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Merge pull request #105 from odnura/master
Merge v0.6.2 to branch develop
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README.md

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# Anomaly Detection Toolkit (ADTK)
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[![Build Status](https://travis-ci.com/arundo/adtk.svg?branch=master)](https://travis-ci.com/arundo/adtk)
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[![Docs](https://readthedocs.com/projects/arundo-adtk/badge/?version=stable)](https://arundo-adtk.readthedocs-hosted.com)
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[![Documentation Status](https://readthedocs.org/projects/adtk/badge/?version=stable)](https://adtk.readthedocs.io/en/stable)
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[![Coverage Status](https://coveralls.io/repos/github/arundo/adtk/badge.svg?branch=master&service=github)](https://coveralls.io/github/arundo/adtk?branch=master)
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[![PyPI](https://img.shields.io/pypi/v/adtk)](https://pypi.org/project/adtk/)
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[![Downloads](https://pepy.tech/badge/adtk)](https://pepy.tech/project/adtk)
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models. It also provides some functions to process and visualize time series
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and anomaly events.
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See https://arundo-adtk.readthedocs-hosted.com for complete documentation.
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See https://adtk.readthedocs.io for complete documentation.
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## Installation
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## Examples
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Please see [Quick Start](https://arundo-adtk.readthedocs-hosted.com/en/stable/quickstart.html) for a simple example.
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Please see [Quick Start](https://adtk.readthedocs.io/en/stable/quickstart.html) for a simple example.
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For more detailed examples of each module of ADTK, please refer to
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[Examples](https://arundo-adtk.readthedocs-hosted.com/en/stable/examples.html)
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[Examples](https://adtk.readthedocs.io/en/stable/examples.html)
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section in the documentation or [an interactive demo notebook](https://mybinder.org/v2/gh/arundo/adtk/master?filepath=docs%2Fnotebooks%2Fdemo.ipynb).
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## Contributing
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Please make sure to update unit tests as appropriate.
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Please see [Contributing](https://arundo-adtk.readthedocs-hosted.com/en/stable/developer.html) for more details.
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Please see [Contributing](https://adtk.readthedocs.io/en/stable/developer.html) for more details.
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## License

docs/conf.py

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# The short X.Y version.
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version = "0.6"
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# The full version, including alpha/beta/rc tags.
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release = "0.6.0"
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release = "0.6.2"
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.

docs/releasehistory.rst

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Release History
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***************
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Version 0.6.2 (Apr 16, 2020)
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===================================
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- Hot fix of wrong documentation url
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Version 0.6.1 (Apr 16, 2020)
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===================================
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- Migrated the documentation to a new host
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- Fixed minor typos in the documentation
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- Fixed a minor type hinting bug
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Version 0.6.0 (Mar 10, 2020)
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===================================
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- Re-designed the API of :py:mod:`adtk.visualization.plot`
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- Removed :py:mod:`adtk.data.resample` because its functionality is highly overlapped with pandas resampler module
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- Made :py:mod:`adtk.data.expand_event` accept events in the form of pandas Series/DataFrame
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- Made :py:mod:`adtk.data.expand_event` accept time delta in the form of `str` or `int`
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- Made :py:mod:`adtk.data.expand_events` accept events in the form of pandas Series/DataFrame
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- Made :py:mod:`adtk.data.expand_events` accept time delta in the form of `str` or `int`
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- Changed the output type of :py:mod:`adtk.data.split_train_test` from a 2-tuple of lists to a list of 2-tuples
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- Turned the following model parameters required from optional
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setup.cfg

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[metadata]
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name = adtk
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version = 0.6.0
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version = 0.6.2
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author = Arundo Analytics, Inc.
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maintainer = Tailai Wen
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maintainer_email = [email protected]
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coveralls>=1.7
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mypy>=0.641
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doc =
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sphinx>=2.0
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sphinx>=2.4,<3
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sphinx_rtd_theme<0.5
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nbsphinx>=0.4
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python-dateutil>=2.5

src/adtk/__init__.py

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models. It also provides some functions to process and visualize time series
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and anomaly events.
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See https://arundo-adtk.readthedocs-hosted.com for complete documentation.
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See https://adtk.readthedocs.io for complete documentation.
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"""
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__version__ = "0.6.0"
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__version__ = "0.6.2"

src/adtk/detector/_detector_hd.py

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if df.dropna().empty:
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raise RuntimeError("Valid values are not enough for training.")
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clustering_result = self.model.fit_predict(df.dropna())
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cluster_count = Counter(clustering_result)
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cluster_count = Counter(clustering_result) # type: Counter
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self._anomalous_cluster_id = cluster_count.most_common()[-1][0]
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def _predict_core(self, df: pd.DataFrame) -> pd.Series:

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