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#include "TArrow.h"
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#include "TEllipse.h"
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#include "TPaveLabel.h"
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#include "TCanvas.h"
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#include "TH2F.h"
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#include "TFile.h"
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#include "TString.h"
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#include "TDirectory.h"
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#include "TKey.h"
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#include "TText.h"
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#include "tmvaglob.C"
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// this macro prints out a neural network generated by MethodMLP graphically
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// @author: Matt Jachowski, jachowski@stanford.edu
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TFile* Network_GFile = 0;
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static Int_t c_DarkBackground = TColor::GetColor( "#6e7a85" );
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void draw_layer_labels( Int_t nLayers );
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void draw_layer ( TCanvas* c, TH2F* h, Int_t iHist, Int_t nLayers, Double_t maxWeight );
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void draw_synapse ( Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
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Double_t rad1, Double_t rad2, Double_t weightNormed );
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TString* get_var_names ( Int_t nVars );
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Bool_t MovieMode = kFALSE;
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void draw_network( TFile* f, TDirectory* d, const TString& hName = "weights_hist",
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Bool_t movieMode = kFALSE, const TString& epoch = "" )
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{
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Bool_t __PRINT_LOGO__ = kTRUE;
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Network_GFile = f;
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MovieMode = movieMode;
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if (MovieMode) c_DarkBackground = TColor::GetColor( "#707F7F" );
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// create canvas
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TStyle* TMVAStyle = gROOT->GetStyle("TMVA"); // the TMVA style
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Int_t canvasColor = TMVAStyle->GetCanvasColor(); // backup
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TMVAStyle->SetCanvasColor( c_DarkBackground );
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Int_t titleFillColor = TMVAStyle->GetTitleFillColor();
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Int_t titleTextColor = TMVAStyle->GetTitleTextColor();
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Int_t borderSize = TMVAStyle->GetTitleBorderSize();
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TMVAStyle->SetTitleFillColor( c_DarkBackground );
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TMVAStyle->SetTitleTextColor( TColor::GetColor( "#FFFFFF" ) );
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TMVAStyle->SetTitleBorderSize( 0 );
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static Int_t icanvas = -1;
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Int_t ixc = 100 + (icanvas)*40;
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Int_t iyc = 0 + (icanvas+1)*20;
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if (MovieMode) ixc = iyc = 0;
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TString canvasnumber = Form( "c%i", icanvas );
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TString canvastitle = Form("Neural Network Layout for: %s", d->GetName());
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TCanvas* c = new TCanvas( canvasnumber, canvastitle,
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ixc, 0 + (icanvas+1)*20, 1000, 650 );
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icanvas++;
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TIter next = d->GetListOfKeys();
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TKey *key( 0 );
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Int_t numHists = 0;
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// loop over all histograms with hName in name again
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next.Reset();
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Double_t maxWeight = 0;
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// find max weight
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while ((key = (TKey*)next())) {
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TClass *cl = gROOT->GetClass(key->GetClassName());
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if (!cl->InheritsFrom("TH2F")) continue;
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TH2F* h = (TH2F*)key->ReadObj();
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if (!h) {
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cout << "Big troubles in \"draw_network\" (1)" << endl;
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exit(1);
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}
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if (TString(h->GetName()).Contains( hName )){
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numHists++;
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Int_t n1 = h->GetNbinsX();
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Int_t n2 = h->GetNbinsY();
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for (Int_t i = 0; i < n1; i++) {
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for (Int_t j = 0; j < n2; j++) {
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Double_t weight = TMath::Abs(h->GetBinContent(i+1, j+1));
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if (maxWeight < weight) maxWeight = weight;
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}
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}
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}
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}
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if (numHists == 0) {
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cout << "Error: could not find histograms" << endl;
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//exit(1);
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}
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// draw network
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next.Reset();
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//cout << "check4a" << endl;
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Int_t count = 0;
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while ((key = (TKey*)next())) {
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//cout << "check4b" << endl;
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TClass *cl = gROOT->GetClass(key->GetClassName());
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if (!cl->InheritsFrom("TH2F")) continue;
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//cout << "check4c" << endl;
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TH2F* h = (TH2F*)key->ReadObj();
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//cout << (h->GetName()) << endl;
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if (!h) {
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cout << "Big troubles in \"draw_network\" (2)" << endl;
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exit(1);
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}
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//cout << (h->GetName()) << endl;
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if (TString(h->GetName()).Contains( hName )) {
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//cout << (h->GetName()) << endl;
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draw_layer(c, h, count++, numHists+1, maxWeight);
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}
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//cout << "check4d" << endl;
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}
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draw_layer_labels(numHists+1);
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// add epoch
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if (MovieMode) {
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TText* t = new TText();
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t->SetTextSize( 0.04 );
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t->SetTextColor( 0 );
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t->SetTextAlign( 31 );
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t->DrawTextNDC( 1 - c->GetRightMargin(), 1 - c->GetTopMargin() - 0.033,
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Form( "Epoch: %s", epoch.Data() ) );
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}
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// ============================================================
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if (__PRINT_LOGO__) TMVAGlob::plot_logo();
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// ============================================================
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c->Update();
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if (MovieMode) {
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// save to file
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TString dirname = "movieplots";
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TString foutname = dirname + "/" + hName;
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foutname.Resize( foutname.Length()-5 );
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foutname.ReplaceAll("epochmonitoring___","");
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foutname += ".gif";
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cout << "storing file: " << foutname << endl;
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c->Print(foutname);
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c->Clear();
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delete c;
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}
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else {
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TString fname = "plots/network";
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TMVAGlob::imgconv( c, fname );
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}
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// reset global style changes so that it does not affect other plots
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TMVAStyle->SetCanvasColor ( canvasColor );
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TMVAStyle->SetTitleFillColor ( titleFillColor );
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TMVAStyle->SetTitleTextColor ( titleTextColor );
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TMVAStyle->SetTitleBorderSize( borderSize );
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}
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void draw_layer_labels(Int_t nLayers)
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{
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const Double_t LABEL_HEIGHT = 0.032;
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const Double_t LABEL_WIDTH = 0.20;
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Double_t effWidth = 0.8*(1.0-LABEL_WIDTH)/nLayers;
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Double_t height = 0.8*LABEL_HEIGHT;
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Double_t margY = LABEL_HEIGHT - height;
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for (Int_t i = 0; i < nLayers; i++) {
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TString label = Form("Layer %i", i);
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if (i == nLayers-1) label = "Output layer";
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Double_t cx = i*(1.0-LABEL_WIDTH)/nLayers+1.0/(2.0*nLayers)+LABEL_WIDTH;
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Double_t x1 = cx-0.8*effWidth/2.0;
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Double_t x2 = cx+0.8*effWidth/2.0;
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Double_t y1 = margY;
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Double_t y2 = margY + height;
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TPaveLabel *p = new TPaveLabel(x1, y1, x2, y2, label+"", "br");
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p->SetFillColor(gStyle->GetTitleFillColor());
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p->SetTextColor(gStyle->GetTitleTextColor());
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p->SetFillStyle(1001);
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p->SetBorderSize( 0 );
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p->Draw();
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}
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}
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void draw_input_labels(Int_t nInputs, Double_t* cy,
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Double_t rad, Double_t layerWidth)
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{
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const Double_t LABEL_HEIGHT = 0.04;
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const Double_t LABEL_WIDTH = 0.20;
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Double_t width = LABEL_WIDTH + (layerWidth-4*rad);
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Double_t margX = 0.01;
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Double_t effHeight = 0.8*LABEL_HEIGHT;
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TString *varNames = get_var_names(nInputs);
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if (varNames == 0) exit(1);
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TString input;
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for (Int_t i = 0; i < nInputs; i++) {
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if (i != nInputs-1) input = varNames[i];
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else input = "Bias node";
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Double_t x1 = margX;
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Double_t x2 = margX + width;
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Double_t y1 = cy[i] - effHeight;
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Double_t y2 = cy[i] + effHeight;
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TText* t = new TText();
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t->SetTextColor(gStyle->GetTitleTextColor());
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t->SetTextAlign(31);
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t->SetTextSize(LABEL_HEIGHT);
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if (i == nInputs-1) t->SetTextColor( TColor::GetColor( "#AFDCEC" ) );
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t->DrawText( x2, y1+0.018, input + " :");
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}
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delete[] varNames;
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}
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TString* get_var_names( Int_t nVars )
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{
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const TString directories[6] = { "InputVariables_NoTransform",
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"InputVariables_DecorrTransform",
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"InputVariables_PCATransform",
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"InputVariables_Id",
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"InputVariables_Norm",
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"InputVariables_Deco"};
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TDirectory* dir = 0;
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for (Int_t i=0; i<6; i++) {
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dir = (TDirectory*)Network_GFile->Get( directories[i] );
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if (dir != 0) break;
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}
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if (dir==0) {
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cout << "*** Big troubles in macro \"network.C\": could not find directory for input variables, "
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<< "and hence could not determine variable names --> abort" << endl;
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return 0;
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}
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dir->cd();
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TString* vars = new TString[nVars];
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Int_t ivar = 0;
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// loop over all objects in directory
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TIter next(dir->GetListOfKeys());
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TKey* key = 0;
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while ((key = (TKey*)next())) {
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if (key->GetCycle() != 1) continue;
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if (!TString(key->GetName()).Contains("__S") &&
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!TString(key->GetName()).Contains("__r") &&
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!TString(key->GetName()).Contains("Regression"))
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continue;
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if (TString(key->GetName()).Contains("target"))
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continue;
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// make sure, that we only look at histograms
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TClass *cl = gROOT->GetClass(key->GetClassName());
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if (!cl->InheritsFrom("TH1")) continue;
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TH1 *sig = (TH1*)key->ReadObj();
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TString hname = sig->GetTitle();
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vars[ivar] = hname; ivar++;
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if (ivar > nVars-1) break;
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}
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if (ivar != nVars-1) { // bias layer and targets are also in nVars counts
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cout << "*** Troubles in \"network.C\": did not reproduce correct number of "
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<< "input variables: " << ivar << " != " << nVars << endl;
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}
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return vars;
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}
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void draw_activation(TCanvas* c, Double_t cx, Double_t cy,
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Double_t radx, Double_t rady, Int_t whichActivation)
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{
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TImage *activation = NULL;
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switch (whichActivation) {
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case 0:
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activation = TImage::Open("sigmoid-small.png");
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break;
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case 1:
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activation = TImage::Open("line-small.png");
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break;
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default:
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cout << "Activation index " << whichActivation << " is not known." << endl;
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cout << "You messed up or you need to modify network.C to introduce a new "
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<< "activation function (and image) corresponding to this index" << endl;
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}
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if (activation == NULL) {
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cout << "Could not create an image... exit" << endl;
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return;
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}
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activation->SetConstRatio(kFALSE);
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radx *= 0.7;
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rady *= 0.7;
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TString name = Form("activation%f%f", cx, cy);
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TPad* p = new TPad(name+"", name+"", cx-radx, cy-rady, cx+radx, cy+rady);
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p->Draw();
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p->cd();
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activation->Draw();
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c->cd();
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}
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void draw_layer(TCanvas* c, TH2F* h, Int_t iHist,
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Int_t nLayers, Double_t maxWeight)
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{
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const Double_t MAX_NEURONS_NICE = 12;
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const Double_t LABEL_HEIGHT = 0.03;
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const Double_t LABEL_WIDTH = 0.20;
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Double_t ratio = ((Double_t)(c->GetWindowHeight())) / c->GetWindowWidth();
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Double_t rad, cx1, *cy1, cx2, *cy2;
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// this is the smallest radius that will still display the activation images
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rad = 0.04*650/c->GetWindowHeight();
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Int_t nNeurons1 = h->GetNbinsX();
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cx1 = iHist*(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0*nLayers) + LABEL_WIDTH;
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cy1 = new Double_t[nNeurons1];
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Int_t nNeurons2 = h->GetNbinsY();
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cx2 = (iHist+1)*(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0*nLayers) + LABEL_WIDTH;
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cy2 = new Double_t[nNeurons2];
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Double_t effRad1 = rad;
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if (nNeurons1 > MAX_NEURONS_NICE)
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effRad1 = 0.8*(1.0-LABEL_HEIGHT)/(2.0*nNeurons1);
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for (Int_t i = 0; i < nNeurons1; i++) {
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cy1[nNeurons1-i-1] = i*(1.0-LABEL_HEIGHT)/nNeurons1 + 1.0/(2.0*nNeurons1) + LABEL_HEIGHT;
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| 343 |
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| 344 |
if (iHist == 0) {
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| 345 |
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TEllipse *ellipse = new TEllipse( cx1, cy1[nNeurons1-i-1],
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| 347 |
effRad1*ratio, effRad1, 0, 360, 0 );
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ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
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| 349 |
ellipse->SetFillStyle(1001);
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ellipse->Draw();
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| 351 |
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| 352 |
if (i == 0) ellipse->SetLineColor(9);
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| 353 |
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| 354 |
if (nNeurons1 > MAX_NEURONS_NICE) continue;
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| 355 |
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| 356 |
Int_t whichActivation = 0;
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| 357 |
if (iHist==0 || iHist==nLayers-1 || i==0) whichActivation = 1;
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| 358 |
draw_activation(c, cx1, cy1[nNeurons1-i-1],
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| 359 |
rad*ratio, rad, whichActivation);
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| 360 |
}
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| 361 |
}
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| 362 |
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| 363 |
if (iHist == 0) draw_input_labels(nNeurons1, cy1, rad, (1.0-LABEL_WIDTH)/nLayers);
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| 364 |
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| 365 |
Double_t effRad2 = rad;
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| 366 |
if (nNeurons2 > MAX_NEURONS_NICE)
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| 367 |
effRad2 = 0.8*(1.0-LABEL_HEIGHT)/(2.0*nNeurons2);
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| 368 |
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| 369 |
for (Int_t i = 0; i < nNeurons2; i++) {
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| 370 |
cy2[nNeurons2-i-1] = i*(1.0-LABEL_HEIGHT)/nNeurons2 + 1.0/(2.0*nNeurons2) + LABEL_HEIGHT;
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| 371 |
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| 372 |
TEllipse *ellipse =
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| 373 |
new TEllipse(cx2, cy2[nNeurons2-i-1], effRad2*ratio, effRad2, 0, 360, 0);
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| 374 |
ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
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| 375 |
ellipse->SetFillStyle(1001);
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| 376 |
ellipse->Draw();
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| 377 |
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| 378 |
if (i == 0 && nNeurons2 > 1) ellipse->SetLineColor(9);
|
| 379 |
|
| 380 |
if (nNeurons2 > MAX_NEURONS_NICE) continue;
|
| 381 |
|
| 382 |
Int_t whichActivation = 0;
|
| 383 |
if (iHist+1==0 || iHist+1==nLayers-1 || i==0) whichActivation = 1;
|
| 384 |
draw_activation(c, cx2, cy2[nNeurons2-i-1], rad*ratio, rad, whichActivation);
|
| 385 |
}
|
| 386 |
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| 387 |
for (Int_t i = 0; i < nNeurons1; i++) {
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| 388 |
for (Int_t j = 0; j < nNeurons2; j++) {
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| 389 |
draw_synapse(cx1, cy1[i], cx2, cy2[j], effRad1*ratio, effRad2*ratio,
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| 390 |
h->GetBinContent(i+1, j+1)/maxWeight);
|
| 391 |
}
|
| 392 |
}
|
| 393 |
|
| 394 |
delete [] cy1;
|
| 395 |
delete [] cy2;
|
| 396 |
}
|
| 397 |
|
| 398 |
void draw_synapse(Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
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| 399 |
Double_t rad1, Double_t rad2, Double_t weightNormed)
|
| 400 |
{
|
| 401 |
const Double_t TIP_SIZE = 0.01;
|
| 402 |
const Double_t MAX_WEIGHT = 8;
|
| 403 |
const Double_t MAX_COLOR = 100; // red
|
| 404 |
const Double_t MIN_COLOR = 60; // blue
|
| 405 |
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| 406 |
if (weightNormed == 0) return;
|
| 407 |
|
| 408 |
// gStyle->SetPalette(100, NULL);
|
| 409 |
|
| 410 |
TArrow *arrow = new TArrow(cx1+rad1, cy1, cx2-rad2, cy2, TIP_SIZE, ">");
|
| 411 |
arrow->SetFillColor(1);
|
| 412 |
arrow->SetFillStyle(1001);
|
| 413 |
arrow->SetLineWidth((Int_t)(TMath::Abs(weightNormed)*MAX_WEIGHT+0.5));
|
| 414 |
arrow->SetLineColor((Int_t)((weightNormed+1.0)/2.0*(MAX_COLOR-MIN_COLOR)+MIN_COLOR+0.5));
|
| 415 |
arrow->Draw();
|
| 416 |
}
|
| 417 |
|
| 418 |
// input: - Input file (result from TMVA),
|
| 419 |
// - use of TMVA plotting TStyle
|
| 420 |
void network( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
|
| 421 |
{
|
| 422 |
// set style and remove existing canvas'
|
| 423 |
TMVAGlob::Initialize( useTMVAStyle );
|
| 424 |
|
| 425 |
// checks if file with name "fin" is already open, and if not opens one
|
| 426 |
TFile* file = TMVAGlob::OpenFile( fin );
|
| 427 |
TIter next(file->GetListOfKeys());
|
| 428 |
TKey *key(0);
|
| 429 |
while( (key = (TKey*)next()) ) {
|
| 430 |
if (!TString(key->GetName()).BeginsWith("Method_MLP")) continue;
|
| 431 |
if( ! gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory") ) continue;
|
| 432 |
|
| 433 |
cout << "--- Found directory: " << ((TDirectory*)key->ReadObj())->GetName() << endl;
|
| 434 |
|
| 435 |
TDirectory* mDir = (TDirectory*)key->ReadObj();
|
| 436 |
|
| 437 |
TIter keyIt(mDir->GetListOfKeys());
|
| 438 |
TKey *titkey;
|
| 439 |
while((titkey = (TKey*)keyIt())) {
|
| 440 |
if( ! gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory") ) continue;
|
| 441 |
|
| 442 |
TDirectory* dir = (TDirectory *)titkey->ReadObj();
|
| 443 |
dir->cd();
|
| 444 |
TList titles;
|
| 445 |
UInt_t ni = TMVAGlob::GetListOfTitles( dir, titles );
|
| 446 |
if (ni==0) {
|
| 447 |
cout << "No titles found for Method_MLP" << endl;
|
| 448 |
return;
|
| 449 |
}
|
| 450 |
draw_network( file, dir );
|
| 451 |
}
|
| 452 |
}
|
| 453 |
|
| 454 |
return;
|
| 455 |
}
|
| 456 |
|
