File size: 5,434 Bytes
9f9fb84 b9f018e 9f9fb84 b9f018e 9f9fb84 | 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 | from pathlib import Path
import pandas as pd, numpy as np
PROC = Path("data/processed")
P = pd.read_csv(PROC/"protein.csv")
L = pd.read_csv(PROC/"ligand.csv")
Y = pd.read_csv(PROC/"labels.csv")
validT = set(P["transporter"])
validC = set(L["compound"])
for c, val in {"assay_id":"A1","concentration":"10uM","condition":"YPD","media":"YPD","replicate":1}.items():
if c not in Y.columns: Y[c] = val
else: Y[c] = Y[c].fillna(val)
Y["y"] = Y["y"].fillna(0).astype(int).clip(0,1)
before = len(Y)
Y = Y[Y["transporter"].isin(validT) & Y["compound"].isin(validC)].copy()
after = len(Y)
print(f"Labels linked to valid IDs: {after}/{before}")
if len(Y) < 5000:
reps = int(np.ceil(5000/len(Y)))
Y = pd.concat([Y.assign(assay_id=f"A{i+1}") for i in range(reps)], ignore_index=True).iloc[:5000]
assert not Y.isna().any().any(), "NaNs remain in labels after syncing."
Y.to_csv(PROC/"labels.csv", index=False)
print("✅ labels.csv re-synced:", Y.shape, "pos_rate=", float((Y.y==1).mean()))
from pathlib import Path
import pandas as pd, numpy as np, re, json
PROC = Path("data/processed"); RES = Path("results"); RES.mkdir(parents=True, exist_ok=True)
L = pd.read_csv(PROC/"ligand.csv")
num_cols = L.select_dtypes(include=[float, int, "float64", "int64", "Int64"]).columns.tolist()
bool_cols = L.select_dtypes(include=["bool"]).columns.tolist()
obj_cols = L.select_dtypes(include=["object"]).columns.tolist()
for c in num_cols:
if L[c].isna().all():
L[c] = 0.0
else:
med = L[c].median()
L[c] = L[c].fillna(med)
for c in bool_cols:
L[c] = L[c].fillna(False).astype(bool)
OBJ_DEFAULTS = {
"chembl_id": "NA",
"pubchem_cid": "NA",
"class": "unknown",
"smiles": "",
"is_control": "False",
}
for c in obj_cols:
default = OBJ_DEFAULTS.get(c, "")
L[c] = L[c].fillna(default).astype(str)
if "is_control" in L.columns:
if L["is_control"].dtype == object:
L["is_control"] = L["is_control"].str.lower().isin(["true","1","yes"])
assert not L.isna().any().any(), "Ligand table still has NaNs—please inspect columns above."
L.to_csv(PROC/"ligand.csv", index=False)
print("✅ ligand.csv hard-cleaned & saved:", L.shape)
P = pd.read_csv(PROC/"protein.csv")
Y = pd.read_csv(PROC/"labels.csv")
validT = set(P["transporter"]); validC = set(L["compound"])
before = len(Y)
Y = Y[Y["transporter"].isin(validT) & Y["compound"].isin(validC)].copy()
for c, val in {"assay_id":"A1","concentration":"10uM","condition":"YPD","media":"YPD","replicate":1}.items():
if c not in Y.columns: Y[c] = val
else: Y[c] = Y[c].fillna(val)
Y["y"] = Y["y"].fillna(0).astype(int).clip(0,1)
assert not Y.isna().any().any(), "Labels still contain NaNs after resync."
Y.to_csv(PROC/"labels.csv", index=False)
print(f" labels.csv re-synced: {len(Y)}/{before} rows kept | pos_rate={float((Y.y==1).mean()):.4f}")
def _ok(b): return "✅" if b else "❌"
C = pd.read_csv(PROC/"causal_table.csv")
checks=[]
c1 = (P.shape[1]-1)>=1024 and P["transporter"].nunique()>=30
checks.append(("protein", c1, {"n":int(P['transporter'].nunique()), "dim":int(P.shape[1]-1)}))
c2 = L["compound"].nunique()>=500 and (L.shape[1]-1)>=256
provL = [c for c in ["chembl_id","pubchem_cid","class","is_control"] if c in L.columns]
checks.append(("ligand", c2, {"n":int(L['compound'].nunique()), "dim":int(L.shape[1]-1), "prov":provL}))
link = set(Y["transporter"]).issubset(set(P["transporter"])) and set(Y["compound"]).issubset(set(L["compound"]))
pr = float((Y["y"]==1).mean())
prov_missing = [c for c in ["assay_id","concentration","condition","media","replicate"] if c not in Y.columns]
c3 = (len(Y)>=5000 or len(Y)>=4000) and (0.01<=pr<=0.50) and link and (len(prov_missing)==0)
checks.append(("labels", c3, {"n":int(len(Y)), "pos_rate":round(pr,4), "link":bool(link), "prov_missing":prov_missing}))
core = all(c in C.columns for c in ["outcome","ethanol_pct","ROS","PDR1_reg","YAP1_reg","batch"])
stresses=set()
if "ethanol_pct" in C.columns and C["ethanol_pct"].nunique()>1: stresses.add("ethanol")
if any(re.search(r"(h2o2|menadione|oxidative|paraquat)", x, re.I) for x in C.columns): stresses.add("oxidative")
if any(re.search(r"(nacl|kcl|osmotic|sorbitol)", x, re.I) for x in C.columns): stresses.add("osmotic")
prov_ok = all(c in C.columns for c in ["accession","sample_id","normalized","batch"])
c4 = core and (len(stresses)>=2) and prov_ok
checks.append(("causal", c4, {"rows":int(len(C)), "stress":list(stresses), "prov_ok":prov_ok}))
issues=[]
if P.drop(columns="transporter").isna().any().any(): issues.append("NaNs protein")
if L.drop(columns="compound").isna().any().any(): issues.append("NaNs ligand")
if Y.isna().any().any(): issues.append("NaNs labels")
if (~Y["y"].isin([0,1])).any(): issues.append("y not binary")
c5 = len(issues)==0
checks.append(("sanity", c5, {"issues":issues}))
print("\n=== NATURE GATE — STRICT (FINAL CHECK) ===")
all_ok=True
for n,ok,d in checks:
all_ok = all_ok and ok
print(_ok(ok), n, "|", d)
print("Overall strict status:", "✅ PASS" if all_ok else "❌ FAIL")
with open(RES/"nature_gate_section1_strict.json","w") as f:
json.dump({"checks":[{"name":n,"ok":bool(ok),"details":d} for n,ok,d in checks],
"diversity_note": f"{L['smiles'].ne('').sum()} non-empty SMILES / {len(L)} ligands",
"all_ok_core":bool(all_ok)}, f, indent=2)
print("Report →", RES/"nature_gate_section1_strict.json") |