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black formatting on freqai files

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This commit is contained in:
robcaulk
2022-07-03 10:59:38 +02:00
parent 106131ff0f
commit ffb39a5029
7 changed files with 508 additions and 427 deletions
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32
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
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@@ -45,8 +45,9 @@ class CatboostPredictionModel(IFreqaiModel):
return dataframe["s"]
def train(self, unfiltered_dataframe: DataFrame,
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
@@ -57,8 +58,7 @@ class CatboostPredictionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info('--------------------Starting training '
f'{pair} --------------------')
logger.info("--------------------Starting training " f"{pair} --------------------")
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
# filter the features requested by user in the configuration file and elegantly handle NaNs
@@ -78,13 +78,14 @@ class CatboostPredictionModel(IFreqaiModel):
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
' features')
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
logger.info(f'--------------------done training {pair}--------------------')
logger.info(f"--------------------done training {pair}--------------------")
return model
@@ -110,14 +111,17 @@ class CatboostPredictionModel(IFreqaiModel):
model = CatBoostRegressor(
allow_writing_files=False,
verbose=100, early_stopping_rounds=400, **self.model_training_parameters
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data)
return model
def predict(self, unfiltered_dataframe: DataFrame,
dk: FreqaiDataKitchen, first: bool = False) -> Tuple[DataFrame, DataFrame]:
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
@@ -141,8 +145,10 @@ class CatboostPredictionModel(IFreqaiModel):
pred_df = DataFrame(predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = ((pred_df[label] + 1) *
(dk.data["labels_max"][label] -
dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
return (pred_df, dk.do_predict)