text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  import time
  
  import numpy as np
  
  import tvm
  from tvm import te
  from tvm import autotvm
  from tvm.autotvm import MeasureInput, MeasureResult
  from tvm.autotvm.tuner.xgboost_cost_model import XGBoostCostModel
  
  from test_autotvm_common import get_sample_task, get_sample_records
  
  
  def test_fit():
      task, target = get_sample_task()
      records = get_sample_records(n=500)
  
      base_model = XGBoostCostModel(task, feature_type="itervar", loss_type="rank")
      base_model.fit_log(records, plan_size=32)
  
      upper_model = XGBoostCostModel(task, feature_type="itervar", loss_type="rank")
      upper_model.load_basemodel(base_model)
  
      xs = np.arange(10)
      ys = np.arange(10)
  
      upper_model.fit(xs, ys, plan_size=32)
  
  
  def test_tuner():
      task, target = get_sample_task()
      records = get_sample_records(n=100)
  
      tuner = autotvm.tuner.XGBTuner(task)
      tuner.load_history(records)
  
  
  if __name__ == "__main__":
      test_fit()
      test_tuner()
  