private bool IsWindowsInNightLightMode()
{
try
{
var data = (byte[])(_stateKeyInsider ?? _stateKeyWin10).GetValue("Data");
if (data == null)
{
const string msg = "could not read or find any data for the night light mode in the registry.";
_log.Error(msg);
throw new Exception(msg);
}
if (_lastData != null && data.SequenceEqual(_lastData))
{
return(_lastResult);
}
if (_log.IsDebugEnabled)
{
_log.Debug("read regval: " + data.Select(b => b.ToString()).Aggregate((s1, s2) => s1 + "," + s2));
}
var stateString = data.Select(b => b.ToString()).Aggregate((s1, s2) => s1 + "," + s2);
if (_predictor == null)
{
_context = new Microsoft.ML.MLContext();
using (var zipStream = Assembly.GetExecutingAssembly().GetManifestResourceStream("adrilight.Resources.NightLightDetectionModel.zip"))
{
_model = _context.Model.Load(zipStream, out var inputSchema) as Microsoft.ML.Data.TransformerChain <Microsoft.ML.ITransformer>;
_predictor = _context.Model.CreatePredictionEngine <NightLightDataRow, NightLightState>(_model);
}
}
var predicted = _predictor.Predict(new NightLightDataRow(data, true));
var isUnclearResult = predicted.Probability <= 0.9f && predicted.Probability >= 0.1f;
if (isUnclearResult)
{
_log.Warn($"predicted: {predicted.PredictedLabel}, {predicted.Probability:0.00000}");
}
else
{
_log.Debug($"predicted: {predicted.PredictedLabel}, {predicted.Probability:0.00000}");
}
_lastData = data;
_lastResult = predicted.PredictedLabel;
return(predicted.PredictedLabel);
}
catch (Exception ex)
{
_log.Error(ex, "IsWindowsInNightLightMode() failed");
throw;
}
}
public void MLnet_Parse_File()
{
// https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/load-data-ml-net
//Create MLContext
Microsoft.ML.MLContext mlContext = new Microsoft.ML.MLContext();
//Load Data
IDataView data = mlContext.Data.LoadFromTextFile <AndroidX>
(
"androidx-class-mapping.csv",
separatorChar: ',',
hasHeader: true
);
}
private static void HyperParameterExploration(MLContext mlContext
, IEstimator <ITransformer> dataPreProcessingPipeLine
, IDataView trainDataView)
{
var result = new List <(double RMSE
, double RSQ
, int iterations
, int approximationRank)>();
for (int iterations = 5; iterations < 100; iterations += 5)
{
for (int approximationRank = 50; approximationRank < 250; approximationRank += 50)
{
var option = new MatrixFactorizationTrainer.Options
{
MatrixColumnIndexColumnName = "UserIdEncoded",
MatrixRowIndexColumnName = "RestaurantNameEncoded",
LabelColumnName = "TotalRating",
NumberOfIterations = iterations,
ApproximationRank = approximationRank,
Quiet = true
};
var trainer = mlContext.Recommendation()
.Trainers.MatrixFactorization(option);
var completePipeLine = dataPreProcessingPipeLine.Append(trainer);
var cvMetrics = mlContext.Recommendation()
.CrossValidate(trainDataView, completePipeLine, labelColumnName: "TotalRating");
result.Add((
cvMetrics.Average(cv => cv.Metrics.RootMeanSquaredError),
cvMetrics.Average(cv => cv.Metrics.RSquared),
iterations,
approximationRank));
}
}
BCCConsole.Write(BCCConsoleColor.DarkGreen, false, "\n", "--- Hyper Parameter Exploration Result Metrics ---");
foreach (var res in result.OrderByDescending(r => r.RSQ))
{
BCCConsole.Write(BCCConsoleColor.DarkGreen, false, "\n",
$"RSQ => RSquared : {res.RSQ:#.000}",
$"RMSE => Root Error : {res.RMSE:#.000}",
$"I => Iterations : {res.iterations}",
$"AR => ApproximationRank : {res.approximationRank}"
);
}
BCCConsole.Write(BCCConsoleColor.DarkGreen, false, "\n", "---------------------------------------");
}
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);
}