def test_lasso_cv():
X, y, X_test, y_test = build_dataset()
max_iter = 150
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, cv=3).fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
clf = LassoCV(n_alphas=10,
eps=1e-3,
max_iter=max_iter,
precompute=True,
cv=3)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
# Check that the lars and the coordinate descent implementation
# select a similar alpha
lars = LassoLarsCV(normalize=False, max_iter=30, cv=3).fit(X, y)
# for this we check that they don't fall in the grid of
# clf.alphas further than 1
assert np.abs(
np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1
# check that they also give a similar MSE
mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
clf.mse_path_[5].mean(),
significant=2)
# test set
assert clf.score(X_test, y_test) > 0.99
def test_1d_multioutput_lasso_and_multitask_lasso_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = LassoCV(n_alphas=5, eps=2e-3)
clf.fit(X, y[:, 0])
clf1 = MultiTaskLassoCV(n_alphas=5, eps=2e-3)
clf1.fit(X, y)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])
def test_lassoCV_does_not_set_precompute(monkeypatch, precompute,
inner_precompute):
X, y, _, _ = build_dataset()
calls = 0
class LassoMock(Lasso):
def fit(self, X, y):
super().fit(X, y)
nonlocal calls
calls += 1
assert self.precompute == inner_precompute
monkeypatch.setattr("sklearn_lib.linear_model._coordinate_descent.Lasso",
LassoMock)
clf = LassoCV(precompute=precompute)
clf.fit(X, y)
assert calls > 0
def test_lasso_cv_positive_constraint():
X, y, X_test, y_test = build_dataset()
max_iter = 500
# Ensure the unconstrained fit has a negative coefficient
clf_unconstrained = LassoCV(n_alphas=3,
eps=1e-1,
max_iter=max_iter,
cv=2,
n_jobs=1)
clf_unconstrained.fit(X, y)
assert min(clf_unconstrained.coef_) < 0
# On same data, constrained fit has non-negative coefficients
clf_constrained = LassoCV(n_alphas=3,
eps=1e-1,
max_iter=max_iter,
positive=True,
cv=2,
n_jobs=1)
clf_constrained.fit(X, y)
assert min(clf_constrained.coef_) >= 0