本文整理汇总了Python中rep.test.test_estimators.check_classifier函数的典型用法代码示例。如果您正苦于以下问题：Python check_classifier函数的具体用法？Python check_classifier怎么用？Python check_classifier使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了check_classifier函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_pybrain_SoftMax_Tanh
def test_pybrain_SoftMax_Tanh():
check_classifier(PyBrainClassifier(epochs=10, layers=[5, 2], hiddenclass=['TanhLayer', 'SoftmaxLayer'],
use_rprop=True),
**classifier_params)
check_regression(
PyBrainRegressor(epochs=2, layers=[10, 5, 2], hiddenclass=['TanhLayer', 'SoftmaxLayer', 'TanhLayer']),
**regressor_params)示例2: test_models
def test_models():
for _ in range(3):
clf = CacheClassifier('clf', SGDClassifier(loss='log'))
check_classifier(clf, has_staged_pp=False, has_importances=False)
reg = CacheRegressor('reg', SGDRegressor())
check_regression(reg, has_staged_predictions=False, has_importances=False)
cache_helper.clear_cache()示例3: test_theanets_configurations
def test_theanets_configurations():
check_classifier(
TheanetsClassifier(layers=[20], scaler=False,
trainers=[{'optimize': 'nag', 'learning_rate': 0.3, 'min_improvement': 0.5}]),
**classifier_params)
check_classifier(
TheanetsClassifier(layers=[5, 5], trainers=[{'optimize': 'nag', 'learning_rate': 0.3, 'min_improvement': 0.5}]),
**classifier_params)示例4: test_theanets_configurations
def test_theanets_configurations():
check_classifier(
TheanetsClassifier(layers=[13], scaler=False,
trainers=[{'algo': 'nag', 'learning_rate': 0.1}]),
**classifier_params)
check_classifier(
TheanetsClassifier(layers=[5, 5], scaler='minmax',
trainers=[{'algo': 'adadelta', 'learning_rate': 0.1}]),
**classifier_params)示例5: test_theanets_configurations
def test_theanets_configurations():
check_classifier(
TheanetsClassifier(layers=[13], scaler=False,
trainers=[dict(algo='nag', learning_rate=0.1, **impatient)]),
**classifier_params)
check_classifier(
TheanetsClassifier(layers=[5, 5],
trainers=[dict(algo='adam', learning_rate=0.01, momentum=0.9)]
),
**classifier_params)示例6: test_theanets_single_classification
def test_theanets_single_classification():
check_classifier(TheanetsClassifier(),
supports_weight=False, has_staged_pp=False, has_importances=False)
check_classifier(TheanetsClassifier(layers=[]),
supports_weight=False, has_staged_pp=False, has_importances=False)
check_classifier(TheanetsClassifier(layers=[20], trainers=[{'optimize': 'sgd', 'learning_rate': 0.3}]),
supports_weight=False, has_staged_pp=False, has_importances=False)
check_classifier(TheanetsClassifier(layers=[5, 5], trainers=[{'optimize': 'sgd', 'learning_rate': 0.3}]),
supports_weight=False, has_staged_pp=False, has_importances=False)
check_classifier(TheanetsClassifier(layers=[5, 5], trainers=[{'optimize': 'sgd', 'learning_rate': 0.3}]),
supports_weight=False, has_staged_pp=False, has_importances=False)示例7: test_neurolab_single_classification
def test_neurolab_single_classification():
check_classifier(NeurolabClassifier(layers=[], epochs=N_EPOCHS2, trainf=None),
**classifier_params)
check_classifier(NeurolabClassifier(layers=[2], epochs=N_EPOCHS2),
**classifier_params)
check_classifier(NeurolabClassifier(layers=[1, 1], epochs=N_EPOCHS2),
**classifier_params)示例8: test_nolearn_classification
def test_nolearn_classification():
cl = NolearnClassifier()
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False, supports_weight=False)
cl = NolearnClassifier(layers=[])
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False, supports_weight=False)
cl = NolearnClassifier(layers=[5, 5])
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False, supports_weight=False)示例9: test_tmva
def test_tmva():
# check classifier
check_classifier(TMVAClassifier(), check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(method='kSVM', Gamma=0.25, Tol=0.001, sigmoid_function='identity')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(method='kCuts', FitMethod='GA', EffMethod='EffSel', sigmoid_function='sig_eff=0.9')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(), check_instance=True, has_staged_predictions=False, has_importances=False)示例10: test_tmva
def test_tmva():
# check classifier
factory_options = "Silent=True:V=False:DrawProgressBar=False"
cl = TMVAClassifier(factory_options=factory_options, method='kBDT', NTrees=10)
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(factory_options=factory_options, method='kSVM', Gamma=0.25, Tol=0.001,
sigmoid_function='identity')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
cl = TMVAClassifier(factory_options=factory_options, method='kCuts',
FitMethod='GA', EffMethod='EffSel', sigmoid_function='sig_eff=0.9')
check_classifier(cl, check_instance=True, has_staged_pp=False, has_importances=False)
# check regressor, need to run twice to check for memory leak.
for i in range(2):
check_regression(TMVARegressor(factory_options=factory_options, method='kBDT', NTrees=10), check_instance=True,
has_staged_predictions=False, has_importances=False)示例11: test_simple_stacking_pybrain
def test_simple_stacking_pybrain():
base_pybrain = PyBrainClassifier(epochs=2)
base_bagging = BaggingClassifier(base_estimator=base_pybrain, n_estimators=3)
check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)示例12: test_complex_stacking_xgboost
def test_complex_stacking_xgboost():
# Ada over kFold over xgboost
base_kfold = FoldingClassifier(base_estimator=XGBoostClassifier())
check_classifier(SklearnClassifier(clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)),
has_staged_pp=False, has_importances=False)示例13: test_complex_stacking_tmva
def test_complex_stacking_tmva():
# Ada over kFold over TMVA
base_kfold = FoldingClassifier(base_estimator=TMVAClassifier(), random_state=13)
check_classifier(SklearnClassifier(clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)),
has_staged_pp=False, has_importances=False)示例14: test_simple_stacking_tmva
def test_simple_stacking_tmva():
base_tmva = TMVAClassifier()
check_classifier(SklearnClassifier(clf=BaggingClassifier(base_estimator=base_tmva, n_estimators=3, random_state=13)),
has_staged_pp=False, has_importances=False)示例15: test_theanets_single_classification
def test_theanets_single_classification():
check_classifier(TheanetsClassifier(trainers=[{'patience': 0}]), **classifier_params)
check_classifier(TheanetsClassifier(layers=[], scaler='minmax',
trainers=[{'patience': 0}]), **classifier_params)