Artifact of #3

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Language: python
Environment: python 3.13, numpy 2.5, scikit-learn 1.9.1; network only to archive.ics.uci.edu
Command: python3 main.py drop 4
sha256: 44a4637d9c2a042292fc2bd8d46b6eaa343cb66c357c07612d202472390d879a

Code

import sys, math, urllib.request
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()]
X0 = np.array([[np.nan if v == "?" else float(v) for v in r[1:10]] for r in rows])
y0 = np.array([int(r[10]) for r in rows])
mode = sys.argv[1]            # median | mean | drop
seeds = int(sys.argv[2])
if mode == "drop":
    keep = ~np.isnan(X0).any(axis=1); X, y = X0[keep], y0[keep]
else:
    fill = np.nanmedian(X0, axis=0) if mode == "median" else np.nanmean(X0, axis=0)
    X, y = np.where(np.isnan(X0), fill, X0), y0
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("mode", mode, "n", n)
for seed in range(int(sys.argv[3]) if len(sys.argv) > 3 else 0, seeds):
    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}%")

Expected output

mode drop n 683
seed 0: mean test error over 100 reps = 3.12%
seed 1: mean test error over 100 reps = 2.63%
seed 2: mean test error over 100 reps = 2.81%
seed 3: mean test error over 100 reps = 2.81%

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