def test__get_tunable_no_conditionals(self):
"""If there are no conditionals, tunables are returned unmodified."""
# setup
init_params = {
'an_init_param': 'a_value'
}
hyperparameters = {
'tunable': {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
}
}
}
# run
tunable = MLBlock._get_tunable(hyperparameters, init_params)
# assert
expected = {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
}
}
assert tunable == expected
def test__get_tunable_no_condition(self):
"""If there is a conditional but no condition, the default is used."""
# setup
init_params = {
'an_init_param': 'a_value'
}
hyperparameters = {
'tunable': {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'conditional',
'condition': 'a_condition',
'default': {
'type': 'float',
'default': 0.1,
'values': [0, 1]
},
'values': {
'not_a_match': {
'type': 'str',
'default': 'a',
'values': ['a', 'b']
},
'neither_a_match': {
'type': 'int',
'default': 0,
'range': [1, 10]
}
}
}
}
}
# run
tunable = MLBlock._get_tunable(hyperparameters, init_params)
# assert
expected = {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'float',
'default': 0.1,
'values': [0, 1]
}
}
assert tunable == expected
def test__get_tunable_condition_match(self):
"""If there is a conditional and it matches, only that part is returned."""
# setup
init_params = {
'a_condition': 'a_match'
}
hyperparameters = {
'tunable': {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'conditional',
'condition': 'a_condition',
'default': {
'type': 'float',
'default': 0.1,
'values': [0, 1]
},
'values': {
'not_a_match': {
'type': 'str',
'default': 'a',
'values': ['a', 'b']
},
'a_match': {
'type': 'int',
'default': 0,
'range': [1, 10]
}
}
}
}
}
# run
tunable = MLBlock._get_tunable(hyperparameters, init_params)
# assert
expected = {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'int',
'default': 0,
'range': [1, 10]
}
}
assert tunable == expected
def test__get_tunable_condition_match_null(self):
"""If there is a match and it is null (None), this param is not included.
This stands even if the default is not null.
"""
# setup
init_params = {
'a_condition': 'a_match'
}
hyperparameters = {
'tunable': {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'conditional',
'condition': 'a_condition',
'default': {
'type': 'float',
'default': 0.1,
'values': [0, 1]
},
'values': {
'not_a_match': {
'type': 'str',
'default': 'a',
'values': ['a', 'b']
},
'a_match': None
}
}
}
}
# run
tunable = MLBlock._get_tunable(hyperparameters, init_params)
# assert
expected = {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
}
}
assert tunable == expected
def test__get_tunable_condition_default_null(self):
"""If there is no match and default is null (None), this param is not included."""
# setup
init_params = {
'a_condition': 'not_a_match'
}
hyperparameters = {
'tunable': {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
},
'this_is_conditional': {
'type': 'conditional',
'condition': 'a_condition',
'default': None,
'values': {
'also_not_a_match': {
'type': 'str',
'default': 'a',
'values': ['a', 'b']
},
'neither_a_match': {
'type': 'int',
'default': 0,
'range': [1, 10]
}
}
}
}
}
# run
tunable = MLBlock._get_tunable(hyperparameters, init_params)
# assert
expected = {
'this_is_not_conditional': {
'type': 'int',
'default': 1,
'range': [1, 10]
}
}
assert tunable == expected