wehub-resource-sync
356 lines
11 KiB
JavaScript
356 lines
11 KiB
JavaScript
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// utility functions
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Array.prototype.contains = function(v) {
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for(var i = 0; i < this.length; i++) {
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if(this[i] === v) return true;
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}
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return false;
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};
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Array.prototype.unique = function() {
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var arr = [];
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for(var i = 0; i < this.length; i++) {
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if(!arr.contains(this[i])) {
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arr.push(this[i]);
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}
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}
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return arr;
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}
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function FAIL(outdivid, msg) {
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$(outdivid).prepend("<div class=\"msg\" style=\"background-color:#FCC;\">"+msg+"</div>")
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}
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function SUCC(outdivid, msg) {
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$(outdivid).prepend("<div class=\"msg\" style=\"background-color:#CFC;\">"+msg+"</div>")
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}
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// looks at a column i of data and guesses what's in it
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// returns results of analysis: is column numeric? How many unique entries and what are they?
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function guessColumn(data, c) {
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var numeric = true;
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var vs = [];
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for(var i=0,n=data.length;i<n;i++) {
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var v = data[i][c];
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vs.push(v);
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if(isNaN(v)) numeric = false;
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}
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var u = vs.unique();
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if(!numeric) {
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// if we have a non-numeric we will map it through uniques to an index
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return {numeric:numeric, num:u.length, uniques:u};
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} else {
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return {numeric:numeric, num:u.length};
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}
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}
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// returns arr (csv parse)
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// and colstats, which contains statistics about the columns of the input
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// parsing results will be appended to a div with id outdivid
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function importData(arr, outdivid) {
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$(outdivid).empty(); // flush messages
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// find number of datapoints
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N = arr.length;
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var t = [];
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SUCC(outdivid, "found " + N + " data points");
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if(N === 0) { FAIL(outdivid, 'no data points found?'); return; }
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// find dimensionality and enforce consistency
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D = arr[0].length;
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for(var i=0;i<N;i++) {
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var d = arr[i].length;
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if(d !== D) { FAIL(outdivid, 'data dimension not constant: line ' + i + ' has ' + d + ' entries.'); return; }
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}
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SUCC(outdivid, "data dimensionality is " + (D-1));
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// go through columns of data and figure out what they are
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var colstats = [];
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for(var i=0;i<D;i++) {
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var res = guessColumn(arr, i);
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colstats.push(res);
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if(D > 20 && i>3 && i < D-3) {
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if(i==4) {
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SUCC(outdivid, "..."); // suppress output for too many columns
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}
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} else {
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SUCC(outdivid, "column " + i + " looks " + (res.numeric ? "numeric" : "NOT numeric") + " and has " + res.num + " unique elements");
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}
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}
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return {arr: arr, colstats: colstats};
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}
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// process input mess into vols and labels
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function makeDataset(arr, colstats) {
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var labelix = parseInt($("#labelix").val());
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if(labelix < 0) labelix = D + labelix; // -1 should turn to D-1
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var data = [];
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var labels = [];
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for(var i=0;i<N;i++) {
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var arri = arr[i];
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// create the input datapoint Vol()
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var p = arri.slice(0, D-1);
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var xarr = [];
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for(var j=0;j<D;j++) {
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if(j===labelix) continue; // skip!
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if(colstats[j].numeric) {
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xarr.push(parseFloat(arri[j]));
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} else {
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var u = colstats[j].uniques;
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var ix = u.indexOf(arri[j]); // turn into 1ofk encoding
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for(var q=0;q<u.length;q++) {
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if(q === ix) { xarr.push(1.0); }
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else { xarr.push(0.0); }
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}
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}
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}
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var x = new convnetjs.Vol(xarr);
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// process the label (last column)
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if(colstats[labelix].numeric) {
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var L = parseFloat(arri[labelix]); // regression
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} else {
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var L = colstats[labelix].uniques.indexOf(arri[labelix]); // classification
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if(L==-1) {
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console.log('whoa label not found! CRITICAL ERROR, very fishy.');
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}
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}
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data.push(x);
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labels.push(L);
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}
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var dataset = {};
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dataset.data = data;
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dataset.labels = labels;
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return dataset;
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}
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// optionally provide a magic net
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function testEval(optional_net) {
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if (typeof optional_net !== 'undefined') {
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var net = optional_net;
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} else {
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var net = magicNet;
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}
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// set options for magic net
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net.ensemble_size = parseInt($("#ensemblenum").val())
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// read in the data in the text field
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var test_dataset = importTestData();
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// use magic net to predict
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var n = test_dataset.data.length;
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var acc = 0.0;
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for(var i=0;i<n;i++) {
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var yhat = net.predict(test_dataset.data[i]);
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if(yhat === -1) {
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$("#testresult").html("The MagicNet is not yet ready! It must process at least one batch of candidates across all folds first. Wait a bit.");
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$("#testresult").css('background-color', '#FCC');
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return;
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}
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var l = test_dataset.labels[i];
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acc += (yhat === l ? 1 : 0); // 0-1 loss
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console.log('test example ' + i + ': predicting ' + yhat + ', ground truth is ' + l);
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}
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acc /= n;
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// report accuracy
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$("#testresult").html("Test set accuracy: " + acc);
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$("#testresult").css('background-color', '#CFC');
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}
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function reinitGraph() {
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var legend = [];
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for(var i=0;i<magicNet.candidates.length;i++) {
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legend.push('model ' + i);
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}
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valGraph = new cnnvis.MultiGraph(legend, {miny: 0, maxy: 1});
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}
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var folds_evaluated = 0;
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function finishedFold() {
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folds_evaluated++;
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$("#foldreport").html("So far evaluated a total of " + folds_evaluated + "/" + magicNet.num_folds + " folds in current batch");
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reinitGraph();
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}
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var batches_evaluated = 0;
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function finishedBatch() {
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batches_evaluated++;
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$("#candsreport").html("So far evaluated a total of " + batches_evaluated + " batches of candidates");
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}
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var magicNet = null;
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function startCV() { // takes in train_dataset global
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var opts = {}
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opts.train_ratio = parseInt($("#trainp").val())/100.0;
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opts.num_folds = parseInt($("#foldsnum").val());
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opts.num_candidates = parseInt($("#candsnum").val());
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opts.num_epochs = parseInt($("#epochsnum").val());
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opts.neurons_min = parseInt($("#nnmin").val());
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opts.neurons_max = parseInt($("#nnmin").val());
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magicNet = new convnetjs.MagicNet(train_dataset.data, train_dataset.labels, opts);
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magicNet.onFinishFold(finishedFold);
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magicNet.onFinishBatch(finishedBatch);
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folds_evaluated = 0;
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batches_evaluated = 0;
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$("#candsreport").html("So far evaluated a total of " + batches_evaluated + " batches of candidates");
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$("#foldreport").html("So far evaluated a total of " + folds_evaluated + "/" + magicNet.num_folds + " folds in current batch");
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reinitGraph();
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var legend = [];
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for(var i=0;i<magicNet.candidates.length;i++) {
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legend.push('model ' + i);
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}
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valGraph = new cnnvis.MultiGraph(legend, {miny: 0, maxy: 1});
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setInterval(step, 0);
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}
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var fold;
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var cands = [];
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var dostep = false;
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var valGraph;
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var iter = 0;
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function step() {
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iter++;
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magicNet.step();
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if(iter % 300 == 0) {
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var vals = magicNet.evalValErrors();
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valGraph.add(magicNet.iter, vals);
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valGraph.drawSelf(document.getElementById("valgraph"));
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// print out the best models so far
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var cands = magicNet.candidates; // naughty: get pointer to internal data
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var scores = [];
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for(var k=0;k<cands.length;k++) {
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var c = cands[k];
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var s = c.acc.length === 0 ? 0 : c.accv / c.acc.length;
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scores.push(s);
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}
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var mm = convnetjs.maxmin(scores);
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var cm = cands[mm.maxi];
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var t = '';
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if(c.acc.length > 0) {
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t += 'Results based on ' + c.acc.length + ' folds:';
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t += 'best model in current batch (validation accuracy ' + mm.maxv + '):<br>';
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t += '<b>Net layer definitions:</b><br>';
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t += JSON.stringify(cm.layer_defs);
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t += '<br><b>Trainer definition:</b><br>';
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t += JSON.stringify(cm.trainer_def);
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t += '<br>';
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}
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$('#bestmodel').html(t);
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// also print out the best model so far
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var t = '';
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if(magicNet.evaluated_candidates.length > 0) {
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var cm = magicNet.evaluated_candidates[0];
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t += 'validation accuracy of best model so far, overall: ' + cm.accv / cm.acc.length + '<br>';
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t += '<b>Net layer definitions:</b><br>';
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t += JSON.stringify(cm.layer_defs);
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t += '<br><b>Trainer definition:</b><br>';
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t += JSON.stringify(cm.trainer_def);
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t += '<br>';
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}
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$('#bestmodeloverall').html(t);
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}
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}
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// TODO: MOVE TO CONVNETJS UTILS
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var randperm = function(n) {
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var i = n,
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j = 0,
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temp;
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var array = [];
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for(var q=0;q<n;q++)array[q]=q;
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while (i--) {
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j = Math.floor(Math.random() * (i+1));
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temp = array[i];
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array[i] = array[j];
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array[j] = temp;
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}
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return array;
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}
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var train_dataset, train_import_data; // globals
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function importTrainData() {
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var csv_txt = $('#data-ta').val();
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var arr = $.csv.toArrays(csv_txt);
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var arr_train = arr;
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var arr_test = [];
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var test_ratio = Math.floor($("#testsplit").val());
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if(test_ratio !== 0) {
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// send some lines to test set
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var test_lines_num = Math.floor(arr.length * test_ratio / 100.0);
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var rp = randperm(arr.length);
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arr_train = [];
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for(var i=0;i<arr.length;i++) {
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if(i<test_lines_num) {
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arr_test.push(arr[rp[i]]);
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} else {
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arr_train.push(arr[rp[i]]);
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}
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}
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// enter test lines to test box
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var t = "";
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for(var i=0;i<arr_test.length;i++) {
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t+= arr_test[i].join(",")+"\n";
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}
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$("#data-te").val(t);
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$("#datamsgtest").empty();
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}
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$("#prepromsg").empty(); // flush
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SUCC("#prepromsg", "Sent " + arr_test.length + " data to test, keeping " + arr_train.length + " for train.");
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train_import_data = importData(arr_train,'#datamsg');
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train_dataset = makeDataset(train_import_data.arr, train_import_data.colstats);
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return train_dataset;
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}
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function importTestData() {
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var csv_txt = $('#data-te').val();
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var arr = $.csv.toArrays(csv_txt);
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var import_data = importData(arr,'#datamsgtest');
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// note important that we use colstats of train data!
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test_dataset = makeDataset(import_data.arr, train_import_data.colstats);
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return test_dataset;
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}
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function loadDB(url) {
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// load a dataset from a url with ajax
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$.ajax({
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url: url,
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dataType: "text",
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success: function(txt) {
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$("#data-ta").val(txt);
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}
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});
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}
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function start() {
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loadDB('data/car.data.txt');
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}
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function exportMagicNet() {
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$("#taexport").val(JSON.stringify(magicNet.toJSON()));
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/*
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// for debugging
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var j = JSON.parse($("#taexport").val());
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var m = new convnetjs.MagicNet();
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m.fromJSON(j);
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testEval(m);
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*/
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}
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function changeNNRange() {
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magicNet.neurons_min = parseInt($("#nnmin").val());
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magicNet.neurons_max = parseInt($("#nnmax").val());
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}
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