def net() -> BayesNet:
with Model() as m:
m.gamma = Gamma(1., 1.)
m.exp = Exponential(1.)
m.cauchy = Cauchy(m.gamma, m.exp)
return m.to_bayes_net()
def model() -> Model:
with Model() as m:
m.temperature = Uniform(0., 100.)
m.thermometer_one = Gaussian(m.temperature, 1.0)
m.thermometer_two = Gaussian(m.temperature, 1.0)
return m
def model() -> Model:
with Model() as m:
m.a = Gaussian(0., 50.)
m.b = Gaussian(0., 50.)
m.c = m.a + m.b
m.d = Gaussian(m.c, 1.)
m.d.observe(20.0)
return m
def tensor_net() -> BayesNet:
with Model() as m:
m.gamma = Gamma(
np.array([1., 1., 1., 1.]).reshape((2, 2)),
np.array([2., 2., 2., 2.]).reshape((2, 2)))
m.exp = Exponential(np.array([1., 1., 1., 1.]).reshape((2, 2)))
m.add = m.gamma + m.exp
return m.to_bayes_net()
def model() -> Model:
KeanuRandom.set_default_random_seed(1)
with Model() as m:
m.a = Gaussian(0., 50.)
m.b = Gaussian(0., 50.)
m.c = m.a + m.b
m.d = Gaussian(m.c, 1.)
m.d.observe(20.0)
return m
def test_autocorr_returns_ndarray_of_correct_dtype() -> None:
with Model() as m:
m.uniform = Uniform(0, 1000)
net = m.to_bayes_net()
samples = sample(net=net, sample_from=net.iter_latent_vertices(), draws=10)
valid_key = list(samples.keys())[0]
sample_ = samples.get(valid_key)
assert sample_ is not None
autocorr = stats.autocorrelation(sample_)
assert type(autocorr) == np.ndarray
def test_to_bayes_net_excludes_non_vertices() -> None:
with Model() as m:
m.not_a_vertex = 1
m.vertex = Gamma(0.5, 0.1)
net = m.to_bayes_net()
assert isinstance(net, BayesNet)
net_vertex_ids = [vertex.get_id() for vertex in net.iter_latent_or_observed_vertices()]
assert len(net_vertex_ids) == 1
assert m.vertex.get_id() in net_vertex_ids
def autocorrelation_example_nd():
with Model() as m:
m.a = Gaussian(np.array([[20., 30.], [40., 60.]]),
np.array([[1., 1.], [1., 1.]]))
bayes_net = m.to_bayes_net()
# %%SNIPPET_START%% PythonNdAutocorrelation
algo = MetropolisHastingsSampler()
posterior_samples = sample(net=bayes_net,
sample_from=bayes_net.get_latent_vertices(),
sampling_algorithm=algo,
draws=100)
vertex_samples = posterior_samples.get('a')
ac = stats.autocorrelation(vertex_samples, (0, 1))
def model(self) -> Model:
start_year, end_year = (self._data.index.min(), self._data.index.max())
with Model() as m:
m.switchpoint = UniformInt(start_year, end_year + 1)
m.early_rate = Exponential(1.0)
m.late_rate = Exponential(1.0)
m.years = np.array(self._data.index)
m.rates = If(m.switchpoint > m.years, m.early_rate, m.late_rate)
m.disasters = Poisson(m.rates)
return m
def test_thermometers_example():
# %%SNIPPET_START%% PythonTwoThermometers
with Model() as m:
m.temperature = Uniform(20., 30.)
m.first_thermometer = Gaussian(m.temperature, 2.5)
m.second_thermometer = Gaussian(m.temperature, 5.)
m.first_thermometer.observe(25.)
m.second_thermometer.observe(30.)
bayes_net = m.to_bayes_net()
optimizer = GradientOptimizer(bayes_net)
optimizer.max_a_posteriori()
calculated_temperature = m.temperature.get_value()
def autocorrelation_example_scalar():
with Model() as m:
m.a = Gaussian(20, 1.)
m.b = Gaussian(20, 1.)
m.c = Gaussian(m.a + m.b, 1.)
m.c.observe(43.)
m.a.set_value(20.)
m.b.set_value(20.)
bayes_net = m.to_bayes_net()
# %%SNIPPET_START%% PythonScalarAutocorrelation
algo = MetropolisHastingsSampler()
posterior_samples = sample(net=bayes_net,
sample_from=bayes_net.get_latent_vertices(),
sampling_algorithm=algo,
draws=100)
vertex_samples = posterior_samples.get('a')
ac = stats.autocorrelation(vertex_samples)
def test_autocorrelation_same_for_streaming_as_batch() -> None:
with Model() as model:
model.uniform = Uniform(0, 1000)
net = model.to_bayes_net()
draws = 15
set_starting_state(model)
samples = sample(net=net, sample_from=net.get_latent_vertices(), algo="metropolis", draws=draws)
set_starting_state(model)
iter_samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), algo="metropolis")
samples_dataframe = pd.DataFrame()
for next_sample in islice(iter_samples, draws):
samples_dataframe = samples_dataframe.append(next_sample, ignore_index=True)
for vertex_id in samples_dataframe:
autocorr_streaming = stats.autocorrelation(list(samples_dataframe[vertex_id].values))
autocorr_batch = stats.autocorrelation(samples[vertex_id])
np.testing.assert_array_equal(autocorr_batch, autocorr_streaming)
def inference_example_metropolis():
# %%SNIPPET_START%% PythonMetropolisExample
with Model() as m:
m.a = Gaussian(20., 1.)
m.b = Gaussian(20., 1.)
m.c = Gaussian(m.a + m.b, 1.)
m.c.observe(43.)
m.a.set_value(20.)
m.b.set_value(20.)
bayes_net = m.to_bayes_net()
posterior_samples = sample(net=bayes_net,
sample_from=bayes_net.get_latent_vertices(),
algo="metropolis",
draws=100000)
average_posterior_a = np.average(posterior_samples.get('a'))
average_posterior_b = np.average(posterior_samples.get('b'))
actual = average_posterior_a + average_posterior_b
def test_to_bayes_net() -> None:
with Model() as m:
m.mu = Exponential(1.)
m.sigma = Gamma(0.5, 0.1)
m.gaussian = Gaussian(m.mu, m.sigma)
net = m.to_bayes_net()
assert isinstance(net, BayesNet)
net_vertex_ids = [
vertex.get_id() for vertex in net.get_latent_or_observed_vertices()
]
assert len(net_vertex_ids) == 3
assert m.mu.get_id() in net_vertex_ids
assert m.sigma.get_id() in net_vertex_ids
assert m.gaussian.get_id() in net_vertex_ids
def inference_example_hmc_nuts():
with Model() as m:
m.a = Gaussian(20., 1.)
m.b = Gaussian(20., 1.)
m.c = Gaussian(m.a + m.b, 1.)
m.c.observe(43.)
m.a.set_value(20.)
m.b.set_value(20.)
bayes_net = m.to_bayes_net()
# %%SNIPPET_START%% PythonHamiltonianExample
posterior_samples = sample(net=bayes_net,
sample_from=bayes_net.get_latent_vertices(),
algo="hamiltonian",
draws=2000)
# %%SNIPPET_END%% PythonHamiltonianExample
# %%SNIPPET_START%% PythonNUTSExample
posterior_samples = sample(net=bayes_net,
sample_from=bayes_net.get_latent_vertices(),
algo="NUTS",
draws=2000)
def net() -> BayesNet:
with Model() as m:
m.gamma = Gamma(1., 1.)
m.gaussian = Gaussian(0., m.gamma)
return m.to_bayes_net()
def build_model():
with Model() as m:
m.temperature = Uniform(20., 30.)
m.first_thermometer = Gaussian(m.temperature, 2.5)
m.second_thermometer = Gaussian(m.temperature, 5.)
return m
