def test_read_and_write_pcs(self):
configuration_space_path = os.path.abspath(HPOlibConfigSpace.__file__)
configuration_space_path = os.path.dirname(configuration_space_path)
configuration_space_path = os.path.join(configuration_space_path,
"..", "test",
"test_searchspaces",
"mini_autosklearn_original.pcs")
with open(configuration_space_path) as fh:
cs = pcs_parser.read(fh)
pcs = pcs_parser.write(cs)
with open(configuration_space_path) as fh:
lines = fh.readlines()
num_asserts = 0
for line in lines:
line = line.replace("\n", "")
line = line.split("#")[0] # Remove comments
line = line.strip()
if line:
num_asserts += 1
self.assertIn(line, pcs)
self.assertEqual(21, num_asserts)
# Sample a little bit
rs = RandomSampler(cs, 1)
print cs
for i in range(1000):
c = rs.sample_configuration()
def test_read_and_write_pcs(self):
configuration_space_path = os.path.abspath(HPOlibConfigSpace.__file__)
configuration_space_path = os.path.dirname(configuration_space_path)
configuration_space_path = os.path.join(
configuration_space_path, "..", "test", "test_searchspaces",
"mini_autosklearn_original.pcs")
with open(configuration_space_path) as fh:
cs = pcs_parser.read(fh)
pcs = pcs_parser.write(cs)
with open(configuration_space_path) as fh:
lines = fh.readlines()
num_asserts = 0
for line in lines:
line = line.replace("\n", "")
line = line.split("#")[0] # Remove comments
line = line.strip()
if line:
num_asserts += 1
self.assertIn(line, pcs)
self.assertEqual(21, num_asserts)
# Sample a little bit
rs = RandomSampler(cs, 1)
print cs
for i in range(1000):
c = rs.sample_configuration()
from ParamSklearn.classification import ParamSklearnClassifier
from HPOlibConfigSpace.random_sampler import RandomSampler
import sklearn.datasets
import sklearn.metrics
import numpy as np
iris = sklearn.datasets.load_iris()
X = iris.data
Y = iris.target
indices = np.arange(X.shape[0])
np.random.shuffle(indices)
configuration_space = ParamSklearnClassifier.get_hyperparameter_search_space()
sampler = RandomSampler(configuration_space, 1)
for i in range(10000):
configuration = sampler.sample_configuration()
auto = ParamSklearnClassifier(configuration)
try:
auto = auto.fit(X[indices[:100]], Y[indices[:100]])
except Exception as e:
print configuration
print e
continue
predictions = auto.predict(X[indices[100:]])
print sklearn.metrics.accuracy_score(predictions, Y[indices[100:]])
