fix tensorboard path so that users can track all historical models
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2493e0c8a5
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240b529533
@ -72,8 +72,8 @@
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"5m",
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"30m"
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],
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"indicator_max_period_candles": 10,
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"indicator_periods_candles": [5]
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [14]
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},
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"data_split_parameters": {
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"test_size": 0.5,
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@ -28,14 +28,14 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
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if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(dk.data_path / "tensorboard"),
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tensorboard_log=Path(
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dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
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**self.freqai_info['model_training_parameters']
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)
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else:
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logger.info('Continual training activated - starting training from previously '
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'trained agent.')
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model = self.dd.model_dictionary[dk.pair]
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model.tensorboard_log = Path(dk.data_path / "tensorboard")
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model.set_env(self.train_env)
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model.learn(
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@ -31,14 +31,14 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
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if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(dk.full_path / "tensorboard"),
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tensorboard_log=Path(
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dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
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**self.freqai_info['model_training_parameters']
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)
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else:
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logger.info('Continual learning activated - starting training from previously '
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'trained agent.')
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model = self.dd.model_dictionary[dk.pair]
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model.tensorboard_log = Path(dk.data_path / "tensorboard")
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model.set_env(self.train_env)
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model.learn(
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