def test_find_constrained_prior_error_too_large(distribution, lower, upper,
init_guess, fixed_params):
with pytest.warns(UserWarning, match="instead of the requested 95%"):
pm.find_constrained_prior(
distribution,
lower=lower,
upper=upper,
mass=0.95,
init_guess=init_guess,
fixed_params=fixed_params,
)
def test_find_constrained_prior_input_errors():
# missing param
with pytest.raises(TypeError, match="required positional argument"):
pm.find_constrained_prior(
pm.StudentT,
lower=0.1,
upper=0.4,
mass=0.95,
init_guess={
"mu": 170,
"sigma": 3
},
)
# mass too high
with pytest.raises(AssertionError,
match="has to be between 0.01 and 0.99"):
pm.find_constrained_prior(
pm.StudentT,
lower=0.1,
upper=0.4,
mass=0.995,
init_guess={
"mu": 170,
"sigma": 3
},
fixed_params={"nu": 7},
)
# mass too low
with pytest.raises(AssertionError,
match="has to be between 0.01 and 0.99"):
pm.find_constrained_prior(
pm.StudentT,
lower=0.1,
upper=0.4,
mass=0.005,
init_guess={
"mu": 170,
"sigma": 3
},
fixed_params={"nu": 7},
)
# non-scalar params
with pytest.raises(NotImplementedError,
match="does not work with non-scalar parameters yet"):
pm.find_constrained_prior(
pm.MvNormal,
lower=0,
upper=1,
mass=0.95,
init_guess={
"mu": 5,
"cov": np.asarray([[1, 0.2], [0.2, 1]])
},
)
def test_find_constrained_prior(distribution, lower, upper, init_guess,
fixed_params, mass):
with pytest.warns(None) as record:
opt_params = pm.find_constrained_prior(
distribution,
lower=lower,
upper=upper,
mass=mass,
init_guess=init_guess,
fixed_params=fixed_params,
)
assert len(record) == 0
opt_distribution = distribution.dist(**opt_params)
mass_in_interval = (
pm.math.exp(pm.logcdf(opt_distribution, upper)) -
pm.math.exp(pm.logcdf(opt_distribution, lower))).eval()
assert np.abs(mass_in_interval - mass) <= 1e-5