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Implementation of the Adaptive XGBoost classifier for evolving data streams
TypeError Traceback (most recent call last)
/tmp/ipykernel_97027/1701108376.py in
37 evaluator.evaluate(stream=stream,
38 model=[AXGBp, AXGBr],
---> 39 model_names=['AXGBp', 'AXGBr'])
~/lib/python3.7/site-packages/skmultiflow/evaluation/evaluate_prequential.py in evaluate(self, stream, model, model_names)
248 self._init_file()
249
--> 250 self.model = self._train_and_test()
251
252 if self.show_plot:
~/lib/python3.7/site-packages/skmultiflow/evaluation/evaluate_prequential.py in _train_and_test(self)
373
374 if len(set(self.metrics).difference({constants.DATA_POINTS})) > 0:
--> 375 self.evaluation_summary()
376 else:
377 print('Done')
~/lib/python3.7/site-packages/skmultiflow/evaluation/base_evaluator.py in evaluation_summary(self)
610 print('{} - Accuracy : {:.4f}'.format(
611 self.model_names[i],
--> 612 self._data_buffer.get_data(metric_id=constants.ACCURACY, data_id=constants.MEAN)[i]))
613 if constants.KAPPA in self.metrics:
614 print('{} - Kappa : {:.4f}'.format(
TypeError: 'NoneType' object is not subscriptable
Hi, I want to save self._ensemble and convert to pmml format for online deployment. Do you have any example? Thank you so much in advance.
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