def test_empty_model():
clf = CDClassifier(C=1e-5, penalty="l1")
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
clf = CDClassifier(C=1e-5, penalty="l1", debiasing=True)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
def test_empty_model():
clf = CDClassifier(C=1e-5, penalty="l1")
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
clf = CDClassifier(C=1e-5, penalty="l1", debiasing=True)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
def test_warm_start_l1r():
clf = CDClassifier(warm_start=True, random_state=0, penalty="l1")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
n_nz = clf.n_nonzero()
clf.C = 0.2
clf.fit(bin_dense, bin_target)
n_nz2 = clf.n_nonzero()
assert_true(n_nz < n_nz2)
def test_warm_start_l1r():
clf = CDClassifier(warm_start=True, random_state=0, penalty="l1")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
n_nz = clf.n_nonzero()
clf.C = 0.2
clf.fit(bin_dense, bin_target)
n_nz2 = clf.n_nonzero()
assert_true(n_nz < n_nz2)
def test_debiasing_warm_start():
clf = CDClassifier(penalty="l1", max_iter=10,
warm_start=True, random_state=0)
clf.C = 0.5
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 74)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
clf.C = 1.0
clf.fit(bin_dense, bin_target)
# FIXME: not the same sparsity as without warm start...
assert_equal(clf.n_nonzero(), 77)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
def test_debiasing_warm_start():
clf = CDClassifier(penalty="l1", max_iter=10,
warm_start=True, random_state=0)
clf.C = 0.5
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 74)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
clf.C = 1.0
clf.fit(bin_dense, bin_target)
# FIXME: not the same sparsity as without warm start...
assert_equal(clf.n_nonzero(), 77)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
def test_empty_model(bin_dense_train_data):
bin_dense, bin_target = bin_dense_train_data
clf = CDClassifier(C=1e-5, penalty="l1")
clf.fit(bin_dense, bin_target)
assert clf.n_nonzero() == 0
acc = clf.score(bin_dense, bin_target)
assert acc == 0.5
clf = CDClassifier(C=1e-5, penalty="l1", debiasing=True)
clf.fit(bin_dense, bin_target)
assert clf.n_nonzero() == 0
acc = clf.score(bin_dense, bin_target)
assert acc == 0.5
def test_debiasing_l1():
for warm_debiasing in (True, False):
clf = CDClassifier(penalty="l1", debiasing=True,
warm_debiasing=warm_debiasing,
C=0.05, Cd=1.0, max_iter=10, random_state=0)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 22)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.955, 3)
def test_debiasing_l1():
for warm_debiasing in (True, False):
clf = CDClassifier(penalty="l1", debiasing=True,
warm_debiasing=warm_debiasing,
C=0.05, Cd=1.0, max_iter=10, random_state=0)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_nonzero(), 22)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.955, 3)
def test_fit_linear_binary_l1r():
clf = CDClassifier(C=1.0, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
n_nz = clf.n_nonzero()
perc = clf.n_nonzero(percentage=True)
assert_equal(perc, float(n_nz) / bin_dense.shape[1])
clf = CDClassifier(C=0.1, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.97)
n_nz2 = clf.n_nonzero()
perc2 = clf.n_nonzero(percentage=True)
assert_equal(perc2, float(n_nz2) / bin_dense.shape[1])
assert_true(n_nz > n_nz2)
def test_fit_linear_binary_l1r():
clf = CDClassifier(C=1.0, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
n_nz = clf.n_nonzero()
perc = clf.n_nonzero(percentage=True)
assert_equal(perc, float(n_nz) / bin_dense.shape[1])
clf = CDClassifier(C=0.1, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.97)
n_nz2 = clf.n_nonzero()
perc2 = clf.n_nonzero(percentage=True)
assert_equal(perc2, float(n_nz2) / bin_dense.shape[1])
assert_true(n_nz > n_nz2)
def test_debiasing_l1l2():
for warm_debiasing in (True, False):
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=False,
debiasing=True,
warm_debiasing=warm_debiasing,
max_iter=20, C=0.01, random_state=0)
clf.fit(mult_csc, mult_target)
assert_greater(clf.score(mult_csc, mult_target), 0.75)
assert_equal(clf.n_nonzero(percentage=True), 0.08)
def test_debiasing_l1l2():
for warm_debiasing in (True, False):
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=False,
debiasing=True,
warm_debiasing=warm_debiasing,
max_iter=20, C=0.01, random_state=0)
clf.fit(mult_csc, mult_target)
assert_greater(clf.score(mult_csc, mult_target), 0.75)
assert_equal(clf.n_nonzero(percentage=True), 0.08)
def test_fit_linear_binary_l1r(bin_dense_train_data):
bin_dense, bin_target = bin_dense_train_data
clf = CDClassifier(C=1.0, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
assert not hasattr(clf, 'predict_proba')
acc = clf.score(bin_dense, bin_target)
np.testing.assert_almost_equal(acc, 1.0)
n_nz = clf.n_nonzero()
perc = clf.n_nonzero(percentage=True)
assert perc == n_nz / bin_dense.shape[1]
clf = CDClassifier(C=0.1, random_state=0, penalty="l1")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
np.testing.assert_almost_equal(acc, 0.97)
n_nz2 = clf.n_nonzero()
perc2 = clf.n_nonzero(percentage=True)
assert perc2 == n_nz2 / bin_dense.shape[1]
assert n_nz > n_nz2
def test_fit_squared_loss_l1():
clf = CDClassifier(C=0.5, random_state=0, penalty="l1",
loss="squared", max_iter=100, shrinking=False)
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.985, 3)
y = bin_target.copy()
y[y == 0] = -1
assert_array_almost_equal(np.dot(bin_dense, clf.coef_.ravel()) - y,
clf.errors_.ravel())
n_nz = clf.n_nonzero()
assert_equal(n_nz, 89)
def test_fit_squared_loss_l1():
clf = CDClassifier(C=0.5, random_state=0, penalty="l1",
loss="squared", max_iter=100, shrinking=False)
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.985, 3)
y = bin_target.copy()
y[y == 0] = -1
assert_array_almost_equal(np.dot(bin_dense, clf.coef_.ravel()) - y,
clf.errors_.ravel())
n_nz = clf.n_nonzero()
assert_equal(n_nz, 89)
def test_debiasing_l1(bin_dense_train_data, warm_debiasing):
bin_dense, bin_target = bin_dense_train_data
clf = CDClassifier(penalty="l1",
debiasing=True,
warm_debiasing=warm_debiasing,
C=0.05,
Cd=1.0,
max_iter=10,
random_state=0)
clf.fit(bin_dense, bin_target)
assert clf.n_nonzero() == 22
np.testing.assert_almost_equal(clf.score(bin_dense, bin_target), 0.955, 3)
def test_debiasing_l1l2(mult_sparse_train_data, warm_debiasing):
mult_sparse, mult_target = mult_sparse_train_data
clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=False,
debiasing=True,
warm_debiasing=warm_debiasing,
max_iter=20,
C=0.01,
random_state=0)
clf.fit(mult_sparse, mult_target)
assert clf.score(mult_sparse, mult_target) > 0.75
assert 0.0 <= clf.n_nonzero(percentage=True) <= 0.1
