ConversionsExtensionsCatalog.MapValue Método

Definição

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Pacote:

Microsoft.ML v2.0.0

Sobrecargas

MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String)	Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas do lookupMap local em que as keyColumn chaves especificam e o valueColumn respectivo valor.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String)	Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas de keyValuePairs.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean)	Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas de keyValuePairs.

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

Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas do lookupMap local em que as keyColumn chaves especificam e o valueColumn respectivo valor.

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

Parâmetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo da transformação de conversão

outputColumnName

String

Nome da coluna resultante da transformação de inputColumnName. Os tipos de dados podem ser primitivos ou vetores de tipos numéricos, de texto, DateTimeDateTimeOffset boolianos ou DataViewRowId de tipos.

lookupMap

IDataView

Uma instância que IDataView contém as colunas e valueColumn as keyColumn colunas.

keyColumn

DataViewSchema.Column

A coluna de chave em lookupMap.

valueColumn

DataViewSchema.Column

A coluna de valor em lookupMap.

inputColumnName

String

Nome da coluna a ser transformada. Se definido como null, o valor do outputColumnName será usado como origem. Os tipos de dados podem ser primitivos ou vetores de tipos numéricos, de texto, DateTimeDateTimeOffset boolianos ou DataViewRowId de tipos.

Retornos

ValueMappingEstimator

Exemplos

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)

Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas de 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)

Parâmetros de tipo

TInputType

O tipo principal.

TOutputType

O tipo de valor.

Parâmetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo da transformação de conversão

outputColumnName

String

Nome da coluna resultante da transformação de inputColumnName. Os tipos de dados podem ser primitivos ou vetores de tipos numéricos, de texto, DateTimeDateTimeOffset boolianos ou DataViewRowId de texto, conforme especificado no TOutputType.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType[]>>

Especifica o mapeamento que será executado. As chaves serão mapeadas para os valores conforme especificado no keyValuePairs.

inputColumnName

String

Nome da coluna a ser transformada. Se definido como null, o valor do outputColumnName será usado como origem. Os tipos de dados podem ser primitivos ou vetores de tipos numéricos, de texto, DateTimeDateTimeOffset boolianos ou DataViewRowId de texto, conforme especificado no TInputType.

Retornos

ValueMappingEstimator<TInputType,TOutputType>

Exemplos

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)

Crie um ValueMappingEstimator, que converte tipos de valor em chaves, carregando as chaves a serem usadas de 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)

Parâmetros de tipo

TInputType

O tipo principal.

TOutputType

O tipo de valor.

Parâmetros

catalog

TransformsCatalog.ConversionTransforms

Catálogo da transformação de conversão

outputColumnName

String

Nome da coluna resultante da transformação de inputColumnName. Os tipos de dados de saída podem ser primitivos ou vetores de tipos, texto, booliano DateTimeDateTimeOffset ou DataViewRowId numéricos.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType>>

Especifica o mapeamento que será executado. As chaves serão mapeadas para os valores conforme especificado no keyValuePairs.

inputColumnName

String

Nome da coluna a ser transformada. Se definido como null, o valor do outputColumnName será usado como origem. Os tipos de dados de entrada podem ser primitivos ou vetores de tipos numéricos, de texto, DateTimeDateTimeOffset boolianos ou DataViewRowId de entrada.

treatValuesAsKeyType

Boolean

Se os valores devem ser tratados como uma chave.

Retornos

ValueMappingEstimator<TInputType,TOutputType>

Uma instância do ValueMappingEstimator

Exemplos

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; }
        }
    }
}