本文整理汇总了Python中imblearn.pipeline.Pipeline.fit_predict方法的典型用法代码示例。如果您正苦于以下问题：Python Pipeline.fit_predict方法的具体用法？Python Pipeline.fit_predict怎么用？Python Pipeline.fit_predict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imblearn.pipeline.Pipeline的用法示例。
在下文中一共展示了Pipeline.fit_predict方法的2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_fit_predict_with_intermediate_fit_params
# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import fit_predict [as 别名]
def test_fit_predict_with_intermediate_fit_params():
# tests that Pipeline passes fit_params to intermediate steps
# when fit_predict is invoked
pipe = Pipeline([('transf', TransfFitParams()), ('clf', FitParamT())])
pipe.fit_predict(
X=None, y=None, transf__should_get_this=True, clf__should_succeed=True)
assert pipe.named_steps['transf'].fit_params['should_get_this']
assert pipe.named_steps['clf'].successful
assert 'should_succeed' not in pipe.named_steps['transf'].fit_params示例2: test_fit_predict_on_pipeline
# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import fit_predict [as 别名]
def test_fit_predict_on_pipeline():
# test that the fit_predict method is implemented on a pipeline
# test that the fit_predict on pipeline yields same results as applying
# transform and clustering steps separately
iris = load_iris()
scaler = StandardScaler()
km = KMeans(random_state=0)
# first compute the transform and clustering step separately
scaled = scaler.fit_transform(iris.data)
separate_pred = km.fit_predict(scaled)
# use a pipeline to do the transform and clustering in one step
pipe = Pipeline([('scaler', scaler), ('Kmeans', km)])
pipeline_pred = pipe.fit_predict(iris.data)
assert_array_almost_equal(pipeline_pred, separate_pred)