def test_fit_linear_binary_auc():
for data in (bin_dense, bin_csr):
for loss in ("l1", "l2"):
clf = LinearSVC(loss=loss, criterion="auc", random_state=0,
max_iter=25)
clf.fit(data, bin_target)
assert_equal(clf.score(data, bin_target), 1.0)
def test_fit_linear_multi():
for data in (mult_dense, mult_sparse):
clf = LinearSVC(random_state=0)
clf.fit(data, mult_target)
y_pred = clf.predict(data)
acc = np.mean(y_pred == mult_target)
assert_greater(acc, 0.85)
def test_fit_linear_binary_auc():
for data in (bin_dense, bin_csr):
for loss in ("l1", "l2"):
clf = LinearSVC(loss=loss, criterion="auc", random_state=0,
max_iter=25)
clf.fit(data, bin_target)
assert_equal(clf.score(data, bin_target), 1.0)
def test_fit_linear_binary():
for data in (bin_dense, bin_csr):
for loss in ("l1", "l2"):
clf = LinearSVC(loss=loss, random_state=0, max_iter=10)
clf.fit(data, bin_target)
assert_equal(clf.score(data, bin_target), 1.0)
y_pred = clf.decision_function(data).ravel()
def test_fit_linear_multi():
for data in (mult_dense, mult_sparse):
clf = LinearSVC(random_state=0)
clf.fit(data, mult_target)
y_pred = clf.predict(data)
acc = np.mean(y_pred == mult_target)
assert_greater(acc, 0.85)
def test_fit_linear_multi(data, request):
X, y = request.getfixturevalue(data)
clf = LinearSVC(random_state=0)
clf.fit(X, y)
assert list(clf.classes_) == [0, 1, 2]
y_pred = clf.predict(X)
acc = np.mean(y_pred == y)
assert acc > 0.85
def test_warm_start():
clf = LinearSVC(warm_start=True, loss="l1", random_state=0, max_iter=100)
for C in (0.1, 0.2):
clf.C = C
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_greater(acc, 0.99)
def test_fit_linear_multi():
for data in (mult_dense, mult_sparse):
clf = LinearSVC(random_state=0)
clf.fit(data, mult_target)
assert list(clf.classes_) == [0, 1, 2]
y_pred = clf.predict(data)
acc = np.mean(y_pred == mult_target)
assert acc > 0.85
def test_warm_start(bin_dense_train_data, C):
bin_dense, bin_target = bin_dense_train_data
clf = LinearSVC(warm_start=True, loss="l1", random_state=0, max_iter=100)
clf.C = C
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert acc > 0.99
def test_fit_linear_binary(data, loss, request):
X, y = request.getfixturevalue(data)
clf = LinearSVC(loss=loss, random_state=0, max_iter=10)
clf.fit(X, y)
assert list(clf.classes_) == [0, 1]
assert clf.score(X, y) == 1.0
y_pred = clf.decision_function(X).ravel()
def test_fit_linear_binary():
for data in (bin_dense, bin_csr):
for loss in ("l1", "l2"):
clf = LinearSVC(loss=loss, random_state=0, max_iter=10)
clf.fit(data, bin_target)
assert_equal(clf.score(data, bin_target), 1.0)
y_pred = clf.decision_function(data).ravel()
def test_warm_start():
clf = LinearSVC(warm_start=True, loss="l1", random_state=0, max_iter=100)
for C in (0.1, 0.2):
clf.C = C
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_greater(acc, 0.99)
def test_fit_linear_binary_auc(data, loss, request):
X, y = request.getfixturevalue(data)
clf = LinearSVC(loss=loss, criterion="auc", random_state=0, max_iter=25)
clf.fit(X, y)
assert clf.score(X, y) == 1.0
