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Ejecutando con el conjunto completo

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This commit is contained in:
Miguel Á M.S.
2023-01-18 15:20:57 +01:00
parent 7f68e262f4
commit 105e2e471e
1 changed files with 102 additions and 47 deletions
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149
testML/Program.cs
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@@ -7,15 +7,18 @@ using Microsoft.SqlServer.Server;
using NPOI.XSSF.UserModel;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Dynamic;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Reflection;
using System.Security.AccessControl;
using System.Security.Cryptography;
using System.Text;
using System.Threading.Tasks;
using System.Xml.Linq;
using static TorchSharp.torch.utils;
namespace testML
{
@@ -98,20 +101,16 @@ namespace testML
{
rowData.Add(finalColumnName, value?.ToString() ?? "");
}
}
tmpData.Add(rowData);
}
var columnToPredict = "DESCENDIENTE_S4i001";
//Eliminamos las columnas en blanco
var firstRow = tmpData[0] as IDictionary<string, object>;
foreach (var key in firstRow.Keys.ToArray())
{
var firstValue = (from x in tmpData where x.ContainsKey(key) && x[key] != null && !string.IsNullOrEmpty(x[key] as string) select x[key]).FirstOrDefault();
var values = (from x in tmpData where x.ContainsKey(key) && x[key] != null && !string.IsNullOrEmpty(x[key] as string) select x[key]);
var firstValue = values.FirstOrDefault();
if (firstValue == null)
{
foreach (var item in tmpData)
@@ -121,10 +120,66 @@ namespace testML
item.Remove(key);
}
}
}
}
foreach (var key in firstRow.Keys)
{
if (key.StartsWith("DESCENDIENTE_S4i") ||
key.StartsWith("DESCENDIENTE_SNP"))
{
var values = (from x in tmpData where x.ContainsKey(key) && x[key] != null && !string.IsNullOrEmpty(x[key] as string) select x[key]).Distinct().ToArray();
if (values.Length > 1)
{
try
{
var sw = new Stopwatch();
sw.Start();
MakePrediction(tmpData, key);
sw.Stop();
Console.WriteLine("Elapsed: " + sw.Elapsed.ToString());
GC.Collect();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
}
else
{
}
}
}
#endregion
Console.WriteLine();
Console.WriteLine("Press enter to Exit");
Console.ReadLine();
}
private static void MakePrediction(List<Dictionary<string, object>> tmpData, string columnToPredict)
{
var firstRow = tmpData[0] as IDictionary<string, object>;
var hashKey = new StringBuilder();
foreach (var key in firstRow.Keys.Where(x => !x.StartsWith("DESCENDIENTE_") && (x.Contains("_S4i") || x.Contains("_SNP"))).OrderBy(x => x))
{
if (hashKey.Length > 0) { hashKey.Append("+"); }
hashKey.Append(key);
}
var md5 = MD5.Create();
var hash = string.Join("", md5.ComputeHash(new MemoryStream(new UTF8Encoding(false).GetBytes(hashKey.ToString()))).Select(x => x.ToString("X2").ToUpper()).ToArray());
var modelFilename = columnToPredict + "." + hash + ".zip";
#endregion
MLContext mlContext = new MLContext();
@@ -147,42 +202,48 @@ namespace testML
//}
};
var dataConverted = DictionaryToObjectConverter.Convert(tmpData, columnToPredict, out Type classType, out Type classPredictionType, out DataViewSchema schema);
tmpData = null; //Liberamos la memoria
var loadMethod = mlContext.Data.GetType().GetMethods().Where(x => x.Name == "LoadFromEnumerable" && x.IsGenericMethodDefinition).FirstOrDefault();
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)
DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.5);
IDataView trainData = dataSplit.TrainSet;
IDataView testData = dataSplit.TestSet;
#endregion
#region Preparamos los datos de entrada y salida
var columnNameAndTypes = new Dictionary<string, Type>();
foreach (var item in (from x in firstRow.Keys
select new { Key = x, Type = (from y in dataConverted.Cast<IDictionaryToObjectConverter>() where y.GetValue(x) != null select y.GetValue(x).GetType()).FirstOrDefault() })
)
ITransformer _trainedModel;
if (!File.Exists(modelFilename))
{
columnNameAndTypes.Add(item.Key, item.Type);
var loadMethod = mlContext.Data.GetType().GetMethods().Where(x => x.Name == "LoadFromEnumerable" && x.IsGenericMethodDefinition).FirstOrDefault();
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)
DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.5);
IDataView trainData = dataSplit.TrainSet;
IDataView testData = dataSplit.TestSet;
#endregion
#region Preparamos los datos de entrada y salida
var columnNameAndTypes = new Dictionary<string, Type>();
foreach (var item in (from x in firstRow.Keys
select new { Key = x, Type = (from y in dataConverted.Cast<IDictionaryToObjectConverter>() where y.GetValue(x) != null select y.GetValue(x).GetType()).FirstOrDefault() })
)
{
columnNameAndTypes.Add(item.Key, item.Type);
}
var pipeline = ProcessData(mlContext, columnToPredict, columnNameAndTypes);
var trainingPipeline = BuildAndTrainModel(mlContext, trainData, pipeline, classType, classPredictionType);
Console.WriteLine("Training...");
_trainedModel = trainingPipeline.Fit(trainData);
mlContext.Model.Save(_trainedModel, data.Schema, modelFilename);
}
else
{
_trainedModel = mlContext.Model.Load(modelFilename, out schema);
}
var pipeline = ProcessData(mlContext, columnToPredict, columnNameAndTypes);
var trainingPipeline = BuildAndTrainModel(mlContext, trainData, pipeline, classType, classPredictionType);
Console.WriteLine("Training...");
var _trainedModel = trainingPipeline.Fit(trainData);
mlContext.Model.Save(_trainedModel, data.Schema, columnToPredict + ".zip");
var createPredictionEngineMethod = mlContext.Model.GetType().GetMethods().Where(x => x.Name == "CreatePredictionEngine" && x.IsGenericMethodDefinition).FirstOrDefault();
var createPredictionEngineMethodObj = createPredictionEngineMethod.MakeGenericMethod(classType, classPredictionType);
@@ -228,7 +289,6 @@ namespace testML
Console.WriteLine(string.Format("Ok: {0}, Fail: {1}, Percent: {2}%", ok, fail, (((double)ok / (double)(ok + fail)) * 100.0).ToString("##0.0000")));
#endregion
/*
@@ -266,13 +326,8 @@ namespace testML
#endregion
*/
Console.WriteLine();
Console.WriteLine("Press enter to Exit");
Console.ReadLine();
}
private static IEstimator<ITransformer> ProcessData(MLContext mlContext, string predictColumnName, Dictionary<string, Type> columnNames)
{
IEstimator<ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey(inputColumnName: predictColumnName, outputColumnName: "Label");
@@ -300,7 +355,7 @@ namespace testML
public static IEstimator<ITransformer> BuildAndTrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> pipeline, Type modelType, Type prodelPredictionType)
{
var trainingPipeline = pipeline.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features", maximumNumberOfIterations: 1000))
var trainingPipeline = pipeline.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features", maximumNumberOfIterations: 1000))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
return trainingPipeline;