TimeSeries - fix confidence parameter type for some detectors (#4058) (…

…#5623) * TimeSeries - fix confidence parameter type for some detectors. - The public API exposed confidence parameters as int even though it's internally implemented as double - There was no workaround since all classes where double is used are internal - This caused major issues for software requiring high precision predictions - This change to API should be backwards compatible since int can be passed to parameter of type double * TimeSeries - reintroduce original methods with confidence parameter of type int (to not break the API). * TimeSeries - make catalog API methods with int confidence parameter deprecated. - Tests adjusted to not use the deprecated methods
dotnet · Feb 18, 2021 · 5ec4472 · 5ec4472
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diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/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.

diff --git a/...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs b/...crosoft.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

diff --git a/...amples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs b/...amples/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.

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/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,

diff --git a/...Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs b/...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

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/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,

diff --git a/...mples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs b/...mples/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.

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/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.

diff --git a/...les/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs b/...les/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

diff --git a/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs b/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.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 Microsoft.ML.Data;
 using Microsoft.ML.Runtime;
@@ -32,8 +33,33 @@ 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);
+
+        /// <summary>
+        /// Create <see cref="IidChangePointEstimator"/>, which predicts change points in an
+        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables">independent identically distributed (i.i.d.)</a>
+        /// time series based on adaptive kernel density estimations and martingale scores.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="confidence">The confidence for change point detection in the range [0, 100].</param>
+        /// <param name="changeHistoryLength">The length of the sliding window on p-values for computing the martingale score.</param>
+        /// <param name="martingale">The martingale used for scoring.</param>
+        /// <param name="eps">The epsilon parameter for the Power martingale.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[DetectIidChangePoint](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
+            double confidence, int changeHistoryLength, MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
             => new IidChangePointEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, changeHistoryLength, inputColumnName, martingale, eps);
 
         /// <summary>
@@ -56,8 +82,33 @@ 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);
+
+        /// <summary>
+        /// Create <see cref="IidSpikeEstimator"/>, which predicts spikes in
+        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a>
+        /// time series based on adaptive kernel density estimations and martingale scores.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="confidence">The confidence for spike detection in the range [0, 100].</param>
+        /// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
+        /// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[DetectIidSpike](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
+             double confidence, int pvalueHistoryLength, AnomalySide side = AnomalySide.TwoSided)
             => new IidSpikeEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, pvalueHistoryLength, inputColumnName, side);
 
         /// <summary>
@@ -83,9 +134,38 @@ 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)
+            => DetectChangePointBySsa(catalog, outputColumnName, inputColumnName, (double)confidence, changeHistoryLength, trainingWindowSize, seasonalityWindowSize, errorFunction, martingale, eps);
+
+        /// <summary>
+        /// Create <see cref="SsaChangePointEstimator"/>, which predicts change points in time series
+        /// using <a href="https://en.wikipedia.org/wiki/Singular_spectrum_analysis">Singular Spectrum Analysis (SSA)</a>.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="confidence">The confidence for change point detection in the range [0, 100].</param>
+        /// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
+        /// <param name="changeHistoryLength">The size of the sliding window for computing the p-value.</param>
+        /// <param name="seasonalityWindowSize">An upper bound on the largest relevant seasonality in the input time-series.</param>
+        /// <param name="errorFunction">The function used to compute the error between the expected and the observed value.</param>
+        /// <param name="martingale">The martingale used for scoring.</param>
+        /// <param name="eps">The epsilon parameter for the Power martingale.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[DetectChangePointBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
+            double confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize, ErrorFunction errorFunction = ErrorFunction.SignedDifference,
+            MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
             => new SsaChangePointEstimator(CatalogUtils.GetEnvironment(catalog), new SsaChangePointDetector.Options
             {
                 Name = outputColumnName,
@@ -121,7 +201,34 @@ 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);
+
+        /// <summary>
+        /// Create <see cref="SsaSpikeEstimator"/>, which predicts spikes in time series
+        /// using <a href="https://en.wikipedia.org/wiki/Singular_spectrum_analysis">Singular Spectrum Analysis (SSA)</a>.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="confidence">The confidence for spike detection in the range [0, 100].</param>
+        /// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
+        /// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
+        /// <param name="seasonalityWindowSize">An upper bound on the largest relevant seasonality in the input time-series.</param>
+        /// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
+        /// <param name="errorFunction">The function used to compute the error between the expected and the observed value.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[DetectSpikeBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static SsaSpikeEstimator DetectSpikeBySsa(this TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int pvalueHistoryLength,
             int trainingWindowSize, int seasonalityWindowSize, AnomalySide side = AnomalySide.TwoSided, ErrorFunction errorFunction = ErrorFunction.SignedDifference)
             => new SsaSpikeEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, pvalueHistoryLength, trainingWindowSize, seasonalityWindowSize, inputColumnName, side, errorFunction);
 

diff --git a/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs b/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs
@@ -219,7 +219,7 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat
     /// ]]>
     /// </format>
     /// </remarks>
-    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)" />
+    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Double,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)" />
     public sealed class IidChangePointEstimator : TrivialEstimator<IidChangePointDetector>
     {
         /// <summary>
@@ -233,7 +233,7 @@ public sealed class IidChangePointEstimator : TrivialEstimator<IidChangePointDet
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="martingale">The martingale used for scoring.</param>
         /// <param name="eps">The epsilon parameter for the Power martingale.</param>
-        internal IidChangePointEstimator(IHostEnvironment env, string outputColumnName, int confidence,
+        internal IidChangePointEstimator(IHostEnvironment env, string outputColumnName, double confidence,
             int changeHistoryLength, string inputColumnName, MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
             : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(IidChangePointEstimator)),
                 new IidChangePointDetector(env, new IidChangePointDetector.Options

diff --git a/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs b/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs
@@ -199,7 +199,7 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat
     /// ]]>
     /// </format>
     /// </remarks>
-    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidSpike(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.AnomalySide)" />
+    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidSpike(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Double,System.Int32,Microsoft.ML.Transforms.TimeSeries.AnomalySide)" />
     public sealed class IidSpikeEstimator : TrivialEstimator<IidSpikeDetector>
     {
         /// <summary>
@@ -212,7 +212,7 @@ public sealed class IidSpikeEstimator : TrivialEstimator<IidSpikeDetector>
         /// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
-        internal IidSpikeEstimator(IHostEnvironment env, string outputColumnName, int confidence, int pvalueHistoryLength, string inputColumnName, AnomalySide side = AnomalySide.TwoSided)
+        internal IidSpikeEstimator(IHostEnvironment env, string outputColumnName, double confidence, int pvalueHistoryLength, string inputColumnName, AnomalySide side = AnomalySide.TwoSided)
             : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(IidSpikeDetector)),
                 new IidSpikeDetector(env, new IidSpikeDetector.Options
                 {