83 lines
2.7 KiB
Python
Executable File
83 lines
2.7 KiB
Python
Executable File
#!/usr/bin/python
|
|
import numpy as np
|
|
import scipy.sparse
|
|
import pickle
|
|
import xgboost as xgb
|
|
|
|
### simple example
|
|
# load file from text file, also binary buffer generated by xgboost
|
|
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
|
|
dtest = xgb.DMatrix('../data/agaricus.txt.test')
|
|
|
|
# specify parameters via map, definition are same as c++ version
|
|
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
|
|
|
|
# specify validations set to watch performance
|
|
watchlist = [(dtest,'eval'), (dtrain,'train')]
|
|
num_round = 2
|
|
bst = xgb.train(param, dtrain, num_round, watchlist)
|
|
|
|
# this is prediction
|
|
preds = bst.predict(dtest)
|
|
labels = dtest.get_label()
|
|
print ('error=%f' % ( sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
|
|
bst.save_model('0001.model')
|
|
# dump model
|
|
bst.dump_model('dump.raw.txt')
|
|
# dump model with feature map
|
|
bst.dump_model('dump.nice.txt','../data/featmap.txt')
|
|
|
|
# save dmatrix into binary buffer
|
|
dtest.save_binary('dtest.buffer')
|
|
# save model
|
|
bst.save_model('xgb.model')
|
|
# load model and data in
|
|
bst2 = xgb.Booster(model_file='xgb.model')
|
|
dtest2 = xgb.DMatrix('dtest.buffer')
|
|
preds2 = bst2.predict(dtest2)
|
|
# assert they are the same
|
|
assert np.sum(np.abs(preds2-preds)) == 0
|
|
|
|
# alternatively, you can pickle the booster
|
|
pks = pickle.dumps(bst2)
|
|
# load model and data in
|
|
bst3 = pickle.loads(pks)
|
|
preds3 = bst3.predict(dtest2)
|
|
# assert they are the same
|
|
assert np.sum(np.abs(preds3-preds)) == 0
|
|
|
|
###
|
|
# build dmatrix from scipy.sparse
|
|
print ('start running example of build DMatrix from scipy.sparse CSR Matrix')
|
|
labels = []
|
|
row = []; col = []; dat = []
|
|
i = 0
|
|
for l in open('../data/agaricus.txt.train'):
|
|
arr = l.split()
|
|
labels.append(int(arr[0]))
|
|
for it in arr[1:]:
|
|
k,v = it.split(':')
|
|
row.append(i); col.append(int(k)); dat.append(float(v))
|
|
i += 1
|
|
csr = scipy.sparse.csr_matrix((dat, (row,col)))
|
|
dtrain = xgb.DMatrix(csr, label = labels)
|
|
watchlist = [(dtest,'eval'), (dtrain,'train')]
|
|
bst = xgb.train(param, dtrain, num_round, watchlist)
|
|
|
|
print ('start running example of build DMatrix from scipy.sparse CSC Matrix')
|
|
# we can also construct from csc matrix
|
|
csc = scipy.sparse.csc_matrix((dat, (row,col)))
|
|
dtrain = xgb.DMatrix(csc, label=labels)
|
|
watchlist = [(dtest,'eval'), (dtrain,'train')]
|
|
bst = xgb.train(param, dtrain, num_round, watchlist)
|
|
|
|
print ('start running example of build DMatrix from numpy array')
|
|
# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation
|
|
# then convert to DMatrix
|
|
npymat = csr.todense()
|
|
dtrain = xgb.DMatrix(npymat, label = labels)
|
|
watchlist = [(dtest,'eval'), (dtrain,'train')]
|
|
bst = xgb.train(param, dtrain, num_round, watchlist)
|
|
|
|
|