本文整理匯總了Python中sklearn.feature_selection.SelectFdr.fit_transform方法的典型用法代碼示例。如果您正苦於以下問題：Python SelectFdr.fit_transform方法的具體用法？Python SelectFdr.fit_transform怎麽用？Python SelectFdr.fit_transform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.SelectFdr的用法示例。
在下文中一共展示了SelectFdr.fit_transform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: featureFitting
# 需要導入模塊: from sklearn.feature_selection import SelectFdr [as 別名]
# 或者: from sklearn.feature_selection.SelectFdr import fit_transform [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"
'''
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示例2: str
# 需要導入模塊: from sklearn.feature_selection import SelectFdr [as 別名]
# 或者: from sklearn.feature_selection.SelectFdr import fit_transform [as 別名]
fileName = r'\trainingSetFeatures.csv'
# filePath = r'E:\Dropbox\Dropbox\BioInformatics Lab\AA_Information\CODE\Feature_Extract\test_seq\Chap'
filePath = str(input('Input DIRRectory containing TrainingData csv '))
## features, labels, lb_encoder,featureNames = load_data(filename, 'file')
features, labels, lb_encoder,featureNames = load_data(filePath+fileName, 'file')
X, y = features, labels
print('len(set(y)',len(set(y)))
print(X.shape,"X = samples, features")
scale = StandardScaler(copy=False)
X = scale.fit_transform(X)
FD = SelectFdr(alpha=0.0005)
FD_K = SelectPercentile(percentile=70)
X = FD.fit_transform(X,y)
print(X.shape,"X post FDR alpha filter")
X_FD = FD_K.fit_transform(X,y)
print(X_FD.shape,"X post FDR+K-best alpha filter")
print("\n BASE X models: \n")
ModelParam_GridSearch(X,y,cv=Kcv)
'''
pca = PCA(n_components='mle')
X_PCA = pca.fit_transform(X)
print(X_PCA.shape,"X - PCA,mle")
ModelParam_GridSearch(X_PCA,y,cv=Kcv)
'''