TransformExtensionsCatalog.DropColumns(TransformsCatalog, String[]) Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Erstellen Sie eine ColumnSelectingEstimator, die eine bestimmte Liste von Spalten aus einer IDataView. Alle nicht angegebenen Spalten werden in der Ausgabe beibehalten.

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

Parameter

catalog

TransformsCatalog

Der Katalog der Transformation.

columnNames

String[]

Das Array der zu ablegenden Spaltennamen. Dieser Schätzer wird über Spalten eines beliebigen Datentyps betrieben.

Gibt zurück

ColumnSelectingEstimator

Beispiele

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; }
        }
    }
}