static void Main(string[] args)
{
var dataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData.csv");
var testDataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData-test.csv");
var pipeline = new LearningPipeline
{
new TextLoader(dataset).CreateFrom <SalaryData>(useHeader: true, separator: ','),
new ColumnConcatenator("Features", "YearsExperience"),
new GeneralizedAdditiveModelRegressor()
};
var crossValidator = new CrossValidator()
{
Kind = MacroUtilsTrainerKinds.SignatureRegressorTrainer,
NumFolds = 5
};
var crossValidatorOutput = crossValidator.CrossValidate <SalaryData, SalaryPrediction>(pipeline);
Console.Write(Environment.NewLine);
Console.WriteLine("Root Mean Squared for each fold:");
crossValidatorOutput.RegressionMetrics.ForEach(m => Console.WriteLine(m.Rms));
var totalR2 = crossValidatorOutput.RegressionMetrics.Sum(metric => metric.RSquared);
var totalRMS = crossValidatorOutput.RegressionMetrics.Sum(metric => metric.Rms);
Console.Write(Environment.NewLine);
Console.WriteLine($"Average R^2: {totalR2 / crossValidatorOutput.RegressionMetrics.Count}");
Console.WriteLine($"Average RMS: {totalRMS / crossValidatorOutput.RegressionMetrics.Count}");
Console.ReadLine();
}
static void Main(string[] args)
{
var dataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData.csv");
var testDataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData-test.csv");
var env = new LocalEnvironment();
var reader = TextLoader.CreateReader(env, ctx => (
YearsExperience: ctx.LoadFloat(0),
Target: ctx.LoadFloat(1)
), hasHeader: true, separator: ',');
var data = reader.Read(new MultiFileSource(dataset));
var regression = new RegressionContext(env);
var pipeline = reader.MakeNewEstimator()
.Append(r => (
r.Target,
Prediction: regression.Trainers.FastTree(label: r.Target, features: r.YearsExperience.AsVector())
));
var model = pipeline.Fit(data).AsDynamic;
var predictionFunc = model.MakePredictionFunction <SalaryData, SalaryPrediction>(env);
var prediction = predictionFunc.Predict(new SalaryData {
YearsExperience = 8
});
Console.WriteLine($"Predicted salary - {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.Read();
}
static void Main(string[] args)
{
var modelPath = MLNetUtilities.GetModelFilePath("model.zip");
var model = PredictionModel.ReadAsync <SalaryData, SalaryPrediction>(modelPath).Result;
var prediction = model.Predict(new SalaryData {
YearsExperience = YEARS_EXPERIENCE
});
Console.WriteLine($"Prediction for {YEARS_EXPERIENCE} years - {prediction.PredictedSalary}");
// Can write it back to the file system
// model.WriteAsync("model.zip");
Console.ReadLine();
}
static void Main(string[] args)
{
var dataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData.csv");
var testDataset = MLNetUtilities.GetDataPathByDatasetName("SalaryData-test.csv");
var pipeline = new LearningPipeline
{
new TextLoader(dataset).CreateFrom <SalaryData>(useHeader: true, separator: ','),
new ColumnConcatenator("Features", "YearsExperience"),
new GeneralizedAdditiveModelRegressor()
};
Console.WriteLine("--------------Training----------------");
var model = pipeline.Train <SalaryData, SalaryPrediction>();
model.WriteAsync(MLNetUtilities.GetModelFilePath("model.zip"));
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Evaluating----------------");
var testData = new TextLoader(testDataset).CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Root Mean Squared: {metrics.Rms}");
Console.WriteLine($"R^2: {metrics.RSquared}");
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Predicting----------------");
var prediction = model.Predict(new SalaryData {
YearsExperience = PREDICTION_YEARS
});
Console.WriteLine($"After {PREDICTION_YEARS} years you would earn around {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.ReadLine();
}
