def test_refit_shuffle_on_fail(backend, dask_client):
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
failing_model.fit_transformer.side_effect = [
ValueError(), ValueError(), (None, {})]
failing_model.get_max_iter.return_value = 100
auto = AutoML(backend, 30, 5, dask_client=dask_client)
ensemble_mock = unittest.mock.Mock()
ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
auto.ensemble_ = ensemble_mock
auto.InputValidator = InputValidator()
for budget_type in [None, 'iterations']:
auto._budget_type = budget_type
auto.models_ = {(1, 1, 50.0): failing_model}
# Make sure a valid 2D array is given to automl
X = np.array([1, 2, 3]).reshape(-1, 1)
y = np.array([1, 2, 3])
auto.InputValidator.fit(X, y)
auto.refit(X, y)
assert failing_model.fit.call_count == 3
assert failing_model.fit_transformer.call_count == 3
del auto
def test_subsample_classification_unique_labels_stay_in_training_set(task, y):
n_samples = 10000
X = np.random.random(size=(n_samples, 3))
memory_limit = 1 # Force subsampling
mock = unittest.mock.Mock()
# Make sure our test assumptions are correct
assert len(y) == n_samples, "Ensure tests are correctly setup"
values, counts = np.unique(y, axis=0, return_counts=True)
unique_labels = [
value for value, count in zip(values, counts) if count == 1
]
assert len(unique_labels), "Ensure we have unique labels in the test"
_, y_sampled = AutoML.subsample_if_too_large(X,
y,
logger=mock,
seed=1,
memory_limit=memory_limit,
task=task)
assert len(y_sampled) <= len(y), \
"Ensure sampling took place"
assert all(label in y_sampled for label in unique_labels), \
"All unique labels present in the return sampled set"
def test_subsample_if_too_large(memory_limit, task):
fixture = {
BINARY_CLASSIFICATION: {
1: 436,
10: 569,
None: 569
},
MULTICLASS_CLASSIFICATION: {
1: 204,
10: 1797,
None: 1797
},
MULTILABEL_CLASSIFICATION: {
1: 204,
10: 1797,
None: 1797
},
REGRESSION: {
1: 1310,
10: 1326,
None: 1326
},
MULTIOUTPUT_REGRESSION: {
1: 1310,
10: 1326,
None: 1326
}
}
mock = unittest.mock.Mock()
if task == BINARY_CLASSIFICATION:
X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
elif task == MULTICLASS_CLASSIFICATION:
X, y = sklearn.datasets.load_digits(return_X_y=True)
elif task == MULTILABEL_CLASSIFICATION:
X, y_ = sklearn.datasets.load_digits(return_X_y=True)
y = np.zeros((X.shape[0], 10))
for i, j in enumerate(y_):
y[i, j] = 1
elif task == REGRESSION:
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
X = np.vstack((X, X, X))
y = np.vstack((y.reshape((-1, 1)), y.reshape(
(-1, 1)), y.reshape((-1, 1))))
elif task == MULTIOUTPUT_REGRESSION:
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
y = np.vstack((y, y)).transpose()
X = np.vstack((X, X, X))
y = np.vstack((y, y, y))
else:
raise ValueError(task)
assert X.shape[0] == y.shape[0]
X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit,
task)
assert X_new.shape[0] == fixture[task][memory_limit]
if memory_limit == 1:
assert mock.warning.call_count == 1
else:
assert mock.warning.call_count == 0
def test_subsample_if_too_large(memory_limit, precision, task):
fixture = {
BINARY_CLASSIFICATION: {
1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655},
100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000},
None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000},
},
MULTICLASS_CLASSIFICATION: {
1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102},
100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
},
MULTILABEL_CLASSIFICATION: {
1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102},
100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
},
REGRESSION: {
1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327},
100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
},
MULTIOUTPUT_REGRESSION: {
1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327},
100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
}
}
mock = unittest.mock.Mock()
if task == BINARY_CLASSIFICATION:
X, y = sklearn.datasets.make_hastie_10_2()
elif task == MULTICLASS_CLASSIFICATION:
X, y = sklearn.datasets.load_digits(return_X_y=True)
elif task == MULTILABEL_CLASSIFICATION:
X, y_ = sklearn.datasets.load_digits(return_X_y=True)
y = np.zeros((X.shape[0], 10))
for i, j in enumerate(y_):
y[i, j] = 1
elif task == REGRESSION:
X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20)
elif task == MULTIOUTPUT_REGRESSION:
X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20)
y = np.vstack((y, y)).transpose()
else:
raise ValueError(task)
X = X.astype(precision)
assert X.shape[0] == y.shape[0]
X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task)
assert X_new.shape[0] == fixture[task][memory_limit][precision]
if memory_limit == 1:
if precision in (np.float128, np.float64, float):
assert mock.warning.call_count == 2
else:
assert mock.warning.call_count == 1
else:
assert mock.warning.call_count == 0
def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
failing_model.fit_transformer.side_effect = [
ValueError(), ValueError(), (None, {})]
failing_model.get_max_iter.return_value = 100
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
auto.ensemble_ = ensemble_mock
for budget_type in [None, 'iterations']:
auto._budget_type = budget_type
auto.models_ = {(1, 1, 50.0): failing_model}
# Make sure a valid 2D array is given to automl
X = np.array([1, 2, 3]).reshape(-1, 1)
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
self.assertEqual(failing_model.fit_transformer.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_smbo_metalearning_configurations(backend, context, dask_client):
# Get the inputs to the optimizer
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
config_space = AutoML(backend=backend,
metric=autosklearn.metrics.accuracy,
time_left_for_this_task=20,
per_run_time_limit=5).fit(
X_train,
Y_train,
task=BINARY_CLASSIFICATION,
only_return_configuration_space=True)
watcher = StopWatch()
# Create an optimizer
smbo = AutoMLSMBO(
config_space=config_space,
dataset_name='iris',
backend=backend,
total_walltime_limit=10,
func_eval_time_limit=5,
memory_limit=4096,
metric=autosklearn.metrics.accuracy,
watcher=watcher,
n_jobs=1,
dask_client=dask_client,
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
start_num_run=1,
data_memory_limit=None,
num_metalearning_cfgs=25,
pynisher_context=context,
)
assert smbo.pynisher_context == context
# Create the inputs to metalearning
datamanager = XYDataManager(
X_train,
Y_train,
X_test,
Y_test,
task=BINARY_CLASSIFICATION,
dataset_name='iris',
feat_type={i: 'numerical'
for i in range(X_train.shape[1])},
)
backend.save_datamanager(datamanager)
smbo.task = BINARY_CLASSIFICATION
smbo.reset_data_manager()
metalearning_configurations = smbo.get_metalearning_suggestions()
# We should have 25 metalearning configurations
assert len(metalearning_configurations) == 25
assert [
isinstance(config, Configuration)
for config in metalearning_configurations
]
def test_refit_shuffle_on_fail(self):
output = os.path.join(self.test_dir, '..', '.tmp_refit_shuffle_on_fail')
context = BackendContext(output, output, False, False)
backend = Backend(context)
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend, 30, 30)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_selected_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_refit_shuffle_on_fail(self):
output = os.path.join(self.test_dir, '..', '.tmp_refit_shuffle_on_fail')
context = BackendContext(output, output, False, False)
backend = Backend(context)
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend, 20, 5)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_selected_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
