SvmLightLoaderSaverCatalog.CreateSvmLightLoader Método

Definición

Espacio de nombres:

Microsoft.ML.Data

Ensamblado:

Microsoft.ML.Transforms.dll

Paquete:

Microsoft.ML v2.0.0

Crea un cargador que carga archivos de formato SVM-light. SvmLightLoader.

public static Microsoft.ML.Data.SvmLightLoader CreateSvmLightLoader (this Microsoft.ML.DataOperationsCatalog catalog, long? numberOfRows = default, int inputSize = 0, bool zeroBased = false, Microsoft.ML.Data.IMultiStreamSource dataSample = default);

static member CreateSvmLightLoader : Microsoft.ML.DataOperationsCatalog * Nullable<int64> * int * bool * Microsoft.ML.Data.IMultiStreamSource -> Microsoft.ML.Data.SvmLightLoader

<Extension()>
Public Function CreateSvmLightLoader (catalog As DataOperationsCatalog, Optional numberOfRows As Nullable(Of Long) = Nothing, Optional inputSize As Integer = 0, Optional zeroBased As Boolean = false, Optional dataSample As IMultiStreamSource = Nothing) As SvmLightLoader

Parámetros

catalog

DataOperationsCatalog

Catálogo DataOperationsCatalog .

numberOfRows

Nullable<Int64>

Número de filas del ejemplo que se va a usar para determinar el número de características.

inputSize

Int32

Número de características de la columna Características. Si se especifica 0, el cargador lo determinará examinando el ejemplo de archivo proporcionado en dataSample.

zeroBased

Boolean

Si el archivo contiene índices de base cero, este parámetro debe establecerse en true. Si se basan en uno, debe establecerse en false.

dataSample

IMultiStreamSource

Ejemplo de datos que se va a usar para determinar el número de características de la columna Características.

Devoluciones

SvmLightLoader

Ejemplos

using System;
using System.IO;
using System.Text;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.DataOperations
{
    public static class LoadingSvmLight
    {
        // This examples shows how to load data with SvmLightLoader.
        public static void Example()
        {
            // Create a random SVM light format file.
            var random = new Random(42);
            var dataDirectoryName = "DataDir";
            Directory.CreateDirectory(dataDirectoryName);
            var fileName = Path.Combine(dataDirectoryName, $"SVM_Data.csv");
            using (var fs = File.CreateText(fileName))
            {
                // Write random lines in SVM light format
                for (int line = 0; line < 10; line++)
                {
                    var sb = new StringBuilder();
                    if (random.NextDouble() > 0.5)
                        sb.Append("1 ");
                    else
                        sb.Append("-1 ");
                    if (line % 2 == 0)
                        sb.Append("cost:1 ");
                    else
                        sb.Append("cost:2 ");
                    for (int i = 1; i <= 10; i++)
                    {
                        if (random.NextDouble() > 0.5)
                            continue;
                        sb.Append($"{i}:{random.NextDouble()} ");
                    }
                    fs.WriteLine(sb.ToString());
                }
            }

            // Create an SvmLightLoader.
            var mlContext = new MLContext();
            var file = new MultiFileSource(fileName);
            var loader = mlContext.Data.CreateSvmLightLoader(dataSample: file);

            // Load a single file from path.
            var svmData = loader.Load(file);

            PrintSchema(svmData);

            // Expected Output:
            // Column Label type Single
            // Column Weight type Single
            // Column GroupId type Key<UInt64, 0 - 18446744073709551613>
            // Column Comment type String
            // Column Features type Vector<Single, 10>

            PrintData(svmData);

            // Expected Output:
            // 1 1 0 0 0.2625927 0 0 0.7612506 0.2573214 0 0.3809696 0.5174511
            // -1 1 0 0 0 0.7051522 0 0 0.7111546 0.9062127 0 0
            // -1 1 0 0 0 0.535722 0 0 0.1491191 0.05100901 0 0
            // -1 1 0 0.6481459 0.04449836 0 0 0.4203662 0 0 0.01325378 0.2674384
            // -1 1 0 0 0.7978093 0.5134962 0.008952909 0 0.003074009 0.6541431 0.9135142 0
            // -1 1 0 0.3727672 0.4369507 0 0 0.2973725 0 0 0 0.8816807
            // 1 1 0 0.1031429 0.3332489 0 0.1346936 0.5916625 0 0 0 0
            // 1 1 0 0 0 0.3454075 0 0.2197472 0.03848049 0.5923384 0.09373277 0
            // -1 1 0 0.7511514 0 0.0420841 0 0 0.9262196 0 0.545344 0
            // 1 1 0 0.02958358 0.9334617 0 0 0.8833956 0.2947684 0 0 0

            // If the loader is created without a data sample we need to specify the number of features expected in the file.
            loader = mlContext.Data.CreateSvmLightLoader(inputSize: 10);
            svmData = loader.Load(file);

            PrintSchema(svmData);
            PrintData(svmData);
        }

        private static void PrintSchema(IDataView svmData)
        {
            foreach (var col in svmData.Schema)
                Console.WriteLine($"Column {col.Name} type {col.Type}");
        }

        private static void PrintData(IDataView svmData)
        {
            using (var cursor = svmData.GetRowCursor(svmData.Schema))
            {
                var labelGetter = cursor.GetGetter<float>(svmData.Schema["Label"]);
                var weightGetter = cursor.GetGetter<float>(svmData.Schema["Weight"]);
                var featuresGetter = cursor.GetGetter<VBuffer<float>>(svmData.Schema["Features"]);

                VBuffer<float> features = default;
                while (cursor.MoveNext())
                {
                    float label = default;
                    labelGetter(ref label);

                    float weight = default;
                    weightGetter(ref weight);

                    featuresGetter(ref features);

                    Console.WriteLine($"{label} {weight} {string.Join(' ', features.DenseValues())}");
                }
            }
        }
    }
}