Fix constant PCA
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@ -206,11 +206,15 @@ class FreqaiDataKitchen:
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drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
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drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
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ft_params = self.freqai_config["feature_parameters"]
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if (training_filter):
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const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
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if const_cols:
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filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
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logger.warning(f"Removed features {const_cols} with constant values.")
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if not ft_params.get(
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"principal_component_analysis", False
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):
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const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
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if const_cols:
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filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
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logger.warning(f"Removed features {const_cols} with constant values.")
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# we don't care about total row number (total no. datapoints) in training, we only care
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# about removing any row with NaNs
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# if labels has multiple columns (user wants to train multiple modelEs), we detect here
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@ -241,7 +245,10 @@ class FreqaiDataKitchen:
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self.data["filter_drop_index_training"] = drop_index
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else:
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filtered_df = self.check_pred_labels(filtered_df)
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if not ft_params.get(
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"principal_component_analysis", False
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):
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filtered_df = self.check_pred_labels(filtered_df)
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# we are backtesting so we need to preserve row number to send back to strategy,
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# so now we use do_predict to avoid any prediction based on a NaN
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drop_index = pd.isnull(filtered_df).any(axis=1)
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