60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
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import json
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import logging
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from pathlib import Path
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from typing import Any, Dict, Tuple
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# import pickle as pk
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import numpy as np
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logger = logging.getLogger(__name__)
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class FreqaiDataDrawer:
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"""
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Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
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/loading to/from disk.
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This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
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reinstantiated for each coin.
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"""
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def __init__(self, full_path: Path):
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# dictionary holding all pair metadata necessary to load in from disk
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self.pair_dict: Dict[str, Any] = {}
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# dictionary holding all actively inferenced models in memory given a model filename
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self.model_dictionary: Dict[str, Any] = {}
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self.full_path = full_path
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self.load_drawer_from_disk()
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def load_drawer_from_disk(self):
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exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists()
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if exists:
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with open(self.full_path / str('pair_dictionary.json'), "r") as fp:
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self.pair_dict = json.load(fp)
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else:
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logger.info("Could not find existing datadrawer, starting from scratch")
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return exists
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def save_drawer_to_disk(self):
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with open(self.full_path / str('pair_dictionary.json'), "w") as fp:
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json.dump(self.pair_dict, fp, default=self.np_encoder)
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def np_encoder(self, object):
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if isinstance(object, np.generic):
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return object.item()
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def get_pair_dict_info(self, metadata: dict) -> Tuple[str, int, bool]:
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pair_in_dict = self.pair_dict.get(metadata['pair'])
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if pair_in_dict:
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model_filename = self.pair_dict[metadata['pair']]['model_filename']
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trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp']
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coin_first = self.pair_dict[metadata['pair']]['first']
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else:
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self.pair_dict[metadata['pair']] = {}
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model_filename = self.pair_dict[metadata['pair']]['model_filename'] = ''
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coin_first = self.pair_dict[metadata['pair']]['first'] = True
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trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0
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return model_filename, trained_timestamp, coin_first
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