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:]])