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Minor cleanup (#2342)

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* Clean up demo of multiclass classification

* Remove extra space
This commit is contained in:
Juang, Yi-Lin 2017-05-26 08:40:41 -05:00 committed by Yuan (Terry) Tang
parent f1dc82e3e1
commit 6776292951
2 changed files with 20 additions and 17 deletions
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35
demo/multiclass_classification/train.py
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@ -1,22 +1,25 @@
#! /usr/bin/python
#!/usr/bin/python
from __future__ import division
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 } )
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_X = train[:, :33]
train_Y = train[:, 34]
test_X = test[:,0:33]
test_X = test[:, :33]
test_Y = test[:, 34]
xg_train = xgb.DMatrix( train_X, label=train_Y)
xg_train = xgb.DMatrix(train_X, label=train_Y)
xg_test = xgb.DMatrix(test_X, label=test_Y)
# setup parameters for xgboost
param = {}
@ -29,20 +32,20 @@ param['silent'] = 1
param['nthread'] = 4
param['num_class'] = 6
watchlist = [ (xg_train,'train'), (xg_test, 'test') ]
watchlist = [(xg_train, 'train'), (xg_test, 'test')]
num_round = 5
bst = xgb.train(param, xg_train, num_round, watchlist );
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)) ))
pred = bst.predict(xg_test)
error_rate = np.sum(pred != test_Y) / test_Y.shape[0]
print('Test error using softmax = {}'.format(error_rate))
# do the same thing again, but output probabilities
param['objective'] = 'multi:softprob'
bst = xgb.train(param, xg_train, num_round, watchlist );
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)) ))
pred_prob = bst.predict(xg_test).reshape(test_Y.shape[0], 6)
pred_label = np.argmax(pred_prob, axis=1)
error_rate = np.sum(pred != test_Y) / test_Y.shape[0]
print('Test error using softprob = {}'.format(error_rate))

2
python-package/xgboost/core.py
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@ -848,7 +848,7 @@ class Booster(object):
def eval_set(self, evals, iteration=0, feval=None):
# pylint: disable=invalid-name
"""Evaluate a set of data.
"""Evaluate a set of data.
Parameters
----------