GLAST/LAT analysis group 31Jan05 - T. Burnett 1 Creating Decision Trees for GLAST analysis: A new C++-based procedure Toby Burnett Frank Golf.

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Presentation transcript:

GLAST/LAT analysis group 31Jan05 - T. Burnett 1 Creating Decision Trees for GLAST analysis: A new C++-based procedure Toby Burnett Frank Golf

GLAST/LAT analysis group 31Jan05 - T. Burnett 2 Outline Quick review of classification (or decision) trees Training and testing How Bill does it with Insightful Miner Application to the “good-gamma” trees: how does it compare?

GLAST/LAT analysis group 31Jan05 - T. Burnett 3 Quick Review of Decision Trees Introduced to GLAST by Bill Atwood, using InsightfulMiner Each branch node is a predicate, or cut on a variable, like CalCsIRLn > If true, this defines the right branch, otherwise the left branch. If there is no branch, the node is a leaf; a leaf contains the purity of the sample that reaches that point Thus the tree defines a function of the event variables used, returning a value for the purity from the training sample predicate purity true: right false: left

GLAST/LAT analysis group 31Jan05 - T. Burnett 4 Training and testing procedure Analyze a training sample containing a mixture of “good” and “bad” events: I use the even events Choose set of variables and find the optimal cut for such that the left and right subsets are purer than the orignal. Two standard criteria for this: “Gini” and entropy. I currently use the former. W S : sum of signal weights W B : sum of background weights Gini = 2 W S W B /(W S +W B ) Thus Gini wants to be small. Actually maximize the improvement: Gini(parent)-Gini(left child)-Gini(right child) Apply this recursively until too few events. (100 for now) Finally test with the odd events: measure purity for each node

GLAST/LAT analysis group 31Jan05 - T. Burnett 5 Evaluate by Comparing with Bill CAL-Low CT Probabilities CAL-High CT Probabilities All Good Bad Good All Bad Good From Bill’s Rome ’03 talk: The “good cal” analysis

GLAST/LAT analysis group 31Jan05 - T. Burnett 6 Compare with Bill, cont Since Rome: Three energy ranges, three trees. CalEnergySum: 5-350; ; >3500 Resulting classification trees implemented in Gleam by a “classification” package: results saved to the IMgoodCal tuple variable. Data set for training, comparison: the all_gamma from v4r2 760 K events E from 16 MeV to 160 GeV (uniform in log(E), and 0<  < 90 (uniform in cos(  ) ). Contains IMgoodCal for comparison

GLAST/LAT analysis group 31Jan05 - T. Burnett 7 Bill-type plots for all_gamma

GLAST/LAT analysis group 31Jan05 - T. Burnett 8 Another way of looking at performance Define efficiency as fraction of good events after given cut on node purity; determine bad fraction for that cut

GLAST/LAT analysis group 31Jan05 - T. Burnett 9 The new classifier package Properties Implements Gini separation (entropy on todo list) Reads ROOT files Flexible specification of variables to use Simple and compact ascii representation of decision trees Not (yet) implemented: multiple trees, pruning Currently in users/burnett, I plan, after suitable notification, to copy it to the cvs root.

GLAST/LAT analysis group 31Jan05 - T. Burnett 10 Growing trees For simplicity, run a single tree: initially try all of Bill’s classification tree variables: list at right. The run over the full 750 K events, trying each of the 70 variables takes only a few minutes! "AcdActiveDist", "AcdTotalEnergy", "CalBkHalfRatio", "CalCsIRLn", "CalDeadTotRat", "CalDeltaT", "CalEnergySum", "CalLATEdge", "CalLRmsRatio", "CalLyr0Ratio", "CalLyr7Ratio", "CalMIPDiff", "CalTotRLn", "CalTotSumCorr", "CalTrackDoca", "CalTrackSep", "CalTwrEdge", "CalTwrGap", "CalXtalRatio", "CalXtalsTrunc", "EvtCalETLRatio", "EvtCalETrackDoca", "EvtCalETrackSep", "EvtCalEXtalRatio", "EvtCalEXtalTrunc", "EvtCalEdgeAngle", "EvtEnergySumOpt", "EvtLogESum", "EvtTkr1EChisq", "EvtTkr1EFirstChisq", "EvtTkr1EFrac", "EvtTkr1EQual", "EvtTkr1PSFMdRat", "EvtTkr2EChisq", "EvtTkr2EFirstChisq", "EvtTkrComptonRatio", "EvtTkrEComptonRatio", "EvtTkrEdgeAngle", "EvtVtxEAngle", "EvtVtxEDoca", "EvtVtxEEAngle", "EvtVtxEHeadSep", "Tkr1Chisq", "Tkr1DieEdge", "Tkr1FirstChisq", "Tkr1FirstLayer", "Tkr1Hits", "Tkr1KalEne", "Tkr1PrjTwrEdge", "Tkr1Qual", "Tkr1TwrEdge", "Tkr1ZDir", "Tkr2Chisq", "Tkr2KalEne", "TkrBlankHits", "TkrHDCount", "TkrNumTracks", "TkrRadLength", "TkrSumKalEne", "TkrThickHits", "TkrThinHits", "TkrTotalHits", "TkrTrackLength", "TkrTwrEdge", "VtxAngle", "VtxDOCA", "VtxHeadSep", "VtxS1", "VtxTotalWgt", "VtxZDir"

GLAST/LAT analysis group 31Jan05 - T. Burnett 11 Performance of the truncated list Note: this is cheating: evaluation using the same events as for training 1691 leaf nodes 3381 total Nameimprovement CalCsIRLn21000 CalTrackDoca3000 AcdTotalEnergy1800 CalTotSumCorr800 EvtCalEXtalTrunc600 EvtTkr1EFrac560 CalLyr7Ratio470 CalTotRLn400 CalEnergySum370 EvtLogESum370 EvtEnergySumOpt310 EvtTkrEComptonRatio290 EvtCalETrackDoca220 EvtVtxEEAngle140

GLAST/LAT analysis group 31Jan05 - T. Burnett 12 Plans Implement multiple trees: “boosting” Averaging Try truncation to reduce size of tree without losing performance This is only one of Bill’s variables: also need trees for: PSF tail reduction But we do not implement “regression trees” vertex choice: one track or vertex good-gamma prediction