log(tmpCurFeatureList)
newX = pd.DataFrame()
for tmpFeature in tmpCurFeatureList:
path = _basePath + tmpFeature + "_train.csv"
dr = DataReader()
tmpX = dr.cvtPathListToDfList(path, "test")
newX = pd.concat([newX, tmpX], axis=1)
#log("feature len: " , len(newX))
# Get all best model from newX
fab = ModelFactory()
fab._setXgboostTheradToOne = True
fab._gridSearchFlag = True
fab._onlyTreeBasedModels = True
fab._subFolderName = "one_hot_each_" + str(i)
fab._n_iter_search = 30
fab._expInfo = expInfo
# fab.getAllModels(newX, newY)
fab.getRandomForestClf(newX, newY)
# fab.getXgboostClf(newX, newY)
log ( i , "/32 done..." )
# t = pd.get_dummies(newY)
# finalList = []
# for tmp in range(0,len(t)):
# tmpList =[]
# for i in range(0, len(t.ix[tmp])):
# tmpList.append( int( t.ix[tmp][i]))
# finalList.append(tmpList)
# print finalList
#exit()
#print len(newX)
# Get all best model from newX
fab = ModelFactory()
fab._setXgboostTheradToOne = False
fab._gridSearchFlag = True
fab._subFolderName = "ismail3"
fab._n_iter_search = 1
fab._expInfo = expInfo
#clf = fab.getXgboostClf(newX, newY)
clf = fab.getRandomForestClf(newX, newY)
#print fab.getLogloss(clf,newX,newY)
def llfun(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
