btag:tmva
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| btag:tmva [2014/12/17 14:43] – vlambert | btag:tmva [2014/12/17 14:53] (current) – vlambert | ||
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| **2)** 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)** 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 // | ||
| - | *For the training, one can either combine these ntuples with hadd or leave them as is for the rest of the processing. | + | *//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 // | **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. | + | **4)** Assuming the evaluation sample vertex category weights have been produced (look at procedures for Evaluation Samples, step 3), 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 " | **5)** Create 2D Pt/Eta Histograms for the weighted ntuples with **createEtaPtWeightHists.py** (make sure " | ||
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| -**weight** | -**weight** | ||
| + | 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% (this can easily be modified) of the events from each of the flavour/ | ||
| + | |||
| + | *//Remember not to use the same skimming process for the evaluation as done for the training since one wants to keep the physical vertex category distribution for the process in the evaluation.// | ||
| + | |||
| + | **3)** Create the vertex category weights for the training samples with **biasTTbar.C** and save the output to a text file such as // | ||
btag/tmva.1418823810.txt.gz · Last modified: 2014/12/17 14:43 by vlambert
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