BuildDivisorTransitions application added.

EvenPerfectBitScanner/MLModel1.training.cs

@@ -0,0 +1,100 @@
+// This file was auto-generated by ML.NET Model Builder.
+using System;
+using System.IO;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Trainers;
+using Microsoft.ML.Trainers.FastTree;
+
+namespace EvenPerfectBitScanner
+{
+    public partial class MLModel1
+    {
+        public const string RetrainFilePath =  @"C:\Users\marek\source\repos\even-perfect-numbers-scanner\BuildDivisorTransitions\bin\Release\net8.0\NextKForecastTraning.csv";
+        public const char RetrainSeparatorChar = ',';
+        public const bool RetrainHasHeader =  true;
+        public const bool RetrainAllowQuoting =  false;
+
+         /// <summary>
+        /// Train a new model with the provided dataset.
+        /// </summary>
+        /// <param name="outputModelPath">File path for saving the model. Should be similar to "C:\YourPath\ModelName.mlnet"</param>
+        /// <param name="inputDataFilePath">Path to the data file for training.</param>
+        /// <param name="separatorChar">Separator character for delimited training file.</param>
+        /// <param name="hasHeader">Boolean if training file has a header.</param>
+        public static void Train(string outputModelPath, string inputDataFilePath = RetrainFilePath, char separatorChar = RetrainSeparatorChar, bool hasHeader = RetrainHasHeader, bool allowQuoting = RetrainAllowQuoting)
+        {
+            var mlContext = new MLContext();
+
+            var data = LoadIDataViewFromFile(mlContext, inputDataFilePath, separatorChar, hasHeader, allowQuoting);
+            var model = RetrainModel(mlContext, data);
+            SaveModel(mlContext, model, data, outputModelPath);
+        }
+
+        /// <summary>
+        /// Load an IDataView from a file path.
+        /// </summary>
+        /// <param name="mlContext">The common context for all ML.NET operations.</param>
+        /// <param name="inputDataFilePath">Path to the data file for training.</param>
+        /// <param name="separatorChar">Separator character for delimited training file.</param>
+        /// <param name="hasHeader">Boolean if training file has a header.</param>
+        /// <returns>IDataView with loaded training data.</returns>
+        public static IDataView LoadIDataViewFromFile(MLContext mlContext, string inputDataFilePath, char separatorChar, bool hasHeader, bool allowQuoting)
+        {
+            return mlContext.Data.LoadFromTextFile<ModelInput>(inputDataFilePath, separatorChar, hasHeader, allowQuoting: allowQuoting);
+        }
+
+
+        /// <summary>
+        /// Save a model at the specified path.
+        /// </summary>
+        /// <param name="mlContext">The common context for all ML.NET operations.</param>
+        /// <param name="model">Model to save.</param>
+        /// <param name="data">IDataView used to train the model.</param>
+        /// <param name="modelSavePath">File path for saving the model. Should be similar to "C:\YourPath\ModelName.mlnet.</param>
+        public static void SaveModel(MLContext mlContext, ITransformer model, IDataView data, string modelSavePath)
+        {
+            // Pull the data schema from the IDataView used for training the model
+            DataViewSchema dataViewSchema = data.Schema;
+
+            using (var fs = File.Create(modelSavePath))
+            {
+                mlContext.Model.Save(model, dataViewSchema, fs);
+            }
+        }
+
+
+        /// <summary>
+        /// Retrain model using the pipeline generated as part of the training process.
+        /// </summary>
+        /// <param name="mlContext"></param>
+        /// <param name="trainData"></param>
+        /// <returns></returns>
+        public static ITransformer RetrainModel(MLContext mlContext, IDataView trainData)
+        {
+            var pipeline = BuildPipeline(mlContext);
+            var model = pipeline.Fit(trainData);
+
+            return model;
+        }
+
+        /// <summary>
+        /// build the pipeline that is used from model builder. Use this function to retrain model.
+        /// </summary>
+        /// <param name="mlContext"></param>
+        /// <returns></returns>
+        public static IEstimator<ITransformer> BuildPipeline(MLContext mlContext)
+        {
+            // Data process configuration with pipeline data transformations
+            var pipeline = mlContext.Transforms.ReplaceMissingValues(new []{new InputOutputColumnPair(@"p", @"p"),new InputOutputColumnPair(@"divisor", @"divisor"),new InputOutputColumnPair(@"lastDigit", @"lastDigit"),new InputOutputColumnPair(@"k", @"k"),new InputOutputColumnPair(@"lastMersenneDigit", @"lastMersenneDigit")})      
+                                    .Append(mlContext.Transforms.Concatenate(@"Features", new []{@"p",@"divisor",@"lastDigit",@"k",@"lastMersenneDigit"}))      
+                                    .Append(mlContext.Regression.Trainers.FastTree(new FastTreeRegressionTrainer.Options(){NumberOfLeaves=4,MinimumExampleCountPerLeaf=9,NumberOfTrees=8,MaximumBinCountPerFeature=317,FeatureFraction=0.9843433146312323,LearningRate=0.3857390791447961,LabelColumnName=@"isNextKHigher",FeatureColumnName=@"Features",DiskTranspose=false}));
+
+            return pipeline;
+        }
+    }
+ }