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remove remnants of single threaded version, ensure pair queue priority is checked before retraining

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This commit is contained in:
robcaulk 2022-05-28 14:55:07 +02:00
parent 2a4d1e2d64
commit e54614fa2f
1 changed files with 16 additions and 6 deletions
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22
freqtrade/freqai/freqai_interface.py
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@ -85,7 +85,7 @@ class IFreqaiModel(ABC):
# determine what the current pair will do # determine what the current pair will do
if self.live: if self.live:
if (not self.training_on_separate_thread and if (not self.training_on_separate_thread and
self.data_drawer.training_queue == 1): self.data_drawer.training_queue[metadata['pair']] == 1):
self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
self.live, metadata["pair"]) self.live, metadata["pair"])
@ -321,16 +321,26 @@ class IFreqaiModel(ABC):
base_dataframes, base_dataframes,
metadata) metadata)
self.model = self.train(unfiltered_dataframe, metadata, dh) try:
model = self.train(unfiltered_dataframe, metadata, dh)
except ValueError:
logger.warning('Value error encountered during training')
self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
self.training_on_separate_thread = False
self.retrain = False
return
self.data_drawer.pair_dict[metadata['pair']][ self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = new_trained_timerange.stopts 'trained_timestamp'] = new_trained_timerange.stopts
dh.set_new_model_names(metadata, new_trained_timerange) dh.set_new_model_names(metadata, new_trained_timerange)
self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) logger.info('Training queue'
dh.save_data(self.model, coin=metadata['pair']) f'{sorted(self.data_drawer.training_queue.items(), key=lambda item: item[1])}')
dh.save_data(model, coin=metadata['pair'])
self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
self.training_on_separate_thread = False self.training_on_separate_thread = False
self.retrain = False self.retrain = False
return
def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict, def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
strategy: IStrategy, dh: FreqaiDataKitchen): strategy: IStrategy, dh: FreqaiDataKitchen):
@ -344,13 +354,13 @@ class IFreqaiModel(ABC):
base_dataframes, base_dataframes,
metadata) metadata)
self.model = self.train(unfiltered_dataframe, metadata, dh) model = self.train(unfiltered_dataframe, metadata, dh)
self.data_drawer.pair_dict[metadata['pair']][ self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = new_trained_timerange.stopts 'trained_timestamp'] = new_trained_timerange.stopts
dh.set_new_model_names(metadata, new_trained_timerange) dh.set_new_model_names(metadata, new_trained_timerange)
self.data_drawer.pair_dict[metadata['pair']]['first'] = False self.data_drawer.pair_dict[metadata['pair']]['first'] = False
dh.save_data(self.model, coin=metadata['pair']) dh.save_data(model, coin=metadata['pair'])
self.retrain = False self.retrain = False
# Methods which are overridden by user made prediction models. # Methods which are overridden by user made prediction models.