public static void GetMyPrediction()
{
Console.WriteLine("Begin ML.NET demo run");
Console.WriteLine("Income from age, sex, politics");
var pipeline = new LearningPipeline();
string dataPath = AppDomain.CurrentDomain.BaseDirectory + "/PeopleData.txt";
pipeline.Add(new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' '));
pipeline.Add(new ColumnCopier(("Income", "Label")));
//pipeline.Add(new CategoricalOneHotVectorizer("Politic"));
pipeline.Add(new ColumnConcatenator("Features", "pre10",
"pre9", "pre8", "pre7", "pre6", "pre5", "pre4", "pre3"
, "pre2", "pre1"));
var sdcar = new StochasticDualCoordinateAscentRegressor();
sdcar.MaxIterations = 1000;
sdcar.NormalizeFeatures = NormalizeOption.Auto;
pipeline.Add(sdcar);
// pipeline.N
Console.WriteLine("\nStarting training \n");
var model = pipeline.Train <myLottery, myPrediction>();
Console.WriteLine("\nTraining complete \n");
string modelPath = AppDomain.CurrentDomain.BaseDirectory + "/IncomeModel.zip";
Task.Run(async() =>
{
await model.WriteAsync(modelPath);
}).GetAwaiter().GetResult();
var testData = new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' ');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
double rms = metrics.Rms;
Console.WriteLine("Root mean squared error = " +
rms.ToString("F4"));
Console.WriteLine("Income age 40 conservative male: ");
myLottery newPatient = new myLottery()
{
pre10 = 6824298f,
pre9 = 2589916f,
pre8 = 2602089f,
pre7 = 2915497f,
pre6 = 8507838f,
pre5 = 7679324f,
pre4 = 607461f,
pre3 = 5806877,
pre2 = 6776442f,
pre1 = 9975203
};
myPrediction prediction = model.Predict(newPatient);
float predIncome = prediction.Income;
Console.WriteLine("Predicted income = $" +
predIncome.ToString("F2"));
Console.WriteLine("\nEnd ML.NET demo");
Console.ReadLine();
}
public static void GetMyPrediction()
{
Console.WriteLine("Begin ML.NET demo run");
Console.WriteLine("Income from age, sex, politics");
var pipeline = new LearningPipeline();
string dataPath = AppDomain.CurrentDomain.BaseDirectory + "/datamodel/PeopleData.txt";
pipeline.Add(new TextLoader(dataPath).
CreateFrom <IncomeData>(separator: ','));
pipeline.Add(new ColumnCopier(("Income", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("Politic"));
pipeline.Add(new ColumnConcatenator("Features", "Age",
"Sex", "Politic"));
var sdcar = new StochasticDualCoordinateAscentRegressor();
sdcar.MaxIterations = 1000;
sdcar.NormalizeFeatures = NormalizeOption.Auto;
pipeline.Add(sdcar);
// pipeline.N
Console.WriteLine("\nStarting training \n");
var model = pipeline.Train <IncomeData, IncomePrediction>();
Console.WriteLine("\nTraining complete \n");
string modelPath = AppDomain.CurrentDomain.BaseDirectory + "/IncomeModel.zip";
Task.Run(async() =>
{
await model.WriteAsync(modelPath);
}).GetAwaiter().GetResult();
var testData = new TextLoader(dataPath).
CreateFrom <IncomeData>(separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
double rms = metrics.Rms;
Console.WriteLine("Root mean squared error = " +
rms.ToString("F4"));
Console.WriteLine("Income age 40 conservative male: ");
IncomeData newPatient = new IncomeData()
{
Age = 40.0f,
Sex = -1f,
Politic = "conservative"
};
IncomePrediction prediction = model.Predict(newPatient);
float predIncome = prediction.Income * 10000;
Console.WriteLine("Predicted income = $" +
predIncome.ToString("F2"));
Console.WriteLine("\nEnd ML.NET demo");
Console.ReadLine();
} // Main
