From 6669714a737f32e9536f7bcb0a87d64e8bccb4f4 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 24 Oct 2022 06:53:42 +0200 Subject: [PATCH] Update mal-formatted docstrings --- freqtrade/freqai/data_kitchen.py | 3 +-- freqtrade/freqai/prediction_models/XGBoostRFClassifier.py | 7 +++---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index ed64fb162..c0becd5ae 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -468,8 +468,7 @@ class FreqaiDataKitchen: def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame: """ Check that prediction feature labels match training feature labels. - :params: - :df_predictions: incoming predictions + :param df_predictions: incoming predictions """ constant_labels = self.data['constant_features_list'] df_predictions = df_predictions.filter( diff --git a/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py index 1aba8df85..470c283ea 100644 --- a/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py +++ b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py @@ -26,9 +26,8 @@ class XGBoostRFClassifier(BaseClassifierModel): def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ X = data_dictionary["train_features"].to_numpy() @@ -65,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel): ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: """ 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: :pred_df: dataframe containing the predictions :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove