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Merged
merged 1 commit into from
Apr 30, 2019
Merged

Update live with current master #859

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2 changes: 1 addition & 1 deletion machine-learning/tutorials/GitHubIssueClassification/GitHubIssueClassification.csproj
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
</ItemGroup>

<ItemGroup>
<PackageReference Include="Microsoft.ML" Version="0.11.0" />
<PackageReference Include="Microsoft.ML" Version="1.0.0-preview" />
</ItemGroup>

<ItemGroup>
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30 changes: 11 additions & 19 deletions machine-learning/tutorials/GitHubIssueClassification/Program.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,7 @@
using System;
using System.IO;
using System.Linq;
using Microsoft.Data.DataView;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
// </SnippetAddUsings>

namespace GitHubIssueClassification
Expand Down Expand Up @@ -55,7 +52,7 @@ static void Main(string[] args)
// </SnippetCallBuildAndTrainModel>

// <SnippetCallEvaluate>
Evaluate();
Evaluate(_trainingDataView.Schema);
// </SnippetCallEvaluate>

// <SnippetCallPredictIssue>
Expand Down Expand Up @@ -95,7 +92,7 @@ public static IEstimator<ITransformer> BuildAndTrainModel(IDataView trainingData
// Use the multi-class SDCA algorithm to predict the label using features.
//Set the trainer/algorithm and map label to value (original readable state)
// <SnippetAddTrainer>
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label, DefaultColumnNames.Features))
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// </SnippetAddTrainer>

Expand All @@ -112,7 +109,7 @@ public static IEstimator<ITransformer> BuildAndTrainModel(IDataView trainingData

// Create prediction engine related to the loaded trained model
// <SnippetCreatePredictionEngine1>
_predEngine = _trainedModel.CreatePredictionEngine<GitHubIssue, IssuePrediction>(_mlContext);
_predEngine = _mlContext.Model.CreatePredictionEngine<GitHubIssue, IssuePrediction>(_trainedModel);
// </SnippetCreatePredictionEngine1>
// <SnippetCreateTestIssue1>
GitHubIssue issue = new GitHubIssue() {
Expand All @@ -135,7 +132,7 @@ public static IEstimator<ITransformer> BuildAndTrainModel(IDataView trainingData

}

public static void Evaluate()
public static void Evaluate(DataViewSchema trainingDataViewSchema)
{
// STEP 5: Evaluate the model in order to get the model's accuracy metrics
Console.WriteLine($"=============== Evaluating to get model's accuracy metrics - Starting time: {DateTime.Now.ToString()} ===============");
Expand All @@ -155,28 +152,24 @@ public static void Evaluate()
Console.WriteLine($"*************************************************************************************************************");
Console.WriteLine($"* Metrics for Multi-class Classification model - Test Data ");
Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
Console.WriteLine($"* MicroAccuracy: {testMetrics.AccuracyMicro:0.###}");
Console.WriteLine($"* MacroAccuracy: {testMetrics.AccuracyMacro:0.###}");
Console.WriteLine($"* MicroAccuracy: {testMetrics.MicroAccuracy:0.###}");
Console.WriteLine($"* MacroAccuracy: {testMetrics.MacroAccuracy:0.###}");
Console.WriteLine($"* LogLoss: {testMetrics.LogLoss:#.###}");
Console.WriteLine($"* LogLossReduction: {testMetrics.LogLossReduction:#.###}");
Console.WriteLine($"*************************************************************************************************************");
// </SnippetDisplayMetrics>

// Save the new model to .ZIP file
// <SnippetCallSaveModel>
SaveModelAsFile(_mlContext, _trainedModel);
SaveModelAsFile(_mlContext, trainingDataViewSchema, _trainedModel);
// </SnippetCallSaveModel>

}

public static void PredictIssue()
{
// <SnippetLoadModel>
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = _mlContext.Model.Load(stream);
}
ITransformer loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
// </SnippetLoadModel>

// <SnippetAddTestIssue>
Expand All @@ -185,7 +178,7 @@ public static void PredictIssue()

//Predict label for single hard-coded issue
// <SnippetCreatePredictionEngine>
_predEngine = loadedModel.CreatePredictionEngine<GitHubIssue, IssuePrediction>(_mlContext);
_predEngine = _mlContext.Model.CreatePredictionEngine<GitHubIssue, IssuePrediction>(loadedModel);
// </SnippetCreatePredictionEngine>

// <SnippetPredictIssue>
Expand All @@ -198,11 +191,10 @@ public static void PredictIssue()

}

private static void SaveModelAsFile(MLContext mlContext, ITransformer model)
private static void SaveModelAsFile(MLContext mlContext,DataViewSchema trainingDataViewSchema, ITransformer model)
{
// <SnippetSaveModel>
using (var fs = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(model, fs);
mlContext.Model.Save(model, trainingDataViewSchema, _modelPath);
// </SnippetSaveModel>

Console.WriteLine("The model is saved to {0}", _modelPath);
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