class_docstr: LightGBM Model for high frequency prediction
class_docstr_tok: ["LightGBM","Model","for","high","frequency","prediction"]
docstr: finetune model
  
  Parameters
  ----------
  dataset : DatasetH
      dataset for finetuning
  num_boost_round : int
      number of round to finetune model
  verbose_eval : int
      verbose level
docstr_tok: ["finetune","model","Parameters","--","--","--","--","--","dataset",":","DatasetH","dataset","for","finetuning","num_boost_round",":","int","number","of","round","to","finetune","model","verbose_eval",":","int","verbose","level"]
code: def finetune(
      self, dataset: DatasetH, num_boost_round=10, verbose_eval=20
  ):
      """
      finetune model
  
      Parameters
      ----------
      dataset : DatasetH
          dataset for finetuning
      num_boost_round : int
          number of round to finetune model
      verbose_eval : int
          verbose level
      """
      # Based on existing model and finetune by train more rounds
      dtrain, _ = self._prepare_data(dataset)
      verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
      self.model = lgb.train(
          self.params,
          dtrain,
          num_boost_round=num_boost_round,
          init_model=self.model,
          valid_sets=[dtrain],
          valid_names=["train"],
          callbacks=[verbose_eval_callback],
      )
  
file_path: qlib/qlib/contrib/model/highfreq_gdbt_model.py
imports: import warnings
  import numpy
  import pandas
  import lightgbm
  from ...model.base import ModelFT
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...model.interpret.base import LightGBMFInt
label_desc: Use image node_id 7 for calling the HFLGBModel obj's underlying member method code with example usage: obj.finetune(dataset, num_boost_round, verbose_eval) without return types