TimeSeries - make catalog API methods with int confidence parameter deprecated.

- Tests adjusted to not use the deprecated methods

Author: esso23

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

@@ -52,7 +52,7 @@ public static void Example()
             // Setup SsaChangePointDetector arguments
             var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(ChangePointPrediction.Prediction);
-            int confidence = 95;
+            double confidence = 95;
             int changeHistoryLength = 8;
 
             // Train the change point detector.

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

@@ -59,7 +59,7 @@ public static void Example()
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectChangePointBySsa(
-                outputColumnName, inputColumnName, 95, 8, TrainingSize,
+                outputColumnName, inputColumnName, 95.0d, 8, TrainingSize,
                 SeasonalitySize + 1).Fit(dataView).Transform(dataView);
 
             // Getting the data of the newly created column as an IEnumerable of

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

@@ -52,7 +52,7 @@ public static void Example()
             // Setup SsaChangePointDetector arguments
             var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(ChangePointPrediction.Prediction);
-            int confidence = 95;
+            double confidence = 95;
             int changeHistoryLength = 8;
 
             // Train the change point detector.

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

@@ -55,7 +55,7 @@ public static void Example()
 
             // Time Series model.
             ITransformer model = ml.Transforms.DetectIidChangePoint(
-                outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
+                outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView);
 
             // Create a time series prediction engine from the model.
             var engine = model.CreateTimeSeriesEngine<TimeSeriesData,

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

@@ -53,7 +53,7 @@ public static void Example()
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectIidChangePoint(
-                outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView)
+                outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView)
                 .Transform(dataView);
 
             // Getting the data of the newly created column as an IEnumerable of

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

@@ -47,7 +47,7 @@ public static void Example()
 
             // The transformed model.
             ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName,
-                inputColumnName, 95, Size).Fit(dataView);
+                inputColumnName, 95.0d, Size).Fit(dataView);
 
             // Create a time series prediction engine from the model.
             var engine = model.CreateTimeSeriesEngine<TimeSeriesData,

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

@@ -45,7 +45,7 @@ public static void Example()
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectIidSpike(outputColumnName,
-                inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
+                inputColumnName, 95.0d, Size / 4).Fit(dataView).Transform(dataView);
 
             // Getting the data of the newly created column as an IEnumerable of
             // IidSpikePrediction.

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

@@ -53,7 +53,7 @@ public static void Example()
 
             // Train the change point detector.
             ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName,
-                inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(
+                inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
                 dataView);
 
             // Create a prediction engine from the model for feeding new data.

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

@@ -61,7 +61,7 @@ public static void Example()
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName,
-                inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(
+                inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
                 dataView).Transform(dataView);
 
             // Getting the data of the newly created column as an IEnumerable of

src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs

@@ -33,6 +33,7 @@ public static class TimeSeriesCatalog
         /// ]]>
         /// </format>
         /// </example>
+        [Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
         public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
             int confidence, int changeHistoryLength, MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
             => DetectIidChangePoint(catalog, outputColumnName, inputColumnName, (double)confidence, changeHistoryLength, martingale, eps);
@@ -81,6 +82,7 @@ public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalo
         /// ]]>
         /// </format>
         /// </example>
+        [Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
         public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
              int confidence, int pvalueHistoryLength, AnomalySide side = AnomalySide.TwoSided)
             => DetectIidSpike(catalog, outputColumnName, inputColumnName, (double)confidence, pvalueHistoryLength, side);
@@ -132,6 +134,7 @@ public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, s
         /// ]]>
         /// </format>
         /// </example>
+        [Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
         public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
             int confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize, ErrorFunction errorFunction = ErrorFunction.SignedDifference,
             MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
@@ -198,6 +201,7 @@ public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCata
         /// ]]>
         /// </format>
         /// </example>
+        [Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
         public static SsaSpikeEstimator DetectSpikeBySsa(this TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength,
             int trainingWindowSize, int seasonalityWindowSize, AnomalySide side = AnomalySide.TwoSided, ErrorFunction errorFunction = ErrorFunction.SignedDifference)
             => DetectSpikeBySsa(catalog, outputColumnName, inputColumnName, (double)confidence, pvalueHistoryLength, trainingWindowSize, seasonalityWindowSize, side, errorFunction);

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

@@ -48,7 +48,7 @@ public void ChangeDetection()
                 data.Add(new Data((float)(5 + i * 1.1)));
 
             // Build the pipeline
-            var learningPipeline = ML.Transforms.DetectIidChangePoint("Data", "Value", 80, size);
+            var learningPipeline = ML.Transforms.DetectIidChangePoint("Data", "Value", 80.0d, size);
 
             // Train
             var detector = learningPipeline.Fit(dataView);
@@ -92,7 +92,7 @@ public void ChangePointDetectionWithSeasonality()
                 data.Add(new Data(i * 100));
 
             // Build the pipeline
-            var learningPipeline = ML.Transforms.DetectChangePointBySsa("Data", "Value", 95, changeHistorySize, maxTrainingSize, seasonalitySize);
+            var learningPipeline = ML.Transforms.DetectChangePointBySsa("Data", "Value", 95.0d, changeHistorySize, maxTrainingSize, seasonalitySize);
             // Train
             var detector = learningPipeline.Fit(dataView);
             // Transform
@@ -133,7 +133,7 @@ public void SpikeDetection()
                 data.Add(new Data(5));
 
             // Build the pipeline
-            var learningPipeline = ML.Transforms.DetectIidSpike("Data", "Value", 80, pvalHistoryLength);
+            var learningPipeline = ML.Transforms.DetectIidSpike("Data", "Value", 80.0d, pvalHistoryLength);
             // Train
             var detector = learningPipeline.Fit(dataView);
             // Transform
@@ -185,7 +185,7 @@ public void SsaSpikeDetection()
                 data.Add(new Data(5));
 
             // Build the pipeline
-            var learningPipeline = ML.Transforms.DetectSpikeBySsa("Data", "Value", 80, changeHistoryLength, trainingWindowSize, seasonalityWindowSize);
+            var learningPipeline = ML.Transforms.DetectSpikeBySsa("Data", "Value", 80.0d, changeHistoryLength, trainingWindowSize, seasonalityWindowSize);
             // Train
             var detector = learningPipeline.Fit(dataView);
             // Transform