def test_Parameter_1D():
"""Tests a 1D Parameter"""
# Create 1D parameter
param = Parameter(shape=5)
# kl_loss should still be scalar
kl_loss = param.kl_loss()
assert isinstance(kl_loss, tf.Tensor)
assert kl_loss.ndim == 0
# posterior_mean should return mean
sample1 = param.posterior_mean()
sample2 = param.posterior_mean()
assert sample1.ndim == 1
assert sample2.ndim == 1
assert sample1.shape[0] == 5
assert sample2.shape[0] == 5
assert np.all(sample1 == sample2)
# posterior_sample should return samples
sample1 = param.posterior_sample()
sample2 = param.posterior_sample()
assert sample1.ndim == 1
assert sample2.ndim == 1
assert sample1.shape[0] == 5
assert sample2.shape[0] == 5
assert np.all(sample1 != sample2)
# posterior_sample should be able to return multiple samples
sample1 = param.posterior_sample(10)
sample2 = param.posterior_sample(10)
assert sample1.ndim == 2
assert sample2.ndim == 2
assert sample1.shape[0] == 10
assert sample1.shape[1] == 5
assert sample2.shape[0] == 10
assert sample2.shape[1] == 5
assert np.all(sample1 != sample2)
# prior_sample should still be 1D
prior_sample = param.prior_sample()
assert prior_sample.ndim == 0
prior_sample = param.prior_sample(n=7)
assert prior_sample.ndim == 1
assert prior_sample.shape[0] == 7
# n_parameters property
nparams = param.n_parameters
assert isinstance(nparams, int)
assert nparams == 5
def test_Parameter_no_prior():
"""Tests a parameter with no prior"""
# Create parameter with no prior
param = Parameter(prior=None)
# kl_loss should return 0
kl_loss = param.kl_loss()
assert isinstance(kl_loss, tf.Tensor)
assert kl_loss.ndim == 0
assert kl_loss.numpy() == 0.0
# prior_sample should return nans with prior=None
prior_sample = param.prior_sample()
assert prior_sample.ndim == 1
assert prior_sample.shape[0] == 1
assert np.all(np.isnan(prior_sample))
prior_sample = param.prior_sample(n=7)
assert prior_sample.ndim == 1
assert prior_sample.shape[0] == 7
assert np.all(np.isnan(prior_sample))
def test_Parameter_2D():
"""Tests a 2D Parameter"""
# Create 1D parameter
param = Parameter(shape=[5, 4], name="lala")
# repr
pstr = param.__repr__()
assert pstr == "<pf.Parameter lala shape=[5, 4]>"
# kl_loss should still be scalar
kl_loss = param.kl_loss()
assert isinstance(kl_loss, tf.Tensor)
assert kl_loss.ndim == 0
# posterior_mean should return mean
sample1 = param.posterior_mean()
sample2 = param.posterior_mean()
assert sample1.ndim == 2
assert sample2.ndim == 2
assert sample1.shape[0] == 5
assert sample1.shape[1] == 4
assert sample2.shape[0] == 5
assert sample2.shape[1] == 4
assert np.all(sample1 == sample2)
# posterior_sample should return samples
sample1 = param.posterior_sample()
sample2 = param.posterior_sample()
assert sample1.ndim == 2
assert sample2.ndim == 2
assert sample1.shape[0] == 5
assert sample1.shape[1] == 4
assert sample2.shape[0] == 5
assert sample2.shape[1] == 4
assert np.all(sample1 != sample2)
# posterior_sample should be able to return multiple samples
sample1 = param.posterior_sample(10)
sample2 = param.posterior_sample(10)
assert sample1.ndim == 3
assert sample2.ndim == 3
assert sample1.shape[0] == 10
assert sample1.shape[1] == 5
assert sample1.shape[2] == 4
assert sample2.shape[0] == 10
assert sample2.shape[1] == 5
assert sample2.shape[2] == 4
assert np.all(sample1 != sample2)
# prior_sample should still be 1D
prior_sample = param.prior_sample()
assert prior_sample.ndim == 0
prior_sample = param.prior_sample(n=7)
assert prior_sample.ndim == 1
assert prior_sample.shape[0] == 7
# n_parameters property
nparams = param.n_parameters
assert isinstance(nparams, int)
assert nparams == 20
def test_Parameter_scalar():
"""Tests the generic scalar Parameter"""
# Create scalar parameter
param = Parameter()
# Check defaults
assert isinstance(param.shape, list)
assert param.shape[0] == 1
assert isinstance(param.untransformed_variables, dict)
assert all(isinstance(p, str) for p in param.untransformed_variables)
assert all(
isinstance(p, tf.Variable)
for _, p in param.untransformed_variables.items())
# Shape should be >0
with pytest.raises(ValueError):
Parameter(shape=-1)
with pytest.raises(ValueError):
Parameter(shape=[20, 0, 1])
# trainable_variables should be a property returning list of vars
assert all(isinstance(v, tf.Variable) for v in param.trainable_variables)
# variables should be a property returning dict of transformed vars
assert isinstance(param.variables, dict)
assert all(isinstance(v, str) for v in param.variables)
# loc should be variable, while scale should have been transformed->tensor
assert isinstance(param.variables["loc"], tf.Variable)
assert isinstance(param.variables["scale"], tf.Tensor)
# posterior should be a distribution object
assert isinstance(param.posterior, BaseDistribution)
assert isinstance(param.posterior(), tfd.Normal)
# __call__ should return the MAP estimate by default
sample1 = param()
sample2 = param()
assert sample1.ndim == 1
assert sample2.ndim == 1
assert sample1.shape[0] == 1
assert sample2.shape[0] == 1
assert sample1.numpy() == sample2.numpy()
# within a Sampling statement, should randomly sample from the dist
with Sampling(n=1):
sample1 = param()
sample2 = param()
assert sample1.ndim == 1
assert sample2.ndim == 1
assert sample1.shape[0] == 1
assert sample2.shape[0] == 1
assert sample1.numpy() != sample2.numpy()
# sampling statement should effect N samples
with Sampling(n=10):
sample1 = param()
sample2 = param()
assert sample1.ndim == 2
assert sample2.ndim == 2
assert sample1.shape[0] == 10
assert sample1.shape[1] == 1
assert sample2.shape[0] == 10
assert sample2.shape[1] == 1
assert np.all(sample1.numpy() != sample2.numpy())
# sampling statement should allow static samples
sample1 = param()
with Sampling(static=True):
with Sampling(n=1):
sample2 = param()
sample3 = param()
with Sampling(static=True):
with Sampling(n=1):
sample4 = param()
sample5 = param()
assert sample1.ndim == 1
assert sample2.ndim == 1
assert sample3.ndim == 1
assert sample4.ndim == 1
assert sample5.ndim == 1
assert sample1.shape[0] == 1
assert sample2.shape[0] == 1
assert sample3.shape[0] == 1
assert sample4.shape[0] == 1
assert sample5.shape[0] == 1
assert sample1.numpy() != sample2.numpy()
assert sample1.numpy() != sample3.numpy()
assert sample2.numpy() == sample3.numpy()
assert sample1.numpy() != sample4.numpy()
assert sample1.numpy() != sample5.numpy()
assert sample4.numpy() == sample5.numpy()
assert sample2.numpy() != sample4.numpy()
# sampling statement should allow static samples (and work w/ n>1)
with Sampling(static=True):
with Sampling(n=5):
sample1 = param()
sample2 = param()
with Sampling(static=True):
with Sampling(n=5):
sample3 = param()
sample4 = param()
assert sample1.ndim == 2
assert sample2.ndim == 2
assert sample3.ndim == 2
assert sample4.ndim == 2
assert sample1.shape[0] == 5
assert sample1.shape[1] == 1
assert sample2.shape[0] == 5
assert sample2.shape[1] == 1
assert sample3.shape[0] == 5
assert sample3.shape[1] == 1
assert sample4.shape[0] == 5
assert sample4.shape[1] == 1
assert np.all(sample1.numpy() == sample2.numpy())
assert np.all(sample1.numpy() != sample3.numpy())
assert np.all(sample1.numpy() != sample4.numpy())
assert np.all(sample2.numpy() != sample3.numpy())
assert np.all(sample2.numpy() != sample4.numpy())
assert np.all(sample3.numpy() == sample4.numpy())
# kl_loss should return sum of kl divergences
kl_loss = param.kl_loss()
assert isinstance(kl_loss, tf.Tensor)
assert kl_loss.ndim == 0
# prior_sample should be 1D
prior_sample = param.prior_sample()
assert prior_sample.ndim == 0
prior_sample = param.prior_sample(n=7)
assert prior_sample.ndim == 1
assert prior_sample.shape[0] == 7
# prior and posterior shouldn't be the same (post was randomly initialized)
assert tf.reduce_all(param.prior.loc != param.posterior.loc).numpy()
assert tf.reduce_all(param.prior.scale != param.posterior.scale).numpy()
# but they should be the same after running bayesian_update
param.bayesian_update()
assert tf.reduce_all(param.prior.loc == param.posterior.loc).numpy()
assert tf.reduce_all(param.prior.scale == param.posterior.scale).numpy()