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**6)** Make the final weighted ntuples making sure that the new Pt/Eta histogram files are pointed to in **addWeightBranch.py**. There should be six new branches created:\\ | **6)** Make the final weighted ntuples making sure that the new Pt/Eta histogram files are pointed to in **addWeightBranch.py**. There should be six new branches created:\\ | ||
- | -weight_etaPt | + | -**weight_etaPt** : the Pt/Eta weight, specific for a flavour/ |
- | -weight_etaPtInc: | + | -**weight_etaPtInc**: the Pt/Eta weight, inclusive for the flavour\\ |
- | -weight_category: | + | -**weight_category**: the category weight from the evaluation sample\\ |
- | -weight_norm | + | -**weight_norm** : the normalization weight from the training sample\\ |
- | -weight_flavour : the ratio of the flavour prevalences in the evaluation process\\ | + | -**weight_flavour** : the ratio of the flavour prevalences in the evaluation process\\ |
- | -weight | + | -**weight** : (weight_etaPtInc) x (weight_norm x weight_category) x (weight_flavour) |
+ | The training samples are now ready for the training process with **tmva_training.py**. Make sure to create a directory called "// | ||
=== Evaluation Samples === | === Evaluation Samples === | ||
+ | **1)** Make the trees really flat without vectors and set variables that are not defined for a given vertex category to a default value. For this, run your ntuples through **createNewTree.py** which will produce sets of new flat ntuples split in event range such as // | ||
+ | **2)** The evaluation trees can be skimmed as well to make the evaluation process faster. The script **skimTT.py** will reference the event ranges in the file names for the flat trees and copy new skimmed trees that contain 10% of the events from each of the flavour/ |