public static void Evaluate(MLContext context, string outputModelPath = "famoccupancy_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Evaluate family room occupancy Model");
ITransformer trainedModel;
using (var stream = File.OpenRead(outputModelPath))
{
trainedModel = context.Model.Load(stream);
}
var predictionFunct = trainedModel.MakePredictionFunction <OccupancyTrainer, OccupancyPrediction>(context);
Console.WriteLine("** Testing sample date **");
// Build sample data
var dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("08-12-2018"),
Occupied = 0,
TotalRoom = 22,
Prev = 0,
};
// Predict sample data
var prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 09-12-2018, - Real value: 7, Predicted Forecast: {prediction.Score}");
Console.WriteLine("\n\n=============== Attempt to predict the next 14 days occupancy ===============");
float ps = 0;
float oed = 7;
var datey = DateTime.Parse("09-12-2018");
for (int i = 0; i < 14; i++)
{
dataSample = new OccupancyTrainer()
{
Date = datey,
Occupied = oed,
TotalRoom = 22,
Prev = ps,
};
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date to predict: {dataSample.Date.AddDays(1)}, - Predicted Forecast: {prediction.Score}");
datey = datey.AddDays(1);
ps = oed;
oed = prediction.Score;
}
}
private static IList <OccupancyTrainer> GetOccupancyData()
{
IList <OccupancyTrainer> occupancyList = new List <OccupancyTrainer>();
using (var context = new AppContext())
{
var dblist = context.Occupancy.ToList();
foreach (var item in dblist)
{
OccupancyTrainer modelOcc = new OccupancyTrainer();
modelOcc.Date = item.Date;
modelOcc.Occupied = item.RoomOccupied;
modelOcc.TotalRoom = item.TotalRoom;
modelOcc.Next = GetNextDayValue(item.Date);
modelOcc.Prev = GetPrevDayValue(item.Date);
occupancyList.Add(modelOcc);
}
}
return(occupancyList);
}
public static void Evaluate(MLContext context, string outputModelPath = "occupancy_fastTreeTweedie.zip")
{
ConsoleWriteHeader("Evaluate Model");
ITransformer trainedModel;
using (var stream = File.OpenRead(outputModelPath))
{
trainedModel = context.Model.Load(stream);
}
var predictionFunct = trainedModel.MakePredictionFunction <OccupancyTrainer, OccupancyPrediction>(context);
Console.WriteLine("** Testing sample date **");
// Build sample data
var dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("25-10-2018"),
Occupied = 40,
TotalRoom = 71,
Prev = 51,
};
// Predict sample data
var prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 26-10-2018, - Real value: 40, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("26-10-2018"),
Occupied = 40,
TotalRoom = 71,
Prev = 40,
};
// Predict sample data
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 27-10-2018, - Real value: 44, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("05-12-2018"),
Occupied = 25,
TotalRoom = 71,
Prev = 28,
};
// Predict sample data
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 06-12-2018, - Real value: 14, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("06-12-2018"),
Occupied = 14,
TotalRoom = 71,
Prev = 25,
};
// Predict sample data
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 07-12-2018, - Real value: 16, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("07-12-2018"),
Occupied = 16,
TotalRoom = 71,
Prev = 14,
};
// Predict sample data
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 08-12-2018, - Real value: 13, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("08-12-2018"),
Occupied = 13,
TotalRoom = 71,
Prev = 16,
};
// Predict sample data
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 09-12-2018, - Real value: 27, Predicted Forecast: {prediction.Score}");
dataSample = new OccupancyTrainer()
{
Date = DateTime.Parse("09-12-2018"),
Occupied = 27,
TotalRoom = 71,
Prev = 13,
};
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date: {dataSample.Date}, date to predict: 10-12-2018, - Predicted Forecast: {prediction.Score}");
Console.WriteLine("\n\n=============== Attempt to predict the next 14 days occupancy ===============");
var appcontext = new AppContext();
var latestDate = appcontext.Occupancy.Max(r => r.Date);
var latestItem = appcontext.Occupancy.Where(i => i.Date == latestDate).FirstOrDefault();
var previousItem = appcontext.Occupancy.Where(i => i.Date == latestDate.AddDays(-1)).FirstOrDefault();
float ps = previousItem.RoomOccupied;
float oed = latestItem.RoomOccupied;
var datey = latestItem.Date;
for (int i = 0; i < 14; i++)
{
dataSample = new OccupancyTrainer()
{
Date = datey,
Occupied = oed,
TotalRoom = latestItem.TotalRoom,
Prev = ps,
};
prediction = predictionFunct.Predict(dataSample);
Console.WriteLine($"Date to predict: {dataSample.Date.AddDays(1)}, - Predicted Forecast: {prediction.Score}");
datey = datey.AddDays(1);
ps = oed;
oed = prediction.Score;
}
}
