabels(labels7, fontsize=7)
|
||||
ax.set(ylabel="verifier accuracy")
|
||||
ax.legend()
|
||||
letter(ax, "B")
|
||||
|
||||
a = pd.read_parquet("results/llm_epistasis/results.parquet")
|
||||
b = pd.read_parquet("results/llm_epistasis_compat/results.parquet")
|
||||
dfe = pd.concat([a, b], ignore_index=True)
|
||||
ax = axes[1, 0]
|
||||
for mode, c, mk, lab in (("conflict", "#d62728", "o", "conflicting conventions"),
|
||||
("duration", "#2c7fb8", "s", "divergence only"),
|
||||
("compat", "#41ab5d", "^", "overlap, no conflict")):
|
||||
sub = dfe[dfe["mode"] == mode]
|
||||
ax.scatter(sub["epi_conf"], sub["merge_penalty"], c=c, marker=mk, s=26, alpha=0.75, label=lab)
|
||||
ax.axhline(0, color="#999", lw=0.6)
|
||||
ax.set(xlabel="pre-merge functional conflict (confidence-weighted)",
|
||||
ylabel="merge penalty")
|
||||
ax.legend()
|
||||
letter(ax, "C")
|
||||
|
||||
preds = [("dis_raw", "raw\ndisagreement"), ("epi_conf", "conf-weighted\nconflict"),
|
||||
("grad_cos", "gradient\nalignment"), ("delta_cos", "weight\ncosine"),
|
||||
("delta_l2", "weight\ndistance"), ("cross_perf", "cross-task\naccuracy")]
|
||||
ax = axes[1, 1]
|
||||
rhos_ = [abs(spearmanr(dfe[c], dfe["merge_penalty"])[0]) for c, _ in preds]
|
||||
cols = ["#fc9272", "#d62728", "#9ecae1", "#9ecae1", "#9ecae1", "#9ecae1"]
|
||||
ax.bar(np.arange(len(preds)), rhos_, 0.6, color=cols)
|
||||
ax.set_xticks(np.arange(len(preds)))
|
||||
ax.set_xticklabels([l for _, l in preds], fontsize=6.5)
|
||||
ax.set(ylabel="|Spearman ρ| vs merge penalty", ylim=(0, 0.8))
|
||||
letter(ax, "D")
|
||||
save(fig, "fig6")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
for f in (fig1, fig2, fig3, fig4, fig5, fig6):
|
||||
f()
|
||||
Loading…
Add table
Add a link
Reference in a new issue