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_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:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
"rescaling:__choice__": "min/max"
})
cls = ParamSklearnClassifier(config)
# Multiclass
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(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',
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(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_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:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
"rescaling:__choice__": "min/max"})
cls = ParamSklearnClassifier(config)
# Multiclass
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(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',
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(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_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):
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_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_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_configurations_categorical_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={'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)
categorical = [True, True, True, False, False, True, True, True,
False, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, False,
False, False, True, True, True]
this_directory = os.path.dirname(__file__)
X = np.loadtxt(os.path.join(this_directory, "components",
"data_preprocessing", "dataset.pkl"))
y = X[:, -1].copy()
X = X[:,:-1]
X_train, X_test, Y_train, Y_test = \
sklearn.cross_validation.train_test_split(X, y)
cls = ParamSklearnClassifier(config, random_state=1,)
try:
cls.fit(X_train, Y_train,
init_params={'one_hot_encoding:categorical_features': categorical})
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_weighting_effect(self):
for name, clf, acc_no_weighting, acc_weighting in \
[('adaboost', AdaboostClassifier, 0.692, 0.719),
('decision_tree', DecisionTree, 0.712, 0.668),
('extra_trees', ExtraTreesClassifier, 0.901, 0.919),
('gradient_boosting', GradientBoostingClassifier, 0.879, 0.883),
('random_forest', RandomForest, 0.886, 0.885),
('libsvm_svc', LibSVM_SVC, 0.915, 0.937),
('liblinear_svc', LibLinear_SVC, 0.920, 0.923),
('sgd', SGD, 0.811, 0.902)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
# Fit
X_train, Y_train, X_test, Y_test = get_dataset(
dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
# pre_transform and fit_estimator
X_train, Y_train, X_test, Y_test = get_dataset(
dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, fit_params=fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
for name, pre, acc_no_weighting, acc_weighting in \
[('extra_trees_preproc_for_classification',
ExtraTreesPreprocessor, 0.892, 0.910),
('liblinear_svc_preprocessor', LibLinear_Preprocessor,
0.906, 0.909)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
X_train, Y_train, X_test, Y_test = get_dataset(
dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={
'classifier': ['sgd'],
'preprocessor': [name]
})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
# pre_transform and fit_estimator
X_train, Y_train, X_test, Y_test = get_dataset(
dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={
'classifier': ['sgd'],
'preprocessor': [name]
})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, fit_params=fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
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:]])
def test_multilabel(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
dataset_properties = {'multilabel': True}
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties=dataset_properties)
print(cs)
cs.seed(5)
for i in range(50):
X, Y = sklearn.datasets.\
make_multilabel_classification(n_samples=150,
n_features=20,
n_classes=5,
n_labels=2,
length=50,
allow_unlabeled=True,
sparse=False,
return_indicator=True,
return_distributions=False,
random_state=1)
X_train = X[:100, :]
Y_train = Y[:100, :]
X_test = X[101:, :]
Y_test = Y[101:, ]
config = cs.sample_configuration()
config._populate_values()
if 'classifier:passive_aggressive:n_iter' in config:
config._values['classifier:passive_aggressive:n_iter'] = 5
if 'classifier:sgd:n_iter' in config:
config._values['classifier:sgd:n_iter'] = 5
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_probabilities = cls.predict_proba(X_test_)
[
self.assertIsInstance(i, np.ndarray)
for i in predicted_probabilities
]
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_categorical_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={'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)
categorical = [
True, True, True, False, False, True, True, True, False, True,
True, True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True, True,
True, True, False, False, False, True, True, True
]
this_directory = os.path.dirname(__file__)
X = np.loadtxt(
os.path.join(this_directory, "components",
"data_preprocessing", "dataset.pkl"))
y = X[:, -1].copy()
X = X[:, :-1]
X_train, X_test, Y_train, Y_test = \
sklearn.cross_validation.train_test_split(X, y)
cls = ParamSklearnClassifier(
config,
random_state=1,
)
try:
cls.fit(X_train,
Y_train,
init_params={
'one_hot_encoding:categorical_features':
categorical
})
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_multilabel(self):
# Use a limit of ~4GiB
limit = 4000 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
dataset_properties = {'multilabel': True}
cs = ParamSklearnClassifier.get_hyperparameter_search_space(dataset_properties=dataset_properties)
print(cs)
cs.seed(5)
for i in range(50):
X, Y = sklearn.datasets.\
make_multilabel_classification(n_samples=150,
n_features=20,
n_classes=5,
n_labels=2,
length=50,
allow_unlabeled=True,
sparse=False,
return_indicator=True,
return_distributions=False,
random_state=1)
X_train = X[:100, :]
Y_train = Y[:100, :]
X_test = X[101:, :]
Y_test = Y[101:, ]
config = cs.sample_configuration()
config._populate_values()
if 'classifier:passive_aggressive:n_iter' in config:
config._values['classifier:passive_aggressive:n_iter'] = 5
if 'classifier:sgd:n_iter' in config:
config._values['classifier:sgd:n_iter'] = 5
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_probabilities = cls.predict_proba(X_test_)
[self.assertIsInstance(i, np.ndarray) for i in predicted_probabilities]
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_weighting_effect(self):
for name, clf, acc_no_weighting, acc_weighting in \
[('adaboost', AdaboostClassifier, 0.692, 0.719),
('decision_tree', DecisionTree, 0.712, 0.668),
('extra_trees', ExtraTreesClassifier, 0.901, 0.919),
('gradient_boosting', GradientBoostingClassifier, 0.879, 0.883),
('random_forest', RandomForest, 0.886, 0.885),
('libsvm_svc', LibSVM_SVC, 0.915, 0.937),
('liblinear_svc', LibLinear_SVC, 0.920, 0.923),
('sgd', SGD, 0.811, 0.902)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
# Fit
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(predictions, Y_test),
places=3)
# pre_transform and fit_estimator
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, fit_params=fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
for name, pre, acc_no_weighting, acc_weighting in \
[('extra_trees_preproc_for_classification',
ExtraTreesPreprocessor, 0.892, 0.910),
('liblinear_svc_preprocessor', LibLinear_Preprocessor,
0.906, 0.909)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': ['sgd'], 'preprocessor': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)
# pre_transform and fit_estimator
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': ['sgd'], 'preprocessor': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = ParamSklearnClassifier(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, fit_params=fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.accuracy_score(
predictions, Y_test),
places=3)