func AlgorithmRunOnDataSet(classifier algo.Classifier, train_dataset, test_dataset *core.DataSet, pred_path string, params map[string]string) (float64, []*eval.LabelPrediction) {
if train_dataset != nil {
classifier.Train(train_dataset)
}
predictions := []*eval.LabelPrediction{}
var pred_file *os.File
if pred_path != "" {
pred_file, _ = os.Create(pred_path)
}
for _, sample := range test_dataset.Samples {
prediction := classifier.Predict(sample)
if pred_file != nil {
pred_file.WriteString(strconv.FormatFloat(prediction, 'g', 5, 64) + "\n")
}
predictions = append(predictions, &(eval.LabelPrediction{Label: sample.Label, Prediction: prediction}))
}
if pred_path != "" {
defer pred_file.Close()
}
auc := eval.AUC(predictions)
return auc, predictions
}
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 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 predict(res http.ResponseWriter, req *http.Request) {
driverID, _ := strconv.Atoi(req.URL.Query().Get(":id"))
var model algo.Classifier
if _, ok := driversModels[driverID]; !ok {
model = NewModel(driverID)
} else {
model = driversModels[driverID]
}
fs := make(map[string]float64)
fs["hour"], _ = strconv.ParseFloat(req.URL.Query().Get("hour"), 64)
fs["dayOfWeek"], _ = strconv.ParseFloat(req.URL.Query().Get("dayOfWeek"), 64)
fs["distance_from_order_on_creation"], _ = strconv.ParseFloat(req.URL.Query().Get("distance_from_order_on_creation"), 64)
fs["driver_location_key"], _ = strconv.ParseFloat(req.URL.Query().Get("driver_location_key"), 64)
fs["driver_latitude"], _ = strconv.ParseFloat(req.URL.Query().Get("driver_latitude"), 64)
fs["driver_longitude"], _ = strconv.ParseFloat(req.URL.Query().Get("driver_longitude"), 64)
fs["origin_location_key"], _ = strconv.ParseFloat(req.URL.Query().Get("origin_location_key"), 64)
fs["origin_latitude"], _ = strconv.ParseFloat(req.URL.Query().Get("origin_latitude"), 64)
fs["origin_longitude"], _ = strconv.ParseFloat(req.URL.Query().Get("origin_longitude"), 64)
sample := NewSample(fs)
pr := model.Predict(sample)
renderJSON(res, http.StatusOK, map[string]interface{}{"predict": pr})
}
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
}