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create a separate folder for the historic_predictions parquet files. Modify save/load functions to accommodate new structure

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robcaulk
2023-03-25 13:19:53 +01:00
parent 0883198f40
commit b383654378
1 changed files with 26 additions and 14 deletions
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40
freqtrade/freqai/data_drawer.py
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@@ -74,10 +74,9 @@ class FreqaiDataDrawer:
self.historic_predictions: Dict[str, DataFrame] = {}
self.full_path = full_path
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.historic_predictions_path_parquet = Path(
self.full_path / "historic_predictions.parquet")
self.historic_predictions_bkp_path_parquet = Path(
self.full_path / "historic_predictions.backup.parquet")
self.historic_predictions_folder = Path(self.full_path / "historic_predictions")
self.historic_predictions_bkp_folder = Path(
self.full_path / "historic_predictions_backup")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.global_metadata_path = Path(self.full_path / "global_metadata.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
@@ -165,12 +164,16 @@ class FreqaiDataDrawer:
Locate and load a previously saved historic predictions.
:return: bool - whether or not the drawer was located
"""
exists = self.historic_predictions_path_parquet.is_file()
exists = self.historic_predictions_folder.exists()
convert = self.historic_predictions_path.is_file()
if exists:
try:
self.historic_predictions = pd.read_parquet(self.historic_predictions_path_parquet)
for file_path in self.historic_predictions_folder.glob("*.parquet"):
key = file_path.stem
key.replace("_", "/")
self.historic_predictions[key] = pd.read_parquet(file_path)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for "
@@ -178,17 +181,20 @@ class FreqaiDataDrawer:
)
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
self.historic_predictions = pd.read_parquet(
self.historic_predictions_bkp_path_parquet)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
'Historical prediction files were corrupted. Trying to load backup files.')
for file_path in self.historic_predictions_folder.glob("*.parquet"):
key = file_path.stem
key.replace("_", "/")
self.historic_predictions[key] = pd.read_parquet(file_path)
logger.warning('FreqAI successfully loaded the backup '
'historical predictions files.')
elif not exists and convert:
logger.info("Converting your historic predictions pkl to parquet"
"to improve performance.")
with Path.open(self.historic_predictions_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
self.historic_predictions.to_parquet(self.historic_predictions_path_parquet)
self.save_historic_predictions_to_disk()
exists = True
else:
@@ -203,10 +209,16 @@ class FreqaiDataDrawer:
"""
Save historic predictions pickle to disk
"""
self.historic_predictions.to_parquet(self.historic_predictions_path_parquet)
self.historic_predictions_folder.mkdir(parents=True, exist_ok=True)
for key, value in self.historic_predictions.items():
key = key.replace("/", "_")
# pytest.set_trace()
filename = Path(self.historic_predictions_folder / f"{key}.parquet")
value.to_parquet(filename)
# create a backup
shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path_parquet)
shutil.copytree(self.historic_predictions_folder, self.historic_predictions_bkp_folder)
def save_metric_tracker_to_disk(self):
"""
@@ -688,7 +700,7 @@ class FreqaiDataDrawer:
Returns timerange information based on historic predictions file
:return: timerange calculated from saved live data
"""
if not self.historic_predictions_path.is_file():
if not self.historic_predictions_folder.exists():
raise OperationalException(
'Historic predictions not found. Historic predictions data is required '
'to run backtest with the freqai-backtest-live-models option '