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69 lines
3.2 KiB
Python
69 lines
3.2 KiB
Python
import logging
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from typing import Any, Dict
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from pandas import DataFrame
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from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
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from sb3_contrib.common.maskable.utils import is_masking_supported
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from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
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from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
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logger = logging.getLogger(__name__)
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class ReinforcementLearner_multiproc(ReinforcementLearner):
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"""
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Demonstration of how to build vectorized environments
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"""
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def set_train_and_eval_environments(self, data_dictionary: Dict[str, Any],
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prices_train: DataFrame, prices_test: DataFrame,
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dk: FreqaiDataKitchen):
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"""
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User can override this if they are using a custom MyRLEnv
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:param data_dictionary: dict = common data dictionary containing train and test
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features/labels/weights.
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:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in
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the environment during training
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or testing
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:param dk: FreqaiDataKitchen = the datakitchen for the current pair
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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if self.train_env:
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self.train_env.close()
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if self.eval_env:
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self.eval_env.close()
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env_info = self.pack_env_dict(dk.pair)
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eval_freq = len(train_df) // self.max_threads
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env_id = "train_env"
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self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
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train_df, prices_train,
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env_info=env_info) for i
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in range(self.max_threads)]))
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eval_env_id = 'eval_env'
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self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
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test_df, prices_test,
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env_info=env_info) for i
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in range(self.max_threads)]))
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self.eval_callback = MaskableEvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=str(dk.data_path),
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use_masking=(self.model_type == 'MaskablePPO' and
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is_masking_supported(self.eval_env)))
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# TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
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# IT WILL RETURN FALSE INFORMATION, NEVERTHELESS NOT THREAD SAFE WITH SB3!!!
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actions = self.train_env.env_method("get_actions")[0]
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self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
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