Merge pull request #1 from ML-KULeuven/setup-github

Setup GitHub
ML-KULeuven · Oct 8, 2024 · 6feb43e · 6feb43e
2 parents bcd6aed + 5bb9964
commit 6feb43e
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diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
@@ -10,22 +10,15 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
-    # You can also specify other tool versions:
-    # nodejs: "19"
-    # rust: "1.64"
-    # golang: "1.19"
 
 # Build documentation in the "docs/" directory with Sphinx
 sphinx:
   configuration: docs/conf.py
 
-# Optionally build your docs in additional formats such as PDF and ePub
-formats:
-   - pdf
-
 # Optional but recommended, declare the Python requirements required
 # to build your documentation
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
 python:
    install:
+   - requirements: requirements.txt
    - requirements: requirements-dev.txt
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -3,6 +3,9 @@ All notable changes to this project will be documented in this file.
 
 ## Latest
 
+In this release, all files were transferred from GitLab to GitHub. Therefore, 
+older links may no longer function as desired. 
+
 ### Added
 
 ### Changed

diff --git a/README.md b/README.md
@@ -1,20 +1,19 @@
 # Dtaianomaly for Time Series Anomaly Detection
 
-[![pipeline status](https://gitlab.kuleuven.be/u0143709/dtaianomaly/badges/main/pipeline.svg)](https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/pipelines)
-[![coverage report](https://gitlab.kuleuven.be/u0143709/dtaianomaly/badges/main/coverage.svg)](https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/commits/main)
-[![Latest Release](https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/badges/release.svg)](https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/releases)
+[![PyPi Version](https://img.shields.io/pypi/v/dtaianomaly.svg)](https://pypi.org/project/dtaianomaly/)
 [![Downloads](https://static.pepy.tech/badge/dtaianomaly)](https://pepy.tech/project/dtaianomaly)
 [![PyPI pyversions](https://img.shields.io/pypi/pyversions/dtaianomaly)](https://pypi.python.org/pypi/dtaianomaly/)
 [![PyPI license](https://img.shields.io/pypi/l/dtaianomaly.svg)](https://pypi.python.org/pypi/dtaianomaly/)
 
 A simple-to-use Python package for the development and analysis of time series anomaly 
 detection techniques. Here we describe the main usage of `dtaianomaly`, but be sure to
-check out the [documentation](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/index.html) 
+check out the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html) 
 for more information. 
 
 ## Installation
 
-The preferred way to install `dtaianomaly` is via PyPi. See the [documentation](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/installation.html) for more options.
+The preferred way to install `dtaianomaly` is via PyPi. See the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html) 
+for more options.
 ```
 pip install dtaianomaly
 ```
@@ -23,18 +22,18 @@ pip install dtaianomaly
 
 The three key features of `dtaianomaly` are as follows:
 1. State-of-the-art time series anomaly detection via a simple API.
-   [Learn more!](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/anomaly_detection.html)
+   [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/anomaly_detection.html)
 2. Develop custom models for anomaly detection.
-   [Learn more!](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/custom_models.html)
+   [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/custom_models.html)
 3. Quantitative evaluation of time series anomaly detection.
-   [Learn more!](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/quantitative_evaluation.html)
+   [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/quantitative_evaluation.html)
 
 ## Example
 
 Below code shows a simple example of `dtaianomaly`, which is part of the 
 [anomaly detection notebook](notebooks/Anomaly-detection.ipynb). More examples 
 are provided in the [other notebooks](notebooks) and in the 
-[documentation](https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/anomaly_detection.html).
+[documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html).
 
 ```python
 from dtaianomaly.data import demonstration_time_series
@@ -56,7 +55,7 @@ detector.fit(X_)
 decision_scores = detector.decision_function(X_)
 anomaly_probabilities = detector.predict_proba(X_)
 ```
-![Anomaly scores](https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/raw/main/notebooks/Demonstration-time-series-detected-anomalies.svg?inline=false)
+![Demonstration-time-series-detected-anomalies.svg](notebooks%2FDemonstration-time-series-detected-anomalies.svg)
 
 ## Contact
 

diff --git a/data/README.rst b/data/README.rst
@@ -22,7 +22,7 @@ described on this page.
 
 .. note::
     You can also read custom data by implementing a custom :py:class:`~dtaianomaly.data.LazyDataLoader`,
-    as described in the `documentation <https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/custom_models.html#custom-data-loader>`_.
+    as described in the `documentation <https://dtaianomaly.readthedocs.io/en/stable/index.html>`_.
 
 Synthetic data
 --------------

diff --git a/docs/getting_started/anomaly_detection.rst b/docs/getting_started/anomaly_detection.rst
@@ -7,7 +7,7 @@ be detected in time series using ``dtaianomaly``.
 
 .. note::
     Some of the code has not been added to this webpage for clarity reasons. The full
-    code can be found in the `anomaly detection notebook <https://gitlab.kuleuven.be/u0143709/dtaianomaly/-/raw/main/notebooks/Anomaly-detection.ipynb>`_.
+    code can be found in the `anomaly detection notebook <https://github.com/ML-KULeuven/dtaianomaly/blob/main/notebooks/Anomaly-detection.ipynb>`_.
 
 Load the data
 -------------
@@ -41,7 +41,7 @@ of the noise from the time series.
     from dtaianomaly.preprocessing import MovingAverage
     preprocessor = MovingAverage(window_size=10)
 
-In general, `any anomaly detector <https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/api/anomaly_detection.html>`_
+In general, `any anomaly detector <https://dtaianomaly.readthedocs.io/en/stable/api/anomaly_detection.html>`_
 in ``dtaianomaly`` can be used to detect anomalies in this time series. Here, we use the
 :py:class:`~dtaianomaly.anomaly_detection.MatrixProfileDetector`
 

diff --git a/docs/getting_started/installation.rst b/docs/getting_started/installation.rst
@@ -18,38 +18,38 @@ To install version ``X.Y.Z``, use the following command:
     pip install dtaianomaly==X.Y.Z
 
 
-From GitLab
+From GitHub
 -----------
 
-It is also possible to install ``dtaianomaly`` directly from `GitLab`_:
+It is also possible to install ``dtaianomaly`` directly from `GitHub`_:
 
 .. code-block:: bash
 
-    pip install git+https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly
+    pip install git+https://github.com/ML-KULeuven/dtaianomaly
 
 Note that this will install the latest, *unreleased* version. Similarly as installation
 through PyPi, it is also possible to install version ``X.Y.Z`` as follows:
 
 .. code-block:: bash
 
-    pip install git+https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/[email protected]
+    pip install git+https://github.com/ML-KULeuven/[email protected]
 
-See the `release page <https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly/-/releases>`_
+See the `release page <https://github.com/ML-KULeuven/dtaianomaly/releases>`_
 for more information regarding the different versions.
 
 
 From source
 -----------
 
 Finally, it is also possible to install ``dtaianomaly`` from the source code. First
-download the source from `GitLab`_.
+download the source from `GitHub`_.
 It is also possible to download the source code for a specific release on
-`this webpage <https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly/-/releases>`_.
+`this webpage <https://github.com/ML-KULeuven/dtaianomaly/releases>`_.
 Unzip the files, and navigate to the root directory of the repository in the terminal.
 Then, ``dtaianomaly`` can be installed through the following command:
 
 .. code-block:: bash
 
     pip install .
 
-.. _GitLab: https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly
+.. _GitHub: https://github.com/ML-KULeuven/dtaianomaly
diff --git a/docs/getting_started/quantitative_evaluation.rst b/docs/getting_started/quantitative_evaluation.rst
@@ -12,7 +12,7 @@ There are two ways to run a :py:class:`~dtaianomaly.workflow.Workflow`: from :re
 or from a :ref:`configuration file <with-config>`.
 
 .. note::
-    You can also evaluate `custom components <https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/custom_models.html>`_
+    You can also evaluate `custom components <https://dtaianomaly.readthedocs.io/en/stable/getting_started/custom_models.html>`_
     in ``dtaianomaly`` via a :py:class:`~dtaianomaly.workflow.Workflow` in :ref:`Python <with-code>`. However,
     this is not possible via a :ref:`configuration file <with-config>` without extending the functionality of
     the :py:func:`~dtaianomaly.workflow.workflow_from_config` function!
@@ -138,5 +138,5 @@ Below, we show a simplified version of the configuration in `Config.json`_.
    :language: json
    :tab-width: 4
 
-.. _Config.json: https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly/-/blob/main/notebooks/Config.json
+.. _Config.json: https://github.com/ML-KULeuven/dtaianomaly/blob/main/notebooks/Config.json
 
diff --git a/docs/index.rst b/docs/index.rst
@@ -8,7 +8,7 @@ state-of-the-art methods. Additionally, ``dtaianomaly`` provides a benchmark fra
 quantitatively evaluate time series anomaly detection methods on a large repository of
 time series.
 
-The source is available on `GitLab <https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/dtaianomaly>`_.
+The source is available on `GitHub <https://github.com/ML-KULeuven/dtaianomaly>`_.
 
 .. toctree::
    :maxdepth: 1

diff --git a/dtaianomaly/anomaly_detection/__init__.py b/dtaianomaly/anomaly_detection/__init__.py
@@ -5,7 +5,7 @@
 
 >>> from dtaianomaly import anomaly_detection
 
-We refer to the `documentation <https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/anomaly_detection.html>`_
+We refer to the `documentation <https://dtaianomaly.readthedocs.io/en/stable/getting_started/anomaly_detection.html>`_
 for more information regarding detecting anomalies using ``dtaianomaly``.
 """
 

diff --git a/dtaianomaly/workflow/__init__.py b/dtaianomaly/workflow/__init__.py
@@ -25,7 +25,7 @@
 ...     metrics=[AreaUnderROC(), AreaUnderPR()]
 ... )
 
-We refer to the `documentation <https://m-group-campus-brugge.pages.gitlab.kuleuven.be/dtai_public/dtaianomaly/getting_started/quantitative_evaluation.html>`_
+We refer to the `documentation <https://dtaianomaly.readthedocs.io/en/stable/getting_started/quantitative_evaluation.html>`_
 for more information regarding the configuration and use of a Workflow.
 """