def test_sag_adaptive():
"""Check that the adaptive step size strategy yields the same
solution as the non-adaptive"""
np.random.seed(0)
X = sparse.rand(100, 10, density=.5, random_state=0).tocsr()
y = np.random.randint(0, high=2, size=100)
for alpha in np.logspace(-3, 1, 5):
clf_adaptive = SAGClassifier(eta='line-search',
random_state=0,
alpha=alpha)
clf_adaptive.fit(X, y)
clf = SAGClassifier(eta='auto', random_state=0, alpha=alpha)
clf.fit(X, y)
np.testing.assert_almost_equal(clf_adaptive.score(X, y),
clf.score(X, y), 1)
clf_adaptive = SAGAClassifier(eta='line-search',
loss='log',
random_state=0,
alpha=alpha,
max_iter=20)
clf_adaptive.fit(X, y)
assert np.isnan(clf_adaptive.coef_.sum()) == False
clf = SAGAClassifier(eta='auto',
loss='log',
random_state=0,
alpha=alpha,
max_iter=20)
clf.fit(X, y)
np.testing.assert_almost_equal(clf_adaptive.score(X, y),
clf.score(X, y), 1)
def test_sag_sparse():
# FIX for https://github.com/mblondel/lightning/issues/33
# check that SAG has the same results with dense
# and sparse data
X = sparse.rand(100, 50, density=.5, random_state=0)
y = np.random.randint(0, high=2, size=100)
for alpha in np.logspace(-3, 3, 10):
clf_sparse = SAGClassifier(eta=1.,
max_iter=1,
random_state=0,
alpha=alpha)
clf_sparse.fit(X, y)
clf_dense = SAGClassifier(eta=1.,
max_iter=1,
random_state=0,
alpha=alpha)
clf_dense.fit(X.toarray(), y)
assert clf_sparse.score(X, y) == clf_dense.score(X, y)
clf_sparse = SAGAClassifier(eta=1.,
max_iter=1,
random_state=0,
alpha=alpha)
clf_sparse.fit(X, y)
clf_dense = SAGAClassifier(eta=1.,
max_iter=1,
random_state=0,
alpha=alpha)
clf_dense.fit(X.toarray(), y)
assert clf_sparse.score(X, y) == clf_dense.score(X, y)
def test_sag_sparse():
# FIX for https://github.com/mblondel/lightning/issues/33
# check that SAG has the same results with dense
# and sparse data
X = sparse.rand(100, 50, density=.5, random_state=0)
y = np.random.randint(0, high=2, size=100)
for alpha in np.logspace(-3, 3, 10):
clf_sparse = SAGClassifier(max_iter=1, random_state=0, alpha=alpha)
clf_sparse.fit(X, y)
clf_dense = SAGClassifier(max_iter=1, random_state=0, alpha=alpha)
clf_dense.fit(X.toarray(), y)
assert_equal(clf_sparse.score(X, y), clf_dense.score(X, y))
def test_sag_score():
X, y = make_classification(1000, random_state=0)
pysag = PySAGClassifier(eta=1e-3, alpha=0.0, beta=0.0, max_iter=10, random_state=0)
sag = SAGClassifier(eta=1e-3, alpha=0.0, beta=0.0, max_iter=10, random_state=0)
pysag.fit(X, y)
sag.fit(X, y)
assert_equal(pysag.score(X, y), sag.score(X, y))
def test_sag_score():
X, y = make_classification(1000, random_state=0)
pysag = PySAGClassifier(eta=1e-3, alpha=0.0, beta=0.0, max_iter=10,
random_state=0)
sag = SAGClassifier(eta=1e-3, alpha=0.0, beta=0.0, max_iter=10,
random_state=0)
pysag.fit(X, y)
sag.fit(X, y)
assert_equal(pysag.score(X, y), sag.score(X, y))
def test_sag_adaptive():
"""Check that the adaptive step size strategy yields the same
solution as the non-adaptive"""
np.random.seed(0)
X = sparse.rand(100, 10, density=.5, random_state=0).tocsr()
y = np.random.randint(0, high=2, size=100)
for alpha in np.logspace(-3, 1, 5):
clf_adaptive = SAGClassifier(
eta='line-search', random_state=0, alpha=alpha)
clf_adaptive.fit(X, y)
clf = SAGClassifier(
eta='auto', random_state=0, alpha=alpha)
clf.fit(X, y)
assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)
clf_adaptive = SAGAClassifier(
eta='line-search', loss='log', random_state=0, alpha=alpha, max_iter=20)
clf_adaptive.fit(X, y)
assert np.isnan(clf_adaptive.coef_.sum()) == False
clf = SAGAClassifier(
eta='auto', loss='log', random_state=0, alpha=alpha, max_iter=20)
clf.fit(X, y)
assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)
def test_sag():
clf = SAGClassifier(eta=1e-3, max_iter=20, verbose=0, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sag():
clf = SAGClassifier(eta=1e-3, max_iter=20, verbose=0, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
