def generate_random_hyperparameters(self):
hyperparameters = {}
for hp in self._hyperparameters:
hyperparameters[hp.name] = hp.generate()
hyperparameters = ppp.dot_map_dict_to_nested_dict(hyperparameters)
return ppp.merge_recursive_dicts(
hyperparameters,
copy.deepcopy(self._default_kwargs),
ignore_duplicate_keys_in_second_dict=True,
)
def test_merge_recursive_dicts(self):
a = {'foo': 1, 'bar': {'baz': 3}}
b = {'foo2': 2, 'bar': {'baz2': 4}}
result = ppp.merge_recursive_dicts(a, b)
expected = {
'foo': 1,
'foo2': 2,
'bar': {
'baz': 3,
'baz2': 4,
}
}
self.assertDictEqual(result, expected)
def iterate_hyperparameters(self):
"""
Iterate over the hyperparameters in a grid-manner.
:return: List of dictionaries. Each dictionary is a map from name to
hyperpameter.
"""
return [
ppp.merge_recursive_dicts(
hyperparameters,
copy.deepcopy(self._default_kwargs),
ignore_duplicate_keys_in_second_dict=True,
) for hyperparameters in self._hyperparameters_dicts
]
def wrapped_function(params):
start_time = time.time()
if flatten_choice_dictionary:
params = flatten_hyperopt_choice_dict(params)
if dotmap_to_nested_dictionary:
params = dot_map_dict_to_nested_dict(params)
loss = function(
merge_recursive_dicts(
params,
extra_function_kwargs,
ignore_duplicate_keys_in_second_dict=True,
))
if maximize:
loss = -loss
if np.isnan(loss):
loss = max_magnitude
return {
'loss': loss,
'status': STATUS_OK,
'params': params,
# -- store other results like this
'eval_time': time.time() - start_time,
}