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Ova Multi class codegen support (dotnet#321)
* dummy * multiova implementation * fix tests * remove inclusion list * fix tests and console helper
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...est/ApprovalTests/ConsoleCodeGeneratorTests.TrainProgramCSFileContentOvaTest.approved.txt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| //***************************************************************************************** | ||
| //* * | ||
| //* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. * | ||
| //* * | ||
| //***************************************************************************************** | ||
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| using System; | ||
| using System.IO; | ||
| using System.Linq; | ||
| using Microsoft.ML; | ||
| using Microsoft.ML.Data; | ||
| using Microsoft.Data.DataView; | ||
| using TestNamespace.Model.DataModels; | ||
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| namespace TestNamespace.Train | ||
| { | ||
| class Program | ||
| { | ||
| private static string TRAIN_DATA_FILEPATH = @"x:\dummypath\dummy_train.csv"; | ||
| private static string TEST_DATA_FILEPATH = @"x:\dummypath\dummy_test.csv"; | ||
| private static string MODEL_FILEPATH = @"../../../../TestNamespace.Model/MLModel.zip"; | ||
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| static void Main(string[] args) | ||
| { | ||
| // Create MLContext to be shared across the model creation workflow objects | ||
| // Set a random seed for repeatable/deterministic results across multiple trainings. | ||
| MLContext mlContext = new MLContext(seed: 1); | ||
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| // Load Data | ||
| IDataView trainingDataView = mlContext.Data.LoadFromTextFile<SampleObservation>( | ||
| path: TRAIN_DATA_FILEPATH, | ||
| hasHeader: true, | ||
| separatorChar: ',', | ||
| allowQuoting: true, | ||
| allowSparse: true); | ||
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| IDataView testDataView = mlContext.Data.LoadFromTextFile<SampleObservation>( | ||
| path: TEST_DATA_FILEPATH, | ||
| hasHeader: true, | ||
| separatorChar: ',', | ||
| allowQuoting: true, | ||
| allowSparse: true); | ||
| // Build training pipeline | ||
| IEstimator<ITransformer> trainingPipeline = BuildTrainingPipeline(mlContext); | ||
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| // Train Model | ||
| ITransformer mlModel = TrainModel(mlContext, trainingDataView, trainingPipeline); | ||
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| // Evaluate quality of Model | ||
| EvaluateModel(mlContext, mlModel, testDataView); | ||
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| // Save model | ||
| SaveModel(mlContext, mlModel, MODEL_FILEPATH); | ||
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| Console.WriteLine("=============== End of process, hit any key to finish ==============="); | ||
| Console.ReadKey(); | ||
| } | ||
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| public static IEstimator<ITransformer> BuildTrainingPipeline(MLContext mlContext) | ||
| { | ||
| // Data process configuration with pipeline data transformations | ||
| var dataProcessPipeline = mlContext.Transforms.Concatenate("Out", new[] { "In" }) | ||
| .AppendCacheCheckpoint(mlContext); | ||
|
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| // Set the training algorithm | ||
| var trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(mlContext.BinaryClassification.Trainers.FastForest(numLeaves: 2, labelColumnName: "Label", featureColumnName: "Features"), labelColumnName: "Label"); | ||
| var trainingPipeline = dataProcessPipeline.Append(trainer); | ||
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| return trainingPipeline; | ||
| } | ||
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| public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline) | ||
| { | ||
| Console.WriteLine("=============== Training model ==============="); | ||
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| ITransformer model = trainingPipeline.Fit(trainingDataView); | ||
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| Console.WriteLine("=============== End of training process ==============="); | ||
| return model; | ||
| } | ||
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| private static void EvaluateModel(MLContext mlContext, ITransformer mlModel, IDataView testDataView) | ||
| { | ||
| // Evaluate the model and show accuracy stats | ||
| Console.WriteLine("===== Evaluating Model's accuracy with Test data ====="); | ||
| IDataView predictions = mlModel.Transform(testDataView); | ||
| var metrics = mlContext.MulticlassClassification.Evaluate(predictions, "Label", "Score"); | ||
| ConsoleHelper.PrintMultiClassClassificationMetrics(metrics); | ||
| } | ||
| private static void SaveModel(MLContext mlContext, ITransformer mlModel, string modelRelativePath) | ||
| { | ||
| // Save/persist the trained model to a .ZIP file | ||
| Console.WriteLine($"=============== Saving the model ==============="); | ||
| using (var fs = new FileStream(GetAbsolutePath(modelRelativePath), FileMode.Create, FileAccess.Write, FileShare.Write)) | ||
| mlContext.Model.Save(mlModel, fs); | ||
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| Console.WriteLine("The model is saved to {0}", GetAbsolutePath(modelRelativePath)); | ||
| } | ||
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| public static string GetAbsolutePath(string relativePath) | ||
| { | ||
| FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location); | ||
| string assemblyFolderPath = _dataRoot.Directory.FullName; | ||
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| string fullPath = Path.Combine(assemblyFolderPath, relativePath); | ||
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| return fullPath; | ||
| } | ||
| } | ||
| } |
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