def test_float(self):
self.pc["parameter_config"] = [
{"name": "x1", "type": "float", "range": [0, 1], "interval":0.2},
{"name": "x2", "type": "float", "range": [0, 1]}
]
rp = get_proposer("sequence")(self.pc)
self.assertEqual(rp.nSamples, 12)
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 0, "x2": 0})
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 0.2, "x2": 0})
for i in range(4):
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 1, "x2": 0})
while not rp.finished:
param1 = rp.get()
self.assertTrue(param1 == {"x1": 1, "x2": 1})
self.pc["parameter_config"] = [
{"name": "x1", "type": "float", "range": [0, 1], "interval": 0.2},
{"name": "x2", "type": "float", "range": [0, 1], "n": 2}
]
rp = get_proposer("sequence")(self.pc)
self.assertEqual(rp.nSamples, 12)
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 0, "x2": 0})
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 0.2, "x2": 0})
for i in range(4):
param1 = rp.get()
self.assertDictEqual(param1, {"x1": 1, "x2": 0})
while not rp.finished:
param1 = rp.get()
self.assertTrue(param1 == {"x1": 1, "x2": 1})
def test_int_interval(self):
self.pc["parameter_config"] = [
{
"name": "x1",
"type": "int",
"range": [0, 10],
"interval": 2
}, # 0 2 4 6 8 10
{
"name": "x2",
"type": "int",
"range": [0, 10],
"n": 3
} # 0 5 10
]
rp = get_proposer("sequence")(self.pc)
self.assertEqual(rp.nSamples, 18)
param1 = rp.get()
self.assertTrue(param1 == {"x1": 0, "x2": 0})
param1 = rp.get()
self.assertTrue(param1 == {"x1": 2, "x2": 0})
for i in range(10):
param1 = rp.get()
self.assertTrue(param1 == {"x1": 10, "x2": 5})
while not rp.finished:
param1 = rp.get()
self.assertTrue(param1 == {"x1": 10, "x2": 10})
def setup_config(): # pragma: no cover
config = dict()
config['max_iter'] = int(input("max iteration [81]:") or 81)
config['eta'] = int(input("ita [3]") or 3)
config["skip_last"] = int(input("skip last [0]") or '0')
config["engine"] = get_from_options("Hyperparameter sampling engine", ["random", "sequence"])
config.update(get_proposer(config['engine']).setup_config())
return config
def test_failed(self):
self.pc["parameter_config"] = [
{"name": "x1", "type": "choice", "range": [2, 4, 6, 8, 10]},
{"name": "x2", "type": "choice", "range": ["a", "b", "c"]}
]
p = get_proposer("sequence")(self.pc)
c = p.get()
job = Job("none", c)
p.failed(job)
def test_choice(self):
self.pc["parameter_config"] = [
{"name": "x1", "type": "choice", "range": [2, 4, 6, 8, 10]},
{"name": "x2", "type": "choice", "range": ["a", "b", "c"]}
]
rp = get_proposer("sequence")(self.pc)
self.assertEqual(rp.nSamples, 15)
param1 = rp.get()
self.assertTrue(param1 == {"x1": 2, "x2": "a"})
while not rp.finished:
param1 = rp.get()
self.assertTrue(param1 == {"x1": 10, "x2": "c"})
def test_int(self):
self.pc["parameter_config"] = [
{"name": "x1", "type": "int", "range": [0, 10]},
{"name": "x2", "type": "int", "range": [0, 10]}
]
rp = get_proposer("sequence")(self.pc)
self.assertEqual(rp.nSamples, 121)
param1 = rp.get()
self.assertTrue(param1 == {"x1": 0, "x2": 0})
param1 = rp.get()
self.assertTrue(param1 == {"x1": 1, "x2": 0})
while not rp.finished:
param1 = rp.get()
self.assertTrue(param1 == {"x1": 10, "x2": 10})
def setup(self, s):
# Follow the hyperband paper, set up internal variables based on s_max
self.s = s
self.n = int(ceil(self.B / self.max_iter / (s + 1) * self.eta ** s))
self.r = self.max_iter * self.eta ** (-s)
gen_config = self.config.copy()
gen_config["n_samples"] = self.n
gen_config["random_seed"] = random.randint(0, 100)
gen_config["proposer"] = gen_config["engine"]
proposer = get_proposer(gen_config['proposer'])(gen_config)
self.test_set = [proposer.get for _ in range(self.n)]
self.t = 0
self.i = 0
self.n_configs = self.n * self.eta ** (-self.i)
self.n_iterations = self.r * self.eta ** self.i
