TimeSeries - reintroduce original methods with confidence parameter of type int (to not break the API).

Author: esso23

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;
@@ -12,6 +13,30 @@ namespace Microsoft.ML
 {
     public static class TimeSeriesCatalog
     {
+        /// <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,
+            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>
@@ -36,6 +61,30 @@ public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalo
             double confidence, int changeHistoryLength, MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
             => new IidChangePointEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, changeHistoryLength, inputColumnName, martingale, eps);
 
+        /// <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,
+             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>
@@ -60,6 +109,34 @@ public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, s
              double confidence, int pvalueHistoryLength, AnomalySide side = AnomalySide.TwoSided)
             => new IidSpikeEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, pvalueHistoryLength, inputColumnName, side);
 
+        /// <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,
+            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>.
@@ -99,6 +176,32 @@ public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCata
                 ErrorFunction = 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, 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>.