TransformExtensionsCatalog.CopyColumns メソッド
定義
重要
一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。
新しい列に ColumnCopyingEstimator指定された inputColumnName 列からデータをコピーする、作成します outputColumnName。
public static Microsoft.ML.Transforms.ColumnCopyingEstimator CopyColumns (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName);static member CopyColumns : Microsoft.ML.TransformsCatalog * string * string -> Microsoft.ML.Transforms.ColumnCopyingEstimator<Extension()>
Public Function CopyColumns (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String) As ColumnCopyingEstimatorパラメーター
- catalog
- TransformsCatalog
変換のカタログ。
- outputColumnName
- String
の変換の結果として得られる列の inputColumnName名前。
この列のデータ型は、入力列のデータ型と同じになります。
- inputColumnName
- String
データのコピー元の列の名前。 この推定機能は、任意のデータ型で動作します。
戻り値
例
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class CopyColumns
{
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();
// Create a small dataset as an IEnumerable.
var samples = new List<InputData>()
{
new InputData(){ ImageId = 1, Features = new [] { 1.0f, 1.0f,
1.0f } },
new InputData(){ ImageId = 2, Features = new [] { 2.0f, 2.0f,
2.0f } },
new InputData(){ ImageId = 3, Features = new [] { 3.0f, 3.0f,
3.0f } },
new InputData(){ ImageId = 4, Features = new [] { 4.0f, 4.0f,
4.0f } },
new InputData(){ ImageId = 5, Features = new [] { 5.0f, 5.0f,
5.0f } },
new InputData(){ ImageId = 6, Features = new [] { 6.0f, 6.0f,
6.0f } },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// CopyColumns is commonly used to rename columns.
// For example, if you want to train towards ImageId, and your trainer
// expects a "Label" column, you can use CopyColumns to rename ImageId
// to Label. Technically, the ImageId column still exists, but it won't
// be materialized unless you actually need it somewhere (e.g. if you
// were to save the transformed data without explicitly dropping the
// column). This is a general property of IDataView's lazy evaluation.
var pipeline = mlContext.Transforms.CopyColumns("Label", "ImageId");
// Now we can transform the data and look at the output to confirm the
// behavior of CopyColumns. Don't forget that this operation doesn't
// actually evaluate data until we read the data below.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// We can extract the newly created column as an IEnumerable of
// SampleInfertDataTransformed, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
Console.WriteLine($"Label and ImageId columns obtained " +
$"post-transformation.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Label: {row.Label} ImageId: {row.ImageId}");
// Expected output:
// ImageId and Label columns obtained post-transformation.
// Label: 1 ImageId: 1
// Label: 2 ImageId: 2
// Label: 3 ImageId: 3
// Label: 4 ImageId: 4
// Label: 5 ImageId: 5
// Label: 6 ImageId: 6
}
private class InputData
{
public int ImageId { get; set; }
public float[] Features { get; set; }
}
private class TransformedData : InputData
{
public int Label { get; set; }
}
}
}