/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static async Task TestPrediction(string outputModelPath = "country_month_fastTreeTweedie.zip")
{
Console.WriteLine(" ");
Console.WriteLine("*********************************");
Console.WriteLine("Testing Country Sales Forecast model");
// Read the model that has been previously saved by the method SaveModel
var model = await PredictionModel.ReadAsync <CountryData, CountrySalesPrediction>(outputModelPath);
Console.WriteLine("** Testing Country 1 **");
// Build sample data
var dataSample = new CountryData()
{
country = "United Kingdom",
month = 10,
year = 2017,
med = 309.945F,
max = 587.902F,
min = 135.640F,
std = 1063.932092F,
prev = 856548.78F,
count = 1724,
sales = 873612.9F,
};
// Predict sample data
var prediction = model.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(6.0084501F,10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score,10)}");
dataSample = new CountryData()
{
country = "United Kingdom",
month = 11,
year = 2017,
med = 288.72F,
max = 501.488F,
min = 134.5360F,
std = 707.5642F,
prev = 873612.9F,
count = 2387,
sales = 1019647.67F,
};
prediction = model.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score,10)}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Country 2 **");
dataSample = new CountryData()
{
country = "United States",
month = 10,
year = 2017,
med = 400.17F,
max = 573.63F,
min = 340.395F,
std = 340.3959F,
prev = 4264.94F,
count = 10,
sales = 5322.56F
};
prediction = model.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(3.805769F,10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score,10)}");
dataSample = new CountryData()
{
country = "United States",
month = 11,
year = 2017,
med = 317.9F,
max = 1135.99F,
min = 249.44F,
std = 409.75528F,
prev = 5322.56F,
count = 11,
sales = 6393.96F,
};
prediction = model.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score,10)}");
}
/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static void TestPrediction(MLContext mlContext, string outputModelPath = "country_month_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Testing Country Sales Forecast model");
ITransformer trainedModel;
using (var stream = File.OpenRead(outputModelPath))
{
trainedModel = mlContext.Model.Load(stream);
}
//ITransformer trainedModel;
//using (var file = File.OpenRead(outputModelPath))
//{
// trainedModel = TransformerChain
// .LoadFrom(mlContext, file);
//}
var predictionEngine = trainedModel.CreatePredictionEngine <CountryData, CountrySalesPrediction>(mlContext);
Console.WriteLine("** Testing Country 1 **");
// Build sample data
var dataSample = new CountryData()
{
country = "United Kingdom",
month = 10,
year = 2017,
med = 309.945F,
max = 587.902F,
min = 135.640F,
std = 1063.932092F,
prev = 856548.78F,
count = 1724,
sales = 873612.9F,
};
// Predict sample data
var prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(6.0084501F, 10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
dataSample = new CountryData()
{
country = "United Kingdom",
month = 11,
year = 2017,
med = 288.72F,
max = 501.488F,
min = 134.5360F,
std = 707.5642F,
prev = 873612.9F,
count = 2387,
sales = 1019647.67F,
};
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Country 2 **");
dataSample = new CountryData()
{
country = "United States",
month = 10,
year = 2017,
med = 400.17F,
max = 573.63F,
min = 340.395F,
std = 340.3959F,
prev = 4264.94F,
count = 10,
sales = 5322.56F
};
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(3.805769F, 10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
dataSample = new CountryData()
{
country = "United States",
month = 11,
year = 2017,
med = 317.9F,
max = 1135.99F,
min = 249.44F,
std = 409.75528F,
prev = 5322.56F,
count = 11,
sales = 6393.96F,
};
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
}
/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static void TestPrediction(string outputModelPath = "country_month_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Testing Country Sales Forecast model");
var env = new LocalEnvironment(seed: 1); //Seed set to any number so you have a deterministic environment
ITransformer model;
using (var file = File.OpenRead(outputModelPath))
{
model = TransformerChain
.LoadFrom(env, file);
}
var predictor = model.MakePredictionFunction <CountryData, CountrySalesPrediction>(env);
Console.WriteLine("** Testing Country 1 **");
// Build sample data
var dataSample = new CountryData()
{
country = "United Kingdom",
month = 10,
year = 2017,
med = 309.945F,
max = 587.902F,
min = 135.640F,
std = 1063.932092F,
prev = 856548.78F,
count = 1724,
sales = 873612.9F,
};
// Predict sample data
var prediction = predictor.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(6.0084501F, 10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
dataSample = new CountryData()
{
country = "United Kingdom",
month = 11,
year = 2017,
med = 288.72F,
max = 501.488F,
min = 134.5360F,
std = 707.5642F,
prev = 873612.9F,
count = 2387,
sales = 1019647.67F,
};
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Country 2 **");
dataSample = new CountryData()
{
country = "United States",
month = 10,
year = 2017,
med = 400.17F,
max = 573.63F,
min = 340.395F,
std = 340.3959F,
prev = 4264.94F,
count = 10,
sales = 5322.56F
};
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Real value (US$): {Math.Pow(3.805769F, 10)}, Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
dataSample = new CountryData()
{
country = "United States",
month = 11,
year = 2017,
med = 317.9F,
max = 1135.99F,
min = 249.44F,
std = 409.75528F,
prev = 5322.56F,
count = 11,
sales = 6393.96F,
};
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Country: {dataSample.country}, month to predict: {dataSample.month + 1}, year: {dataSample.year} - Predicted Forecast (US$): {Math.Pow(prediction.Score, 10)}");
}