def test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = RandomTreesEmbedding(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = RandomTreesEmbedding(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
def test_predict_proba_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
# Multiclass
cls = ParamSklearnClassifier(default)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
# The object behind the last step in the pipeline
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertEqual((1647, 10), prediction.shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
cls = ParamSklearnClassifier(default)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertIsInstance(prediction, list)
self.assertEqual(2, len(prediction))
self.assertEqual((1647, 10), prediction[0].shape)
self.assertEqual((1647, 10), prediction[1].shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
cls = ParamSklearnClassifier(default)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, ), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
cls = ParamSklearnClassifier(default)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647,), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_proba_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
# Multiclass
cls = ParamSklearnClassifier(default)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
# The object behind the last step in the pipeline
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertEqual((1647, 10), prediction.shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
cls = ParamSklearnClassifier(default)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertIsInstance(prediction, list)
self.assertEqual(2, len(prediction))
self.assertEqual((1647, 10), prediction[0].shape)
self.assertEqual((1647, 10), prediction[1].shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = RandomTreesEmbedding(random_state=1,
**{hp_name: default[hp_name] for
hp_name in
default})
preprocessor.fit(X_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = RandomTreesEmbedding.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = RandomTreesEmbedding(random_state=1,
**{hp_name: default[hp_name] for
hp_name in
default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
def _test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = Nystroem.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Nystroem(random_state=1,
**{hp.hyperparameter.name: hp.value
for hp
in
default.values.values()})
preprocessor.fit(X_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = Nystroem.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Nystroem(random_state=1,
**{hp.hyperparameter.name: hp.value
for hp
in
default.values.values()})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
# Sparse
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
self.assertEqual(X_train.dtype, np.float32)
configuration_space = Nystroem.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Nystroem(random_state=1,
**{hp.hyperparameter.name: hp.value
for hp
in
default.values.values()})
preprocessor.fit(X_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
X_train = X_train.astype(np.float64)
configuration_space = Nystroem.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Nystroem(random_state=1,
**{hp.hyperparameter.name: hp.value
for hp
in
default.values.values()})
preprocessor.fit(X_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
def test_predict_proba_batched_sparse(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
config = Configuration(
cs,
values={
"balancing:strategy": "none",
"classifier:__choice__": "random_forest",
"imputation:strategy": "mean",
"one_hot_encoding:minimum_fraction": 0.01,
"one_hot_encoding:use_minimum_fraction": 'True',
"preprocessor:__choice__": "no_preprocessing",
'classifier:random_forest:bootstrap': 'True',
'classifier:random_forest:criterion': 'gini',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:min_samples_split': 2,
'classifier:random_forest:min_samples_leaf': 2,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
'classifier:random_forest:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
"rescaling:__choice__": "min/max"
})
# Multiclass
cls = ParamSklearnClassifier(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
# The object behind the last step in the pipeline
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertEqual((1647, 10), prediction.shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
cls = ParamSklearnClassifier(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertIsInstance(prediction, list)
self.assertEqual(2, len(prediction))
self.assertEqual((1647, 10), prediction[0].shape)
self.assertEqual((1647, 10), prediction[1].shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_proba_batched_sparse(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
config = Configuration(cs,
values={"balancing:strategy": "none",
"classifier:__choice__": "random_forest",
"imputation:strategy": "mean",
"one_hot_encoding:minimum_fraction": 0.01,
"one_hot_encoding:use_minimum_fraction": 'True',
"preprocessor:__choice__": "no_preprocessing",
'classifier:random_forest:bootstrap': 'True',
'classifier:random_forest:criterion': 'gini',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:min_samples_split': 2,
'classifier:random_forest:min_samples_leaf': 2,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
'classifier:random_forest:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
"rescaling:__choice__": "min/max"})
# Multiclass
cls = ParamSklearnClassifier(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
# The object behind the last step in the pipeline
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertEqual((1647, 10), prediction.shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
cls = ParamSklearnClassifier(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict_proba(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
cls.pipeline_.steps[-1] = ("estimator", cls_predict)
prediction = cls.predict_proba(X_test, batch_size=20)
self.assertIsInstance(prediction, list)
self.assertEqual(2, len(prediction))
self.assertEqual((1647, 10), prediction[0].shape)
self.assertEqual((1647, 10), prediction[1].shape)
self.assertEqual(84, cls_predict.predict_proba.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
# Sparse
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
X_train = X_train.astype(np.float64)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
def setUp(self):
self.X_train, self.Y_train, self.X_test, self.Y_test = \
get_dataset('iris')
eliminate_class_two = self.Y_train != 2
self.X_train = self.X_train[eliminate_class_two]
self.Y_train = self.Y_train[eliminate_class_two]
def test_default_configuration(self):
transformation, original = _test_preprocessing(
SelectPercentileClassification)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(
SelectPercentileClassification, make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(
random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default if default[hp_name] is not None
})
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(
X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
def test_fit(self):
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(self.output, self.output, 10, 10)
automl.fit(X_train, Y_train)
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.9)
self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
def test_fit(self):
X_train, Y_train, X_test, Y_test = putil.get_dataset("iris")
automl = autosklearn.automl.AutoML(self.output, self.output, 10, 10)
automl.fit(X_train, Y_train)
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.9)
self.assertEqual(automl.task_, MULTICLASS_CLASSIFICATION)
def test_default_configuration(self):
transformation, original = _test_preprocessing(Nystroem)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 100)
self.assertFalse((transformation == 0).all())
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = Nystroem.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Nystroem(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(
X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 100)
def test_fit_pSMAC(self):
output = os.path.join(self.test_dir, '..', '.tmp_estimator_fit_pSMAC')
self._setUp(output)
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = AutoSklearnClassifier(time_left_for_this_task=15,
per_run_time_limit=15,
output_folder=output,
tmp_folder=output,
shared_mode=True,
seed=1,
initial_configurations_via_metalearning=0,
ensemble_size=0)
automl.fit(X_train, Y_train)
# Create a 'dummy model' for the first run, which has an accuracy of
# more than 99%; it should be in the final ensemble if the ensemble
# building of the second AutoSklearn classifier works correct
true_targets_ensemble_path = os.path.join(output, '.auto-sklearn',
'true_targets_ensemble.npy')
true_targets_ensemble = np.load(true_targets_ensemble_path)
true_targets_ensemble[-1] = 1 if true_targets_ensemble[-1] != 1 else 0
probas = np.zeros((len(true_targets_ensemble), 3), dtype=float)
for i, value in enumerate(true_targets_ensemble):
probas[i, value] = 1.0
dummy_predictions_path = os.path.join(output, '.auto-sklearn',
'predictions_ensemble',
'predictions_ensemble_1_00030.npy')
with open(dummy_predictions_path, 'wb') as fh:
np.save(fh, probas)
probas_test = np.zeros((len(Y_test), 3), dtype=float)
for i, value in enumerate(Y_test):
probas_test[i, value] = 1.0
dummy = ArrayReturningDummyPredictor(probas_test)
backend = Backend(output, output)
backend.save_model(dummy, 30, 1)
automl = AutoSklearnClassifier(time_left_for_this_task=15,
per_run_time_limit=15,
output_folder=output,
tmp_folder=output,
shared_mode=True,
seed=2,
initial_configurations_via_metalearning=0,
ensemble_size=0)
automl.fit(X_train, Y_train)
automl.run_ensemble_builder(0, 1, 50).wait()
score = automl.score(X_test, Y_test)
self.assertEqual(len(os.listdir(os.path.join(output, '.auto-sklearn',
'ensemble_indices'))), 1)
self.assertGreaterEqual(score, 0.90)
self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
del automl
self._tearDown(output)
def test_default_configuration(self):
transformation, original = _test_preprocessing(SelectPercentileClassification)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1]/2))
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(SelectPercentileClassification, make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1]/2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = SelectPercentileClassification.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectPercentileClassification(random_state=1,
**{hp_name: default[hp_name] for hp_name in
default if default[hp_name] is not None})
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
def test_evaluate_multiclass_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25,]
Y_valid = Y_test[:25,]
X_test = X_test[25:,]
Y_test = Y_test[25:,]
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False, 'target_num': 3}
D.data = {'X_train': X_train, 'Y_train': Y_train,
'X_valid': X_valid, 'X_test': X_test}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(D.info,
include_estimators = ['ridge'],
include_preprocessors = ['select_rates'])
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print "Evaluate configuration: %d; result:" % i,
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
print
continue
err[i] = evaluator.predict()
print err[i]
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
print "Number of times it was worse than random guessing:" + str(np.sum(err > 1))
def test_default_configuration(self):
configuration_space = RidgeRegression.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
configuration_space_preproc = RandomKitchenSinks.get_hyperparameter_search_space()
default_preproc = configuration_space_preproc.get_default_configuration()
for i in range(10):
# This should be a bad results
predictions, targets = _test_regressor(RidgeRegression,)
self.assertAlmostEqual(0.32614416980439365,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
# This should be much more better
X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes',
make_sparse=False)
preprocessor = RandomKitchenSinks(
random_state=1,
**{hp_name: default_preproc[hp_name] for hp_name in
default_preproc if default_preproc[hp_name] is not None})
transformer = preprocessor.fit(X_train, Y_train)
X_train_transformed = transformer.transform(X_train)
X_test_transformed = transformer.transform(X_test)
regressor = RidgeRegression(
random_state=1,
**{hp_name: default[hp_name] for hp_name in
default if default[hp_name] is not None})
predictor = regressor.fit(X_train_transformed, Y_train)
predictions = predictor.predict(X_test_transformed)
self.assertAlmostEqual(0.37183512452087852,
sklearn.metrics.r2_score(y_true=Y_test, y_pred=predictions))
def test_evaluate_multiclass_classification_all_metrics(self):
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_valid = X_test[:25,]
Y_valid = Y_test[:25,]
X_test = X_test[25:,]
Y_test = Y_test[25:,]
D = Dummy()
D.info = {"metric": BAC_METRIC, "task": MULTICLASS_CLASSIFICATION, "is_sparse": False, "label_num": 3}
D.data = {"X_train": X_train, "Y_train": Y_train, "X_valid": X_valid, "X_test": X_test}
D.feat_type = ["numerical", "Numerical", "numerical", "numerical"]
configuration_space = get_configuration_space(D.info, include_estimators=["lda"], include_preprocessors=["pca"])
# Test all scoring functions
err = []
for i in range(N_TEST_RUNS):
print("Evaluate configuration: %d; result:" % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration, all_scoring_functions=True)
if not self._fit(evaluator):
continue
err.append(evaluator.predict())
print(err[-1])
self.assertIsInstance(err[-1], dict)
for key in err[-1]:
self.assertEqual(len(err[-1]), 5)
self.assertTrue(np.isfinite(err[-1][key]))
self.assertGreaterEqual(err[-1][key], 0.0)
print("Number of times it was worse than random guessing:" + str(np.sum(err > 1)))
def test_configurations_signed_data(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'signed': True})
print(cs)
for i in range(10):
config = cs.sample_configuration()
config._populate_values()
if config['classifier:passive_aggressive:n_iter'] is not None:
config._values['classifier:passive_aggressive:n_iter'] = 5
if config['classifier:sgd:n_iter'] is not None:
config._values['classifier:sgd:n_iter'] = 5
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls = ParamSklearnClassifier(config, random_state=1)
print(config)
try:
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
predictions = cls.predict(X_test)
self.assertIsInstance(predictions, np.ndarray)
predicted_probabiliets = cls.predict_proba(X_test_)
self.assertIsInstance(predicted_probabiliets, np.ndarray)
except ValueError as e:
if "Floating-point under-/overflow occurred at epoch" in \
e.args[0] or \
"removed all features" in e.args[0] or \
"all features are discarded" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except RuntimeWarning as e:
if "invalid value encountered in sqrt" in e.args[0]:
continue
elif "divide by zero encountered in" in e.args[0]:
continue
elif "invalid value encountered in divide" in e.args[0]:
continue
elif "invalid value encountered in true_divide" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except UserWarning as e:
if "FastICA did not converge" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except MemoryError as e:
continue
def test_configurations_signed_data(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'signed': True})
print(cs)
for i in range(10):
config = cs.sample_configuration()
config._populate_values()
if config['classifier:passive_aggressive:n_iter'] is not None:
config._values['classifier:passive_aggressive:n_iter'] = 5
if config['classifier:sgd:n_iter'] is not None:
config._values['classifier:sgd:n_iter'] = 5
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls = ParamSklearnClassifier(config, random_state=1)
print(config)
try:
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
predictions = cls.predict(X_test)
self.assertIsInstance(predictions, np.ndarray)
predicted_probabiliets = cls.predict_proba(X_test_)
self.assertIsInstance(predicted_probabiliets, np.ndarray)
except ValueError as e:
if "Floating-point under-/overflow occurred at epoch" in \
e.args[0] or \
"removed all features" in e.args[0] or \
"all features are discarded" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except RuntimeWarning as e:
if "invalid value encountered in sqrt" in e.args[0]:
continue
elif "divide by zero encountered in" in e.args[0]:
continue
elif "invalid value encountered in divide" in e.args[0]:
continue
elif "invalid value encountered in true_divide" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except UserWarning as e:
if "FastICA did not converge" in e.args[0]:
continue
else:
print(config)
print(traceback.format_exc())
raise e
except MemoryError as e:
continue
def test_default_configuration(self):
transformation, original = _test_preprocessing(SelectRates)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 3)
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(SelectRates,
make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(
X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
# I don't know why its 52 here and not 32 which would be half of the
# number of features. Seems to be related to a runtime warning raised
# by sklearn
self.assertEqual(transformation.shape[1], 52)
def setUp(self):
self.X_train, self.Y_train, self.X_test, self.Y_test = \
get_dataset('iris')
eliminate_class_two = self.Y_train != 2
self.X_train = self.X_train[eliminate_class_two]
self.Y_train = self.Y_train[eliminate_class_two]
def test_default_configuration(self):
transformation, original = _test_preprocessing(SelectRates)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 3)
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(SelectRates, make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(
random_state=1, **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None}
)
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
# I don't know why its 52 here and not 32 which would be half of the
# number of features. Seems to be related to a runtime warning raised
# by sklearn
self.assertEqual(transformation.shape[1], 52)
def test_evaluate_multiclass_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False, 'target_num': 3}
D.data = {'X_train': X_train, 'Y_train': Y_train,
'X_valid': X_valid, 'X_test': X_test}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(D.info,
include_estimators=['ridge'],
include_preprocessors=['select_rates'])
err = np.zeros([N_TEST_RUNS])
num_models_better_than_random = 0
for i in range(N_TEST_RUNS):
print "Evaluate configuration: %d; result:" % i,
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, configuration,
with_predictions=True)
if not self._fit(evaluator):
print
continue
e_, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
evaluator.predict()
err[i] = e_
print err[i], configuration['classifier']
num_targets = len(np.unique(Y_train))
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
# Test that ten models were trained
self.assertEqual(len(evaluator.models), 10)
self.assertEqual(Y_optimization_pred.shape[0], Y_train.shape[0])
self.assertEqual(Y_optimization_pred.shape[1], num_targets)
self.assertEqual(Y_valid_pred.shape[0], Y_valid.shape[0])
self.assertEqual(Y_valid_pred.shape[1], num_targets)
self.assertEqual(Y_test_pred.shape[0], Y_test.shape[0])
self.assertEqual(Y_test_pred.shape[1], num_targets)
# Test some basic statistics of the dataset
if err[i] < 0.5:
self.assertTrue(0.3 < Y_valid_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_valid_pred.std(), 0.01)
self.assertTrue(0.3 < Y_test_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_test_pred.std(), 0.01)
num_models_better_than_random += 1
self.assertGreater(num_models_better_than_random, 5)
def test_default_configuration(self):
for i in range(2):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris')
auto = ParamSklearnClassifier(default)
auto = auto.fit(X_train, Y_train)
predictions = auto.predict(X_test)
self.assertAlmostEqual(0.9599999999999995,
sklearn.metrics.accuracy_score(predictions, Y_test))
scores = auto.predict_proba(X_test)
def test_fit_OneHotEncoder(self):
# Test if the OneHotEncoder is called
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
X_train = np.hstack((X_train,
np.arange(X_train.shape[0]).reshape((-1, 1))))
cls = autosklearn.AutoSklearnClassifier(time_left_for_this_task=5,
per_run_time_limit=5)
cls.fit(X_train, Y_train,
feat_type=['NUMERICAL', 'NUMERICAL', 'NUMERICAL', 'NUMERICAL',
'CATEGORICAL'])
self.assertEqual([False, False, False, False, True],
cls._ohe.categorical_features)
def test_default_configuration(self):
for i in range(2):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris')
auto = ParamSklearnClassifier(default)
auto = auto.fit(X_train, Y_train)
predictions = auto.predict(X_test)
self.assertAlmostEqual(
0.9599999999999995,
sklearn.metrics.accuracy_score(predictions, Y_test))
scores = auto.predict_proba(X_test)
def test_fit(self):
output = os.path.join(self.test_dir, '..', '.tmp_test_fit')
self._setUp(output)
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
automl = autosklearn.automl.AutoML(output, output, 12, 12)
automl.fit(X_train, Y_train)
score = automl.score(X_test, Y_test)
self.assertGreaterEqual(score, 0.8)
self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
del automl
self._tearDown(output)
def test_default_configuration(self):
for i in range(2):
cs = ParamSklearnRegressor.get_hyperparameter_search_space()
default = cs.get_default_configuration()
X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes')
auto = ParamSklearnRegressor(default)
auto = auto.fit(X_train, Y_train)
predictions = auto.predict(copy.deepcopy(X_test))
# The lower the worse
r2_score = sklearn.metrics.r2_score(Y_test, predictions)
self.assertAlmostEqual(0.41626416529791199, r2_score)
model_score = auto.score(copy.deepcopy(X_test), Y_test)
self.assertEqual(model_score, r2_score)
def _test_helper(self, Preprocessor, dataset=None, make_sparse=False):
X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset,
make_sparse=make_sparse)
original_X_train = X_train.copy()
configuration_space = Preprocessor.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Preprocessor(random_state=1,
**{hp_name: default[hp_name] for hp_name in
default if default[hp_name] is not None})
preprocessor = preprocessor.choice
transformer = preprocessor.fit(X_train, Y_train)
return transformer.transform(X_train), original_X_train
def test_evaluate_multiclass_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_valid = X_test[:25,]
Y_valid = Y_test[:25,]
X_test = X_test[25:,]
Y_test = Y_test[25:,]
D = Dummy()
D.info = {"metric": BAC_METRIC, "task": MULTICLASS_CLASSIFICATION, "is_sparse": False, "label_num": 3}
D.data = {"X_train": X_train, "Y_train": Y_train, "X_valid": X_valid, "X_test": X_test}
D.feat_type = ["numerical", "Numerical", "numerical", "numerical"]
configuration_space = get_configuration_space(
D.info, include_estimators=["extra_trees"], include_preprocessors=["select_rates"]
)
err = np.zeros([N_TEST_RUNS])
num_models_better_than_random = 0
for i in range(N_TEST_RUNS):
print("Evaluate configuration: %d; result:" % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = CVEvaluator(D_, configuration, with_predictions=True)
if not self._fit(evaluator):
print()
continue
e_, Y_optimization_pred, Y_valid_pred, Y_test_pred = evaluator.predict()
err[i] = e_
print(err[i], configuration["classifier:__choice__"])
num_targets = len(np.unique(Y_train))
self.assertTrue(np.isfinite(err[i]))
self.assertGreaterEqual(err[i], 0.0)
# Test that ten models were trained
self.assertEqual(len(evaluator.models), 10)
self.assertEqual(Y_optimization_pred.shape[0], Y_train.shape[0])
self.assertEqual(Y_optimization_pred.shape[1], num_targets)
self.assertEqual(Y_valid_pred.shape[0], Y_valid.shape[0])
self.assertEqual(Y_valid_pred.shape[1], num_targets)
self.assertEqual(Y_test_pred.shape[0], Y_test.shape[0])
self.assertEqual(Y_test_pred.shape[1], num_targets)
# Test some basic statistics of the dataset
if err[i] < 0.5:
self.assertTrue(0.3 < Y_valid_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_valid_pred.std(), 0.01)
self.assertTrue(0.3 < Y_test_pred.mean() < 0.36666)
self.assertGreaterEqual(Y_test_pred.std(), 0.01)
num_models_better_than_random += 1
self.assertGreater(num_models_better_than_random, 5)
def test_configurations_sparse(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
print(cs)
for i in range(10):
config = cs.sample_configuration()
config._populate_values()
if config['classifier:passive_aggressive:n_iter'] is not None:
config._values['classifier:passive_aggressive:n_iter'] = 5
if config['classifier:sgd:n_iter'] is not None:
config._values['classifier:sgd:n_iter'] = 5
print(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls = ParamSklearnClassifier(config, random_state=1)
try:
cls.fit(X_train, Y_train)
predictions = cls.predict(X_test)
except ValueError as e:
if "Floating-point under-/overflow occurred at epoch" in \
e.args[0] or \
"removed all features" in e.args[0] or \
"all features are discarded" in e.args[0]:
continue
else:
print(config)
traceback.print_tb(sys.exc_info()[2])
raise e
except RuntimeWarning as e:
if "invalid value encountered in sqrt" in e.args[0]:
continue
elif "divide by zero encountered in" in e.args[0]:
continue
elif "invalid value encountered in divide" in e.args[0]:
continue
elif "invalid value encountered in true_divide" in e.args[0]:
continue
else:
print(config)
raise e
except UserWarning as e:
if "FastICA did not converge" in e.args[0]:
continue
else:
print(config)
raise e
def test_configurations_sparse(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
print(cs)
for i in range(10):
config = cs.sample_configuration()
config._populate_values()
if config['classifier:passive_aggressive:n_iter'] is not None:
config._values['classifier:passive_aggressive:n_iter'] = 5
if config['classifier:sgd:n_iter'] is not None:
config._values['classifier:sgd:n_iter'] = 5
print(config)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls = ParamSklearnClassifier(config, random_state=1)
try:
cls.fit(X_train, Y_train)
predictions = cls.predict(X_test)
except ValueError as e:
if "Floating-point under-/overflow occurred at epoch" in \
e.args[0] or \
"removed all features" in e.args[0] or \
"all features are discarded" in e.args[0]:
continue
else:
print(config)
traceback.print_tb(sys.exc_info()[2])
raise e
except RuntimeWarning as e:
if "invalid value encountered in sqrt" in e.args[0]:
continue
elif "divide by zero encountered in" in e.args[0]:
continue
elif "invalid value encountered in divide" in e.args[0]:
continue
elif "invalid value encountered in true_divide" in e.args[0]:
continue
else:
print(config)
raise e
except UserWarning as e:
if "FastICA did not converge" in e.args[0]:
continue
else:
print(config)
raise e
def test_metalearning(self):
dataset_name = 'digits'
initial_challengers = {
ACC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
AUC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'",
BAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
F1_METRIC: "--initial-challengers \" "
"-balancing:strategy 'weighting' "
"-classifier:__choice__ 'proj_logit'",
PAC_METRIC: "--initial-challengers \" "
"-balancing:strategy 'none' "
"-classifier:__choice__ 'random_forest'"
}
for metric in initial_challengers:
configuration_space = get_configuration_space(
{
'metric': metric,
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False
},
include_preprocessors=['no_preprocessing'])
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
categorical = [False] * X_train.shape[1]
meta_features_label = calc_meta_features(X_train, Y_train,
categorical, dataset_name)
meta_features_encoded_label = calc_meta_features_encoded(X_train,
Y_train,
categorical,
dataset_name)
initial_configuration_strings_for_smac = \
create_metalearning_string_for_smac_call(
meta_features_label,
meta_features_encoded_label,
configuration_space, dataset_name, metric,
MULTICLASS_CLASSIFICATION, False, 1, None)
print(metric)
print(initial_configuration_strings_for_smac[0])
self.assertTrue(initial_configuration_strings_for_smac[
0].startswith(initial_challengers[metric]))
def test_evaluate_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': AUC_METRIC,
'task': BINARY_CLASSIFICATION,
'is_sparse': False,
'label_num': 2
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info,
include_estimators=['lda'],
include_preprocessors=['pca'])
err = np.zeros([N_TEST_RUNS])
for i in range(N_TEST_RUNS):
print('Evaluate configuration: %d; result:' % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration)
if not self._fit(evaluator):
continue
err[i] = evaluator.predict()
self.assertTrue(np.isfinite(err[i]))
print(err[i])
self.assertGreaterEqual(err[i], 0.0)
def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {
'metric': BAC_METRIC,
'task': task_type,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info,
include_estimators=['lda'],
include_preprocessors=['select_rates'])
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
def test_evaluate_multiclass_classification_all_metrics(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': 'bac_metric',
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False,
'target_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info,
include_estimators=['ridge'],
include_preprocessors=['select_rates'])
# Test all scoring functions
err = []
for i in range(N_TEST_RUNS):
print('Evaluate configuration: %d; result:' % i)
configuration = configuration_space.sample_configuration()
D_ = copy.deepcopy(D)
evaluator = HoldoutEvaluator(D_, configuration,
all_scoring_functions=True)
if not self._fit(evaluator):
continue
err.append(evaluator.predict())
print(err[-1])
self.assertIsInstance(err[-1], dict)
for key in err[-1]:
self.assertEqual(len(err[-1]), 5)
self.assertTrue(np.isfinite(err[-1][key]))
self.assertGreaterEqual(err[-1][key], 0.0)
print('Number of times it was worse than random guessing:' +
str(np.sum(err > 1)))
def test_fit_OneHotEncoder(self):
# Test if the OneHotEncoder is called
X_train, Y_train, X_test, Y_test = putil.get_dataset("iris")
X_train = np.hstack((X_train, np.arange(X_train.shape[0]).reshape(
(-1, 1))))
cls = autosklearn.AutoSklearnClassifier(time_left_for_this_task=5,
per_run_time_limit=5)
cls.fit(X_train,
Y_train,
feat_type=[
"NUMERICAL", "NUMERICAL", "NUMERICAL", "NUMERICAL",
"CATEGORICAL"
])
self.assertEqual([False, False, False, False, True],
cls.ohe_.categorical_features)
def test_predict_batched(self):
cs = ParamSklearnRegressor.get_hyperparameter_search_space()
default = cs.get_default_configuration()
cls = ParamSklearnRegressor(default)
X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((356,), prediction.shape)
self.assertEqual(18, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), '.test')
try:
shutil.rmtree(output_dir)
except Exception:
pass
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': 'bac_metric',
'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False,
'target_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
D.basename = 'test'
configuration_space = get_configuration_space(D.info)
while True:
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration,
with_predictions=True,
all_scoring_functions=True,
output_dir=output_dir,
output_y_test=True)
if not self._fit(evaluator):
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(output_dir,
'y_optimization.npy')))
break
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), '.test')
try:
shutil.rmtree(output_dir)
except Exception:
pass
X_train, Y_train, X_test, Y_test = get_dataset('boston')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {
'metric': R2_METRIC,
'task': REGRESSION,
'is_sparse': False,
'label_num': 3
}
D.data = {
'X_train': X_train,
'Y_train': Y_train,
'X_valid': X_valid,
'X_test': X_test
}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
D.name = 'test'
configuration_space = get_configuration_space(D.info)
while True:
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration,
with_predictions=True,
all_scoring_functions=True,
output_dir=output_dir,
output_y_test=True)
if not self._fit(evaluator):
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(
output_dir, '.auto-sklearn', 'true_targets_ensemble.npy')))
break
def _test_helper(self, Preprocessor, dataset=None, make_sparse=False):
X_train, Y_train, X_test, Y_test = get_dataset(dataset=dataset,
make_sparse=make_sparse)
original_X_train = X_train.copy()
configuration_space = Preprocessor.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = Preprocessor(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
preprocessor = preprocessor.choice
transformer = preprocessor.fit(X_train, Y_train)
return transformer.transform(X_train), original_X_train
def test_default_configuration_predict_proba(self):
for i in range(10):
predictions, targets = _test_classifier_predict_proba(
LibSVM_SVC,
sparse=True,
dataset='digits',
train_size_maximum=500)
self.assertAlmostEqual(
4.6680593525563063,
sklearn.metrics.log_loss(targets, predictions))
for i in range(10):
predictions, targets = _test_classifier_predict_proba(
LibSVM_SVC, sparse=True, dataset='iris')
self.assertAlmostEqual(
0.8649665185853217,
sklearn.metrics.log_loss(targets, predictions))
# 2 class
for i in range(10):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris')
remove_training_data = Y_train == 2
remove_test_data = Y_test == 2
X_train = X_train[~remove_training_data]
Y_train = Y_train[~remove_training_data]
X_test = X_test[~remove_test_data]
Y_test = Y_test[~remove_test_data]
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = LibSVM_SVC.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
cls = LibSVM_SVC(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
cls = cls.fit(X_train, Y_train)
prediction = cls.predict_proba(X_test)
self.assertAlmostEqual(
sklearn.metrics.log_loss(Y_test, prediction),
0.69323680119641773)
def test_default_configuration_regression(self):
for i in range(5):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes')
configuration_space = FastICA.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = FastICA(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = Ridge()
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.r2_score(Y_test, predictions)
self.assertAlmostEqual(accuracy, 0.32614416980439365)
def test_file_output(self):
output_dir = os.path.join(os.getcwd(), ".test")
try:
shutil.rmtree(output_dir)
except:
pass
X_train, Y_train, X_test, Y_test = get_dataset('iris')
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False, 'target_num': 3}
D.data = {'X_train': X_train, 'Y_train': Y_train,
'X_valid': X_valid, 'X_test': X_test}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
D.basename = "test"
configuration_space = get_configuration_space(D.info)
while True:
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration,
with_predictions=True,
all_scoring_functions=True,
output_dir=output_dir,
output_y_test=True)
if not self._fit(evaluator):
print
continue
evaluator.predict()
evaluator.file_output()
self.assertTrue(os.path.exists(os.path.join(output_dir,
"y_optimization.npy")))
break
def test_default_configuration(self):
configuration_space = RidgeRegression.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
configuration_space_preproc = RandomKitchenSinks.get_hyperparameter_search_space(
)
default_preproc = configuration_space_preproc.get_default_configuration(
)
for i in range(10):
# This should be a bad results
predictions, targets = _test_regressor(RidgeRegression, )
self.assertAlmostEqual(
0.32614416980439365,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
# This should be much more better
X_train, Y_train, X_test, Y_test = get_dataset(dataset='diabetes',
make_sparse=False)
preprocessor = RandomKitchenSinks(
random_state=1,
**{
hp_name: default_preproc[hp_name]
for hp_name in default_preproc
if default_preproc[hp_name] is not None
})
transformer = preprocessor.fit(X_train, Y_train)
X_train_transformed = transformer.transform(X_train)
X_test_transformed = transformer.transform(X_test)
regressor = RidgeRegression(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
predictor = regressor.fit(X_train_transformed, Y_train)
predictions = predictor.predict(X_test_transformed)
self.assertAlmostEqual(
0.37183512452087852,
sklearn.metrics.r2_score(y_true=Y_test, y_pred=predictions))
def test_default_configuration_classify(self):
for i in range(3):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=False)
configuration_space = GEM.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = GEM(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = ProjLogitCLassifier(max_epochs=5, random_state=1)
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
self.assertGreaterEqual(accuracy, 0.94)
def test_default_configuration_negative_values(self):
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
configuration_space = MultinomialNB.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
cls = MultinomialNB(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
cls = cls.fit(X_train, Y_train)
prediction = cls.predict(X_test)
self.assertAlmostEqual(np.nanmean(prediction == Y_test),
0.88888888888888884)
def test_default_configuration_classify(self):
for i in range(2):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=False)
configuration_space = ExtraTreesPreprocessor.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = ExtraTreesPreprocessor(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = RidgeClassifier()
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
self.assertAlmostEqual(accuracy, 0.87310261080752882, places=2)
def test_default_configuration_classify(self):
for i in range(3):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=False)
configuration_space = FeatureAgglomeration.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = FeatureAgglomeration(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = RandomForestClassifier(random_state=1)
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
self.assertAlmostEqual(accuracy, 0.8026715)
def test_predict_proba_binary_classification(self):
X_train, Y_train, X_test, Y_test = get_dataset('iris')
eliminate_class_two = Y_train != 2
X_train = X_train[eliminate_class_two]
Y_train = Y_train[eliminate_class_two]
eliminate_class_two = Y_test != 2
X_test = X_test[eliminate_class_two]
Y_test = Y_test[eliminate_class_two]
X_valid = X_test[:25, ]
Y_valid = Y_test[:25, ]
X_test = X_test[25:, ]
Y_test = Y_test[25:, ]
class Dummy2(object):
def predict_proba(self, y, batch_size=200):
return np.array([[0.1, 0.9], [0.7, 0.3]])
model = Dummy2()
task_type = BINARY_CLASSIFICATION
D = Dummy()
D.info = {'metric': 'bac_metric', 'task': task_type,
'is_sparse': False, 'target_num': 3}
D.data = {'X_train': X_train, 'Y_train': Y_train,
'X_valid': X_valid, 'X_test': X_test}
D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']
configuration_space = get_configuration_space(
D.info, include_estimators=['ridge'],
include_preprocessors=['select_rates'])
configuration = configuration_space.sample_configuration()
evaluator = HoldoutEvaluator(D, configuration)
pred = evaluator.predict_proba(None, model, task_type)
expected = [[0.9], [0.3]]
for i in range(len(expected)):
self.assertEqual(expected[i], pred[i])
def test_default_configuration_classify(self):
for i in range(5):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=False)
configuration_space = KernelPCA.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = KernelPCA(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = RidgeClassifier()
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
self.assertAlmostEqual(accuracy, 0.096539162112932606)
def test_min_max_scaler_sparse_boston_data(self):
# Use the boston housing dataset, because column three is 1HotEncoded!
# This is important to test; because the normal sklearn rescaler
# would set all values of the 1Hot Encoded column to zero, while we
# keep the values at 1.
X_train, Y_train, X_test, Y_test = get_dataset('boston',
make_sparse=True)
num_data_points = len(X_train.data)
expected_max_values = [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
expected_max_values = np.array(expected_max_values).reshape((1, -1))
scaler = MinMaxScaler()
scaler.fit(X_train, Y_train)
transformation = scaler.transform(X_train)
assert_array_almost_equal(
np.array(transformation.todense().min(axis=0)), np.zeros((1, 13)))
assert_array_almost_equal(
np.array(transformation.todense().max(axis=0)),
expected_max_values)
# Test that the matrix is still sparse
self.assertTrue(sparse.issparse(transformation))
self.assertEqual(num_data_points, len(transformation.data))
def test_default_configuration_classify(self):
for i in range(2):
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
configuration_space = TruncatedSVD.get_hyperparameter_search_space(
)
default = configuration_space.get_default_configuration()
preprocessor = TruncatedSVD(random_state=1,
**{
hp_name: default[hp_name]
for hp_name in default
if default[hp_name] is not None
})
preprocessor.fit(X_train, Y_train)
X_train_trans = preprocessor.transform(X_train)
X_test_trans = preprocessor.transform(X_test)
# fit a classifier on top
classifier = RidgeClassifier()
predictor = classifier.fit(X_train_trans, Y_train)
predictions = predictor.predict(X_test_trans)
accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
self.assertAlmostEqual(accuracy, 0.44201578627808136, places=2)