TextLoaderSaverCatalog.SaveAsText Метод

Определение

Пространство имен:

Microsoft.ML

Сборка:

Microsoft.ML.Data.dll

Пакет:

Microsoft.ML v3.0.1

Сохраните IDataView текст в виде текста.

public static void SaveAsText (this Microsoft.ML.DataOperationsCatalog catalog, Microsoft.ML.IDataView data, System.IO.Stream stream, char separatorChar = '\t', bool headerRow = true, bool schema = true, bool keepHidden = false, bool forceDense = false);

static member SaveAsText : Microsoft.ML.DataOperationsCatalog * Microsoft.ML.IDataView * System.IO.Stream * char * bool * bool * bool * bool -> unit

<Extension()>
Public Sub SaveAsText (catalog As DataOperationsCatalog, data As IDataView, stream As Stream, Optional separatorChar As Char = '\t', Optional headerRow As Boolean = true, Optional schema As Boolean = true, Optional keepHidden As Boolean = false, Optional forceDense As Boolean = false)

Параметры

catalog

DataOperationsCatalog

Каталог DataOperationsCatalog .

data

IDataView

Представление данных для сохранения.

stream

Stream

Поток, в который требуется выполнить запись.

separatorChar

Char

Разделитель столбцов.

headerRow

Boolean

Следует ли записывать строку заголовка.

schema

Boolean

Следует ли записывать комментарий заголовка со схемой.

keepHidden

Boolean

Следует ли хранить скрытые столбцы в наборе данных.

forceDense

Boolean

Следует ли сохранять столбцы в плотном формате, даже если они являются разреженными векторами.

Примеры

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

namespace Samples.Dynamic
{
    public static class SaveAndLoadFromText
    {
        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. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = new List<DataPoint>()
            {
                new DataPoint(){ Label = 0, Features = 4},
                new DataPoint(){ Label = 0, Features = 5},
                new DataPoint(){ Label = 0, Features = 6},
                new DataPoint(){ Label = 1, Features = 8},
                new DataPoint(){ Label = 1, Features = 9},
            };

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            IDataView data = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Create a FileStream object and write the IDataView to it as a text
            // file.
            using (FileStream stream = new FileStream("data.tsv", FileMode.Create))
                mlContext.Data.SaveAsText(data, stream);

            // Create an IDataView object by loading the text file.
            IDataView loadedData = mlContext.Data.LoadFromTextFile("data.tsv");

            // Inspect the data that is loaded from the previously saved text file.
            var loadedDataEnumerable = mlContext.Data
                .CreateEnumerable<DataPoint>(loadedData, reuseRowObject: false);

            foreach (DataPoint row in loadedDataEnumerable)
                Console.WriteLine($"{row.Label}, {row.Features}");

            // Preview of the loaded data.
            // 0, 4
            // 0, 5
            // 0, 6
            // 1, 8
            // 1, 9
        }

        // Example with label and feature values. A data set is a collection of such
        // examples.
        private class DataPoint
        {
            public float Label { get; set; }

            public float Features { get; set; }
        }
    }
}