}, function(err){
var testEnd = new Date().getTime()
currentStats.calculateStats()
callback(err, { 'data': data_stats,
'stats': currentStats,
'testtime': testEnd - testStart})
})
}, function(err){
var testEnd = new Date().getTime()
currentStats.calculateStats()
console.vlog("TEST&TRAIN: currentStats: "+JSON.stringify(currentStats, null, 4))
callback(err, { 'data': data_stats,
'stats': currentStats,
'testtime': testEnd - testStart})
})
module.exports.test_hash = function( classifier, testSet1, verbosity) {
var stat_hash = {}
var sentence_hash = {}
var data_stats = []
var currentStats = []
var indexes = []
var explain = 50;
var testStart = new Date().getTime()
testSet = JSON.parse(JSON.stringify(testSet1, null, 4))
currentStats = new PrecisionRecall()
for (var i=0; i<testSet.length; ++i)
{
expectedClasses = testSet[i].output
classesWithExplanation = classifier.classify(testSet[i].input, verbosity)
console.log("*********RESULTS**************")
console.log(JSON.stringify(classesWithExplanation, null, 4))
if (verbosity) actualClasses = classesWithExplanation.classes
else
actualClasses = classesWithExplanation
// actualClasses = bars.filterlabels(actualClasses)
// expl = currentStats.addCasesHash(expectedClasses, actualClasses, (verbosity>2));
// console.log(JSON.stringify(expl, null, 4))
var sentence_hash = {}
sentence_hash.input = testSet[i].input
sentence_hash.output = testSet[i].output
sentence_hash.classified = actualClasses
sentence_hash.explanation = currentStats.addCasesHash(expectedClasses, actualClasses, (verbosity>2));
currentStats.addIntentHash(expectedClasses, actualClasses, true)
// sentence_hash['expected'] = expectedClasses
// sentence_hash['id'] = id
// sentence_hash['explanation_source'] = classesWithExplanation
data_stats.push(sentence_hash);
}
currentStats.calculateStats()
var testEnd = new Date().getTime()
return {
'data': data_stats,
'stats': currentStats,
'testtime': testEnd - testStart
}
};
module.exports.trainAndTest_batch = function(
classifierType,
trainSet, testSet,
verbosity, microAverage, macroSum) {
var startTime = new Date();
var classifier = new classifierType();
testSet1 = JSON.parse(JSON.stringify(testSet))
testSet2 = JSON.parse(JSON.stringify(testSet))
trainSet1 = JSON.parse(JSON.stringify(trainSet))
classifier.trainBatch(trainSet1);
var output = classifier.classifyBatch(testSet1);
if (output.length != testSet1.length)
throw new Error('Lengths are not equal');
var currentStats = new PrecisionRecall();
var data_stats = []
_.each(testSet2, function(value, key, list){
var sentence_hash = {}
sentence_hash['input'] = value.input;
sentence_hash['expected'] = value.output
sentence_hash['classified'] = output[key].output
sentence_hash['explanation'] = currentStats.addCasesHash(value.output, output[key].output, (verbosity>2));
data_stats.push(sentence_hash)
}, this)
currentStats.calculateStats()
var testEnd = new Date().getTime()
classifierstats = {}
classifierstats['labels'] = currentStats.retrieveLabels()
classifierstats['data'] = data_stats
classifierstats['stats'] = currentStats
return classifierstats
};
module.exports.test = function(
classifier, testSet,
verbosity, microAverage, macroSum) {
if (typeof classifier.OutputSplitLabel === 'function') {
testSet = classifier.outputToFormat(testSet)
}
var currentStats = new PrecisionRecall();
for (var i=0; i<testSet.length; ++i) {
var expectedClasses = normalizeClasses(testSet[i].output);
var actualClasses = classifier.classify(testSet[i].input);
var explanations = currentStats.addCases(expectedClasses, actualClasses, (verbosity>2));
currentStats.addCasesLabels(expectedClasses, actualClasses);
if (verbosity>1 && explanations.length>0) console.log("\t"+testSet[i].input+": \n"+explanations.join("\n"));
if (microAverage) microAverage.addCases(expectedClasses, actualClasses);
}
currentStats.calculateStats();
if (macroSum) hash.add(macroSum, currentStats.fullStats());
return currentStats;
};
module.exports.testLite = function(classifier, testSet, explain) {
var currentStats = new PrecisionRecall();
labeltree = {}
if ((typeof classifier.TestSplitLabel === 'function')) {
testSet = classifier.outputToFormat(testSet)
}
for (var i=0; i<testSet.length; ++i) {
expectedClasses = list.listembed(testSet[i].output)
// classified = classifier.classify(testSet[i].input, 10, testSet[i].input);
classified = classifier.classify(testSet[i].input);
// console.log(JSON.stringify(classified, null, 4))
// process.exit(0)
actualClasses = list.listembed(classified)
for (type in classified.explanation)
{
for (label in classified.explanation[type])
{
if (!(label in labeltree))
{
labeltree[label] = {}
labeltree[label]['positive'] = {}
labeltree[label]['negative'] = {}
}
_.each(classified.explanation[type][label], function(value, key, list){
if (!(value[0] in labeltree[label][type]))
{
labeltree[label][type][value[0]] = {}
labeltree[label][type][value[0]]['count'] = 0
labeltree[label][type][value[0]]['weight'] = 0
}
labeltree[label][type][value[0]]['count'] = labeltree[label][type][value[0]]['count']+ 1
labeltree[label][type][value[0]]['weight'] = labeltree[label][type][value[0]]['weight'] + value[1]
}, this)
}
}
// var expectedClasses = normalizeClasses(dataset[i].output);
// var actualClassesWithExplanations = classifier.classify(dataset[i].input, explain, dataset[i].input);
// actualClasses = (actualClassesWithExplanations.classes? actualClassesWithExplanations.classes: actualClassesWithExplanations);
// actualClasses.sort();
// if (!_(expectedClasses).isEqual(actualClasses)) {
// console.log(
// "\t"+JSON.stringify(dataset[i].input)+":"+
// " expected "+expectedClasses+
// " but got "+(explain>0? JSON.stringify(actualClassesWithExplanations,null,"\t"): actualClasses));
// }
// currentStats.addCases(expectedClasses, actualClasses);
}
for (label in labeltree)
{
for (type in labeltree[label])
{
lis = []
for (label1 in labeltree[label][type])
{
lis.push([label1,labeltree[label][type][label1]['count'], labeltree[label][type][label1]['weight']])
}
labeltree[label][type]['LIST'] = _.sortBy(lis, function(num){ return Math.abs(num[2]); });
}
}
for (label in labeltree)
{
for (type in labeltree[label])
{
console.log(label+" "+type)
console.log(JSON.stringify(labeltree[label][type]['LIST'].reverse().slice(0,10), null, 4))
}
}
console.log("SUMMARY: "+currentStats.calculateStats().shortStats());
return currentStats;
};