ConversionsExtensionsCatalog.MapValue Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Überlädt

MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String)	Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die Schlüssel, die verwendet werden sollen, aus der lookupMap die keyColumn Schlüssel angegeben werden, und den valueColumn jeweiligen Wert.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String)	Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die zu verwendenden Schlüssel aus keyValuePairs.
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean)	Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die zu verwendenden Schlüssel aus keyValuePairs.

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

Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die Schlüssel, die verwendet werden sollen, aus der lookupMap die keyColumn Schlüssel angegeben werden, und den valueColumn jeweiligen Wert.

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

Parameter

catalog

TransformsCatalog.ConversionTransforms

Katalog der Konvertierungstransformation

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Die Datentypen können Grundtypen oder Vektoren numerischer, Text, boolescher DateTimeDateTimeOffset Oder DataViewRowId Typen sein.

lookupMap

IDataView

Eine Instanz, die IDataView die keyColumn Spalten valueColumn enthält.

keyColumn

DataViewSchema.Column

Die Schlüsselspalte in lookupMap.

valueColumn

DataViewSchema.Column

Die Wertspalte in lookupMap.

inputColumnName

String

Name der zu transformierenden Spalte. Wenn dieser Wert als nullQuelle festgelegt ist, wird der Wert des Werts outputColumnName als Quelle verwendet. Die Datentypen können Grundtypen oder Vektoren numerischer, Text, boolescher DateTimeDateTimeOffset Oder DataViewRowId Typen sein.

Gibt zurück

ValueMappingEstimator

Beispiele

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)

Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die zu verwendenden Schlüssel aus 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)

Typparameter

TInputType

Der Schlüsseltyp.

TOutputType

Der Werttyp.

Parameter

catalog

TransformsCatalog.ConversionTransforms

Katalog der Konvertierungstransformation

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Die Datentypen können Grundtypen oder Vektoren numerischer, Text, boolescher DateTimeDateTimeOffset oder DataViewRowId typen sein, wie in der TOutputTypeAngegebenen.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType[]>>

Gibt die Zuordnung an, die ausgeführt wird. Die Schlüssel werden den Werten zugeordnet, wie in der keyValuePairs.

inputColumnName

String

Name der zu transformierenden Spalte. Wenn dieser Wert als nullQuelle festgelegt ist, wird der Wert des Werts outputColumnName als Quelle verwendet. Die Datentypen können Grundtypen oder Vektoren numerischer, Text, boolescher DateTimeDateTimeOffset oder DataViewRowId typen sein, wie in der TInputTypeAngegebenen.

Gibt zurück

ValueMappingEstimator<TInputType,TOutputType>

Beispiele

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)

Erstellen Sie eine ValueMappingEstimator, die Werttypen in Schlüssel konvertiert, und laden Sie die zu verwendenden Schlüssel aus 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)

Typparameter

TInputType

Der Schlüsseltyp.

TOutputType

Der Werttyp.

Parameter

catalog

TransformsCatalog.ConversionTransforms

Katalog der Konvertierungstransformation

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Die Ausgabedatentypen können Grundtypen oder Vektoren numerischer, text-, boolescher DateTimeDateTimeOffset oder DataViewRowId typen sein.

keyValuePairs

IEnumerable<KeyValuePair<TInputType,TOutputType>>

Gibt die Zuordnung an, die ausgeführt wird. Die Schlüssel werden den Werten zugeordnet, wie in der keyValuePairs.

inputColumnName

String

Name der zu transformierenden Spalte. Wenn dieser Wert als nullQuelle festgelegt ist, wird der Wert des Werts outputColumnName als Quelle verwendet. Die Eingabedatentypen können Grundtypen oder Vektoren numerischer, Text, boolescher DateTimeDateTimeOffset Oder DataViewRowId Typen sein.

treatValuesAsKeyType

Boolean

Gibt an, ob die Werte als Schlüssel behandelt werden sollen.

Gibt zurück

ValueMappingEstimator<TInputType,TOutputType>

Eine Instanz der ValueMappingEstimator

Beispiele

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; }
        }
    }
}