Migrate remaining inprogress samples to v1.0.0-preview (dotnet#360)

* Minor changes in ReadMe.md

Changed the scenarios wording from real time to cross cutting

* Casing changes in Readme File

* Migrating BikeSharingDemand sample to ML.Net v1.0.0-preview.
-added new solution file with v1.0.0
-Changed common  Directory.Build.props file to v1.0.0-preview

* Changed ReadMe file of BikeSharing Demand sample.
Added build.Props file in remaining samples to fix build errors

* Minor change: BikesharingDemand sample -Removed unnecessary nuget package

* Migrated Credit Card Fraud Detection Sample.

* Migrated sample Customer segmentation

* Commiting model file of migrated sample Customer Segmentation

* Migrated Heart Disease Console APP.

* WIP: Migrating IRIS Classification sample

* Migrated IRIS Clustering sample to v1.0.0-preview

* Migrated Movie Recommender Sample to v1.0.0-preview.

* Migrated Movie Recommender E2e Sample to v1.0.0-preview

* Migrated Timeseries -PowerAnomalyDetection sample to ML.Net v1.0.0-preview

* Migrated Product Recommendation sample to v1.0.0-preview

* Power Anomaly Detection sample: some minor changes and updated ReadMe file to ML.Net 1.0.0-preview

* Migrated Sentiment Anlysis Sample to ML.Net v1.0.0-preview.

Commented Previewing of features column as it displays a number of 0's which is difficult to read.

* Migrated ShampooSales Spike Detection sample to v1.0.0-preview

* Migrated TaxiFare Prediction sample to ML.net v1.0.0-preview

* Migrated TensorFlow scorer-Image Classification Sample

* Migrated Sales Forecast sample to v1.0.0-preview

* changes in common files.

* Changed the solution file name

* Removed the unnecessary solution files.

* Migrated MNIST sample to ML.Net 1.0.0-preview.

Changed the trainer name to sdcaMaximumEntropy.

* ReadMe file of MNIST sample.

* Updated version number in Readme file of MNIST sample.

* Migrated MultiClass Classification_Iris sample to ML.Net 1.0.0-preview

* Migrated Github labeler sample to Ml.Net 1.0.0-preview.

* updated solution file

* migrated Scalable WEBAPI sample to v1.0.0-preview

* Minor changes in GITHUB labeler sample

* Minor changes in Sentiment Anlaysis sample. Using the model path directly intead of filestream.

* WIP: migrating TensorFlow Estimator sample.

* For TesnorFlow Estiamtor sample, the model got updated. storing the updated model in Predict project.

* Minor change

* Migrated Spam Detection Sample to Ml.Net 1.0.0-preview

* Updated sln file.

* Refactored Bike Sharing sample code to directly pass the filepath instead of file stream

* Refactored CreditCard Fraus Detection sample to use filepath instead of filestream

* Minor changes in Customer Segmenation sample

* no need to use filestream

* Removed old build.props file.

* Heart Disease Sample: Replaced file stream with filepath

* Iris Clustering  Sample: Replaced file stream with filepath

* IRIS Classification sample: Replaced file stream with filepath

* Movei Recommender E2E sample: Refactored code to replace filestream with fielpath

* Minor changes

* Sales Forecast sample:
Refactored code to replace filestream with file path

* Migrated shampoosales spike detection sample to Ml.Net 1.0.0-preview

* Migrated ReadMe file for Shampoo sales E2E sample.

* ReadMe file of TensorFlow Estimator is migrated to 1.0.0-preview

* Refactored TaxiFare sample to replace filestream with filepath

* Refactored Powet Anomaly detection sample to repalce filestream with filepath

* Changed the training algorithm to use linear classification

* Refactored SpamDetection Sample and ReadMe file.

* Updated ReadMe of Github Labeler sample

* minor change

* Changed the name of sample in ReadME file

* Minor changes

* Minor changes

* updated build file.

Changed solution file.
Added new solution to include only cosnole proj to make the build successful

Author: prathyusha12345

.vsts-dotnet-ci.yml

@@ -142,4 +142,4 @@ phases:
   - task: DotNetCoreCLI@2
     displayName: Build Shampoo Sales spike and change Detection E2E(Time Series)
     inputs:
-      projects: '.\samples\csharp\csharp\end-to-end-apps\AnomalyDetection-ShampooSales-WinForms\AnomalyDetectionConsoleApp\AnomalyDetectionConsoleApp.csproj'
+      projects: '.\samples\csharp\end-to-end-apps\SpikeDetection-ShampooSales-WinForms\ShampooSalesSpikeDetectionConsole.sln'

README.md

@@ -120,7 +120,7 @@ The official ML.NET samples are divided in multiple categories depending on the
   <td>
       <h4>Spike Detection-Shampoo Sales &nbsp;&nbsp;&nbsp;
       <a href="samples/csharp/getting-started/SpikeDetection_ShampooSales">C#</a> &nbsp; &nbsp; <img src="images/app-type-getting-started.png" alt="Getting started icon">
-      <a href="samples/csharp/end-to-end-apps/AnomalyDetection-ShampooSales-WinForms">C#</a> &nbsp;&nbsp;&nbsp;<img src="images/app-type-e2e.png" alt="End-to-end app icon"></h4>      
+      <a href="samples/csharp/end-to-end-apps/SpikeDetection-ShampooSales-WinForms">C#</a> &nbsp;&nbsp;&nbsp;<img src="images/app-type-e2e.png" alt="End-to-end app icon"></h4>      
      <h4>Spike Detection-PowerMeter Readings &nbsp;&nbsp;&nbsp;
       <a href="samples/csharp/getting-started/TimeSeries_PowerAnomalyDetection">C#</a> &nbsp; &nbsp; <img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
   </td>

samples/csharp/common/ConsoleHelper.cs

@@ -90,7 +90,7 @@ public static void PrintRegressionFoldsAverageMetrics(string algorithmName, IRea
 
         public static void PrintMulticlassClassificationFoldsAverageMetrics(
                                          string algorithmName,
-                                        CrossValidationResult<MulticlassClassificationMetrics>[] crossValResults
+                                       IReadOnlyList<CrossValidationResult<MulticlassClassificationMetrics>> crossValResults
                                                                            )
         {
             var metricsInMultipleFolds = crossValResults.Select(r => r.Metrics);

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/Directory.Build.props

@@ -13,6 +13,10 @@
     <None Remove="datasets\**" />
   </ItemGroup>
 
+  <ItemGroup>
+    <Compile Include="..\..\..\..\common\ConsoleHelper.cs" Link="Common\ConsoleHelper.cs" />
+  </ItemGroup>
+
   <ItemGroup>
     <PackageReference Include="Microsoft.Extensions.Configuration" Version="2.1.1" />
     <PackageReference Include="Microsoft.Extensions.Configuration.Json" Version="2.1.1" />
@@ -27,6 +31,7 @@
   </ItemGroup>
 
   <ItemGroup>
+    <Folder Include="Common\" />
     <Folder Include="Data\" />
   </ItemGroup>
 

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/GitHubLabeler/GitHubLabelerConsoleApp/Common/ConsoleHelper.cs

@@ -33,14 +33,10 @@ public Labeler(string modelPath, string repoOwner = "", string repoName = "", st
             _mlContext = new MLContext();
 
             //Load model from file
-            
-            using (var stream = new FileStream(_modelPath, System.IO.FileMode.Open, FileAccess.Read, FileShare.Read))
-            {
-                _trainedModel = _mlContext.Model.Load(stream);
-            }
+            _trainedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
 
             // Create prediction engine related to the loaded trained model
-            _predEngine = _trainedModel.CreatePredictionEngine<GitHubIssue, GitHubIssuePrediction>(_mlContext);
+            _predEngine = _mlContext.Model.CreatePredictionEngine<GitHubIssue, GitHubIssuePrediction>(_trainedModel);
 
             //Configure Client to access a GitHub repo
             if (accessToken != string.Empty)

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/GitHubLabeler/GitHubLabelerConsoleApp/GitHubLabeler.csproj

@@ -34,7 +34,7 @@ private static async Task Main(string[] args)
             SetupAppConfiguration();
 
             //1. ChainedBuilderExtensions and Train the model
-            BuildAndTrainModel(DataSetLocation, ModelPath, MyTrainerStrategy.SdcaMultiClassTrainer);
+            BuildAndTrainModel(DataSetLocation, ModelPath, MyTrainerStrategy.OVAAveragedPerceptronTrainer);
 
             //2. Try/test to predict a label for a single hard-coded Issue
             TestSingleLabelPrediction(ModelPath);
@@ -56,10 +56,10 @@ public static void BuildAndTrainModel(string DataSetLocation, string ModelPath,
             var trainingDataView = mlContext.Data.LoadFromTextFile<GitHubIssue>(DataSetLocation, hasHeader: true, separatorChar:'\t', allowSparse: false);
 
             // STEP 2: Common data process configuration with pipeline data transformations
-            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: DefaultColumnNames.Label,inputColumnName:nameof(GitHubIssue.Area))
+            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: "Label",inputColumnName:nameof(GitHubIssue.Area))
                             .Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "TitleFeaturized",inputColumnName:nameof(GitHubIssue.Title)))
                             .Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "DescriptionFeaturized", inputColumnName: nameof(GitHubIssue.Description)))
-                            .Append(mlContext.Transforms.Concatenate(outputColumnName:DefaultColumnNames.Features, "TitleFeaturized", "DescriptionFeaturized"))
+                            .Append(mlContext.Transforms.Concatenate(outputColumnName:"Features", "TitleFeaturized", "DescriptionFeaturized"))
                             .AppendCacheCheckpoint(mlContext);  
                             // Use in-memory cache for small/medium datasets to lower training time. 
                             // Do NOT use it (remove .AppendCacheCheckpoint()) when handling very large datasets.
@@ -72,15 +72,12 @@ public static void BuildAndTrainModel(string DataSetLocation, string ModelPath,
             switch(selectedStrategy)
             {
                 case MyTrainerStrategy.SdcaMultiClassTrainer:                 
-                     trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label, 
-                                                                                                          DefaultColumnNames.Features);
+                     trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features");
                      break;
                 case MyTrainerStrategy.OVAAveragedPerceptronTrainer:
                 {
                     // Create a binary classification trainer.
-                    var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron(DefaultColumnNames.Label,
-                                                                                                                     DefaultColumnNames.Features,
-                                                                                                                     numIterations: 10);
+                    var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron("Label", "Features",numberOfIterations: 10);
                     // Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
                     // In this strategy, a binary classification algorithm is used to train one classifier for each class, "
                     // which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
@@ -95,7 +92,7 @@ public static void BuildAndTrainModel(string DataSetLocation, string ModelPath,
 
             //Set the trainer/algorithm and map label to value (original readable state)
             var trainingPipeline = dataProcessPipeline.Append(trainer)
-                    .Append(mlContext.Transforms.Conversion.MapKeyToValue(DefaultColumnNames.PredictedLabel));
+                    .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
 
             // STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
             // in order to evaluate and get the model's accuracy metrics
@@ -105,7 +102,7 @@ public static void BuildAndTrainModel(string DataSetLocation, string ModelPath,
             //Measure cross-validation time
             var watchCrossValTime = System.Diagnostics.Stopwatch.StartNew();
 
-            CrossValidationResult<MultiClassClassifierMetrics>[] crossValidationResults = mlContext.MulticlassClassification.CrossValidate(data:trainingDataView, estimator:trainingPipeline, numFolds: 6, labelColumn:DefaultColumnNames.Label);
+            var crossValidationResults= mlContext.MulticlassClassification.CrossValidate(data:trainingDataView, estimator:trainingPipeline, numberOfFolds: 6, labelColumnName:"Label");
 
             //Stop measuring time
             watchCrossValTime.Stop();
@@ -131,16 +128,15 @@ public static void BuildAndTrainModel(string DataSetLocation, string ModelPath,
             // (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
             GitHubIssue issue = new GitHubIssue() { ID = "Any-ID", Title = "WebSockets communication is slow in my machine", Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.." };
             // Create prediction engine related to the loaded trained model
-            var predEngine = trainedModel.CreatePredictionEngine<GitHubIssue, GitHubIssuePrediction>(mlContext);
+            var predEngine = mlContext.Model.CreatePredictionEngine<GitHubIssue, GitHubIssuePrediction>(trainedModel);
             //Score
             var prediction = predEngine.Predict(issue);
             Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
             //
 
             // STEP 6: Save/persist the trained model to a .ZIP file
             Console.WriteLine("=============== Saving the model to a file ===============");
-            using (var fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
-                mlContext.Model.Save(trainedModel, fs);
+            mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath);
 
             Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
         }

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/GitHubLabeler/GitHubLabelerConsoleApp/Labeler.cs

@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data sources | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v0.11           | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier |
+| v1.0.0-preview           | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
 
 
 This is a simple prototype application to demonstrate how to use [ML.NET](https://www.nuget.org/packages/Microsoft.ML/) APIs. The main focus is on creating, training, and using ML (Machine Learning) model that is implemented in Predictor.cs class.

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/GitHubLabeler/GitHubLabelerConsoleApp/Program.cs

@@ -44,12 +44,8 @@ public ActionResult Recommend(int id)
             // 1. Create the ML.NET environment and load the already trained model
             MLContext mlContext = new MLContext();
 
-            ITransformer trainedModel;
-            using (FileStream stream = new FileStream(_movieService.GetModelPath(), FileMode.Open, FileAccess.Read, FileShare.Read))
-            {
-                trainedModel = mlContext.Model.Load(stream, out var modelInputSchema);
-            }
-
+            ITransformer trainedModel = mlContext.Model.Load(_movieService.GetModelPath(), out var modelInputSchema);
+            
             //2. Create a prediction function
             var predictionEngine = mlContext.Model.CreatePredictionEngine<MovieRating, MovieRatingPrediction>(trainedModel);
 

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/GitHubLabeler/MLModels/GitHubLabelerModel.zip

@@ -5,16 +5,6 @@
     <LangVersion>latest</LangVersion>
   </PropertyGroup>
 
-  <ItemGroup>
-    <None Remove="Models\model.zip" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <Content Include="Models\model.zip">
-      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
-    </Content>
-  </ItemGroup>
-
   <ItemGroup>
     <PackageReference Include="CsvHelper" Version="12.1.1" />
     <PackageReference Include="Microsoft.AspNetCore.App" />
@@ -34,5 +24,11 @@
     </Content>
   </ItemGroup>
 
+  <ItemGroup>
+    <None Update="Models\model.zip">
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
 </Project>
 

samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md

@@ -86,12 +86,7 @@ static void Main(string[] args)
 
             //STEP 8:  Save model to disk
             Console.WriteLine("=============== Writing model to the disk ===============", color);
-            Console.WriteLine();
-
-            using (FileStream fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
-            {
-                mlContext.Model.Save(model, trainingDataView.Schema, fs);
-            }
+            Console.WriteLine();mlContext.Model.Save(model, trainingDataView.Schema, ModelPath);
 
             Console.WriteLine("=============== Re-Loading model from the disk ===============", color);
             Console.WriteLine();

samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/Controllers/MoviesController.cs

@@ -55,9 +55,8 @@ private static void CreateCountryModel(MLContext mlContext, string dataPath, str
 
             // Create and Train the model
             var model = trainingPipeline.Fit(trainingDataView);
-
-            using (var file = File.OpenWrite(outputModelPath))
-                mlContext.Model.Save(model, trainingDataView.Schema, file);
+            //Save model
+            mlContext.Model.Save(model, trainingDataView.Schema, outputModelPath);
         }
 
         /// <summary>

samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/Models/model.zip

@@ -56,8 +56,7 @@ private static void CreateProductModelUsingPipeline(MLContext mlContext, string
             var model = trainingPipeline.Fit(trainingDataView);
 
             // Save the model for later comsumption from end-user apps
-            using (var file = File.OpenWrite(outputModelPath))
-                mlContext.Model.Save(model, trainingDataView.Schema, file);
+            mlContext.Model.Save(model, trainingDataView.Schema, outputModelPath);
         }
 
         /// <summary>

samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj

@@ -5,7 +5,7 @@
 
 | ML.NET version | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v0.11           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
+| v1.0.0-preview           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
 
 
 **This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.

samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender_Model/Model/model.zip

@@ -1,10 +1,9 @@
 using Microsoft.ML;
 using Microsoft.Extensions.ObjectPool;
 using System.IO;
-using Microsoft.Data.DataView;
 
 namespace Scalable.Model.Engine
-{   
+{
     public class MLModelEngine<TData, TPrediction> 
                     where TData : class
                     where TPrediction : class, new()
@@ -33,7 +32,7 @@ public MLModelEngine(string modelFilePathName, int maxObjectsRetained = -1)
             //Load the ProductSalesForecast model from the .ZIP file
             using (var fileStream = File.OpenRead(modelFilePathName))
             {
-                _mlModel = _mlContext.Model.Load(fileStream);
+                _mlModel = _mlContext.Model.Load(fileStream, out var modelInputSchema);
             }
 
             _maxObjectsRetained = maxObjectsRetained;

samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender_Model/Program.cs

@@ -20,7 +20,7 @@ public PredictionEngine<TData, TPrediction> Create()
             // Measuring CreatePredictionengine() time
             var watch = System.Diagnostics.Stopwatch.StartNew();
 
-            var predictionEngine = _model.CreatePredictionEngine<TData, TPrediction>(_mlContext);
+            var predictionEngine = _mlContext.Model.CreatePredictionEngine<TData, TPrediction>(_model);
 
             watch.Stop();
             long elapsedMs = watch.ElapsedMilliseconds;

samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopForecastModelsTrainer/CountryModelHelper.cs

@@ -6,7 +6,7 @@
 
   <ItemGroup>
     <PackageReference Include="Microsoft.Extensions.ObjectPool" Version="2.2.0" />
-    <PackageReference Include="Microsoft.ML" Version="0.11.0" />
+    <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
   </ItemGroup>
 
   <ItemGroup>

samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopForecastModelsTrainer/ProductModelHelper.cs

@@ -6,9 +6,10 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML" Version="0.11.0" />
+    <PackageReference Include="Microsoft.ML" Version="1.0.0-preview" />
     <PackageReference Include="Microsoft.AspNetCore.App" />
     <PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
+    <PackageReference Include="Microsoft.ML.FastTree" Version="1.0.0-preview" />
     <PackageReference Include="Microsoft.VisualStudio.Web.CodeGeneration.Design" Version="2.2.0" />
   </ItemGroup>
 

samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/Directory.Build.props

@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v0.11         | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Shampoo Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.0.0-preview         | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Shampoo Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
 
 ![Alt Text](./ShampooSalesAnomalyDetection/images/shampoosales.gif)
 
@@ -77,7 +77,7 @@ IDataView dataView = mlcontext.Data.LoadFromTextFile<AnomalyExample>(path: fileP
 string outputColumnName = nameof(AnomalyPrediction.Prediction);
 string inputColumnName = nameof(AnomalyExample.numReported);
 
-var trainingPipeline = mlcontext.Transforms.IidSpikeEstimator(outputColumnName, inputColumnName, confidenceLevel, pValue);
+var trainingPipeLine = mlcontext.Transforms.DetectIidSpike(outputColumnName: nameof(ShampooSalesPrediction.Prediction), inputColumnName: nameof(ShampooSalesData.numSales),confidence: 95, pvalueHistoryLength: size / 4);
 ```
 
 ### 2. Train model
@@ -100,7 +100,7 @@ ITransformer trainedModel;
 // Load model
 using (FileStream stream = new FileStream(modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
 {
-    trainedModel = mlcontext.Model.Load(stream);
+    trainedModel = mlcontext.Model.Load(stream,out var modelInputSchema);
 }
 
 // Apply data transformation to create predictions