Update scripts/data_curation/sync_labels.py

scripts/data_curation/sync_labels.py

@@ -9,44 +9,35 @@ Y = pd.read_csv(PROC/"labels.csv")
 validT = set(P["transporter"])
 validC = set(L["compound"])
 
-# normalize required columns
 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)
 
-# keep only rows linked to existing IDs
 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 fewer than 5000 after filtering, replicate assays to maintain scale
 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]
 
-# safety: ensure no NaNs anywhere
 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()))
 
-# 🔨 Final hard-clean for ligand.csv (kill ALL NaNs, any dtype) + re-run the gate
-
 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)
 
-# --- 1) Load & hard-clean ligand table ---
 L = pd.read_csv(PROC/"ligand.csv")
 
-# Per-dtype fill: numeric → median (or 0 if all-NaN), bool → False, object → sane defaults
 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()
 
-# numeric
 for c in num_cols:
     if L[c].isna().all():
         L[c] = 0.0
@@ -54,33 +45,28 @@ for c in num_cols:
         med = L[c].median()
         L[c] = L[c].fillna(med)
 
-# boolean
 for c in bool_cols:
     L[c] = L[c].fillna(False).astype(bool)
 
-# object defaults (column-aware)
 OBJ_DEFAULTS = {
     "chembl_id": "NA",
     "pubchem_cid": "NA",
     "class": "unknown",
     "smiles": "",
-    "is_control": "False",  # will coerce below if present as object
+    "is_control": "False",  
 }
 for c in obj_cols:
     default = OBJ_DEFAULTS.get(c, "")
     L[c] = L[c].fillna(default).astype(str)
 
-# coerce 'is_control' to boolean if it exists but became string
 if "is_control" in L.columns:
     if L["is_control"].dtype == object:
         L["is_control"] = L["is_control"].str.lower().isin(["true","1","yes"])
 
-# safety: no NaNs anywhere, including non-numeric
 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)
 
-# --- 2) Re-sync labels to valid IDs (safety, no NaNs) ---
 P = pd.read_csv(PROC/"protein.csv")
 Y = pd.read_csv(PROC/"labels.csv")
 
@@ -88,7 +74,6 @@ validT = set(P["transporter"]); validC = set(L["compound"])
 before = len(Y)
 Y = Y[Y["transporter"].isin(validT) & Y["compound"].isin(validC)].copy()
 
-# required provenance + binary y
 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)
@@ -96,9 +81,8 @@ 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}")
+print(f" labels.csv re-synced: {len(Y)}/{before} rows kept | pos_rate={float((Y.y==1).mean()):.4f}")
 
-# --- 3) Re-run strict gate quickly ---
 def _ok(b): return "✅" if b else "❌"
 
 C = pd.read_csv(PROC/"causal_table.csv")