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124eb57 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
"""PROTAC-Bench RF+Morgan baseline reproduction.
Runs 65-fold LOTO evaluation with RandomForest on Morgan fingerprints (1024-bit, radius 2).
Saves predictions to baseline_predictions.csv and runs evaluate.py.
Expected result: AUROC ~0.666
Usage:
python baselines.py
"""
import json
import subprocess
import sys
from pathlib import Path
import numpy as np
import pandas as pd
from rdkit import Chem, RDLogger
from rdkit.Chem import AllChem
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
RDLogger.DisableLog('rdApp.*')
def _find_data_dir():
"""Locate data directory: supports repo layout (../data/) or flat copy (.)."""
candidates = [
Path(__file__).resolve().parent.parent / "data",
Path(__file__).resolve().parent / "data",
Path(__file__).resolve().parent, # flat: CSV/JSON beside script
]
for d in candidates:
if (d / "protac_bench.csv").exists() and (d / "loto_folds.json").exists():
return d
return candidates[0] # fallback to original
DATA_DIR = _find_data_dir()
SEED = 42
N_SEEDS = 3
def auroc_safe(y_true, y_prob):
if len(np.unique(y_true)) < 2:
return 0.5
return roc_auc_score(y_true, y_prob)
def compute_morgan(smiles_list, nbits=2048, radius=2):
X = np.zeros((len(smiles_list), nbits), dtype=np.float32)
for i, smi in enumerate(smiles_list):
mol = Chem.MolFromSmiles(str(smi))
if mol:
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nbits)
X[i] = np.array(fp)
return X
def main():
# Load data
df = pd.read_csv(DATA_DIR / "protac_bench.csv")
with open(DATA_DIR / "loto_folds.json") as f:
folds = json.load(f)
print(f"Dataset: {len(df)} entries, {len(folds)} LOTO folds")
# Compute Morgan fingerprints
print("Computing Morgan fingerprints (1024-bit, radius 2)...")
X = compute_morgan(list(df["smiles"].values), nbits=1024, radius=2)
labels = df["label"].values
# LOTO evaluation with 3 seeds
all_probs = np.zeros(len(df), dtype=np.float64)
all_counts = np.zeros(len(df), dtype=np.int32)
fold_results = []
for uid, fold in sorted(folds.items()):
te_idx = np.array(fold["test_indices"])
tr_idx = np.array([i for i in range(len(df)) if i not in set(te_idx)])
y_te = labels[te_idx]
seed_aurocs = []
seed_probs_list = []
for s in range(N_SEEDS):
rf = RandomForestClassifier(
n_estimators=200, max_depth=None, min_samples_leaf=3,
random_state=SEED + s, n_jobs=-1
)
rf.fit(X[tr_idx], labels[tr_idx])
prob = rf.predict_proba(X[te_idx])
prob = prob[:, 1] if rf.classes_[1] == 1 else 1 - prob[:, 0]
seed_probs_list.append(prob)
seed_aurocs.append(auroc_safe(y_te, prob))
avg_probs = np.mean(seed_probs_list, axis=0)
all_probs[te_idx] = avg_probs
all_counts[te_idx] = 1
mean_auc = float(np.mean(seed_aurocs))
fold_results.append({"target": uid, "n": fold["n_entries"], "auroc": mean_auc})
print(f" {uid}: n={fold['n_entries']:3d} AUROC={mean_auc:.4f}")
# For entries not in any LOTO fold, predict with full model
missing = all_counts == 0
if missing.any():
rf = RandomForestClassifier(
n_estimators=200, max_depth=None, min_samples_leaf=3,
random_state=SEED, n_jobs=-1
)
rf.fit(X[~missing], labels[~missing])
prob = rf.predict_proba(X[missing])
prob = prob[:, 1] if rf.classes_[1] == 1 else 1 - prob[:, 0]
all_probs[missing] = prob
# Summary
aurocs = [r["auroc"] for r in fold_results]
print(f"\nRF+Morgan baseline: mean AUROC = {np.mean(aurocs):.4f} "
f"± {np.std(aurocs):.4f} (n={len(fold_results)} folds)")
# Save predictions
script_dir = Path(__file__).resolve().parent
out_path = script_dir / "baseline_predictions.csv"
pred_df = pd.DataFrame({
"index": range(len(df)),
"predicted_probability": all_probs,
})
pred_df.to_csv(out_path, index=False)
print(f"Predictions saved to {out_path}")
# Run evaluate.py
eval_script = script_dir / "evaluate.py"
result_path = script_dir / "baseline_results.json"
print(f"\nRunning evaluation...")
subprocess.run([
sys.executable, str(eval_script),
"--predictions", str(out_path),
"--output", str(result_path),
], check=True)
if __name__ == "__main__":
main()
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