XGBoost：多分类问题

下面用数据 UCI Dermatology dataset演示XGBoost的多分类问题

首先要安装好XGBoost的C++版本和相应的Python模块，然后执行如下脚本，如果本地没有训练所需要的数据，runexp.sh负责从https://archive.ics.uci.edu/ml/datasets/Dermatology下载数据集，然后调用train.py

1.Run runexp.sh

./runexp.sh

runexp.sh的代码

#!/bin/bash if [ -f dermatology.data ] then echo "use existing data to run multi class classification" else echo "getting data from uci, make sure you are connected to internet" wget https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data fi python train.py

train.py的代码

#! /usr/bin/python import numpy as np import xgboost as xgb # label need to be 0 to num_class -1 data = np.loadtxt('./dermatology.data', delimiter=',',converters={33: lambda x:int(x == '?'), 34: lambda x:int(x)-1 } ) sz = data.shape train = data[:int(sz[0] * 0.7), :] test = data[int(sz[0] * 0.7):, :] train_X = train[:,0:33] train_Y = train[:, 34] test_X = test[:,0:33] test_Y = test[:, 34] xg_train = xgb.DMatrix( train_X, label=train_Y) xg_test = xgb.DMatrix(test_X, label=test_Y) # setup parameters for xgboost param = {} # use softmax multi-class classification param['objective'] = 'multi:softmax' # scale weight of positive examples param['eta'] = 0.1 param['max_depth'] = 6 param['silent'] = 1 param['nthread'] = 4 param['num_class'] = 6 watchlist = [ (xg_train,'train'), (xg_test, 'test') ] num_round = 5 bst = xgb.train(param, xg_train, num_round, watchlist ); # get prediction pred = bst.predict( xg_test ); print ('predicting, classification error=%f' % (sum( int(pred[i]) != test_Y[i] for i in range(len(test_Y))) / float(len(test_Y)) )) # do the same thing again, but output probabilities param['objective'] = 'multi:softprob' bst = xgb.train(param, xg_train, num_round, watchlist ); # Note: this convention has been changed since xgboost-unity # get prediction, this is in 1D array, need reshape to (ndata, nclass) yprob = bst.predict( xg_test ).reshape( test_Y.shape[0], 6 ) ylabel = np.argmax(yprob, axis=1) print ('predicting, classification error=%f' % (sum( int(ylabel[i]) != test_Y[i] for i in range(len(test_Y))) / float(len(test_Y)) ))