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CESARDELATORRECESARDELATORRE
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Merge pull request #184 from CESARDELATORRE/features/objectpooling
Added object pooling for PredictionFunction/PredictionEngine to eShopDashboardML sample
2 parents db4f723 + 651dee7 commit 2674426

18 files changed

+399
-148
lines changed

‎samples/csharp/common/MLModelEngine.cs

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using Microsoft.ML.Core.Data;
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using Microsoft.ML.Runtime.Data;
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using System.IO;
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using Microsoft.ML;
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//using Microsoft.Extensions.Configuration;
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namespace Common
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{
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public class MLModelEngine<TData, TPrediction>
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where TData : class
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where TPrediction : class, new()
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{
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private readonly MLContext _mlContext;
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private readonly ITransformer _model;
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private readonly ObjectPool<PredictionFunction<TData, TPrediction>> _predictionEnginePool;
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private readonly int _minPredictionEngineObjectsInPool;
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private readonly int _maxPredictionEngineObjectsInPool;
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public int CurrentPredictionEnginePoolSize
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{
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get { return _predictionEnginePool.CurrentPoolSize; }
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}
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//Constructor with modelFilePathName to load
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public MLModelEngine(MLContext mlContext, string modelFilePathName, int minPredictionEngineObjectsInPool = 5, int maxPredictionEngineObjectsInPool = 1000)
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{
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_mlContext = mlContext;
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//Load the ProductSalesForecast model from the .ZIP file
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using (var fileStream = File.OpenRead(modelFilePathName))
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{
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_model = mlContext.Model.Load(fileStream);
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}
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_minPredictionEngineObjectsInPool = minPredictionEngineObjectsInPool;
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_maxPredictionEngineObjectsInPool = maxPredictionEngineObjectsInPool;
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//Create PredictionEngine Object Pool
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_predictionEnginePool = CreatePredictionEngineObjectPool();
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}
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//Constructor with ITransformer model already created
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public MLModelEngine(MLContext mlContext, ITransformer model, int minPredictionEngineObjectsInPool = 5, int maxPredictionEngineObjectsInPool = 1000)
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{
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_mlContext = mlContext;
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_model = model;
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_minPredictionEngineObjectsInPool = minPredictionEngineObjectsInPool;
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_maxPredictionEngineObjectsInPool = maxPredictionEngineObjectsInPool;
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//Create PredictionEngine Object Pool
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_predictionEnginePool = CreatePredictionEngineObjectPool();
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}
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private ObjectPool<PredictionFunction<TData, TPrediction>> CreatePredictionEngineObjectPool()
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{
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return new ObjectPool<PredictionFunction<TData, TPrediction>>(() => _model.MakePredictionFunction<TData, TPrediction>(_mlContext),
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_minPredictionEngineObjectsInPool,
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_maxPredictionEngineObjectsInPool);
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}
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public TPrediction Predict(TData dataSample)
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{
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//Get PredictionEngine object from the Object Pool
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PredictionFunction<TData, TPrediction> predictionEngine = _predictionEnginePool.GetObject();
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//Predict
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TPrediction prediction = predictionEngine.Predict(dataSample);
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//Release used PredictionEngine object into the Object Pool
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_predictionEnginePool.PutObject(predictionEngine);
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return prediction;
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}
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}
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}

‎samples/csharp/common/ObjectPool.cs

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using System;
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using System.Collections.Concurrent;
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using System.Collections.Generic;
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using System.Linq;
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using System.Threading.Tasks;
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namespace Common
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{
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public class ObjectPool<T>
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{
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private ConcurrentBag<T> _objects;
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private Func<T> _objectGenerator;
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private int _maxPoolSize;
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public int CurrentPoolSize
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{
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get { return _objects.Count; }
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}
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public ObjectPool(Func<T> objectGenerator, int minPoolSize = 5, int maxPoolSize = 50000)
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{
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if (objectGenerator == null) throw new ArgumentNullException("objectGenerator");
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_objects = new ConcurrentBag<T>();
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_objectGenerator = objectGenerator;
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_maxPoolSize = maxPoolSize;
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//Measure total time of minimum objects creation
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var watch = System.Diagnostics.Stopwatch.StartNew();
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//Create minimum number of objects in pool
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for (int i = 0; i < minPoolSize; i++)
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{
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_objects.Add(_objectGenerator());
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}
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//Stop measuring time
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watch.Stop();
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long elapsedMs = watch.ElapsedMilliseconds;
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}
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public T GetObject()
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{
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T item;
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if (_objects.TryTake(out item))
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{
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return item;
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}
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else
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{
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if(_objects.Count <= _maxPoolSize)
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return _objectGenerator();
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else
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throw new InvalidOperationException("MaxPoolSize reached");
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}
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}
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public void PutObject(T item)
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{
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_objects.Add(item);
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}
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/eShopDashboardML.sln

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.editorconfig = .editorconfig
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EndProjectSection
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TestObjectPoolingConsoleApp", "src\TestObjectPoolingConsoleApp\TestObjectPoolingConsoleApp.csproj", "{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
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{F5DC33CF-35B3-45DD-A4A2-977DEA38060A}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{F5DC33CF-35B3-45DD-A4A2-977DEA38060A}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{F5DC33CF-35B3-45DD-A4A2-977DEA38060A}.Release|Any CPU.Build.0 = Release|Any CPU
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{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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{29DB8569-F5D6-4190-9DF4-8D18CA0AABA8} = {F395612F-24C7-4666-90B2-62E417033B4B}
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{5AB1C510-FEF6-4930-AE05-D16AF802084D} = {F395612F-24C7-4666-90B2-62E417033B4B}
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{F5DC33CF-35B3-45DD-A4A2-977DEA38060A} = {B3AF01E5-D172-47F9-991E-A85504958F43}
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{CF3DE8C7-81D6-4B2B-A2F0-82D15701F10A} = {F395612F-24C7-4666-90B2-62E417033B4B}
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {1E47A71B-4F99-48EA-9267-DEE93B23BA31}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/DataStructures/CountryData.cs

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namespace TestObjectPoolingConsoleApp.DataStructures
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{
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/// <summary>
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/// This is the input to the trained model.
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/// </summary>
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public class CountryData
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{
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// next,country,year,month,max,min,std,count,sales,med,prev
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public CountryData(string country, int year, int month, float max, float min, float std, int count, float sales, float med, float prev)
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{
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this.country = country;
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this.year = year;
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this.month = month;
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this.max = max;
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this.min = min;
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this.std = std;
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this.count = count;
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this.sales = sales;
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this.med = med;
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this.prev = prev;
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}
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public float next;
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public string country;
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public float year;
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public float month;
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public float max;
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public float min;
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public float std;
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public float count;
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public float sales;
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public float med;
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public float prev;
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/DataStructures/CountrySalesPrediction.cs

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namespace TestObjectPoolingConsoleApp.DataStructures
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{
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/// <summary>
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/// This is the output of the scored model, the prediction.
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/// </summary>
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public class CountrySalesPrediction
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{
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// Below columns are produced by the model's predictor.
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public float Score;
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/DataStructures/ProductData.cs

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namespace TestObjectPoolingConsoleApp.DataStructures
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{
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/// <summary>
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/// This is the input to the trained model.
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/// </summary>
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public class ProductData
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{
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// next,productId,year,month,units,avg,count,max,min,prev
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public ProductData(string productId, int year, int month, float units, float avg,
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int count, float max, float min, float prev)
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{
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this.productId = productId;
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this.year = year;
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this.month = month;
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this.units = units;
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this.avg = avg;
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this.count = count;
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this.max = max;
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this.min = min;
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this.prev = prev;
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}
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public float next;
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public string productId;
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public float year;
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public float month;
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public float units;
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public float avg;
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public float count;
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public float max;
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public float min;
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public float prev;
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/DataStructures/ProductUnitPrediction.cs

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namespace TestObjectPoolingConsoleApp.DataStructures
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{
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/// <summary>
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/// This is the output of the scored model, the prediction.
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/// </summary>
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public class ProductUnitPrediction
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{
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// Below columns are produced by the model's predictor.
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public float Score;
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/ModelFiles/country_month_fastTreeTweedie.zip

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‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/ModelFiles/product_month_fastTreeTweedie.zip

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‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/Program.cs

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using Common;
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using Microsoft.ML;
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using System;
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using System.Threading;
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using System.Threading.Tasks;
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using TestObjectPoolingConsoleApp.DataStructures;
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namespace TestObjectPoolingConsoleApp
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{
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class Program
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{
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static void Main(string[] args)
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{
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CancellationTokenSource cts = new CancellationTokenSource();
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// Create an opportunity for the user to cancel.
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Task.Run(() =>
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{
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if (Console.ReadKey().KeyChar == 'c' || Console.ReadKey().KeyChar == 'C')
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cts.Cancel();
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});
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MLContext mlContext = new MLContext(seed:1);
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string modelFolder = $"Forecast/ModelFiles";
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string modelFilePathName = $"ModelFiles/country_month_fastTreeTweedie.zip";
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var countrySalesModel = new MLModelEngine<CountryData, CountrySalesPrediction>(mlContext,
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modelFilePathName,
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minPredictionEngineObjectsInPool: 2);
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Console.WriteLine("Current number of objects in pool: {0:####.####}", countrySalesModel.CurrentPredictionEnginePoolSize);
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//Single Prediction
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var singleCountrySample = new CountryData("Australia", 2017, 1, 477, 164, 2486, 9, 10345, 281, 1029);
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var singleNextMonthPrediction = countrySalesModel.Predict(singleCountrySample);
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Console.WriteLine("Prediction: {0:####.####}", singleNextMonthPrediction.Score);
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// Create a high demand for the modelEngine objects.
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Parallel.For(0, 1000000, (i, loopState) =>
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{
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//Sample country data
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//next,country,year,month,max,min,std,count,sales,med,prev
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//4.23056080166201,Australia,2017,1,477.34,164.916,2486.1346772137,9,10345.71,281.7,1029.11
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var countrySample = new CountryData("Australia", 2017, 1, 477, 164, 2486, 9, 10345, 281, i);
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// This is the bottleneck in our application. All threads in this loop
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// must serialize their access to the static Console class.
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Console.CursorLeft = 0;
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var nextMonthPrediction = countrySalesModel.Predict(countrySample);
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Console.WriteLine("Prediction: {0:####.####}", nextMonthPrediction.Score);
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Console.WriteLine("-----------------------------------------");
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Console.WriteLine("Current number of objects in pool: {0:####.####}", countrySalesModel.CurrentPredictionEnginePoolSize);
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if (cts.Token.IsCancellationRequested)
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loopState.Stop();
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});
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Console.WriteLine("-----------------------------------------");
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Console.WriteLine("Current number of objects in pool: {0:####.####}", countrySalesModel.CurrentPredictionEnginePoolSize);
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Console.WriteLine("Press the Enter key to exit.");
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Console.ReadLine();
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cts.Dispose();
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}
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}
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}

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/TestObjectPoolingConsoleApp/TestObjectPoolingConsoleApp.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.1</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="..\..\..\..\common\MLModelEngine.cs" Link="Common\MLModelEngine.cs" />
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<Compile Include="..\..\..\..\common\ObjectPool.cs" Link="Common\ObjectPool.cs" />
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</ItemGroup>
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<ItemGroup>
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<Folder Include="Common\" />
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
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</ItemGroup>
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<ItemGroup>
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<None Update="ModelFiles\country_month_fastTreeTweedie.zip">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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<None Update="ModelFiles\product_month_fastTreeTweedie.zip">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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</Project>

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