Add another example with options

Author: wschin

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs

@@ -44,7 +44,7 @@ public static void Example()
                 new DataPoint(){ Features= new float[3] {1, 2, 3} },
                 new DataPoint(){ Features= new float[3] {0, 1, 0} },
                 new DataPoint(){ Features= new float[3] {0, 2, 1} },
-                new DataPoint(){ Features= new float[3] {-100, -50, -100} }
+                new DataPoint(){ Features= new float[3] {-100, 50, -100} }
             };
 
             // Convert native C# class to IDataView, a consumble format to ML.NET functions.

src/Microsoft.ML.PCA/PCACatalog.cs

@@ -47,6 +47,12 @@ public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(t
         /// <param name="oversampling">Oversampling parameter for randomized PCA training.</param>
         /// <param name="center">If enabled, data is centered to be zero mean.</param>
         /// <param name="seed">The seed for random number generation.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[RPCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs)]
+        /// ]]></format>
+        /// </example>
         public static RandomizedPcaTrainer RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog,
             string featureColumnName = DefaultColumnNames.Features,
             string exampleWeightColumnName = null,
@@ -65,6 +71,12 @@ public static RandomizedPcaTrainer RandomizedPca(this AnomalyDetectionCatalog.An
         /// </summary>
         /// <param name="catalog">The anomaly detection catalog trainer object.</param>
         /// <param name="options">Advanced options to the algorithm.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[RPCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs)]
+        /// ]]></format>
+        /// </example>
         public static RandomizedPcaTrainer RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog, Options options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));

test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

@@ -50,8 +50,37 @@ public void NoAnomalyTest()
             Assert.Throws<ArgumentOutOfRangeException>(() => ML.AnomalyDetection.Evaluate(transformedData));
         }
 
+        [Fact]
+        public static void RandomizedPcaInMemory()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create an anomaly detector. Its underlying algorithm is randomized PCA.
+            var trainer1 = mlContext.AnomalyDetection.Trainers.RandomizedPca(featureColumnName: nameof(DataPoint.Features), rank: 1, center: false);
+
+            // Test the first detector.
+            ExecutePipelineWithGivenRandomizedPcaTrainer(mlContext, trainer1);
+
+            // Object required in the creation of another detector.
+            var options = new Trainers.RandomizedPcaTrainer.Options()
+            {
+                FeatureColumn = nameof(DataPoint.Features),
+                Rank = 1,
+                Center = false
+            };
+
+            // Create anther anomaly detector. Its underlying algorithm is randomized PCA.
+            var trainer2 = mlContext.AnomalyDetection.Trainers.RandomizedPca(options);
+
+            // Test the second detector.
+            ExecutePipelineWithGivenRandomizedPcaTrainer(mlContext, trainer2);
+        }
+
         /// <summary>
-        /// Example with 3 feature values.
+        /// Example with 3 feature values used in <see cref="ExecutePipelineWithGivenRandomizedPcaTrainer"/>.
         /// </summary>
         private class DataPoint
         {
@@ -60,7 +89,7 @@ private class DataPoint
         }
 
         /// <summary>
-        /// Class used to capture prediction of <see cref="DataPoint"/> in <see cref="RandomizedPcaInMemory"/>.
+        /// Class used to capture prediction of <see cref="DataPoint"/> in <see cref="ExecutePipelineWithGivenRandomizedPcaTrainer"/>.
         /// </summary>
 #pragma warning disable 649
         private class Result
@@ -72,32 +101,26 @@ private class Result
         }
 #pragma warning restore 649
 
-        [Fact]
-        public void RandomizedPcaInMemory()
+        /// <summary>
+        /// Help function used to execute trainers defined in <see cref="RandomizedPcaInMemory"/>.
+        /// </summary>
+        private static void ExecutePipelineWithGivenRandomizedPcaTrainer(MLContext mlContext, Trainers.RandomizedPcaTrainer trainer)
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
-            var mlContext = new MLContext(seed: 0);
-
             var samples = new List<DataPoint>()
             {
                 new DataPoint(){ Features= new float[3] {1, 0, 0} },
                 new DataPoint(){ Features= new float[3] {0, 2, 1} },
                 new DataPoint(){ Features= new float[3] {1, 2, 3} },
                 new DataPoint(){ Features= new float[3] {0, 1, 0} },
                 new DataPoint(){ Features= new float[3] {0, 2, 1} },
-                new DataPoint(){ Features= new float[3] {-100, -50, -100} }
+                new DataPoint(){ Features= new float[3] {-100, 50, -100} }
             };
 
             // Convert native C# class to IDataView, a consumble format to ML.NET functions.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Create an anomaly detector. Its underlying algorithm is randomized PCA.
-            var pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca(featureColumnName: nameof(DataPoint.Features), rank: 1, center: false);
-
             // Train the anomaly detector.
-            var model = pipeline.Fit(data);
+            var model = trainer.Fit(data);
 
             // Apply the trained model on the training data.
             var transformed = model.Transform(data);