Python sklearn.feature_selection.SelectFdr() Examples

The following are 2 code examples of sklearn.feature_selection.SelectFdr(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.feature_selection , or try the search function .
Example #1
Source File: test_feature_selection.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
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)
Example #2
Source File: Model_trainer.py    From ProFET with GNU General Public License v3.0 6 votes vote down vote up 
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