Datasets:
File size: 4,581 Bytes
0613483 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | """Export all plotting CSVs for each figure into benchmark/figure_data/."""
from pathlib import Path
import pandas as pd
import numpy as np
import shutil
BENCH = Path(__file__).resolve().parents[1]
FIGDATA = BENCH / "figure_data"
FIGDATA.mkdir(exist_ok=True)
TARGETS = ["PDL1", "MDM2", "IL23R"]
def main():
# ── fig_ablation_bo_tradeoff ──
ablation = pd.read_csv(BENCH / "ablation" / "ablation_summary_pose_filtered.csv")
ablation.to_csv(FIGDATA / "fig_ablation_bo_tradeoff_ablation_summary.csv", index=False)
tradeoff = pd.read_csv(BENCH / "ablation" / "ablation_tradeoff_pose_filtered.csv")
tradeoff.to_csv(FIGDATA / "fig_ablation_bo_tradeoff_tradeoff.csv", index=False)
bad = pd.read_csv(BENCH / "buried_pose_inventory.csv")
# Ablation Tier-1 reuses the dl D-peptide designs, so the ablation bad-pose
# counts are the D_peptide buried poses per core target.
bad_ab = bad[bad["peptide_mode"] == "D_peptide"]
bad_counts = (
bad_ab.groupby("target")
.size()
.reindex(TARGETS, fill_value=0)
.rename("n_bad_poses")
.reset_index()
)
bad_counts.to_csv(FIGDATA / "fig_ablation_bo_tradeoff_bad_pose_counts.csv", index=False)
print(
f"fig_ablation: {len(ablation)} summary rows + "
f"{len(tradeoff)} trade-off rows + {len(bad_counts)} pose counts"
)
# ── fig_dl_score_distribution ──
for t in TARGETS:
for m in ["D_peptide", "L_peptide"]:
src = BENCH / "dl_downsampled" / f"{t}_{m}_filtered.csv"
dst = FIGDATA / f"fig_dl_score_dist_{t}_{m}.csv"
shutil.copy2(src, dst)
from scipy import stats as sp_stats
rows = []
for t in TARGETS:
d = pd.read_csv(FIGDATA / f"fig_dl_score_dist_{t}_D_peptide.csv")
l = pd.read_csv(FIGDATA / f"fig_dl_score_dist_{t}_L_peptide.csv")
ks, p = sp_stats.ks_2samp(d["score"], l["score"])
rows.append(
{
"target": t,
"n": len(d),
"D_median": d["score"].median(),
"L_median": l["score"].median(),
"D_mean": d["score"].mean(),
"L_mean": l["score"].mean(),
"KS_stat": ks,
"KS_p": p,
}
)
pd.DataFrame(rows).to_csv(FIGDATA / "fig_dl_score_dist_summary.csv", index=False)
print("fig_dl_score_dist: 6 CSVs + summary")
# ── fig_dl_sequence_comparison (t-SNE top-100) ──
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
AAS = list("ACDEFGHIKLMNPQRSTVWY")
TOP_N = 100
aa2i = {a: i for i, a in enumerate(AAS)}
def onehot(seqs, max_len):
vecs = []
for s in seqs:
v = np.zeros(max_len * len(AAS))
for pos, aa in enumerate(str(s)):
if aa in aa2i:
v[pos * len(AAS) + aa2i[aa]] = 1.0
vecs.append(v)
return np.array(vecs)
for t in TARGETS:
d_df = pd.read_csv(FIGDATA / f"fig_dl_score_dist_{t}_D_peptide.csv")
l_df = pd.read_csv(FIGDATA / f"fig_dl_score_dist_{t}_L_peptide.csv")
d_top = d_df.nsmallest(TOP_N, "score").drop_duplicates("sequence").copy()
l_top = l_df.nsmallest(TOP_N, "score").drop_duplicates("sequence").copy()
ml = max(d_top["sequence"].str.len().max(), l_top["sequence"].str.len().max())
dv = onehot(d_top["sequence"], ml)
lv = onehot(l_top["sequence"], ml)
X = np.vstack([dv, lv])
npc = min(50, X.shape[1], X.shape[0] - 1)
Xp = PCA(n_components=npc, random_state=42).fit_transform(X)
Xt = TSNE(
n_components=2, perplexity=30, random_state=42,
learning_rate="auto", init="pca",
).fit_transform(Xp)
nd = len(dv)
d_out = d_top[["sequence", "score", "length", "pose"]].copy()
d_out["tsne_1"] = Xt[:nd, 0]
d_out["tsne_2"] = Xt[:nd, 1]
d_out["type"] = "D"
l_out = l_top[["sequence", "score", "length", "pose"]].copy()
l_out["tsne_1"] = Xt[nd:, 0]
l_out["tsne_2"] = Xt[nd:, 1]
l_out["type"] = "L"
combined = pd.concat([d_out, l_out], ignore_index=True)
combined.to_csv(FIGDATA / f"fig_dl_tsne_{t}.csv", index=False)
print(f"fig_dl_tsne_{t}: {nd} D + {len(l_out)} L = {len(combined)} rows")
print(f"\nAll figure data saved to {FIGDATA}/")
for f in sorted(FIGDATA.iterdir()):
print(f" {f.name} ({f.stat().st_size/1024:.1f} KB)")
if __name__ == "__main__":
main()
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