freqtrade/freqai/data_kitchen.py
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@ -661,7 +661,7 @@ class FreqaiDataKitchen:
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self.data_dictionary['train_features']
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)
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logger.info(f'DBSCAN found eps of {epsilon}.')
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logger.info(f'DBSCAN found eps of {epsilon:.2f}.')
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self.data['DBSCAN_eps'] = epsilon
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self.data['DBSCAN_min_samples'] = MinPts
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@ -680,7 +680,6 @@ class FreqaiDataKitchen:
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logger.info(
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f"DBSCAN tossed {dropped_points.sum()}"
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f" train points from {len(clustering.labels_)}"
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f" ({np.round(dropped_points.sum()/len(clustering.labels_),0)}%)"
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)
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return
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@ -725,7 +724,7 @@ class FreqaiDataKitchen:
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if (len(do_predict) - do_predict.sum()) > 0:
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logger.info(
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f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for "
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f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
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"being too far from training data"
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)
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