359 lines
14 KiB
Python
359 lines
14 KiB
Python
import collections
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import json
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import logging
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import re
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import shutil
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import threading
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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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import pandas as pd
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from pandas import DataFrame
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# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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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, config: dict, follow_mode: bool = False):
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self.config = config
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self.freqai_info = config.get("freqai", {})
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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.model_return_values: Dict[str, Any] = {}
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self.pair_data_dict: Dict[str, Any] = {}
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self.historic_data: Dict[str, Any] = {}
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self.historic_predictions: Dict[str, Any] = {}
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self.follower_dict: Dict[str, Any] = {}
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self.full_path = full_path
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self.follow_mode = follow_mode
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if follow_mode:
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self.create_follower_dict()
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self.load_drawer_from_disk()
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self.load_historic_predictions_from_disk()
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self.training_queue: Dict[str, int] = {}
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self.history_lock = threading.Lock()
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def load_drawer_from_disk(self):
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"""
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Locate and load a previously saved data drawer full of all pair model metadata in
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present model folder.
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:returns:
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exists: bool = whether or not the drawer was located
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"""
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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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elif not self.follow_mode:
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logger.info("Could not find existing datadrawer, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find pair_dictionary at {self.full_path} "
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"sending null values back to strategy"
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)
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return exists
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def load_historic_predictions_from_disk(self):
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"""
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Locate and load a previously saved historic predictions.
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:returns:
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exists: bool = whether or not the drawer was located
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"""
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exists = Path(self.full_path / str("historic_predictions.json")).resolve().exists()
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if exists:
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with open(self.full_path / str("historic_predictions.json"), "r") as fp:
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self.pair_dict = json.load(fp)
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logger.info(f"Found existing historic predictions at {self.full_path}, but beware of "
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"that statistics may be inaccurate if the bot has been offline for "
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"an extended period of time.")
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elif not self.follow_mode:
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logger.info("Could not find existing historic_predictions, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find historic predictions at {self.full_path} "
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"sending null values back to strategy"
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)
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return exists
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def save_drawer_to_disk(self):
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"""
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Save data drawer full of all pair model metadata in present model folder.
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"""
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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 save_historic_predictions_to_disk(self):
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"""
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Save data drawer full of all pair model metadata in present model folder.
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"""
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with open(self.full_path / str("historic_predictions.json"), "w") as fp:
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json.dump(self.historic_predictions, fp, default=self.np_encoder)
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def save_follower_dict_to_disk(self):
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"""
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Save follower dictionary to disk (used by strategy for persistent prediction targets)
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"""
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follower_name = self.config.get("bot_name", "follower1")
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with open(
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self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
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) as fp:
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json.dump(self.follower_dict, fp, default=self.np_encoder)
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def create_follower_dict(self):
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"""
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Create or dictionary for each follower to maintain unique persistent prediction targets
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"""
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follower_name = self.config.get("bot_name", "follower1")
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whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
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exists = (
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Path(self.full_path / str("follower_dictionary-" + follower_name + ".json"))
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.resolve()
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.exists()
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)
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if exists:
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logger.info("Found an existing follower dictionary")
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for pair in whitelist_pairs:
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self.follower_dict[pair] = {}
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with open(
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self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
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) as fp:
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json.dump(self.follower_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, pair: str) -> Tuple[str, int, bool, bool]:
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"""
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Locate and load existing model metadata from persistent storage. If not located,
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create a new one and append the current pair to it and prepare it for its first
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training
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:params:
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metadata: dict = strategy furnished pair metadata
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:returns:
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model_filename: str = unique filename used for loading persistent objects from disk
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trained_timestamp: int = the last time the coin was trained
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coin_first: bool = If the coin is fresh without metadata
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return_null_array: bool = Follower could not find pair metadata
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"""
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pair_in_dict = self.pair_dict.get(pair)
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data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
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return_null_array = False
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if pair_in_dict:
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model_filename = self.pair_dict[pair]["model_filename"]
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trained_timestamp = self.pair_dict[pair]["trained_timestamp"]
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coin_first = self.pair_dict[pair]["first"]
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elif not self.follow_mode:
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self.pair_dict[pair] = {}
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model_filename = self.pair_dict[pair]["model_filename"] = ""
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coin_first = self.pair_dict[pair]["first"] = True
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trained_timestamp = self.pair_dict[pair]["trained_timestamp"] = 0
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self.pair_dict[pair]["priority"] = len(self.pair_dict)
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if not data_path_set and self.follow_mode:
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logger.warning(
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f"Follower could not find current pair {pair} in "
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f"pair_dictionary at path {self.full_path}, sending null values "
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"back to strategy."
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)
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return_null_array = True
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return model_filename, trained_timestamp, coin_first, return_null_array
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def set_pair_dict_info(self, metadata: dict) -> None:
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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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return
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else:
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self.pair_dict[metadata["pair"]] = {}
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self.pair_dict[metadata["pair"]]["model_filename"] = ""
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self.pair_dict[metadata["pair"]]["first"] = True
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self.pair_dict[metadata["pair"]]["trained_timestamp"] = 0
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self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
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return
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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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for p in self.pair_dict:
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self.pair_dict[p]["priority"] -= 1
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# send pair to end of queue
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self.pair_dict[pair]["priority"] = len(self.pair_dict)
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def set_initial_return_values(self, pair: str, dk, pred_df, do_preds) -> None:
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"""
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Set the initial return values to a persistent dataframe. This avoids needing to repredict on
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historical candles, and also stores historical predictions despite retrainings (so stored
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predictions are true predictions, not just inferencing on trained data)
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"""
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# dynamic df returned to strategy and plotted in frequi
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mrv_df = self.model_return_values[pair] = pd.DataFrame()
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for label in dk.label_list:
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mrv_df[label] = pred_df[label]
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mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
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mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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mrv_df["DI_values"] = dk.DI_values
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mrv_df["do_predict"] = do_preds
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def append_model_predictions(self, pair: str, predictions, do_preds, dk, len_df) -> None:
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# strat seems to feed us variable sized dataframes - and since we are trying to build our
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# own return array in the same shape, we need to figure out how the size has changed
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# and adapt our stored/returned info accordingly.
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length_difference = len(self.model_return_values[pair]) - len_df
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i = 0
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if length_difference == 0:
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i = 1
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elif length_difference > 0:
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i = length_difference + 1
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df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
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hp_df = self.historic_predictions[pair]
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# here are some pandas hula hoops to accommodate the possibility of a series
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# or dataframe depending number of labels requested by user
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nan_df = pd.DataFrame(np.nan, index=hp_df.index[-2:] + 2, columns=hp_df.columns)
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hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
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hp_df = pd.concat([hp_df, nan_df[-2:-1]], axis=0)
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for label in dk.label_list:
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df[label].iloc[-1] = predictions[label].iloc[-1]
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df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
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df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
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# df['prediction'].iloc[-1] = predictions[-1]
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df["do_predict"].iloc[-1] = do_preds[-1]
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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df["DI_values"].iloc[-1] = dk.DI_values[-1]
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# append the new predictions to persistent storage
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hp_df.iloc[-1] = df[label].iloc[-1]
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if length_difference < 0:
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prepend_df = pd.DataFrame(
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np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
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)
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df = pd.concat([prepend_df, df], axis=0)
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def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
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"""
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Attach the return values to the strat dataframe
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:params:
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dataframe: DataFrame = strat dataframe
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:returns:
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dataframe: DataFrame = strat dataframe with return values attached
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"""
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df = self.model_return_values[pair]
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to_keep = [col for col in dataframe.columns if not col.startswith("&")]
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dataframe = pd.concat([dataframe[to_keep], df], axis=1)
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return dataframe
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def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
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"""
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Build 0 filled dataframe to return to strategy
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"""
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dk.find_features(dataframe)
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for label in dk.label_list:
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dataframe[label] = 0
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dataframe[f"{label}_mean"] = 0
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dataframe[f"{label}_std"] = 0
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# dataframe['prediction'] = 0
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dataframe["do_predict"] = 0
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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dataframe["DI_value"] = 0
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dk.return_dataframe = dataframe
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def purge_old_models(self) -> None:
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model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
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pattern = re.compile(r"sub-train-(\w+)(\d{10})")
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delete_dict: Dict[str, Any] = {}
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for dir in model_folders:
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result = pattern.match(str(dir.name))
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if result is None:
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break
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coin = result.group(1)
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timestamp = result.group(2)
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if coin not in delete_dict:
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delete_dict[coin] = {}
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delete_dict[coin]["num_folders"] = 1
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delete_dict[coin]["timestamps"] = {int(timestamp): dir}
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else:
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delete_dict[coin]["num_folders"] += 1
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delete_dict[coin]["timestamps"][int(timestamp)] = dir
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for coin in delete_dict:
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if delete_dict[coin]["num_folders"] > 2:
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sorted_dict = collections.OrderedDict(
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sorted(delete_dict[coin]["timestamps"].items())
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)
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num_delete = len(sorted_dict) - 2
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deleted = 0
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for k, v in sorted_dict.items():
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if deleted >= num_delete:
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break
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logger.info(f"Freqai purging old model file {v}")
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shutil.rmtree(v)
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deleted += 1
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def update_follower_metadata(self):
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# follower needs to load from disk to get any changes made by leader to pair_dict
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self.load_drawer_from_disk()
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if self.config.get("freqai", {}).get("purge_old_models", False):
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self.purge_old_models()
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# to be used if we want to send predictions directly to the follower instead of forcing
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# follower to load models and inference
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# def save_model_return_values_to_disk(self) -> None:
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# with open(self.full_path / str('model_return_values.json'), "w") as fp:
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# json.dump(self.model_return_values, fp, default=self.np_encoder)
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# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
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# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
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# if exists:
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# with open(self.full_path / str('model_return_values.json'), "r") as fp:
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# self.model_return_values = json.load(fp)
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# elif not self.follow_mode:
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# logger.info("Could not find existing datadrawer, starting from scratch")
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# else:
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# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
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# 'sending null values back to strategy')
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# return exists, dk
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