print "Acc:", clf.score(X, y)
print clf.coefs_
print "### Equivalent Lightning Cython Implementation ###"
light_clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=True,
max_iter=clf.max_iter,
alpha=1e-4, # clf.alpha,
C=1.0 / X.shape[0],
tol=clf.tol,
permute=False,
verbose=3,
random_state=0).fit(X, y)
print "Acc:", light_clf.score(X, y)
print light_clf.coef_.T
import numpy as np
data = np.load('3ng_train.npz')
X = data['X'].item()
Xaug = data['Xaug'].item()
y = data['y']
groups = data['groups']
clf.fit(Xaug, y, groups)
print clf.score(Xaug, y)
light_clf.verbose=1
light_clf.fit(X, y)
print light_clf.score(X, y)
