import collections import copy import json import logging import re import shutil from pathlib import Path from typing import Any, Dict, Tuple # import pickle as pk import numpy as np from pandas import DataFrame # from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class FreqaiDataDrawer: """ Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving /loading to/from disk. This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is reinstantiated for each coin. """ def __init__(self, full_path: Path, config: dict, follow_mode: bool = False): self.config = config # dictionary holding all pair metadata necessary to load in from disk self.pair_dict: Dict[str, Any] = {} # dictionary holding all actively inferenced models in memory given a model filename self.model_dictionary: Dict[str, Any] = {} self.model_return_values: Dict[str, Any] = {} self.pair_data_dict: Dict[str, Any] = {} self.historic_data: Dict[str, Any] = {} # self.populated_historic_data: Dict[str, Any] = {} ? self.follower_dict: Dict[str, Any] = {} self.full_path = full_path self.follow_mode = follow_mode if follow_mode: self.create_follower_dict() self.load_drawer_from_disk() self.training_queue: Dict[str, int] = {} # self.create_training_queue(pair_whitelist) def load_drawer_from_disk(self): """ Locate and load a previously saved data drawer full of all pair model metadata in present model folder. :returns: exists: bool = whether or not the drawer was located """ exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists() if exists: with open(self.full_path / str('pair_dictionary.json'), "r") as fp: self.pair_dict = json.load(fp) elif not self.follow_mode: logger.info("Could not find existing datadrawer, starting from scratch") else: logger.warning(f'Follower could not find pair_dictionary at {self.full_path} ' 'sending null values back to strategy') return exists def save_drawer_to_disk(self): """ Save data drawer full of all pair model metadata in present model folder. """ with open(self.full_path / str('pair_dictionary.json'), "w") as fp: json.dump(self.pair_dict, fp, default=self.np_encoder) def save_follower_dict_to_disk(self): """ Save follower dictionary to disk (used by strategy for persistent prediction targets) """ follower_name = self.config.get('bot_name', 'follower1') with open(self.full_path / str('follower_dictionary-' + follower_name + '.json'), "w") as fp: json.dump(self.follower_dict, fp, default=self.np_encoder) def create_follower_dict(self): """ Create or dictionary for each follower to maintain unique persistent prediction targets """ follower_name = self.config.get('bot_name', 'follower1') whitelist_pairs = self.config.get('exchange', {}).get('pair_whitelist') exists = Path(self.full_path / str('follower_dictionary-' + follower_name + '.json')).resolve().exists() if exists: logger.info('Found an existing follower dictionary') for pair in whitelist_pairs: self.follower_dict[pair] = {} with open(self.full_path / str('follower_dictionary-' + follower_name + '.json'), "w") as fp: json.dump(self.follower_dict, fp, default=self.np_encoder) def np_encoder(self, object): if isinstance(object, np.generic): return object.item() def get_pair_dict_info(self, metadata: dict) -> Tuple[str, int, bool, bool]: """ Locate and load existing model metadata from persistent storage. If not located, create a new one and append the current pair to it and prepare it for its first training :params: metadata: dict = strategy furnished pair metadata :returns: model_filename: str = unique filename used for loading persistent objects from disk trained_timestamp: int = the last time the coin was trained coin_first: bool = If the coin is fresh without metadata return_null_array: bool = Follower could not find pair metadata """ pair_in_dict = self.pair_dict.get(metadata['pair']) data_path_set = self.pair_dict.get(metadata['pair'], {}).get('data_path', None) return_null_array = False if pair_in_dict: model_filename = self.pair_dict[metadata['pair']]['model_filename'] trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] coin_first = self.pair_dict[metadata['pair']]['first'] elif not self.follow_mode: self.pair_dict[metadata['pair']] = {} model_filename = self.pair_dict[metadata['pair']]['model_filename'] = '' coin_first = self.pair_dict[metadata['pair']]['first'] = True trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 if not data_path_set and self.follow_mode: logger.warning(f'Follower could not find current pair {metadata["pair"]} in ' f'pair_dictionary at path {self.full_path}, sending null values ' 'back to strategy.') return_null_array = True return model_filename, trained_timestamp, coin_first, return_null_array def set_pair_dict_info(self, metadata: dict) -> None: pair_in_dict = self.pair_dict.get(metadata['pair']) if pair_in_dict: return else: self.pair_dict[metadata['pair']] = {} self.pair_dict[metadata['pair']]['model_filename'] = '' self.pair_dict[metadata['pair']]['first'] = True self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 self.pair_dict[metadata['pair']]['priority'] = len(self.pair_dict) return def pair_to_end_of_training_queue(self, pair: str) -> None: # march all pairs up in the queue for p in self.pair_dict: self.pair_dict[p]['priority'] -= 1 # send pair to end of queue self.pair_dict[pair]['priority'] = len(self.pair_dict) def set_initial_return_values(self, pair: str, dh): self.model_return_values[pair] = {} self.model_return_values[pair]['predictions'] = dh.full_predictions self.model_return_values[pair]['do_preds'] = dh.full_do_predict self.model_return_values[pair]['target_mean'] = dh.full_target_mean self.model_return_values[pair]['target_std'] = dh.full_target_std self.model_return_values[pair]['DI_values'] = dh.full_DI_values # if not self.follow_mode: # self.save_model_return_values_to_disk() def append_model_predictions(self, pair: str, predictions, do_preds, target_mean, target_std, dh, len_df) -> None: # strat seems to feed us variable sized dataframes - and since we are trying to build our # own return array in the same shape, we need to figure out how the size has changed # and adapt our stored/returned info accordingly. length_difference = len(self.model_return_values[pair]['predictions']) - len_df i = 0 if length_difference == 0: i = 1 elif length_difference > 0: i = length_difference + 1 self.model_return_values[pair]['predictions'] = np.append( self.model_return_values[pair]['predictions'][i:], predictions[-1]) self.model_return_values[pair]['DI_values'] = np.append( self.model_return_values[pair]['DI_values'][i:], dh.DI_values[-1]) self.model_return_values[pair]['do_preds'] = np.append( self.model_return_values[pair]['do_preds'][i:], do_preds[-1]) self.model_return_values[pair]['target_mean'] = np.append( self.model_return_values[pair]['target_mean'][i:], target_mean) self.model_return_values[pair]['target_std'] = np.append( self.model_return_values[pair]['target_std'][i:], target_std) if length_difference < 0: prepend = np.zeros(abs(length_difference) - 1) self.model_return_values[pair]['predictions'] = np.insert( self.model_return_values[pair]['predictions'], 0, prepend) self.model_return_values[pair]['DI_values'] = np.insert( self.model_return_values[pair]['DI_values'], 0, prepend) self.model_return_values[pair]['do_preds'] = np.insert( self.model_return_values[pair]['do_preds'], 0, prepend) self.model_return_values[pair]['target_mean'] = np.insert( self.model_return_values[pair]['target_mean'], 0, prepend) self.model_return_values[pair]['target_std'] = np.insert( self.model_return_values[pair]['target_std'], 0, prepend) dh.full_predictions = copy.deepcopy(self.model_return_values[pair]['predictions']) dh.full_do_predict = copy.deepcopy(self.model_return_values[pair]['do_preds']) dh.full_target_mean = copy.deepcopy(self.model_return_values[pair]['target_mean']) dh.full_target_std = copy.deepcopy(self.model_return_values[pair]['target_std']) dh.full_DI_values = copy.deepcopy(self.model_return_values[pair]['DI_values']) # if not self.follow_mode: # self.save_model_return_values_to_disk() def return_null_values_to_strategy(self, dataframe: DataFrame, dh) -> None: len_df = len(dataframe) dh.full_predictions = np.zeros(len_df) dh.full_do_predict = np.zeros(len_df) dh.full_target_mean = np.zeros(len_df) dh.full_target_std = np.zeros(len_df) dh.full_DI_values = np.zeros(len_df) def purge_old_models(self) -> None: model_folders = [x for x in self.full_path.iterdir() if x.is_dir()] pattern = re.compile(r"sub-train-(\w+)(\d{10})") delete_dict: Dict[str, Any] = {} for dir in model_folders: result = pattern.match(str(dir.name)) if result is None: break coin = result.group(1) timestamp = result.group(2) if coin not in delete_dict: delete_dict[coin] = {} delete_dict[coin]['num_folders'] = 1 delete_dict[coin]['timestamps'] = {int(timestamp): dir} else: delete_dict[coin]['num_folders'] += 1 delete_dict[coin]['timestamps'][int(timestamp)] = dir for coin in delete_dict: if delete_dict[coin]['num_folders'] > 2: sorted_dict = collections.OrderedDict( sorted(delete_dict[coin]['timestamps'].items())) num_delete = len(sorted_dict) - 2 deleted = 0 for k, v in sorted_dict.items(): if deleted >= num_delete: break logger.info(f'Freqai purging old model file {v}') shutil.rmtree(v) deleted += 1 def update_follower_metadata(self): # follower needs to load from disk to get any changes made by leader to pair_dict self.load_drawer_from_disk() if self.config.get('freqai', {}).get('purge_old_models', False): self.purge_old_models() # to be used if we want to send predictions directly to the follower instead of forcing # follower to load models and inference # def save_model_return_values_to_disk(self) -> None: # with open(self.full_path / str('model_return_values.json'), "w") as fp: # json.dump(self.model_return_values, fp, default=self.np_encoder) # def load_model_return_values_from_disk(self, dh: FreqaiDataKitchen) -> FreqaiDataKitchen: # exists = Path(self.full_path / str('model_return_values.json')).resolve().exists() # if exists: # with open(self.full_path / str('model_return_values.json'), "r") as fp: # self.model_return_values = json.load(fp) # elif not self.follow_mode: # logger.info("Could not find existing datadrawer, starting from scratch") # else: # logger.warning(f'Follower could not find pair_dictionary at {self.full_path} ' # 'sending null values back to strategy') # return exists, dh