#!/usr/bin/env python3
"""Profile clusters so they can be named, checked and acted on.
Input: a CSV with a cluster-label column (from any algorithm; -1 is treated as noise) plus the
features. For each cluster:
size and share
numeric features: mean, the standardised difference from the rest of the data
(mean_in - mean_out) / pooled_sd, and the most distinctive features ranked by |d|
categorical features: the level most over-represented relative to the population (lift)
outcome (--outcome): mean of a business outcome per cluster (churn rate, spend), to check that
segments actually behave differently
Warnings: tiny clusters (< --min-share), clusters with no feature |d| >= 0.5 (hard to tell apart), and an
outcome that barely differs across clusters.
Usage:
python cluster_profile.py segments.csv --label cluster
python cluster_profile.py segments.csv --label cluster --outcome churned --drop customer_id --top 4
python cluster_profile.py segments.csv --label cluster --json
Exit codes: 0 profiled, 2 usage or read error.
"""
import argparse
import csv
import json
import math
import sys
from collections import Counter, defaultdict
MISSING = {"", "na", "n/a", "nan", "null", "none", "?"}
def num(v):
try:
x = float(v)
return x if math.isfinite(x) else None
except (TypeError, ValueError):
return None
def mean_sd(xs):
if not xs:
return None, None
m = sum(xs) / len(xs)
return m, math.sqrt(sum((x - m) ** 2 for x in xs) / max(1, len(xs) - 1))
def main():
ap = argparse.ArgumentParser(description="Profile clusters for naming and validation.", epilog=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("csv")
ap.add_argument("--label", required=True, help="cluster label column")
ap.add_argument("--outcome", help="business outcome column to compare across clusters")
ap.add_argument("--drop", nargs="*", default=[])
ap.add_argument("--top", type=int, default=3)
ap.add_argument("--min-share", type=float, default=0.02)
ap.add_argument("--json", action="store_true")
a = ap.parse_args()
try:
with (sys.stdin if a.csv == "-" else open(a.csv, newline="", encoding="utf-8-sig")) as fh:
rd = csv.DictReader(fh)
header = rd.fieldnames or []
rows = list(rd)
for c in (a.label, a.outcome):
if c and c not in header:
raise ValueError(f"column {c!r} not found")
except (OSError, ValueError) as e:
print(f"error: {e}", file=sys.stderr)
return 2
feats = [c for c in header if c not in (a.label, a.outcome) and c not in a.drop]
numeric = [c for c in feats if all(num(r[c]) is not None for r in rows if (r[c] or "").strip().lower() not in MISSING)]
categorical = [c for c in feats if c not in numeric and len({r[c] for r in rows}) <= 50]
groups = defaultdict(list)
for r in rows:
groups[r[a.label].strip()].append(r)
n = len(rows)
out = {"rows": n, "clusters": []}
pop_cat = {c: Counter(r[c] for r in rows) for c in categorical}
outcome_means = []
for lab in sorted(groups, key=lambda l: (l == "-1", -len(groups[l]))):
g = groups[lab]
rest = [r for r in rows if r[a.label].strip() != lab]
prof = {"label": lab, "size": len(g), "share": len(g) / n, "noise": lab == "-1", "numeric": [], "categorical": []}
for c in numeric:
xin = [num(r[c]) for r in g if num(r[c]) is not None]
xout = [num(r[c]) for r in rest if num(r[c]) is not None]
mi, si = mean_sd(xin)
mo, so = mean_sd(xout)
if mi is None or mo is None:
continue
pooled = math.sqrt(((len(xin) - 1) * si ** 2 + (len(xout) - 1) * so ** 2) / max(1, len(xin) + len(xout) - 2)) or 1e-12
prof["numeric"].append({"feature": c, "mean": mi, "rest_mean": mo, "d": (mi - mo) / pooled})
prof["numeric"].sort(key=lambda e: -abs(e["d"]))
for c in categorical:
cnt = Counter(r[c] for r in g)
best = max(cnt, key=lambda v: (cnt[v] / len(g)) / (pop_cat[c][v] / n))
prof["categorical"].append({"feature": c, "level": best, "share_in": cnt[best] / len(g),
"share_pop": pop_cat[c][best] / n,
"lift": (cnt[best] / len(g)) / (pop_cat[c][best] / n)})
prof["categorical"].sort(key=lambda e: -e["lift"])
if a.outcome:
ys = [num(r[a.outcome]) for r in g if num(r[a.outcome]) is not None]
prof["outcome_mean"] = sum(ys) / len(ys) if ys else None
if ys and lab != "-1":
outcome_means.append(prof["outcome_mean"])
warn = []
if prof["share"] < a.min_share and lab != "-1":
warn.append(f"tiny cluster ({prof['share']:.1%})")
if prof["numeric"] and abs(prof["numeric"][0]["d"]) < 0.5 and not any(e["lift"] > 1.5 for e in prof["categorical"]):
warn.append("no feature separates it clearly (max |d| < 0.5): hard to name or act on")
prof["warnings"] = warn
out["clusters"].append(prof)
if a.outcome and len(outcome_means) >= 2:
lo, hi = min(outcome_means), max(outcome_means)
out["outcome_spread"] = {"min": lo, "max": hi, "ratio": hi / lo if lo else None}
if a.json:
print(json.dumps(out, indent=2))
return 0
print(f"{n} rows, {len(groups)} clusters; {len(numeric)} numeric, {len(categorical)} categorical features")
for p in out["clusters"]:
tag = " (noise)" if p["noise"] else ""
oc = f", {a.outcome} mean {p['outcome_mean']:.3g}" if a.outcome and p.get("outcome_mean") is not None else ""
print(f"\ncluster {p['label']}{tag}: {p['size']} rows ({p['share']:.1%}){oc}")
for e in p["numeric"][:a.top]:
arrow = "higher" if e["d"] > 0 else "lower"
print(f" {e['feature']:<22} mean {e['mean']:.4g} vs {e['rest_mean']:.4g} elsewhere (d {e['d']:+.2f}, {arrow})")
for e in p["categorical"][:2]:
if e["lift"] > 1.2:
print(f" {e['feature']}={e['level']:<16} {e['share_in']:.0%} vs {e['share_pop']:.0%} overall (lift {e['lift']:.1f}x)")
for w in p["warnings"]:
print(f" warning: {w}")
if "outcome_spread" in out:
sp = out["outcome_spread"]
print(f"\n{a.outcome} ranges {sp['min']:.3g} .. {sp['max']:.3g} across clusters" + (
"; segments barely differ on the outcome - check whether they need different actions"
if sp["ratio"] and sp["ratio"] < 1.2 else ""))
return 0
if __name__ == "__main__":
sys.exit(main())
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