def test_skopt_tuner_basic():
pipeline_hyperparameter_ranges = {
'Mock Classifier': {
'parameter a': Integer(0, 10),
'parameter b': Real(0, 10),
'parameter c': (0, 10),
'parameter d': (0, 10.0),
'parameter e': ['option a', 'option b', 'option c'],
'parameter f': ['option a 💩', 'option b 💩', 'option c 💩'],
'parameter g': ['option a', 'option b', 100, np.inf]
}
}
tuner = SKOptTuner(pipeline_hyperparameter_ranges, random_seed=random_seed)
assert isinstance(tuner, Tuner)
proposed_params = tuner.propose()
assert proposed_params == {
'Mock Classifier': {
'parameter a': 5,
'parameter b': 8.442657485810175,
'parameter c': 3,
'parameter d': 8.472517387841256,
'parameter e': 'option b',
'parameter f': 'option b 💩',
'parameter g': 'option b'
}
}
tuner.add(proposed_params, 0.5)
def test_skopt_tuner_propose():
pipeline_hyperparameter_ranges = {
'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': ['option a', 'option b', 'option c']
}
}
tuner = SKOptTuner(pipeline_hyperparameter_ranges, random_seed=random_seed)
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 1.0,
'param c': 'option a'
}
}, 0.5)
parameters = tuner.propose()
assert parameters == {
'Mock Classifier': {
'param a': 5,
'param b': 8.442657485810175,
'param c': 'option c'
}
}
def test_skopt_tuner_single_value():
SKOptTuner({'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': 'Value'
}}, random_state=random_state)
tuner = SKOptTuner({'Mock Classifier': {
'param c': 10
}}, random_state=random_state)
proposed_params = tuner.propose()
assert proposed_params == {'Mock Classifier': {}}
def test_skopt_tuner_init():
with pytest.raises(
ValueError,
match=
'pipeline_hyperparameter_ranges must be a dict but is of type <class \'set\'>'
):
SKOptTuner({'My Component'})
with pytest.raises(
ValueError,
match=
'pipeline_hyperparameter_ranges has invalid entry for My Component: True'
):
SKOptTuner({'My Component': True})
with pytest.raises(
ValueError,
match=
'pipeline_hyperparameter_ranges has invalid entry for My Component'
):
SKOptTuner({'My Component': 0})
with pytest.raises(
ValueError,
match=
'pipeline_hyperparameter_ranges has invalid entry for My Component'
):
SKOptTuner({'My Component': None})
with pytest.raises(
ValueError,
match=
'pipeline_hyperparameter_ranges has invalid dimensions for My Component parameter param a: None'
):
SKOptTuner({'My Component': {'param a': None}})
SKOptTuner({})
SKOptTuner({'My Component': {}})
def test_skopt_tuner_invalid_ranges():
SKOptTuner({'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': ['option a', 'option b', 'option c']
}}, random_state=random_state)
with pytest.raises(ValueError, match="Invalid dimension \\[\\]. Read the documentation for supported types."):
SKOptTuner({'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': []
}}, random_state=random_state)
with pytest.raises(ValueError, match="pipeline_hyperparameter_ranges has invalid dimensions for Mock Classifier parameter param c"):
SKOptTuner({'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': None
}}, random_state=random_state)
def test_skopt_tuner_raises_deprecated_random_state_warning():
with warnings.catch_warnings(record=True) as warn:
warnings.simplefilter("always")
pipeline_hyperparameter_ranges = {
'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': ['option a', 'option b', 'option c']
}
}
SKOptTuner(pipeline_hyperparameter_ranges, random_state=15)
assert any(
str(w.message).startswith(
"Argument 'random_state' has been deprecated in favor of 'random_seed'"
) for w in warn)
def test_skopt_tuner_is_search_space_exhausted():
tuner = SKOptTuner({})
assert not tuner.is_search_space_exhausted()
def test_skopt_tuner_invalid_parameters_score():
pipeline_hyperparameter_ranges = {
'Mock Classifier': {
'param a': Integer(0, 10),
'param b': Real(0, 10),
'param c': ['option a', 'option b', 'option c']
}
}
tuner = SKOptTuner(pipeline_hyperparameter_ranges, random_seed=random_seed)
with pytest.raises(
TypeError,
match=
'Pipeline parameters missing required field "param a" for component "Mock Classifier"'
):
tuner.add({}, 0.5)
with pytest.raises(
TypeError,
match=
'Pipeline parameters missing required field "param a" for component "Mock Classifier"'
):
tuner.add({'Mock Classifier': {}}, 0.5)
with pytest.raises(
TypeError,
match=
'Pipeline parameters missing required field "param b" for component "Mock Classifier"'
):
tuner.add({'Mock Classifier': {'param a': 0}}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{'Mock Classifier': {
'param a': 0,
'param b': 0.0,
'param c': 0
}}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{
'Mock Classifier': {
'param a': -1,
'param b': 0.0,
'param c': 'option a'
}
}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 11.0,
'param c': 'option a'
}
}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 0.0,
'param c': 'option d'
}
}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{
'Mock Classifier': {
'param a': np.nan,
'param b': 0.0,
'param c': 'option a'
}
}, 0.5)
with pytest.raises(ValueError,
match="is not within the bounds of the space"):
tuner.add(
{
'Mock Classifier': {
'param a': np.inf,
'param b': 0.0,
'param c': 'option a'
}
}, 0.5)
with pytest.raises(ParameterError,
match="Invalid parameters specified to SKOptTuner.add"):
tuner.add(
{
'Mock Classifier': {
'param a': None,
'param b': 0.0,
'param c': 'option a'
}
}, 0.5)
with patch(
'evalml.tuners.skopt_tuner.Optimizer.tell') as mock_optimizer_tell:
msg = 'Mysterious internal error'
mock_optimizer_tell.side_effect = Exception(msg)
with pytest.raises(Exception, match=msg):
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 0.0,
'param c': 'option a'
}
}, 0.5)
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 1.0,
'param c': 'option a'
}
}, 0.5)
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 1.0,
'param c': 'option a'
}
}, np.nan)
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 1.0,
'param c': 'option a'
}
}, np.inf)
tuner.add(
{
'Mock Classifier': {
'param a': 0,
'param b': 1.0,
'param c': 'option a'
}
}, None)
tuner.propose()