def make_mode_test(data, seed, configuration, metric):
global evaluator
evaluator = TestEvaluator(data, configuration, seed=seed, all_scoring_functions=True, with_predictions=True)
evaluator.fit()
signal.signal(15, empty_signal_handler)
scores, _, _, _ = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ";".join(["%s: %s" % (m_, value) for m_, value in scores.items()])
additional_run_info += ";" + "duration: " + str(duration)
print(
"Result for ParamILS: %s, %f, 1, %f, %d, %s"
% ("SAT", abs(duration), score, evaluator.seed, additional_run_info)
)
def test_datasets(self):
for getter in get_dataset_getters():
testname = '%s_%s' % (os.path.basename(__file__).
replace('.pyc', '').replace('.py', ''),
getter.__name__)
with self.subTest(testname):
D = getter()
output_directory = os.path.join(os.path.dirname(__file__),
'.%s' % testname)
self.output_directories.append(output_directory)
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
D_ = copy.deepcopy(D)
evaluator = TestEvaluator(D_, output_directory, None)
err[i] = evaluator.fit_predict_and_loss()[0]
self.assertTrue(np.isfinite(err[i]))
def make_mode_test(data, seed, configuration, metric):
global evaluator
evaluator = TestEvaluator(data,
configuration,
seed=seed,
all_scoring_functions=True,
with_predictions=True)
evaluator.fit()
signal.signal(15, empty_signal_handler)
scores, _, _, _ = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ';'.join(
['%s: %s' % (m_, value) for m_, value in scores.items()])
additional_run_info += ';' + 'duration: ' + str(duration)
print('Result for ParamILS: %s, %f, 1, %f, %d, %s' %
('SAT', abs(duration), score, evaluator.seed, additional_run_info))
def test_datasets(self):
for getter in get_dataset_getters():
testname = '%s_%s' % (os.path.basename(__file__).replace(
'.pyc', '').replace('.py', ''), getter.__name__)
with self.subTest(testname):
backend_mock = unittest.mock.Mock(spec=Backend)
backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau'
D = getter()
D_ = copy.deepcopy(D)
y = D.data['Y_train']
if len(y.shape) == 2 and y.shape[1] == 1:
D_.data['Y_train'] = y.flatten()
queue_ = multiprocessing.Queue()
evaluator = TestEvaluator(D_, backend_mock, queue_)
evaluator.fit_predict_and_loss()
duration, result, seed, run_info, status = evaluator.queue.get(
timeout=1)
self.assertTrue(np.isfinite(result))
