def test_astep_multidmodel_uniform(self):
"""Test a single step of DREAM with a multi-dimensional model and uniform prior."""
self.param, self.like = multidmodel_uniform()
model = Model(self.like, self.param)
dream = Dream(model=model, save_history=False, verbose=False)
# Even though we're calling the steps separately we need to call these functions
# to initialize the shared memory arrays that are called in the step fxn
pool = _setup_mp_dream_pool(nchains=3, niterations=10, step_instance=dream)
pool._initializer(*pool._initargs)
# Test initial step (when last logp and prior values aren't set)
q_new, last_prior, last_like = dream.astep(q0=np.array([-5, 4, -3, 0]))
self.assertTrue(isinstance(q_new, np.ndarray))
self.assertTrue(isinstance(last_prior, numbers.Number))
self.assertTrue(isinstance(last_like, numbers.Number))
# Test later iteration after last logp and last prior have been set
q_new, last_prior, last_like = dream.astep(q0=np.array([8, 4, -2, 9]), last_logprior=100, last_loglike=-600)
self.assertTrue(isinstance(q_new, np.ndarray))
self.assertTrue(isinstance(last_prior, numbers.Number))
self.assertTrue(isinstance(last_like, numbers.Number))
if np.any(q_new != np.array(8)):
self.assertTrue(last_prior + last_like >= -500)
else:
self.assertTrue(last_prior == 100)
self.assertTrue(last_like == -600)
def test_boundaries_obeyed_aftersampling(self):
"""Test that boundaries are respected if included."""
self.param, self.like = multidmodel_uniform()
model = Model(self.like, self.param)
step = Dream(model=model,
save_history=True,
history_thin=1,
model_name='test_boundaries',
adapt_crossover=False,
hardboundaries=True,
nverbose=10)
sampled_params, logps = run_dream(self.param,
self.like,
niterations=1000,
nchains=5,
save_history=True,
history_thin=1,
model_name='test_boundaries',
adapt_crossover=False,
verbose=True,
hardboundaries=True,
nverbose=10)
history = np.load('test_boundaries_DREAM_chain_history.npy')
variables = model.sampled_parameters
dim = 0
for var in variables:
interval = var.interval()
dim += var.dsize
lowerbound = interval[0]
upperbound = interval[1]
npoints = int(len(history) / float(dim))
reshaped_history = np.reshape(history, (npoints, dim))
print('reshaped history: ', reshaped_history)
print('upper ', upperbound, ' and lower ', lowerbound)
print('lower bounds: ', (reshaped_history < lowerbound).any())
print('upper bounds: ', (reshaped_history > upperbound).any())
print('disobeyed lower: ',
reshaped_history[reshaped_history < lowerbound])
print('disobeyed upper: ',
reshaped_history[reshaped_history > upperbound])
self.assertFalse((reshaped_history < lowerbound).any())
self.assertFalse((reshaped_history > upperbound).any())
remove('test_boundaries_DREAM_chain_adapted_crossoverprob.npy')
remove('test_boundaries_DREAM_chain_adapted_gammalevelprob.npy')
remove('test_boundaries_DREAM_chain_history.npy')