def test_model_target_log_prob_fn():
model = pm.Model()
@model.define
def simple(cfg):
ed.Normal(0., 1., name='normal')
model.target_log_prob_fn()
def test_model_session():
model = pm.Model()
@model.define
def simple(cfg):
ed.Normal(0., 1., name='normal')
assert isinstance(model.session, tf.Session)
def test_model_definition_type1():
model = pm.Model(name="testName")
@model.define
def simple(cfg):
ed.Normal(0., 1., name='normal')
assert 'normal' in model.variables
assert [] == model.variables['normal'].shape.as_list()
assert model.name == "testName"
def test_variables():
model = pm.Model()
@model.define
def simple(cfg):
ed.Normal(0., 1., name='normal')
assert len(model.variables) == 1
assert len(model.unobserved) == 1
assert "normal" in model.variables
def test_sample():
model = pm.Model()
@model.define
def sample(cfg):
mu = ed.Normal(0., 1., name="mu")
trace = pm.sample(model)
assert 0. == pytest.approx(trace["mu"].mean(), 1)
assert 1. == pytest.approx(trace["mu"].std(), 1)
def test_model_log_prob_fn():
model = pm.Model()
@model.define
def simple(cfg):
mu = ed.Normal(0., 1., name="mu")
log_prob_fn = model.target_log_prob_fn()
with tf.Session():
assert -0.91893853 == pytest.approx(log_prob_fn(0).eval(), 0.00001)
def test_model_observe():
model = pm.Model()
@model.define
def simple(cfg):
ed.Normal(0., 1., name='normal')
model.observe(normal=1)
assert len(model.observed) == 1
assert not model.unobserved
def test_model_config():
model = pm.Model()
assert model.cfg == {}
model = pm.Model(var1=123)
@model.define
def simple(cfg):
assert cfg["var1"] == 123
model = pm.Model(var1=123)
@model.define
def simple(cfg):
pass
model = model.configure(var1=12)
@model.define
def simple(cfg):
assert cfg["var1"] == 12
import numpy as np
import pymc4 as pm4
import tensorflow as tf # For Random Variable operation
from tensorflow_probability import edward2 as ed # For defining random variables
J = 8 # No. of schools
y = np.array([28., 8., -3., 7., -1., 1., 18., 12.])
sigma = np.array([15., 10., 16., 11., 9., 11., 10., 18.])
pymc4_non_centered_eight = pm4.Model(num_schools=J, y=y, sigma=sigma)
@pymc4_non_centered_eight.define
def process(cfg):
mu = ed.Normal(loc=0., scale=10., name="mu")
log_tau = ed.Normal(loc=5., scale=1., name="log_tau")
theta_prime = ed.Normal(loc=tf.zeros(cfg.num_schools),
scale=tf.ones(cfg.num_schools),
name="theta_prime")
theta = mu + tf.exp(log_tau) * theta_prime
y = ed.Normal(loc=theta, scale=np.float32(cfg.sigma), name="y")
return y
pymc4_non_centered_eight.observe(y=y)
pymc4_trace = pm4.sample(pymc4_non_centered_eight)
pymc4_theta = pymc4_trace['mu'][:, np.newaxis] + np.exp(
pymc4_trace['log_tau'])[:, np.newaxis] * pymc4_trace['theta_prime']