ensure ohlc is dropped from both train and predict
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@ -235,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_df, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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@ -314,14 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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prices_test.rename(columns=rename_dict, inplace=True)
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prices_test.reset_index(drop=True)
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if self.rl_config["drop_ohlc_from_features"]:
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train_df.drop(rename_dict.keys(), axis=1, inplace=True)
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test_df.drop(rename_dict.keys(), axis=1, inplace=True)
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feature_list = dk.training_features_list
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feature_list = [e for e in feature_list if e not in rename_dict.keys()]
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train_df = self.drop_ohlc_from_df(train_df, dk)
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test_df = self.drop_ohlc_from_df(test_df, dk)
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return prices_train, prices_test
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def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
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"""
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Given a dataframe, drop the ohlc data
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"""
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drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
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if self.rl_config["drop_ohlc_from_features"]:
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df.drop(drop_list, axis=1, inplace=True)
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feature_list = dk.training_features_list
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feature_list = [e for e in feature_list if e not in drop_list]
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return df
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def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
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"""
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Can be used by user if they are trying to limit_ram_usage *and*
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