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_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_fit():
"""Test the entire fitting procedure for the Poisson GLM."""
# compare after 50 iterations
poisson = Poisson(max_iter=50, fit_intercept=False)
poisson.fit(X, y, init=0.5 * np.ones(beta.size))
beta_new_true = np.array([
-3.865910169523823, 6.243915946623266, -0.728804736275411,
-0.463706073765083, -3.622620769371424
])
assert np.allclose(beta_new_true, poisson.coef_)
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_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)
def test_Poisson_response_constant():
"""Test that UoI Poisson correctly fits the data when the response variable
is constant."""
n_features = 5
n_samples = 100
X = np.random.normal(size=(n_samples, n_features))
y = np.zeros(n_samples)
poisson = Poisson()
poisson.fit(X, y)
assert_equal(poisson.intercept_, -np.inf)
assert_equal(poisson.coef_, np.zeros(n_features))
y += 1.
poisson.fit(X, y)
assert_equal(poisson.intercept_, 0.)
assert_equal(poisson.coef_, np.zeros(n_features))