def test_update_term_priors_after_init(diabetes_data):
model = Model(diabetes_data)
model.add('BMI')
model.add('S1')
model.add(random='age_grp|BP')
p1 = Prior('Normal', mu=-10, sd=10)
p2 = Prior('Beta', alpha=2, beta=2)
model.set_priors({'BMI': 0.3, 'S1': p2})
assert model.terms['S1'].prior.args['beta'] == 2
assert model.terms['BMI'].prior == 0.3
model.set_priors({('S1', 'BMI'): p1})
assert model.terms['S1'].prior.args['sd'] == 10
assert model.terms['BMI'].prior.args['mu'] == -10
p3 = Prior('Normal', mu=0, sd=Prior('Normal', mu=0, sd=7))
model.set_priors(fixed=0.4, random=p3)
assert model.terms['BMI'].prior == 0.4
assert model.terms['age_grp|BP'].prior.args['sd'].args['sd'] == 7
# Invalid names should raise error
with pytest.raises(ValueError):
model.set_priors({'nonexistent_term': 0.3})
# Test for partial names, e.g., 'threecats' should match 'threecats[0]'.
model = Model(diabetes_data)
model.add(random='age_grp|BP', categorical='age_grp')
model.set_priors({'age_grp|BP': 0.5})
assert model.terms['age_grp[T.1]|BP'].prior == 0.5
assert model.terms['1|BP'].prior == 0.5
def test_set_prior_unexisting_term():
data = pd.DataFrame(
{
"y": np.random.normal(size=100),
"x": np.random.normal(size=100),
}
)
prior = Prior("Uniform", lower=0, upper=50)
model = Model("y ~ x", data)
with pytest.raises(ValueError):
model.set_priors(priors={("x", "z"): prior})
def test_set_prior_with_tuple():
data = pd.DataFrame(
{
"y": np.random.normal(size=100),
"x": np.random.normal(size=100),
"z": np.random.normal(size=100),
}
)
prior = Prior("Uniform", lower=0, upper=50)
model = Model("y ~ x + z", data)
model.set_priors(priors={("x", "z"): prior})
assert model.terms["x"].prior == prior
assert model.terms["z"].prior == prior
def test_set_priors():
data = pd.DataFrame(
{
"y": np.random.normal(size=100),
"x": np.random.normal(size=100),
"g": np.random.choice(["A", "B"], size=100),
}
)
model = Model("y ~ x + (1|g)", data)
prior = Prior("Uniform", lower=0, upper=50)
# Common
model.set_priors(common=prior)
assert model.terms["Intercept"].prior == prior
assert model.terms["x"].prior == prior
# Group-specific
model.set_priors(group_specific=prior)
assert model.terms["1|g"].prior == prior
# By name
model = Model("y ~ x + (1|g)", data)
model.set_priors(priors={"x": prior})
model.set_priors(priors={"1|g": prior})
assert model.terms["x"].prior == prior
assert model.terms["1|g"].prior == prior
def test_update_term_priors_after_init(diabetes_data):
model = Model(diabetes_data)
model.add("Y ~ BMI")
model.add("S1")
model.add(random="age_grp|BP")
p1 = Prior("Normal", mu=-10, sigma=10)
p2 = Prior("Beta", alpha=2, beta=2)
model.set_priors({"BMI": 0.3, "S1": p2})
model.build(backend="pymc")
assert model.terms["S1"].prior.args["beta"] == 2
assert model.terms["BMI"].prior.scale == 0.3
assert np.isclose(model.terms["BMI"].prior.args["sigma"], 4.7, rtol=0.1)[0]
model.set_priors({("S1", "BMI"): p1})
model.build(backend="pymc")
assert model.terms["S1"].prior.args["sigma"] == 10
assert model.terms["BMI"].prior.args["mu"] == -10
p3 = Prior("Normal", mu=0, sigma=Prior("Normal", mu=0, sigma=7))
model.set_priors(fixed=0.3, random=p3)
model.build(backend="pymc")
assert model.terms["BMI"].prior.scale == 0.3
assert np.isclose(model.terms["BMI"].prior.args["sigma"], 4.7, rtol=0.1)[0]
assert model.terms["age_grp|BP"].prior.args["sigma"].args["sigma"] == 7
# Invalid names should raise error
with pytest.raises(ValueError):
model.set_priors({"nonexistent_term": 0.3})
model.build(backend="pymc")
# Test for partial names, e.g., 'threecats' should match 'threecats[0]'.
model = Model(diabetes_data)
model.add("Y ~ 1", random="age_grp|BP", categorical="age_grp")
model.set_priors({"age_grp|BP": 0.5})
model.build(backend="pymc")
assert model.terms["age_grp[T.1]|BP"].prior.scale == 0.5
assert np.isclose(
model.terms["age_grp[T.1]|BP"].prior.args["sigma"].args["sigma"], 94, rtol=0.2
)
assert model.terms["1|BP"].prior.scale == 0.5
def test_set_response_prior():
data = pd.DataFrame({
"y": np.random.randint(3, 10, size=50),
"x": np.random.normal(size=50)
})
priors = {"sigma": Prior("Uniform", lower=0, upper=50)}
model = Model("y ~ x", data)
model.set_priors(priors)
assert model.response.prior.args["sigma"] == Prior("Uniform",
lower=0,
upper=50)
priors = {"alpha": Prior("Uniform", lower=1, upper=20)}
model = Model("y ~ x", data, family="negativebinomial")
model.set_priors(priors)
assert model.response.prior.args["alpha"] == Prior("Uniform",
lower=1,
upper=20)
priors = {"alpha": Prior("Uniform", lower=0, upper=50)}
model = Model("y ~ x", data, family="gamma")
model.set_priors(priors)
assert model.response.prior.args["alpha"] == Prior("Uniform",
lower=0,
upper=50)
def test_update_term_priors_after_init(diabetes_data):
model = Model(diabetes_data)
model.add_term('BMI')
model.add_term('S1')
model.add_term('age_grp', random=True, over='BP')
p1 = Prior('Normal', mu=-10, sd=10)
p2 = Prior('Beta', alpha=2, beta=2)
model.set_priors({'BMI': 0.3, 'S1': p2})
assert model.terms['S1'].prior.args['beta'] == 2
assert model.terms['BMI'].prior == 0.3
model.set_priors({('S1', 'BMI'): p1})
assert model.terms['S1'].prior.args['sd'] == 10
assert model.terms['BMI'].prior.args['mu'] == -10
p3 = Prior('Normal', mu=0, sd=Prior('Normal', mu=0, sd=7))
model.set_priors(fixed=0.4, random=p3)
assert model.terms['BMI'].prior == 0.4
assert model.terms['age_grp|BP'].prior.args['sd'].args['sd'] == 7
