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it would be good to firstly list variables set in fit here, and secondly be able to summarise this over resamples and problems to help understand how they are operating
Describe your proposed solution
so input a dictionary, generate a file for each parameter over resamples
for example collate_paras("estimator",["para1", "para2"]) create files para1 with 112x30 of para1 and para2
Other comments and alternatives considered
No response
The text was updated successfully, but these errors were encountered:
Describe the feature or idea you want to propose
Line two in the results files contains a dictionary for classifier parameters
e.g.
{'batch_size': 100, 'contract_max_n_shapelet_samples': inf, 'estimator': None, 'max_shapelet_length': None, 'max_shapelets': 1000, 'n_jobs': 1, 'n_shapelet_samples': 50000, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': 0, 'transform_limit_in_minutes': 0}
it would be good to firstly list variables set in fit here, and secondly be able to summarise this over resamples and problems to help understand how they are operating
Describe your proposed solution
so input a dictionary, generate a file for each parameter over resamples
for example collate_paras("estimator",["para1", "para2"]) create files para1 with 112x30 of para1 and para2
Other comments and alternatives considered
No response
The text was updated successfully, but these errors were encountered: