def test_fit_reg_squared_l1():
clf = CDRegressor(C=1.0, random_state=0, penalty="l1",
loss="squared", max_iter=100)
clf.fit(digit.data, digit.target)
y_pred = (clf.predict(digit.data) > 0.5).astype(int)
acc = np.mean(digit.target == y_pred)
assert_almost_equal(acc, 1.0, 3)
def test_fit_reg_squared_l1():
clf = CDRegressor(C=1.0, random_state=0, penalty="l1",
loss="squared", max_iter=100)
clf.fit(digit.data, digit.target)
y_pred = (clf.predict(digit.data) > 0.5).astype(int)
acc = np.mean(digit.target == y_pred)
assert_almost_equal(acc, 1.0, 3)
def test_fit_reg_squared_loss_nn_l2():
K = pairwise_kernels(digit.data, metric="poly", degree=4)
clf = CDRegressor(C=1, random_state=0, penalty="nnl2",
loss="squared", max_iter=100)
clf.fit(K, digit.target)
y_pred = (clf.predict(K) > 0.5).astype(int)
acc = np.mean(digit.target == y_pred)
assert_almost_equal(acc, 0.9444, 3)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
loss="squared", max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=1.0, random_state=0, penalty="l2",
loss="squared", max_iter=100)
Y = np.zeros((len(digit.target), 2))
Y[:, 0] = digit.target
Y[:, 1] = digit.target
reg.fit(digit.data, Y)
y_pred = reg.predict(digit.data)
assert_equal(y_pred.shape[0], len(digit.target))
assert_equal(y_pred.shape[1], 2)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=1.0, random_state=0, penalty="l2",
loss="squared", max_iter=100)
Y = np.zeros((len(digit.target), 2))
Y[:, 0] = digit.target
Y[:, 1] = digit.target
reg.fit(digit.data, Y)
y_pred = reg.predict(digit.data)
assert_equal(y_pred.shape[0], len(digit.target))
assert_equal(y_pred.shape[1], 2)
def test_fit_reg_squared_l1(train_data):
X, y = train_data
clf = CDRegressor(C=1.0,
random_state=0,
penalty="l1",
loss="squared",
max_iter=100)
clf.fit(X, y)
y_pred = (clf.predict(X) > 0.5).astype(int)
acc = np.mean(y == y_pred)
np.testing.assert_almost_equal(acc, 1.0, 3)
def test_fit_reg_squared_loss_nn_l2():
K = pairwise_kernels(digit.data, metric="poly", degree=4)
clf = CDRegressor(C=1,
random_state=0,
penalty="nnl2",
loss="squared",
max_iter=100)
clf.fit(K, digit.target)
y_pred = (clf.predict(K) > 0.5).astype(int)
acc = np.mean(digit.target == y_pred)
assert_almost_equal(acc, 0.9444, 3)
def test_warm_start_l1r_regression():
clf = CDRegressor(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_fit_reg_squared_multiple_outputs(train_data):
X, y = train_data
reg = CDRegressor(C=1.0,
random_state=0,
penalty="l2",
loss="squared",
max_iter=100)
Y = np.zeros((len(y), 2))
Y[:, 0] = y
Y[:, 1] = y
reg.fit(X, Y)
y_pred = reg.predict(X)
assert y_pred.shape[0] == len(y)
assert y_pred.shape[1] == 2
def test_warm_start_l1r_regression():
clf = CDRegressor(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_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
loss="squared", max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)