public static void Metrics()
{
IDataView predictions = Data.Transformer.Transform(Data.DataViewTesting);
Microsoft.ML.Data.RegressionMetrics metrics_endomorphic = null;
Microsoft.ML.Data.RegressionMetrics metrics_mesomorphic = null;
Microsoft.ML.Data.RegressionMetrics metrics_ectomorphic = null;
metrics_endomorphic = ml_dotnet_context.Regression.Evaluate(predictions, "Label", "EndomorphicComponent");
metrics_mesomorphic = ml_dotnet_context.Regression.Evaluate(predictions, "Label", "MesomorphicComponent");
metrics_ectomorphic = ml_dotnet_context.Regression.Evaluate(predictions, "Label", "EctomorphicComponent");
Console.WriteLine();
Console.WriteLine($"*************************************************");
Console.WriteLine($"* Model quality metrics evaluation ");
Console.WriteLine($"*------------------------------------------------");
Console.WriteLine($"* EndomorphicComponent");
Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
Console.WriteLine($"* MesomorphicComponent");
Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
Console.WriteLine($"* EctomorphicComponent");
Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
return;
}
private static void PrintMetrics(string caption, Microsoft.ML.Data.RegressionMetrics metrics)
{
Console.WriteLine($"*************************************************");
Console.WriteLine($"* 回帰モデル: {caption} のメトリック");
Console.WriteLine($"*");
Console.WriteLine($"* 損失関数(LossFn): {metrics.LossFunction}");
Console.WriteLine($"* 決定係数(R2 Score): {metrics.RSquared}");
Console.WriteLine($"* 平均絶対誤差(Absolute loss): {metrics.MeanAbsoluteError}");
Console.WriteLine($"* 平均二乗誤差(Squared loss): {metrics.MeanSquaredError}");
Console.WriteLine($"* 平均二乗誤差平方根(RMS loss): {metrics.RootMeanSquaredError}");
Console.WriteLine($"*************************************************");
}
public void Train(string trainingFileName)
{
if (!File.Exists(trainingFileName))
{
Console.WriteLine($"Failed to find training data file ({trainingFileName}");
return;
}
//The first change is the use of a comma to separate the data
IDataView trainingDataView = MlContext.Data.LoadFromTextFile <EmploymentHistory>(trainingFileName, ',');
DataOperationsCatalog.TrainTestData dataSplit = MlContext.Data.TrainTestSplit(trainingDataView, testFraction: 0.4);
Microsoft.ML.Data.EstimatorChain <Microsoft.ML.Data.TransformerChain <Microsoft.ML.Data.ColumnConcatenatingTransformer> > dataProcessPipeline = MlContext.Transforms.CopyColumns("Label", nameof(EmploymentHistory.DurationInMonths))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.IsMarried)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.BSDegree)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.MSDegree)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.YearsExperience))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.AgeAtHire)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.HasKids)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.WithinMonthOfVesting)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.DeskDecorations)))
.Append(MlContext.Transforms.NormalizeMeanVariance(nameof(EmploymentHistory.LongCommute)))
.Append(MlContext.Transforms.Concatenate("Features",
typeof(EmploymentHistory).ToPropertyList <EmploymentHistory>(nameof(EmploymentHistory.DurationInMonths)))));
//We can then create the Sdca trainer using the default parameters
Microsoft.ML.Trainers.SdcaRegressionTrainer trainer = MlContext.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
Microsoft.ML.Data.EstimatorChain <Microsoft.ML.Data.RegressionPredictionTransformer <Microsoft.ML.Trainers.LinearRegressionModelParameters> > trainingPipeline = dataProcessPipeline.Append(trainer);
ITransformer trainedModel = trainingPipeline.Fit(dataSplit.TrainSet);
MlContext.Model.Save(trainedModel, dataSplit.TrainSet.Schema, ModelPath);
IDataView testSetTransform = trainedModel.Transform(dataSplit.TestSet);
//Lastly, we call the Regression.Evaluate method to provide regression specific metrics
Microsoft.ML.Data.RegressionMetrics modelMetrics = MlContext.Regression.Evaluate(testSetTransform);
Console.WriteLine($"Loss Function: {modelMetrics.LossFunction:0.##}{Environment.NewLine}" +
$"Mean Absolute Error: {modelMetrics.MeanAbsoluteError:#.##}{Environment.NewLine}" +
$"Mean Squared Error: {modelMetrics.MeanSquaredError:#.##}{Environment.NewLine}" +
$"RSquared: {modelMetrics.RSquared:0.##}{Environment.NewLine}" +
$"Root Mean Squared Error: {modelMetrics.RootMeanSquaredError:#.##}");
}
private static void EvaluateTestMLdotnet(MLContext mlc, ITransformer m)
{
IDataView predictions = m.Transform(testData);
Microsoft.ML.Data.RegressionMetrics metrics = null;
metrics = mlc.Regression.Evaluate(predictions, "Label", "EndomorphicComponent");
Console.WriteLine();
Console.WriteLine($"*************************************************");
Console.WriteLine($"* Model quality metrics evaluation ");
Console.WriteLine($"*------------------------------------------------");
Console.WriteLine($"* RSquared Score: {metrics.RSquared:0.##}");
Console.WriteLine($"* Root Mean Squared Error: {metrics.RootMeanSquaredError:#.##}");
return;
}
public static void Metrics()
{
//Define DataViewSchema for data preparation pipeline and trained model
DataViewSchema schema = Data.DataViewSchema;
// Load trained model
ITransformer trainedModel = Data.Context.Model.Load
(
Data.ModelPathEndomorphic,
out schema
);
Data.Transformer = trainedModel;
IDataView predictions = Data.Transformer.Transform(Data.DataViewTesting);
Microsoft.ML.Data.RegressionMetrics metrics_endomorphic = null;
Microsoft.ML.Data.RegressionMetrics metrics_mesomorphic = null;
Microsoft.ML.Data.RegressionMetrics metrics_ectomorphic = null;
metrics_endomorphic = Data.Context.Regression.Evaluate(predictions, "Label", "EndomorphicComponent");
metrics_mesomorphic = Data.Context.Regression.Evaluate(predictions, "Label", "MesomorphicComponent");
metrics_ectomorphic = Data.Context.Regression.Evaluate(predictions, "Label", "EctomorphicComponent");
Console.WriteLine();
Console.WriteLine($"*************************************************");
Console.WriteLine($"* Model quality metrics evaluation ");
Console.WriteLine($"*------------------------------------------------");
Console.WriteLine($"* EndomorphicComponent");
Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
//Console.WriteLine($"* MesomorphicComponent");
//Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
//Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
//Console.WriteLine($"* EctomorphicComponent");
//Console.WriteLine($"* RSquared Score: {metrics_endomorphic.RSquared:0.##}");
//Console.WriteLine($"* Root Mean Squared Error: {metrics_endomorphic.RootMeanSquaredError:#.##}");
return;
}
