Artifact of #2

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Language: python
Environment: python 3.13.16, numpy 2.5.3, scikit-learn 1.9.1; network only to archive.ics.uci.edu (data download)
Command: python3 main.py 4
sha256: 6c45cda0cb2780b4016bbd8611e6b03e03feda2675afb43884331af24432b350

Code

import sys, math, urllib.request, io
import numpy as np
from sklearn.ensemble import RandomForestClassifier

URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"
raw = urllib.request.urlopen(URL, timeout=60).read().decode()
rows = [r.split(",") for r in raw.strip().splitlines()]
X = np.array([[np.nan if v == "?" else float(v) for v in r[1:10]] for r in rows])
y = np.array([int(r[10]) for r in rows])
med = np.nanmedian(X, axis=0)                      # 16 missing Bare Nuclei values -> column median
X = np.where(np.isnan(X), med, X)
n, M = X.shape
F2 = int(math.log2(M) + 1)                         # Breiman: first integer below log2 M + 1 read as int(log2 M + 1) = 4
print("n", n, "M", M, "F values", 1, F2)

def one_rep(rng):
    idx = rng.permutation(n)
    nt = n // 10                                    # random 10% set aside
    te, tr = idx[:nt], idx[nt:]
    best = None
    for F in (1, F2):
        rf = RandomForestClassifier(n_estimators=100, max_features=F, bootstrap=True,
                                    oob_score=True, n_jobs=-1,
                                    random_state=int(rng.integers(2**31 - 1)))
        rf.fit(X[tr], y[tr])
        oob_err = 1 - rf.oob_score_
        te_err = np.mean(rf.predict(X[te]) != y[te])
        if best is None or oob_err < best[0]:       # choose run with lower out-of-bag error
            best = (oob_err, te_err)
    return best[1]

for seed in range(int(sys.argv[1]) if len(sys.argv) > 1 else 3):
    rng = np.random.default_rng(seed)
    errs = [one_rep(rng) for _ in range(100)]       # 100 repetitions as in the paper
    print(f"seed {seed}: mean test error over 100 reps = {100*np.mean(errs):.2f}%  (sd of rep errors {100*np.std(errs):.2f})")

Expected output

n 699 M 9 F values 1 4
seed 0: mean test error over 100 reps = 3.10%  (sd of rep errors 2.19)
seed 1: mean test error over 100 reps = 3.07%  (sd of rep errors 1.90)
seed 2: mean test error over 100 reps = 3.19%  (sd of rep errors 1.98)
seed 3: mean test error over 100 reps = 3.06%  (sd of rep errors 1.82)

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