ConversionsExtensionsCatalog.MapValueToKey 메서드
정의
중요
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오버로드
| 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 = Nothing) As ValueToKeyMappingEstimator매개 변수
변환 변환의 카탈로그입니다.
- columns
- InputOutputColumnPair[]
입력 및 출력 열입니다. 입력 데이터 형식은 숫자, 텍스트, 부울 DateTime 또는 DateTimeOffset.
- maximumNumberOfKeys
- Int32
학습할 때 열당 유지할 최대 키 수입니다.
- keyOrdinality
- ValueToKeyMappingEstimator.KeyOrdinality
키가 할당되는 순서입니다. 설정 ByOccurrence하면 키가 발생한 순서대로 할당됩니다. 로 ByValue설정하면 값이 정렬되고 정렬 순서에 따라 키가 할당됩니다.
- addKeyValueAnnotationsAsText
- Boolean
true로 설정하면 실제 입력 형식에 관계없이 값에 텍스트 형식을 사용합니다. 역방향 매핑을 수행할 때 값은 원래 입력 형식이 아닌 텍스트입니다.
- keyData
- IDataView
학습 중에 입력 데이터에서 매핑을 빌드하는 대신 값과 키 간에 미리 정의된 매핑을 사용합니다. 지정한 경우 값을 포함하는 단일 열 IDataView 이어야 합니다. 키는 keyOrdinality 값에 따라 할당됩니다.
반환
예제
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/main/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; }
}
}
}
설명
이 변환은 여러 열 쌍에서 작동하여 각 쌍에 대한 매핑을 만들 수 있습니다.
적용 대상
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 = Nothing, 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 = Nothing) As ValueToKeyMappingEstimator매개 변수
변환 변환의 카탈로그입니다.
- outputColumnName
- String
키가 포함된 열의 이름입니다.
- inputColumnName
- String
범주 값을 포함하는 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 사용됩니다.
입력 데이터 형식은 숫자, 텍스트, 부울 DateTime 또는 DateTimeOffset.
- maximumNumberOfKeys
- Int32
학습할 때 열당 유지할 최대 키 수입니다.
- keyOrdinality
- ValueToKeyMappingEstimator.KeyOrdinality
키가 할당되는 순서입니다. 설정 ByOccurrence하면 키가 발생한 순서대로 할당됩니다. 로 ByValue설정하면 값이 정렬되고 정렬 순서에 따라 키가 할당됩니다.
- addKeyValueAnnotationsAsText
- Boolean
true로 설정하면 실제 입력 형식에 관계없이 값에 텍스트 형식을 사용합니다. 역방향 매핑을 수행할 때 값은 원래 입력 형식이 아닌 텍스트입니다.
- keyData
- IDataView
학습 중에 입력 데이터에서 매핑을 빌드하는 대신 값과 키 간에 미리 정의된 매핑을 사용합니다. 지정한 경우 값을 포함하는 단일 열 IDataView 이어야 합니다. 키는 keyOrdinality 값에 따라 할당됩니다.
반환
예제
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; }
}
}
}