TransformExtensionsCatalog.SelectColumns 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。
オーバーロード
| SelectColumns(TransformsCatalog, String[]) |
列の特定の ColumnSelectingEstimatorリストを保持し、他の列を IDataView 削除する、作成します。 |
| SelectColumns(TransformsCatalog, String[], Boolean) |
列の特定の ColumnSelectingEstimatorリストを保持し、他の列を IDataView 削除する、作成します。 |
SelectColumns(TransformsCatalog, String[])
列の特定の ColumnSelectingEstimatorリストを保持し、他の列を IDataView 削除する、作成します。
public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, params string[] columnNames);static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] -> Microsoft.ML.Transforms.ColumnSelectingEstimator<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, ParamArray columnNames As String()) As ColumnSelectingEstimatorパラメーター
- catalog
- TransformsCatalog
変換のカタログ。
- columnNames
- String[]
保持する列名の配列。
戻り値
例
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class SelectColumns
{
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(){ Age = 21, Gender = "Male", Education = "BS",
ExtraColumn = 1 },
new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
ExtraColumn = 2 },
new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
ExtraColumn = 3 },
new InputData(){ Age = 22, Gender = "Male", Education = "BS",
ExtraColumn = 4 },
new InputData(){ Age = 23, Gender = "Female", Education = "MS",
ExtraColumn = 5 },
new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
ExtraColumn = 6 },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// Select a subset of columns to keep.
var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");
// Now we can transform the data and look at the output to confirm the
// behavior of SelectColumns. Don't forget that this operation doesn't
// actually evaluate data until we read the data below, as
// transformations are lazy in ML.NET.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// Print the number of columns in the schema
Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
$" in the dataset.");
// Expected output:
// There are 2 columns in the dataset.
// We can extract the newly created column as an IEnumerable of
// TransformedData, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
Console.WriteLine($"Age and Educations columns obtained " +
$"post-transformation.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Age: {row.Age} Education: {row.Education}");
// Expected output:
// Age and Educations columns obtained post-transformation.
// Age: 21 Education: BS
// Age: 23 Education: MBA
// Age: 28 Education: PhD
// Age: 22 Education: BS
// Age: 23 Education: MS
// Age: 27 Education: PhD
}
private class InputData
{
public int Age { get; set; }
public string Gender { get; set; }
public string Education { get; set; }
public float ExtraColumn { get; set; }
}
private class TransformedData
{
public int Age { get; set; }
public string Education { get; set; }
}
}
}
適用対象
SelectColumns(TransformsCatalog, String[], Boolean)
列の特定の ColumnSelectingEstimatorリストを保持し、他の列を IDataView 削除する、作成します。
public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, string[] columnNames, bool keepHidden);static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] * bool -> Microsoft.ML.Transforms.ColumnSelectingEstimator<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, columnNames As String(), keepHidden As Boolean) As ColumnSelectingEstimatorパラメーター
- catalog
- TransformsCatalog
変換のカタログ。
- columnNames
- String[]
保持する列名の配列。
- keepHidden
- Boolean
非表示の列を保持し、false非表示の列を削除する場合true。
非表示の列を削除する代わりに、パイプラインの入力をデバッグ目的でパイプラインの出力にマップする方法を理解する必要がある場合は、非表示の列を保持することをお勧めします。
戻り値
例
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class SelectColumns
{
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(){ Age = 21, Gender = "Male", Education = "BS",
ExtraColumn = 1 },
new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
ExtraColumn = 2 },
new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
ExtraColumn = 3 },
new InputData(){ Age = 22, Gender = "Male", Education = "BS",
ExtraColumn = 4 },
new InputData(){ Age = 23, Gender = "Female", Education = "MS",
ExtraColumn = 5 },
new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
ExtraColumn = 6 },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// Select a subset of columns to keep.
var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");
// Now we can transform the data and look at the output to confirm the
// behavior of SelectColumns. Don't forget that this operation doesn't
// actually evaluate data until we read the data below, as
// transformations are lazy in ML.NET.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// Print the number of columns in the schema
Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
$" in the dataset.");
// Expected output:
// There are 2 columns in the dataset.
// We can extract the newly created column as an IEnumerable of
// TransformedData, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
Console.WriteLine($"Age and Educations columns obtained " +
$"post-transformation.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Age: {row.Age} Education: {row.Education}");
// Expected output:
// Age and Educations columns obtained post-transformation.
// Age: 21 Education: BS
// Age: 23 Education: MBA
// Age: 28 Education: PhD
// Age: 22 Education: BS
// Age: 23 Education: MS
// Age: 27 Education: PhD
}
private class InputData
{
public int Age { get; set; }
public string Gender { get; set; }
public string Education { get; set; }
public float ExtraColumn { get; set; }
}
private class TransformedData
{
public int Age { get; set; }
public string Education { get; set; }
}
}
}