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btag:tmva [2014/12/17 14:17] – vlambert | btag:tmva [2014/12/17 14:41] – vlambert | ||
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Below are the subsequent steps for preparing the training samples for the TMVA: | Below are the subsequent steps for preparing the training samples for the TMVA: | ||
- | **1)** | + | **1)** |
- | **2)** | + | **2)** |
+ | |||
+ | *For the training, one can either combine these ntuples with hadd or leave them as is for the rest of the processing. | ||
+ | |||
+ | **3)** Produce the category normalization weights for the training sample with **Normalization_Weights.C** and save the output to a text such as // | ||
+ | |||
+ | **4)** Assuming the evaluation sample vertex category weights have been produced (look at procedures for Evaluation Samples), add the normalization and category weight branches to the flat ntuples with **addWeightBranch.py**. The combination of these weights will remove the training sample vertex category information and match it with that of the evaluation sample. | ||
+ | |||
+ | **5)** Create 2D Pt/Eta Histograms for the weighted ntuples with **createEtaPtWeightHists.py** (make sure " | ||
+ | |||
+ | **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_etaPtInc**: | ||
+ | -**weight_category**: | ||
+ | -**weight_norm** | ||
+ | -**weight_flavour** : the ratio of the flavour prevalences in the evaluation process\\ | ||
+ | -**weight** | ||