#!/usr/bin/env python3
"""Check a randomised experiment before uplift modelling: randomisation, balance, average and segment effects.
Input: CSV with a treatment indicator (1 = treated, 0 = control), a binary or numeric outcome, and
optional covariates / segments.
Reports:
randomisation treated share and a sample-ratio-mismatch test against --expected-share (chi-square, 1 df):
p < 0.001 means the assignment or the logging is broken - stop and investigate
balance standardised mean difference (SMD) per numeric covariate and per level of categorical ones;
|SMD| > 0.1 is imbalance worth explaining in a randomised test
ATE treated mean - control mean with a 95% CI (difference in means, Welch / Wald) and the
relative lift
segments the same effect per level of each --segments column, with CI; a heterogeneity test across
levels (Cochran's Q on the segment effects, df = levels - 1): uplift modelling only pays off
when effects differ
power the minimum detectable effect (80% power, 5% two-sided) for the overall comparison
Usage:
python rct_check.py uplift.csv --treatment-col treatment --outcome-col converted --covariates age segment --segments segment
python rct_check.py exp.csv --treatment-col variant --outcome-col revenue --expected-share 0.2 --json
Exit codes: 0 checks passed, 1 sample-ratio mismatch (p < 0.001) or a covariate with |SMD| > 0.1, 2 usage error.
"""
import argparse
import csv
import json
import math
import os
import sys
from collections import defaultdict
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from _uplift_io import chi2_sf, num # noqa: E402
def mean_var(xs):
n = len(xs)
m = sum(xs) / n
v = sum((x - m) ** 2 for x in xs) / (n - 1) if n > 1 else 0.0
return m, v
def effect(t, c):
mt, vt = mean_var(t)
mc, vc = mean_var(c)
d = mt - mc
se = math.sqrt(vt / len(t) + vc / len(c))
return {"treated_mean": mt, "control_mean": mc, "n_treated": len(t), "n_control": len(c), "effect": d, "se": se,
"ci": (d - 1.959964 * se, d + 1.959964 * se), "relative": d / mc if mc else None}
def main():
ap = argparse.ArgumentParser(description="Check a randomised experiment.", epilog=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("csv")
ap.add_argument("--treatment-col", default="treatment")
ap.add_argument("--outcome-col", default="converted")
ap.add_argument("--covariates", nargs="*", default=[])
ap.add_argument("--segments", nargs="*", default=[])
ap.add_argument("--expected-share", type=float, default=0.5)
ap.add_argument("--json", action="store_true")
a = ap.parse_args()
try:
with open(a.csv, newline="", encoding="utf-8") as f:
rows = list(csv.DictReader(f))
if not rows:
raise ValueError("no rows")
need = [a.treatment_col, a.outcome_col] + a.covariates + a.segments
miss = [c for c in need if c not in rows[0]]
if miss:
raise ValueError(f"columns not found: {', '.join(miss)}")
data = []
for r in rows:
t, y = num(r[a.treatment_col]), num(r[a.outcome_col])
if t is None or y is None:
continue
if t not in (0, 1):
raise ValueError("treatment must be 0/1")
data.append((int(t), y, r))
tr = [d for d in data if d[0] == 1]
co = [d for d in data if d[0] == 0]
if len(tr) < 2 or len(co) < 2:
raise ValueError("need treated and control rows")
except (OSError, ValueError) as e:
print(f"error: {e}", file=sys.stderr)
return 2
n = len(data)
res = {"n": n, "flags": []}
exp_t = n * a.expected_share
chi = (len(tr) - exp_t) ** 2 / exp_t + (len(co) - (n - exp_t)) ** 2 / (n - exp_t)
res["srm"] = {"treated_share": len(tr) / n, "expected": a.expected_share, "chi2": chi, "p": chi2_sf(chi, 1)}
if res["srm"]["p"] < 0.001:
res["flags"].append(f"sample ratio mismatch: treated share {len(tr) / n:.3f} vs expected {a.expected_share} "
f"(p = {res['srm']['p']:.2g}) - check assignment and logging before analysing")
res["balance"] = []
for c in a.covariates:
vt = [num(d[2][c]) for d in tr]
vc = [num(d[2][c]) for d in co]
if all(v is not None for v in vt + vc):
mt, s2t = mean_var(vt)
mc, s2c = mean_var(vc)
pooled = math.sqrt((s2t + s2c) / 2) or 1.0
res["balance"].append({"covariate": c, "smd": (mt - mc) / pooled})
else:
levels = sorted({d[2][c] for d in data})
for lv in levels:
pt = sum(1 for d in tr if d[2][c] == lv) / len(tr)
pc = sum(1 for d in co if d[2][c] == lv) / len(co)
pooled = math.sqrt((pt * (1 - pt) + pc * (1 - pc)) / 2) or 1.0
res["balance"].append({"covariate": f"{c}={lv}", "smd": (pt - pc) / pooled})
for b in res["balance"]:
if abs(b["smd"]) > 0.1:
res["flags"].append(f"imbalance on {b['covariate']} (SMD {b['smd']:+.3f})")
res["ate"] = effect([d[1] for d in tr], [d[1] for d in co])
sd_pooled = math.sqrt((mean_var([d[1] for d in tr])[1] + mean_var([d[1] for d in co])[1]) / 2)
res["mde"] = (1.959964 + 0.841621) * sd_pooled * math.sqrt(1 / len(tr) + 1 / len(co))
res["segments"] = {}
for c in a.segments:
by = defaultdict(lambda: ([], []))
for t, y, r in data:
by[r[c]][0 if t else 1].append(y)
effs = {}
for lv, (yt, yc) in sorted(by.items()):
if len(yt) >= 2 and len(yc) >= 2:
effs[lv] = effect(yt, yc)
ws = [(e["effect"], 1 / e["se"] ** 2) for e in effs.values() if e["se"] > 0]
q = p = None
if len(ws) >= 2:
pooled = sum(e * w for e, w in ws) / sum(w for _, w in ws)
q = sum(w * (e - pooled) ** 2 for e, w in ws)
p = chi2_sf(q, len(ws) - 1)
res["segments"][c] = {"effects": effs, "q": q, "df": len(ws) - 1, "p": p}
if a.json:
print(json.dumps(res, indent=2))
else:
s = res["srm"]
print(f"n {n} treated {len(tr)} ({s['treated_share']:.1%}, expected {a.expected_share:.0%}) "
f"SRM chi2 {s['chi2']:.3f} p {s['p']:.3g}")
if res["balance"]:
print("balance (SMD treated vs control): " + ", ".join(f"{b['covariate']} {b['smd']:+.3f}" for b in res["balance"]))
e = res["ate"]
rel = f", relative {e['relative']:+.1%}" if e["relative"] is not None else ""
print(f"ATE: treated {e['treated_mean']:.4f} vs control {e['control_mean']:.4f} -> {e['effect']:+.4f} "
f"[95% CI {e['ci'][0]:+.4f}, {e['ci'][1]:+.4f}]{rel} (MDE at 80% power: {res['mde']:.4f})")
for c, d in res["segments"].items():
print(f"\nby {c}:")
for lv, e in d["effects"].items():
sig = " *" if e["ci"][0] > 0 or e["ci"][1] < 0 else ""
print(f" {lv:<12} n {e['n_treated']}/{e['n_control']} effect {e['effect']:+.4f} "
f"[{e['ci'][0]:+.4f}, {e['ci'][1]:+.4f}]{sig}")
if d["q"] is not None:
verdict = ("effects differ: uplift modelling / targeting can help" if d["p"] < 0.05
else "no evidence that effects differ")
print(f" heterogeneity: Q = {d['q']:.3f}, df = {d['df']}, p = {d['p']:.4g} -> {verdict}")
for fl in res["flags"]:
print(f"FLAG: {fl}")
return 1 if res["flags"] else 0
if __name__ == "__main__":
sys.exit(main())
anomaly-detection-model-builder
Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.
catboost-model-builder
Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).
categorical-encoding-selector
Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).
classification-model-selector
Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.
clustering-algorithm-selector
Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.
collaborative-filtering-implementer
Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.