def test_bool():
api_config = {
"x": {
"type": "bool"
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(fit_function_bool)
assert 1 == proposal["x"]
def test_categorical():
api_config = {
"x": {
"type": "cat",
"values": ["exp", "log", "abs"]
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(fit_function_cat)
assert "exp" == proposal["x"]
def test_log_space():
api_config = {
"x": {
"type": "real",
"space": "log",
"range": (0.1, 100)
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(basic_fit_function_10)
assert pytest.approx(10, 0.1) == proposal["x"]
def test_happy_path():
api_config = {
"x": {
"type": "real",
"space": "linear",
"range": (-10, 10)
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(basic_fit_function)
assert pytest.approx(-1, 0.1) == proposal["x"]
def test_int_values():
api_config = {
"x": {
"type": "int",
"space": "linear",
"values": (-10, -2, -1, 0, 10)
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(basic_fit_function)
assert pytest.approx(-1, 0.1) == proposal["x"]
def test_timeout_callback():
api_config = {
"x": {
"type": "real",
"space": "linear",
"range": (-10, 10)
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
start_time = time()
proposal = gs.suggest(basic_fit_function, timeout=0.001)
end_time = time()
assert pytest.approx(-1, 1) == proposal["x"]
assert end_time - start_time < 0.1
def test_bounds_log():
min_max_iter = 10
max_max_iter = 5000
api_config = {
"max_iter": {
"type": "int",
"space": "log",
"range": (min_max_iter, max_max_iter)
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
for i in range(10):
proposal = gs.suggest(more_better_fit_function, seed=i)
print(proposal)
assert min_max_iter <= proposal["max_iter"] <= max_max_iter
def test_mixed_types():
api_config = {
"x": {
"type": "real",
"space": "linear",
"range": (-10, 10)
},
"b": {
"type": "bool"
},
"c": {
"type": "cat",
"values": ["exp", "log", "abs"]
},
}
transformer = HyperTransformer(api_config)
gs = GeneticSearch(transformer)
proposal = gs.suggest(fit_function_mixed_type)
assert (pytest.approx(-1, 0.1) == proposal["x"] and 1 == proposal["b"]
and "exp" == proposal["c"])