def test_glm_gaussian(make_gaus_data, make_random):
X, y, Xs, ys = make_gaus_data
basis = LinearBasis(onescol=True)
lhood = Gaussian()
# simple SGD
glm = GeneralizedLinearModel(lhood, basis, random_state=make_random)
glm.fit(X, y)
Ey = glm.predict(Xs)
assert smse(ys, Ey) < 0.1
# Test BasisCat
basis = LinearBasis(onescol=True) \
+ RandomRBF(nbases=20, Xdim=X.shape[1]) \
+ RandomMatern52(nbases=20, Xdim=X.shape[1])
glm = GeneralizedLinearModel(lhood, basis, random_state=make_random)
glm.fit(X, y)
Ey = glm.predict(Xs)
assert smse(ys, Ey) < 0.1
# Test upper quantile estimates
py, _, _ = glm.predict_cdf(Xs, 1e5)
assert np.allclose(py, 1.)
# Test log probability
lpy, _, _ = glm.predict_logpdf(Xs, Ey)
assert np.all(lpy > -100)
EyQn, EyQx = glm.predict_interval(Xs, 0.9)
assert all(Ey <= EyQx)
assert all(Ey >= EyQn)
def test_glm_binomial(make_binom_data, make_random):
# This is more to test the logic than to test if the model can overfit,
# hence more relaxed SMSE. This is because this is a harder problem than
# the previous case. We also haven't split training ans test sets, since we
# want to check the latent function and bounds
X, y, p, n = make_binom_data
f = p * n
basis = LinearBasis(onescol=True) \
+ RandomRBF(nbases=20, Xdim=X.shape[1]) \
+ RandomMatern52(nbases=20, Xdim=X.shape[1])
lhood = Binomial()
largs = (n, )
# SGD
glm = GeneralizedLinearModel(lhood, basis, random_state=make_random)
glm.fit(X, y, likelihood_args=largs)
Ey = glm.predict(X, likelihood_args=largs)
assert smse(f, Ey) < 1
# Test upper quantile estimates
py, _, _ = glm.predict_cdf(X, 1e5, likelihood_args=largs)
assert np.allclose(py, 1.)
EyQn, EyQx = glm.predict_interval(X, 0.9, likelihood_args=largs)
assert all(Ey <= EyQx)
assert all(Ey >= EyQn)
def test_glm_binomial(make_binom_data):
# This is more to test the logic than to test if the model can overfit,
# hence more relaxed SMSE. This is because this is a harder problem than
# the previous case.
X, y, p, n = make_binom_data
f = p * n
basis = LinearBasis(onescol=True) \
+ RandomRBF(nbases=20, Xdim=X.shape[1]) \
+ RandomMatern52(nbases=20, Xdim=X.shape[1])
lhood = Binomial()
largs = (n,)
# SGD
glm = GeneralisedLinearModel(lhood, basis, random_state=randstate)
glm.fit(X, y, likelihood_args=largs)
Ey = glm.predict(X, likelihood_args=largs)
assert smse(f, Ey) < 1
# Test upper quantile estimates
py, _, _ = glm.predict_cdf(1e5, X, likelihood_args=largs)
assert np.allclose(py, 1.)
EyQn, EyQx = glm.predict_interval(0.9, X, likelihood_args=largs)
assert all(Ey <= EyQx)
assert all(Ey >= EyQn)
def test_regression(make_data):
X, y, w = make_data
basis = LinearBasis(onescol=False)
params = regression.learn(X, y, basis, [])
Ey, Vf, Vy = regression.predict(X, basis, *params)
assert rsquare(Ey, y) > 0.9
basis = LinearBasis(onescol=False) + RandomRBF(nbases=10, Xdim=X.shape[1])
params = regression.learn(X, y, basis, [1.])
Ey, Vf, Vy = regression.predict(X, basis, *params)
assert rsquare(Ey, y) > 0.9
def test_glm(make_data):
X, y, w = make_data
basis = LinearBasis(onescol=False)
lhood = Gaussian()
params = glm.learn(X, y, lhood, [1.], basis, [])
Ey, _, _, _ = glm.predict_meanvar(X, lhood, basis, *params)
assert rsquare(Ey, y) > 0.9
basis = LinearBasis(onescol=False) + RandomRBF(nbases=10, Xdim=X.shape[1])
params = glm.learn(X, y, lhood, [1.], basis, [1.])
Ey, _, _, _ = glm.predict_meanvar(X, lhood, basis, *params)
assert rsquare(Ey, y) > 0.9
def test_slm(make_gaus_data):
X, y, Xs, ys = make_gaus_data
basis = LinearBasis(onescol=False)
slm = StandardLinearModel(basis)
slm.fit(X, y)
Ey = slm.predict(Xs)
assert smse(ys, Ey) < 0.1
basis = LinearBasis(onescol=False) \
+ RandomRBF(nbases=10, Xdim=X.shape[1]) \
+ RandomMatern52(nbases=10, Xdim=X.shape[1])
slm = StandardLinearModel(basis)
slm.fit(X, y)
Ey = slm.predict(Xs)
assert smse(ys, Ey) < 0.1