ConversionsExtensionsCatalog.MapValue 메서드

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.Data.dll

패키지:

Microsoft.ML v3.0.1

오버로드

MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String)	Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from the lookupMap where the keyColumn specifies the keys, and the valueColumn the respective value.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String)	Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean)	Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.

MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String)

Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from the lookupMap where the keyColumn specifies the keys, and the valueColumn the respective value.

public static Microsoft.ML.Transforms.ValueMappingEstimator MapValue (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, Microsoft.ML.IDataView lookupMap, Microsoft.ML.DataViewSchema.Column keyColumn, Microsoft.ML.DataViewSchema.Column valueColumn, string inputColumnName = default);

static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * Microsoft.ML.IDataView * Microsoft.ML.DataViewSchema.Column * Microsoft.ML.DataViewSchema.Column * string -> Microsoft.ML.Transforms.ValueMappingEstimator

<Extension()>
Public Function MapValue (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, lookupMap As IDataView, keyColumn As DataViewSchema.Column, valueColumn As DataViewSchema.Column, Optional inputColumnName As String = Nothing) As ValueMappingEstimator

매개 변수

catalog

TransformsCatalog.ConversionTransforms

변환 변환의 카탈로그

outputColumnName

String

의 변환에서 생성된 열의 inputColumnName이름입니다. 데이터 형식은 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다.

lookupMap

IDataView

열과 valueColumn 열이 keyColumn 포함된 인스턴스 IDataView 입니다.

keyColumn

DataViewSchema.Column

의 키 열입니다 lookupMap.

valueColumn

DataViewSchema.Column

의 값 열입니다 lookupMap.

inputColumnName

String

변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다. 데이터 형식은 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다.

반환

ValueMappingEstimator

예제

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

namespace Samples.Dynamic
{
    public static class MapValueIdvLookup
    {
        /// This example demonstrates the use of MapValue by mapping floats to
        /// strings, looking up the mapping in an IDataView. This is useful to map
        /// types to a grouping. 
        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() { Price = 3.14f },
                new DataPoint() { Price = 2000f },
                new DataPoint() { Price = 1.19f },
                new DataPoint() { Price = 2.17f },
                new DataPoint() { Price = 33.784f },

            };

            // Convert to IDataView
            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // Create the lookup map data IEnumerable.   
            var lookupData = new[] {
                new LookupMap { Value = 3.14f, Category = "Low" },
                new LookupMap { Value = 1.19f , Category = "Low" },
                new LookupMap { Value = 2.17f , Category = "Low" },
                new LookupMap { Value = 33.784f, Category = "Medium" },
                new LookupMap { Value = 2000f, Category = "High"}

            };

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

            // Constructs the ValueMappingEstimator making the ML.NET pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValue("PriceCategory",
                lookupIdvMap, lookupIdvMap.Schema["Value"], lookupIdvMap.Schema[
                    "Category"], "Price");

            // Fits the ValueMappingEstimator and transforms the data converting the
            // Price to PriceCategory.
            IDataView transformedData = pipeline.Fit(data).Transform(data);

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

            Console.WriteLine($" Price   PriceCategory");
            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.Price}\t\t" +
                $"{featureRow.PriceCategory}");

            // TransformedData obtained post-transformation.
            //
            // Price        PriceCategory
            // 3.14            Low
            // 2000            High
            // 1.19            Low
            // 2.17            Low
            // 33.784          Medium
        }

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

        private class DataPoint
        {
            public float Price { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public string PriceCategory { get; set; }
        }
    }
}

MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String)

Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.

public static Microsoft.ML.Transforms.ValueMappingEstimator<TInputType,TOutputType> MapValue<TInputType,TOutputType> (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, System.Collections.Generic.IEnumerable<System.Collections.Generic.KeyValuePair<TInputType,TOutputType[]>> keyValuePairs, string inputColumnName = default);

static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * seq<System.Collections.Generic.KeyValuePair<'InputType, 'OutputType[]>> * string -> Microsoft.ML.Transforms.ValueMappingEstimator<'InputType, 'OutputType>

<Extension()>
Public Function MapValue(Of TInputType, TOutputType) (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, keyValuePairs As IEnumerable(Of KeyValuePair(Of TInputType, TOutputType())), Optional inputColumnName As String = Nothing) As ValueMappingEstimator(Of TInputType, TOutputType)

형식 매개 변수

TInputType

키 형식입니다.

TOutputType

값 형식입니다.

매개 변수

catalog

TransformsCatalog.ConversionTransforms

변환 변환의 카탈로그

outputColumnName

String

의 변환에서 생성된 열의 inputColumnName이름입니다. 데이터 형식은 에 지정된 대로 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다TOutputType.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType[]>>

수행할 매핑을 지정합니다. 키는 에 지정된 대로 값에 keyValuePairs매핑됩니다.

inputColumnName

String

변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다. 데이터 형식은 에 지정된 대로 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다TInputType.

반환

ValueMappingEstimator<TInputType,TOutputType>

예제

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

namespace Samples.Dynamic
{
    public static class MapValueToArray
    {
        /// This example demonstrates the use of MapValue by mapping strings to
        /// array values, which allows for mapping data to numeric arrays. This
        /// functionality is useful when the generated column will serve as the
        /// Features column for a trainer. Most of the trainers take a numeric
        /// vector, as the Features column. In this example, we are mapping the
        /// Timeframe data to arbitrary integer arrays.
        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 = "0-4yrs" },
                new DataPoint() { Timeframe = "6-11yrs" },
                new DataPoint() { Timeframe = "12-25yrs" },
                new DataPoint() { Timeframe = "0-5yrs" },
                new DataPoint() { Timeframe = "12-25yrs" },
                new DataPoint() { Timeframe = "25+yrs" },
            };

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

            // Creating a list of key-value pairs to indicate the mapping between
            // the DataPoint values, and the arrays they should map to. 
            var timeframeMap = new Dictionary<string, int[]>();
            timeframeMap["0-4yrs"] = new int[] { 0, 5, 300 };
            timeframeMap["0-5yrs"] = new int[] { 0, 5, 300 };
            timeframeMap["6-11yrs"] = new int[] { 6, 11, 300 };
            timeframeMap["12-25yrs"] = new int[] { 12, 50, 300 };
            timeframeMap["25+yrs"] = new int[] { 12, 50, 300 };

            // Constructs the ValueMappingEstimator making the ML.NET pipeline.
            var pipeline = mlContext.Transforms.Conversion.MapValue("Features",
                timeframeMap, "Timeframe");

            // Fits the ValueMappingEstimator and transforms the data adding the
            // Features column.
            IDataView transformedData = pipeline.Fit(data).Transform(data);

            // Getting the resulting data as an IEnumerable.
            IEnumerable<TransformedData> featuresColumn = mlContext.Data
                .CreateEnumerable<TransformedData>(transformedData, reuseRowObject:
                false);

            Console.WriteLine($"Timeframe     Features");
            foreach (var featureRow in featuresColumn)
                Console.WriteLine($"{featureRow.Timeframe}\t\t " +
                $"{string.Join(",", featureRow.Features)}");

            // Timeframe      Features
            // 0-4yrs       0, 5, 300
            // 6-11yrs      6, 11, 300
            // 12-25yrs     12, 50, 300
            // 0-5yrs       0, 5, 300
            // 12-25yrs     12, 50,300
            // 25+yrs       12, 50, 300
        }

        public class DataPoint
        {
            public string Timeframe { get; set; }
        }

        public class TransformedData : DataPoint
        {
            public int[] Features { get; set; }
        }
    }
}

MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean)

Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.

public static Microsoft.ML.Transforms.ValueMappingEstimator<TInputType,TOutputType> MapValue<TInputType,TOutputType> (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, System.Collections.Generic.IEnumerable<System.Collections.Generic.KeyValuePair<TInputType,TOutputType>> keyValuePairs, string inputColumnName = default, bool treatValuesAsKeyType = false);

static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * seq<System.Collections.Generic.KeyValuePair<'InputType, 'OutputType>> * string * bool -> Microsoft.ML.Transforms.ValueMappingEstimator<'InputType, 'OutputType>

<Extension()>
Public Function MapValue(Of TInputType, TOutputType) (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, keyValuePairs As IEnumerable(Of KeyValuePair(Of TInputType, TOutputType)), Optional inputColumnName As String = Nothing, Optional treatValuesAsKeyType As Boolean = false) As ValueMappingEstimator(Of TInputType, TOutputType)

형식 매개 변수

TInputType

키 형식입니다.

TOutputType

값 형식입니다.

매개 변수

catalog

TransformsCatalog.ConversionTransforms

변환 변환의 카탈로그

outputColumnName

String

의 변환에서 생성된 열의 inputColumnName이름입니다. 출력 데이터 형식은 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType>>

수행할 매핑을 지정합니다. 키는 에 지정된 대로 값에 keyValuePairs매핑됩니다.

inputColumnName

String

변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다. 입력 데이터 형식은 숫자, 텍스트, 부울 DateTimeDateTimeOffset 또는 형식의 기본 형식 또는 DataViewRowId 벡터일 수 있습니다.

treatValuesAsKeyType

Boolean

값을 키로 처리할지 여부입니다.

반환

ValueMappingEstimator<TInputType,TOutputType>

의 인스턴스 ValueMappingEstimator

예제

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

namespace Samples.Dynamic
{
    public static class MapValue
    {
        /// This example demonstrates the use of the ValueMappingEstimator by 
        /// mapping strings to other string values, or floats to strings. This is
        /// useful to map types to a 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() { Timeframe = "0-4yrs" , Score = 1 },
                new DataPoint() { Timeframe = "6-11yrs" , Score = 2 },
                new DataPoint() { Timeframe = "12-25yrs" , Score = 3 },
                new DataPoint() { Timeframe = "0-5yrs" , Score = 4 },
                new DataPoint() { Timeframe = "12-25yrs" , Score = 5 },
                new DataPoint() { Timeframe = "25+yrs" , Score = 5 },
            };

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

            // Construct the mapping to other strings for the Timeframe column.  
            var timeframeMap = new Dictionary<string, string>();
            timeframeMap["0-4yrs"] = "Short";
            timeframeMap["0-5yrs"] = "Short";
            timeframeMap["6-11yrs"] = "Medium";
            timeframeMap["12-25yrs"] = "Long";
            timeframeMap["25+yrs"] = "Long";

            // Construct the mapping of strings to keys(uints) for the Timeframe
            // column. 
            var timeframeKeyMap = new Dictionary<string, uint>();
            timeframeKeyMap["0-4yrs"] = 1;
            timeframeKeyMap["0-5yrs"] = 1;
            timeframeKeyMap["6-11yrs"] = 2;
            timeframeKeyMap["12-25yrs"] = 3;
            timeframeKeyMap["25+yrs"] = 3;

            // Construct the mapping of ints to strings for the Score column. 
            var scoreMap = new Dictionary<int, string>();
            scoreMap[1] = "Low";
            scoreMap[2] = "Low";
            scoreMap[3] = "Average";
            scoreMap[4] = "High";
            scoreMap[5] = "High";

            // Constructs the ML.net pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValue(
                "TimeframeCategory", timeframeMap, "Timeframe").Append(mlContext.
                Transforms.Conversion.MapValue("ScoreCategory", scoreMap, "Score"))
                // on the MapValue below, the treatValuesAsKeyType is set to true.
                // The type of the Label column will be a KeyDataViewType type, 
                // and it can be used as input for trainers performing multiclass
                // classification.
                .Append(mlContext.Transforms.Conversion.MapValue("Label",
                timeframeKeyMap, "Timeframe", treatValuesAsKeyType: 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(" Timeframe   TimeframeCategory   Label    Score   " +
                "ScoreCategory");

            foreach (var featureRow in features)
                Console.WriteLine($"{featureRow.Timeframe}\t\t" +
                    $"{featureRow.TimeframeCategory}\t\t\t{featureRow.Label}\t\t" +
                    $"{featureRow.Score}\t{featureRow.ScoreCategory}");

            // TransformedData obtained post-transformation.
            //
            //  Timeframe   TimeframeCategory   Label    Score   ScoreCategory
            // 0-4yrs         Short              1       1       Low
            // 6-11yrs        Medium             2       2       Low
            // 12-25yrs       Long               3       3       Average
            // 0-5yrs         Short              1       4       High
            // 12-25yrs       Long               3       5       High
            // 25+yrs         Long               3       5       High
        }

        private class DataPoint
        {
            public string Timeframe { get; set; }
            public int Score { get; set; }
        }

        private class TransformedData : DataPoint
        {
            public string TimeframeCategory { get; set; }
            public string ScoreCategory { get; set; }
            public uint Label { get; set; }
        }
    }
}