/// <summary>
/// Help function used to execute trainers defined in <see cref="RandomizedPcaInMemory"/>.
/// </summary>
private static void ExecuteRandomizedPcaTrainerChangeThreshold(MLContext mlContext, Trainers.RandomizedPcaTrainer trainer)
{
var samples = new List <DataPoint>()
{
new DataPoint()
{
Features = new float[3] {
0, 2, 1
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 3
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 4
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 1
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 2
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 3
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 4
}
},
new DataPoint()
{
Features = new float[3] {
1, 0, 0
}
}
};
// Convert the List<DataPoint> to IDataView, a consumble format to ML.NET functions.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Train the anomaly detector.
var model = trainer.Fit(data);
var transformer = mlContext.AnomalyDetection.ChangeModelThreshold(model, 0.3f);
// Apply the trained model on the training data.
var transformed = transformer.Transform(data);
// Read ML.NET predictions into IEnumerable<Result>.
var results = mlContext.Data.CreateEnumerable <Result>(transformed, reuseRowObject: false).ToList();
// Outlier should be predicted as true.
Assert.True(results[0].PredictedLabel);
Assert.InRange(results[0].Score, 0.3, 1);
// Inlier should be predicted as false.
Assert.False(results[1].PredictedLabel);
Assert.InRange(results[1].Score, 0, 0.3);
// Inlier should be predicted as false.
Assert.False(results[2].PredictedLabel);
Assert.InRange(results[2].Score, 0, 0.3);
// Outlier should be predicted as true.
Assert.True(results[3].PredictedLabel);
Assert.InRange(results[3].Score, 0.3, 1);
// Inlier should be predicted as false.
Assert.False(results[4].PredictedLabel);
Assert.InRange(results[4].Score, 0, 0.3);
// Inlier should be predicted as false.
Assert.False(results[5].PredictedLabel);
Assert.InRange(results[5].Score, 0, 0.3);
// Inlier should be predicted as false.
Assert.False(results[6].PredictedLabel);
Assert.InRange(results[6].Score, 0, 0.3);
// Outlier should be predicted as true.
Assert.True(results[7].PredictedLabel);
Assert.InRange(results[7].Score, 0.3, 1);
}
/// <summary>
/// Help function used to execute trainers defined in <see cref="RandomizedPcaInMemory"/>.
/// </summary>
private static void ExecutePipelineWithGivenRandomizedPcaTrainer(MLContext mlContext, Trainers.RandomizedPcaTrainer trainer)
{
var samples = new List <DataPoint>()
{
new DataPoint()
{
Features = new float[3] {
0, 2, 1
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 3
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 4
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 1
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 2
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 3
}
},
new DataPoint()
{
Features = new float[3] {
0, 2, 4
}
},
new DataPoint()
{
Features = new float[3] {
1, 0, 0
}
}
};
// Convert the List<DataPoint> to IDataView, a consumable format to ML.NET functions.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Train the anomaly detector.
var model = trainer.Fit(data);
// Apply the trained model on the training data.
var transformed = model.Transform(data);
// Read ML.NET predictions into IEnumerable<Result>.
var results = mlContext.Data.CreateEnumerable <Result>(transformed, reuseRowObject: false).ToList();
// First 5 examples are inliers.
for (int i = 0; i < 7; ++i)
{
// Inlier should be predicted as false.
Assert.False(results[i].PredictedLabel);
// Higher score means closer to inlier.
Assert.InRange(results[i].Score, 0, 0.5);
}
// Last example is outlier. Note that outlier should be predicted as true.
Assert.True(results[7].PredictedLabel);
Assert.InRange(results[7].Score, 0.5, 1);
}