def trainRF(x_train, x_test, y_train, y_test, path):
rf = model_zoo.RF('RF')
rf.build(x_train, y_train, path, n_estimators=200, max_depth=6)
rf.modelEvaluate(x_test, y_test, path)
rf.featureImportance(x_train, path)
ml.modelDump(rf, path + "rf.txt")
return rf
def trainDT(x_train, x_test, y_train, y_test, path):
dt = model_zoo.DecisionTree('dt')
dt.build(x_train, y_train, path, max_depth=10)
dt.modelEvaluate(x_test, y_test, path)
dt.saveTree(path)
ml.modelDump(dt, path + 'dt.txt')
return dt
def trainSVM(x_train, x_test, y_train, y_test, path):
svm = model_zoo.SVM('SVM')
svm.build(x_train,
y_train,
path,
C=10.0,
kernel='rbf',
class_weight=np.asarray([1.0, 10.0]),
gamma=1,
shrinking=True,
probability=True)
svm.modelEvaluate(x_test, y_test, path)
ml.modelDump(svm, path + "SVM.txt")
return svm
def trainLasso(x_train, x_test, y_train, y_test, path):
lasso = model_zoo.lasso('lasso')
lasso.build(x_train,
y_train,
path,
penalty='l1',
C=1,
class_weight={
0: 1,
1: 20
},
max_iter=100)
lasso.modelEvaluate(x_test, y_test, path)
# lasso.top_probality(x_test,path)
ml.modelDump(lasso, path + "lasso.txt")
return lasso
def trainGBDT(x_train, x_test, y_train, y_test, path):
GBDT = model_zoo.GBDT('GBDT')
GBDT.build(x_train, y_train, path, subsample=0.7)
GBDT.modelEvaluate(x_test, y_test, path)
ml.modelDump(GBDT, path + "gbdt.txt")
return GBDT