Adding samples for data save and load from text and binary files

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromBinary.cs

@@ -0,0 +1,58 @@
+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; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromText.cs

@@ -0,0 +1,59 @@
+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; }
+        }
+    }
+}
+

src/Microsoft.ML.Data/DataLoadSave/Binary/BinaryLoaderSaverCatalog.cs

@@ -37,6 +37,13 @@ public static IDataView LoadFromBinary(this DataOperationsCatalog catalog, IMult
         /// </summary>
         /// <param name="catalog">The catalog.</param>
         /// <param name="path">The path to the file to load from.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[LoadFromBinary](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromBinary.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public static IDataView LoadFromBinary(this DataOperationsCatalog catalog, string path)
         {
             Contracts.CheckNonEmpty(path, nameof(path));
@@ -54,6 +61,13 @@ public static IDataView LoadFromBinary(this DataOperationsCatalog catalog, strin
         /// <param name="data">The data view to save.</param>
         /// <param name="stream">The stream to write to.</param>
         /// <param name="keepHidden">Whether to keep hidden columns in the dataset.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[SaveAsBinary](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromBinary.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public static void SaveAsBinary(this DataOperationsCatalog catalog, IDataView data, Stream stream,
             bool keepHidden = false)
         {

src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderSaverCatalog.cs

@@ -164,6 +164,13 @@ public static IDataView LoadFromTextFile<TInput>(this DataOperationsCatalog cata
         /// <param name="catalog">The <see cref="DataOperationsCatalog"/> catalog.</param>
         /// <param name="path">Specifies a file from which to load.</param>
         /// <param name="options">Defines the settings of the load operation.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[LoadFromTextFile](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromText.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public static IDataView LoadFromTextFile(this DataOperationsCatalog catalog, string path,
             TextLoader.Options options = null)
         {
@@ -186,6 +193,13 @@ public static IDataView LoadFromTextFile(this DataOperationsCatalog catalog, str
         /// <param name="schema">Whether to write the header comment with the schema.</param>
         /// <param name="keepHidden">Whether to keep hidden columns in the dataset.</param>
         /// <param name="forceDense">Whether to save columns in dense format even if they are sparse vectors.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[SaveAsText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SaveAndLoadFromText.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public static void SaveAsText(this DataOperationsCatalog catalog,
             IDataView data,
             Stream stream,