def test_set_choices_updates(self, fit_dictionary_tabular):
dataset_properties = {
'numerical_columns': [1],
'categorical_columns': [2],
'task_type': 'tabular_classification'
}
config_dict = TabularClassificationPipeline(dataset_properties=dataset_properties). \
get_hyperparameter_search_space()._hyperparameters
updates = HyperparameterSearchSpaceUpdates()
for i, (name, hyperparameter) in enumerate(config_dict.items()):
if '__choice__' not in name:
continue
name = name.split(':')
hyperparameter_name = ':'.join(name[1:])
# Using NoEmbedding is safer for this test
# to avoid forbidden configuration errors
if name[0] == 'network_embedding' and hyperparameter_name == '__choice__':
value_range = ('NoEmbedding', )
default_value = 'NoEmbedding'
else:
value_range = (hyperparameter.choices[0], )
default_value = hyperparameter.choices[0]
updates.append(node_name=name[0],
hyperparameter=hyperparameter_name,
value_range=value_range,
default_value=default_value)
pipeline = TabularClassificationPipeline(
dataset_properties=dataset_properties,
search_space_updates=updates)
self._assert_pipeline_search_space(pipeline, updates)
def test_set_range_search_space_updates(self, fit_dictionary_tabular):
dataset_properties = {
'numerical_columns': [1],
'categorical_columns': [2],
'task_type': 'tabular_classification'
}
config_dict = TabularClassificationPipeline(dataset_properties=dataset_properties). \
get_hyperparameter_search_space()._hyperparameters
updates = HyperparameterSearchSpaceUpdates()
for i, (name, hyperparameter) in enumerate(config_dict.items()):
if '__choice__' in name:
continue
name = name.split(':')
hyperparameter_name = ':'.join(name[1:])
if '_' in hyperparameter_name:
if any(l_.isnumeric() for l_ in hyperparameter_name.split('_')
[-1]) and 'network' in name[0]:
hyperparameter_name = '_'.join(
hyperparameter_name.split('_')[:-1])
if isinstance(hyperparameter, CategoricalHyperparameter):
value_range = (hyperparameter.choices[0], )
default_value = hyperparameter.choices[0]
else:
value_range = (0, 1)
default_value = 1
updates.append(node_name=name[0],
hyperparameter=hyperparameter_name,
value_range=value_range,
default_value=default_value)
pipeline = TabularClassificationPipeline(
dataset_properties=dataset_properties,
search_space_updates=updates)
try:
self._assert_pipeline_search_space(pipeline, updates)
except AssertionError as e:
# As we are setting num_layers to 1 for fully connected
# head, units_layer does not exist in the configspace
assert 'fully_connected:units_layer' in e.args[0], e.args[0]