ConversionsCatalog.MapKeyToBinaryVector Metode
Definisi
Penting
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Buat KeyToBinaryVectorMappingEstimator, yang mengonversi jenis kunci ke representasi biner yang sesuai dari nilai aslinya.
public static Microsoft.ML.Transforms.KeyToBinaryVectorMappingEstimator MapKeyToBinaryVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default);
static member MapKeyToBinaryVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string -> Microsoft.ML.Transforms.KeyToBinaryVectorMappingEstimator
<Extension()>
Public Function MapKeyToBinaryVector (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing) As KeyToBinaryVectorMappingEstimator
Parameter
Katalog transformasi kategoris.
- outputColumnName
- String
Nama kolom yang dihasilkan dari transformasi inputColumnName
.
Jenis data adalah vektor ukuran yang diketahui mewakili Single nilai input.
- inputColumnName
- String
Nama kolom yang akan diubah. Jika diatur ke null
, nilai outputColumnName
akan digunakan sebagai sumber.
Jenis data adalah kunci atau vektor kunci ukuran yang diketahui.
Mengembalikan
Contoh
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
class MapKeyToBinaryVector
{
/// This example demonstrates the use of MapKeyToVector by mapping keys to
/// floats[] of 0 and 1, representing the number in binary format.
/// Because the ML.NET KeyType maps the missing value to zero, counting
/// starts at 1, so the uint values converted to KeyTypes will appear
/// skewed by one.
/// See https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
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 = 9 },
new DataPoint() { Timeframe = 8 },
new DataPoint() { Timeframe = 8 },
new DataPoint() { Timeframe = 9 },
new DataPoint() { Timeframe = 2 },
new DataPoint() { Timeframe = 3 }
};
var data = mlContext.Data.LoadFromEnumerable(rawData);
// Constructs the ML.net pipeline
var pipeline = mlContext.Transforms.Conversion.MapKeyToBinaryVector(
"TimeframeVector", "Timeframe");
// 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($" Timeframe TimeframeVector");
foreach (var featureRow in features)
Console.WriteLine($"{featureRow.Timeframe}\t\t\t" +
$"{string.Join(',', featureRow.TimeframeVector)}");
// Timeframe TimeframeVector
// 10 0,1,0,0,1 //binary representation of 9, the original value
// 9 0,1,0,0,0 //binary representation of 8, the original value
// 9 0,1,0,0,0
// 10 0,1,0,0,1
// 3 0,0,0,1,0
// 4 0,0,0,1,1
}
private class DataPoint
{
[KeyType(10)]
public uint Timeframe { get; set; }
}
private class TransformedData : DataPoint
{
public float[] TimeframeVector { get; set; }
}
}
}