def test_parameters(self):
def to_lookup_config(dictionary):
tmp = LookupConfig()
tmp.update(dictionary)
return tmp
test_cases = [({
"a": 1,
"b": [1, 2],
"c": {
"d": 3,
"e": 56
}
}, {
"f": 1,
"g": {
"h": {
"i": {
"a": 3
}
}
}
}, {
"j": 1,
"k": 2
}, {}, "e", 56, "a", "q")]
for case in test_cases:
with self.subTest(case=case):
fixed_model_params, variable_model_params, \
fixed_training_params, variable_training_params, \
valid_nested_key, valid_nested_value, doubled_key, \
invalid_key = case
fixed_model_params = to_lookup_config(fixed_model_params)
variable_model_params = to_lookup_config(variable_model_params)
fixed_training_params = to_lookup_config(fixed_training_params)
variable_training_params = to_lookup_config(
variable_training_params)
params = Parameters(fixed_params={
"model": fixed_model_params,
"training": fixed_training_params
},
variable_params={
"model": variable_model_params,
"training": variable_training_params
})
self.assertFalse(params.training_on_top)
self.assertTrue(params.variability_on_top)
self.assertEqual(
params.fixed,
to_lookup_config({
"model": fixed_model_params,
"training": fixed_training_params
}))
self.assertEqual(
params.variable,
to_lookup_config({
"model": variable_model_params,
"training": variable_training_params
}))
params = params.permute_training_on_top()
self.assertFalse(params.variability_on_top)
self.assertTrue(params.training_on_top)
self.assertEqual(
params.model,
to_lookup_config({
"fixed": fixed_model_params,
"variable": variable_model_params
}))
self.assertEqual(
params.training,
to_lookup_config({
"fixed": fixed_training_params,
"variable": variable_training_params
}))
params_copy = params.deepcopy()
params = params.permute_variability_on_top(
).permute_training_on_top()
self.assertEqual(params_copy, params)
self.assertEqual(params.nested_get(valid_nested_key),
valid_nested_value)
with self.assertRaises(KeyError):
params.nested_get(doubled_key)
with self.assertRaises(KeyError):
params.nested_get(invalid_key)
self.assertEqual("default",
params.nested_get(invalid_key, "default"))
self.assertEqual(
"default", params.nested_get(invalid_key,
default="default"))
def test_parameters(fixed_model_params, variable_model_params,
fixed_training_params, variable_training_params,
valid_nested_key, valid_nested_value, doubled_key,
invalid_key):
def to_lookup_config(dictionary):
tmp = LookupConfig()
tmp.update(dictionary)
return tmp
fixed_model_params = to_lookup_config(fixed_model_params)
variable_model_params = to_lookup_config(variable_model_params)
fixed_training_params = to_lookup_config(fixed_training_params)
variable_training_params = to_lookup_config(variable_training_params)
params = Parameters(fixed_params={
"model": fixed_model_params,
"training": fixed_training_params
},
variable_params={
"model": variable_model_params,
"training": variable_training_params
})
assert params.training_on_top == False
assert params.variability_on_top
assert params.fixed == to_lookup_config({
"model": fixed_model_params,
"training": fixed_training_params
})
assert params.variable == to_lookup_config({
"model":
variable_model_params,
"training":
variable_training_params
})
params = params.permute_training_on_top()
assert params.variability_on_top == False
assert params.training_on_top
print(
params.model.difference_config(
to_lookup_config({
"fixed": fixed_model_params,
"variable": variable_model_params
})))
assert params.model == to_lookup_config({
"fixed": fixed_model_params,
"variable": variable_model_params
})
assert params.training == to_lookup_config({
"fixed":
fixed_training_params,
"variable":
variable_training_params
})
params_copy = params.deepcopy()
params = params.permute_variability_on_top().permute_training_on_top()
assert params_copy == params
assert params.nested_get(valid_nested_key) == valid_nested_value
try:
params.nested_get(doubled_key)
assert False
except KeyError:
assert True
try:
params.nested_get(invalid_key)
assert False
except KeyError:
assert True
assert "default" == params.nested_get(invalid_key, "default")
assert "default" == params.nested_get(invalid_key, default="default")