ConversionsExtensionsCatalog.MapValueToKey Metode
Definisi
Penting
Beberapa informasi terkait produk prarilis yang dapat diubah secara signifikan sebelum dirilis. Microsoft tidak memberikan jaminan, tersirat maupun tersurat, sehubungan dengan informasi yang diberikan di sini.
Overload
| MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView) |
Buat ValueToKeyMappingEstimator, yang mengonversi nilai kategoris menjadi kunci. |
| MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView) |
Buat ValueToKeyMappingEstimator, yang mengonversi nilai kategoris menjadi kunci numerik. |
MapValueToKey(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)
Buat ValueToKeyMappingEstimator, yang mengonversi nilai kategoris menjadi kunci.
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 ValueToKeyMappingEstimatorParameter
Katalog transformasi konversi.
- columns
- InputOutputColumnPair[]
Kolom input dan output. Jenis data input dapat berupa numerik, teks, boolean, DateTime atau DateTimeOffset.
- maximumNumberOfKeys
- Int32
Jumlah maksimum kunci untuk disimpan per kolom saat pelatihan.
- keyOrdinality
- ValueToKeyMappingEstimator.KeyOrdinality
Urutan penetapan kunci. Jika diatur ke ByOccurrence, kunci ditetapkan dalam urutan yang ditemui. Jika diatur ke ByValue, nilai diurutkan, dan kunci ditetapkan berdasarkan urutan pengurutan.
- addKeyValueAnnotationsAsText
- Boolean
Jika diatur ke true, gunakan jenis teks untuk nilai, terlepas dari jenis input aktual. Saat melakukan pemetaan terbalik, nilainya adalah teks daripada jenis input asli.
- keyData
- IDataView
Gunakan pemetaan yang telah ditentukan sebelumnya antara nilai dan kunci, alih-alih membangun pemetaan dari data input selama pelatihan. Jika ditentukan, ini harus berupa satu kolom IDataView yang berisi nilai. Kunci dialokasikan berdasarkan nilai keyOrdinality.
Mengembalikan
Contoh
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; }
}
}
}
Keterangan
Transformasi ini dapat beroperasi melalui beberapa pasang kolom, membuat pemetaan untuk setiap pasangan.
Berlaku untuk
MapValueToKey(TransformsCatalog+ConversionTransforms, String, String, Int32, ValueToKeyMappingEstimator+KeyOrdinality, Boolean, IDataView)
Buat ValueToKeyMappingEstimator, yang mengonversi nilai kategoris menjadi kunci numerik.
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 ValueToKeyMappingEstimatorParameter
Katalog transformasi konversi.
- outputColumnName
- String
Nama kolom yang berisi kunci.
- inputColumnName
- String
Nama kolom yang berisi nilai kategoris. Jika diatur ke null, nilai digunakan outputColumnName .
Jenis data input dapat berupa numerik, teks, boolean, DateTime atau DateTimeOffset.
- maximumNumberOfKeys
- Int32
Jumlah maksimum kunci untuk disimpan per kolom saat pelatihan.
- keyOrdinality
- ValueToKeyMappingEstimator.KeyOrdinality
Urutan penetapan kunci. Jika diatur ke ByOccurrence, kunci ditetapkan dalam urutan yang ditemui. Jika diatur ke ByValue, nilai diurutkan, dan kunci ditetapkan berdasarkan urutan pengurutan.
- addKeyValueAnnotationsAsText
- Boolean
Jika diatur ke true, gunakan jenis teks untuk nilai, terlepas dari jenis input aktual. Saat melakukan pemetaan terbalik, nilainya adalah teks daripada jenis input asli.
- keyData
- IDataView
Gunakan pemetaan yang telah ditentukan sebelumnya antara nilai dan kunci, alih-alih membangun pemetaan dari data input selama pelatihan. Jika ditentukan, ini harus berupa satu kolom IDataView yang berisi nilai. Kunci dialokasikan berdasarkan nilai keyOrdinality.
Mengembalikan
Contoh
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; }
}
}
}