Add validation scenario tests

test/Microsoft.ML.Functional.Tests/Validation.cs

@@ -3,11 +3,14 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.Data.DataView;
+using Microsoft.ML.Core.Data;
 using Microsoft.ML.Data;
+using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers.HalLearners;
 using Xunit;
+using static Microsoft.ML.RunTests.TestDataViewBase;
 
 namespace Microsoft.ML.Functional.Tests
 {
@@ -23,20 +26,20 @@ public class ValidationScenarios
         [Fact]
         void CrossValidation()
         {
-            var mlContext = new MLContext(seed: 789);
+            var mlContext = new MLContext(seed: 1, conc: 1);
 
-            // Get the dataset
+            // Get the dataset.
             var data = mlContext.Data.CreateTextLoader(TestDatasets.housing.GetLoaderColumns(), hasHeader: true)
                 .Read(BaseTestClass.GetDataPath(TestDatasets.housing.trainFilename));
 
-            // Create a pipeline to train on the sentiment data
+            // Create a pipeline to train on the sentiment data.
             var pipeline = mlContext.Transforms.Concatenate("Features", new string[] {
                     "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling",
                     "PercentPre40s", "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio"})
                 .Append(mlContext.Transforms.CopyColumns("Label", "MedianHomeValue"))
                 .Append(mlContext.Regression.Trainers.OrdinaryLeastSquares());
 
-            // Compute the CV result
+            // Compute the CV result.
             var cvResult = mlContext.Regression.CrossValidate(data, pipeline, numFolds: 5);
 
             // Check that the results are valid
@@ -45,9 +48,58 @@ void CrossValidation()
             Assert.True(cvResult[0].ScoredHoldOutSet is IDataView);
             Assert.Equal(5, cvResult.Length);
 
-            // And validate the metrics
+            // And validate the metrics.
             foreach (var result in cvResult)
                 Common.CheckMetrics(result.Metrics);
         }
+
+        /// <summary>
+        /// Train with validation set.
+        /// </summary>
+        [Fact]
+        public void TrainWithValidationSet()
+        {
+            var mlContext = new MLContext(seed: 1, conc: 1);
+
+            // Get the dataset.
+            var data = mlContext.Data.CreateTextLoader(TestDatasets.housing.GetLoaderColumns(), hasHeader: true)
+                .Read(BaseTestClass.GetDataPath(TestDatasets.housing.trainFilename));
+            var dataSplit = mlContext.Regression.TrainTestSplit(data, testFraction: 0.2);
+            var trainData = dataSplit.TrainSet;
+            var validData = dataSplit.TestSet;
+
+            // Create a pipeline to featurize the dataset.
+            var pipeline = mlContext.Transforms.Concatenate("Features", new string[] {
+                    "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling",
+                    "PercentPre40s", "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio"})
+                .Append(mlContext.Transforms.CopyColumns("Label", "MedianHomeValue"))
+                .AppendCacheCheckpoint(mlContext) as IEstimator<ITransformer>;
+
+            // Preprocess the datasets.
+            var preprocessor = pipeline.Fit(trainData);
+            var preprocessedTrainData = preprocessor.Transform(trainData);
+            var preprocessedValidData = preprocessor.Transform(validData);
+
+            // Train the model with a validation set.
+            var trainedModel = mlContext.Regression.Trainers.FastTree(new Trainers.FastTree.FastTreeRegressionTrainer.Options {
+                    NumTrees = 2,
+                    EarlyStoppingMetrics = 2,
+                    EarlyStoppingRule = new GLEarlyStoppingCriterion.Arguments()
+                })
+                .Train(trainData: preprocessedTrainData, validationData: preprocessedValidData);
+
+            // Combine the model.
+            var model = preprocessor.Append(trainedModel);
+
+            // Score the data sets.
+            var scoredTrainData = model.Transform(trainData);
+            var scoredValidData = model.Transform(validData);
+
+            var trainMetrics = mlContext.Regression.Evaluate(scoredTrainData);
+            var validMetrics = mlContext.Regression.Evaluate(scoredValidData);
+
+            Common.CheckMetrics(trainMetrics);
+            Common.CheckMetrics(validMetrics);
+        }
     }
 }