def test_log_pdf(self, dtype, alpha, alpha_isSamples, beta, beta_isSamples,
rv, rv_isSamples, num_samples):
import scipy as sp
isSamples_any = any([alpha_isSamples, beta_isSamples, rv_isSamples])
rv_shape = rv.shape[1:] if rv_isSamples else rv.shape
n_dim = 1 + len(
rv.shape) if isSamples_any and not rv_isSamples else len(rv.shape)
alpha_np = numpy_array_reshape(alpha, alpha_isSamples, n_dim)
beta_np = numpy_array_reshape(beta, beta_isSamples, n_dim)
rv_np = numpy_array_reshape(rv, rv_isSamples, n_dim)
log_pdf_np = sp.stats.gamma.logpdf(rv_np,
a=alpha_np,
loc=0,
scale=1. / beta_np)
gamma = Gamma.define_variable(shape=rv_shape, dtype=dtype).factor
alpha_mx = mx.nd.array(alpha, dtype=dtype)
if not alpha_isSamples:
alpha_mx = add_sample_dimension(mx.nd, alpha_mx)
beta_mx = mx.nd.array(beta, dtype=dtype)
if not beta_isSamples:
beta_mx = add_sample_dimension(mx.nd, beta_mx)
rv_mx = mx.nd.array(rv, dtype=dtype)
if not rv_isSamples:
rv_mx = add_sample_dimension(mx.nd, rv_mx)
variables = {
gamma.alpha.uuid: alpha_mx,
gamma.beta.uuid: beta_mx,
gamma.random_variable.uuid: rv_mx
}
log_pdf_rt = gamma.log_pdf(F=mx.nd, variables=variables)
assert np.issubdtype(log_pdf_rt.dtype, dtype)
assert is_sampled_array(mx.nd, log_pdf_rt) == isSamples_any
if isSamples_any:
assert get_num_samples(mx.nd, log_pdf_rt) == num_samples
if np.issubdtype(dtype, np.float64):
rtol, atol = 1e-7, 1e-10
else:
rtol, atol = 1e-4, 1e-5
assert np.allclose(log_pdf_np,
log_pdf_rt.asnumpy(),
rtol=rtol,
atol=atol)
def test_draw_samples(self, dtype, alpha, alpha_isSamples, beta,
beta_isSamples, rv_shape, num_samples):
n_dim = 1 + len(rv_shape)
out_shape = (num_samples, ) + rv_shape
alpha_np = mx.nd.array(np.broadcast_to(numpy_array_reshape(
alpha, alpha_isSamples, n_dim),
shape=out_shape),
dtype=dtype)
beta_np = mx.nd.array(np.broadcast_to(numpy_array_reshape(
beta, beta_isSamples, n_dim),
shape=out_shape),
dtype=dtype)
gamma = Gamma.define_variable(shape=rv_shape, dtype=dtype).factor
alpha_mx = mx.nd.array(alpha, dtype=dtype)
if not alpha_isSamples:
alpha_mx = add_sample_dimension(mx.nd, alpha_mx)
beta_mx = mx.nd.array(beta, dtype=dtype)
if not beta_isSamples:
beta_mx = add_sample_dimension(mx.nd, beta_mx)
variables = {gamma.alpha.uuid: alpha_mx, gamma.beta.uuid: beta_mx}
mx.random.seed(0)
rv_samples_rt = gamma.draw_samples(F=mx.nd,
variables=variables,
num_samples=num_samples)
mx.random.seed(0)
rv_samples_mx = mx.nd.random.gamma(alpha=alpha_np,
beta=beta_np,
dtype=dtype)
assert np.issubdtype(rv_samples_rt.dtype, dtype)
assert is_sampled_array(mx.nd, rv_samples_rt)
assert get_num_samples(mx.nd, rv_samples_rt) == num_samples
if np.issubdtype(dtype, np.float64):
rtol, atol = 1e-7, 1e-10
else:
rtol, atol = 1e-4, 1e-5
assert np.allclose(rv_samples_mx.asnumpy(),
rv_samples_rt.asnumpy(),
rtol=rtol,
atol=atol)
def test_draw_samples_non_mock(self, plot=False):
# Also make sure the non-mock sampler works
dtype = np.float32
num_samples = 100000
alpha = np.array([9])
beta = np.array([0.5])
rv_shape = (1, )
location_mx = add_sample_dimension(mx.nd,
mx.nd.array(alpha, dtype=dtype))
scale_mx = add_sample_dimension(mx.nd, mx.nd.array(beta, dtype=dtype))
rand_gen = None
var = Gamma.define_variable(shape=rv_shape,
rand_gen=rand_gen,
dtype=dtype).factor
variables = {var.alpha.uuid: location_mx, var.beta.uuid: scale_mx}
rv_samples_rt = var.draw_samples(F=mx.nd,
variables=variables,
num_samples=num_samples)
assert array_has_samples(mx.nd, rv_samples_rt)
assert get_num_samples(mx.nd, rv_samples_rt) == num_samples
assert rv_samples_rt.dtype == dtype
if plot:
plot_univariate(samples=rv_samples_rt,
dist=gamma,
a=alpha[0],
scale=beta[0])
alpha_est, location_est, scale_est = gamma.fit(
rv_samples_rt.asnumpy().ravel())
beta_est = scale_est
alpha_tol = 0.2
beta_tol = 0.2
assert np.abs(alpha[0] - alpha_est) < alpha_tol
assert np.abs(beta[0] - beta_est) < beta_tol