def test_check_flow_model_reset_not_trained(sampler):
"""
Verify that the flow model is not reset if it has never been trained.
"""
sampler.proposal = MagicMock()
sampler.proposal.reset_model_weights = MagicMock()
sampler.proposal.training_count = 0
NestedSampler.check_flow_model_reset(sampler)
sampler.proposal.reset_model_weights.assert_not_called()
def test_check_flow_model_reset_weights(sampler, training_count):
"""Assert flow model only weights are reset"""
sampler.proposal = MagicMock()
sampler.proposal.reset_model_weights = MagicMock()
sampler.reset_acceptance = False
sampler.reset_weights = 10
sampler.reset_permutations = 0
sampler.proposal.training_count = training_count
NestedSampler.check_flow_model_reset(sampler)
sampler.proposal.reset_model_weights.assert_called_once_with(weights=True)
def test_check_flow_model_reset_both(sampler, training_count):
"""Assert flow model only permutations are reset"""
sampler.proposal = MagicMock()
sampler.proposal.reset_model_weights = MagicMock()
sampler.reset_acceptance = False
sampler.reset_weights = 10
sampler.reset_permutations = 10
sampler.proposal.training_count = training_count
NestedSampler.check_flow_model_reset(sampler)
calls = [call(weights=True), call(weights=False, permutations=True)]
sampler.proposal.reset_model_weights.assert_has_calls(calls)
def test_check_flow_model_reset_acceptance(sampler):
"""
Assert flow model is reset based on acceptance is reset_acceptance is True.
"""
sampler.proposal = MagicMock()
sampler.proposal.reset_model_weights = MagicMock()
sampler.reset_acceptance = True
sampler.mean_block_acceptance = 0.1
sampler.acceptance_threshold = 0.5
sampler.proposal.training_count = 1
NestedSampler.check_flow_model_reset(sampler)
sampler.proposal.reset_model_weights.assert_called_once_with(
weights=True, permutations=True)