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Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v4.0.1

Package:

Microsoft.ML v1.0.0

Package:

Microsoft.ML v1.1.0

Package:

Microsoft.ML v1.2.0

Package:

Microsoft.ML v1.3.1

Package:

Microsoft.ML v1.4.0

Package:

Microsoft.ML v1.5.5

Package:

Microsoft.ML v1.6.0

Package:

Microsoft.ML v1.7.0

Package:

Microsoft.ML v2.0.1

Package:

Microsoft.ML v3.0.1

Package:

Microsoft.ML v5.0.0-preview.1.25125.4

Source:

ExtensionsCatalog.cs

Source:

ExtensionsCatalog.cs

Source:

ExtensionsCatalog.cs

Important

Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

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

Parameters

Returns

Examples

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; }
 }
 }
}