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86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
import logging
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from typing import Any
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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(
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self,
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data_dictionary: dict[str, Any],
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prices_train: DataFrame,
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prices_test: DataFrame,
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dk: FreqaiDataKitchen,
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):
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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(
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SubprocVecEnv(
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[
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make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train, env_info=env_info)
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for i in range(self.max_threads)
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]
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)
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)
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eval_env_id = "eval_env"
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self.eval_env = VecMonitor(
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SubprocVecEnv(
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[
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make_env(
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self.MyRLEnv, eval_env_id, i, 1, test_df, prices_test, env_info=env_info
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)
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for i in range(self.max_threads)
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]
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)
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)
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self.eval_callback = MaskableEvalCallback(
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self.eval_env,
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deterministic=True,
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render=False,
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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 is_masking_supported(self.eval_env)),
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)
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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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