def test_objective(tmpdir):
dfs = load_iris(as_frame=True)
df = dfs["data"]
df["label"] = dfs["target"]
df = df[df.label <= 1]
t = Trial(
"x",
params={
"max_iter": RandInt(2, 4),
SPACE_MODEL_NAME: "sklearn.linear_model.LogisticRegression",
},
dfs={TUNE_DATASET_DF_DEFAULT_NAME: df},
)
obj = SKCVObjective(scoring="accuracy")
runner = HyperoptRunner(5, 0)
def v(report):
print(report.jsondict)
assert report.sort_metric < 0
assert "cv_scores" in report.metadata
# assert report.trial.params["max_iter"] >= 2
validate_noniterative_objective(obj, t, v, runner=runner)
obj = SKCVObjective(scoring="accuracy", checkpoint_path=str(tmpdir))
def v2(report):
print(report.jsondict)
assert report.sort_metric < 0
assert "cv_scores" in report.metadata
assert "checkpoint_path" in report.metadata
# assert report.trial.params["max_iter"] >= 2
validate_noniterative_objective(obj, t, v2, runner=runner)
def test_hyperopt():
params = dict(a=Rand(-10.0, 10.0), b=RandInt(-100, 100), c=2.0)
trial = Trial("a", params, metadata={})
h = HyperoptRunner(max_iter=200, seed=0)
@noniterative_objective
def objective(a, b, c) -> Tuple[float, Dict[str, Any]]:
return a**2 + b**2 + c, dict(a=1)
def v(report):
assert report.metric < 7
assert report.params["a"]**2 < 2
assert report.params["b"]**2 < 2
assert 2.0 == report.params["c"]
validate_noniterative_objective(objective, trial, v, runner=h)
def test_optimization_dummy(self):
params = dict(a=1, b=2, c=3)
trial = Trial("a", params, metadata={})
o = self.make_optimizer(max_iter=5)
@noniterative_objective
def objective(a, b, c) -> Tuple[float, Dict[str, Any]]:
return a**2 + b**2 + c, dict(a="x")
def v(report):
assert 1 == report.params.simple_value["a"]
assert 2 == report.params.simple_value["b"]
assert 3 == report.params.simple_value["c"]
assert report.metric == 8
assert "x" == report.metadata["a"]
validate_noniterative_objective(objective, trial, v, optimizer=o)
def test_optimization(self):
params = dict(a=Rand(-10.0, 10.0), b=RandInt(-100, 100), c=2.0)
trial = Trial("a", params, metadata={})
o = self.make_optimizer(max_iter=200)
@noniterative_objective
def objective(a, b, c) -> Tuple[float, Dict[str, Any]]:
return a**2 + b**2 + c, dict(a="x")
def v(report):
print(report.metric)
assert report.metric < 7
assert report.params.simple_value["a"]**2 < 2
assert report.params.simple_value["b"]**2 < 2
assert 2.0 == report.params.simple_value["c"]
assert "x" == report.metadata["a"]
validate_noniterative_objective(objective, trial, v, optimizer=o)