Merge pull request #831 from dotnet/master

Update live with master

Author: JRAlexander

machine-learning/tutorials/SentimentAnalysis/Program.cs

@@ -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>
@@ -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>
 
 
@@ -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>        
@@ -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>
@@ -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>
@@ -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[]
             {
@@ -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);
@@ -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();
@@ -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);
-        }
-
     }
 }

machine-learning/tutorials/SentimentAnalysis/SentimentAnalysis.csproj

@@ -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>

machine-learning/tutorials/SentimentAnalysis/SentimentData.cs

@@ -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>