TransformExtensionsCatalog.DropColumns(TransformsCatalog, String[]) 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。
から列の ColumnSelectingEstimator特定のリストを削除する 、を作成します IDataView。 指定されていない列は、出力に保持されます。
public static Microsoft.ML.Transforms.ColumnSelectingEstimator DropColumns (this Microsoft.ML.TransformsCatalog catalog, params string[] columnNames);static member DropColumns : Microsoft.ML.TransformsCatalog * string[] -> Microsoft.ML.Transforms.ColumnSelectingEstimator<Extension()>
Public Function DropColumns (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[]
削除する列名の配列。 この推定機能は、任意のデータ型の列に対して動作します。
戻り値
例
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class DropColumns
{
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);
// Drop the ExtraColumn from the dataset.
var pipeline = mlContext.Transforms.DropColumns("ExtraColumn");
// Now we can transform the data and look at the output.
// Don't forget that this operation doesn't actually operate on data
// until we perform an action that requires
// the data to be materialized.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// Now let's take a look at what the DropColumns operations did.
// We can extract the transformed data as an IEnumerable of InputData,
// the class we define below. When we try to pull out the Age, Gender,
// Education and ExtraColumn columns, ML.NET will raise an exception on
// the ExtraColumn
try
{
var failingRowEnumerable = mlContext.Data.CreateEnumerable<
InputData>(transformedData, reuseRowObject: false);
}
catch (ArgumentOutOfRangeException exception)
{
Console.WriteLine($"ExtraColumn is not available, so an exception" +
$" is thrown: {exception.Message}.");
}
// Expected output:
// ExtraColumn is not available, so an exception is thrown: Could not find column 'ExtraColumn'.
// Parameter name: Schema
// And we can write a few columns out to see that the rest of the data
// is still available.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
Console.WriteLine($"The columns we didn't drop are still available.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Age: {row.Age} Gender: {row.Gender} " +
$"Education: {row.Education}");
// Expected output:
// The columns we didn't drop are still available.
// Age: 21 Gender: Male Education: BS
// Age: 23 Gender: Female Education: MBA
// Age: 28 Gender: Male Education: PhD
// Age: 22 Gender: Male Education: BS
// Age: 23 Gender: Female Education: MS
// Age: 27 Gender: Female 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 Gender { get; set; }
public string Education { get; set; }
}
}
}