102 lines
3.8 KiB
Python
102 lines
3.8 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',
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'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))
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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 + 1)
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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))
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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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# test maximize parameter
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def neg_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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bst2 = xgb.train(param, dtrain, num_round, watchlist, logregobj, neg_evalerror, maximize=True)
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preds2 = bst2.predict(dtest)
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err2 = sum(1 for i in range(len(preds2))
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if int(preds2[i] > 0.5) != labels[i]) / float(len(preds2))
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assert err == err2
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def test_fpreproc(self):
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param = {'max_depth': 2, 'eta': 1, 'silent': 1,
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'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,
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'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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