SvmLightLoaderSaverCatalog.CreateSvmLightLoader Methode

Definition

Namespace:

Microsoft.ML.Data

Assembly:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v2.0.0

Erstellt ein Ladeprogramm, das SVM-Light-Formatdateien lädt. 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

Parameter

catalog

DataOperationsCatalog

Der DataOperationsCatalog Katalog.

numberOfRows

Nullable<Int64>

Die Anzahl der Zeilen aus dem Beispiel, die zum Bestimmen der Anzahl der Features verwendet werden sollen.

inputSize

Int32

Die Anzahl der Features in der Spalte "Features". Wenn "0" angegeben ist, bestimmt der Ladeer ihn, indem er sich das dateibeispiel anschaut, das in dataSample.

zeroBased

Boolean

Wenn die Datei nullbasierte Indizes enthält, sollte dieser Parameter auf "true" festgelegt werden. Wenn sie einsbasiert sind, sollte er auf "false" festgelegt werden.

dataSample

IMultiStreamSource

Ein Datenbeispiel, das zum Ermitteln der Anzahl der Features in der Spalte "Features" verwendet werden soll.

Gibt zurück

SvmLightLoader

Beispiele

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())}");
                }
            }
        }
    }
}