def test_to_noniterative_objective():
def f1(a, b: int) -> float:
return a - b
func = to_noniterative_objective(f1)
assert func.min_better
trial = Trial("abc", dict(b=20, a=10), dict(c=3))
report = func.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == trial.params
assert report.metadata == {}
func = to_noniterative_objective("f1", min_better=False)
assert not func.min_better
trial = Trial("abc", dict(b=20, a=10), dict(c=3))
report = func.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == trial.params
assert report.metadata == {}
assert -1 == func(1, 2)
def f2(a, b: int) -> Tuple[float, Dict[str, Any]]:
return a - b, dict(c=5)
func = to_noniterative_objective(f2)
trial = Trial("abc", dict(b=20, a=10), dict(c=3))
report = func.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == trial.params
assert report.metadata == dict(c=5)
def f3(t: Trial) -> TrialReport:
return TrialReport(
t, t.params["a"] - t.params["b"], params=dict(a=1), metadata=dict(c=6)
)
func = to_noniterative_objective(f3)
trial = Trial("abc", dict(b=20, a=10), dict(c=3))
report = func.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == dict(a=1)
assert report.metadata == dict(c=6)
class F4(NonIterativeObjectiveFunc):
def run(self, t: Trial) -> TrialReport:
return TrialReport(
t, t.params["a"] - t.params["b"], params=dict(a=1), metadata=dict(c=6)
)
f4 = F4()
f4_ = to_noniterative_objective(f4)
assert isinstance(f4_, F4)
assert f4 is not f4_
raises(TuneCompileError, lambda: to_noniterative_objective("abc"))
def test_trial_report_heap():
t1 = Trial("a", {})
r1 = TrialReport(t1, 0.1)
t2 = Trial("b", {})
r2 = TrialReport(t2, 0.2)
t3 = Trial("c", {})
r3 = TrialReport(t3, 0.3)
r4 = TrialReport(t3, -0.3)
h = TrialReportHeap(min_heap=True)
for r in [r1, r2, r3, r4]:
h.push(r)
assert "a" in h
assert "x" not in h
for r in [r4, r1, r2]:
assert h.pop() is r
assert 0 == len(h)
h = TrialReportHeap(min_heap=False)
for r in [r1, r2, r3, r4]:
h.push(r)
for r in [r1, r2, r4]:
assert r in list(h.values())
for r in [r2, r1, r4]:
assert h.pop() is r
assert 0 == len(h)
# test __lt__, the sort key is sort_metric!
r5 = TrialReport(t1, metric=0.1, sort_metric=-0.1)
r6 = TrialReport(t2, metric=0.2, sort_metric=-0.2)
r7 = TrialReport(t3, metric=0.3, sort_metric=-0.3)
h = TrialReportHeap(min_heap=True)
for r in [r7, r6, r5]:
h.push(r)
for r in [r7, r6, r5]:
assert h.pop() is r
assert 0 == len(h)
r5 = TrialReport(t1, metric=0.1, cost=0.2, rung=5)
r6 = TrialReport(t2, metric=0.1, cost=0.3, rung=5)
r7 = TrialReport(t3, metric=0.1, cost=0.3, rung=6)
h = TrialReportHeap(min_heap=True)
for r in [r7, r6, r5]:
h.push(r)
for r in [r5, r6, r7]:
assert h.pop() is r
assert 0 == len(h)
# equal case
r8 = TrialReport(t1, metric=0.1, cost=0.3, rung=6)
r9 = TrialReport(t2, metric=0.1, cost=0.3, rung=6)
h = TrialReportHeap(min_heap=False)
for r in [r8, r9]:
h.push(r)
for r in [r8, r9]:
assert h.pop() is r
assert 0 == len(h)
def test_noniterative_objective():
@noniterative_objective
def f1(a, b: int) -> float:
return a - b
assert isinstance(f1, NonIterativeObjectiveFunc)
trial = Trial("abc", dict(b=20, a=10), dict(c=3))
report = f1.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == trial.params
assert report.metadata == {}
func = to_noniterative_objective("f1")
report = func.run(trial)
assert report.trial is trial
assert report.metric == -10
assert report.params == trial.params
assert report.metadata == {}
@noniterative_objective(min_better=False)
def f2(a, b: int) -> float:
return a - b
assert isinstance(f2, NonIterativeObjectiveFunc)
assert not f2.min_better
def test_encode_decode():
p = {"a": 1, "b": Rand(1, 2)}
trial = Trial("abc", p, {}, keys=["x", "y"], dfs={"v": ""})
d = trial.jsondict
assert isinstance(d["params"]["b"], dict)
t = Trial.from_jsondict(d)
assert isinstance(t.params["b"], Rand)
assert ["x", "y"] == t.keys
assert {} == t.dfs # dfs will not be serialized
def test_copy():
trial = Trial("abc", {"a": 1})
t1 = trial.with_dfs({"c": pd.DataFrame([[0]])})
t2 = copy.copy(t1)
t3 = copy.deepcopy(t1)
assert trial.trial_id == t2.trial_id == t3.trial_id
assert t1.dfs is t2.dfs is t3.dfs
assert 0 == len(trial.dfs)
assert 1 == len(t1.dfs)
assert [] == t3.keys
def test_trial():
trial = Trial("abc", {"a": 1}, {"b": 2}, keys=["a", "b"])
assert "abc" == trial.trial_id
assert {"a": 1} == trial.params
assert {"b": 2} == trial.metadata
assert ["a", "b"] == trial.keys
t2 = trial.with_params({"c": 3})
assert "abc" == t2.trial_id
assert {"c": 3} == t2.params
assert {"b": 2} == t2.metadata
assert ["a", "b"] == t2.keys
def test_validator():
m = M()
for cont in [True, False]:
validate_iterative_objective(
F(),
Trial("abc", {"a": 1}),
[3, 3, 2],
lambda reports: [-3.0, -6.0, -8.0] ==
[x.sort_metric for x in reports],
continuous=cont,
monitor=m,
)
assert 6 == len(m._reports)
def test_trial_report():
trial = Trial("abc", {"a": Rand(3, 4)}, {"b": 2})
report = copy.copy(
TrialReport(
trial,
metric=np.float(0.1),
params={"c": Rand(1, 2)},
metadata={"d": 4},
cost=2.0,
))
assert trial is report.trial
assert 0.1 == report.metric
assert type(report.metric) == float
assert {"c": Rand(1, 2)} == report.params
assert {"d": 4} == report.metadata
assert 2.0 == report.cost
assert 0 == report.rung
assert 0.1 == report.sort_metric
report = copy.deepcopy(
TrialReport(trial,
metric=np.float(0.111),
cost=2.0,
rung=4,
sort_metric=1.23))
assert trial is report.trial
assert 0.111 == report.metric
assert type(report.metric) == float
assert {"a": Rand(3, 4)} == report.params
assert {} == report.metadata
assert 2.0 == report.cost
assert 4 == report.rung
r1 = report.generate_sort_metric(True, 2)
r2 = report.generate_sort_metric(False, 1)
r3 = report.with_sort_metric(0.234)
assert 1.23 == report.sort_metric
assert 0.11 == r1.sort_metric
assert -0.1 == r2.sort_metric
assert 0.234 == r3.sort_metric
report = TrialReport.from_jsondict(report.jsondict)
assert trial.trial_id == report.trial_id
assert 0.111 == report.metric
assert type(report.metric) == float
assert {"a": Rand(3, 4)} == report.params
assert {} == report.metadata
assert 2.0 == report.cost
assert 3.0 == report.with_cost(3.0).cost
assert 5 == report.with_rung(5).rung
def test_objective_func(tmpdir):
fs = FileSystem().opendir(str(tmpdir))
j = J([3, 3, 2])
f = F().copy()
t = Trial("abc", {"a": 1})
f.run(t, judge=j, checkpoint_basedir_fs=fs)
assert -10 == f.v
f.run(t, judge=j, checkpoint_basedir_fs=fs)
assert -10 == f.v
assert 6.0 == j.report.metric
assert -6.0 == j.report.sort_metric
f.run(t, judge=j, checkpoint_basedir_fs=fs)
assert -10 == f.v
assert 8.0 == j.report.metric
assert -8.0 == j.report.sort_metric
def _to_trail_row(data: Dict[str, Any], metadata: Dict[str,
Any]) -> Dict[str, Any]:
key_names = sorted(k for k in data.keys() if not k.startswith(TUNE_PREFIX))
keys = [data[k] for k in key_names]
trials: Dict[str, Dict[str, Any]] = {}
for param in pickle.loads(data[TUNE_DATASET_PARAMS_PREFIX]):
p = ParamDict(
sorted(((k, v) for k, v in param.items()), key=lambda x: x[0]))
tid = to_uuid(keys, p)
trials[tid] = Trial(trial_id=tid,
params=p,
metadata=metadata,
keys=keys).jsondict
data[TUNE_DATASET_TRIALS] = json.dumps(list(trials.values()))
del data[TUNE_DATASET_PARAMS_PREFIX]
return data
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_trial_decision():
trial = Trial("abc", {"a": 1}, {"b": Rand(0, 2)})
report = TrialReport(trial,
metric=np.float(0.1),
params={"c": Rand(0, 3)},
metadata={"d": 4})
decision = TrialDecision(report,
budget=0.0,
should_checkpoint=True,
metadata={"x": 1},
reason="p")
assert trial is decision.trial
assert report is decision.report
assert decision.should_stop
assert decision.should_checkpoint
assert {"x": 1} == decision.metadata
assert "p" == decision.reason
assert 0.0 == decision.budget
assert copy.copy(decision) is decision
assert copy.deepcopy(decision) is decision
d2 = TrialDecision.from_jsondict(decision.jsondict)
assert d2.trial_id == trial.trial_id
assert Rand(0, 3) == d2.report.params["c"]
assert decision.should_stop
assert decision.should_checkpoint
assert {"x": 1} == decision.metadata
assert "p" == decision.reason
decision = TrialDecision(report,
budget=1.0,
should_checkpoint=True,
metadata={"x": 1})
assert 1.0 == decision.budget
assert not decision.should_stop
def rp(tid, metric, rung=0, keys=[]):
t = Trial(tid, {}, keys=keys)
return TrialReport(t, metric=metric, rung=rung)
def test_to_trial_row():
data1 = {
"b":
2,
"a":
1,
TUNE_DATASET_DF_PREFIX + "x":
"x",
TUNE_DATASET_PARAMS_PREFIX:
pickle.dumps([{
"b": 10,
"a": 11
}, {
"a": 11,
"b": 10
}, {
"b": 100,
"a": 110
}], ),
}
res1 = _to_trail_row(data1, {"m": 1})
trials1 = [Trial(**p) for p in json.loads(res1[TUNE_DATASET_TRIALS])]
assert 2 == len(trials1)
data2 = {
"a":
1,
"b":
2,
TUNE_DATASET_DF_PREFIX + "y":
"x",
TUNE_DATASET_PARAMS_PREFIX:
pickle.dumps([{
"b": 10,
"a": 11
}, {
"b": 100,
"a": 110
}], ),
}
res2 = _to_trail_row(data2, {"m": 1})
assert TUNE_DATASET_PARAMS_PREFIX not in res2
trials2 = [Trial(**p) for p in json.loads(res2[TUNE_DATASET_TRIALS])]
assert 2 == len(trials2)
assert any(trials2[0].trial_id == x.trial_id for x in trials1)
assert any(trials2[1].trial_id == x.trial_id for x in trials1)
data3 = {
"a":
10,
"b":
2,
TUNE_DATASET_DF_PREFIX + "y":
"x",
TUNE_DATASET_PARAMS_PREFIX:
pickle.dumps([{
"b": 10,
"a": 11
}, {
"b": 100,
"a": 110
}], ),
}
res3 = _to_trail_row(data3, {"m": 1})
trials3 = [Trial(**p) for p in json.loads(res3[TUNE_DATASET_TRIALS])]
assert 2 == len(trials2)
assert not any(trials3[0].trial_id == x.trial_id for x in trials1)
assert not any(trials3[1].trial_id == x.trial_id for x in trials1)