def regtree(par, *data):
X_train, X_test, Y_train, Y_test = data
regTreeModel=tree.DecisionTreeRegressor\
(max_features=par[0],min_samples_split=par[1],min_samples_leaf=par[2],
min_weight_fraction_leaf=par[3],max_leaf_nodes=int(par[4]))
fitModel = linear_model.LinearRegression()
Yp,Yptrain,regTreeModel,fitModelList,predind=\
SSRS.RegressionTree(X_train,X_test,Y_train,Y_test,regTreeModel,fitModel,Field,
doFitSelection=0,doMultiBand=1)
rmse, rmse_band = SSRS.RMSEcal(Yp, Y_test)
print(rmse)
return rmse
# nn = Regressor(
# layers=[
# Layer("Sigmoid", units=200),
# Layer("Sigmoid", units=200),
# Layer("Linear")],
# learning_rate=0.1,
# n_iter=200,verbose=1)
X_train,X_test,Y_train,Y_test = cross_validation.train_test_split(\
Xn,Y,test_size=0.2,random_state=0)
# predict correlation
Yp,Yptrain,regModelList=SSRS.Regression\
(X_train,X_test,Y_train,Y_test,multiband=1,regModel=regModel,doplot=0)
rmse, rmse_band = SSRS.RMSEcal(Yp, Y_test)
rmse_train, rmse_band_train = SSRS.RMSEcal(Yptrain, Y_train)
print(rmse)
print(rmse_train)
print(np.corrcoef(Yp[:, 0], Y_test[:, 0]))
par = [1.0, 16, 6, 20, 0.15]
regTreeModel=tree.DecisionTreeRegressor\
(max_features=par[0],max_depth=par[1],min_samples_split=par[2],min_samples_leaf=par[3],
min_weight_fraction_leaf=par[4],max_leaf_nodes=18)
fitModel = linear_model.LinearRegression()
Yp,Yptrain,regTreeModel,fitModelList,predind=SSRS.RegressionTree\
(X_train,X_test,Y_train,Y_test,regTreeModel,fitModel,Field,doFitSelection=0)
rmse, rmse_band = SSRS.RMSEcal(Yp, Y_test)
rmse_train, rmse_band_train = SSRS.RMSEcal(Yptrain, Y_train)
print(rmse)
