func SplitFile(dataset *core.DataSet, total, part int) (*core.DataSet, *core.DataSet) {
train := core.NewDataSet()
test := core.NewDataSet()
for i, sample := range dataset.Samples {
if i%total == part {
test.AddSample(sample)
} else {
train.AddSample(sample)
}
}
return train, test
}
func main() {
train_path, _, _, method, params := hector.PrepareParams()
global, _ := strconv.ParseInt(params["global"], 10, 64)
profile, _ := params["profile"]
dataset := core.NewDataSet()
dataset.Load(train_path, global)
cv, _ := strconv.ParseInt(params["cv"], 10, 32)
total := int(cv)
if profile != "" {
fmt.Println(profile)
f, err := os.Create(profile)
if err != nil {
fmt.Println("%v", err)
log.Fatal(err)
}
pprof.StartCPUProfile(f)
defer pprof.StopCPUProfile()
}
average_auc := 0.0
for part := 0; part < total; part++ {
train, test := SplitFile(dataset, total, part)
classifier := hector.GetClassifier(method)
classifier.Init(params)
auc, _ := hector.AlgorithmRunOnDataSet(classifier, train, test, "", params)
fmt.Println("AUC:")
fmt.Println(auc)
average_auc += auc
classifier = nil
}
fmt.Println(average_auc / float64(total))
}
func main() {
train_path, _, _, method, params := hector.PrepareParams()
global, _ := strconv.ParseInt(params["global"], 10, 64)
profile, _ := params["profile"]
dataset := core.NewDataSet()
dataset.Load(train_path, global)
cv, _ := strconv.ParseInt(params["cv"], 10, 32)
total := int(cv)
if profile != "" {
f, err := os.Create(profile)
if err != nil {
log.Fatal(err)
}
pprof.StartCPUProfile(f)
defer pprof.StopCPUProfile()
}
average_accuracy := 0.0
for part := 0; part < total; part++ {
train, test := SplitFile(dataset, total, part)
classifier := hector.GetMutliClassClassifier(method)
classifier.Init(params)
accuracy := hector.MultiClassRunOnDataSet(classifier, train, test, "", params)
fmt.Println("accuracy : ", accuracy)
average_accuracy += accuracy
classifier = nil
train = nil
test = nil
runtime.GC()
}
fmt.Println(average_accuracy / float64(total))
}
func AlgorithmRun(classifier algo.Classifier, train_path string, test_path string, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return 0.5, nil, err
}
test_dataset := core.NewDataSet()
err = test_dataset.Load(test_path, global)
if err != nil {
return 0.5, nil, err
}
classifier.Init(params)
auc, predictions := AlgorithmRunOnDataSet(classifier, train_dataset, test_dataset, pred_path, params)
return auc, predictions, nil
}
func MultiClassRun(classifier algo.MultiClassClassifier, train_path string, test_path string, pred_path string, params map[string]string) (float64, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return 0.5, err
}
test_dataset := core.NewDataSet()
err = test_dataset.Load(test_path, global)
if err != nil {
return 0.5, err
}
classifier.Init(params)
accuracy := MultiClassRunOnDataSet(classifier, train_dataset, test_dataset, pred_path, params)
return accuracy, nil
}
func (c *L1VM) Train(dataset *core.DataSet) {
c.sv = []*core.Vector{}
kernel_dataset := core.NewDataSet()
positive := []int{}
negative := []int{}
for i, si := range dataset.Samples {
if si.Label > 0.0 {
positive = append(positive, i)
} else {
negative = append(negative, i)
}
}
perm_positive := rand.Perm(len(positive))
for i, k := range perm_positive {
if i > c.count {
break
}
c.sv = append(c.sv, dataset.Samples[positive[k]].GetFeatureVector())
}
perm_negative := rand.Perm(len(negative))
for i, k := range perm_negative {
if i > c.count {
break
}
c.sv = append(c.sv, dataset.Samples[negative[k]].GetFeatureVector())
}
for _, si := range dataset.Samples {
xi := si.GetFeatureVector()
tsample := core.NewSample()
tsample.Label = si.Label
for j, xj := range c.sv {
tsample.AddFeature(core.Feature{Id: int64(j), Value: RBFKernel(xi, xj, c.radius)})
}
kernel_dataset.AddSample(tsample)
}
c.ftrl.Train(kernel_dataset)
}
func AlgorithmTest(classifier algo.Classifier, test_path string, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
model_path, _ := params["model"]
classifier.Init(params)
if model_path != "" {
classifier.LoadModel(model_path)
} else {
return 0.0, nil, nil
}
test_dataset := core.NewDataSet()
err := test_dataset.Load(test_path, global)
if err != nil {
return 0.0, nil, err
}
auc, predictions := AlgorithmRunOnDataSet(classifier, nil, test_dataset, pred_path, params)
return auc, predictions, nil
}
func AlgorithmTrain(classifier algo.Classifier, train_path string, params map[string]string) error {
global, _ := strconv.ParseInt(params["global"], 10, 64)
train_dataset := core.NewDataSet()
err := train_dataset.Load(train_path, global)
if err != nil {
return err
}
classifier.Init(params)
classifier.Train(train_dataset)
model_path, _ := params["model"]
if model_path != "" {
classifier.SaveModel(model_path)
}
return nil
}
func MultiClassTest(classifier algo.MultiClassClassifier, test_path string, pred_path string, params map[string]string) (float64, error) {
global, _ := strconv.ParseInt(params["global"], 10, 64)
model_path, _ := params["model"]
classifier.Init(params)
if model_path != "" {
classifier.LoadModel(model_path)
} else {
return 0.0, nil
}
test_dataset := core.NewDataSet()
err := test_dataset.Load(test_path, global)
if err != nil {
return 0.0, err
}
accuracy := MultiClassRunOnDataSet(classifier, nil, test_dataset, pred_path, params)
return accuracy, nil
}
func EncodeLabelAction(e *core.LabelEncoder, data_path string) {
dataset := core.NewDataSet()
err := dataset.Load(data_path, -1)
if err != nil {
log.Fatal(err)
return
}
encoded_label_dataset := e.TransformDataset(dataset)
var output_file *os.File
output_file, _ = os.Create(data_path + ".hector")
for _, sample := range encoded_label_dataset.Samples {
output_file.WriteString(string(sample.ToString(false)) + "\n")
}
if output_file != nil {
defer output_file.Close()
}
}
func main() {
train_path, test_path, pred_path, _, params := hector.PrepareParams()
total := 5
methods := []string{"ftrl", "fm"}
all_methods_predictions := [][]*eval.LabelPrediction{}
all_methods_test_predictions := [][]*eval.LabelPrediction{}
for _, method := range methods {
fmt.Println(method)
average_auc := 0.0
all_predictions := []*eval.LabelPrediction{}
for part := 0; part < total; part++ {
train, test, _ := SplitFile(train_path, total, part)
classifier := hector.GetClassifier(method)
auc, predictions, _ := hector.AlgorithmRun(classifier, train, test, "", params)
fmt.Println("AUC:")
fmt.Println(auc)
average_auc += auc
os.Remove(train)
os.Remove(test)
classifier = nil
for _, pred := range predictions {
all_predictions = append(all_predictions, pred)
}
}
all_methods_predictions = append(all_methods_predictions, all_predictions)
fmt.Println(average_auc / float64(total))
classifier := hector.GetClassifier(method)
fmt.Println(test_path)
_, test_predictions, _ := hector.AlgorithmRun(classifier, train_path, test_path, "", params)
all_methods_test_predictions = append(all_methods_test_predictions, test_predictions)
}
var wait sync.WaitGroup
wait.Add(2)
dataset := core.NewDataSet()
go func() {
for i, _ := range all_methods_predictions[0] {
sample := core.NewSample()
sample.Label = all_methods_predictions[0][i].Label
for j, _ := range all_methods_predictions {
feature := core.Feature{Id: int64(j), Value: all_methods_predictions[j][i].Prediction}
sample.AddFeature(feature)
}
dataset.Samples <- sample
}
close(dataset.Samples)
wait.Done()
}()
ensembler := lr.LinearRegression{}
go func() {
ensembler.Init(params)
ensembler.Train(dataset)
wait.Done()
}()
wait.Wait()
fmt.Println(ensembler.Model)
wait.Add(2)
test_dataset := hector.NewDataSet()
go func() {
for i, _ := range all_methods_test_predictions[0] {
sample := hector.NewSample()
sample.Label = all_methods_test_predictions[0][i].Prediction
for j, _ := range all_methods_test_predictions {
feature := hector.Feature{Id: int64(j), Value: all_methods_test_predictions[j][i].Prediction}
sample.AddFeature(feature)
}
test_dataset.Samples <- sample
}
close(test_dataset.Samples)
wait.Done()
}()
go func() {
pred_file, _ := os.Create(test_path + ".out")
for sample := range test_dataset.Samples {
prediction := sample.Label //ensembler.Predict(sample)
pred_file.WriteString(strconv.FormatFloat(prediction, 'g', 5, 64) + "\n")
}
defer pred_file.Close()
wait.Done()
}()
wait.Wait()
}