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In order to save the trained model with a name decided by the user, it is sufficient to write it in the configuration json file in the field "description": SHAUN: I think that we need to make this as human understandable as possible since there will be a lot of similarly trained models for each algorithm (e.g., "algorithm_abovec_24_0_00_Br_all" for system trained for flares above C1.0 over 24-hr windows at 0-hr latency issued from 00:00UT SHARPs using Br properties that correspond to all available). This would lead to the following list of configurations for a single algorithm using Br data:
algorithm_cclass_24_0_00_Br_all
algorithm_mclass_24_0_00_Br_all
algorithm_xclass_24_0_00_Br_all
algorithm_abovem_24_0_00_Br_all
algorithm_abovec_24_0_00_Br_all
and the same algorithm using Blos data:
algorithm_cclass_24_0_00_Blos_all
algorithm_mclass_24_0_00_Blos_all
algorithm_xclass_24_0_00_Blos_all
algorithm_abovem_24_0_00_Blos_all
algorithm_abovec_24_0_00_Blos_all
There is no explicit reason why we should use all properties, so another set of configuration files should be prepared and run for a reduced "optimized"/"feature selected" property set.
NOTE: there may be no point doing X-class, given the rarity of their occurrence in the training time period.
"algorithm":{ "phase": "training", <--- do not touch
"config_name": "HybridLasso", <--- do not touch
"description": "HybridLasso_test", <---- HERE
"HybridLasso": true, <--- do not touch
...
"first_flare_class":false
},
SHAUN: Question to [@msoldati] / [@dario.vischi] Does the following 'flare' structure need to be separate from the 'dataset' structure?
"flare":{"class":1, <-- flare_class = {'A': 0.01, 'B': 0.1, 'C': 1, 'M': 10, 'X': 100} is this conversion table ok?
"class_max" : 1, <-- new field where define the flare upper bound
"window":24,
"latency":0, <-- SHAUN: Question to [@msoldati] / [@dario.vischi] Do these need to be present to filter same-format predictions (e.g., for ensemble forecasting)?
"issuing":00 <-- SHAUN: Question to [@msoldati] / [@dario.vischi] Do these need to be present to filter same-format predictions (e.g., for ensemble forecasting)?
},
Please select here all the properties you want to take into account
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