def test_l1l2_multi_task_log_loss():
clf = CDClassifier(penalty="l1/l2", loss="log",
multiclass=False,
max_steps=30,
max_iter=20, C=5.0, random_state=0)
clf.fit(mult_dense, mult_target)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8633, 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_l2r_modified_huber():
clf = CDClassifier(C=1.0,
random_state=0,
penalty="l2",
loss="modified_huber")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
def test_l1r_shrinking():
for shrinking in (True, False):
clf = CDClassifier(C=0.5,
penalty="l1",
random_state=0,
shrinking=shrinking)
clf.fit(bin_dense, bin_target)
assert_equal(clf.score(bin_dense, bin_target), 1.0)
def test_fit_linear_binary_l2r_log():
clf = CDClassifier(C=1.0,
random_state=0,
penalty="l2",
loss="log",
max_iter=5)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
def test_l1l2_multiclass_log_loss_no_linesearch():
data = mult_csc
clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
selection="uniform", max_steps=0,
max_iter=30, C=1.0, random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.88, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 297)
def test_fit_linear_binary_l1r_no_linesearch():
clf = CDClassifier(C=1.0,
selection="uniform",
max_steps=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)
def test_fit_squared_loss():
clf = CDClassifier(C=1.0, random_state=0, penalty="l2",
loss="squared", max_iter=100)
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.99)
y = bin_target.copy()
y[y == 0] = -1
assert_array_almost_equal(np.dot(bin_dense, clf.coef_.ravel()) - y,
clf.errors_.ravel())
def test_l1l2_multi_task_square_loss():
clf = CDClassifier(penalty="l1/l2",
loss="squared",
multiclass=False,
max_iter=20,
C=5.0,
random_state=0)
clf.fit(mult_dense, mult_target)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8066, 3)
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_warm_start_l2r():
clf = CDClassifier(warm_start=True, random_state=0, penalty="l2")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
clf.C = 0.2
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
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_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_fit_squared_loss():
clf = CDClassifier(C=1.0,
random_state=0,
penalty="l2",
loss="squared",
max_iter=100)
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.99)
y = bin_target.copy()
y[y == 0] = -1
assert_array_almost_equal(
np.dot(bin_dense, clf.coef_.ravel()) - y, clf.errors_.ravel())
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_l2r():
clf = CDClassifier(warm_start=True, random_state=0, penalty="l2")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
clf.C = 0.2
clf.fit(bin_dense, bin_target)
assert_almost_equal(clf.score(bin_dense, bin_target), 1.0)
def test_l1l2_multiclass_log_loss_no_linesearch():
data = mult_csc
clf = CDClassifier(penalty="l1/l2",
loss="log",
multiclass=True,
selection="uniform",
max_steps=0,
max_iter=30,
C=1.0,
random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.88, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 297)
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_l1l2_multi_task_squared_hinge_loss():
Y = LabelBinarizer(neg_label=-1).fit_transform(mult_target)
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=False,
max_iter=20, C=5.0, random_state=0)
clf.fit(mult_dense, mult_target)
df = clf.decision_function(mult_dense)
assert_array_almost_equal(clf.errors_.T, 1 - Y * df)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8633, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 300)
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=False,
max_iter=20, C=0.05, random_state=0)
clf.fit(mult_dense, mult_target)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8266, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 231)
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_l1l2_multiclass_log_loss():
for data in (mult_dense, mult_csc):
clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
max_steps=30, max_iter=5, C=1.0, random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.8766, 3)
df = clf.decision_function(data)
sel = np.array([df[i, int(mult_target[i])] for i in xrange(df.shape[0])])
df -= sel[:, np.newaxis]
df = np.exp(df)
assert_array_almost_equal(clf.errors_, df.T)
for i in xrange(data.shape[0]):
assert_almost_equal(clf.errors_[mult_target[i], i], 1.0)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 297)
clf = CDClassifier(penalty="l1/l2", loss="log", multiclass=True,
max_steps=30, max_iter=5, C=0.3, random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.8566, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 213)
assert_true(nz % 3 == 0) # should be a multiple of n_classes
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_l1l2_multiclass_squared_hinge_loss_no_linesearch():
data = mult_csc
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=True, shrinking=False, selection="uniform",
max_steps=0, max_iter=200, C=1.0, random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.9166, 3)
df = clf.decision_function(data)
n_samples, n_vectors = df.shape
diff = np.zeros_like(clf.errors_)
for i in xrange(n_samples):
for k in xrange(n_vectors):
diff[k, i] = 1 - (df[i, mult_target[i]] - df[i, k])
assert_array_almost_equal(clf.errors_, diff)
assert_equal(np.sum(clf.coef_ != 0), 300)
clf = CDClassifier(penalty="l1/l2", loss="squared_hinge",
multiclass=True,
max_iter=20, C=0.05, random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.83, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 207)
assert_true(nz % 3 == 0) # should be a multiple of n_classes
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_l1l2_multiclass_squared_hinge_loss_no_linesearch():
data = mult_csc
clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=True,
shrinking=False,
selection="uniform",
max_steps=0,
max_iter=200,
C=1.0,
random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.9166, 3)
df = clf.decision_function(data)
n_samples, n_vectors = df.shape
diff = np.zeros_like(clf.errors_)
for i in xrange(n_samples):
for k in xrange(n_vectors):
diff[k, i] = 1 - (df[i, mult_target[i]] - df[i, k])
assert_array_almost_equal(clf.errors_, diff)
assert_equal(np.sum(clf.coef_ != 0), 300)
clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=True,
max_iter=20,
C=0.05,
random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.83, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 207)
assert_true(nz % 3 == 0) # should be a multiple of n_classes
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_log_loss_no_linesearch():
clf = CDClassifier(C=1.0, max_steps=0, random_state=0,
selection="uniform", penalty="l1", loss="log")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.995)
def test_l1l2_multiclass_log_loss():
for data in (mult_dense, mult_csc):
clf = CDClassifier(penalty="l1/l2",
loss="log",
multiclass=True,
max_steps=30,
max_iter=5,
C=1.0,
random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.8766, 3)
df = clf.decision_function(data)
sel = np.array(
[df[i, int(mult_target[i])] for i in xrange(df.shape[0])])
df -= sel[:, np.newaxis]
df = np.exp(df)
assert_array_almost_equal(clf.errors_, df.T)
for i in xrange(data.shape[0]):
assert_almost_equal(clf.errors_[mult_target[i], i], 1.0)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 297)
clf = CDClassifier(penalty="l1/l2",
loss="log",
multiclass=True,
max_steps=30,
max_iter=5,
C=0.3,
random_state=0)
clf.fit(data, mult_target)
assert_almost_equal(clf.score(data, mult_target), 0.8566, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 213)
assert_true(nz % 3 == 0) # should be a multiple of n_classes
def test_l1r_shrinking():
for shrinking in (True, False):
clf = CDClassifier(C=0.5, penalty="l1", random_state=0,
shrinking=shrinking)
clf.fit(bin_dense, bin_target)
assert_equal(clf.score(bin_dense, bin_target), 1.0)
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_fit_linear_binary_l1r_log_loss():
clf = CDClassifier(C=1.0, random_state=0, penalty="l1", loss="log")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.995)
def test_l1l2_multi_task_squared_hinge_loss():
Y = LabelBinarizer(neg_label=-1).fit_transform(mult_target)
clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=False,
max_iter=20,
C=5.0,
random_state=0)
clf.fit(mult_dense, mult_target)
df = clf.decision_function(mult_dense)
assert_array_almost_equal(clf.errors_.T, 1 - Y * df)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8633, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 300)
clf = CDClassifier(penalty="l1/l2",
loss="squared_hinge",
multiclass=False,
max_iter=20,
C=0.05,
random_state=0)
clf.fit(mult_dense, mult_target)
assert_almost_equal(clf.score(mult_dense, mult_target), 0.8266, 3)
nz = np.sum(clf.coef_ != 0)
assert_equal(nz, 231)
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_multi_l2r():
clf = CDClassifier(C=1.0, random_state=0, penalty="l2")
clf.fit(mult_dense, mult_target)
acc = clf.score(mult_dense, mult_target)
assert_almost_equal(acc, 0.8833, 4)
def test_fit_linear_multi_l2r():
clf = CDClassifier(C=1.0, random_state=0, penalty="l2")
clf.fit(mult_dense, mult_target)
acc = clf.score(mult_dense, mult_target)
assert_almost_equal(acc, 0.8833, 4)
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_fit_linear_binary_l2r():
clf = CDClassifier(C=1.0, random_state=0, penalty="l2")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 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_fit_linear_binary_l1r_log_loss():
clf = CDClassifier(C=1.0, random_state=0, penalty="l1", loss="log")
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.995)
verbose=0)
clf.fit(X, y)
training_time = time.time() - s
print "Numba"
print training_time
print clf.score(X, y)
print clf.n_nonzero(percentage=True)
print
from lightning.primal_cd import CDClassifier
clf = CDClassifier(C=1. / X.shape[0],
alpha=1e-4,
tol=1e-3,
max_iter=20,
multiclass=True,
penalty="l1/l2",
shrinking=False,
max_steps=0,
selection="uniform",
verbose=0)
s = time.time()
clf.fit(X, y)
training_time = time.time() - s
print "Cython"
print training_time
print clf.score(X, y)
print clf.n_nonzero(percentage=True)
print
