DataOperationsCatalog.Cache(IDataView, String[]) Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Membuat cache dalam memori malas dari input.

public Microsoft.ML.IDataView Cache (Microsoft.ML.IDataView input, params string[] columnsToPrefetch);

member this.Cache : Microsoft.ML.IDataView * string[] -> Microsoft.ML.IDataView

Public Function Cache (input As IDataView, ParamArray columnsToPrefetch As String()) As IDataView

Parameter

input

IDataView

Data input.

columnsToPrefetch

String[]

Kolom yang harus di-cache setiap kali ada yang di-cache. Array kosong atau nilai null berarti bahwa kolom di-cache pada akses pertamanya.

Mengembalikan

IDataView

Contoh

using System;
using Microsoft.ML;
using Microsoft.ML.SamplesUtils;

namespace Samples.Dynamic
{
    public static class Cache
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for except
            // ion tracking and logging, as a catalog of available operations and as
            // the source of randomness.
            var mlContext = new MLContext();

            var data = DatasetUtils.LoadHousingRegressionDataset(mlContext);

            // Time how long it takes to page through the records if we don't cache.
            (int lines, double columnAverage, double elapsedSeconds) =
                TimeToScanIDataView(mlContext, data);

            Console.WriteLine($"Lines={lines}," +
                $"averageOfColumn0={columnAverage:0.00} and took {elapsedSeconds}" +
                $"seconds.");
            // Expected output (time is approximate):
            // Lines=506, averageOfColumn0=564.17 and took 0.314 seconds.

            // Now create a cached view of the data.
            var cachedData = mlContext.Data.Cache(data);

            // Time how long it takes to page through the records the first time
            // they're accessed after a cache is applied. This iteration will be
            // longer than subsequent calls, as the dataset is being accessed and
            // stored for later. Note that this operation may be relatively quick,
            // as the system may have cached the file.
            (lines, columnAverage, elapsedSeconds) = TimeToScanIDataView(mlContext,
                cachedData);

            Console.WriteLine($"Lines={lines}," +
                $"averageOfColumn0={columnAverage:0.00} and took {elapsedSeconds}" +
                $"seconds.");
            // Expected output (time is approximate):
            // Lines=506, averageOfColumn0=564.17 and took 0.056 seconds.

            // Time how long it takes to page through the records now that the data
            // is cached. After the first iteration that caches the IDataView,
            // future iterations, like this one, are faster because they are pulling
            // from data cached in memory.
            (lines, columnAverage, elapsedSeconds) = TimeToScanIDataView(mlContext,
                cachedData);

            Console.WriteLine(
                $"Lines={lines}, averageOfColumn0={columnAverage:0.00} and took " +
                $"{elapsedSeconds} seconds.");
            // Expected output (time is approximate):
            // Lines=506, averageOfColumn0=564.17 and took 0.006 seconds.
        }

        private static (int lines, double columnAverage, double elapsedSeconds)
            TimeToScanIDataView(MLContext mlContext, IDataView data)
        {
            int lines = 0;
            double columnAverage = 0.0;
            var enumerable = mlContext.Data
                .CreateEnumerable<HousingRegression>(data, reuseRowObject: true);

            var watch = System.Diagnostics.Stopwatch.StartNew();
            foreach (var row in enumerable)
            {
                lines++;
                columnAverage += row.MedianHomeValue + row.CrimesPerCapita +
                    row.PercentResidental + row.PercentNonRetail + row.CharlesRiver
                    + row.NitricOxides + row.RoomsPerDwelling + row.PercentPre40s +
                    row.EmploymentDistance + row.HighwayDistance + row.TaxRate +
                    row.TeacherRatio;
            }
            watch.Stop();
            columnAverage /= lines;
            var elapsed = watch.Elapsed;

            return (lines, columnAverage, elapsed.Seconds);
        }

        /// <summary>
        /// A class to hold the raw housing regression rows.
        /// </summary>
        public sealed class HousingRegression
        {
            public float MedianHomeValue { get; set; }
            public float CrimesPerCapita { get; set; }
            public float PercentResidental { get; set; }
            public float PercentNonRetail { get; set; }
            public float CharlesRiver { get; set; }
            public float NitricOxides { get; set; }
            public float RoomsPerDwelling { get; set; }
            public float PercentPre40s { get; set; }
            public float EmploymentDistance { get; set; }
            public float HighwayDistance { get; set; }
            public float TaxRate { get; set; }
            public float TeacherRatio { get; set; }
        }

    }
}

Keterangan

Penembolokan terjadi per kolom. Kolom hanya di-cache saat pertama kali diakses. Selain itu, columnsToPrefetch dianggap 'selalu diperlukan', sehingga kolom ini akan di-cache saat pertama kali data diminta.