private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
SettingUpBox.IsChecked = false;
TrainingBox.IsChecked = false;
TestingBox.IsChecked = false;
PlottingBox.IsChecked = false;
RestartButton.IsEnabled = false;
PredictButton.IsEnabled = false;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare diagram
var plotModel = Diagram.Model;
plotModel.PlotAreaBorderThickness = new OxyThickness(1, 0, 0, 1);
Diagram.InvalidatePlot();
// Prepare the input files
DatasetBox.IsChecked = true;
var testDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/test.tsv");
// Configure data transformations.
SettingUpBox.IsChecked = true;
var trainingDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/training.tsv");
await ViewModel.Build(trainingDataPath);
// Create and train the model
TrainingBox.IsChecked = true;
await ViewModel.Train();
// Save the model.
await ViewModel.Save("classificationModel.zip");
// Test and evaluate the model
TestingBox.IsChecked = true;
var metrics = await ViewModel.Evaluate(testDataPath);
// Diagram
PlottingBox.IsChecked = true;
var bars = new List <BarItem>();
foreach (var logloss in metrics.PerClassLogLoss)
{
bars.Add(new BarItem {
Value = logloss
});
}
(plotModel.Series[0] as BarSeries).ItemsSource = bars;
plotModel.InvalidatePlot(true);
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
RestartButton.IsEnabled = true;
PredictButton.IsEnabled = true;
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
SettingUpBox.IsChecked = false;
TrainingBox.IsChecked = false;
TestingBox.IsChecked = false;
PlottingBox.IsChecked = false;
RestartButton.IsEnabled = false;
PredictButton.IsEnabled = false;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare the input files
DatasetBox.IsChecked = true;
// var testDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/test.tsv");
// Configure data transformations.
SettingUpBox.IsChecked = true;
// var trainingDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/training.tsv");
// await ViewModel.Build(trainingDataPath);
// Create and train the model
TrainingBox.IsChecked = true;
// await ViewModel.Train();
// Save the model.
// await ViewModel.Save("classificationModel.zip");
// Test and evaluate the model
TestingBox.IsChecked = true;
// var metrics = await ViewModel.Evaluate(testDataPath);
// Diagram
PlottingBox.IsChecked = true;
var foreground = OxyColors.SteelBlue;
var plotModel = new PlotModel
{
PlotAreaBorderThickness = new OxyThickness(1, 0, 0, 1),
PlotAreaBorderColor = foreground,
TextColor = foreground,
TitleColor = foreground,
SubtitleColor = foreground
};
// ...
Diagram.Model = plotModel;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
RestartButton.IsEnabled = true;
PredictButton.IsEnabled = true;
}
private async void HyperParametrisation_Clicked(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
StartButton.IsEnabled = false;
HyperButton.IsEnabled = false;
_experimentNumber = 0;
PrepareDiagram();
await ViewModel.HyperParameterize();
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
StartButton.IsEnabled = true;
HyperButton.IsEnabled = true;
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
TrainingBox.IsChecked = false;
SavingBox.IsChecked = false;
TestingBox.IsChecked = false;
RestartButton.IsEnabled = false;
TravelerTypesCombo.SelectedIndex = -1;
SeasonsCombo.SelectedIndex = -1;
ResultBlock.Text = string.Empty;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare diagram
Diagram.Model.PlotAreaBorderThickness = new OxyThickness(1, 0, 0, 1);
Diagram.InvalidatePlot();
// Prepare the input files
DatasetBox.IsChecked = true;
var dataPath = await MlDotNet.FilePath(@"ms-appx:///Data/LasVegasTripAdvisorReviews.csv");
var data = await ViewModel.Load(dataPath);
TravelerTypesCombo.ItemsSource = ViewModel.TravelerTypes;
SeasonsCombo.ItemsSource = ViewModel.Seasons;
HotelsCombo.ItemsSource = ViewModel.Hotels;
// Create and train the model
TrainingBox.IsChecked = true;
await ViewModel.Build();
// Save the model.
SavingBox.IsChecked = true;
await ViewModel.Save("FfmRecommendationModel.zip");
// Test and evaluate the model
TestingBox.IsChecked = true;
var metrics = await ViewModel.Evaluate(dataPath);
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
RestartButton.IsEnabled = true;
TravelerTypesCombo.SelectedIndex = 0;
SeasonsCombo.SelectedIndex = 0;
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
DataViewBox.IsChecked = false;
SetUpExperimentBox.IsChecked = false;
RunExperimentBox.IsChecked = false;
ProgressTextBlock.Text = string.Empty;
StartButton.IsEnabled = false;
AlgorithmTextBlock.Text = string.Empty;
HyperButton.IsEnabled = false;
_experimentNumber = 0;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare diagram.
PrepareDiagram();
// Prepare datasets.
DatasetBox.IsChecked = true;
_trainingDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/winequality_white_train.csv");
_validationDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/winequality_white_test.csv");
// Create and load dataview.
DataViewBox.IsChecked = true;
await ViewModel.CreateDataViews(_trainingDataPath, _validationDataPath);
// Set up experiment.
SetUpExperimentBox.IsChecked = true;
await ViewModel.SetUpExperiment();
// Run experiment.
RunExperimentBox.IsChecked = true;
ProgressTextBlock.Text = "Starting";
AlgorithmTextBlock.Text = "The winner is " + await ViewModel.RunExperiment() + ". 🏆";
ProgressTextBlock.Text = string.Empty;
StartButton.IsEnabled = true;
HyperButton.IsEnabled = true;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
PerceptronBox.IsChecked = false;
LinearSvmBox.IsChecked = false;
LogisticRegressionBox.IsChecked = false;
SdcaBox.IsChecked = false;
StartButton.IsEnabled = false;
CalculateButton.IsEnabled = false;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare datasets.
DatasetBox.IsChecked = true;
var trainingDataLocation = await MlDotNet.FilePath(@"ms-appx:///Data/winequality_white_train.csv");
_testDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/winequality_white_test.csv");
// Prepare diagram.
PrepareDiagram(out ColumnSeries accuracySeries, out ColumnSeries areaUnderCurveSeries, out ColumnSeries f1ScoreSeries, out ColumnSeries positiveRecallSeries);
//// This raises a ArgumentOutOfRangeException because of different Label type expected:
//// var priorModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.Prior());
//// https://github.com/dotnet/machinelearning/issues/3119
//// These raise an exception on System.Diagnostics.Process
//// 'PlatformNotSupportedException: Retrieving information about local processes is not supported on this platform.'
////
//// var fastTreeBinaryModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.FastTree());
//// var fastForestBinaryModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.FastForest());
//// https://github.com/dotnet/machinelearning/issues/2444
// Perceptron
PerceptronBox.IsChecked = true;
_perceptronBinaryModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.AveragedPerceptron());
await ViewModel.Save(_perceptronBinaryModel, "perceptronModel.zip");
BinaryClassificationMetrics metrics = await ViewModel.EvaluateNonCalibrated(_perceptronBinaryModel, _testDataPath);
accuracySeries.Items.Add(new ColumnItem {
CategoryIndex = 0, Value = metrics.Accuracy
});
areaUnderCurveSeries.Items.Add(new ColumnItem {
CategoryIndex = 0, Value = metrics.AreaUnderRocCurve
});
f1ScoreSeries.Items.Add(new ColumnItem {
CategoryIndex = 0, Value = metrics.F1Score
});
positiveRecallSeries.Items.Add(new ColumnItem {
CategoryIndex = 0, Value = metrics.PositiveRecall
});
// Update diagram
Diagram.InvalidatePlot();
// Linear SVM
LinearSvmBox.IsChecked = true;
_linearSvmModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.LinearSvm());
await ViewModel.Save(_linearSvmModel, "linearSvmModel.zip");
metrics = await ViewModel.EvaluateNonCalibrated(_linearSvmModel, _testDataPath);
accuracySeries.Items.Add(new ColumnItem {
CategoryIndex = 1, Value = metrics.Accuracy
});
areaUnderCurveSeries.Items.Add(new ColumnItem {
CategoryIndex = 1, Value = metrics.AreaUnderRocCurve
});
f1ScoreSeries.Items.Add(new ColumnItem {
CategoryIndex = 1, Value = metrics.F1Score
});
positiveRecallSeries.Items.Add(new ColumnItem {
CategoryIndex = 1, Value = metrics.PositiveRecall
});
// Update diagram
Diagram.InvalidatePlot();
// Logistic Regression
LogisticRegressionBox.IsChecked = true;
_logisticRegressionModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.LbfgsLogisticRegression());
await ViewModel.Save(_logisticRegressionModel, "logisticRegressionModel.zip");
metrics = await ViewModel.Evaluate(_logisticRegressionModel, _testDataPath);
accuracySeries.Items.Add(new ColumnItem {
CategoryIndex = 2, Value = metrics.Accuracy
});
areaUnderCurveSeries.Items.Add(new ColumnItem {
CategoryIndex = 2, Value = metrics.AreaUnderRocCurve
});
f1ScoreSeries.Items.Add(new ColumnItem {
CategoryIndex = 2, Value = metrics.F1Score
});
positiveRecallSeries.Items.Add(new ColumnItem {
CategoryIndex = 2, Value = metrics.PositiveRecall
});
// Update diagram
Diagram.InvalidatePlot();
// Stochastic Dual Coordinate Ascent
SdcaBox.IsChecked = true;
_sdcabModel = await ViewModel.BuildAndTrain(trainingDataLocation, ViewModel.MLContext.BinaryClassification.Trainers.SdcaLogisticRegression());
await ViewModel.Save(_sdcabModel, "sdcabModel.zip");
metrics = await ViewModel.Evaluate(_sdcabModel, _testDataPath);
accuracySeries.Items.Add(new ColumnItem {
CategoryIndex = 3, Value = metrics.Accuracy
});
areaUnderCurveSeries.Items.Add(new ColumnItem {
CategoryIndex = 3, Value = metrics.AreaUnderRocCurve
});
f1ScoreSeries.Items.Add(new ColumnItem {
CategoryIndex = 3, Value = metrics.F1Score
});
positiveRecallSeries.Items.Add(new ColumnItem {
CategoryIndex = 3, Value = metrics.PositiveRecall
});
// Update diagram
Diagram.InvalidatePlot();
StartButton.IsEnabled = true;
CalculateButton.IsEnabled = true;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
SettingUpBox.IsChecked = false;
TrainingBox.IsChecked = false;
TestingBox.IsChecked = false;
PlottingBox.IsChecked = false;
RestartButton.IsEnabled = false;
PredictButton.IsEnabled = false;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare the input files
DatasetBox.IsChecked = true;
var testDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/test.tsv");
// Configure data transformations.
SettingUpBox.IsChecked = true;
var trainingDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/training.tsv");
await ViewModel.Build(trainingDataPath);
// Create and train the model
TrainingBox.IsChecked = true;
await ViewModel.Train();
// Save the model.
await ViewModel.Save("classificationModel.zip");
// Test and evaluate the model
TestingBox.IsChecked = true;
var metrics = await ViewModel.Evaluate(testDataPath);
// Diagram
PlottingBox.IsChecked = true;
var foreground = OxyColors.SteelBlue;
var plotModel = new PlotModel
{
Subtitle = "Model Quality",
PlotAreaBorderThickness = new OxyThickness(1, 0, 0, 1),
PlotAreaBorderColor = foreground,
TextColor = foreground,
TitleColor = foreground,
SubtitleColor = foreground
};
var bars = new List <BarItem>();
foreach (var logloss in metrics.PerClassLogLoss)
{
bars.Add(new BarItem {
Value = logloss
});
}
var barSeries = new BarSeries
{
ItemsSource = bars,
LabelPlacement = LabelPlacement.Inside,
TextColor = OxyColors.Wheat,
LabelFormatString = "{0:0.00}",
FillColor = OxyColors.Firebrick
};
plotModel.Series.Add(barSeries);
plotModel.Axes.Add(new CategoryAxis
{
Position = AxisPosition.Left,
Key = "LogLossAxis",
ItemsSource = new[]
{
"German",
"English",
"French",
"Italian",
"Romanian",
"Spanish"
},
TextColor = foreground,
TicklineColor = foreground,
TitleColor = foreground
});
var linearAxisX = new LinearAxis
{
Position = AxisPosition.Bottom,
Title = "Logarithmic loss per class (lower is better)",
TextColor = foreground,
TicklineColor = foreground,
TitleColor = foreground
};
plotModel.Axes.Add(linearAxisX);
Diagram.Model = plotModel;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
RestartButton.IsEnabled = true;
PredictButton.IsEnabled = true;
}
private async void Page_Loaded(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
DatasetBox.IsChecked = false;
SettingUpBox.IsChecked = false;
TrainingBox.IsChecked = false;
TestingBox.IsChecked = false;
PlottingBox.IsChecked = false;
RestartButton.IsEnabled = false;
DraftSlider.IsEnabled = false;
AgeSlider.IsEnabled = false;
WinsSlider.IsEnabled = false;
BoxSlider.IsEnabled = false;
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Visible;
BusyIndicator.PlayAnimation();
// Prepare the input files
DatasetBox.IsChecked = true;
var trainingDataPath = await MlDotNet.FilePath(@"ms-appx:///Data/2017-18_NBA_salary.csv");
// Read training data
var trainingData = await ViewModel.Load(trainingDataPath);
// Configure data transformations.
SettingUpBox.IsChecked = true;
// Create and train the model
TrainingBox.IsChecked = true;
await ViewModel.BuildAndTrain();
// Save the model.
await ViewModel.Save("regressionModel.zip");
// Visual evaluation of the model.
TestingBox.IsChecked = true;
var predictions = await ViewModel.PredictTrainingData();
var result = predictions.OrderBy((p) => p.Salary).ToList();
// Diagram
PlottingBox.IsChecked = true;
var foreground = OxyColors.SteelBlue;
var plotModel = new PlotModel
{
PlotAreaBorderThickness = new OxyThickness(1, 0, 0, 1),
PlotAreaBorderColor = foreground,
TextColor = foreground,
TitleColor = foreground,
SubtitleColor = foreground,
LegendPosition = LegendPosition.TopCenter,
LegendOrientation = LegendOrientation.Horizontal
};
var axisX = new LinearAxis
{
Position = AxisPosition.Bottom,
Title = "Test Data",
TextColor = foreground,
TicklineColor = foreground,
TitleColor = foreground
};
plotModel.Axes.Add(axisX);
var axisY = new LinearAxis
{
Title = "Salary",
TextColor = foreground,
TicklineColor = foreground,
TitleColor = foreground
};
plotModel.Axes.Add(axisY);
var realSeries = new ScatterSeries
{
Title = "Real",
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerFill = OxyColors.SteelBlue
};
plotModel.Series.Add(realSeries);
var predictedSeries = new ScatterSeries
{
Title = "Predicted",
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerFill = OxyColors.Firebrick
};
plotModel.Series.Add(predictedSeries);
for (int i = 0; i < result.Count; i++)
{
realSeries.Points.Add(new ScatterPoint(i, result[i].Salary));
predictedSeries.Points.Add(new ScatterPoint(i, result[i].Score));
}
// Just to put an entry in the Legend.
var singlePredictionSeries = new ScatterSeries
{
Title = "Single Prediction",
MarkerType = MarkerType.Circle,
MarkerSize = 2,
MarkerFill = OxyColors.Green
};
plotModel.Series.Add(singlePredictionSeries);
Diagram.Model = plotModel;
Slider_ValueChanged(this, null);
BusyIndicator.Visibility = Windows.UI.Xaml.Visibility.Collapsed;
BusyIndicator.PauseAnimation();
RestartButton.IsEnabled = true;
DraftSlider.IsEnabled = true;
AgeSlider.IsEnabled = true;
WinsSlider.IsEnabled = true;
BoxSlider.IsEnabled = true;
}