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add PTH ruff selection

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This commit is contained in:
Matthias 2023-02-25 17:17:05 +01:00
parent d014e4590e
commit 26315b6bc2
3 changed files with 18 additions and 18 deletions
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32
freqtrade/freqai/data_drawer.py
View File

@ -126,7 +126,7 @@ class FreqaiDataDrawer:
"""
exists = self.global_metadata_path.is_file()
if exists:
with open(self.global_metadata_path, "r") as fp:
with self.global_metadata_path.open("r") as fp:
metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
return metatada_dict
return {}
@ -139,7 +139,7 @@ class FreqaiDataDrawer:
"""
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
with self.pair_dictionary_path.open("r") as fp:
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
else:
logger.info("Could not find existing datadrawer, starting from scratch")
@ -152,7 +152,7 @@ class FreqaiDataDrawer:
if self.freqai_info.get('write_metrics_to_disk', False):
exists = self.metric_tracker_path.is_file()
if exists:
with open(self.metric_tracker_path, "r") as fp:
with self.metric_tracker_path.open("r") as fp:
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
logger.info("Loading existing metric tracker from disk.")
else:
@ -166,7 +166,7 @@ class FreqaiDataDrawer:
exists = self.historic_predictions_path.is_file()
if exists:
try:
with open(self.historic_predictions_path, "rb") as fp:
with self.historic_predictions_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
@ -176,7 +176,7 @@ class FreqaiDataDrawer:
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
with open(self.historic_predictions_bkp_path, "rb") as fp:
with self.historic_predictions_bkp_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
@ -189,7 +189,7 @@ class FreqaiDataDrawer:
"""
Save historic predictions pickle to disk
"""
with open(self.historic_predictions_path, "wb") as fp:
with self.historic_predictions_path.open("wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
# create a backup
@ -200,16 +200,16 @@ class FreqaiDataDrawer:
Save metric tracker of all pair metrics collected.
"""
with self.save_lock:
with open(self.metric_tracker_path, 'w') as fp:
with self.metric_tracker_path.open('w') as fp:
rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_drawer_to_disk(self):
def save_drawer_to_disk(self) -> None:
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with self.save_lock:
with open(self.pair_dictionary_path, 'w') as fp:
with self.pair_dictionary_path.open('w') as fp:
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
@ -218,7 +218,7 @@ class FreqaiDataDrawer:
Save global metadata json to disk
"""
with self.save_lock:
with open(self.global_metadata_path, 'w') as fp:
with self.global_metadata_path.open('w') as fp:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
@ -424,7 +424,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
return
@ -457,7 +457,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
# save the train data to file so we can check preds for area of applicability later
@ -471,7 +471,7 @@ class FreqaiDataDrawer:
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
dk.pca, (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("wb")
)
self.model_dictionary[coin] = model
@ -491,7 +491,7 @@ class FreqaiDataDrawer:
Load only metadata into datakitchen to increase performance during
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
@ -514,7 +514,7 @@ class FreqaiDataDrawer:
dk.data = self.meta_data_dictionary[coin]["meta_data"]
dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
else:
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.data_dictionary["train_features"] = pd.read_pickle(
@ -552,7 +552,7 @@ class FreqaiDataDrawer:
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
(dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("rb")
)
return model

2
freqtrade/freqai/utils.py
View File

@ -211,7 +211,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
"pairs": config.get('exchange', {}).get('pair_whitelist')
}
with open(params_record_path, "w") as handle:
with params_record_path.open("w") as handle:
rapidjson.dump(
run_params,
handle,

2
pyproject.toml
View File

@ -68,5 +68,5 @@ extend-select = [
# "DTZ", # flake8-datetimez
# "RSE", # flake8-raise
# "TCH", # flake8-type-checking
# "PTH", # flake8-use-pathlib
"PTH", # flake8-use-pathlib
]