def test_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['densifier']},
dataset_properties={'sparse': True})
self.assertEqual(cs.get_hyperparameter('classifier:__choice__').default,
'qda')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['nystroem_sampler']})
self.assertEqual(cs.get_hyperparameter('classifier:__choice__').default,
'sgd')
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_get_hyperparameter_search_space_preprocessor_contradicts_default_classifier(
self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['densifier']},
dataset_properties={'sparse': True})
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__').default, 'qda')
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['nystroem_sampler']})
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__').default, 'sgd')
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 _get_classification_configuration_space(info, include):
task_type = info['task']
multilabel = False
multiclass = False
sparse = False
if task_type == MULTILABEL_CLASSIFICATION:
multilabel = True
if task_type == REGRESSION:
raise NotImplementedError()
if task_type == MULTICLASS_CLASSIFICATION:
multiclass = True
if task_type == BINARY_CLASSIFICATION:
pass
if info['is_sparse'] == 1:
sparse = True
dataset_properties = {
'multilabel': multilabel,
'multiclass': multiclass,
'sparse': sparse
}
return ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties=dataset_properties,
include=include)
def _get_classification_configuration_space(info,
include_estimators=None,
include_preprocessors=None):
task_type = info['task']
multilabel = False
multiclass = False
sparse = False
if task_type == MULTILABEL_CLASSIFICATION:
multilabel = True
if task_type == REGRESSION:
raise NotImplementedError()
if task_type == MULTICLASS_CLASSIFICATION:
multiclass = True
pass
if task_type == BINARY_CLASSIFICATION:
pass
if info['is_sparse'] == 1:
sparse = True
dataset_properties = {
'multilabel': multilabel,
'multiclass': multiclass,
'sparse': sparse
}
return ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties=dataset_properties,
include_estimators=include_estimators,
include_preprocessors=include_preprocessors)
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_get_hyperparameter_search_space_include_exclude_models(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': ['libsvm_svc']})
self.assertEqual(cs.get_hyperparameter('classifier:__choice__'),
CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
exclude={'classifier': ['libsvm_svc']})
self.assertNotIn('libsvm_svc', str(cs))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['select_percentile_classification']})
self.assertEqual(cs.get_hyperparameter('preprocessor:__choice__'),
CategoricalHyperparameter('preprocessor:__choice__',
['select_percentile_classification']))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
exclude={'preprocessor': ['select_percentile_classification']})
self.assertNotIn('select_percentile_classification', str(cs))
def test_get_hyperparameter_search_space_dataset_properties(self):
cs_mc = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'multiclass': True})
self.assertNotIn('bernoulli_nb', str(cs_mc))
cs_ml = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'multilabel': True})
self.assertNotIn('k_nearest_neighbors', str(cs_ml))
self.assertNotIn('liblinear', str(cs_ml))
self.assertNotIn('libsvm_svc', str(cs_ml))
self.assertNotIn('sgd', str(cs_ml))
cs_sp = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
self.assertIn('extra_trees', str(cs_sp))
self.assertIn('gradient_boosting', str(cs_sp))
self.assertIn('random_forest', str(cs_sp))
cs_mc_ml = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'multilabel': True, 'multiclass': True})
self.assertEqual(cs_ml, cs_mc_ml)
def test_get_hyperparameter_search_space_include_exclude_models(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'classifier': ['libsvm_svc']})
self.assertEqual(
cs.get_hyperparameter('classifier:__choice__'),
CategoricalHyperparameter('classifier:__choice__', ['libsvm_svc']))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
exclude={'classifier': ['libsvm_svc']})
self.assertNotIn('libsvm_svc', str(cs))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
include={'preprocessor': ['select_percentile_classification']})
self.assertEqual(
cs.get_hyperparameter('preprocessor:__choice__'),
CategoricalHyperparameter('preprocessor:__choice__',
['select_percentile_classification']))
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
exclude={'preprocessor': ['select_percentile_classification']})
self.assertNotIn('select_percentile_classification', str(cs))
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_get_hyperparameter_search_space_dataset_properties(self):
cs_mc = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'multiclass': True})
self.assertNotIn('bernoulli_nb', str(cs_mc))
cs_ml = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'multilabel': True})
self.assertNotIn('k_nearest_neighbors', str(cs_ml))
self.assertNotIn('liblinear', str(cs_ml))
self.assertNotIn('libsvm_svc', str(cs_ml))
self.assertNotIn('sgd', str(cs_ml))
cs_sp = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
self.assertIn('extra_trees', str(cs_sp))
self.assertIn('gradient_boosting', str(cs_sp))
self.assertIn('random_forest', str(cs_sp))
cs_mc_ml = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={
'multilabel': True,
'multiclass': True
})
self.assertEqual(cs_ml, cs_mc_ml)
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_get_hyperparameter_search_space(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
self.assertIsInstance(cs, ConfigurationSpace)
conditions = cs.get_conditions()
self.assertEqual(len(cs.get_hyperparameter(
'rescaling:__choice__').choices), 4)
self.assertEqual(len(cs.get_hyperparameter(
'classifier:__choice__').choices), 16)
self.assertEqual(len(cs.get_hyperparameter(
'preprocessor:__choice__').choices), 14)
hyperparameters = cs.get_hyperparameters()
self.assertEqual(145, len(hyperparameters))
#for hp in sorted([str(h) for h in hyperparameters]):
# print hp
# The four parameters which are always active are classifier,
# preprocessor, imputation strategy and scaling strategy
self.assertEqual(len(hyperparameters) - 6, len(conditions))
def test_get_hyperparameter_search_space(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
self.assertIsInstance(cs, ConfigurationSpace)
conditions = cs.get_conditions()
self.assertEqual(
len(cs.get_hyperparameter('rescaling:__choice__').choices), 4)
self.assertEqual(
len(cs.get_hyperparameter('classifier:__choice__').choices), 16)
self.assertEqual(
len(cs.get_hyperparameter('preprocessor:__choice__').choices), 14)
hyperparameters = cs.get_hyperparameters()
self.assertEqual(145, len(hyperparameters))
#for hp in sorted([str(h) for h in hyperparameters]):
# print hp
# The four parameters which are always active are classifier,
# preprocessor, imputation strategy and scaling strategy
self.assertEqual(len(hyperparameters) - 6, len(conditions))
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_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)
D = autosklearn.data.competition_data_manager.CompetitionDataManager(path)
X = D.data['X_train']
y = D.data['Y_train']
X_valid = D.data['X_valid']
X_test = D.data['X_test']
# Replace the following array by a new ensemble
choices = \
[(1.0, ParamSklearnClassifier(configuration={
'balancing:strategy': 'weighting',
'classifier:__choice__': 'sgd',
'classifier:sgd:loss': 'hinge',
'classifier:sgd:penalty': 'l2',
'classifier:sgd:alpha': 0.0001,
'classifier:sgd:fit_intercept': True,
'classifier:sgd:n_iter': 5,
'classifier:sgd:learning_rate': 'optimal',
'classifier:sgd:eta0': 0.01,
'classifier:sgd:average': True,
'imputation:strategy': 'mean',
'one_hot_encoding:use_minimum_fraction': 'True',
'one_hot_encoding:minimum_fraction': 0.1,
'preprocessor:__choice__': 'no_preprocessing',
'rescaling:__choice__': 'min/max'}))]
classifiers = []
targets = []
predictions = []
predictions_valid = []
predictions_test = []
# Make predictions and weight them
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_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_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_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_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_repr(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
representation = repr(ParamSklearnClassifier(default))
cls = eval(representation)
self.assertIsInstance(cls, ParamSklearnClassifier)
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_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)
y = D.data['Y_train']
X_valid = D.data['X_valid']
X_test = D.data['X_test']
# Replace the following array by a new ensemble
choices = \
[(0.480000, ParamSklearnClassifier(configuration={
'balancing:strategy': 'none',
'classifier:__choice__': 'random_forest',
'classifier:random_forest:bootstrap': 'True',
'classifier:random_forest:criterion': 'entropy',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:max_features': 4.885151102990943,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:min_samples_leaf': 2,
'classifier:random_forest:min_samples_split': 2,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
'classifier:random_forest:n_estimators': 100,
'imputation:strategy': 'median',
'one_hot_encoding:minimum_fraction': 0.059297498551361,
'one_hot_encoding:use_minimum_fraction': 'True',
'preprocessor:__choice__': 'gem',
'preprocessor:gem:N': 13,
'preprocessor:gem:precond': 0.31299029323203487,
'rescaling:__choice__': 'min/max'})),
(0.300000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'none',
'classifier:__choice__': 'random_forest',
'classifier:random_forest:bootstrap': 'False',
'classifier:random_forest:criterion': 'entropy',
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)
y = D.data['Y_train']
X_valid = D.data['X_valid']
X_test = D.data['X_test']
# Replace the following array by a new ensemble
choices = \
[(0.580000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'weighting',
'classifier:__choice__': 'extra_trees',
'classifier:extra_trees:bootstrap': 'True',
'classifier:extra_trees:criterion': 'gini',
'classifier:extra_trees:max_depth': 'None',
'classifier:extra_trees:max_features': 1.4927328322706173,
'classifier:extra_trees:min_samples_leaf': 1,
'classifier:extra_trees:min_samples_split': 5,
'classifier:extra_trees:min_weight_fraction_leaf': 0.0,
'classifier:extra_trees:n_estimators': 100,
'imputation:strategy': 'mean',
'one_hot_encoding:use_minimum_fraction': 'False',
'preprocessor:__choice__': 'select_rates',
'preprocessor:select_rates:alpha': 0.4308279694614349,
'preprocessor:select_rates:mode': 'fwe',
'preprocessor:select_rates:score_func': 'f_classif',
'rescaling:__choice__': 'min/max'})),
(0.200000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'none',
'classifier:__choice__': 'sgd',
'classifier:sgd:alpha': 5.707045187542232e-06,
'classifier:sgd:average': 'True',
def test_repr(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
representation = repr(ParamSklearnClassifier(default))
cls = eval(representation)
self.assertIsInstance(cls, ParamSklearnClassifier)
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:]])
classifiers = []
predictions_valid = []
predictions_test = []
# Make predictions and weight them
for weight, configuration in zip(weights, configurations):
for param in configuration:
try:
configuration[param] = int(configuration[param])
except Exception:
try:
configuration[param] = float(configuration[param])
except Exception:
pass
classifier = ParamSklearnClassifier(configuration, 1)
classifiers.append(classifier)
try:
classifier.fit(X.copy(), y.copy())
predictions_valid.append(
classifier.predict_proba(X_valid.copy()) * weight)
predictions_test.append(
classifier.predict_proba(X_test.copy()) * weight)
except Exception as e:
print e
print configuration
# Output the predictions
for name, predictions in [('valid', predictions_valid),
('test', predictions_test)]:
predictions = np.array(predictions)
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)
X_valid = D.data['X_valid']
X_test = D.data['X_test']
# Replace the following array by a new ensemble
choices = \
[(0.140000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'none',
'classifier:__choice__': 'random_forest',
'classifier:random_forest:bootstrap': 'False',
'classifier:random_forest:criterion': 'gini',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:max_features': 4.649151092701434,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:min_samples_leaf': 3,
'classifier:random_forest:min_samples_split': 5,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
'classifier:random_forest:n_estimators': 100,
'imputation:strategy': 'most_frequent',
'one_hot_encoding:minimum_fraction': 0.006861808529548735,
'one_hot_encoding:use_minimum_fraction': 'True',
'preprocessor:__choice__': 'select_rates',
'preprocessor:select_rates:alpha': 0.03408255008474342,
'preprocessor:select_rates:mode': 'fwe',
'preprocessor:select_rates:score_func': 'f_classif',
'rescaling:__choice__': 'normalize'})),
(0.100000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'weighting',
'classifier:__choice__': 'random_forest',
'classifier:random_forest:bootstrap': 'False',
classifiers = []
predictions_valid = []
predictions_test = []
# Make predictions and weight them
for weight, configuration in zip(weights, configurations):
for param in configuration:
try:
configuration[param] = int(configuration[param])
except Exception:
try:
configuration[param] = float(configuration[param])
except Exception:
pass
classifier = ParamSklearnClassifier(configuration, 1)
classifiers.append(classifier)
try:
classifier.fit(X.copy(), y.copy())
predictions_valid.append(classifier.predict_proba(X_valid.copy()) * weight)
predictions_test.append(classifier.predict_proba(X_test.copy()) * weight)
except Exception as e:
print e
print configuration
# Output the predictions
for name, predictions in [('valid', predictions_valid),
('test', predictions_test)]:
predictions = np.array(predictions)
predictions = np.sum(predictions, axis=0)
predictions = predictions[:, 1].reshape((-1, 1))
def get_model(configuration, seed):
if 'classifier' in configuration:
return ParamSklearnClassifier(configuration, seed)
elif 'regressor' in configuration:
return ParamSklearnRegressor(configuration, seed)
D = autosklearn.data.competition_data_manager.CompetitionDataManager(path)
X = D.data['X_train']
y = D.data['Y_train']
X_valid = D.data['X_valid']
X_test = D.data['X_test']
# Replace the following array by a new ensemble
choices = \
[(0.220000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'weighting',
'classifier:__choice__': 'passive_aggressive',
'classifier:passive_aggressive:C': 0.0022574783522003694,
'classifier:passive_aggressive:fit_intercept': 'True',
'classifier:passive_aggressive:loss': 'hinge',
'classifier:passive_aggressive:n_iter': 119,
'imputation:strategy': 'most_frequent',
'one_hot_encoding:minimum_fraction': 0.1898871876010834,
'one_hot_encoding:use_minimum_fraction': 'True',
'preprocessor:__choice__': 'gem',
'preprocessor:gem:N': 20,
'preprocessor:gem:precond': 0.27540716190663134,
'rescaling:__choice__': 'min/max'})),
(0.160000, ParamSklearnClassifier(
configuration={
'balancing:strategy': 'none',
'classifier:__choice__': 'passive_aggressive',
'classifier:passive_aggressive:C': 8.011168723835382,
'classifier:passive_aggressive:fit_intercept': 'True',
'classifier:passive_aggressive:loss': 'hinge',
'classifier:passive_aggressive:n_iter': 20,
