def test_hyperparams_to_flat():
dict_values = {'hp': 1, 'stepa': {'hp': 2, 'stepb': {'hp': 3}}}
r = HyperparameterSamples(**dict_values)
r = r.to_flat()
expected_dict_values = {'hp': 1, 'stepa__hp': 2, 'stepa__stepb__hp': 3}
assert r == HyperparameterSamples(**expected_dict_values)
def _set_hyperparams(self, hyperparams: HyperparameterSamples) -> BaseStep:
"""
Set hyperparams for base step, and the wrapped sklearn_predictor.
:param hyperparams:
:return: self
"""
# flatten the step hyperparams, and set the wrapped sklearn predictor params
hyperparams = HyperparameterSamples(hyperparams)
BaseStep._set_hyperparams(self, hyperparams.to_flat())
self.wrapped_sklearn_predictor.set_params(**hyperparams.with_separator(
RecursiveDict.DEFAULT_SEPARATOR).to_flat_as_dict_primitive())
return self.hyperparams.to_flat()
def get_hyperparams(self, flat=True) -> HyperparameterSamples:
hyperparams = dict()
for k, v in self.steps.items():
hparams = v.get_hyperparams() # TODO: oop diamond problem?
if hasattr(v, "hyperparams"):
hparams.update(v.hyperparams)
if len(hparams) > 0:
hyperparams[k] = hparams
hyperparams = HyperparameterSamples(hyperparams)
if flat:
hyperparams = hyperparams.to_flat()
else:
hyperparams = hyperparams.to_nested_dict()
return hyperparams
