public IActionResult TrainMLAsync(int select = 0)
{
if (select == 0)
{
var metrics = MultiClassification.Train($"{dataPath}/*");
return(Ok(metrics));
}
if (select == 1)
{
var metrics = MultiClassification.Train(Path.Combine(dataPath, labeled_compare_data));
return(Ok(metrics));
}
return(BadRequest($"Select parameter {select} is not valid."));
}
private static int Main(string[] args)
{
// Test if input arguments were supplied.
Parser.Default.ParseArguments <Options>(args)
.WithParsed <Options>(o =>
{
// set logger
LoggingLevelSwitch levelSwitch = new LoggingLevelSwitch
{
MinimumLevel = LogEventLevel.Debug
};
Logger log = new LoggerConfiguration()
.MinimumLevel.ControlledBy(levelSwitch)
.WriteTo.Console(restrictedToMinimumLevel: LogEventLevel.Debug)
.WriteTo.File("backend_component_model_log-.txt", outputTemplate: "{Timestamp:yyyy-MM-dd HH:mm:ss.fff zzz} [{Level:u3}] {Message:lj}{NewLine}{Exception}",
rollingInterval: RollingInterval.Day, restrictedToMinimumLevel: LogEventLevel.Information)
.CreateLogger();
log.ForContext <MachineLearning>();
// load environmental variables
// DB connection configuration
JObject o1 = JObject.Parse(File.ReadAllText(@"C:\Users\Administrator\Documents\GitHub\machine-fault-diagnosis\backend\components\model\model_app\model_app\config\DBconfig.json"));
Environment.SetEnvironmentVariable("DBURL", (string)o1["DBURL"]);
Environment.SetEnvironmentVariable("DBUSER", (string)o1["DBUSER"]);
Environment.SetEnvironmentVariable("DBPW", (string)o1["DBPW"]);
// Model training configuration
JObject o2 = JObject.Parse(File.ReadAllText(@"C:\Users\Administrator\Documents\GitHub\machine-fault-diagnosis\backend\components\model\model_app\model_app\config\Trainconfig.json"));
Environment.SetEnvironmentVariable("ModelFile", (string)o2["ModelFile"]);
Environment.SetEnvironmentVariable("DataFile", (string)o2["DataFile"]);
Environment.SetEnvironmentVariable("TrainedModelFile", (string)o2["TrainedModelFile"]);
Environment.SetEnvironmentVariable("TrainingLogFile", (string)o2["TrainingLogFile"]);
if (o.mode == "I" || o.mode == "Interactive")
{
// Opens the interactive C# app
}
else
{
// Run machine learning engine in the background
switch (o.ModelType)
{
case "ProbabilisticNetwork":
log.Information("Start Probabilistic Graphical Model Engine");
Console.WriteLine("Please choose a task (Train, Inference or All)");
string t = Console.ReadLine();
string estimateStructure1;
if (t != "Inference")
{
Console.WriteLine("Please choose whether to estimated the graph structure or not ( Yes or No)");
estimateStructure1 = Console.ReadLine();
}
else
{
estimateStructure1 = "No";
}
PGM BNEngine = new PGM();
int result = BNEngine.Run(task: t, estimateStructure: estimateStructure1);
break;
case "TimeSeriesAnalysis":
log.Information("Start Time-series Analysis Engine");
break;
case "BinaryClassification":
log.Information("Start Binary Classification Engine");
string[] algorithms1 = { "AveragedPerceptronTrainer",
"SdcaLogisticRegressionBinaryTrainer",
"SdcaNonCalibratedBinaryTrainer",
"SymbolicSgdLogisticRegressionBinaryTrainer",
"LbfgsLogisticRegressionBinaryTrainer",
"LightGbmBinaryTrainer",
"FastTreeBinaryTrainer",
"FastForestBinaryTrainer",
"GamBinaryTrainer",
"FieldAwareFactorizationMachineTrainer",
"PriorTrainer",
"LinearSvmTrainer" };
Console.WriteLine("Please choose an algorithm from below");
Console.WriteLine(algorithms1);
string al1 = Console.ReadLine();
BinaryClassification bc = new BinaryClassification(al1);
int bcResult = bc.Run();
break;
case "MultiClassification":
log.Information("Start Multi Classification Engine");
string[] algorithms2 = { "LightGbmMulticlassTrainer",
"SdcaMaximumEntropyMulticlassTrainer",
"SdcaNonCalibratedMulticlassTrainer",
"LbfgsMaximumEntropyMulticlassTrainer",
"NaiveBayesMulticlassTrainer",
"OneVersusAllTrainer",
"PairwiseCouplingTrainer",
"ImageClassificationTrainer" };
Console.WriteLine("Please choose an algorithm from below");
Console.WriteLine(algorithms2);
string al2 = Console.ReadLine();
MultiClassification mc = new MultiClassification(al2);
int mcResult = mc.Run();
break;
case "Regression":
log.Information("Start Regression Engine");
string[] algorithms3 = { "LbfgsPoissonRegressionTrainer",
"LightGbmRegressionTrainer",
"SdcaRegressionTrainer",
"OlsTrainer",
"OnlineGradientDescentTrainer",
"FastTreeRegressionTrainer",
"FastTreeTweedieTrainer",
"FastForestRegressionTrainer",
"GamRegressionTrainer" };
Console.WriteLine("Please choose an algorithm from below");
Console.WriteLine(algorithms3);
string al3 = Console.ReadLine();
Regression rg = new Regression(al3);
int rgResult = rg.Run();
break;
case "Clustering":
log.Information("Start Clustering Engine. Use KMeans algorithm");
string al4 = "KMeans";
Clustering cluster = new Clustering(al4);
int clusResult = cluster.Run();
break;
case "AnomalyDetection":
log.Information("Start Anomaly Detection Engine. Use Randomized PCA algorithm");
string al5 = "RandomPCA";
Anomaly ano = new Anomaly(al5);
int anoResult = ano.Run();
break;
case "Ranking":
log.Information("Start Ranking Engine");
string[] algorithms6 = { "LightGbmRankingTrainer",
"FastTreeRankingTrainer" };
Console.WriteLine("Please choose an algorithm from below");
Console.WriteLine(algorithms6);
string al6 = Console.ReadLine();
Ranking rank = new Ranking(al6);
int rankResult = rank.Run();
break;
case "Recommendation":
log.Information("Start Recommendation Engine");
string al7 = "MatrixFactorizationTrainer";
Recommendation recomm = new Recommendation(al7);
int recommResult = recomm.Run();
break;
case "Forecast":
log.Information("Start Forecasting Engine");
string al8 = "ForecastBySsa";
Forecast forecast = new Forecast(al8);
int forecastResult = forecast.Run();
break;
default:
log.Information("Start AutoML Engine");
break;
}
}
}
).WithNotParsed(HandleParseError);
return(0);
}
