demo

demo/multi_classification/train.py

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+
+import sys
+import numpy as np
+sys.path.append('../../python/')
+import xgboost as xgb
+
+
+
+data = np.loadtxt('./dermatology.data', delimiter=',',converters={33: lambda x:int(x == '?'), 34: lambda x:int(x) } )
+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 logistic regression loss, use raw prediction before logistic transformation
+# since we only need the rank
+param['objective'] = 'multi:softmax'
+# scale weight of positive examples
+param['bst:eta'] = 0.1
+param['bst:max_depth'] = 6
+param['eval_metric'] = 'auc'
+param['silent'] = 1
+param['nthread'] = 4
+param['num_class'] = 5
+
+watchlist = [ (xg_train,'train'), (xg_test, 'test') ]
+num_round = 5
+bst = xgb.train(param, xg_train, num_round, watchlist );
+
+
+

demo/multi_classification/wgetdata.sh

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+#! /bin/bash
+wget https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data