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Miguel Á M.S.
2023-02-02 17:28:12 +01:00
parent 704890c0dd
commit bf77ea937b
4 changed files with 37 additions and 26 deletions
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43
testML/Program.cs
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@@ -1,4 +1,7 @@
using Microsoft.ML;
#define SIMULATION
#undef TEST_MODELS
using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
@@ -31,7 +34,7 @@ namespace testML
{
XSSFWorkbook wb;
//using (FileStream file = new FileStream(@"C:\Users\miguel.maldonado\Downloads\entrenar_IAMenos.xlsx", FileMode.Open, FileAccess.Read))
using (FileStream file = new FileStream(@"C:\Users\miki_\Downloads\entrenar_IA_2.xlsx", FileMode.Open, FileAccess.Read))
using (FileStream file = new FileStream(@"C:\Users\miguel.maldonado\Downloads\entrenar_IA.xlsx", FileMode.Open, FileAccess.Read))
{
wb = new XSSFWorkbook(file);
}
@@ -136,9 +139,11 @@ namespace testML
}
}
#if SIMULATION
S4i_Simulador.S4i_SimularCruces.Run(tmpData);
/*foreach (var key in firstRow.Keys)
#else
foreach (var key in firstRow.Keys)
{
if (key.StartsWith("DESCENDIENTE_S4i") ||
key.StartsWith("DESCENDIENTE_SNP"))
@@ -170,7 +175,8 @@ namespace testML
}
}
}*/
}
#endif
Console.WriteLine();
Console.WriteLine("Press enter to Exit");
@@ -205,7 +211,7 @@ namespace testML
var objectFilename = columnToPredict + "." + hash + ".dll";
#endregion
#endregion
MLContext mlContext = new MLContext();
@@ -237,16 +243,16 @@ namespace testML
var loadMethodObj = loadMethod.MakeGenericMethod(classType);
var data = (IDataView)loadMethodObj.Invoke(mlContext.Data, new object[] { dataConverted, null });
#region Cortamos los datos de entrenamiento en (Datos para entenar y Datos para hacer el test de precisión)
#region Cortamos los datos de entrenamiento en (Datos para entenar y Datos para hacer el test de precisión)
DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.5);
IDataView trainData = dataSplit.TrainSet;
IDataView testData = dataSplit.TestSet;
#endregion
#endregion
#region Preparamos los datos de entrada y salida
#region Preparamos los datos de entrada y salida
var columnNameAndTypes = new Dictionary<string, Type>();
@@ -317,14 +323,15 @@ namespace testML
#endregion
/*
//Entrenamos el modelo
//ITransformer model = pipe.Fit(trainData);
#region Hacemos un test para medir el % de error
#if TEST_MODELS
//Entrenamos el modelo
//ITransformer model = pipe.Fit(trainData);
// Use trained model to make inferences on test data
IDataView testDataPredictions = result.Model.Transform(testData);
#region Hacemos un test para medir el % de error
// Use trained model to make inferences on test data
IDataView testDataPredictions = result.Model.Transform(testData);
// Extract model metrics and get RSquared
RegressionMetrics trainedModelMetrics = mlContext.Regression.Evaluate(testDataPredictions, labelColumnName: columnInference.LabelColumnName);
@@ -332,10 +339,10 @@ namespace testML
Console.WriteLine("ModelMetrics: {0}", rSquared);
#endregion
#endregion
#region Ponemos a prueba haciendo algunas predicciones
#region Ponemos a prueba haciendo algunas predicciones
var predictionFunction = mlContext.Model.CreatePredictionEngine<Data, DataPrediction>(result.Model);
@@ -350,7 +357,7 @@ namespace testML
Console.WriteLine("Found: {0:#,##0.00}\tExpected: {1:#,##0.00}\t\tDiff: {2:#,##0.00}", p.IntegerNumber, expected, expected - p.IntegerNumber);
}
#endregion
#endregion
*/
}