BinaryLoaderSaverCatalog.LoadFromBinary Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

Overloads

LoadFromBinary(DataOperationsCatalog, IMultiStreamSource)	Load a IDataView from an IMultiStreamSource on a binary file. Note that IDataView's are lazy, so no actual loading happens here, just schema validation.
LoadFromBinary(DataOperationsCatalog, String)	Load a IDataView from a binary file. Note that IDataView's are lazy, so no actual loading happens here, just schema validation.

LoadFromBinary(DataOperationsCatalog, IMultiStreamSource)

Load a IDataView from an IMultiStreamSource on a binary file. Note that IDataView's are lazy, so no actual loading happens here, just schema validation.

public static Microsoft.ML.IDataView LoadFromBinary (this Microsoft.ML.DataOperationsCatalog catalog, Microsoft.ML.Data.IMultiStreamSource fileSource);

static member LoadFromBinary : Microsoft.ML.DataOperationsCatalog * Microsoft.ML.Data.IMultiStreamSource -> Microsoft.ML.IDataView

<Extension()>
Public Function LoadFromBinary (catalog As DataOperationsCatalog, fileSource As IMultiStreamSource) As IDataView

Parameters

catalog

DataOperationsCatalog

The catalog.

fileSource

IMultiStreamSource

The file source to load from. This can be a MultiFileSource, for example.

Returns

IDataView

LoadFromBinary(DataOperationsCatalog, String)

Load a IDataView from a binary file. Note that IDataView's are lazy, so no actual loading happens here, just schema validation.

public static Microsoft.ML.IDataView LoadFromBinary (this Microsoft.ML.DataOperationsCatalog catalog, string path);

static member LoadFromBinary : Microsoft.ML.DataOperationsCatalog * string -> Microsoft.ML.IDataView

<Extension()>
Public Function LoadFromBinary (catalog As DataOperationsCatalog, path As String) As IDataView

Parameters

catalog

DataOperationsCatalog

The catalog.

path

String

The path to the file to load from.

Returns

IDataView

Examples

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

namespace Samples.Dynamic
{
    public static class SaveAndLoadFromBinary
    {
        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 binary
            // IDV file. 
            using (FileStream stream = new FileStream("data.idv", FileMode.Create))
                mlContext.Data.SaveAsBinary(data, stream);

            // Create an IDataView object by loading the binary IDV file.
            IDataView loadedData = mlContext.Data.LoadFromBinary("data.idv");

            // Inspect the data that is loaded from the previously saved binary 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; }
        }
    }
}