#!/usr/bin/env python3
"""Audit a train/test (or train/validation, or train/production) split for leakage and shift.
Checks (codes):
SA001 high exact duplicate feature rows present in both sets (label ignored)
SA002 high --id-col / --group-col values present in both sets (entity leakage)
SA003 high with --time-col and --expect forward: test rows earlier than the latest training row
SA004 medium feature drift: PSI > 0.25 (numeric: train deciles; categorical: level shares), with KS for numerics
SA005 medium a single feature separates train from test (univariate "adversarial" AUC > 0.7)
SA006 medium target distribution differs (rate / mean outside the train CI, or PSI > 0.25)
SA007 low categories in test never seen in train (share of test rows affected)
SA008 low missing-value share differs by > 10 points
SA009 info size and ratio of the sets
PSI = sum((p_test - p_train) * ln(p_test / p_train)) over bins; < 0.1 stable, 0.1-0.25 moderate, > 0.25 large.
Usage:
python split_audit.py train.csv test.csv --target churned --id-col customer_id
python split_audit.py train.csv test.csv --target churned --time-col signup_date --expect forward
python split_audit.py train.csv prod_sample.csv --drop customer_id --json
Exit codes: 0 no high findings, 1 high findings, 2 usage or read error.
"""
import argparse
import csv
import datetime as dt
import json
import math
import sys
from collections import Counter
MISSING = {"", "na", "n/a", "nan", "null", "none", "?"}
def read(path):
with open(path, newline="", encoding="utf-8-sig") as fh:
rd = csv.DictReader(fh)
return rd.fieldnames or [], list(rd)
def num(v):
try:
x = float(v)
return x if math.isfinite(x) else None
except (TypeError, ValueError):
pass
if isinstance(v, str) and len(v) >= 10 and v[4] in "-/" and v[7] in "-/":
try: # ISO-like dates compare as day numbers, not as categories
return float(dt.date(int(v[:4]), int(v[5:7]), int(v[8:10])).toordinal())
except ValueError:
return None
return None
def psi(p, q):
eps = 1e-4
return sum((b - a) * math.log((b + eps) / (a + eps)) for a, b in zip(p, q))
def ks(a, b):
a, b = sorted(a), sorted(b)
i = j = 0
d = 0.0
while i < len(a) and j < len(b):
if a[i] <= b[j]:
i += 1
else:
j += 1
d = max(d, abs(i / len(a) - j / len(b)))
return d
def auc(s, y):
pairs = sorted(zip(s, y))
pos = sum(y)
neg = len(y) - pos
rank, i = [0.0] * len(pairs), 0
while i < len(pairs):
j = i
while j + 1 < len(pairs) and pairs[j + 1][0] == pairs[i][0]:
j += 1
for k in range(i, j + 1):
rank[k] = (i + j) / 2 + 1
i = j + 1
a = (sum(r for r, (_, t) in zip(rank, pairs) if t) - pos * (pos + 1) / 2) / (pos * neg)
return max(a, 1 - a)
def main():
ap = argparse.ArgumentParser(description="Audit a train/test split for leakage and drift.", epilog=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("train")
ap.add_argument("test")
ap.add_argument("--target")
ap.add_argument("--id-col", help="row/entity identifier that must not appear in both sets")
ap.add_argument("--group-col", help="entity whose rows must stay on one side")
ap.add_argument("--time-col")
ap.add_argument("--expect", choices=["forward", "random"], default="random",
help="forward: test must come after train in time")
ap.add_argument("--drop", nargs="*", default=[])
ap.add_argument("--psi-max", type=float, default=0.25)
ap.add_argument("--json", action="store_true")
a = ap.parse_args()
try:
h1, tr = read(a.train)
h2, te = read(a.test)
except OSError as e:
print(f"error: {e}", file=sys.stderr)
return 2
common = [c for c in h1 if c in h2]
for c in (a.target, a.id_col, a.group_col, a.time_col):
if c and c not in common:
print(f"error: column {c!r} must exist in both files", file=sys.stderr)
return 2
findings = []
def add(code, sev, col, msg):
findings.append({"code": code, "severity": sev, "column": col, "message": msg})
add("SA009", "info", "(sets)", f"train {len(tr)} rows, test {len(te)} rows (test share "
f"{len(te) / max(1, len(tr) + len(te)):.1%}); {len(set(h1) ^ set(h2))} column(s) not shared")
feats = [c for c in common if c not in (a.target, a.id_col, a.group_col, a.time_col) and c not in a.drop]
key = lambda r: tuple((r.get(c) or "").strip() for c in feats)
tr_keys = Counter(key(r) for r in tr)
dup = sum(1 for r in te if key(r) in tr_keys)
if dup:
add("SA001", "high", "(rows)", f"{dup} test row(s) ({dup / len(te):.1%}) have an identical feature row in train: "
"the model has seen them (dedupe or group duplicates on one side)")
for col in (a.id_col, a.group_col):
if col:
s1 = {r[col] for r in tr}
shared = {r[col] for r in te if r[col] in s1}
if shared:
rows_aff = sum(1 for r in te if r[col] in shared)
add("SA002", "high", col, f"{len(shared)} {col} value(s) in both sets, covering {rows_aff} test rows "
f"({rows_aff / len(te):.1%}): split by {col} (grouped split)")
if a.time_col:
t1 = sorted(r[a.time_col] for r in tr if r[a.time_col])
t2 = sorted(r[a.time_col] for r in te if r[a.time_col])
if t1 and t2:
early = sum(1 for v in t2 if v <= t1[-1])
if a.expect == "forward" and early:
add("SA003", "high", a.time_col, f"{early} test row(s) ({early / len(t2):.1%}) are not after the last "
f"training time {t1[-1]}: train and test overlap in time")
elif a.expect == "random" and t2[0] > t1[-1]:
add("SA009", "info", a.time_col, "test is entirely after train (a forward split)")
for c in feats + ([a.target] if a.target else []):
v1 = [(r.get(c) or "").strip() for r in tr]
v2 = [(r.get(c) or "").strip() for r in te]
m1 = sum(v.lower() in MISSING for v in v1) / len(v1)
m2 = sum(v.lower() in MISSING for v in v2) / len(v2)
if abs(m1 - m2) > 0.1 and c != a.target:
add("SA008", "low", c, f"missing share {m1:.0%} in train vs {m2:.0%} in test")
p1 = [v for v in v1 if v.lower() not in MISSING]
p2 = [v for v in v2 if v.lower() not in MISSING]
if not p1 or not p2:
continue
n1 = [num(v) for v in p1]
n2 = [num(v) for v in p2]
numeric = all(x is not None for x in n1 + n2) and len(set(p1)) > 10
if numeric:
srt = sorted(n1)
edges = sorted({srt[int(len(srt) * q / 10)] for q in range(1, 10)})
def bins(xs):
cnt = Counter(sum(1 for e in edges if x >= e) for x in xs)
return [cnt.get(b, 0) / len(xs) for b in range(len(edges) + 1)]
pv = psi(bins(n1), bins(n2))
kv = ks(n1, n2)
adv = auc(n1 + n2, [0] * len(n1) + [1] * len(n2))
detail = f"PSI {pv:.3f}, KS {kv:.3f}"
else:
c1, c2 = Counter(p1), Counter(p2)
levels = sorted(set(c1) | set(c2))
pv = psi([c1[l] / len(p1) for l in levels], [c2[l] / len(p2) for l in levels])
unseen = sum(n for l, n in c2.items() if l not in c1)
if unseen and c != a.target:
add("SA007", "low", c, f"{unseen} test row(s) ({unseen / len(p2):.1%}) have levels unseen in train")
rate = {l: c1[l] / len(p1) for l in c1}
adv = auc([rate.get(v, 0.0) for v in p1] + [rate.get(v, 0.0) for v in p2], [0] * len(p1) + [1] * len(p2)) \
if len(levels) > 1 else 0.5
detail = f"PSI {pv:.3f}"
if c == a.target:
if pv > a.psi_max or (numeric is False and len(set(p1)) == 2 and _rate_shift(p1, p2)):
add("SA006", "medium", c, f"target distribution shifted ({detail}): rates/means differ between sets")
continue
if pv > a.psi_max:
add("SA004", "medium", c, f"drift: {detail}")
if adv > 0.7 and c not in (a.id_col,):
add("SA005", "medium", c, f"{c} alone separates train from test (AUC {adv:.2f}): a time-ordered or "
"selected split, or a feature that encodes the split")
order = {"high": 0, "medium": 1, "low": 2, "info": 3}
findings.sort(key=lambda f: (order[f["severity"]], f["code"]))
high = sum(f["severity"] == "high" for f in findings)
if a.json:
print(json.dumps({"findings": findings, "high": high}, indent=2))
return 1 if high else 0
for f in findings:
print(f"{f['code']} [{f['severity']}] {f['column']}: {f['message']}")
print(f"\n{len(findings)} finding(s), {high} high")
return 1 if high else 0
def _rate_shift(p1, p2):
"""Binary target: is the test positive rate outside train rate +/- 3 standard errors?"""
lv = sorted(set(p1) | set(p2))
pos = min(lv, key=lambda l: p1.count(l))
r1, r2 = p1.count(pos) / len(p1), p2.count(pos) / len(p2)
se = math.sqrt(max(r1 * (1 - r1), 1e-9) / len(p2))
return abs(r2 - r1) > 3 * se
if __name__ == "__main__":
sys.exit(main())
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