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)
for alpha in (1e-3, 1e-2, 0.1, 0.2, 0.3):
print "alpha=", alpha
clf.alpha = alpha
clf.fit(X_train, y_train)
print clf.score(X_test, y_test)
print rank(clf.coef_)
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)
for alpha in (1e-3, 1e-2, 0.1, 0.2, 0.3):
print "alpha=", alpha
clf.alpha = alpha
clf.fit(X_train, y_train)
print clf.score(X_test, y_test)
print rank(clf.coef_)
