del

demo/multi_classification/train.py

@@ -1,42 +0,0 @@
-
-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

@@ -1,2 +0,0 @@
-#! /bin/bash
-wget https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data