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Apr 18, 2019
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Update live with master #831

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95 changes: 34 additions & 61 deletions machine-learning/tutorials/SentimentAnalysis/Program.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.Data.DataView;
using Microsoft.ML;
using Microsoft.ML.Data;
using static Microsoft.ML.DataOperationsCatalog;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms.Text;
// </SnippetAddUsings>
Expand All @@ -22,14 +22,13 @@ class Program
static void Main(string[] args)
{
// Create ML.NET context/local environment - allows you to add steps in order to keep everything together
// during the learning process.
//Create ML Context with seed for repeatable/deterministic results
// as you discover the ML.NET trainers and transforms
// <SnippetCreateMLContext>
MLContext mlContext = new MLContext();
// </SnippetCreateMLContext>

// <SnippetCallLoadData>
TrainCatalogBase.TrainTestData splitDataView = LoadData(mlContext);
TrainTestData splitDataView = LoadData(mlContext);
// </SnippetCallLoadData>


Expand All @@ -45,26 +44,28 @@ static void Main(string[] args)
UseModelWithSingleItem(mlContext, model);
// </SnippetCallUseModelWithSingleItem>

// <SnippetCallUseLoadedModelWithBatchItems>
UseLoadedModelWithBatchItems(mlContext);
// </SnippetCallUseLoadedModelWithBatchItems>
// <SnippetCallUseModelWithBatchItems>
UseModelWithBatchItems(mlContext, model);
// </SnippetCallUseModelWithBatchItems>

Console.WriteLine();
Console.WriteLine("=============== End of process ===============");
}

public static TrainCatalogBase.TrainTestData LoadData(MLContext mlContext)
public static TrainTestData LoadData(MLContext mlContext)
{

//Note that this case, loading your training data from a file,
//is the easiest way to get started, but ML.NET also allows you
//to load data from databases or in-memory collections.
// Note that this case, loading your training data from a file,
// is the easiest way to get started, but ML.NET also allows you
// to load data from databases or in-memory collections.
// <SnippetLoadData>
IDataView dataView = mlContext.Data.LoadFromTextFile<SentimentData>(_dataPath,hasHeader:false);
IDataView dataView = mlContext.Data.LoadFromTextFile<SentimentData>(_dataPath, hasHeader: false);
// </SnippetLoadData>

// You need both a training dataset to train the model and a test dataset to evaluate the model.
// Split the loaded dataset into train and test datasets
// Specify test dataset percentage with the `testFraction`parameter
// <SnippetSplitData>
TrainCatalogBase.TrainTestData splitDataView = mlContext.BinaryClassification.TrainTestSplit(dataView, testFraction: 0.2);
TrainTestData splitDataView = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
// </SnippetSplitData>

// <SnippetReturnSplitData>
Expand All @@ -74,22 +75,21 @@ public static TrainCatalogBase.TrainTestData LoadData(MLContext mlContext)

public static ITransformer BuildAndTrainModel(MLContext mlContext, IDataView splitTrainSet)
{

// Create a flexible pipeline (composed by a chain of estimators) for creating/training the model.
// This is used to format and clean the data.
// Convert the text column to numeric vectors (Features column)
// <SnippetFeaturizeText>
var pipeline = mlContext.Transforms.Text.FeaturizeText(outputColumnName: DefaultColumnNames.Features, inputColumnName: nameof(SentimentData.SentimentText))
var estimator = mlContext.Transforms.Text.FeaturizeText(outputColumnName: "Features", inputColumnName: nameof(SentimentData.SentimentText))
//</SnippetFeaturizeText>
// Adds a FastTreeBinaryClassificationTrainer, the decision tree learner for this project
// append the machine learning task to the estimator
// <SnippetAddTrainer>
.Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 20));
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features"));
// </SnippetAddTrainer>

// Create and train the model based on the dataset that has been loaded, transformed.
// <SnippetTrainModel>
Console.WriteLine("=============== Create and Train the Model ===============");
var model = pipeline.Fit(splitTrainSet);
var model = estimator.Fit(splitTrainSet);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine();
// </SnippetTrainModel>
Expand All @@ -116,38 +116,32 @@ public static void Evaluate(MLContext mlContext, ITransformer model, IDataView s
CalibratedBinaryClassificationMetrics metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
// </SnippetEvaluate>

// The Accuracy metric gets the accuracy of a classifier, which is the proportion
// The Accuracy metric gets the accuracy of a model, which is the proportion
// of correct predictions in the test set.

// The Auc metric gets the area under the ROC curve.
// The area under the ROC curve is equal to the probability that the classifier ranks
// a randomly chosen positive instance higher than a randomly chosen negative one
// (assuming 'positive' ranks higher than 'negative').
// The AreaUnderRocCurve metric is an indicator of how confident the model is
// correctly classifying the positive and negative classes as such.

// The F1Score metric gets the classifier's F1 score.
// The F1 score is the harmonic mean of precision and recall:
// The F1Score metric gets the model's F1 score.
// F1 is a measure of tradeoff between precision and recall.
// 2 * precision * recall / (precision + recall).

// <SnippetDisplayMetrics>
Console.WriteLine();
Console.WriteLine("Model quality metrics evaluation");
Console.WriteLine("--------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.Auc:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine($" Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Area Under Roc Curve: {metrics.AreaUnderRocCurve:P2}");
Console.WriteLine($" F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of model evaluation ===============");
//</SnippetDisplayMetrics>

// Save the new model to .ZIP file
// <SnippetCallSaveModel>
SaveModelAsFile(mlContext, model);
// </SnippetCallSaveModel>
}

private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
// <SnippetCreatePredictionEngine1>
PredictionEngine<SentimentData, SentimentPrediction> predictionFunction = model.CreatePredictionEngine<SentimentData, SentimentPrediction>(mlContext);
PredictionEngine<SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
// </SnippetCreatePredictionEngine1>

// <SnippetCreateTestIssue1>
Expand All @@ -172,9 +166,9 @@ private static void UseModelWithSingleItem(MLContext mlContext, ITransformer mod
// </SnippetOutputPrediction>
}

public static void UseLoadedModelWithBatchItems(MLContext mlContext)
public static void UseModelWithBatchItems(MLContext mlContext, ITransformer model)
{
// Adds some comments to test the trained model's predictions.
// Adds some comments to test the trained model's data points.
// <SnippetCreateTestIssues>
IEnumerable<SentimentData> sentiments = new[]
{
Expand All @@ -189,19 +183,11 @@ public static void UseLoadedModelWithBatchItems(MLContext mlContext)
};
// </SnippetCreateTestIssues>

// <SnippetLoadModel>
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = mlContext.Model.Load(stream);
}
// </SnippetLoadModel>

// Load test data
// Load batch comments just created
// <SnippetPrediction>
IDataView sentimentStreamingDataView = mlContext.Data.LoadFromEnumerable(sentiments);
IDataView batchComments = mlContext.Data.LoadFromEnumerable(sentiments);

IDataView predictions = loadedModel.Transform(sentimentStreamingDataView);
IDataView predictions = model.Transform(batchComments);

// Use model to predict whether comment data is Positive (1) or Negative (0).
IEnumerable<SentimentPrediction> predictedResults = mlContext.Data.CreateEnumerable<SentimentPrediction>(predictions, reuseRowObject: false);
Expand All @@ -210,7 +196,7 @@ public static void UseLoadedModelWithBatchItems(MLContext mlContext)
// <SnippetAddInfoMessage>
Console.WriteLine();

Console.WriteLine("=============== Prediction Test of loaded model with a multiple samples ===============");
Console.WriteLine("=============== Prediction Test of loaded model with multiple samples ===============");
// </SnippetAddInfoMessage>

Console.WriteLine();
Expand All @@ -227,21 +213,8 @@ public static void UseLoadedModelWithBatchItems(MLContext mlContext)

}
Console.WriteLine("=============== End of predictions ===============");

// </SnippetDisplayResults>
}

// Saves the model we trained to a zip file.

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

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

}
}
2 changes: 1 addition & 1 deletion machine-learning/tutorials/SentimentAnalysis/SentimentAnalysis.csproj
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Expand Up @@ -10,7 +10,7 @@
</PropertyGroup>

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

<ItemGroup>
Expand Down
2 changes: 0 additions & 2 deletions machine-learning/tutorials/SentimentAnalysis/SentimentData.cs
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Expand Up @@ -19,10 +19,8 @@ public class SentimentPrediction
[ColumnName("PredictedLabel")]
public bool Prediction { get; set; }

// [ColumnName("Probability")]
public float Probability { get; set; }

// [ColumnName("Score")]
public float Score { get; set; }
}
// </SnippetDeclareTypes>
Expand Down