DataOperationsCatalog.ShuffleRows 方法

定義

命名空間:

Microsoft.ML

組件:

Microsoft.ML.Data.dll

套件:

Microsoft.ML v5.0.0-preview.1.25125.4

來源:

DataOperationsCatalog.cs

將 的行 input順序洗牌。

public Microsoft.ML.IDataView ShuffleRows(Microsoft.ML.IDataView input, int? seed = default, int shufflePoolSize = 1000, bool shuffleSource = true);

member this.ShuffleRows : Microsoft.ML.IDataView * Nullable<int> * int * bool -> Microsoft.ML.IDataView

Public Function ShuffleRows (input As IDataView, Optional seed As Nullable(Of Integer) = Nothing, Optional shufflePoolSize As Integer = 1000, Optional shuffleSource As Boolean = true) As IDataView

參數

input

IDataView

輸入資料。

seed

Nullable<Int32>

隨機種子。 若未指定，則隨機狀態將由 推導 MLContext出。

shufflePoolSize

Int32

泳池中要保持的排數。 將此設定為 1 會關閉池子洗牌，只能 ShuffleRows(IDataView, Nullable<Int32>, Int32, Boolean) 隨機讀取 input 順序來進行洗牌。

shuffleSource

Boolean

若 false，變換不會嘗試隨機讀取 input ，僅使用池化洗牌。 若 CanShuffle 的 input 性質為 false，此參數無效。

傳回

IDataView

範例

using System;
using System.Collections.Generic;
using Microsoft.ML;

namespace Samples.Dynamic
{
    public static class ShuffleRows
    {
        // Sample class showing how to shuffle rows in 
        // IDataView.
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness.
            var mlContext = new MLContext();

            // Get a small dataset as an IEnumerable.
            var enumerableOfData = GetSampleTemperatureData(5);
            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);

            // Before we apply a filter, examine all the records in the dataset.
            Console.WriteLine($"Date\tTemperature");
            foreach (var row in enumerableOfData)
            {
                Console.WriteLine($"{row.Date.ToString("d")}" +
                    $"\t{row.Temperature}");
            }
            Console.WriteLine();
            // Expected output:
            //  Date    Temperature
            //  1/2/2012        36
            //  1/3/2012        36
            //  1/4/2012        34
            //  1/5/2012        35
            //  1/6/2012        35

            // Shuffle the dataset.
            var shuffledData = mlContext.Data.ShuffleRows(data, seed: 123);

            // Look at the shuffled data and observe that the rows are in a
            // randomized order.
            var enumerable = mlContext.Data
                .CreateEnumerable<SampleTemperatureData>(shuffledData,
                reuseRowObject: true);

            Console.WriteLine($"Date\tTemperature");
            foreach (var row in enumerable)
            {
                Console.WriteLine($"{row.Date.ToString("d")}" +
                $"\t{row.Temperature}");
            }
            // Expected output:
            //  Date    Temperature
            //  1/4/2012        34
            //  1/2/2012        36
            //  1/5/2012        35
            //  1/3/2012        36
            //  1/6/2012        35
        }

        private class SampleTemperatureData
        {
            public DateTime Date { get; set; }
            public float Temperature { get; set; }
        }

        /// <summary>
        /// Get a fake temperature dataset.
        /// </summary>
        /// <param name="exampleCount">The number of examples to return.</param>
        /// <returns>An enumerable of <see cref="SampleTemperatureData"/>.</returns>
        private static IEnumerable<SampleTemperatureData> GetSampleTemperatureData(
            int exampleCount)

        {
            var rng = new Random(1234321);
            var date = new DateTime(2012, 1, 1);
            float temperature = 39.0f;

            for (int i = 0; i < exampleCount; i++)
            {
                date = date.AddDays(1);
                temperature += rng.Next(-5, 5);
                yield return new SampleTemperatureData
                {
                    Date = date,
                    Temperature =
                    temperature
                };

            }
        }
    }
}

備註

ShuffleRows(IDataView, Nullable<Int32>, Int32, Boolean) 會用串流方式重新排列任何輸入 IDataView 的列。 為了避免整個資料集載入記憶體，會使用一組 shufflePoolSize 資料集隨機選取資料列輸出。 池子由 中的input前shufflePoolSize排組成。 接著會從池中隨機產生列，並從下一列 input 中替換，直到所有列都被產生，產生 IDataView 一個大小相同 input 但列數隨機排列的新列。 若 的 CanShuffle 性質為真，則該性質 input 也會以隨機順序讀入池中，提供兩個隨機來源。