def test_poisson_with_sparsity():
"""Tests the Poisson fitter with no intercept."""
n_features = 3
n_samples = 10000
# create data
X, y, beta, _ = make_poisson_regression(n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0., 1.0, 1.5]),
random_state=2332)
# lbfgs
poisson = Poisson(alpha=0.1, l1_ratio=1., fit_intercept=False,
solver='lbfgs', max_iter=5000)
poisson.fit(X, y)
assert_equal(np.abs(poisson.coef_[0]), 0)
# coordinate descent
poisson = Poisson(alpha=0.1, l1_ratio=1., fit_intercept=False, solver='cd',
max_iter=5000)
poisson.fit(X, y)
assert_equal(np.abs(poisson.coef_[0]), 0.)
def test_poisson_with_intercept():
"""Tests the Poisson fitter with no intercept."""
n_features = 3
n_samples = 10000
# create data
X, y, beta, intercept = make_poisson_regression(
n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0.5, 1.0, 1.5]),
include_intercept=True,
random_state=2332)
# lbfgs
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=True,
solver='lbfgs', max_iter=5000)
poisson.fit(X, y)
assert_allclose(poisson.coef_, beta, rtol=0.5)
assert_allclose(poisson.intercept_, intercept, rtol=0.5)
# coordinate descent
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=True, solver='cd',
max_iter=5000)
poisson.fit(X, y)
assert_allclose(poisson.coef_, beta, rtol=0.5)
assert_allclose(poisson.intercept_, intercept, rtol=0.5)
def test_poisson_warm_start():
"""Tests if the warm start initializes coefficients correctly."""
n_features = 3
n_samples = 10000
# create data
X, y, beta, _ = make_poisson_regression(n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0.5, 1.0, 1.5]),
random_state=2332)
# lbfgs
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=False,
solver='lbfgs', max_iter=5000, warm_start=True)
poisson.fit(X, y)
first_coef = poisson.coef_
poisson.coef_ = np.zeros(n_features)
poisson.fit(X, y)
second_coef = poisson.coef_
assert_allclose(first_coef, second_coef, rtol=0.1)
# cd
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=False,
solver='cd', max_iter=5000, warm_start=True)
poisson.fit(X, y)
first_coef = poisson.coef_
poisson.coef_ = np.zeros(n_features)
poisson.fit(X, y)
second_coef = poisson.coef_
assert_allclose(first_coef, second_coef, rtol=0.1)
def test_poisson_no_intercept():
"""Tests the Poisson fitter with no intercept."""
n_features = 3
n_samples = 10000
# create data
X, y, beta, _ = make_poisson_regression(n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0.5, 1.0, 1.5]),
random_state=2332)
# lbfgs
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=False,
solver='lbfgs', max_iter=5000)
poisson.fit(X, y)
assert_allclose(poisson.coef_, beta, rtol=0.5)
# coordinate descent
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=False, solver='cd',
max_iter=5000)
poisson.fit(X, y)
assert_allclose(poisson.coef_, beta, rtol=0.5)
# broken solver
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=False, solver='ruff',
max_iter=5000)
assert_raises(ValueError, poisson.fit, X, y)
def test_poisson_reg_params():
"""Test whether the upper bound on the regularization parameters correctly
zero out the coefficients."""
n_features = 5
n_samples = 1000
X, y, beta, intercept = make_poisson_regression(
n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
random_state=2332)
alpha_sets = [np.array([0.5]), np.array([1.0])]
for alpha_set in alpha_sets:
uoi_poisson = UoI_Poisson(alphas=alpha_set)
reg_params = uoi_poisson.get_reg_params(X, y)
alpha = reg_params[0]['alpha']
l1_ratio = reg_params[0]['l1_ratio']
# check that coefficients get set to zero
poisson = Poisson(alpha=1.01 * alpha,
l1_ratio=l1_ratio,
standardize=False,
fit_intercept=True)
poisson.fit(X, y)
assert_equal(poisson.coef_, 0.)
# check that coefficients below the bound are not set to zero
poisson = Poisson(alpha=0.99 * alpha,
l1_ratio=l1_ratio,
standardize=False,
fit_intercept=True)
poisson.fit(X, y)
assert np.count_nonzero(poisson.coef_) > 0
def test_poisson():
"""Test basic functionality of UoI_Lasso and that it finds right model"""
n_features = 20
n_inf = 10
X, y, w, b = make_poisson_regression(n_samples=200,
n_features=n_features,
n_informative=n_inf,
random_state=10)
poisson = UoI_Poisson(comm=MPI.COMM_WORLD)
poisson.fit(X, y)
assert (np.sign(abs(w)) == np.sign(abs(poisson.coef_))).mean() >= .6
def test_UoI_Poisson():
"""Tests the UoI Poisson fitter with lbfgs solver."""
n_features = 3
n_samples = 5000
# create data
X, y, beta, _ = make_poisson_regression(n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0., 1.0, 1.5]),
random_state=2332)
# lbfgs
poisson = UoI_Poisson(n_lambdas=48, n_boots_sel=30, n_boots_est=30,
alphas=np.array([1.0]), warm_start=False)
poisson.fit(X, y)
assert_allclose(poisson.coef_, beta, atol=0.5)
def test_poisson_standardize():
"""Tests the Poisson fitter `standardize=True`."""
n_features = 3
n_samples = 200
# create data
X, y, beta, intercept = make_poisson_regression(
n_samples=n_samples,
n_features=n_features,
n_informative=n_features,
beta=np.array([0.5, 1.0, 1.5]),
include_intercept=True,
random_state=2332)
# lbfgs
poisson = Poisson(alpha=0., l1_ratio=0., fit_intercept=True,
solver='lbfgs', standardize=True)
poisson.fit(X, y)