/*program's entry point. use VIC algorithm in partitions of 2 and 3 clusters.
* Write the AUC of each classificator for each partitions to a csv file.
* Write to the standard output the VIC for each partition
*/
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
Clustering clustering = new Clustering(@"D:\Code\Migue\Assignment_2\VIC_classifiers\VIC\data2.csv");
double[,] AUCs2 = vic(clustering.get2clustered, 2, 50, get_model, 7, mlContext, 6);
double[,] AUCs3 = vic(clustering.get3clustered, 3, 50, get_model, 7, mlContext, 6);
Util.save2csv("2c.csv", AUCs2);
Util.save2csv("3c.csv", AUCs3);
double[] vic2 = max_axis1(AUCs2);
double[] vic3 = max_axis1(AUCs3);
System.Console.WriteLine("VIC for 2-clusters partitions");
print(vic2);
System.Console.WriteLine("VIC for 3-clusters partitions");
print(vic3);
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
// 1. Import or create training data
// Binary data
string csv_path = @"D:\Actividades\Maestría\Segundo semestre\Machine learning\Assignment_2\VIC-master\VIC-master\VIC\data.csv";
IDataView trainingData = mlContext.Data.LoadFromTextFile <ModelInput>(csv_path, separatorChar: ',', hasHeader: true);
//Multi-class data
string csv_path2 = @"D:\Actividades\Maestría\Segundo semestre\Machine learning\Assignment_2\VIC-master\VIC-master\VIC\data2.csv";
IDataView trainingData2 = mlContext.Data.LoadFromTextFile <ModelInput2>(csv_path2, separatorChar: ',', hasHeader: true);
Clustering clustering = new Clustering();
IDataView trainingData3 = clustering.get2clustered();
string[] attributes =
{
"nn",
"nnr",
"nn_nn",
"nnr_nnr",
"dn",
"df",
"dnr",
"dfr",
"dn_dn",
"dnr_dnr",
"alphan",
"alphaf",
"alphann",
"alphanf",
"betann",
"betaf",
"alphan_betan" //,
//"typeEncoded"
};
// 2. Feature selection
/*
*
* string[] attributes = {
* "nn15",
* "nn30" ,
* "nn45" ,
* "nn60" ,
* "nn75" ,
* "nn90" ,
* "nn105" ,
* "nn120" ,
* "nn135" ,
* "nn150" ,
* "nn165" ,
* "nn180" ,
* "nn195" ,
* "nn210" ,
* "nn225" ,
* "nn240" ,
* "nn255" ,
* "nn270" ,
* "nn285" ,
* "nn300" ,
* "nn315" ,
* "nn330" ,
* "nn345" ,
* "nn360" ,
* "nn375" ,
* "nn390" ,
* "nn405" ,
* "nn420" ,
* "nn435" ,
* "nn450" ,
* "nn465" ,
* "nn480" ,
* "nn495" ,
* "nn510" ,
* "nn525" ,
* "nn540" ,
* "nn555" ,
* "nn570" ,
* "nn585" ,
* "nn600" ,
* "nn607" ,
* "nn15r" ,
* "nn30r" ,
* "nn45r" ,
* "nn60r" ,
* "nn75r" ,
* "nn90r" ,
* "nn105r" ,
* "nn120r" ,
* "nn135r" ,
* "nn150r" ,
* "nn165r" ,
* "nn180r" ,
* "nn195r" ,
* "nn210r" ,
* "nn225r" ,
* "nn240r" ,
* "nn255r" ,
* "nn270r" ,
* "nn285r" ,
* "nn300r" ,
* "nn315r" ,
* "nn330r" ,
* "nn345r" ,
* "nn360r" ,
* "nn375r" ,
* "nn390r" ,
* "nn405r" ,
* "nn420r" ,
* "nn435r" ,
* "nn450r" ,
* "nn465r" ,
* "nn480r" ,
* "nn495r" ,
* "nn510r" ,
* "nn525r" ,
* "nn540r" ,
* "nn555r" ,
* "nn570r" ,
* "nn585r" ,
* "nn600r" ,
* "nn607r" ,
* "nn30-nn15" ,
* "nn45-nn30" ,
* "nn60-nn45" ,
* "nn75-nn60" ,
* "nn90-nn75" ,
* "nn105-nn90" ,
* "nn120-nn105" ,
* "nn135-nn120" ,
* "nn150-nn135" ,
* "nn165-nn150" ,
* "nn180-nn165" ,
* "nn195-nn180" ,
* "nn210-nn195" ,
* "nn225-nn210" ,
* "nn240-nn225" ,
* "nn255-nn240" ,
* "nn270-nn255" ,
* "nn285-nn270" ,
* "nn300-nn285" ,
* "nn315-nn300" ,
* "nn330-nn315" ,
* "nn345-nn330" ,
* "nn360-nn345" ,
* "nn375-nn360" ,
* "nn390-nn375" ,
* "nn405-nn390" ,
* "nn420-nn405" ,
* "nn435-nn420" ,
* "nn450-nn435" ,
* "nn465-nn450" ,
* "nn480-nn465" ,
* "nn495-nn480" ,
* "nn510-nn495" ,
* "nn525-nn510" ,
* "nn540-nn525" ,
* "nn555-nn540" ,
* "nn570-nn555" ,
* "nn585-nn570" ,
* "nn600-nn585" ,
* "nn607-nn600" ,
* "nn30r-nn15r" ,
* "nn45r-nn30r" ,
* "nn60r-nn45r" ,
* "nn75r-nn60r" ,
* "nn90r-nn75r" ,
* "nn105r-nn90r" ,
* "nn120r-nn105r" ,
* "nn135r-nn120r" ,
* "nn150r-nn135r" ,
* "nn165r-nn150r" ,
* "nn180r-nn165r" ,
* "nn195r-nn180r" ,
* "nn210r-nn195r" ,
* "nn225r-nn210r" ,
* "nn240r-nn225r" ,
* "nn255r-nn240r" ,
* "nn270r-nn255r" ,
* "nn285r-nn270r" ,
* "nn300r-nn285r" ,
* "nn315r-nn300r" ,
* "nn330r-nn315r" ,
* "nn345r-nn330r" ,
* "nn360r-nn345r" ,
* "nn375r-nn360r" ,
* "nn390r-nn375r" ,
* "nn405r-nn390r" ,
* "nn420r-nn405r" ,
* "nn435r-nn420r" ,
* "nn450r-nn435r" ,
* "nn465r-nn450r" ,
* "nn480r-nn465r" ,
* "nn495r-nn480r" ,
* "nn510r-nn495r" ,
* "nn525r-nn510r" ,
* "nn540r-nn525r" ,
* "nn555r-nn540r" ,
* "nn570r-nn555r" ,
* "nn585r-nn570r" ,
* "nn600r-nn585r" ,
* "nn607r-nn600r" ,
* "d1" ,
* "d2" ,
* "d3" ,
* "d4" ,
* "d5" ,
* "d6" ,
* "d7" ,
* "d8" ,
* "d9" ,
* "d10" ,
* "d11" ,
* "d12" ,
* "df" ,
* "d1r" ,
* "d2r" ,
* "d3r" ,
* "d4r" ,
* "d5r" ,
* "d6r" ,
* "d7r" ,
* "d8r" ,
* "d9r" ,
* "d10r" ,
* "d11r" ,
* "d12r" ,
* "dfr" ,
* "d2-d1" ,
* "d3-d2" ,
* "d4-d3" ,
* "d5-d4" ,
* "d6-d5" ,
* "d7-d6" ,
* "d8-d7" ,
* "d9-d8" ,
* "d10-d9" ,
* "d11-d10" ,
* "d12-d11" ,
* "df-d12" ,
* "d2r-d1r" ,
* "d3r-d2r" ,
* "d4r-d3r" ,
* "d5r-d4r" ,
* "d6r-d5r" ,
* "d7r-d6r" ,
* "d8r-d7r" ,
* "d9r-d8r" ,
* "d10r-d9r" ,
* "d11r-d10r" ,
* "d12r-d11r" ,
* "dfr-d12r" ,
* "alpha1" ,
* "alpha2" ,
* "alpha3" ,
* "alpha4" ,
* "alpha5" ,
* "alpha6" ,
* "alpha7" ,
* "alpha8" ,
* "alpha9" ,
* "alpha10" ,
* "alpha11" ,
* "alpha12" ,
* "alphaf" ,
* "alphan1" ,
* "alphan2" ,
* "alphan3" ,
* "alphan4" ,
* "alphan5" ,
* "alphan6" ,
* "alphan7" ,
* "alphan8" ,
* "alphan9" ,
* "alphan10" ,
* "alphan11" ,
* "alphan12" ,
* "alphanf" ,
* "beta1" ,
* "beta2" ,
* "beta3" ,
* "beta4" ,
* "beta5" ,
* "beta6" ,
* "beta7" ,
* "beta8" ,
* "beta9" ,
* "beta10" ,
* "beta11" ,
* "beta12" ,
* "betaf" ,
* "alpha1-beta1" ,
* "alpha2-beta2" ,
* "alpha3-beta3" ,
* "alpha4-beta4" ,
* "alpha5-beta5" ,
* "alpha6-beta6" ,
* "alpha7-beta7" ,
* "alpha8-beta8" ,
* "alpha9-beta9" ,
* "alpha10-beta10" ,
* "alpha11-beta11" ,
* "alpha12-beta12" ,
* "alphaf-betaf",
* "typeEncoded"
* };
*
*/
/*
* double aucSingleModel = CalculateModelAUC(mlContext, 1, trainingData3, attributes,2);
* Console.WriteLine("Aquí está el AUC:");
* Console.WriteLine(aucSingleModel);
*/
//int selector = 0;
double[] aucArray = new double[7];
int[] indexArrray = { 0, 1, 2, 3, 4, 5, 6 };
Parallel.ForEach(indexArrray, new ParallelOptions {
MaxDegreeOfParallelism = 7
}, (x) =>
{
//Console.WriteLine("El valor de AUC con cross-val de 10 folds usando el modelo " + x + " es: ");
aucArray[x] = CalculateModelAUC(mlContext, x, trainingData3, attributes, 2);
//Console.WriteLine(CalculateModelAUC(mlContext, x, trainingData3, attributes, 2)); //result.
//close lambda expression and method invocation
});
for (int i = 0; i < 7; i++)
{
// Console.WriteLine("El valor de AUC con cross-val de 10 folds usando el modelo " + i + " es: ");
Console.WriteLine(aucArray[i]); //result.
}
Console.WriteLine("El VIC es: ");
Console.WriteLine(aucArray.Max());
/*
*
* for (int i = 0; i < 7; i++)
* {
* Console.WriteLine("El valor de AUC con cross-val de 10 folds usando el modelo " + i + " es: ");
* aucArray[i] = CalculateModelAUC(mlContext, i, trainingData3, attributes,2);
* Console.WriteLine(aucArray[i]); //result.
* }
*
* ¨*/
}
