public AnalysisDem()
{
InitializeComponent();
Constants.RawData Data = Constants.Item.GetRawData();
int n = Constants.Item.GetCount();
var PredictedBP = new List <double>();
var ResidSup = new List <double>();
double ResidBP = 0;
double[,] Regressors = FittingFunctions.FitLin(Data, n);
for (var i = 0; i < n; i++)
{
var Qs = Data.SellQuantity.ElementAt(i);
double BP = (Qs - Regressors[1, 1]) / Regressors[0, 1];
ResidBP = Qs - BP;
PredictedBP.Add(BP);
ResidSup.Add(ResidBP);
}
var StrdResid = DataPreparer.ListStrd(ResidSup);
//create observable points for graphing
AnalysisDis = Visualiser.SeriesBuilder(PredictedBP);
DataContext = this;
}
/// <summary>
/// scatter plots of EWMA vs. raw
/// </summary>
public ScatterPlots()
{
InitializeComponent();
Constants.RawData data = Constants.Item.GetRawData();
int n = Constants.Item.GetCount();
var Ewma = DataPreparer.Weighter(data, 0.5);
//LINQ, $ formatting
PriceFormatter = value => value.ToString("C");
List <double> BuyQ = data.BuyQuantity.Select <int, double>(i => i).ToList();
List <double> BuyP = data.BuyPrice.Select <int, double>(i => i).ToList();
List <double> SellQ = data.SellQuantity.Select <int, double>(i => i).ToList();
List <double> SellP = data.SellPrice.Select <int, double>(i => i).ToList();
//~3500 data points possible with current config = 7000 total
Object[] ScatterPoints = Visualiser.ScatterListBuilder(BuyQ, BuyP, SellQ, SellP, n);
Object[] WScatterPoints = Visualiser.ScatterListBuilder(Ewma._strdBuyQuant, Ewma._strdBuyPrice, Ewma._strdSellQuant, Ewma._strdSellPrice, n);
//note explicit conversion of types
Supply = (ChartValues <ObservablePoint>)ScatterPoints[0];
Demand = (ChartValues <ObservablePoint>)ScatterPoints[1];
EwmaSupply = (ChartValues <ObservablePoint>)WScatterPoints[0];
EwmaDemand = (ChartValues <ObservablePoint>)WScatterPoints[1];
DataContext = this;
}
static void Main(string[] args)
{
var dataSource = Config.GetDataSource();
var dataset = new DataPreparer(dataSource).GetCustomerAndVehicleData();
Solution solution = new Solution();
switch (Config.GetSolverType())
{
case "GurobiSolver":
solution = new GSolver(dataset).Run();
break;
case "BendersSolver":
solution = new BSolver(dataset).Run();
break;
case "IterativeLocalSearch":
solution = new IterativeLocalSearch(dataset).Run();
break;
case "TabuSearch":
solution = new TabuSearch(dataset).Run();
break;
}
var outputPathString = Config.GetFileOperation().OutputPath +
Config.GetFileOperation().OutputName;
using StreamWriter file = File.CreateText(outputPathString);
JsonSerializer serializer = new JsonSerializer();
serializer.Serialize(file, solution);
}
public TestContext()
{
DataClient = new MockRedisClientWrapper ();
Settings = new DataManagerSettings ();
Settings.IsVerbose = true;
Keys = new DataKeys (Settings);
IdManager = new DataIdManager (Keys, DataClient);
TypeManager = new DataTypeManager (Keys, DataClient);
EntityLinker = new EntityLinker ();
var preparer = new DataPreparer (DataClient);
Preparer = preparer;
var reader = new DataReader (TypeManager, IdManager, Keys, DataClient);
Reader = reader;
var checker = new DataChecker (reader, Settings);
Checker = checker;
var saver = new DataSaver (Settings, TypeManager, IdManager, Keys, preparer, null, checker, DataClient); // The linker argument is null because it needs to be set after it's created below
Saver = saver;
var updater = new DataUpdater (Settings, Keys, null, preparer, checker, DataClient); // The linker argument is null because it needs to be set after it's created below
Updater = updater;
var linker = new DataLinker (Settings, reader, saver, updater, checker, EntityLinker);
Linker = linker;
// TODO: Is there a way to avoid this messy hack?
// Make sure the linker is set to the saver and updater
saver.Linker = linker;
updater.Linker = linker;
}
static void Main(string[] args)
{
BCCConsole.Write(BCCConsoleColor.DarkBlue, false, "Restaurant Recommender Is Started . . .");
MLContext mlContext = new MLContext(0);
var trainingDataFile = Environment.CurrentDirectory + @"\Data\TrainingFile.tsv";
DataPreparer.PreprocessData(trainingDataFile);
IDataView trainingDataView = mlContext.Data
.LoadFromTextFile <ModelInput>(trainingDataFile, hasHeader: true);
var dataPreProcessingPipeLine = mlContext.Transforms.Conversion
.MapValueToKey("UserIdEncoded", nameof(ModelInput.UserId))
.Append(mlContext.Transforms.Conversion
.MapValueToKey("RestaurantNameEncoded", nameof(ModelInput.RestaurantName)));
var options = new MatrixFactorizationTrainer.Options
{
MatrixColumnIndexColumnName = "UserIdEncoded",
MatrixRowIndexColumnName = "RestaurantNameEncoded",
LabelColumnName = "TotalRating",
NumberOfIterations = 10,
ApproximationRank = 200,
Quiet = true
};
var trainer = mlContext.Recommendation().Trainers.MatrixFactorization(options);
var trainerPipeLine = dataPreProcessingPipeLine.Append(trainer);
#region Not Using CV
BCCConsole.Write(BCCConsoleColor.DarkBlue, false, "\n", "Training Model");
var model = trainerPipeLine.Fit(trainingDataView);
////Test
//var testUserId = "U1134";
var predictionEngine = mlContext.Model
.CreatePredictionEngine <ModelInput, ModelOutput>(model);
//var alreadyRatedRestaurant = mlContext.Data
// .CreateEnumerable<ModelInput>(trainingDataView, false)
// .Where(r => r.UserId == testUserId)
// .Select(r => r.RestaurantName)
// .Distinct();
//var allRestaurantNames = trainingDataView
// .GetColumn<string>("RestaurantName")
// .Distinct().Where(r => !alreadyRatedRestaurant.Contains(r));
//var scoredRestaurant = allRestaurantNames
// .Select(rn =>
// {
// var prediction = predictionEngine.Predict(
// new ModelInput()
// {
// UserId = testUserId,
// RestaurantName = rn
// });
// return (RestaurantName: rn, PredictedScore: prediction.Score);
// });
//var top10Restaurant = scoredRestaurant
// .OrderByDescending(r => r.PredictedScore)
// .Take(10);
//BCCConsole.Write(BCCConsoleColor.DarkGreen,false,
// "\n",
// $"Top 10 Restaurant Name & Rate For User {testUserId}",
// "----------------------------------------------------");
//foreach (var top in top10Restaurant)
//{
// BCCConsole.Write(BCCConsoleColor.DarkGreen,false,$"Prediction Score [{top.PredictedScore:#.0}] | Restaurant Name [{top.RestaurantName}] ");
//}
//BCCConsole.Write(BCCConsoleColor.DarkGreen,false, "----------------------------------------------------");
#endregion
#region Using CV
//var cvMetrics = mlContext.Recommendation()
// .CrossValidate(trainingDataView, trainerPipeLine, labelColumnName: "TotalRating");
//var averageRMSE = cvMetrics.Average(cv => cv.Metrics.RootMeanSquaredError);
//var averageRSquared = cvMetrics.Average(cv => cv.Metrics.RSquared);
//BCCConsole.Write(BCCConsoleColor.DarkGreen, false,
// "\n",
// "Training Result Before Cross Validation (Metrics) ",
// "--------------------------------------------------",
// $"RMSE => Root Error : {averageRMSE:#.000}",
// $"RSQ => RSquared : {averageRSquared:#.000}",
// "--------------------------------------------------");
#endregion
Console.WriteLine("Enter Restaurant Name");
string rn = Console.ReadLine();
if (string.IsNullOrEmpty(rn))
{
"Error".Red();
Environment.Exit(-1);
}
var prediction = predictionEngine.Predict(new ModelInput()
{
UserId = "CLONED",
RestaurantName = rn
});
BCCConsole.Write(BCCConsoleColor.Green, false, "\n", $"Prediction Result Score : {prediction.Score:#.0} For Rincon Huasteco");
//HyperParameterExploration(mlContext, dataPreProcessingPipeLine, trainingDataView);
}
