ConversionsExtensionsCatalog.MapValueToKey Method

Definition

Overloads

MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Create a ValueToKeyMappingEstimator, which converts categorical values into keys.

MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Create a ValueToKeyMappingEstimator, which converts categorical values into numerical keys.

MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Create a ValueToKeyMappingEstimator, which converts categorical values into keys.

public static Microsoft.ML.Transforms.ValueToKeyMappingEstimator MapValueToKey (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, int maximumNumberOfKeys = 1000000, Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality keyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, bool addKeyValueAnnotationsAsText = false, Microsoft.ML.IDataView keyData = default);
static member MapValueToKey : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] * int * Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality * bool * Microsoft.ML.IDataView -> Microsoft.ML.Transforms.ValueToKeyMappingEstimator
<Extension()>
Public Function MapValueToKey (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair(), Optional maximumNumberOfKeys As Integer = 1000000, Optional keyOrdinality As ValueToKeyMappingEstimator.KeyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, Optional addKeyValueAnnotationsAsText As Boolean = false, Optional keyData As IDataView = null) As ValueToKeyMappingEstimator

Parameters

catalog
TransformsCatalog.ConversionTransforms

The conversion transform's catalog.

columns
InputOutputColumnPair[]

The input and output columns. The input data types can be numeric, text, boolean, DateTime or DateTimeOffset.

maximumNumberOfKeys
Int32

Maximum number of keys to keep per column when training.

keyOrdinality
ValueToKeyMappingEstimator.KeyOrdinality

The order in which keys are assigned. If set to ByOccurrence, keys are assigned in the order encountered. If set to ByValue, values are sorted, and keys are assigned based on the sort order.

addKeyValueAnnotationsAsText
Boolean

If set to true, use text type for values, regardless of the actual input type. When doing the reverse mapping, the values are text rather than the original input type.

keyData
IDataView

Use a pre-defined mapping between values and keys, instead of building the mapping from the input data during training. If specified, this should be a single column IDataView containing the values. The keys are allocated based on the value of keyOrdinality.

Returns

Examples

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

namespace Samples.Dynamic
{
    public static class MapValueToKeyMultiColumn
    {
        /// This example demonstrates the use of the ValueToKeyMappingEstimator, by
        /// mapping strings to KeyType values. For more on ML.NET KeyTypes see:
        /// https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types 
        /// It is possible to have multiple values map to the same category.
        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() { StudyTime = "0-4yrs" , Course = "CS" },
                new DataPoint() { StudyTime = "6-11yrs" , Course = "CS" },
                new DataPoint() { StudyTime = "12-25yrs" , Course = "LA" },
                new DataPoint() { StudyTime = "0-5yrs" , Course = "DS" }
            };

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

            // Constructs the ML.net pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValueToKey(new[] {
                new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
                new  InputOutputColumnPair("CourseCategory", "Course")
                },
                keyOrdinality: Microsoft.ML.Transforms.ValueToKeyMappingEstimator
                    .KeyOrdinality.ByValue, addKeyValueAnnotationsAsText: true);

            // 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($" StudyTime   StudyTimeCategory   Course    " +
                $"CourseCategory");

            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
                    $"{featureRow.CourseCategory}");

            // TransformedData obtained post-transformation.
            //
            // StudyTime     StudyTimeCategory   Course    CourseCategory
            // 0-4yrs          1                   CS          1
            // 6-11yrs         4                   CS          1
            // 12-25yrs        3                   LA          3
            // 0-5yrs          2                   DS          2

            // If we wanted to provide the mapping, rather than letting the
            // transform create it, we could do so by creating an IDataView one
            // column containing the values to map to. If the values in the dataset
            // are not found in the lookup IDataView they will get mapped to the
            // missing value, 0. The keyData are shared among the columns, therefore
            // the keys are not contiguous for the column. Create the lookup map
            // data IEnumerable.  
            var lookupData = new[] {
                new LookupMap { Key = "0-4yrs" },
                new LookupMap { Key = "6-11yrs" },
                new LookupMap { Key = "25+yrs"  },
                new LookupMap { Key = "CS" },
                new LookupMap { Key = "DS" },
                new LookupMap { Key = "LA"  }
            };

            // Convert to IDataView
            var lookupIdvMap = mlContext.Data.LoadFromEnumerable(lookupData);

            // Constructs the ML.net pipeline
            var pipelineWithLookupMap = mlContext.Transforms.Conversion
                .MapValueToKey(new[] {
                    new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
                    new  InputOutputColumnPair("CourseCategory", "Course")
                    },
                    keyData: lookupIdvMap);

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

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

            Console.WriteLine($" StudyTime   StudyTimeCategory  " +
                $"Course CourseCategory");

            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
                    $"{featureRow.CourseCategory}");

            // StudyTime    StudyTimeCategory  Course     CourseCategory
            // 0 - 4yrs          1              CS              4
            // 6 - 11yrs         2              CS              4
            // 12 - 25yrs        0              LA              6
            // 0 - 5yrs          0              DS              5

        }

        private class DataPoint
        {
            public string StudyTime { get; set; }
            public string Course { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public uint StudyTimeCategory { get; set; }
            public uint CourseCategory { get; set; }
        }

        // Type for the IDataView that will be serving as the map
        private class LookupMap
        {
            public string Key { get; set; }
        }
    }
}

Remarks

This transform can operate over multiple pairs of columns, creating a mapping for each pair.

MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)

Create a ValueToKeyMappingEstimator, which converts categorical values into numerical keys.

public static Microsoft.ML.Transforms.ValueToKeyMappingEstimator MapValueToKey (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, int maximumNumberOfKeys = 1000000, Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality keyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, bool addKeyValueAnnotationsAsText = false, Microsoft.ML.IDataView keyData = default);
static member MapValueToKey : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * int * Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality * bool * Microsoft.ML.IDataView -> Microsoft.ML.Transforms.ValueToKeyMappingEstimator
<Extension()>
Public Function MapValueToKey (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = null, Optional maximumNumberOfKeys As Integer = 1000000, Optional keyOrdinality As ValueToKeyMappingEstimator.KeyOrdinality = Microsoft.ML.Transforms.ValueToKeyMappingEstimator+KeyOrdinality.ByOccurrence, Optional addKeyValueAnnotationsAsText As Boolean = false, Optional keyData As IDataView = null) As ValueToKeyMappingEstimator

Parameters

catalog
TransformsCatalog.ConversionTransforms

The conversion transform's catalog.

outputColumnName
String

Name of the column containing the keys.

inputColumnName
String

Name of the column containing the categorical values. If set to null, the value of the outputColumnName is used. The input data types can be numeric, text, boolean, DateTime or DateTimeOffset.

maximumNumberOfKeys
Int32

Maximum number of keys to keep per column when training.

keyOrdinality
ValueToKeyMappingEstimator.KeyOrdinality

The order in which keys are assigned. If set to ByOccurrence, keys are assigned in the order encountered. If set to ByValue, values are sorted, and keys are assigned based on the sort order.

addKeyValueAnnotationsAsText
Boolean

If set to true, use text type for values, regardless of the actual input type. When doing the reverse mapping, the values are text rather than the original input type.

keyData
IDataView

Use a pre-defined mapping between values and keys, instead of building the mapping from the input data during training. If specified, this should be a single column IDataView containing the values. The keys are allocated based on the value of keyOrdinality.

Returns

Examples

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

namespace Samples.Dynamic
{
    public class KeyToValueToKey
    {
        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() { Review = "animals birds cats dogs fish horse"},
                new DataPoint() { Review = "horse birds house fish duck cats"},
                new DataPoint() { Review = "car truck driver bus pickup"},
                new DataPoint() { Review = "car truck driver bus pickup horse"},
            };

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

            // A pipeline to convert the terms of the 'Review' column in 
            // making use of default settings.
            var defaultPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
                "TokenizedText"));

            // Another pipeline, that customizes the advanced settings of the
            // ValueToKeyMappingEstimator. We can change the maximumNumberOfKeys to
            // limit how many keys will get generated out of the set of words, and
            // condition the order in which they get evaluated by changing
            // keyOrdinality from the default ByOccurence (order in which they get
            // encountered) to value/alphabetically.
            var customizedPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
                "TokenizedText", maximumNumberOfKeys: 10, keyOrdinality:
                ValueToKeyMappingEstimator.KeyOrdinality.ByValue));

            // The transformed data.
            var transformedDataDefault = defaultPipeline.Fit(trainData).Transform(
                trainData);

            var transformedDataCustomized = customizedPipeline.Fit(trainData)
                .Transform(trainData);

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

            IEnumerable<TransformedData> customizedData = mlContext.Data.
                CreateEnumerable<TransformedData>(transformedDataCustomized,
                reuseRowObject: false);

            Console.WriteLine($"Keys");
            foreach (var dataRow in defaultData)
                Console.WriteLine($"{string.Join(',', dataRow.Keys)}");
            // Expected output:
            //  Keys
            //  1,2,3,4,5,6
            //  6,2,7,5,8,3
            //  9,10,11,12,13
            //  9,10,11,12,13,6

            Console.WriteLine($"Keys");
            foreach (var dataRow in customizedData)
                Console.WriteLine($"{string.Join(',', dataRow.Keys)}");
            // Expected output:
            //  Keys
            //  1,2,4,5,7,8
            //  8,2,9,7,6,4
            //  3,10,0,0,0
            //  3,10,0,0,0,8
            // Retrieve the original values, by appending the KeyToValue estimator to
            // the existing pipelines to convert the keys back to the strings.
            var pipeline = defaultPipeline.Append(mlContext.Transforms.Conversion
                .MapKeyToValue(nameof(TransformedData.Keys)));

            transformedDataDefault = pipeline.Fit(trainData).Transform(trainData);

            // Preview of the DefaultColumnName column obtained.
            var originalColumnBack = transformedDataDefault.GetColumn<VBuffer<
                ReadOnlyMemory<char>>>(transformedDataDefault.Schema[nameof(
                TransformedData.Keys)]);

            foreach (var row in originalColumnBack)
            {
                foreach (var value in row.GetValues())
                    Console.Write($"{value} ");
                Console.WriteLine("");
            }

            // Expected output:
            //  animals birds cats dogs fish horse
            //  horse birds house fish duck cats
            //  car truck driver bus pickup
            //  car truck driver bus pickup horse
        }

        private class DataPoint
        {
            public string Review { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public uint[] Keys { get; set; }
        }
    }
}

Applies to