/// <summary>
/// Export result for specific dataset based on trained model in the mlconfig file
/// </summary>
/// <param name="mlConfigPath"></param>
/// <param name="resultPath"></param>
public static void ExportResult(string mlConfigPath, string resultPath, DataSetType dsType)
{
var device = DeviceDescriptor.UseDefaultDevice();
var task = MLExport.ExportToCSV(mlConfigPath, device, resultPath, dsType);
task.Wait();
}
/// <summary>
/// Prints the performance analysis on the console
/// </summary>
/// <param name="mlConfigPath"></param>
public static void PrintPerformance(string mlConfigPath)
{
try
{
//print evaluation result on console
var performanceData = MLExport.PrintPerformance(mlConfigPath, DataSetType.Validation, DeviceDescriptor.UseDefaultDevice());
performanceData.Wait();
foreach (var s in performanceData.Result)
{
Console.WriteLine(s);
}
}
catch (Exception)
{
throw;
}
}
internal async Task <bool> ExportToCSV(string filepath)
{
try
{
if (string.IsNullOrEmpty(TrainingParameters.LastBestModel))
{
MessageBox.Show("No trained model exist. The model result cannot be exported.");
return(false);
}
//Load ML configuration file
var mlConfigPath = Project.GetMLConfigPath(Settings, Name);
await MLExport.ExportToCSV(mlConfigPath, MLFactory.GetDevice(ProcessDevice.Default), filepath);
return(true);
}
catch (Exception)
{
throw;
}
}
static void Main(string[] args)
{
string root = "C:\\sc\\github\\anndotnet\\src\\tool\\";
//transformDailyLevelVeanaLake();
//return;
//regression
var mlConfigFile1 = $"{root}anndotnet.wnd\\Resources\\Concrete\\ConcreteSlumpProject\\FFNModel.mlconfig";
//binary classification
var mlConfigFile2 = $"{root}anndotnet.wnd\\Resources\\Titanic\\TitanicProject\\DNNModel.mlconfig";
//Multi-class classification
//Famous multi class classification datset: https://archive.ics.uci.edu/ml/datasets/iris
var mlConfigFile3 = "./model_mlconfigs/iris.mlconfig";
//run example
var token2 = new CancellationToken();
//train mlconfig
var result = MachineLearning.Train(mlConfigFile3, trainingProgress, token2, null);
//once the mode is trained you can write performance analysis of the model
MachineLearning.PrintPerformance(mlConfigFile1);
//evaluate model and export the result of testing
MLExport.ExportToCSV(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(), "./model_mlconfigs/iris_result.csv").Wait();
//******run all configurations in the solution******
//string strLocation1 = "C:\\sc\\github\\anndotnet\\src\\tool\\";
//for (int i = 0; i < 10; i++)
// runAllml_configurations(strLocation1);
//*****end of program*****
Console.WriteLine("Press any key to continue!");
Console.ReadKey();
}