# coding: utf-8

import csv

from math import sqrt

import RF as rf

import numpy as np

import pandas as pd

import transferForest as trans

if __name__=='__main__':

dataSet, feature = rf.loadCSV('../../xxx.csv') #your file path, the file consists of source domain data and target domian data

rf.column_to_float(dataSet)

targetID = 'xxx' #specifiy the target domain ID

# parametres in random forests

n_folds = 10

max_depth = 15

min_size = 1

ratio = 1.0

n_features = 35

n_trees = 100

#cross validation

folds = rf.spiltDataSet(dataSet, n_folds)

for fold in folds:

train_set=folds[:]

train_set=sum(train_set,[])

test_set=[]

for row in fold:

row_copy=list(row)

row_copy[-1]=None

test_set.append(row_copy)

actual=[row[-1] for row in fold]

#====================train and test a transfer forest regression model=========================================

# train a transfer forest

transfer_forest = trans.transferForest(train_set, targetID, n_features, max_depth, min_size, n_trees, feature)

# make predictions test data

predict_values_transfer = trans.transfer_forest_predict(transfer_forest, test_set)

# compute error

error = rf.accuracy(predict_values_transfer,actual)

print (error)

#====================train and test a normal random froest regression model======================================

# train a model and make predictions on test data

predict_values = rf.random_forest(train_set, test_set, ratio, n_features, max_depth, min_size, n_trees)

# compute error

error = rf.accuracy(predict_values,actual)

print (error)