def test_lasso_admm_zero(self):
# Check that lasso by admm can handle zero data without crashing
X = [[0], [0], [0]]
y = [0, 0, 0]
clf = LassoADMM(alpha=0.1).fit(X, y)
pred = clf.predict([[1], [2], [3]])
assert_array_almost_equal(clf.coef_, [0])
assert_array_almost_equal(pred, [0, 0, 0])
def test_lasso_admm(self):
X, y, X_test, y_test = build_dataset()
clf = LassoADMM(alpha=0.05, tol=1e-8).fit(X, y)
self.assertGreater(clf.score(X_test, y_test), 0.99)
self.assertLess(clf.n_iter_, 150)
clf = LassoADMM(alpha=0.05, fit_intercept=False).fit(X, y)
self.assertGreater(clf.score(X_test, y_test), 0.99)
# normalize doesn't seem to work well
clf = LassoADMM(alpha=0.144, rho=0.1, normalize=True).fit(X, y)
self.assertGreater(clf.score(X_test, y_test), 0.60)
def test_lasso_admm_toy_multi(self):
# for issue #39
X = np.eye(4)
y = np.array([[1, 1, 0],
[1, 0, 1],
[0, 1, 0],
[0, 0, 1]])
clf = LassoADMM(alpha=0.05, tol=1e-8).fit(X, y)
assert_array_almost_equal(clf.coef_[0], [0.29999988, 0.29999988, -0.29999988, -0.29999988], decimal=3)
assert_array_almost_equal(clf.coef_[1], [0.29999988, -0.29999988, 0.29999988, -0.29999988], decimal=3)
assert_array_almost_equal(clf.coef_[2], [-0.29999988, 0.29999988, -0.29999988, 0.29999988], decimal=3)
from spmimage.linear_model import LassoADMM
if __name__ == '__main__':
data = pd.DataFrame(pd.read_csv('~/Downloads/data.csv'), dtype='float')
scores = np.zeros(0)
param_list = [0.001, 0.01, 0.1, 1, 10, 100]
scoresbyparam = np.zeros(0)
kf = KFold(n_splits=2)
for l in param_list:
print("current lambd is " + str(l))
for train, test in kf.split(data):
train_x = data.iloc[train, 2:7]
train_y = data.iloc[train, 1]
test_x = data.iloc[test, 2:7]
test_y = data.iloc[test, 1]
model = LassoADMM(l)
model.fit(train_x, train_y)
pre = model.predict(test_x)
rmse = np.sqrt(mean_squared_error(test_y, pre))
mae = mean_absolute_error(test_y, pre)
score = 1.253 - (rmse / mae)
print("score : " + str(score))
print("coef_ : " + str(model.coef_))
scores = np.append(scores, score)
scoresbyparam = np.append(scoresbyparam, np.mean(scores))
min = np.min(scoresbyparam)
min_index = np.argmin(scoresbyparam)
print("best lambd is " + str(param_list[min_index]))
model = LassoADMM(param_list[min_index])
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, 2:7],
data.iloc[:, 1],
def test_lasso_admm_toy(self):
# Test LassoADMM for various parameters of alpha and rho, using
# the same test case as Lasso implementation of sklearn.
# (see https://github.com/scikit-learn/scikit-learn/blob/master
# /sklearn/linear_model/tests/test_coordinate_descent.py)
# Actually, the parameters alpha = 0 should not be allowed. However,
# we test it as a border case.
# WARNING:
# LassoADMM can't check the case which is not converged
# because LassoADMM doesn't check dual gap yet.
# This problem will be fixed in future.
X = np.array([[-1.], [0.], [1.]])
Y = [-1, 0, 1] # just a straight line
T = [[2.], [3.], [4.]] # test sample
clf = LassoADMM(alpha=1e-8)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1], decimal=3)
assert_array_almost_equal(pred, [2, 3, 4], decimal=3)
clf = LassoADMM(alpha=0.1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.85], decimal=3)
assert_array_almost_equal(pred, [1.7, 2.55, 3.4], decimal=3)
clf = LassoADMM(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.254], decimal=3)
assert_array_almost_equal(pred, [0.508, 0.762, 1.016], decimal=3)
clf = LassoADMM(alpha=1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.0], decimal=3)
assert_array_almost_equal(pred, [0, 0, 0], decimal=3)
# this is the same test case as the case alpha=1e-8
# because the default rho parameter equals 1.0
clf = LassoADMM(alpha=1e-8, rho=1.0)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1], decimal=3)
assert_array_almost_equal(pred, [2, 3, 4], decimal=3)
clf = LassoADMM(alpha=0.5, rho=0.3, max_iter=50)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.249], decimal=3)
assert_array_almost_equal(pred, [0.498, 0.746, 0.995], decimal=3)
# tests for max_iter parameter(default = 1000)
clf = LassoADMM(alpha=0.5, rho=0.3, max_iter=100)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.249], decimal=3)
assert_array_almost_equal(pred, [0.498, 0.746, 0.995], decimal=3)
clf = LassoADMM(alpha=0.5, rho=0.3, max_iter=500)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.249], decimal=3)
assert_array_almost_equal(pred, [0.498, 0.746, 0.995], decimal=3)
clf = LassoADMM(alpha=0.5, rho=0.3, max_iter=1000)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.249], decimal=3)
assert_array_almost_equal(pred, [0.498, 0.746, 0.995], decimal=3)
clf = LassoADMM(alpha=0.5, rho=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.249], decimal=3)
assert_array_almost_equal(pred, [0.498, 0.746, 0.995], decimal=3)