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using System;
using Microsoft.ML;
namespace Samples.Dynamic.Transforms.Categorical
{
public static class OneHotHashEncodingMultiColumn
{
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();
// Get a small dataset as an IEnumerable.
var samples = new[]
{
new DataPoint {Education = "0-5yrs", ZipCode = "98005"},
new DataPoint {Education = "0-5yrs", ZipCode = "98052"},
new DataPoint {Education = "6-11yrs", ZipCode = "98005"},
new DataPoint {Education = "6-11yrs", ZipCode = "98052"},
new DataPoint {Education = "11-15yrs", ZipCode = "98005"}
};
// Convert training data to IDataView.
IDataView data = mlContext.Data.LoadFromEnumerable(samples);
// Multi column example: A pipeline for one hot has encoding two
// columns 'Education' and 'ZipCode'.
var multiColumnKeyPipeline =
mlContext.Transforms.Categorical.OneHotHashEncoding(
new[]
{
new InputOutputColumnPair("Education"),
new InputOutputColumnPair("ZipCode")
},
numberOfBits: 3);
// Fit and Transform the data.
IDataView transformedData =
multiColumnKeyPipeline.Fit(data).Transform(data);
var convertedData =
mlContext.Data.CreateEnumerable<TransformedData>(transformedData,
true);
Console.WriteLine(
"One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.");
// One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.
foreach (TransformedData item in convertedData)
Console.WriteLine("{0}\t\t\t{1}", string.Join(" ", item.Education),
string.Join(" ", item.ZipCode));
// We have 8 slots, because we used numberOfBits = 3.
// 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
// 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
// 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
// 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
// 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
}
private class DataPoint
{
public string Education { get; set; }
public string ZipCode { get; set; }
}
private class TransformedData
{
public float[] Education { get; set; }
public float[] ZipCode { get; set; }
}
}
}
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