本文整理匯總了Python中sklearn.feature_selection.SelectFdr方法的典型用法代碼示例。如果您正苦於以下問題：Python feature_selection.SelectFdr方法的具體用法？Python feature_selection.SelectFdr怎麽用？Python feature_selection.SelectFdr使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.feature_selection的用法示例。
在下文中一共展示了feature_selection.SelectFdr方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_objectmapper
# 需要導入模塊: from sklearn import feature_selection [as 別名]
# 或者: from sklearn.feature_selection import SelectFdr [as 別名]
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
# 需要導入模塊: from sklearn import feature_selection [as 別名]
# 或者: from sklearn.feature_selection import SelectFdr [as 別名]
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