2c1a439869
* Remove GPU memory usage demo. * Add tests for demos. * Remove `silent`. * Remove shebang as it's not portable.
51 lines
1.5 KiB
Python
Executable File
51 lines
1.5 KiB
Python
Executable File
#!/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})
|
|
sz = data.shape
|
|
|
|
train = data[:int(sz[0] * 0.7), :]
|
|
test = data[int(sz[0] * 0.7):, :]
|
|
|
|
train_X = train[:, :33]
|
|
train_Y = train[:, 34]
|
|
|
|
test_X = test[:, :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['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)
|
|
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)
|
|
# Note: this convention has been changed since xgboost-unity
|
|
# get prediction, this is in 1D array, need reshape to (ndata, nclass)
|
|
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_label != test_Y) / test_Y.shape[0]
|
|
print('Test error using softprob = {}'.format(error_rate))
|