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Update mal-formatted docstrings

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Matthias 2022-10-24 06:53:42 +02:00
parent dd45a3f500
commit 6669714a73
2 changed files with 4 additions and 6 deletions
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3
freqtrade/freqai/data_kitchen.py
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@ -468,8 +468,7 @@ class FreqaiDataKitchen:
def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame: def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
""" """
Check that prediction feature labels match training feature labels. Check that prediction feature labels match training feature labels.
:params: :param df_predictions: incoming predictions
:df_predictions: incoming predictions
""" """
constant_labels = self.data['constant_features_list'] constant_labels = self.data['constant_features_list']
df_predictions = df_predictions.filter( df_predictions = df_predictions.filter(

7
freqtrade/freqai/prediction_models/XGBoostRFClassifier.py
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@ -26,9 +26,8 @@ class XGBoostRFClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
""" """
User sets up the training and test data to fit their desired model here User sets up the training and test data to fit their desired model here
:params: :param data_dictionary: the dictionary constructed by DataHandler to hold
:data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels.
all the training and test data/labels.
""" """
X = data_dictionary["train_features"].to_numpy() X = data_dictionary["train_features"].to_numpy()
@ -65,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]: ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
""" """
Filter the prediction features data and predict with it. Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period. :param unfiltered_df: Full dataframe for the current backtest period.
:return: :return:
:pred_df: dataframe containing the predictions :pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove