Updated the documentation for time series (microsoft#158)

* Update readme with latest feedback (microsoft#39) Updating readme with latest feedback. * Add THIRD-PARTY-NOTICES.txt and move CONTRIBUTING.md to root. (microsoft#40) * Initial checkin * Move to Hosted Mac pool * Update README.md * Manually copied naming changes over from master. * Revert "Merge remote-tracking branch 'upstream/temp/docs'" This reverts commit 93c7347, reversing changes made to 2350069. * Improve documentation regarding contributors. * Fix email address. * Create CODE_OF_CONDUCT.md * Update issue templates * Create PULL_REQUEST_TEMPLATE.md * Update issue templates * Update issue templates * Update issue templates * Fixing link in CONTRIBUTING.md (microsoft#44) * Update contributing.md link. (microsoft#43) * Initial checkin for ML.NET 0.7 upgrade * fix tests * put back columndropper * fix tests * Update scikit-learn links to use https instead of http * restart dotnetcore2 package work * fix build * fix mac & linux * fix build * fix build * dbg build * fix build * fix build * handle py 2.7 * handle py27 * fix py27 * fix build * fix build * fix build * ensure dependencies * ignore exceptions from ensure dependencies * up version * Update cv.py add case for X is data frame * Update cv.py add a space * add a test for cv with data frame * set DOTNET_SYSTEM_GLOBALIZATION_INVARIANT to true to fix app domain error * fix build * up version * Add instructions for editing docstrings. (microsoft#51) * Add instructions for editing docstrings. * Add footnote giving more information. * Fix build failures caused by dotnetcore2 module. (microsoft#67) * Fix importing of the dotnetcore2 module because it has inconsistent folder naming. * Fix file check for unix platforms. * Fix indentation levels. * Reduce number of build legs for PR validations and add nightly build definition with more robust build matrix. (microsoft#69) * Increase version to 0.6.5. (microsoft#71) * Update clr helper function to search multiple folders for clr binaries. (microsoft#72) * Update clr helper function to search multiple folders for clr binaries. * Moved responsiblity for Python version checking to utility functions. * Add clarifying comments. * Fix call to get_nimbusml_libs() * fix drop column param name * Remove restricted permissions on build.sh script. * Fix lightgbm test failures by updating runtime dependencies. * fix TensorFlowScorer model_location paramter name * Fix build.sh defaults so that it detects when running on a mac. * Since OneHotHashVectorizer is broken for output kind Key in ML.NET 0.7, usse ToKey() for unit tests * fix tests * fix pyproj test * fix win 3.6 build * fix comments * expose "parallel" to the fit/fit_transform function by including **param to the argument * add a test for the parallel * update parallel thread * fix tests comparison * Update thread, retry build * modify tests * specify pytest-cov version * update pytest-cov version in build command for linux * for windows use the latest pytest-cov * Enabled strong naming for DoNetBridge.dll (to be used for InternalsVisibleTo in ML.NET) * Changed the keys to be the same as other internal repos * Changed the key filename * Update to ML.NET 0.10.preview (microsoft#77) * Updating ML.NET nugets to latest 0.9 preview. * --generate_entrypoints phase 1 * Fixed Models.CrossValidator * Updated all entrypoints * New manifest.json, picket from Monte's branch * Updated API codegen * Replace ISchema and SchemaImpl with Schema and SchemaBuilder. * Revert "Replace ISchema and SchemaImpl with Schema and SchemaBuilder." This reverts commit dcd749d. * Refactor IRowCursor to RowCursor. * Update ML.NET version in build.csproj. * Update manifest.json to ml.net commit 92e762686989215ddf45d9db3f0a1c989ee54d11 * Updated RunGraph.cs to ml.net 0.10 * Refactor Vbuffer * Added override to RowCursor methods * Update to NimbusML-privileged nugets from ML.NET. * Update to Microsoft.ML namespace without Runtime. * Schema and VBuffer fixes in NativeDataInterop. * API fixes for IRandom and IsText in RmlEnvironment and NativeDataView. * Work on getting VBuffer pointers from Spans. * Some VBuffer fixes * fix some class names * Fix Register Assembly names. * Remove ML.PipelineInference * fixed more classes * Add back columndropper for backward compatability. * Register Entrypoints assembly in environment. * Fix homebrew update problem on VS Hosted Mac images. * Updated all the nuget versions to be the same. * Attempt to fix the dataframe unit tests * Fixed test_pyproj * Optimized VBuffer changes * Changed bridge version value to 0.10 * Addressed PR comments * Simplify by using six.string_types (microsoft#89) * Simplify by using six.string_types * Force a retest * Removed ISchema from DotNetBridge (microsoft#90) * Removed ISchema * Fixed the tests * Addressed PR comments * Addressed Wei-Sheng's comments about documenting the purpose of Column.DetachedColumn. * add configuration for python 3.7 (microsoft#101) * add configuration for python 3.7 * fix broken unit test * Update build.sh * fix build for Windows * Linux py3.7 build * fix pytest version * upgrade pytest * fix pytest-cov version * fix isinstance(., int) for python 2.7 * build urls for Mac * final fixes * fix libomp * Removing 3.7 for now as its not in PyPI * Upgrade to ML.NET version 1.0.0 (microsoft#100) * ref v0.10 ML.NET * fix build * hook up to v0.11.0 ML.NET * fix build errors * fix build * include Microsoft.Data.DataView.dll in build * typo * remove protobuf dll * Regenerate code due to manifest changes * fix missing ep * Update to ML.NET 1.0.0-preview * fix .net build * update nuget for ML.NET * remove Data namespace dll * rollback nuget changes * move to final RC ML.NET * Regenerate classes as per updated manifest * fix maximum_number_of_iterations param name * fix parameter names * fix names * reference official v1.0 of ML.NET * fix tests * fix label column * Fix tests * fix lightgbm tests * fix OLS * fix tests * fix more tests * fix more tests * fix weight column name * more tests * fix normalized metrics * more errors * Fix CV * rename feature_column to feature_column_name * fix cv ranker * Fix lightgbm tests * fix changes due to upgrade of NGramFeaturizer * fix ngram featurizer * fix FactorizationMachine assert error * disable test which is not working now due to change in LightGbm version * fix model name * typo * handle nan in arrays * fix tests * fix tests * fix more tests * fix data type * fix AUC exception * kick the build * fix tests due to data change * fix ngram test * fix mutual info tests * copy libiomp lib * fix mac build * disable SymSgdNative for now * disable SymSgdBinary classifier tests for Linux * fix linux tests * fix linux tests * try linux * fix linux * skip SymSgdBinaryClassifier checks * fix entrypoint compiler * fix entry point generation * fix example tests run * fix typo * fix documentation regression * fix parameter name * fix examples * fix examples * fix tests * fix tests * fix linux * kick build * Fix code_fixer * fix skip take filters * fix estimator checks * Fix latest Windows build issues. (microsoft#105) * Fix build issue on Windows when VS2019 is installed. Note: The -version option could not be added directly to the FOR command due to a command script parsing issue. * Add missing arguments to fix build issue with latest version of autoflake. * Fixes microsoft#50 - summary() fails if called a second time. (microsoft#107) * Fixes microsoft#50 - summary() fails if called a second time. * Fixes microsoft#99. Do not use hardcoded file separator. (microsoft#108) Fixes microsoft#99. Do not use hard coded file separator. * Delete the cached summaries when refitting a pipeline or a predictor. (microsoft#109) * Fix build issue on Windows when VS2019 is installed. Note: The -version option could not be added directly to the FOR command due to a command script parsing issue. * Add missing arguments to fix build issue with latest version of autoflake. * Delete the cached summaries when refitting a pipeline or a predictor. Fixes microsoft#106 * Simplify the code that deletes cached summaries when calling fit. * Fix signature import error when using latest version of scikit-learn. (microsoft#116) * Fix signature import error when using latest version of scikit-learn. Fixes microsoft#111 * Move the conditional import of the signature method in to the utils package. * Package System.Drawing.Common.dll as its missing in dotnetcore2 (microsoft#120) * package System.Drawings.Common.dll as its missing in dotnetcore2 * typo * Add png for Image examples * try linux fix * rollback scikit learn version * test * debug * rollback test * rollback * fix fontconfig err * fix tests * print platform * get os names * test * test * fix linux * Upgrade the pytest-remotedata package to fix missing attribute error. (microsoft#121) * Upgrade the pytest-remotedata package to fix missing attribute error. Fixes microsoft#117 * Remove the RlsMacPy3.6 configuration from .vsts-ci.yml. * Upgrade version (microsoft#122) * package System.Drawings.Common.dll as its missing in dotnetcore2 * typo * Add png for Image examples * try linux fix * rollback scikit learn version * test * debug * rollback test * rollback * fix fontconfig err * fix tests * print platform * get os names * test * test * fix linux * Upgrade version * Support quoted strings by default (microsoft#124) * upgrade to ML.NET 1.1 (microsoft#126) * upgrade to ML.NET 1.1 * by default quote is + * assert changes due to quote * fix tensor flow example * Put long running tests in to their own folder to shorten build times. (microsoft#136) * Temporarily remove the dataframe examples from the test run to see how much that effects the test length. * Remove all examples from the tests to see how it impacts the CI run. * Put long running tests in to their own folder to shorten build times. * Update nimbusml.pyproj to reflect the newly moved test files. Forgot to save the nimbusml.pyproj in visual studio. * Expose ML.NET SSA & IID spike & changepoint detectors. (microsoft#135) * Initial creation of the IidSpikeDetector files to see what works and what doesn't. * Import the Microsoft.ML.TimeSeries assembly in to the project. * Use 'PassAs' in manifest.json to fix the source parameter name. * Use float32 for data dtype in IidSpikeDetector example. * Convert IidSpikeDetector to a standard transform. Add examples and tests. * Add pre-transform to IidSpikeDetector to fix incompatible data types. * Fix issues with the test_estimator_checks IidSpikeDetector tests. * Remove unnecessary TypeConverter import in IidSpikeDetector example. * Initial implementation of IidChangePointDetector. * Initial implementation of SsaSpikeDetector. * Initial implementation of SsaChangePointDetector. * Fix incorrect SsaSpikeDetector instance in test_estimator_checks. * Fix a few minor issues with time series unit tests and examples. (microsoft#139) * Skip Image.py and Image_df.py tests for Ubuntu 14 (microsoft#149) * * Fixed the script for generating the documentation (microsoft#144) * Moved _static to ci_script to solve an error while using sphinx * Removed amek_md.bat and merge the commands of it to make_yaml.bat * Moved metrics.rst to concepts * Rename time_series package to timeseries. (microsoft#150) * Initial checkin * Move to Hosted Mac pool * Manually copied naming changes over from master. * merge master to temp/docs for updating the documentation (microsoft#134) * merge master to documentation branch * fixed the ModuleNotFoundError for WordEmbedding_df.py * Merge branch 'documentation' into temp/docs (microsoft#143) * merge master to documentation branch * fixed the ModuleNotFoundError for WordEmbedding_df.py * Fixed the issue when generating the documentation guide and concepts * Moved _static to the right folder, and change PY36 to PY37 now * Made it work with Python3.6 * Put long running tests in to their own folder to shorten build times. (microsoft#136) * Temporarily remove the dataframe examples from the test run to see how much that effects the test length. * Remove all examples from the tests to see how it impacts the CI run. * Put long running tests in to their own folder to shorten build times. * Update nimbusml.pyproj to reflect the newly moved test files. Forgot to save the nimbusml.pyproj in visual studio. * Added undersocres to the files of time series
Stephen0620 · Jul 4, 2019 · e5a0452 · e5a0452
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diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj
@@ -592,11 +592,10 @@
     <Compile Include="nimbusml\tests\timeseries\test_ssaspikedetector.py" />
     <Compile Include="nimbusml\tests\timeseries\test_iidspikedetector.py" />
     <Compile Include="nimbusml\tests\timeseries\__init__.py" />
-    <Compile Include="nimbusml\timeseries\iidchangepointdetector.py" />
-    <Compile Include="nimbusml\timeseries\iidspikedetector.py" />
-    <Compile Include="nimbusml\timeseries\ssachangepointdetector.py" />
-    <Compile Include="nimbusml\timeseries\ssaforecaster.py" />
-    <Compile Include="nimbusml\timeseries\ssaspikedetector.py" />
+    <Compile Include="nimbusml\timeseries\_iidchangepointdetector.py" />
+    <Compile Include="nimbusml\timeseries\_iidspikedetector.py" />
+    <Compile Include="nimbusml\timeseries\_ssachangepointdetector.py" />
+    <Compile Include="nimbusml\timeseries\_ssaspikedetector.py" />
     <Compile Include="nimbusml\timeseries\__init__.py" />
     <Compile Include="tests\test_estimator_checks.py" />
     <Compile Include="nimbusml\tests\feature_extraction\text\test_lightlda.py" />

diff --git a/src/python/nimbusml/internal/core/timeseries/_iidchangepointdetector.py b/src/python/nimbusml/internal/core/timeseries/_iidchangepointdetector.py
@@ -0,0 +1,107 @@
+# --------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------------------------
+# - Generated by tools/entrypoint_compiler.py: do not edit by hand
+"""
+IidChangePointDetector
+"""
+
+__all__ = ["IidChangePointDetector"]
+
+
+from ...entrypoints.timeseriesprocessingentrypoints_iidchangepointdetector import \
+    timeseriesprocessingentrypoints_iidchangepointdetector
+from ...utils.utils import trace
+from ..base_pipeline_item import BasePipelineItem, DefaultSignature
+
+
+class IidChangePointDetector(BasePipelineItem, DefaultSignature):
+    """
+
+    This transform detects the change-points in an i.i.d. sequence using
+    adaptive kernel density estimation and martingales.
+
+    .. remarks::
+        ``IIDChangePointDetector`` assumes a sequence of data points that are
+        independently sampled from one
+        stationary distribution. `Adaptive kernel density estimation
+        <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_
+        is used to model the distribution.
+
+        This transform detects
+        change points by calculating the martingale score for the sliding
+        window based on the estimated distribution.
+        The idea is based on the `Exchangeability
+        Martingales <https://icml.cc/Conferences/2012/papers/808.pdf>`_ that
+        detects a change of distribution over a stream of i.i.d. values. In
+        short, the value of the
+        martingale score starts increasing significantly when a sequence of
+        small p-values are detected in a row; this
+        indicates the change of the distribution of the underlying data
+        generation process.
+
+    :param confidence: The confidence for change point detection in the range
+        [0, 100]. Used to set the threshold of the martingale score for
+        triggering alert.
+
+    :param change_history_length: The length of the sliding window on p-value
+        for computing the martingale score.
+
+    :param martingale: The type of martingale betting function used for
+        computing the martingale score. Available options are {``Power``,
+        ``Mixture``}.
+
+    :param power_martingale_epsilon: The epsilon parameter for the Power
+        martingale if martingale is set to ``Power``.
+
+    :param params: Additional arguments sent to compute engine.
+
+    .. seealso::
+        :py:func:`IIDSpikeDetector
+        <nimbusml.preprocessing.timeseries.IIDSpikeDetector>`,
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`,
+        :py:func:`SsaChangePointDetector
+        <nimbusml.preprocessing.timeseries.SsaChangePointDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude::
+        /../nimbusml/examples/IidSpikeChangePointDetector.py
+              :language: python
+    """
+
+    @trace
+    def __init__(
+            self,
+            confidence=95.0,
+            change_history_length=20,
+            martingale='Power',
+            power_martingale_epsilon=0.1,
+            **params):
+        BasePipelineItem.__init__(
+            self, type='transform', **params)
+
+        self.confidence = confidence
+        self.change_history_length = change_history_length
+        self.martingale = martingale
+        self.power_martingale_epsilon = power_martingale_epsilon
+
+    @property
+    def _entrypoint(self):
+        return timeseriesprocessingentrypoints_iidchangepointdetector
+
+    @trace
+    def _get_node(self, **all_args):
+        algo_args = dict(
+            source=self.source,
+            name=self._name_or_source,
+            confidence=self.confidence,
+            change_history_length=self.change_history_length,
+            martingale=self.martingale,
+            power_martingale_epsilon=self.power_martingale_epsilon)
+
+        all_args.update(algo_args)
+        return self._entrypoint(**all_args)
diff --git a/src/python/nimbusml/internal/core/timeseries/_iidspikedetector.py b/src/python/nimbusml/internal/core/timeseries/_iidspikedetector.py
@@ -0,0 +1,91 @@
+# --------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------------------------
+# - Generated by tools/entrypoint_compiler.py: do not edit by hand
+"""
+IidSpikeDetector
+"""
+
+__all__ = ["IidSpikeDetector"]
+
+
+from ...entrypoints.timeseriesprocessingentrypoints_iidspikedetector import \
+    timeseriesprocessingentrypoints_iidspikedetector
+from ...utils.utils import trace
+from ..base_pipeline_item import BasePipelineItem, DefaultSignature
+
+
+class IidSpikeDetector(BasePipelineItem, DefaultSignature):
+    """
+
+    This transform detects the spikes in a i.i.d. sequence using adaptive
+    kernel density estimation.
+
+    .. remarks::
+        ``IIDSpikeDetector`` assumes a sequence of data points that are
+        independently sampled from one stationary
+        distribution. `Adaptive kernel density estimation
+        <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_
+        is used to model the distribution.
+        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score
+        indicates the likelihood of the current observation according to
+        the estimated distribution. The lower its value, the more likely the
+        current point is an outlier.
+
+    :param confidence: The confidence for spike detection in the range [0,
+        100].
+
+    :param side: The argument that determines whether to detect positive or
+        negative anomalies, or both. Available options are {``Positive``,
+        ``Negative``, ``TwoSided``}.
+
+    :param pvalue_history_length: The size of the sliding window for computing
+        the p-value.
+
+    :param params: Additional arguments sent to compute engine.
+
+    .. seealso::
+        :py:func:`IIDChangePointDetector
+        <nimbusml.preprocessing.timeseries.IIDChangePointDetector>`,
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`,
+        :py:func:`SsaChangePointDetector
+        <nimbusml.preprocessing.timeseries.SsaChangePointDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude:: /../nimbusml/examples/IidSpikePointDetector.py
+              :language: python
+    """
+
+    @trace
+    def __init__(
+            self,
+            confidence=99.0,
+            side='TwoSided',
+            pvalue_history_length=100,
+            **params):
+        BasePipelineItem.__init__(
+            self, type='transform', **params)
+
+        self.confidence = confidence
+        self.side = side
+        self.pvalue_history_length = pvalue_history_length
+
+    @property
+    def _entrypoint(self):
+        return timeseriesprocessingentrypoints_iidspikedetector
+
+    @trace
+    def _get_node(self, **all_args):
+        algo_args = dict(
+            source=self.source,
+            name=self._name_or_source,
+            confidence=self.confidence,
+            side=self.side,
+            pvalue_history_length=self.pvalue_history_length)
+
+        all_args.update(algo_args)
+        return self._entrypoint(**all_args)
diff --git a/src/python/nimbusml/internal/core/timeseries/_ssachangepointdetector.py b/src/python/nimbusml/internal/core/timeseries/_ssachangepointdetector.py
@@ -0,0 +1,138 @@
+# --------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------------------------
+# - Generated by tools/entrypoint_compiler.py: do not edit by hand
+"""
+SsaChangePointDetector
+"""
+
+__all__ = ["SsaChangePointDetector"]
+
+
+from ...entrypoints.timeseriesprocessingentrypoints_ssachangepointdetector import \
+    timeseriesprocessingentrypoints_ssachangepointdetector
+from ...utils.utils import trace
+from ..base_pipeline_item import BasePipelineItem, DefaultSignature
+
+
+class SsaChangePointDetector(BasePipelineItem, DefaultSignature):
+    """
+
+    This transform detects the change-points in a seasonal time-series
+    using Singular Spectrum Analysis (SSA).
+
+    .. remarks::
+        `Singular Spectrum Analysis (SSA)
+        <https://en.wikipedia.org/wiki/Singular_spectrum_analysis>`_ is a
+        powerful framework for decomposing the time-series into trend,
+        seasonality and noise components as well as forecasting the future
+        values of the time-series. In order to remove the
+        effect of such components on anomaly detection, this transform add
+        SSA as a time-series modeler component in the detection pipeline.
+
+        The SSA component will be trained and it predicts the next expected
+        value on the time-series under normal condition; this expected value
+        is
+        further used to calculate the amount of deviation from the normal
+        behavior at that timestamp.
+        The distribution of this deviation is then modeled using `Adaptive
+        kernel density estimation
+        <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_.
+
+        This transform detects
+        change points by calculating the martingale score for the sliding
+        window based on the estimated distribution of deviations.
+        The idea is based on the `Exchangeability
+        Martingales <https://icml.cc/Conferences/2012/papers/808.pdf>`_ that
+        detects a change of distribution over a stream of i.i.d. values. In
+        short, the value of the
+        martingale score starts increasing significantly when a sequence of
+        small p-values detected in a row; this
+        indicates the change of the distribution of the underlying data
+        generation process.
+
+    :param training_window_size: The number of points, N, from the beginning
+        of the sequence used to train the SSA model.
+
+    :param confidence: The confidence for change point detection in the range
+        [0, 100].
+
+    :param seasonal_window_size: An upper bound, L,  on the largest relevant
+        seasonality in the input time-series, which also
+        determines the order of the autoregression of SSA. It must satisfy 2
+        < L < N/2.
+
+    :param change_history_length: The length of the sliding window on p-value
+        for computing the martingale score.
+
+    :param error_function: The function used to compute the error between the
+        expected and the observed value. Possible values are:
+        {``SignedDifference``, ``AbsoluteDifference``, ``SignedProportion``,
+        ``AbsoluteProportion``, ``SquaredDifference``}.
+
+    :param martingale: The type of martingale betting function used for
+        computing the martingale score. Available options are {``Power``,
+        ``Mixture``}.
+
+    :param power_martingale_epsilon: The epsilon parameter for the Power
+        martingale if martingale is set to ``Power``.
+
+    :param params: Additional arguments sent to compute engine.
+
+    .. seealso::
+        :py:func:`IIDChangePointDetector
+        <nimbusml.preprocessing.timeseries.IIDChangePointDetector>`,
+        :py:func:`IIDSpikeDetector
+        <nimbusml.preprocessing.timeseries.IIDSpikeDetector>`,
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude:: /../nimbusml/examples/SsaChangePointDetector.py
+              :language: python
+    """
+
+    @trace
+    def __init__(
+            self,
+            training_window_size=100,
+            confidence=95.0,
+            seasonal_window_size=10,
+            change_history_length=20,
+            error_function='SignedDifference',
+            martingale='Power',
+            power_martingale_epsilon=0.1,
+            **params):
+        BasePipelineItem.__init__(
+            self, type='transform', **params)
+
+        self.training_window_size = training_window_size
+        self.confidence = confidence
+        self.seasonal_window_size = seasonal_window_size
+        self.change_history_length = change_history_length
+        self.error_function = error_function
+        self.martingale = martingale
+        self.power_martingale_epsilon = power_martingale_epsilon
+
+    @property
+    def _entrypoint(self):
+        return timeseriesprocessingentrypoints_ssachangepointdetector
+
+    @trace
+    def _get_node(self, **all_args):
+        algo_args = dict(
+            source=self.source,
+            name=self._name_or_source,
+            training_window_size=self.training_window_size,
+            confidence=self.confidence,
+            seasonal_window_size=self.seasonal_window_size,
+            change_history_length=self.change_history_length,
+            error_function=self.error_function,
+            martingale=self.martingale,
+            power_martingale_epsilon=self.power_martingale_epsilon)
+
+        all_args.update(algo_args)
+        return self._entrypoint(**all_args)