TextLoaderSaverCatalog.SaveAsText Metoda

Definicja

Przestrzeń nazw:

Microsoft.ML

Zestaw:

Microsoft.ML.Data.dll

Pakiet:

Microsoft.ML v2.0.0

Zapisz jako IDataView tekst.

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

Wykaz DataOperationsCatalog .

data

IDataView

Widok danych do zapisania.

stream

Stream

Strumień do zapisu.

separatorChar

Char

Separator kolumny.

headerRow

Boolean

Czy zapisać wiersz nagłówka.

schema

Boolean

Czy napisać komentarz nagłówka przy użyciu schematu.

keepHidden

Boolean

Czy zachować ukryte kolumny w zestawie danych.

forceDense

Boolean

Czy zapisać kolumny w formacie gęstym, nawet jeśli są rozrzedmiony.

Przykłady

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; }
        }
    }
}