Add samples for MLContext.Model (root) and add links to documentation. (#3352)

* add links to code samples in documentation.

* PR feedback.

Author: codemzs

docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModel.cs

@@ -0,0 +1,58 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.ML;
+
+namespace Samples.Dynamic.ModelOperations
+{
+    public class SaveLoadModel
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Generate sample data.
+            var data = new List<Data>()
+            {
+                new Data() { Value="abc" }
+            };
+
+            // Convert data to IDataView.
+            var dataView = mlContext.Data.LoadFromEnumerable(data);
+            var inputColumnName = nameof(Data.Value);
+            var outputColumnName = nameof(Transformation.Key);
+
+            // Transform.
+            ITransformer model = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName, inputColumnName).Fit(dataView);
+
+            // Save model.
+            mlContext.Model.Save(model, dataView.Schema, "model.zip");
+
+            // Load model.
+            using (var file = File.OpenRead("model.zip"))
+                model = mlContext.Model.Load(file, out DataViewSchema schema);
+
+            // Create a prediction engine from the model for feeding new data.
+            var engine = mlContext.Model.CreatePredictionEngine<Data, Transformation>(model);
+            var transformation = engine.Predict(new Data() { Value = "abc" });
+
+            // Print transformation to console.
+            Console.WriteLine("Value: {0}\t Key:{1}", transformation.Value, transformation.Key);
+            // Value: abc       Key:1
+
+        }
+
+        private class Data
+        {
+            public string Value { get; set; }
+        }
+
+        private class Transformation
+        {
+            public string Value { get; set; }
+            public uint Key { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModelFile.cs

@@ -0,0 +1,57 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.ML;
+
+namespace Samples.Dynamic.ModelOperations
+{
+    public class SaveLoadModelFile
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Generate sample data.
+            var data = new List<Data>()
+            {
+                new Data() { Value="abc" }
+            };
+
+            // Convert data to IDataView.
+            var dataView = mlContext.Data.LoadFromEnumerable(data);
+            var inputColumnName = nameof(Data.Value);
+            var outputColumnName = nameof(Transformation.Key);
+
+            // Transform.
+            ITransformer model = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName, inputColumnName).Fit(dataView);
+
+            // Save model.
+            mlContext.Model.Save(model, dataView.Schema, "model.zip");
+
+            // Load model.
+            model = mlContext.Model.Load("model.zip", out DataViewSchema schema);
+
+            // Create a prediction engine from the model for feeding new data.
+            var engine = mlContext.Model.CreatePredictionEngine<Data, Transformation>(model);
+            var transformation = engine.Predict(new Data() { Value = "abc" });
+
+            // Print transformation to console.
+            Console.WriteLine("Value: {0}\t Key:{1}", transformation.Value, transformation.Key);
+            // Value: abc       Key:1
+
+        }
+
+        private class Data
+        {
+            public string Value { get; set; }
+        }
+
+        private class Transformation
+        {
+            public string Value { get; set; }
+            public uint Key { get; set; }
+        }
+    }
+}

src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs

@@ -102,6 +102,13 @@ public void Save(ITransformer model, DataViewSchema inputSchema, Stream stream)
         /// be an empty <see cref="TransformerChain{TLastTransformer}"/>.</param>
         /// <param name="inputSchema">The schema of the input to the transformer. This can be <see langword="null"/>.</param>
         /// <param name="filePath">Path where model should be saved.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[Save](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModel.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public void Save(ITransformer model, DataViewSchema inputSchema, string filePath)
         {
             _env.CheckValueOrNull(model);
@@ -135,6 +142,13 @@ private void SaveInputSchema(DataViewSchema inputSchema, RepositoryWriter rep)
         /// <param name="inputSchema">Will contain the input schema for the model. If the model was saved without
         /// any description of the input, there will be no input schema. In this case this can be <see langword="null"/>.</param>
         /// <returns>The loaded model.</returns>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[Save](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModel.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public ITransformer Load(Stream stream, out DataViewSchema inputSchema)
         {
             _env.CheckValue(stream, nameof(stream));
@@ -187,6 +201,13 @@ public ITransformer Load(Stream stream, out DataViewSchema inputSchema)
         /// <param name="inputSchema">Will contain the input schema for the model. If the model was saved without
         /// any description of the input, there will be no input schema. In this case this can be <see langword="null"/>.</param>
         /// <returns>The loaded model.</returns>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[Save](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModelFile.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public ITransformer Load(string filePath, out DataViewSchema inputSchema)
         {
             _env.CheckNonEmpty(filePath, nameof(filePath));
@@ -281,6 +302,13 @@ public ITransformer LoadWithDataLoader(string filePath, out IDataLoader<IMultiSt
         /// <typeparamref name="TDst"/>.</param>
         /// <param name="inputSchemaDefinition">Additional settings of the input schema.</param>
         /// <param name="outputSchemaDefinition">Additional settings of the output schema.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[Save](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/ModelOperations/SaveLoadModel.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public PredictionEngine<TSrc, TDst> CreatePredictionEngine<TSrc, TDst>(ITransformer transformer,
             bool ignoreMissingColumns = true, SchemaDefinition inputSchemaDefinition = null, SchemaDefinition outputSchemaDefinition = null)
             where TSrc : class