44811f2330
* Fix #3485, #3540: Don't use dropout for predicting test sets Dropout (for DART) should only be used at training time. * Add regression test
207 lines
8.6 KiB
Python
207 lines
8.6 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, 'nthread': 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 < 0.2
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def test_dart(self):
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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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param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'silent': False}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest, ntree_limit=num_round)
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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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# error must be smaller than 10%
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assert err < 0.1
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# save dmatrix into binary buffer
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dtest.save_binary('dtest.buffer')
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# save model
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bst.save_model('xgb.model.dart')
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# load model and data in
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bst2 = xgb.Booster(params=param, model_file='xgb.model.dart')
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dtest2 = xgb.DMatrix('dtest.buffer')
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preds2 = bst2.predict(dtest2, ntree_limit=num_round)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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# regression test for issues #3485, #3540
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for _ in range(10):
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bst3 = xgb.Booster(params=param, model_file='xgb.model.dart')
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dtest3 = xgb.DMatrix('dtest.buffer')
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preds3 = bst3.predict(dtest3)
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# assert they are the same
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assert np.sum(np.abs(preds3 - preds)) == 0, 'preds3 = {}, preds = {}'.format(preds3, preds)
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# check whether sample_type and normalize_type work
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num_round = 50
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param['silent'] = True
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param['learning_rate'] = 0.1
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param['rate_drop'] = 0.1
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preds_list = []
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for p in [[p0, p1] for p0 in ['uniform', 'weighted'] for p1 in ['tree', 'forest']]:
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param['sample_type'] = p[0]
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param['normalize_type'] = p[1]
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bst = xgb.train(param, dtrain, num_round, watchlist)
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preds = bst.predict(dtest, ntree_limit=num_round)
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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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preds_list.append(preds)
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for ii in range(len(preds_list)):
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for jj in range(ii + 1, len(preds_list)):
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assert np.sum(np.abs(preds_list[ii] - preds_list[jj])) > 0
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def test_eta_decay(self):
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 4
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# learning_rates as a list
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# init eta with 0 to check whether learning_rates work
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param = {'max_depth': 2, 'eta': 0, 'silent': 1, 'objective': 'binary:logistic'}
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evals_result = {}
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
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evals_result=evals_result)
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eval_errors = list(map(float, evals_result['eval']['error']))
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assert isinstance(bst, xgb.core.Booster)
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# validation error should decrease, if eta > 0
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assert eval_errors[0] > eval_errors[-1]
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# init learning_rate with 0 to check whether learning_rates work
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param = {'max_depth': 2, 'learning_rate': 0, 'silent': 1, 'objective': 'binary:logistic'}
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evals_result = {}
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
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evals_result=evals_result)
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eval_errors = list(map(float, evals_result['eval']['error']))
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assert isinstance(bst, xgb.core.Booster)
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# validation error should decrease, if learning_rate > 0
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assert eval_errors[0] > eval_errors[-1]
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# check if learning_rates override default value of eta/learning_rate
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param = {'max_depth': 2, 'silent': 1, 'objective': 'binary:logistic'}
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evals_result = {}
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0, 0, 0, 0],
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evals_result=evals_result)
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eval_errors = list(map(float, evals_result['eval']['error']))
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assert isinstance(bst, xgb.core.Booster)
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# validation error should not decrease, if eta/learning_rate = 0
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assert eval_errors[0] == eval_errors[-1]
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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_multi_eval_metric(self):
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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param = {'max_depth': 2, 'eta': 0.2, 'silent': 1, 'objective': 'binary:logistic'}
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param['eval_metric'] = ["auc", "logloss", 'error']
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evals_result = {}
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bst = xgb.train(param, dtrain, 4, watchlist, evals_result=evals_result)
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assert isinstance(bst, xgb.core.Booster)
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assert len(evals_result['eval']) == 3
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assert set(evals_result['eval'].keys()) == {'auc', 'error', 'logloss'}
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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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def test_feature_names_validation(self):
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X = np.random.random((10, 3))
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y = np.random.randint(2, size=(10,))
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dm1 = xgb.DMatrix(X, y)
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dm2 = xgb.DMatrix(X, y, feature_names=("a", "b", "c"))
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bst = xgb.train([], dm1)
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bst.predict(dm1) # success
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self.assertRaises(ValueError, bst.predict, dm2)
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bst.predict(dm1) # success
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bst = xgb.train([], dm2)
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bst.predict(dm2) # success
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self.assertRaises(ValueError, bst.predict, dm1)
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bst.predict(dm2) # success
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