def test_multitarget():
# Assure that estimators receiving multidimensional y do the right thing
Y = np.vstack([y, y**2]).T
n_targets = Y.shape[1]
estimators = [
linear_model.LassoLars(),
linear_model.Lars(),
# regression test for gh-1615
linear_model.LassoLars(fit_intercept=False),
linear_model.Lars(fit_intercept=False),
]
for estimator in estimators:
estimator.fit(X, Y)
Y_pred = estimator.predict(X)
alphas, active, coef, path = (estimator.alphas_, estimator.active_,
estimator.coef_, estimator.coef_path_)
for k in range(n_targets):
estimator.fit(X, Y[:, k])
y_pred = estimator.predict(X)
assert_array_almost_equal(alphas[k], estimator.alphas_)
assert_array_almost_equal(active[k], estimator.active_)
assert_array_almost_equal(coef[k], estimator.coef_)
assert_array_almost_equal(path[k], estimator.coef_path_)
assert_array_almost_equal(Y_pred[:, k], y_pred)
def test_lasso_lars_path_length():
# Test that the path length of the LassoLars is right
lasso = linear_model.LassoLars()
lasso.fit(X, y)
lasso2 = linear_model.LassoLars(alpha=lasso.alphas_[2])
lasso2.fit(X, y)
assert_array_almost_equal(lasso.alphas_[:3], lasso2.alphas_)
# Also check that the sequence of alphas is always decreasing
assert np.all(np.diff(lasso.alphas_) < 0)
def test_lasso_lars_vs_lasso_cd_ill_conditioned2():
# Create an ill-conditioned situation in which the LARS has to go
# far in the path to converge, and check that LARS and coordinate
# descent give the same answers
# Note it used to be the case that Lars had to use the drop for good
# strategy for this but this is no longer the case with the
# equality_tolerance checks
X = [[1e20, 1e20, 0], [-1e-32, 0, 0], [1, 1, 1]]
y = [10, 10, 1]
alpha = .0001
def objective_function(coef):
return (1. / (2. * len(X)) * linalg.norm(y - np.dot(X, coef))**2 +
alpha * linalg.norm(coef, 1))
lars = linear_model.LassoLars(alpha=alpha, normalize=False)
assert_warns(ConvergenceWarning, lars.fit, X, y)
lars_coef_ = lars.coef_
lars_obj = objective_function(lars_coef_)
coord_descent = linear_model.Lasso(alpha=alpha, tol=1e-4, normalize=False)
cd_coef_ = coord_descent.fit(X, y).coef_
cd_obj = objective_function(cd_coef_)
assert lars_obj < cd_obj * (1. + 1e-8)
def test_lars_lstsq():
# Test that Lars gives least square solution at the end
# of the path
X1 = 3 * X # use un-normalized dataset
clf = linear_model.LassoLars(alpha=0.)
clf.fit(X1, y)
# Avoid FutureWarning about default value change when numpy >= 1.14
rcond = None if LooseVersion(np.__version__) >= '1.14' else -1
coef_lstsq = np.linalg.lstsq(X1, y, rcond=rcond)[0]
assert_array_almost_equal(clf.coef_, coef_lstsq)
def test_rank_deficient_design():
# consistency test that checks that LARS Lasso is handling rank
# deficient input data (with n_features < rank) in the same way
# as coordinate descent Lasso
y = [5, 0, 5]
for X in ([[5, 0], [0, 5], [10, 10]], [[10, 10, 0], [1e-32, 0, 0],
[0, 0, 1]]):
# To be able to use the coefs to compute the objective function,
# we need to turn off normalization
lars = linear_model.LassoLars(.1, normalize=False)
coef_lars_ = lars.fit(X, y).coef_
obj_lars = (1. /
(2. * 3.) * linalg.norm(y - np.dot(X, coef_lars_))**2 +
.1 * linalg.norm(coef_lars_, 1))
coord_descent = linear_model.Lasso(.1, tol=1e-6, normalize=False)
coef_cd_ = coord_descent.fit(X, y).coef_
obj_cd = ((1. / (2. * 3.)) * linalg.norm(y - np.dot(X, coef_cd_))**2 +
.1 * linalg.norm(coef_cd_, 1))
assert obj_lars < obj_cd * (1. + 1e-8)
def test_lasso_lars_vs_lasso_cd():
# Test that LassoLars and Lasso using coordinate descent give the
# same results.
X = 3 * diabetes.data
alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso')
lasso_cd = linear_model.Lasso(fit_intercept=False, tol=1e-8)
for c, a in zip(lasso_path.T, alphas):
if a == 0:
continue
lasso_cd.alpha = a
lasso_cd.fit(X, y)
error = linalg.norm(c - lasso_cd.coef_)
assert error < 0.01
# similar test, with the classifiers
for alpha in np.linspace(1e-2, 1 - 1e-2, 20):
clf1 = linear_model.LassoLars(alpha=alpha, normalize=False).fit(X, y)
clf2 = linear_model.Lasso(alpha=alpha, tol=1e-8,
normalize=False).fit(X, y)
err = linalg.norm(clf1.coef_ - clf2.coef_)
assert err < 1e-3
# same test, with normalized data
X = diabetes.data
alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso')
lasso_cd = linear_model.Lasso(fit_intercept=False,
normalize=True,
tol=1e-8)
for c, a in zip(lasso_path.T, alphas):
if a == 0:
continue
lasso_cd.alpha = a
lasso_cd.fit(X, y)
error = linalg.norm(c - lasso_cd.coef_)
assert error < 0.01
def test_lasso_lars_vs_R_implementation():
# Test that sklearn_lib LassoLars implementation agrees with the LassoLars
# implementation available in R (lars library) under the following
# scenarios:
# 1) fit_intercept=False and normalize=False
# 2) fit_intercept=True and normalize=True
# Let's generate the data used in the bug report 7778
y = np.array(
[-6.45006793, -3.51251449, -8.52445396, 6.12277822, -19.42109366])
x = np.array(
[[0.47299829, 0, 0, 0, 0], [0.08239882, 0.85784863, 0, 0, 0],
[0.30114139, -0.07501577, 0.80895216, 0, 0],
[-0.01460346, -0.1015233, 0.0407278, 0.80338378, 0],
[-0.69363927, 0.06754067, 0.18064514, -0.0803561, 0.40427291]])
X = x.T
###########################################################################
# Scenario 1: Let's compare R vs sklearn_lib when fit_intercept=False and
# normalize=False
###########################################################################
#
# The R result was obtained using the following code:
#
# library(lars)
# model_lasso_lars = lars(X, t(y), type="lasso", intercept=FALSE,
# trace=TRUE, normalize=FALSE)
# r = t(model_lasso_lars$beta)
#
r = np.array([[
0, 0, 0, 0, 0, -79.810362809499026, -83.528788732782829,
-83.777653739190711, -83.784156932888934, -84.033390591756657
], [0, 0, 0, 0, -0.476624256777266, 0, 0, 0, 0, 0.025219751009936],
[
0, -3.577397088285891, -4.702795355871871,
-7.016748621359461, -7.614898471899412,
-0.336938391359179, 0, 0, 0.001213370600853,
0.048162321585148
],
[
0, 0, 0, 2.231558436628169, 2.723267514525966,
2.811549786389614, 2.813766976061531, 2.817462468949557,
2.817368178703816, 2.816221090636795
],
[
0, 0, -1.218422599914637, -3.457726183014808,
-4.021304522060710, -45.827461592423745,
-47.776608869312305, -47.911561610746404,
-47.914845922736234, -48.039562334265717
]])
model_lasso_lars = linear_model.LassoLars(alpha=0,
fit_intercept=False,
normalize=False)
model_lasso_lars.fit(X, y)
skl_betas = model_lasso_lars.coef_path_
assert_array_almost_equal(r, skl_betas, decimal=12)
###########################################################################
###########################################################################
# Scenario 2: Let's compare R vs sklearn_lib when fit_intercept=True and
# normalize=True
#
# Note: When normalize is equal to True, R returns the coefficients in
# their original units, that is, they are rescaled back, whereas sklearn_lib
# does not do that, therefore, we need to do this step before comparing
# their results.
###########################################################################
#
# The R result was obtained using the following code:
#
# library(lars)
# model_lasso_lars2 = lars(X, t(y), type="lasso", intercept=TRUE,
# trace=TRUE, normalize=TRUE)
# r2 = t(model_lasso_lars2$beta)
r2 = np.array(
[[0, 0, 0, 0, 0], [0, 0, 0, 8.371887668009453, 19.463768371044026],
[0, 0, 0, 0, 9.901611055290553],
[
0, 7.495923132833733, 9.245133544334507, 17.389369207545062,
26.971656815643499
], [0, 0, -1.569380717440311, -5.924804108067312,
-7.996385265061972]])
model_lasso_lars2 = linear_model.LassoLars(alpha=0, normalize=True)
model_lasso_lars2.fit(X, y)
skl_betas2 = model_lasso_lars2.coef_path_
# Let's rescale back the coefficients returned by sklearn_lib before comparing
# against the R result (read the note above)
temp = X - np.mean(X, axis=0)
normx = np.sqrt(np.sum(temp**2, axis=0))
skl_betas2 /= normx[:, np.newaxis]
assert_array_almost_equal(r2, skl_betas2, decimal=12)
def test_lasso_lars_vs_lasso_cd_positive():
# Test that LassoLars and Lasso using coordinate descent give the
# same results when using the positive option
# This test is basically a copy of the above with additional positive
# option. However for the middle part, the comparison of coefficient values
# for a range of alphas, we had to make an adaptations. See below.
# not normalized data
X = 3 * diabetes.data
alphas, _, lasso_path = linear_model.lars_path(X,
y,
method='lasso',
positive=True)
lasso_cd = linear_model.Lasso(fit_intercept=False, tol=1e-8, positive=True)
for c, a in zip(lasso_path.T, alphas):
if a == 0:
continue
lasso_cd.alpha = a
lasso_cd.fit(X, y)
error = linalg.norm(c - lasso_cd.coef_)
assert error < 0.01
# The range of alphas chosen for coefficient comparison here is restricted
# as compared with the above test without the positive option. This is due
# to the circumstance that the Lars-Lasso algorithm does not converge to
# the least-squares-solution for small alphas, see 'Least Angle Regression'
# by Efron et al 2004. The coefficients are typically in congruence up to
# the smallest alpha reached by the Lars-Lasso algorithm and start to
# diverge thereafter. See
# https://gist.github.com/michigraber/7e7d7c75eca694c7a6ff
for alpha in np.linspace(6e-1, 1 - 1e-2, 20):
clf1 = linear_model.LassoLars(fit_intercept=False,
alpha=alpha,
normalize=False,
positive=True).fit(X, y)
clf2 = linear_model.Lasso(fit_intercept=False,
alpha=alpha,
tol=1e-8,
normalize=False,
positive=True).fit(X, y)
err = linalg.norm(clf1.coef_ - clf2.coef_)
assert err < 1e-3
# normalized data
X = diabetes.data
alphas, _, lasso_path = linear_model.lars_path(X,
y,
method='lasso',
positive=True)
lasso_cd = linear_model.Lasso(fit_intercept=False,
normalize=True,
tol=1e-8,
positive=True)
for c, a in zip(lasso_path.T[:-1], alphas[:-1]): # don't include alpha=0
lasso_cd.alpha = a
lasso_cd.fit(X, y)
error = linalg.norm(c - lasso_cd.coef_)
assert error < 0.01