ConversionsCatalog.MapKeyToBinaryVector Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v3.0.1

Create a KeyToBinaryVectorMappingEstimator, which converts key types to their corresponding binary representation of the original value.

public static Microsoft.ML.Transforms.KeyToBinaryVectorMappingEstimator MapKeyToBinaryVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default);

static member MapKeyToBinaryVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string -> Microsoft.ML.Transforms.KeyToBinaryVectorMappingEstimator

<Extension()>
Public Function MapKeyToBinaryVector (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing) As KeyToBinaryVectorMappingEstimator

Parameters

catalog

TransformsCatalog.ConversionTransforms

The categorical transform's catalog.

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. The data type is a known-size vector of Single representing the input value.

inputColumnName

String

Name of column to transform. If set to null, the value of the outputColumnName will be used as source. The data type is a key or a known-size vector of keys.

Returns

KeyToBinaryVectorMappingEstimator

Examples

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    class MapKeyToBinaryVector
    {
        /// This example demonstrates the use of MapKeyToVector by mapping keys to
        /// floats[] of 0 and 1, representing the number in binary format.
        /// Because the ML.NET KeyType maps the missing value to zero, counting
        /// starts at 1, so the uint values converted to KeyTypes will appear
        /// skewed by one.
        /// See https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
        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 rawData = new[] {
                new DataPoint() { Timeframe = 9 },
                new DataPoint() { Timeframe = 8 },
                new DataPoint() { Timeframe = 8 },
                new DataPoint() { Timeframe = 9 },
                new DataPoint() { Timeframe = 2 },
                new DataPoint() { Timeframe = 3 }
            };

            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // Constructs the ML.net pipeline
            var pipeline = mlContext.Transforms.Conversion.MapKeyToBinaryVector(
                "TimeframeVector", "Timeframe");

            // Fits the pipeline to the data.
            IDataView transformedData = pipeline.Fit(data).Transform(data);

            // Getting the resulting data as an IEnumerable.
            // This will contain the newly created columns.
            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
                TransformedData>(transformedData, reuseRowObject: false);

            Console.WriteLine($" Timeframe           TimeframeVector");
            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.Timeframe}\t\t\t" +
                    $"{string.Join(',', featureRow.TimeframeVector)}");

            // Timeframe             TimeframeVector
            // 10                      0,1,0,0,1 //binary representation of 9, the original value
            // 9                       0,1,0,0,0 //binary representation of 8, the original value
            // 9                       0,1,0,0,0
            // 10                      0,1,0,0,1
            // 3                       0,0,0,1,0
            // 4                       0,0,0,1,1
        }

        private class DataPoint
        {
            [KeyType(10)]
            public uint Timeframe { get; set; }

        }

        private class TransformedData : DataPoint
        {
            public float[] TimeframeVector { get; set; }
        }
    }
}