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 = CVEvaluator(D_, output_directory, None)
err[i] = evaluator.fit_predict_and_loss()[0]
self.assertTrue(np.isfinite(err[i]))
self.assertEqual(err[i], 1.0)
for model_idx in range(10):
indices = evaluator.indices[model_idx]
self.assertIsNotNone(indices)
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, output_directory, None)
for j in range(5):
evaluator.partial_fit_predict_and_loss(j)
indices = evaluator.indices[j]
self.assertIsNotNone(indices)
for j in range(5, 10):
indices = evaluator.indices[j]
self.assertIsNone(indices)
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 = CVEvaluator(D_, output_directory, None)
err[i] = evaluator.fit_predict_and_loss()[0]
self.assertTrue(np.isfinite(err[i]))
self.assertEqual(err[i], 1.0)
for model_idx in range(10):
indices = evaluator.indices[model_idx]
self.assertIsNotNone(indices)
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, output_directory, None)
for j in range(5):
evaluator.partial_fit_predict_and_loss(j)
indices = evaluator.indices[j]
self.assertIsNotNone(indices)
for j in range(5, 10):
indices = evaluator.indices[j]
self.assertIsNone(indices)
def make_mode_cv(data, seed, configuration, num_run, folds, output_dir):
global evaluator
evaluator = CVEvaluator(data, output_dir, configuration,
cv_folds=folds,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
loss, opt_pred, valid_pred, test_pred = evaluator.fit_predict_and_loss()
evaluator.finish_up(loss, opt_pred, valid_pred, test_pred)
def make_mode_cv(data, seed, configuration, num_run, folds, output_dir):
global evaluator
evaluator = CVEvaluator(data,
output_dir,
configuration,
cv_folds=folds,
seed=seed,
num_run=num_run,
all_scoring_functions=False,
**_get_base_dict())
loss, opt_pred, valid_pred, test_pred = evaluator.fit_predict_and_loss()
evaluator.finish_up(loss, opt_pred, valid_pred, test_pred)
