def test_is_valid(self):
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2", "x2__p3"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1": Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2": Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice",
"int"),
"x2__p1": Parameter("x2__p1", ["a", "b", "c", "d"], "choice",
"string"),
"x2__p2": Parameter("x2__p2", [10, 11, 12], "choice", "int"),
"x2__p3": Parameter("x2__p3", "lol", "constant", "string"),
}
rules = [
ChildRule(applied_to=["x2__p2"], parent="x2__p1", value=["a"])
]
space = Space(scenario=start, sampler=sampler, rules=rules)
assert (space.next_params(history=[("Model",
None), ("x2", None), ("x2__p1",
"a")])[0]
in ["x2__p2", "x2__p3"])
assert (space.has_finite_child(
history=[("Model", None), ("x2", None), ("x2__p1", "b")])[1] == 0)
assert (space.has_finite_child(
history=[("Model", None), ("x2", None), ("x2__p1", "a")])[1] > 0)
def test_playout(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice", "int"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c", "d"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "func"),
}
space = Space(scenario=start, sampler=sampler)
for i in range(10):
space.playout(history=[("Model", None)])
def test_next_params(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2", "x2__p3"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice", "int"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c", "d"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "func"),
"x2__p3":
Parameter("x2__p3", "lol", "constant", "string"),
}
space = Space(scenario=start, sampler=sampler)
for i in range(50):
assert (space.next_params(
history=[("Model",
None), ("x2",
None), ("x2__p1",
"c"), ("x2__p2",
a_func)]) == ("x2__p3", "lol",
True))
assert (space.next_params(
history=[("Model", None), ("x2", None), ("x2__p1",
"c")])[0] == "x2__p2")
assert (space.next_params(
history=[("Model", None), ("x2", None), ("x2__p1",
"a")])[0] == "x2__p2")
assert (space.next_params(history=[("Model",
None), ("x2",
None)])[0] == "x2__p1")
assert (space.next_params(history=[("Model", None), ("x1",
None)])[0]
in ["x1__p1", "x1__p2"])
assert (space.next_params(history=[("Model",
None), ("x1",
None), ("x1__p2",
5)])[0]
in ["x1__p1", "x1__p2"])
assert (space.next_params(history=[("Model", None)])[0]
in ["x1", "x2"])
def test_has_finite_child(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice", "int"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c", "d"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "func"),
}
space = Space(scenario=start, sampler=sampler)
assert (space.has_finite_child(history=[("Model", None)]) == (False,
2))
assert (space.has_finite_child(history=[("Model",
None), ("x1",
None)]) == (False,
17))
assert (space.has_finite_child(
history=[("Model", None), ("x1", None), ("x1__p1",
0.5)]) == (False, 7))
assert (space.has_finite_child(
history=[("Model", None), ("x1", None), ("x1__p1",
0.5), ("x1__p2",
1)]) == (False, 0))
assert (space.has_finite_child(history=[("Model",
None), ("x2",
None)]) == (False, 4))
assert (space.has_finite_child(
history=[("Model", None), ("x2", None), ("x2__p1",
"a")]) == (False, 3))
assert (space.has_finite_child(
history=[("Model", None), ("x2",
None), ("x2__p1",
"c"), ("x2__p2",
c_func)]) == (False, 0))
def test_value_rules(self):
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1": Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2": Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice",
"int"),
"x2__p1": Parameter("x2__p1", ["a", "b", "c", "d"], "choice",
"string"),
"x2__p2": Parameter("x2__p2", [10, 11, 12], "choice", "int")
}
rules = [
ChildRule(applied_to=["x2__p2"], parent="x2__p1", value=["a"]),
ValueRule(constraints=[("x1__p1", 0.5), ("x1__p2", 7)]),
ValueRule(constraints=[("x1__p1", 0.9), ("x1__p2", 6)])
]
space = Space(scenario=start, sampler=sampler, rules=rules)
assert (space.next_params(
history=[("Model", None), ("x2", None), ("x2__p1",
"a")])[0] == "x2__p2")
assert (space.has_finite_child(
history=[("Model", None), ("x2", None), ("x2__p1", "b")])[1] == 0)
assert (space.has_finite_child(
history=[("Model", None), ("x2", None), ("x2__p1", "a")])[1] > 0)
for i in range(10):
assert (space.next_params(
history=[("Model", None), ("x1", None), ("x1__p1", 0.3)]) !=
("x1__p2", 7, True))
assert (space.next_params(
history=[("Model", None), ("x1", None), ("x1__p1",
0.5)]) == ("x1__p2",
7, True))
assert (space.next_params(
history=[("Model", None), ("x1", None), ("x1__p1", 0.8)]) !=
("x1__p2", 7, True))
assert (space.next_params(
history=[("Model", None), ("x1", None), ("x1__p1", 0.9)]) !=
("x1__p2", 7, True))
assert (space.next_params(
history=[("Model", None), ("x1", None), ("x1__p1",
0.9)]) == ("x1__p2",
6, True))
def test_preprocess_moves(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2"])
start = WorkflowChoiceScenario(name="Model", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice", "int"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c", "d"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "func"),
}
space = Space(scenario=start, sampler=sampler)
ex_config = space.playout(history=[("Model", None)])
env = Env(a_func, scenario=start, sampler=sampler)
for i in range(10):
configs = env.preprocess_moves(ex_config)
for model, param in configs:
assert (model in ["Model", "x1", "x2"])
for p, v in param.items():
if model == "x1":
if p == "p1":
assert ((v >= 0) and (v <= 1))
else:
assert (v in [1, 2, 3, 4, 5, 6, 7])
else:
if p == "p1":
assert (v in ["a", "b", "c", "d"])
else:
assert (v in [a_func, b_func, c_func])
def get_configuration_DummyClassifier():
DummyClassifier = WorkflowListTask(is_ordered=False,
name="DummyClassifier",
tasks=["DummyClassifier__strategy"])
sampler = {
"DummyClassifier__strategy":
Parameter("DummyClassifier__strategy",
["stratified", "most_frequent", "prior", "uniform"],
"choice", "string"),
}
rules = []
return DummyClassifier, sampler, rules
def init_mcts(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2"])
x2 = WorkflowListTask(is_ordered=True,
name="x2",
tasks=["x2__p1", "x2__p2"])
start = WorkflowChoiceScenario(name="root", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2], "choice", "int"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "int")
}
env = Env(a_func, scenario=start, sampler=sampler)
def evaluate(config, bestconfig):
return random.uniform(0, 1)
Env.evaluate = evaluate
return MCTS(env=env)
def test_everything_with_rules(self):
def a_func():
return 0
def b_func():
return 0
def c_func():
return 0
x1 = WorkflowListTask(is_ordered=False,
name="x1",
tasks=["x1__p1", "x1__p2", "x1__p3"])
x2 = WorkflowListTask(is_ordered=False,
name="x2",
tasks=["x2__p1", "x2__p2", "x2__p3"])
start = WorkflowChoiceScenario(name="root", scenarios=[x1, x2])
sampler = {
"x1__p1":
Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2":
Parameter("x1__p2", [1, 2], "choice", "int"),
"x1__p3":
Parameter("x1__p3", ["v", "w", "x", "y", "z"], "choice", "string"),
"x2__p1":
Parameter("x2__p1", ["a", "b", "c"], "choice", "string"),
"x2__p2":
Parameter("x2__p2", [a_func, b_func, c_func], "choice", "int"),
"x2__p3":
Parameter("x2__p3", "lol", "constant", "string"),
}
rules = [
ChildRule(applied_to=["x2__p2"], parent="x2__p1", value=["a",
"c"]),
ValueRule(constraints=[("x1__p2", 1), ("x1__p3", "v")]),
ValueRule(constraints=[("x1__p2", 2), ("x1__p3", "w")])
]
def evaluate(config, bestconfig):
return random.uniform(0, 1)
env = Env(evaluate, scenario=start, sampler=sampler, rules=rules)
mcts = MCTS(env=env)
mcts.run(n=100)