save the pickle data with bz2 compression and FP32. it saves up to x5 data size
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@ -420,8 +420,8 @@ class FreqaiDataDrawer:
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rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
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# save the train data to file so we can check preds for area of applicability later
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dk.data_dictionary["train_features"].to_pickle(
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save_path / f"{dk.model_filename}_trained_df.pkl"
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dk.data_dictionary["train_features"].astype("floa32").to_pickle(
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save_path / f"{dk.model_filename}_trained_df.pkl.bz2"
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)
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dk.data_dictionary["train_dates"].to_pickle(
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