Force high priority pair to be trained before anything else.
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@ -123,10 +123,6 @@ class FreqaiDataDrawer:
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self.pair_dict[metadata['pair']]['priority'] = len(self.pair_dict)
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self.pair_dict[metadata['pair']]['priority'] = len(self.pair_dict)
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return
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return
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# def create_training_queue(self, pairs: list) -> None:
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# for i, pair in enumerate(pairs):
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# self.training_queue[pair] = i + 1
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def pair_to_end_of_training_queue(self, pair: str) -> None:
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def pair_to_end_of_training_queue(self, pair: str) -> None:
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# march all pairs up in the queue
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# march all pairs up in the queue
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for p in self.pair_dict:
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for p in self.pair_dict:
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@ -215,7 +215,8 @@ class IFreqaiModel(ABC):
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self.data_drawer.return_null_values_to_strategy(dataframe, dh)
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self.data_drawer.return_null_values_to_strategy(dataframe, dh)
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return dh
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return dh
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if (not self.training_on_separate_thread and not self.follow_mode):
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if (not self.training_on_separate_thread and not self.follow_mode
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and self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1):
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file_exists = False
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file_exists = False
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if trained_timestamp != 0: # historical model available
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if trained_timestamp != 0: # historical model available
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