TextLoaderSaverCatalog.SaveAsText Metoda

Definice

Obor názvů:

Microsoft.ML

Sestavení:

Microsoft.ML.Data.dll

Balíček:

Microsoft.ML v3.0.1

Uložte text IDataView jako text.

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)

Parametry

catalog

DataOperationsCatalog

Katalog DataOperationsCatalog .

data

IDataView

Zobrazení dat, které chcete uložit.

stream

Stream

Datový proud, do který se má zapisovat.

separatorChar

Char

Oddělovač sloupců.

headerRow

Boolean

Určuje, jestli chcete napsat řádek záhlaví.

schema

Boolean

Zda se má napsat komentář záhlaví se schématem.

keepHidden

Boolean

Jestli chcete zachovat skryté sloupce v datové sadě.

forceDense

Boolean

Zda se mají sloupce ukládat v zhuštěných formátech, i když jsou řídké vektory.

Příklady

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; }
        }
    }
}