// 进行N-fold cross-validation，输出评价
func CrossValidate(trainer supervised.Trainer, set data.Dataset,
evals *Evaluators, folds int) (output Evaluation) {
output.Metrics = make(map[string]float64)
for iFold := 0; iFold < folds; iFold++ {
// 裂分训练数据
trainBuckets := []data.SkipBucket{
{false, iFold},
{true, 1},
{false, folds - 1 - iFold},
}
trainSet := data.NewSkipDataset(set, trainBuckets)
// 裂分评价数据
evalBuckets := []data.SkipBucket{
{true, iFold},
{false, 1},
{true, folds - 1 - iFold},
}
evalSet := data.NewSkipDataset(set, evalBuckets)
// 在训练数据上训练模型
model := trainer.Train(trainSet)
// 在评价数据上评价
metrics := evals.Evaluate(model, evalSet)
// 累加评价结果
for m, v := range metrics.Metrics {
output.Metrics[m] += v
}
}
// 评价结果求平均
for m := range output.Metrics {
output.Metrics[m] /= float64(folds)
}
return
}
func (opt *lbfgsOptimizer) OptimizeWeights(
weights *util.Matrix, derivative_func ComputeInstanceDerivativeFunc, set data.Dataset) {
// 学习率计算器
learningRate := NewLearningRate(opt.options)
// 偏导数向量
derivative := weights.Populate()
// 优化循环
step := 0
convergingSteps := 0
oldWeights := weights.Populate()
weightsDelta := weights.Populate()
// 为各个工作协程开辟临时资源
numLbfgsThreads := *lbfgs_threads
if numLbfgsThreads == 0 {
numLbfgsThreads = runtime.NumCPU()
}
workerSet := make([]data.Dataset, numLbfgsThreads)
workerDerivative := make([]*util.Matrix, numLbfgsThreads)
workerInstanceDerivative := make([]*util.Matrix, numLbfgsThreads)
for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
workerBuckets := []data.SkipBucket{
{true, iWorker},
{false, 1},
{true, numLbfgsThreads - 1 - iWorker},
}
workerSet[iWorker] = data.NewSkipDataset(set, workerBuckets)
workerDerivative[iWorker] = weights.Populate()
workerInstanceDerivative[iWorker] = weights.Populate()
}
log.Print("开始L-BFGS优化")
for {
if opt.options.MaxIterations > 0 && step >= opt.options.MaxIterations {
break
}
step++
// 开始工作协程
workerChannel := make(chan int, numLbfgsThreads)
for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
go func(iw int) {
workerDerivative[iw].Clear()
iterator := workerSet[iw].CreateIterator()
iterator.Start()
for !iterator.End() {
instance := iterator.GetInstance()
derivative_func(
weights, instance, workerInstanceDerivative[iw])
// log.Print(workerInstanceDerivative[iw].GetValues(0))
workerDerivative[iw].Increment(
workerInstanceDerivative[iw], float64(1)/float64(set.NumInstances()))
iterator.Next()
}
workerChannel <- iw
}(iWorker)
}
derivative.Clear()
// 等待工作协程结束
for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
<-workerChannel
}
for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
derivative.Increment(workerDerivative[iWorker], 1)
}
// 添加正则化项
derivative.Increment(ComputeRegularization(weights, opt.options), 1.0/float64(set.NumInstances()))
// 计算特征权重的增量
delta := opt.GetDeltaX(weights, derivative)
// 根据学习率更新权重
learning_rate := learningRate.ComputeLearningRate(delta)
weights.Increment(delta, learning_rate)
weightsDelta.WeightedSum(weights, oldWeights, 1, -1)
oldWeights.DeepCopy(weights)
weightsNorm := weights.Norm()
weightsDeltaNorm := weightsDelta.Norm()
log.Printf("#%d |dw|/|w|=%f |w|=%f lr=%1.3g", step, weightsDeltaNorm/weightsNorm, weightsNorm, learning_rate)
// 判断是否溢出
if math.IsNaN(weightsNorm) {
log.Fatal("优化失败：不收敛")
}
// 判断是否收敛
if weightsDeltaNorm/weightsNorm < opt.options.ConvergingDeltaWeight {
convergingSteps++
if convergingSteps > opt.options.ConvergingSteps {
log.Printf("收敛")
break
}
} else {
convergingSteps = 0
}
}
}