public static void TrainAndSave()
{
MLContext mlContext = new MLContext(seed: 1);
// 准备数据
var trainData = mlContext.Data.LoadFromTextFile <WineData>(path: TrainDataPath, separatorChar: ',', hasHeader: true);
var testData = mlContext.Data.LoadFromTextFile <WineData>(path: TestDataPath, separatorChar: ',', hasHeader: true);
var progressHandler = new RegressionExperimentProgressHandler();
uint ExperimentTime = 200;
ExperimentResult <RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRegressionExperiment(ExperimentTime)
.Execute(trainData, "Label", progressHandler: progressHandler);
Debugger.PrintTopModels(experimentResult);
RunDetail <RegressionMetrics> best = experimentResult.BestRun;
ITransformer trainedModel = best.Model;
// 评估 BestRun
var predictions = trainedModel.Transform(testData);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");
Debugger.PrintRegressionMetrics(best.TrainerName, metrics);
// 保存模型
Console.WriteLine("====== Save model to local file =========");
mlContext.Model.Save(trainedModel, trainData.Schema, ModelFilePath);
}
private static ITransformer BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
/* contents of csv file
* vendor_id,rate_code,passenger_count,trip_time_in_secs,trip_distance,payment_type,fare_amount
* VTS,1,1,1140,3.75,CRD,15.5
* VTS,1,1,480,2.72,CRD,10.0
* VTS,1,1,1680,7.8,CSH,26.5
* VTS,1,1,600,4.73,CSH,14.5
*/
IDataView trainingDataView = mlContext.Data.LoadFromTextFile <TaxiTrip>(TrainDataPath, hasHeader: true, separatorChar: ',');
IDataView testDataView = mlContext.Data.LoadFromTextFile <TaxiTrip>(TestDataPath, hasHeader: true, separatorChar: ',');
// Display first few rows of the training data
ConsoleHelper.ShowDataViewInConsole(mlContext, trainingDataView);
// STEP 2: Initialize our user-defined progress handler that AutoML will
// invoke after each model it produces and evaluates.
var progressHandler = new RegressionExperimentProgressHandler();
// STEP 3: Run AutoML regression experiment
ConsoleHelper.ConsoleWriteHeader("=============== Training the model ===============");
Console.WriteLine($"Running AutoML regression experiment for {ExperimentTime} seconds...");
ExperimentResult <RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRegressionExperiment(ExperimentTime)
.Execute(trainingDataView, LabelColumnName, progressHandler: progressHandler);
// Print top models found by AutoML
Console.WriteLine();
PrintTopModels(experimentResult);
// STEP 4: Evaluate the model and print metrics
ConsoleHelper.ConsoleWriteHeader("===== Evaluating model's accuracy with test data =====");
RunDetail <RegressionMetrics> best = experimentResult.BestRun;
ITransformer trainedModel = best.Model;
IDataView predictions = trainedModel.Transform(testDataView);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: LabelColumnName, scoreColumnName: "Score");
// Print metrics from top model
ConsoleHelper.PrintRegressionMetrics(best.TrainerName, metrics);
// STEP 5: Save/persist the trained model - convonnx
using (var stream = File.Create(MODEL_NAME))
{
mlContext.Model.ConvertToOnnx(trainedModel, trainingDataView, stream);
}
Console.WriteLine("The model is saved to {0}", MODEL_NAME);
return(trainedModel);
}
private static ExperimentResult <RegressionMetrics> RunAutoMLExperiment(MLContext mlContext,
ColumnInferenceResults columnInference)
{
// STEP 1: Display first few rows of the training data
ConsoleHelper.ShowDataViewInConsole(mlContext, TrainDataView);
// STEP 2: Build a pre-featurizer for use in the AutoML experiment.
// (Internally, AutoML uses one or more train/validation data splits to
// evaluate the models it produces. The pre-featurizer is fit only on the
// training data split to produce a trained transform. Then, the trained transform
// is applied to both the train and validation data splits.)
IEstimator <ITransformer> preFeaturizer = mlContext.Transforms.Conversion.MapValue("is_cash",
new[] { new KeyValuePair <string, bool>("CSH", true) }, "payment_type");
// STEP 3: Customize column information returned by InferColumns API
ColumnInformation columnInformation = columnInference.ColumnInformation;
columnInformation.CategoricalColumnNames.Remove("payment_type");
columnInformation.IgnoredColumnNames.Add("payment_type");
// STEP 4: Initialize a cancellation token source to stop the experiment.
var cts = new CancellationTokenSource();
// STEP 5: Initialize our user-defined progress handler that AutoML will
// invoke after each model it produces and evaluates.
var progressHandler = new RegressionExperimentProgressHandler();
// STEP 6: Create experiment settings
var experimentSettings = CreateExperimentSettings(mlContext, cts);
// STEP 7: Run AutoML regression experiment
var experiment = mlContext.Auto().CreateRegressionExperiment(experimentSettings);
ConsoleHelper.ConsoleWriteHeader("=============== Running AutoML experiment ===============");
Console.WriteLine($"Running AutoML regression experiment...");
var stopwatch = Stopwatch.StartNew();
// Cancel experiment after the user presses any key
CancelExperimentAfterAnyKeyPress(cts);
ExperimentResult <RegressionMetrics> experimentResult = experiment.Execute(TrainDataView, columnInformation, preFeaturizer, progressHandler);
Console.WriteLine($"{experimentResult.RunDetails.Count()} models were returned after {stopwatch.Elapsed.TotalSeconds:0.00} seconds{Environment.NewLine}");
// Print top models found by AutoML
PrintTopModels(experimentResult);
return(experimentResult);
}
private static ITransformer BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
IDataView trainingDataView = mlContext.Data.LoadFromTextFile <TaxiTrip>(TrainDataPath, hasHeader: true, separatorChar: ',');
IDataView testDataView = mlContext.Data.LoadFromTextFile <TaxiTrip>(TestDataPath, hasHeader: true, separatorChar: ',');
// STEP 2: Display first few rows of the training data
ConsoleHelper.ShowDataViewInConsole(mlContext, trainingDataView);
// STEP 3: Initialize our user-defined progress handler that AutoML will
// invoke after each model it produces and evaluates.
var progressHandler = new RegressionExperimentProgressHandler();
// STEP 4: Run AutoML regression experiment
ConsoleHelper.ConsoleWriteHeader("=============== Training the model ===============");
Console.WriteLine($"Running AutoML regression experiment for {ExperimentTime} seconds...");
ExperimentResult <RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRegressionExperiment(ExperimentTime)
.Execute(trainingDataView, LabelColumnName, progressHandler: progressHandler);
// Print top models found by AutoML
Console.WriteLine();
PrintTopModels(experimentResult);
// STEP 5: Evaluate the model and print metrics
ConsoleHelper.ConsoleWriteHeader("===== Evaluating model's accuracy with test data =====");
RunDetail <RegressionMetrics> best = experimentResult.BestRun;
ITransformer trainedModel = best.Model;
IDataView predictions = trainedModel.Transform(testDataView);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: LabelColumnName, scoreColumnName: "Score");
// Print metrics from top model
ConsoleHelper.PrintRegressionMetrics(best.TrainerName, metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath);
Console.WriteLine("The model is saved to {0}", ModelPath);
return(trainedModel);
}
public void AutoMLTest()
{
string content = File.ReadAllText(@".\Datas\NOPTrainData.json");
List <NOPInput> trainData = SerializerHelper.Deserialize <List <NOPInput> >(content);
content = File.ReadAllText(@".\Datas\NOPTestData.json");
List <NOPInput> testData = SerializerHelper.Deserialize <List <NOPInput> >(content);
Console.WriteLine($"训练数据：{trainData.Count} 个，测试数据：{testData.Count} 个");
MLContext mlContext = new MLContext();
var progressHandler = new RegressionExperimentProgressHandler();
uint ExperimentTime = 200;
IDataView trainDataView = mlContext.Data.LoadFromEnumerable(trainData);
IDataView testDataView = mlContext.Data.LoadFromEnumerable(testData);
ExperimentResult <RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRegressionExperiment(ExperimentTime)
.Execute(trainDataView, nameof(NOPInput.NextOpenningPrice), progressHandler: progressHandler);
var topRuns = experimentResult.RunDetails
.Where(r => r.ValidationMetrics != null && !double.IsNaN(r.ValidationMetrics.RSquared))
.OrderByDescending(r => r.ValidationMetrics.RSquared)
.ToList();
Console.WriteLine("训练模型按照 R-Squared 排序：");
foreach (var run in topRuns)
{
Console.WriteLine($"{run.TrainerName}\t{run.RuntimeInSeconds}s\t{run.ValidationMetrics.RSquared}");
}
Console.WriteLine("最佳模型：");
Console.WriteLine(experimentResult.BestRun.TrainerName);
var predictionEngine = mlContext.Model.CreatePredictionEngine <NOPInput, NOPOutput>(experimentResult.BestRun.Model);
var output = predictionEngine.Predict(testData.First());
Console.WriteLine($"预测结果：{output.NextOpenPrice}");
}
