def test_run_grid(self):
auto_var = AutoVar()
auto_var.add_variable_class(OrdVarClass())
auto_var.add_variable_class(DatasetVarClass())
auto_var.add_variable('random_seed', int)
grid_params = {
"ord": ['1', '2'],
"dataset": ['halfmoon_50', 'halfmoon_10'],
"random_seed": [1126],
}
def fn(auto_var):
return {
"ord": auto_var.var_value['ord'],
"dataset": auto_var.var_value['dataset'],
"random_seed": auto_var.var_value['random_seed'],
}
params, results = auto_var.run_grid_params(fn,
grid_params=grid_params,
n_jobs=1)
self.assertEqual(params[0], {
'ord': '1',
'dataset': 'halfmoon_50',
'random_seed': 1126,
})
del results[0]['var_value']['git_hash']
self.assertEqual(params[0], results[0]['var_value'])
del results[0]['running_time']
del results[0]['var_value']
self.assertEqual(params[0], results[0])
def test_val(self):
auto_var = AutoVar(logging_level=logging.INFO)
with self.assertRaises(VariableNotRegisteredError):
auto_var.get_var('ord')
auto_var.add_variable_class(OrdVarClass())
auto_var.add_variable_class(DatasetVarClass())
auto_var.add_variable('random_seed', int)
with self.assertRaises(VariableValueNotSetError):
auto_var.get_var('ord')
auto_var.set_variable_value_by_dict({
'ord': '1',
'dataset': 'halfmoon_200',
'random_seed': 1126
})
self.assertEqual(auto_var.get_var('ord'), 1)
self.assertEqual(auto_var.get_var('random_seed'), 1126)
self.assertEqual(len(auto_var.get_var('dataset')[0]), 200)
self.assertEqual(
len(auto_var.get_var_with_argument('dataset', 'halfmoon_300')[0]),
300)
with self.assertRaises(ValueError):
auto_var.set_variable_value_by_dict({'ord': 'l2'})
with self.assertRaises(TypeError):
auto_var.set_variable_value_by_dict({'random_seed': '1126.0'})
self.assertEqual(auto_var.get_var_shown_name(var_name="dataset"),
'shown_halfmoon')
assert_array_equal(
auto_var.get_var_with_argument('dataset', 'halfmoon_300')[0],
auto_var.get_var_with_argument('dataset', 'moon_300')[0],
)
argparse_help = auto_var.get_argparser().format_help()
self.assertTrue('halfmoon dataset' in argparse_help)
self.assertTrue('Dataset variable class' in argparse_help)
partial(create_placeholder_file,
get_name_fn=get_file_name),
],
after_experiment_hooks=[
partial(save_result_to_file, get_name_fn=get_file_name),
partial(remove_placeholder_if_error,
get_name_fn=get_file_name),
],
settings={
'file_format': 'pickle',
'server_url': '',
'result_file_dir': './results/'
})
auto_var.add_variable_class(DatasetVarClass())
auto_var.add_variable('random_seed', int)
#auto_var.add_variable('eps', float)
#auto_var.add_variable('optimizer', str)
#auto_var.add_variable('learning_rate', float, default=1e-2)
#auto_var.add_variable('batch_size', int, default=64)
#auto_var.add_variable('momentum', float, default=0.9)
#auto_var.add_variable('epochs', int, default=2)
#auto_var.add_variable('weight_decay', float, default=0.)
#from autovar.base import RegisteringChoiceType, VariableClass, register_var
#class ExampleVarClass(VariableClass, metaclass=RegisteringChoiceType):
# """Example Variable Class"""
# var_name = "example"
#
# @register_var()
# @staticmethod
from autovar.base import RegisteringChoiceType, register_var, VariableClass
from autovar.hooks import check_result_file_exist, save_result_to_file
from autovar.hooks import create_placeholder_file, remove_placeholder_if_error
from autovar.hooks import default_get_file_name as get_file_name
from .datasets import DatasetVarClass
from .preprocessors import PreprocessorVarClass
auto_var = AutoVar(logging_level=logging.INFO,
before_experiment_hooks=[
partial(check_result_file_exist,
get_name_fn=get_file_name),
partial(create_placeholder_file,
get_name_fn=get_file_name),
],
after_experiment_hooks=[
partial(save_result_to_file, get_name_fn=get_file_name),
partial(remove_placeholder_if_error,
get_name_fn=get_file_name),
],
settings={
'file_format': 'pickle',
'server_url': '',
'result_file_dir': './results/'
})
auto_var.add_variable_class(DatasetVarClass())
auto_var.add_variable_class(PreprocessorVarClass())
auto_var.add_variable('random_seed', int)
auto_var.add_variable('rsep', float, default=float(-1.))
class NormVarClass(VariableClass, metaclass=RegisteringChoiceType):
"""Defines which distance measure to use for attack."""
var_name = "norm"
@register_var()
@staticmethod
def inf(auto_var):
"""L infinity norm"""
return np.inf
@register_var(argument='2')
@staticmethod
def l2(auto_var):
"""L2 norm"""
return 2
@register_var(argument='1')
@staticmethod
def l1(auto_var):
"""L1 norm"""
return 1
auto_var.add_variable_class(NormVarClass())
auto_var.add_variable_class(DatasetVarClass())
auto_var.add_variable_class(ModelVarClass())
auto_var.add_variable_class(AttackVarClass())
auto_var.add_variable('random_seed', int)
auto_var.add_variable('eps', float)