public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();
var samples = new List <DataPoint>()
{
new DataPoint()
{
Features = new float[4] {
8, 1, 3, 0
},
Label = true
},
new DataPoint()
{
Features = new float[4] {
6, 2, 2, 0
},
Label = true
},
new DataPoint()
{
Features = new float[4] {
4, 0, 1, 0
},
Label = false
},
new DataPoint()
{
Features = new float[4] {
2, -1, -1, 1
},
Label = false
}
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Create a pipeline to normalize the features and train a binary
// classifier. We use WithOnFitDelegate for the intermediate binning
// normalization step, so that we can inspect the properties of the
// normalizer after fitting.
NormalizingTransformer binningTransformer = null;
var pipeline =
mlContext.Transforms
.NormalizeBinning("Features", maximumBinCount: 3)
.WithOnFitDelegate(
fittedTransformer => binningTransformer = fittedTransformer)
.Append(mlContext.BinaryClassification.Trainers
.LbfgsLogisticRegression());
Console.WriteLine(binningTransformer == null);
// Expected Output:
// True
var model = pipeline.Fit(data);
// During fitting binningTransformer will get assigned a new value
Console.WriteLine(binningTransformer == null);
// Expected Output:
// False
// Inspect some of the properties of the binning transformer
var binningParam = binningTransformer.GetNormalizerModelParameters(0) as
BinNormalizerModelParameters <ImmutableArray <float> >;
for (int i = 0; i < binningParam.UpperBounds.Length; i++)
{
var upperBounds = string.Join(", ", binningParam.UpperBounds[i]);
Console.WriteLine(
$"Bin {i}: Density = {binningParam.Density[i]}, " +
$"Upper-bounds = {upperBounds}");
}
// Expected output:
// Bin 0: Density = 2, Upper-bounds = 3, 7, Infinity
// Bin 1: Density = 2, Upper-bounds = -0.5, 1.5, Infinity
// Bin 2: Density = 2, Upper-bounds = 0, 2.5, Infinity
// Bin 3: Density = 1, Upper-bounds = 0.5, Infinity
}
