feat(SentimentAnalyzer): Improve performance

Cognitive-Library/CognitiveLibrary.sln

@@ -1,12 +1,14 @@
 
 Microsoft Visual Studio Solution File, Format Version 12.00
-# Visual Studio 15
-VisualStudioVersion = 15.0.28307.329
+# Visual Studio Version 16
+VisualStudioVersion = 16.0.30114.105
 MinimumVisualStudioVersion = 10.0.40219.1
 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CustomVision", "CustomVision\CustomVision.csproj", "{5197E68A-CC01-4048-AD8A-EBF806507633}"
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SentimentAnalyzer", "SentimentAnalyzer\SentimentAnalyzer.csproj", "{7F54E5D3-408B-49F2-AFDF-3CC2FDD8C5B6}"
 EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "SentimentAnalyzer.Tests", "SentimentAnalyzer.Tests\SentimentAnalyzer.Tests.csproj", "{4BB7CCDE-C145-4342-BD23-31F8E1955499}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -21,6 +23,10 @@ Global
 		{7F54E5D3-408B-49F2-AFDF-3CC2FDD8C5B6}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{7F54E5D3-408B-49F2-AFDF-3CC2FDD8C5B6}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{7F54E5D3-408B-49F2-AFDF-3CC2FDD8C5B6}.Release|Any CPU.Build.0 = Release|Any CPU
+		{4BB7CCDE-C145-4342-BD23-31F8E1955499}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{4BB7CCDE-C145-4342-BD23-31F8E1955499}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{4BB7CCDE-C145-4342-BD23-31F8E1955499}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{4BB7CCDE-C145-4342-BD23-31F8E1955499}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

Cognitive-Library/SentimentAnalyzer.Tests/Benchmarks.cs

@@ -0,0 +1,57 @@
+using System;
+using System.Diagnostics;
+using System.Linq;
+using System.Reflection;
+using System.Text.Json;
+using System.Threading.Tasks;
+using Xunit;
+using Xunit.Abstractions;
+
+namespace SentimentAnalyzer.Tests
+{
+    public class Benchmarks
+    {
+        private readonly ITestOutputHelper output;
+
+        public Benchmarks(ITestOutputHelper output)
+        {
+            this.output = output;
+        }
+
+        /// <summary>
+        /// Use IMDB dataset of 50k reviews to benchmark analysis.
+        /// See: https://github.com/nas5w/imdb-data
+        /// </summary>
+        /// <returns></returns>
+        [Fact]
+        public async Task Loop_Of_50000_IMDB_Reviews()
+        {
+            var assembly = Assembly.GetExecutingAssembly();
+            var stream = assembly.GetManifestResourceStream("SentimentAnalyzer.Tests.reviews.json");
+
+            Assert.NotNull(stream);
+
+            var reviews = await JsonSerializer.DeserializeAsync<Review[]>(stream);
+            Assert.Equal(50000, reviews.Length);
+
+            var stopWatch = new Stopwatch();
+            stopWatch.Start();
+
+            for(var i = 0; i < reviews.Length; i++)
+            {
+                Sentiments.Predict(reviews[i].t);
+            }
+
+            stopWatch.Stop();
+
+            Assert.True(stopWatch.ElapsedMilliseconds > 0);
+            output.WriteLine($"Loop duration: {stopWatch.ElapsedMilliseconds}ms");
+        }
+    }
+
+    public class Review
+    {
+        public string t { get; set; }
+        public int s { get; set; }
+    }
+}

Cognitive-Library/SentimentAnalyzer.Tests/SentimentAnalyzer.Tests.csproj

@@ -0,0 +1,29 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <TargetFramework>netcoreapp3.1</TargetFramework>
+
+    <IsPackable>false</IsPackable>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <None Remove="reviews.json" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <EmbeddedResource Include="reviews.json" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.5.0" />
+    <PackageReference Include="System.Text.Json" Version="5.0.1" />
+    <PackageReference Include="xunit" Version="2.4.0" />
+    <PackageReference Include="xunit.runner.visualstudio" Version="2.4.0" />
+    <PackageReference Include="coverlet.collector" Version="1.2.0" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\SentimentAnalyzer\SentimentAnalyzer.csproj" />
+  </ItemGroup>
+
+</Project>

Cognitive-Library/SentimentAnalyzer/README.md

@@ -1,4 +1,18 @@
-# SentimentAnalyzer - On device (offline) Sentiment Analysis for .NET Standard apps
+# SentimentAnalyzer - On-device (offline) Sentiment Analysis for .NET Standard apps
 
- This is the library which can be used to consume the model which I created using ML.NET. For further inforamtion, you can always have a look at my [blog](https://www.arafattehsin.com/blog/sentimentanalyzer-ondevice-machine-learning/)
+ This is a library which can be used to consume a model which I created using ML.NET. 
 
+ For further information, you can always have a look at my [blog](https://www.arafattehsin.com/blog/sentimentanalyzer-ondevice-machine-learning/)
+
+## Quick Start
+
+```c#
+using SentimentAnalyzer;
+
+var sentiment = Sentiments.Predict("some string");
+```
+
+`Predict` returns a `SentimentPrediction` which contains:
+
+- `Prediction` (`bool`) - `true` is Positive sentiment, `false` is Negative sentiment.
+- `Score` (`float`) - A score representing the model's accuracy

Cognitive-Library/SentimentAnalyzer/SentimentAnalyzer.csproj

@@ -25,7 +25,7 @@
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML" Version="1.5.4" />
+    <PackageReference Include="Microsoft.ML" Version="1.5.5" />
   </ItemGroup>
 
 </Project>

Cognitive-Library/SentimentAnalyzer/Sentiments.cs

@@ -9,26 +9,47 @@
 
 namespace SentimentAnalyzer
 {
-    public class Sentiments
+    /// <summary>
+    /// Thread-safe singleton to manage initialization of ML training model
+    /// to improve runtime performance.
+    /// </summary>
+    public sealed class Sentiments
     {
-        public static SentimentPrediction Predict(string text)
+        private static readonly Sentiments instance = new Sentiments();
+        private readonly PredictionEngine<Sentiment, SentimentPrediction> predictionEngine;
+
+        /// <summary>
+        /// Explicit static constructor to tell C# compiler
+        /// not to mark type as beforefieldinit
+        /// </summary>
+        static Sentiments()
         {
-            //Create MLContext to be shared across the model creation workflow objects 
-            //Set a random seed for repeatable/deterministic results across multiple trainings.
-            var assembly = Assembly.GetExecutingAssembly();
-            var stream = assembly.GetManifestResourceStream("SentimentAnalyzer.MLModels.SentimentModel.zip");
+        }
 
-            var mlContext = new MLContext(seed: 1);
-            Sentiment statement = new Sentiment { Col0 = text };
-            ITransformer trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
+        private Sentiments()
+        {
+            var assembly = Assembly.GetExecutingAssembly();
+            using (var stream = assembly.GetManifestResourceStream("SentimentAnalyzer.MLModels.SentimentModel.zip"))
+            {
+                var mlContext = new MLContext();
+                ITransformer trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
 
-            // Create prediction engine related to the loaded trained model
-            var predEngine = mlContext.Model.CreatePredictionEngine<Sentiment, SentimentPrediction>(trainedModel);
+                // Create prediction engine related to the loaded trained model
+                predictionEngine = mlContext.Model.CreatePredictionEngine<Sentiment, SentimentPrediction>(trainedModel);
 
-            stream.Close();
+                stream.Close();
+            }
+        }
 
-            //Score
-            return predEngine.Predict(statement);
+        /// <summary>
+        /// Perform a single prediction of the given text
+        /// </summary>
+        /// <param name="text"></param>
+        /// <returns></returns>
+        public static SentimentPrediction Predict(string text)
+        {
+            var statement = new Sentiment { Col0 = text };
+            return instance.predictionEngine.Predict(statement);
         }
 
     }