ML .NET 二手車價格預測之再次訓練與參數調整（二）

再次訓練與參數調整

在UsedCarsPricePredictionMLModel.training.cs文件下，有訓練設置與訓練模型的方法
BuildPipeline方法中是ML .NET自動生成的訓練設置，包括選擇了哪些參數，預測的字段是什么，
以及調用LightGbm方法，參數配置為

{
    NumberOfLeaves=17,
    MinimumExampleCountPerLeaf=25,
    NumberOfIterations=6019,
    MaximumBinCountPerFeature=24,
    LearningRate=1F,
    LabelColumnName=@"Price",
    FeatureColumnName=@"Features",
    Booster=new GradientBooster.Options()
    {
        SubsampleFraction=0.706948120047722F,
        FeatureFraction=0.521537449021549F,
        L1Regularization=0.00247814105551342F,
        L2Regularization=0.00137211480690565F
    }
}

這些都是由ML .NET自動生成好的推薦配置參數，如果本身對機器學習有所研究，可以在此基礎上進行修改，以達到優化模型的作用
參考資料 LightGbmExtensions.LightGbm 方法
完整訓練代碼如下

public static IEstimator<ITransformer> BuildPipeline(MLContext mlContext)
{
    // Data process configuration with pipeline data transformations
    var pipeline = mlContext.Transforms.Categorical.OneHotEncoding(new []{new InputOutputColumnPair(@"Fuel_Type", @"Fuel_Type"),new InputOutputColumnPair(@"Transmission", @"Transmission"),new InputOutputColumnPair(@"Owner_Type", @"Owner_Type")})      
                            .Append(mlContext.Transforms.ReplaceMissingValues(new []{new InputOutputColumnPair(@"Year", @"Year"),new InputOutputColumnPair(@"Kilometers_Driven", @"Kilometers_Driven"),new InputOutputColumnPair(@"Seats", @"Seats")}))      
                            .Append(mlContext.Transforms.Text.FeaturizeText(@"Name", @"Name"))      
                            .Append(mlContext.Transforms.Text.FeaturizeText(@"Location", @"Location"))      
                            .Append(mlContext.Transforms.Text.FeaturizeText(@"Engine", @"Engine"))      
                            .Append(mlContext.Transforms.Text.FeaturizeText(@"Power", @"Power"))      
                            .Append(mlContext.Transforms.Concatenate(@"Features", new []{@"Fuel_Type",@"Transmission",@"Owner_Type",@"Year",@"Kilometers_Driven",@"Seats",@"Name",@"Location",@"Engine",@"Power"}))      
                            .Append(mlContext.Regression.Trainers.LightGbm(new LightGbmRegressionTrainer.Options(){NumberOfLeaves=17,MinimumExampleCountPerLeaf=25,NumberOfIterations=6019,MaximumBinCountPerFeature=24,LearningRate=1F,LabelColumnName=@"Price",FeatureColumnName=@"Features",Booster=new GradientBooster.Options(){SubsampleFraction=0.706948120047722F,FeatureFraction=0.521537449021549F,L1Regularization=0.00247814105551342F,L2Regularization=0.00137211480690565F}}));

    return pipeline;
}

之后可以調用RetrainPipeline方法再次訓練，得到新的模型

public static ITransformer RetrainPipeline(MLContext context, IDataView trainData)
{
    var pipeline = BuildPipeline(context);
    var model = pipeline.Fit(trainData);

    return model;
}

獲取model后保存成文件

//注意，這里使用txt或者tsv格式的文件
string trainCsvPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "TrainData", "train-data.txt");
string testCsvPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "TrainData", "test-data2.txt");
string modelDirectory = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "Model");
string modelPath = Path.Combine(modelDirectory, "UsedCarsPricePredictionMLModel.zip");

MLContext mlContext = new MLContext(seed: 0);
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<ModelInput>(trainCsvPath, hasHeader: true);
var model = UsedCarsPricePredictionMLModel.RetrainPipeline(mlContext, trainingDataView);
if (!Directory.Exists(modelDirectory))
    Directory.CreateDirectory(modelDirectory);

mlContext.Model.Save(model, trainingDataView.Schema, modelPath);

小問題

問題1：

Property 'Column1' is missing the LoadColumnAttribute attribute

根據提示，需要為ModelInput模型輸入類的每個屬性添加LoadColumn特性，指明所在列
問題2：

Schema mismatch for input column 'Name_CharExtractor': expected Expected known-size vector of Single, got Vector<Single> Arg_ParamName_Name

根據ML.NET: Schema mismatch for input column 'AnswerFeaturized_CharExtractor': expected Expected Single or known-size vector of Single, got Vector ，不能使用.csv文件，改為.txt文件或者.tsv文件

示例代碼

UsedCarsPricePrediction

參考資料

10分鍾快速入門
官方示例machinelearning-samples
教程：將回歸與 ML.NET 配合使用以預測價格