Trace norm

Out:

alpha     | score                    | rank
0.001     | 0.7696494954859268       | 19
0.01      | 0.7639405204460966       | 19
0.1       | 0.45698353690918747      | 19
0.2       | 0.2894317578332448       | 12
0.3       | 0.20233669676048857      | 6

print(__doc__)
import numpy as np
from scipy.linalg import svd

from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.feature_selection import SelectKBest, chi2

from lightning.classification import FistaClassifier

def rank(M, eps=1e-9):
    U, s, V = svd(M, full_matrices=False)
    return np.sum(s > eps)


bunch = fetch_20newsgroups_vectorized(subset="train")
X_train = bunch.data
y_train = bunch.target

# Reduces dimensionality to make the example faster
ch2 = SelectKBest(chi2, k=5000)
X_train = ch2.fit_transform(X_train, y_train)

bunch = fetch_20newsgroups_vectorized(subset="test")
X_test = bunch.data
y_test = bunch.target
X_test = ch2.transform(X_test)

clf = FistaClassifier(C=1.0 / X_train.shape[0],
                      max_iter=200,
                      penalty="trace",
                      multiclass=True)

print(f"{'alpha': <10}| {'score': <25}| {'rank': <5}")
for alpha in (1e-3, 1e-2, 0.1, 0.2, 0.3):
    clf.alpha = alpha
    clf.fit(X_train, y_train)
    print(f"{alpha: <10}| {clf.score(X_test, y_test): <25}| {rank(clf.coef_): <5}")