本文整理汇总了Python中sklearn.feature_selection.SelectFdr方法的典型用法代码示例。如果您正苦于以下问题：Python feature_selection.SelectFdr方法的具体用法？Python feature_selection.SelectFdr怎么用？Python feature_selection.SelectFdr使用的例子？那么, 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.SelectFdr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_objectmapper
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFdr [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2: featureFitting
# 需要导入模块: from sklearn import feature_selection [as 别名]
# 或者: from sklearn.feature_selection import SelectFdr [as 别名]
def featureFitting(filename, X, y, featureNames,optimalFlag, kbest=20, alpha=0.05, model=None):
'''
Gets the K-best features (filtered by FDR, then select best ranked by t-test, more advanced options can be implemented).
Save the data/matrix with the resulting/kept features to a new output file, "REDUCED_Feat.csv"
Returns new features matrix, FD scaler, and K-select scaler
'''
a=alpha
FD = SelectFdr(alpha=a)
X = FD.fit_transform(X,y)
selectK = SelectKBest(k=kbest)
selectK.fit(X,y)
selectK_mask=selectK.get_support()
K_featnames = featureNames[selectK_mask]
print("K_featnames: %s" %(K_featnames))
Reduced_df = pd.read_csv(filename, index_col=0)
Reduced_df = Reduced_df[Reduced_df.columns[selectK_mask]]
Reduced_df.to_csv('REDUCED_Feat.csv')
return Reduced_df, FD, selectK