本文整理汇总了Python中sklearn.feature_selection.SelectFpr方法的典型用法代码示例。如果您正苦于以下问题：Python feature_selection.SelectFpr方法的具体用法？Python feature_selection.SelectFpr怎么用？Python feature_selection.SelectFpr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sklearn.feature_selection的用法示例。
在下文中一共展示了feature_selection.SelectFpr方法的5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_clone
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFpr [as 别名]
def test_clone():
# Tests that clone creates a correct deep copy.
# We create an estimator, make a copy of its original state
# (which, in this case, is the current state of the estimator),
# and check that the obtained copy is a correct deep copy.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
new_selector = clone(selector)
assert selector is not new_selector
assert_equal(selector.get_params(), new_selector.get_params())
selector = SelectFpr(f_classif, alpha=np.zeros((10, 2)))
new_selector = clone(selector)
assert selector is not new_selector 示例2: test_objectmapper
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFpr [as 别名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.feature_selection.GenericUnivariateSelect,
fs.GenericUnivariateSelect)
self.assertIs(df.feature_selection.SelectPercentile,
fs.SelectPercentile)
self.assertIs(df.feature_selection.SelectKBest, fs.SelectKBest)
self.assertIs(df.feature_selection.SelectFpr, fs.SelectFpr)
self.assertIs(df.feature_selection.SelectFromModel,
fs.SelectFromModel)
self.assertIs(df.feature_selection.SelectFdr, fs.SelectFdr)
self.assertIs(df.feature_selection.SelectFwe, fs.SelectFwe)
self.assertIs(df.feature_selection.RFE, fs.RFE)
self.assertIs(df.feature_selection.RFECV, fs.RFECV)
self.assertIs(df.feature_selection.VarianceThreshold,
fs.VarianceThreshold) 示例3: test_clone
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFpr [as 别名]
def test_clone():
# Tests that clone creates a correct deep copy.
# We create an estimator, make a copy of its original state
# (which, in this case, is the current state of the estimator),
# and check that the obtained copy is a correct deep copy.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
new_selector = clone(selector)
assert_true(selector is not new_selector)
assert_equal(selector.get_params(), new_selector.get_params())
selector = SelectFpr(f_classif, alpha=np.zeros((10, 2)))
new_selector = clone(selector)
assert_true(selector is not new_selector) 示例4: test_clone_2
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFpr [as 别名]
def test_clone_2():
# Tests that clone doesn't copy everything.
# We first create an estimator, give it an own attribute, and
# make a copy of its original state. Then we check that the copy doesn't
# have the specific attribute we manually added to the initial estimator.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
selector.own_attribute = "test"
new_selector = clone(selector)
assert not hasattr(new_selector, "own_attribute") 示例5: test_clone_2
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFpr [as 别名]
def test_clone_2():
# Tests that clone doesn't copy everything.
# We first create an estimator, give it an own attribute, and
# make a copy of its original state. Then we check that the copy doesn't
# have the specific attribute we manually added to the initial estimator.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
selector.own_attribute = "test"
new_selector = clone(selector)
assert_false(hasattr(new_selector, "own_attribute"))