/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static async Task TestPrediction(string outputModelPath = "product_month_fastTreeTweedie.zip")
{
Console.WriteLine("*********************************");
Console.WriteLine("Testing Product Unit Sales Forecast model");
// Read the model that has been previously saved by the method SaveModel
var model = await PredictionModel.ReadAsync <ProductData, ProductUnitPrediction>(outputModelPath);
Console.WriteLine("** Testing Product 1 **");
// Build sample data
ProductData dataSample = new ProductData()
{
productId = "263", month = 10, year = 2017, avg = 91, max = 370, min = 1,
count = 10, prev = 1675, units = 910
};
//model.Predict() predicts the nextperiod/month forecast to the one provided
ProductUnitPrediction prediction = model.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month+1}, year: {dataSample.year} - Real value (units): 551, Forecast Prediction (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "263", month = 11, year = 2017, avg = 29, max = 221, min = 1,
count = 35, prev = 910, units = 551
};
//model.Predict() predicts the nextperiod/month forecast to the one provided
prediction = model.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month+1}, year: {dataSample.year} - Forecast Prediction (units): {prediction.Score}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Product 2 **");
dataSample = new ProductData()
{
productId = "988", month = 10, year = 2017, avg = 43, max = 220, min = 1,
count = 25, prev = 1036, units = 1094
};
prediction = model.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month+1}, year: {dataSample.year} - Real Value (units): 1076, Forecasting (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "988", month = 11, year = 2017, avg = 41, max = 225, min = 4,
count = 26, prev = 1094, units = 1076
};
prediction = model.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month+1}, year: {dataSample.year} - Forecasting (units): {prediction.Score}");
}
/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static void TestPrediction(string outputModelPath = "product_month_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Testing Product Unit Sales Forecast model");
// Read the model that has been previously saved by the method SaveModel
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 <ProductData, ProductUnitPrediction>(env);
Console.WriteLine("** Testing Product 1 **");
// Build sample data
ProductData dataSample = new ProductData()
{
productId = "263",
month = 10,
year = 2017,
avg = 91,
max = 370,
min = 1,
count = 10,
prev = 1675,
units = 910
};
//model.Predict() predicts the nextperiod/month forecast to the one provided
ProductUnitPrediction prediction = predictor.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Real value (units): 551, Forecast Prediction (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "263",
month = 11,
year = 2017,
avg = 29,
max = 221,
min = 1,
count = 35,
prev = 910,
units = 551
};
//model.Predict() predicts the nextperiod/month forecast to the one provided
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Forecast Prediction (units): {prediction.Score}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Product 2 **");
dataSample = new ProductData()
{
productId = "988",
month = 10,
year = 2017,
avg = 43,
max = 220,
min = 1,
count = 25,
prev = 1036,
units = 1094
};
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Real Value (units): 1076, Forecasting (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "988",
month = 11,
year = 2017,
avg = 41,
max = 225,
min = 4,
count = 26,
prev = 1094,
units = 1076
};
prediction = predictor.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Forecasting (units): {prediction.Score}");
}
/// <summary>
/// Predict samples using saved model
/// </summary>
/// <param name="outputModelPath">Model file path</param>
/// <returns></returns>
public static void TestPrediction(MLContext mlContext, string outputModelPath = "product_month_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Testing Product Unit Sales Forecast model");
// Read the model that has been previously saved by the method SaveModel
ITransformer trainedModel;
using (var stream = File.OpenRead(outputModelPath))
{
trainedModel = mlContext.Model.Load(stream);
}
var predictionEngine = trainedModel.CreatePredictionEngine <ProductData, ProductUnitPrediction>(mlContext);
Console.WriteLine("** Testing Product 1 **");
// Build sample data
ProductData dataSample = new ProductData()
{
productId = "263",
month = 10,
year = 2017,
avg = 91,
max = 370,
min = 1,
count = 10,
prev = 1675,
units = 910
};
// Predict the nextperiod/month forecast to the one provided
ProductUnitPrediction prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Real value (units): 551, Forecast Prediction (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "263",
month = 11,
year = 2017,
avg = 29,
max = 221,
min = 1,
count = 35,
prev = 910,
units = 551
};
// Predicts the nextperiod/month forecast to the one provided
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Forecast Prediction (units): {prediction.Score}");
Console.WriteLine(" ");
Console.WriteLine("** Testing Product 2 **");
dataSample = new ProductData()
{
productId = "988",
month = 10,
year = 2017,
avg = 43,
max = 220,
min = 1,
count = 25,
prev = 1036,
units = 1094
};
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Real Value (units): 1076, Forecasting (units): {prediction.Score}");
dataSample = new ProductData()
{
productId = "988",
month = 11,
year = 2017,
avg = 41,
max = 225,
min = 4,
count = 26,
prev = 1094,
units = 1076
};
prediction = predictionEngine.Predict(dataSample);
Console.WriteLine($"Product: {dataSample.productId}, month: {dataSample.month + 1}, year: {dataSample.year} - Forecasting (units): {prediction.Score}");
}
