code: import xgboost as xgb
  import testing as tm
  import numpy as np
  import unittest
  
  rng = np.random.RandomState(1337)
  
  
  class TestTrainingContinuation(unittest.TestCase):
      num_parallel_tree = 3
  
      xgb_params_01 = {
          'silent': 1,
          'nthread': 1,
      }
  
      xgb_params_02 = {
          'silent': 1,
          'nthread': 1,
          'num_parallel_tree': num_parallel_tree
      }
  
      xgb_params_03 = {
          'silent': 1,
          'nthread': 1,
          'num_class': 5,
          'num_parallel_tree': num_parallel_tree
      }
  
      def test_training_continuation(self):
          tm._skip_if_no_sklearn()
          from sklearn.datasets import load_digits
          from sklearn.metrics import mean_squared_error
  
          digits_2class = load_digits(2)
          digits_5class = load_digits(5)
  
          X_2class = digits_2class['data']
          y_2class = digits_2class['target']
  
          X_5class = digits_5class['data']
          y_5class = digits_5class['target']
  
          dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
          dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
  
          gbdt_01 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10)
          ntrees_01 = len(gbdt_01.get_dump())
          assert ntrees_01 == 10
  
          gbdt_02 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=0)
          gbdt_02.save_model('xgb_tc.model')
  
          gbdt_02a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02)
          gbdt_02b = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model="xgb_tc.model")
          ntrees_02a = len(gbdt_02a.get_dump())
          ntrees_02b = len(gbdt_02b.get_dump())
          assert ntrees_02a == 10
          assert ntrees_02b == 10
  
          res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
          res2 = mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
          assert res1 == res2
  
          res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
          res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
          assert res1 == res2
  
          gbdt_03 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=3)
          gbdt_03.save_model('xgb_tc.model')
  
          gbdt_03a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03)
          gbdt_03b = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model="xgb_tc.model")
          ntrees_03a = len(gbdt_03a.get_dump())
          ntrees_03b = len(gbdt_03b.get_dump())
          assert ntrees_03a == 10
          assert ntrees_03b == 10
  
          res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
          res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
          assert res1 == res2
  
          gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, num_boost_round=3)
          assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
  
          res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
          res2 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
          assert res1 == res2
  
          gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, num_boost_round=7, xgb_model=gbdt_04)
          assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
  
          res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
          res2 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
          assert res1 == res2
  
          gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=7)
          assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
          gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05)
          assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
  
          res1 = gbdt_05.predict(dtrain_5class)
          res2 = gbdt_05.predict(dtrain_5class, ntree_limit=gbdt_05.best_ntree_limit)
          np.testing.assert_almost_equal(res1, res2)
  
apis: numpy.random.RandomState sklearn.datasets.load_digits numpy.testing.assert_almost_equal