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_broadcastable():
"""Test PyMC3 to Theano conversion amid array broadcasting."""
tt.config.compute_test_value = 'ignore'
mu_X = tt.vector('mu_X')
sd_X = tt.vector('sd_X')
mu_Y = tt.vector('mu_Y')
sd_Y = tt.vector('sd_Y')
mu_X.tag.test_value = np.array([0.], dtype=tt.config.floatX)
sd_X.tag.test_value = np.array([1.], dtype=tt.config.floatX)
mu_Y.tag.test_value = np.array([1.], dtype=tt.config.floatX)
sd_Y.tag.test_value = np.array([0.5], dtype=tt.config.floatX)
with pm.Model() as model:
X_rv = pm.Normal('X_rv', mu_X, sd=sd_X, shape=(1, ))
Y_rv = pm.Normal('Y_rv', mu_Y, sd=sd_Y, shape=(1, ))
Z_rv = pm.Normal('Z_rv',
X_rv + Y_rv,
sd=sd_X + sd_Y,
shape=(1, ),
observed=[10.])
with pytest.warns(UserWarning):
fgraph = model_graph(model)
Z_rv_tt = canonicalize(fgraph, return_graph=False)
# This will break comparison if we don't reuse it
rng = Z_rv_tt.owner.inputs[1].owner.inputs[-1]
mu_X_ = mt.vector('mu_X')
sd_X_ = mt.vector('sd_X')
mu_Y_ = mt.vector('mu_Y')
sd_Y_ = mt.vector('sd_Y')
tt.config.compute_test_value = 'ignore'
X_rv_ = mt.NormalRV(mu_X_, sd_X_, (1, ), rng, name='X_rv')
X_rv_ = mt.addbroadcast(X_rv_, 0)
Y_rv_ = mt.NormalRV(mu_Y_, sd_Y_, (1, ), rng, name='Y_rv')
Y_rv_ = mt.addbroadcast(Y_rv_, 0)
Z_rv_ = mt.NormalRV(mt.add(X_rv_, Y_rv_),
mt.add(sd_X_, sd_Y_), (1, ),
rng,
name='Z_rv')
obs_ = mt(Z_rv.observations)
Z_rv_obs_ = mt.observed(obs_, Z_rv_)
Z_rv_meta = canonicalize(Z_rv_obs_.reify(), return_graph=False)
assert mt(Z_rv_tt) == mt(Z_rv_meta)
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_pymc_normal_model():
"""Conduct a more in-depth test of PyMC3/Theano conversions for a specific model."""
tt.config.compute_test_value = 'ignore'
mu_X = tt.dscalar('mu_X')
sd_X = tt.dscalar('sd_X')
mu_Y = tt.dscalar('mu_Y')
mu_X.tag.test_value = np.array(0., dtype=tt.config.floatX)
sd_X.tag.test_value = np.array(1., dtype=tt.config.floatX)
mu_Y.tag.test_value = np.array(1., dtype=tt.config.floatX)
# We need something that uses transforms...
with pm.Model() as model:
X_rv = pm.Normal('X_rv', mu_X, sd=sd_X)
S_rv = pm.HalfCauchy('S_rv',
beta=np.array(0.5, dtype=tt.config.floatX))
Y_rv = pm.Normal('Y_rv', X_rv * S_rv, sd=S_rv)
Z_rv = pm.Normal('Z_rv', X_rv + Y_rv, sd=sd_X, observed=10.)
fgraph = model_graph(model, output_vars=[Z_rv])
Z_rv_tt = canonicalize(fgraph, return_graph=False)
# This will break comparison if we don't reuse it
rng = Z_rv_tt.owner.inputs[1].owner.inputs[-1]
mu_X_ = mt.dscalar('mu_X')
sd_X_ = mt.dscalar('sd_X')
tt.config.compute_test_value = 'ignore'
X_rv_ = mt.NormalRV(mu_X_, sd_X_, None, rng, name='X_rv')
S_rv_ = mt.HalfCauchyRV(np.array(0., dtype=tt.config.floatX),
np.array(0.5, dtype=tt.config.floatX),
None,
rng,
name='S_rv')
Y_rv_ = mt.NormalRV(mt.mul(X_rv_, S_rv_), S_rv_, None, rng, name='Y_rv')
Z_rv_ = mt.NormalRV(mt.add(X_rv_, Y_rv_), sd_X, None, rng, name='Z_rv')
obs_ = mt(Z_rv.observations)
Z_rv_obs_ = mt.observed(obs_, Z_rv_)
Z_rv_meta = mt(canonicalize(Z_rv_obs_.reify(), return_graph=False))
assert mt(Z_rv_tt) == Z_rv_meta
# Now, let's try that with multiple outputs.
fgraph.disown()
fgraph = model_graph(model, output_vars=[Y_rv, Z_rv])
assert len(fgraph.variables) == 25
Y_new_rv = walk(Y_rv, fgraph.memo)
S_new_rv = walk(S_rv, fgraph.memo)
X_new_rv = walk(X_rv, fgraph.memo)
Z_new_rv = walk(Z_rv, fgraph.memo)
# Make sure our new vars are actually in the graph and where
# they should be.
assert Y_new_rv == fgraph.outputs[0]
assert Z_new_rv == fgraph.outputs[1]
assert X_new_rv in fgraph.variables
assert S_new_rv in fgraph.variables
assert isinstance(Z_new_rv.owner.op, Observed)
# Let's only look at the variables involved in the `Z_rv` subgraph.
Z_vars = theano.gof.graph.variables(theano.gof.graph.inputs([Z_new_rv]),
[Z_new_rv])
# Let's filter for only the `RandomVariables` with names.
Z_vars_count = Counter([
n.name for n in Z_vars
if n.name and n.owner and isinstance(n.owner.op, RandomVariable)
])
# Each new RV should be present and only occur once.
assert Y_new_rv.name in Z_vars_count.keys()
assert X_new_rv.name in Z_vars_count.keys()
assert Z_new_rv.owner.inputs[1].name in Z_vars_count.keys()
assert all(v == 1 for v in Z_vars_count.values())