Add an example of random PCA

Fix build

Polish example

Author: wschin

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

@@ -0,0 +1,85 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.AnomalyDetection
+{
+    public static class RandomizedPcaSample
+    {
+        /// <summary>
+        /// Example with 3 feature values.
+        /// </summary>
+        private class DataPoint
+        {
+            [VectorType(3)]
+            public float[] Features;
+        }
+
+        /// <summary>
+        /// Class used to capture prediction of <see cref="DataPoint"/> in <see cref="Example"/>.
+        /// </summary>
+        // We disable this warning because complier doesn't realize those fields below are assigned somewhere.
+#pragma warning disable 649
+        private class Result
+        {
+            // Outlier gets false while inlier has true.
+            public bool PredictedLabel;
+            // Outlier gets smaller score.
+            public float Score;
+        }
+#pragma warning restore 649
+
+        public static void Example()
+        {
+            // 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} }
+            };
+
+            // 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);
+
+            // Apply the trained model on the training data.
+            var transformed = model.Transform(data);
+
+            // Read ML.NET predictions into C# class.
+            var results = mlContext.Data.CreateEnumerable<Result>(transformed, reuseRowObject: false).ToList();
+
+            // Let's go through all predictions.
+            for (int i = 0; i < samples.Count; ++i)
+            {
+                // The i-th example's prediction result.
+                var result = results[i];
+
+                // The i-th example's feature vector in text format.
+                var featuresInText = string.Join(',', samples[i].Features);
+
+                if (result.PredictedLabel)
+                    // The i-th sample is predicted as an inlier.
+                    Console.WriteLine("The {0}-th example with features [{1}] is an inlier with a score of being inlier {2}",
+                        i, featuresInText, result.Score);
+                else
+                    // The i-th sample is predicted as an outlier.
+                    Console.WriteLine("The {0}-th example with features [{1}] is an outlier with a score of being inlier {2}",
+                        i, featuresInText, result.Score);
+            }
+        }
+    }
+}

test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

@@ -3,6 +3,8 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using System.Collections.Generic;
+using System.Linq;
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
@@ -48,6 +50,75 @@ public void NoAnomalyTest()
             Assert.Throws<ArgumentOutOfRangeException>(() => ML.AnomalyDetection.Evaluate(transformedData));
         }
 
+        /// <summary>
+        /// Example with 3 feature values.
+        /// </summary>
+        private class DataPoint
+        {
+            [VectorType(3)]
+            public float[] Features;
+        }
+
+        /// <summary>
+        /// Class used to capture prediction of <see cref="DataPoint"/> in <see cref="RandomizedPcaInMemory"/>.
+        /// </summary>
+#pragma warning disable 649
+        private class Result
+        {
+            // Outlier gets false while inlier has true.
+            public bool PredictedLabel;
+            // Outlier gets smaller score.
+            public float Score;
+        }
+#pragma warning restore 649
+
+        [Fact]
+        public 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);
+
+            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} }
+            };
+
+            // 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);
+
+            // Apply the trained model on the training data.
+            var transformed = model.Transform(data);
+
+            // Read ML.NET predictions into C# class.
+            var results = mlContext.Data.CreateEnumerable<Result>(transformed, reuseRowObject: false).ToList();
+
+            // First 5 examples are inliers.
+            for (int i = 0; i < 5; ++i)
+            {
+                // Inlier should be predicted as true.
+                Assert.True(results[i].PredictedLabel);
+                // Higher score means closer to inlier.
+                Assert.InRange(results[i].Score, 0.3, 1);
+            }
+
+            // Last example is outlier. Note that outlier should be predicted as false.
+            Assert.False(results[5].PredictedLabel);
+            Assert.InRange(results[5].Score, 0, 0.3);
+        }
+
         private IDataView DetectAnomalyInMnistOneClass(string trainPath, string testPath)
         {
             var loader = ML.Data.CreateTextLoader(new[]