def test_pymc3_convert_dists():
"""Just a basic check that all PyMC3 RVs will convert to and from Theano RVs."""
tt.config.compute_test_value = "ignore"
theano.config.cxx = ""
with pm.Model() as model:
norm_rv = pm.Normal("norm_rv", 0.0, 1.0, observed=1.0)
mvnorm_rv = pm.MvNormal("mvnorm_rv",
np.r_[0.0],
np.c_[1.0],
shape=1,
observed=np.r_[1.0])
cauchy_rv = pm.Cauchy("cauchy_rv", 0.0, 1.0, observed=1.0)
halfcauchy_rv = pm.HalfCauchy("halfcauchy_rv", 1.0, observed=1.0)
uniform_rv = pm.Uniform("uniform_rv", observed=1.0)
gamma_rv = pm.Gamma("gamma_rv", 1.0, 1.0, observed=1.0)
invgamma_rv = pm.InverseGamma("invgamma_rv", 1.0, 1.0, observed=1.0)
exp_rv = pm.Exponential("exp_rv", 1.0, observed=1.0)
halfnormal_rv = pm.HalfNormal("halfnormal_rv", 1.0, observed=1.0)
beta_rv = pm.Beta("beta_rv", 2.0, 2.0, observed=1.0)
binomial_rv = pm.Binomial("binomial_rv", 10, 0.5, observed=5)
dirichlet_rv = pm.Dirichlet("dirichlet_rv",
np.r_[0.1, 0.1],
observed=np.r_[0.1, 0.1])
poisson_rv = pm.Poisson("poisson_rv", 10, observed=5)
bernoulli_rv = pm.Bernoulli("bernoulli_rv", 0.5, observed=0)
betabinomial_rv = pm.BetaBinomial("betabinomial_rv",
0.1,
0.1,
10,
observed=5)
categorical_rv = pm.Categorical("categorical_rv",
np.r_[0.5, 0.5],
observed=1)
multinomial_rv = pm.Multinomial("multinomial_rv",
5,
np.r_[0.5, 0.5],
observed=np.r_[2])
# Convert to a Theano `FunctionGraph`
fgraph = model_graph(model)
rvs_by_name = {
n.owner.inputs[1].name: n.owner.inputs[1]
for n in fgraph.outputs
}
pymc_rv_names = {n.name for n in model.observed_RVs}
assert all(
isinstance(rvs_by_name[n].owner.op, RandomVariable)
for n in pymc_rv_names)
# Now, convert back to a PyMC3 model
pymc_model = graph_model(fgraph)
new_pymc_rv_names = {n.name for n in pymc_model.observed_RVs}
pymc_rv_names == new_pymc_rv_names
def test_pymc_convert_dists():
"""Just a basic check that all PyMC3 RVs will convert to and from Theano RVs."""
tt.config.compute_test_value = 'ignore'
theano.config.cxx = ''
with pm.Model() as model:
norm_rv = pm.Normal('norm_rv', 0.0, 1.0, observed=1.0)
mvnorm_rv = pm.MvNormal('mvnorm_rv',
np.r_[0.0],
np.c_[1.0],
shape=1,
observed=np.r_[1.0])
cauchy_rv = pm.Cauchy('cauchy_rv', 0.0, 1.0, observed=1.0)
halfcauchy_rv = pm.HalfCauchy('halfcauchy_rv', 1.0, observed=1.0)
uniform_rv = pm.Uniform('uniform_rv', observed=1.0)
gamma_rv = pm.Gamma('gamma_rv', 1.0, 1.0, observed=1.0)
invgamma_rv = pm.InverseGamma('invgamma_rv', 1.0, 1.0, observed=1.0)
exp_rv = pm.Exponential('exp_rv', 1.0, observed=1.0)
# Convert to a Theano `FunctionGraph`
fgraph = model_graph(model)
rvs_by_name = {
n.owner.inputs[1].name: n.owner.inputs[1]
for n in fgraph.outputs
}
pymc_rv_names = {n.name for n in model.observed_RVs}
assert all(
isinstance(rvs_by_name[n].owner.op, RandomVariable)
for n in pymc_rv_names)
# Now, convert back to a PyMC3 model
pymc_model = graph_model(fgraph)
new_pymc_rv_names = {n.name for n in pymc_model.observed_RVs}
pymc_rv_names == new_pymc_rv_names
def test_normals_to_model():
"""Test conversion to a PyMC3 model."""
tt.config.compute_test_value = 'ignore'
a_tt = tt.vector('a')
R_tt = tt.matrix('R')
F_t_tt = tt.matrix('F')
V_tt = tt.matrix('V')
a_tt.tag.test_value = np.r_[1., 0.]
R_tt.tag.test_value = np.diag([10., 10.])
F_t_tt.tag.test_value = np.c_[-2., 1.]
V_tt.tag.test_value = np.diag([0.5])
beta_rv = MvNormalRV(a_tt, R_tt, name='\\beta')
E_y_rv = F_t_tt.dot(beta_rv)
Y_rv = MvNormalRV(E_y_rv, V_tt, name='Y')
y_val = np.r_[-3.]
def _check_model(model):
assert len(model.observed_RVs) == 1
assert model.observed_RVs[0].name == 'Y'
Y_pm = model.observed_RVs[0].distribution
assert isinstance(Y_pm, pm.MvNormal)
np.testing.assert_array_equal(model.observed_RVs[0].observations.data,
y_val)
assert Y_pm.mu.owner.op == tt.basic._dot
assert Y_pm.cov.name == 'V'
assert len(model.unobserved_RVs) == 1
assert model.unobserved_RVs[0].name == '\\beta'
beta_pm = model.unobserved_RVs[0].distribution
assert isinstance(beta_pm, pm.MvNormal)
y_tt = theano.shared(y_val, name='y')
Y_obs = observed(y_tt, Y_rv)
fgraph = FunctionGraph(tt_inputs([beta_rv, Y_obs]), [beta_rv, Y_obs],
clone=True)
model = graph_model(fgraph)
_check_model(model)
# Now, let `graph_model` create the `FunctionGraph`
model = graph_model(Y_obs)
_check_model(model)
# Use a different type of observation value
y_tt = tt.as_tensor_variable(y_val, name='y')
Y_obs = observed(y_tt, Y_rv)
model = graph_model(Y_obs)
_check_model(model)
# Use an invalid type of observation value
tt.config.compute_test_value = 'ignore'
y_tt = tt.vector('y')
Y_obs = observed(y_tt, Y_rv)
with pytest.raises(TypeError):
model = graph_model(Y_obs)