本文整理汇总了Python中imblearn.pipeline.Pipeline.get_params方法的典型用法代码示例。如果您正苦于以下问题：Python Pipeline.get_params方法的具体用法？Python Pipeline.get_params怎么用？Python Pipeline.get_params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.get_params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_pipeline_init
# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import get_params [as 别名]
def test_pipeline_init():
# Test the various init parameters of the pipeline.
assert_raises(TypeError, Pipeline)
# Check that we can't instantiate pipelines with objects without fit
# method
pipe = assert_raises(TypeError, Pipeline, [('svc', IncorrectT)])
# Smoke test with only an estimator
clf = T()
pipe = Pipeline([('svc', clf)])
assert_equal(
pipe.get_params(deep=True),
dict(
svc__a=None, svc__b=None, svc=clf, **pipe.get_params(deep=False)))
# Check that params are set
pipe.set_params(svc__a=0.1)
assert_equal(clf.a, 0.1)
assert_equal(clf.b, None)
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't use the same stage name twice
assert_raises(ValueError, Pipeline, [('svc', SVC()), ('svc', SVC())])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert_equal(clf.C, 0.1)
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
assert_raises(ValueError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert_equal(params, params2)示例2: test_pipeline_init
# 需要导入模块: from imblearn.pipeline import Pipeline [as 别名]
# 或者: from imblearn.pipeline.Pipeline import get_params [as 别名]
def test_pipeline_init():
# Test the various init parameters of the pipeline.
with raises(TypeError):
Pipeline()
# Check that we can't instantiate pipelines with objects without fit
# method
error_regex = 'Last step of Pipeline should implement fit. .*NoFit.*'
with raises(TypeError, match=error_regex):
Pipeline([('clf', NoFit())])
# Smoke test with only an estimator
clf = NoTrans()
pipe = Pipeline([('svc', clf)])
expected = dict(svc__a=None, svc__b=None, svc=clf,
**pipe.get_params(deep=False))
assert pipe.get_params(deep=True) == expected
# Check that params are set
pipe.set_params(svc__a=0.1)
assert clf.a == 0.1
assert clf.b is None
# Smoke test the repr:
repr(pipe)
# Test with two objects
clf = SVC()
filter1 = SelectKBest(f_classif)
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that we can't instantiate with non-transformers on the way
# Note that NoTrans implements fit, but not transform
error_regex = 'implement fit and transform or sample'
with raises(TypeError, match=error_regex):
Pipeline([('t', NoTrans()), ('svc', clf)])
# Check that params are set
pipe.set_params(svc__C=0.1)
assert clf.C == 0.1
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
with raises(ValueError):
pipe.set_params(anova__C=0.1)
# Test clone
pipe2 = clone(pipe)
assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']
# Check that apart from estimators, the parameters are the same
params = pipe.get_params(deep=True)
params2 = pipe2.get_params(deep=True)
for x in pipe.get_params(deep=False):
params.pop(x)
for x in pipe2.get_params(deep=False):
params2.pop(x)
# Remove estimators that where copied
params.pop('svc')
params.pop('anova')
params2.pop('svc')
params2.pop('anova')
assert params == params2