131 lines
3.5 KiB
C#
131 lines
3.5 KiB
C#
using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.Trainers;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Reflection;
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using System.Text;
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using System.Threading.Tasks;
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using System.Xml.Linq;
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namespace testML
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{
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internal class Program
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{
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static string[] tags = new string[] { "Rojo", "Amarillo", "Verde claro", "Verde oscuro", "Violeta", "Naranja", "Azul", "Blanco" };
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static Random rnd = new Random();
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static void Main(string[] args)
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{
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#region Preparamos los datos de entrenamiento
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var tmpData = new List<Data>();
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for (var c = 0; c < 100000; c++)
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{
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var d = CreateRandomData();
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tmpData.Add(d);
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}
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#endregion
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MLContext mlContext = new MLContext();
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var data = mlContext.Data.LoadFromEnumerable(tmpData);
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#region Cortamos los datos de entrenamiento en (Datos para entenar y Datos para hacer el test de precisión)
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DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.1);
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IDataView trainData = dataSplit.TrainSet;
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IDataView testData = dataSplit.TestSet;
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#endregion
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#region Preparamos los datos de entrada y salida
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var trainer = mlContext.Regression.Trainers.Sdca(maximumNumberOfIterations:1000);
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//var trainer = mlContext.Regression.Trainers.OnlineGradientDescent(numberOfIterations: 100, learningRate: 0.01f );
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var pipeline = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "IntegerNumber")
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//.Append(mlContext.Transforms.Text.NormalizeText("StringTest"))
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//.Append(mlContext.Transforms.Text.FeaturizeText("StringTest"))
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.Append(mlContext.Transforms.Concatenate("Features", "Enum1", "Enum2", "Enum3", "Enum4"))
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.Append(mlContext.Transforms.NormalizeMinMax("Features"))
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.Append(trainer);
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#endregion
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//Entrenamos el modelo
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ITransformer model = pipeline.Fit(trainData);
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#region Hacemos un test para medir el % de error
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// Use trained model to make inferences on test data
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IDataView testDataPredictions = model.Transform(testData);
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// Extract model metrics and get RSquared
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RegressionMetrics trainedModelMetrics = mlContext.Regression.Evaluate(testDataPredictions);
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double rSquared = trainedModelMetrics.RSquared;
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Console.WriteLine("ModelMetrics: {0}", rSquared);
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#endregion
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#region Ponemos a prueba haciendo algunas predicciones
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var predictionFunction = mlContext.Model.CreatePredictionEngine<Data, DataPrediction>(model);
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for (var c = 0; c < 1000; c++)
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{
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var test = CreateRandomData();
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var expected = test.IntegerNumber;
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test.IntegerNumber = 0;
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var p = predictionFunction.Predict(test);
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//p.IntegerNumber = (float)Math.Round(p.IntegerNumber);
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Console.WriteLine("{4} {3}, Found: {0:#,##0.00}\tExpected: {1:#,##0.00}\t\tDiff: {2:#,##0.00}", p.IntegerNumber, expected , expected- p.IntegerNumber, test.Enum4, test.Enum2);
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}
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#endregion
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Console.WriteLine();
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Console.WriteLine("Press enter to Exit");
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Console.ReadLine();
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}
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private static Data CreateRandomData()
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{
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var d = new Data()
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{
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Accession = rnd.Next(0, 99999999).ToString("00000000"),
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Enum1 = rnd.Next(1, 4),
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Enum2 = rnd.Next(1, 11),
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Enum3 = rnd.Next(1, 6),
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Enum4 = rnd.Next(1, 4),
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StringTest = tags[rnd.Next(0, tags.Length)]
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};
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switch (d.Enum4)
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{
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case 1: d.IntegerNumber = 1; break;
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case 2: d.IntegerNumber = rnd.NextDouble() > 0.5 ? 1 : 0; break;
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case 3: d.IntegerNumber = 0; break;
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}
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return d;
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}
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}
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}
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