本文整理汇总了Python中autosklearn.pipeline.classification.SimpleClassificationPipeline.pre_transform方法的典型用法代码示例。如果您正苦于以下问题：Python SimpleClassificationPipeline.pre_transform方法的具体用法？Python SimpleClassificationPipeline.pre_transform怎么用？Python SimpleClassificationPipeline.pre_transform使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类autosklearn.pipeline.classification.SimpleClassificationPipeline的用法示例。
在下文中一共展示了SimpleClassificationPipeline.pre_transform方法的1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_weighting_effect
# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import pre_transform [as 别名]
def test_weighting_effect(self):
data = sklearn.datasets.make_classification(
n_samples=1000, n_features=20, n_redundant=5, n_informative=5,
n_repeated=2, n_clusters_per_class=2, weights=[0.8, 0.2],
random_state=1)
for name, clf, acc_no_weighting, acc_weighting in \
[('adaboost', AdaboostClassifier, 0.709, 0.658),
('decision_tree', DecisionTree, 0.683, 0.701),
('extra_trees', ExtraTreesClassifier, 0.812, 0.8),
('gradient_boosting', GradientBoostingClassifier,
0.800, 0.760),
('random_forest', RandomForest, 0.846, 0.792),
('libsvm_svc', LibSVM_SVC, 0.571, 0.658),
('liblinear_svc', LibLinear_SVC, 0.685, 0.699),
('sgd', SGD, 0.65384615384615385, 0.38795986622073581)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
# Fit
data_ = copy.copy(data)
X_train = data_[0][:700]
Y_train = data_[1][:700]
X_test = data_[0][700:]
Y_test = data_[1][700:]
cs = SimpleClassificationPipeline.\
get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = SimpleClassificationPipeline(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.f1_score(predictions, Y_test),
places=3)
# pre_transform and fit_estimator
data_ = copy.copy(data)
X_train = data_[0][:700]
Y_train = data_[1][:700]
X_test = data_[0][700:]
Y_test = data_[1][700:]
cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
include={'classifier': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = SimpleClassificationPipeline(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, **fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.f1_score(
predictions, Y_test),
places=3)
for name, pre, acc_no_weighting, acc_weighting in \
[('extra_trees_preproc_for_classification',
ExtraTreesPreprocessorClassification, 0.7142857142857143,
0.72180451127819545),
('liblinear_svc_preprocessor', LibLinear_Preprocessor,
0.5934065934065933, 0.71111111111111114)]:
for strategy, acc in [('none', acc_no_weighting),
('weighting', acc_weighting)]:
data_ = copy.copy(data)
X_train = data_[0][:700]
Y_train = data_[1][:700]
X_test = data_[0][700:]
Y_test = data_[1][700:]
cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
include={'classifier': ['sgd'], 'preprocessor': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = SimpleClassificationPipeline(default, random_state=1)
predictor = classifier.fit(X_train, Y_train)
predictions = predictor.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.f1_score(
predictions, Y_test),
places=3)
# pre_transform and fit_estimator
data_ = copy.copy(data)
X_train = data_[0][:700]
Y_train = data_[1][:700]
X_test = data_[0][700:]
Y_test = data_[1][700:]
cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
include={'classifier': ['sgd'], 'preprocessor': [name]})
default = cs.get_default_configuration()
default._values['balancing:strategy'] = strategy
classifier = SimpleClassificationPipeline(default, random_state=1)
Xt, fit_params = classifier.pre_transform(X_train, Y_train)
classifier.fit_estimator(Xt, Y_train, **fit_params)
predictions = classifier.predict(X_test)
self.assertAlmostEqual(acc,
sklearn.metrics.f1_score(
#.........这里部分代码省略.........