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CarClassificationModel.training.cs
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/
CarClassificationModel.training.cs
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// This file was auto-generated by ML.NET Model Builder.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using Microsoft.ML;
namespace CarShopCourseWork
{
public partial class CarClassificationModel
{
/// <summary>
/// Retrains model using the pipeline generated as part of the training process. For more information on how to load data, see aka.ms/loaddata.
/// </summary>
/// <param name="mlContext"></param>
/// <param name="trainData"></param>
/// <returns></returns>
public static ITransformer RetrainPipeline(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.Categorical.OneHotEncoding(new []{new InputOutputColumnPair(@"col0", @"col0"),new InputOutputColumnPair(@"col1", @"col1"),new InputOutputColumnPair(@"col4", @"col4"),new InputOutputColumnPair(@"col5", @"col5")}, outputKind: OneHotEncodingEstimator.OutputKind.Indicator)
.Append(mlContext.Transforms.ReplaceMissingValues(new []{new InputOutputColumnPair(@"col2", @"col2"),new InputOutputColumnPair(@"col3", @"col3")}))
.Append(mlContext.Transforms.Concatenate(@"Features", new []{@"col0",@"col1",@"col4",@"col5",@"col2",@"col3"}))
.Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName:@"col6",inputColumnName:@"col6"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator:mlContext.BinaryClassification.Trainers.FastTree(new FastTreeBinaryTrainer.Options(){NumberOfLeaves=59,MinimumExampleCountPerLeaf=25,NumberOfTrees=6,MaximumBinCountPerFeature=563,FeatureFraction=0.885299443652481,LearningRate=0.999999776672986,LabelColumnName=@"col6",FeatureColumnName=@"Features"}),labelColumnName: @"col6"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName:@"PredictedLabel",inputColumnName:@"PredictedLabel"));
return pipeline;
}
}
}