81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
import numpy as np
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import xgboost as xgb
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import unittest
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dpath = 'demo/data/'
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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rng = np.random.RandomState(1994)
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class TestModels(unittest.TestCase):
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def test_glm(self):
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param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 4
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bst = xgb.train(param, dtrain, num_round, watchlist)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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def test_eta_decay(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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# learning_rates as a list
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.4, 0.3])
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assert isinstance(bst, xgb.core.Booster)
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# learning_rates as a customized decay function
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def eta_decay(ithround, num_boost_round):
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return num_boost_round / ithround
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
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assert isinstance(bst, xgb.core.Booster)
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def test_custom_objective(self):
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param = {'max_depth':2, 'eta':1, 'silent':1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds))
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grad = preds - labels
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hess = preds * (1.0-preds)
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return grad, hess
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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# test custom_objective in training
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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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# test custom_objective in cross-validation
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xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
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obj = logregobj, feval=evalerror)
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def test_fpreproc(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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def fpreproc(dtrain, dtest, param):
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label = dtrain.get_label()
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ratio = float(np.sum(label == 0)) / np.sum(label==1)
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param['scale_pos_weight'] = ratio
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return (dtrain, dtest, param)
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'auc'}, seed = 0, fpreproc = fpreproc)
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def test_show_stdv(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'error'}, seed = 0, show_stdv = False)
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