def test_sdca_hinge_elastic():
clf = SDCAClassifier(alpha=0.5,
l1_ratio=0.85,
loss="hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_hinge_multiclass():
clf = SDCAClassifier(alpha=1e-2,
max_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
assert_almost_equal(clf.score(X, y), 0.947, 3)
def test_sdca_squared_hinge_elastic():
clf = SDCAClassifier(alpha=0.5,
l1_ratio=0.85,
loss="squared_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_squared_hinge_elastic(bin_train_data):
X_bin, y_bin = bin_train_data
clf = SDCAClassifier(alpha=0.5,
l1_ratio=0.85,
loss="squared_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_smooth_hinge_elastic():
clf = SDCAClassifier(alpha=0.5,
l1_ratio=0.85,
loss="smooth_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_hinge_multiclass(train_data):
X, y = train_data
clf = SDCAClassifier(alpha=1e-2,
max_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
np.testing.assert_almost_equal(clf.score(X, y), 0.933, 3)
def test_sdca_smooth_hinge_elastic(bin_train_data):
X_bin, y_bin = bin_train_data
clf = SDCAClassifier(alpha=0.5,
l1_ratio=0.85,
loss="smooth_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_squared_l1_only():
clf = SDCAClassifier(alpha=0.5,
l1_ratio=1.0,
loss="squared",
tol=1e-2,
max_iter=100,
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_smooth_hinge_l1_only():
clf = SDCAClassifier(alpha=0.5,
l1_ratio=1.0,
loss="smooth_hinge",
tol=1e-2,
max_iter=200,
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_squared_l1_only(bin_train_data):
X_bin, y_bin = bin_train_data
clf = SDCAClassifier(alpha=0.5,
l1_ratio=1.0,
loss="squared",
tol=1e-2,
max_iter=100,
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_absolute():
clf = SDCAClassifier(loss="absolute", random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_hinge_multiclass():
clf = SDCAClassifier(alpha=1e-2, max_iter=100, loss="hinge",
random_state=0)
clf.fit(X, y)
assert_almost_equal(clf.score(X, y), 0.947, 3)
def test_sdca_squared():
clf = SDCAClassifier(loss="squared", random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_absolute():
clf = SDCAClassifier(loss="absolute", random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_absolute():
clf = SDCAClassifier(loss="absolute", random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_squared_hinge_elastic():
clf = SDCAClassifier(alpha=0.5, l1_ratio=0.85, loss="squared_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_squared():
clf = SDCAClassifier(loss="squared", random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_hinge(bin_train_data):
X_bin, y_bin = bin_train_data
clf = SDCAClassifier(loss="hinge", random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert clf.score(X_bin, y_bin) == 1.0
def test_sdca_absolute_l1_only():
clf = SDCAClassifier(alpha=0.5, l1_ratio=1.0, loss="absolute",
tol=1e-2, max_iter=200, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_smooth_hinge_elastic():
clf = SDCAClassifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge",
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, 'predict_proba')
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_squared():
clf = SDCAClassifier(loss="squared", random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
import sys
from sklearn.externals import joblib
from lightning.classification import SDCAClassifier
if len(sys.argv) == 1:
print """
Please enter the path to amazon7_uncompressed_pkl/amazon7.pkl
Download data from
http://www.mblondel.org/data/amazon7_uncompressed_pkl.tar.bz2
"""
exit()
data = joblib.load(sys.argv[1], mmap_mode="r")
X = data["X"]
y = data["y"].copy() # copy is needed to modify y.
y[y >= 1] = 1 # Create a binary classification problem.
clf = SDCAClassifier(tol=1e-5, max_iter=10, verbose=1)
clf.fit(X, y)
print clf.score(X, y)
def test_sdca_absolute():
clf = SDCAClassifier(loss="absolute", random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
