Add test and sample.

Author: mengaims

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -45,32 +45,25 @@ public static void Example()
             string inputColumnName = nameof(TimeSeriesData.Value);
 
             // The transformed model.
-            //ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
-            ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 8, 5, 5, 3, 6, 0.3).Fit(dataView);
+            ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 64, 5, 5, 3, 21, 0.25).Fit(dataView);
 
             // Create a time series prediction engine from the model.
             var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
             Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 
-
             // Create non-anomalous data and check for anomaly.
-            for (int index = 0; index < 5; index++)
+            for (int index = 0; index < 100; index++)
             {
                 // Anomaly spike detection.
                 PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
             }
 
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-
             // Spike.
-            PrintPrediction(10, engine.Predict(new TimeSeriesData(10)));
-
-            // 10     1      10.00    0.00  <-- alert is on, predicted spike (check-point model)
+            for (int index = 0; index < 5; index++)
+            {
+                PrintPrediction(15, engine.Predict(new TimeSeriesData(10)));
+            }
 
             // Checkpoint the model.
             var modelPath = "temp.zip";
@@ -85,13 +78,6 @@ public static void Example()
                 // Anomaly spike detection.
                 PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
             }
-
-            // 5      0       5.00    0.26  <-- load model from disk.
-            // 5      0       5.00    0.26
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-
         }
 
         private static void PrintPrediction(float value, SrCnnAnomalyDetection prediction) =>

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnnBatchPrediction.cs

@@ -0,0 +1,73 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+
+namespace Samples.Dynamic
+{
+    public static class DetectAnomalyBySrCnnBatchPrediction
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var ml = new MLContext();
+
+            // Generate sample series data with a spike
+            var data = new List<TimeSeriesData>();
+            for (int index = 0; index < 100; index++)
+            {
+                data.Add(new TimeSeriesData(5));
+            }
+            for (int index = 0; index < 5; index++)
+            {
+                data.Add(new TimeSeriesData(15));
+            }
+            for (int index = 0; index < 5; index++)
+            {
+                data.Add(new TimeSeriesData(5));
+            }
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup the estimator arguments
+            string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+            string inputColumnName = nameof(TimeSeriesData.Value);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 64, 5, 5, 3, 21, 0.25).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+            var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value");
+
+            int k = 0;
+            foreach (var prediction in predictionColumn)
+                PrintPrediction(data[k++].Value, prediction);
+
+        }
+
+        private static void PrintPrediction(float value, SrCnnAnomalyDetection prediction) =>
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0],
+                prediction.Prediction[1], prediction.Prediction[2]);
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class SrCnnAnomalyDetection
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
+        }
+    }
+}

src/Microsoft.ML.TimeSeries/SRCNNAnomalyDetector.cs

@@ -152,7 +152,6 @@ internal SrCnnAnomalyDetector(IHostEnvironment env, Options options)
         internal SrCnnAnomalyDetector(IHostEnvironment env, ModelLoadContext ctx)
             : base(env, ctx, LoaderSignature)
         {
-            //TODO: Some data check here
         }
 
         private SrCnnAnomalyDetector(IHostEnvironment env, SrCnnAnomalyDetector transform)

src/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs

@@ -104,7 +104,7 @@ public SrCnnAnomalyDetectionBase(SrCnnArgumentBase args, string name, IHostEnvir
             public SrCnnAnomalyDetectionBase(IHostEnvironment env, ModelLoadContext ctx, string name, SrCnnAnomalyDetectionBaseWrapper parent)
                 : base(env, ctx, name)
             {
-                Host.CheckDecode(InitialWindowSize == 0);
+                //Host.CheckDecode(InitialWindowSize == 0);
                 StateRef = new State(ctx.Reader);
                 StateRef.InitState(this, Host);
                 Parent = parent;
@@ -132,7 +132,7 @@ private protected override void SaveModel(ModelSaveContext ctx)
             internal void SaveThis(ModelSaveContext ctx)
             {
                 ctx.CheckAtModel();
-                Host.Assert(InitialWindowSize == 0);
+                //Host.Assert(InitialWindowSize == 0);
                 base.SaveModel(ctx);
 
                 // *** Binary format ***
@@ -147,7 +147,7 @@ public State()
                 {
                 }
 
-                internal State(BinaryReader reader)
+                internal State(BinaryReader reader) : base(reader)
                 {
                     WindowedBuffer = TimeSeriesUtils.DeserializeFixedSizeQueueSingle(reader, Host);
                     InitialWindowedBuffer = TimeSeriesUtils.DeserializeFixedSizeQueueSingle(reader, Host);
@@ -229,17 +229,17 @@ private protected override sealed void SpectralResidual(Single input, FixedSizeQ
                     }
                     List<Single> filteredIfftMagList = AverageFilter(ifftMagList, Parent.JudgementWindowSize);
 
-                    // Step 7: Calculate score
+                    // Step 7: Calculate score and set result
                     var score = CalculateSocre(ifftMagList[data.Count-1], filteredIfftMagList[data.Count-1]);
-                    score = (score < 1) ? 0 : score;
-                    score = (score > 10) ? 10 : score;
                     score /= 10.0f;
-                    var detres = score > Parent.AlertThreshold ? 1 : 0;
-                    var mag = ifftMagList[data.Count-1];
+                    result.Values[1] = score;
 
-                    //Step 8: Set result
+                    score = Math.Min(score, 1);
+                    score = Math.Max(score, 0);
+                    var detres = score > Parent.AlertThreshold ? 1 : 0;
                     result.Values[0] = detres;
-                    result.Values[1] = score;
+
+                    var mag = ifftMagList[data.Count-1];
                     result.Values[2] = mag;
                 }
 

src/Microsoft.ML.TimeSeries/SrCnnTransformBase.cs

@@ -84,7 +84,28 @@ private protected SrCnnTransformBase(int windowSize, int initialWindowSize, stri
         private protected SrCnnTransformBase(IHostEnvironment env, ModelLoadContext ctx, string name)
             : base(Contracts.CheckRef(env, nameof(env)).Register(name), ctx)
         {
-            //TODO: Read from binary format
+            OutputLength = 3;
+
+            byte temp;
+            temp = ctx.Reader.ReadByte();
+            BackAddWindowSize = (int)temp;
+            Host.CheckDecode(BackAddWindowSize > 0);
+
+            temp = ctx.Reader.ReadByte();
+            LookaheadWindowSize = (int)temp;
+            Host.CheckDecode(LookaheadWindowSize > 0);
+
+            temp = ctx.Reader.ReadByte();
+            AvergingWindowSize = (int)temp;
+            Host.CheckDecode(AvergingWindowSize > 0);
+
+            temp = ctx.Reader.ReadByte();
+            JudgementWindowSize = (int)temp;
+            Host.CheckDecode(JudgementWindowSize > 0);
+
+            temp = ctx.Reader.ReadByte();
+            AlertThreshold = (double)temp;
+            Host.CheckDecode(AlertThreshold >= 0 && AlertThreshold <= 1);
         }
 
         private protected SrCnnTransformBase(SrCnnArgumentBase args, string name, IHostEnvironment env)
@@ -95,7 +116,23 @@ private protected SrCnnTransformBase(SrCnnArgumentBase args, string name, IHostE
 
         private protected override void SaveModel(ModelSaveContext ctx)
         {
-            //TODO: save to ctx and write to file
+            Host.CheckValue(ctx, nameof(ctx));
+            ctx.CheckAtModel();
+
+            Host.Assert(WindowSize > 0);
+            Host.Assert(InitialWindowSize == WindowSize);
+            Host.Assert(BackAddWindowSize > 0);
+            Host.Assert(LookaheadWindowSize > 0);
+            Host.Assert(AvergingWindowSize > 0);
+            Host.Assert(JudgementWindowSize > 0);
+            Host.Assert(AlertThreshold >= 0 && AlertThreshold <= 1);
+
+            base.SaveModel(ctx);
+            ctx.Writer.Write((byte)BackAddWindowSize);
+            ctx.Writer.Write((byte)LookaheadWindowSize);
+            ctx.Writer.Write((byte)AvergingWindowSize);
+            ctx.Writer.Write((byte)JudgementWindowSize);
+            ctx.Writer.Write((byte)AlertThreshold);
         }
 
         internal override IStatefulRowMapper MakeRowMapper(DataViewSchema schema) => new Mapper(Host, this, schema);
@@ -225,17 +262,17 @@ private protected SrCnnStateBase() { }
 
             private protected override void CloneCore(TState state)
             {
-                //TODO:
+                base.CloneCore(state);
+                Contracts.Assert(state is SrCnnStateBase);
             }
 
             private protected SrCnnStateBase(BinaryReader reader) : base(reader)
             {
-                //TODO:
             }
 
             internal override void Save(BinaryWriter writer)
             {
-                //TODO:
+                base.Save(writer);
             }
 
             private protected override void SetNaOutput(ref VBuffer<double> dst)
@@ -244,7 +281,7 @@ private protected override void SetNaOutput(ref VBuffer<double> dst)
                 var editor = VBufferEditor.Create(ref dst, outputLength);
 
                 for (int i = 0; i < outputLength; ++i)
-                    editor.Values[i] = Double.NaN;
+                    editor.Values[i] = 0;
 
                 dst = editor.Commit();
             }

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -2,6 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
+using System;
 using System.Collections.Generic;
 using System.IO;
 using Microsoft.ML.Data;
@@ -41,6 +42,22 @@ public Data(float value)
             }
         }
 
+        private sealed class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        private sealed class SrCnnAnomalyDetection
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
+        }
+
         [Fact]
         public void ChangeDetection()
         {
@@ -276,5 +293,49 @@ public void ChangePointDetectionWithSeasonalityPredictionEngine()
             Assert.Equal(0.14823824685192111, prediction.Change[2], precision: 5); // P-Value score
             Assert.Equal(1.5292508189989167E-07, prediction.Change[3], precision: 5); // Martingale score
         }
+
+        [Fact]
+        public void AnomalyDetectionWithSrCnn()
+        {
+            var ml = new MLContext();
+
+            // Generate sample series data with a spike
+            var data = new List<TimeSeriesData>();
+            for (int index = 0; index < 100; index++)
+            {
+                data.Add(new TimeSeriesData(5));
+            }
+            for (int index = 0; index < 5; index++)
+            {
+                data.Add(new TimeSeriesData(15));
+            }
+            for (int index = 0; index < 5; index++)
+            {
+                data.Add(new TimeSeriesData(5));
+            }
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup the estimator arguments
+            string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+            string inputColumnName = nameof(TimeSeriesData.Value);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 64, 5, 5, 3, 21, 0.25).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+            var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(transformedData, reuseRowObject: false);
+
+            int k = 0;
+            foreach (var prediction in predictionColumn)
+            {
+                if (k == 101 || k == 106)
+                    Assert.Equal(1, prediction.Prediction[0]);
+                else
+                    Assert.Equal(0, prediction.Prediction[0]);
+                k += 1;
+            }
+        }
     }
 }