import numpy as np
import evaluation
from sklearn.cross_validation import KFold
from sklearn.feature_selection import RFE
from sklearn.feature_selection import SelectKBest
from datahandler import DataHandler
def f_regression(X, Y):
import sklearn
return sklearn.feature_selection.f_regression(X, Y, center=False) # center=True (the default) would not work ("ValueError: center=True only allowed for dense data") but should presumably work in general
if __name__ == '__main__':
[X, y] = DataHandler.getTrainingData()
X = DataHandler.getFeatures(X)
yCasual = y[0]
yRegistered = y[1]
kf = KFold(len(X), n_folds=10)
scoresCasualExtraTreesRegression = []
scoresRegisteredExtraTreesRegression = []
scoresTotalExtraTreesRegression = []
scoresCasualABR = []
scoresRegisteredABR = []
scoresTotalABR = []
mdlExtraTreesRegressorCasual = None
mdlExtraTreesRegressorRegistered = None
from sklearn.feature_selection import RFE
from sklearn.feature_selection import SelectKBest
from datahandler import DataHandler
def f_regression(X, Y):
import sklearn
return sklearn.feature_selection.f_regression(
X, Y, center=False
) # center=True (the default) would not work ("ValueError: center=True only allowed for dense data") but should presumably work in general
if __name__ == '__main__':
[X, y] = DataHandler.getTrainingData()
X = DataHandler.getFeatures(X)
yCasual = y[0]
yRegistered = y[1]
kf = KFold(len(X), n_folds=10)
scoresCasualExtraTreesRegression = []
scoresRegisteredExtraTreesRegression = []
scoresTotalExtraTreesRegression = []
scoresCasualABR = []
scoresRegisteredABR = []
scoresTotalABR = []
mdlExtraTreesRegressorCasual = None
mdlExtraTreesRegressorRegistered = None
