Artifact of #7

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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
Command: python3 -I main.py 6
sha256: ae5aaea5dcdd9dabdac8fc79253bab239ec3e66dcc50476b2db056d041a901e3

Code

import sys, math, urllib.request
import numpy as np
from sklearn.ensemble import RandomForestClassifier
# Breiman (2001) Table 2, row "Sonar", column "Selection" (paper: 15.9 %).
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
raw = urllib.request.urlopen(URL, timeout=60).read().decode()
rows = [r.split(",") for r in raw.strip().splitlines()]
X = np.array([[float(v) for v in r[:-1]] for r in rows])
y = np.array([1 if r[-1] == "M" else 0 for r in rows])
n, M = X.shape
F2 = int(math.log2(M) + 1)
def one_rep(rng):
    idx = rng.permutation(n); nt = n // 10
    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]: best = (oob_err, te_err)
    return best[1]
print(f"rows={n} features={M} F in (1, {F2})")
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)]
    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

rows=208 features=60 F in (1, 6)
seed 0: mean test error over 100 reps = 17.70%  (sd of rep errors 9.78)
seed 1: mean test error over 100 reps = 15.90%  (sd of rep errors 8.98)
seed 2: mean test error over 100 reps = 17.45%  (sd of rep errors 9.15)
seed 3: mean test error over 100 reps = 16.80%  (sd of rep errors 8.32)
seed 4: mean test error over 100 reps = 16.00%  (sd of rep errors 9.43)
seed 5: mean test error over 100 reps = 15.90%  (sd of rep errors 8.56)

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