| Name | aeon JSON |
| Version |
0.11.1
JSON |
| download |
| home_page | None |
| Summary | A toolkit for machine learning from time series |
| upload_time | 2024-09-07 18:14:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | BSD 3-Clause License Copyright (c) The aeon developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
data-science
machine-learning
data-mining
time-series
scikit-learn
forecasting
time-series-analysis
time-series-classification
time-series-clustering
time-series-regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
<p align="center">
<a href="https://aeon-toolkit.org"><img src="https://raw.githubusercontent.com/aeon-toolkit/aeon/main/docs/images/logo/aeon-logo-blue-compact.png" width="50%" alt="aeon logo" /></a>
</p>
# ⌛ Welcome to aeon
`aeon` is an open-source toolkit for learning from time series. It is compatible with
[scikit-learn](https://scikit-learn.org) and provides access to the very latest
algorithms for time series machine learning, in addition to a range of classical
techniques for learning tasks such as forecasting and classification.
We strive to provide a broad library of time series algorithms including the
latest advances, offer efficient implementations using numba, and interfaces with other
time series packages to provide a single framework for algorithm comparison.
The latest `aeon` release is `v0.11.1`. You can view the full changelog
[here](https://www.aeon-toolkit.org/en/stable/changelog.html).
Our webpage and documentation is available at https://aeon-toolkit.org.
The following modules are still considered experimental, and the [deprecation policy](https://www.aeon-toolkit.org/en/stable/developer_guide/deprecation.html)
does not apply:
`anomaly_detection`, `benchmarking`, `segmentation`, `similarity_search`,
`testing`, `transformations/series`, `visualisation`
| Overview | |
|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **CI/CD** | [](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [](https://www.aeon-toolkit.org/en/stable/) [](https://www.aeon-toolkit.org/en/latest/) [](https://codecov.io/gh/aeon-toolkit/aeon) [](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat)|
| **Code** | [](https://pypi.org/project/aeon/) [](https://anaconda.org/conda-forge/aeon) [](https://www.python.org/) [](https://github.com/psf/black) [](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) |
| **Community** | [](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [](https://www.linkedin.com/company/aeon-toolkit/) [](https://twitter.com/aeon_toolkit) |
## ⚙️ Installation
`aeon` requires a Python version of 3.9 or greater. Our full installation guide is
available in our [documentation](https://www.aeon-toolkit.org/en/stable/installation.html).
The easiest way to install `aeon` is via pip:
```bash
pip install aeon
```
Some estimators require additional packages to be installed. If you want to install
the full package with all optional dependencies, you can use:
```bash
pip install aeon[all_extras]
```
Instructions for installation from the [GitHub source](https://github.com/aeon-toolkit/aeon)
can be found [here](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html).
## ⏲️ Getting started
The best place to get started for all `aeon` packages is our [getting started guide](https://www.aeon-toolkit.org/en/stable/getting_started.html).
Below we provide a quick example of how to use `aeon` for forecasting,
classification and clustering.
### Classification
*It's worth mentioning that the classifier used in the example can easily be
swapped out for a regressor, and the labels for numeric targets. This flexibility
allowing for seamless adaptation to different tasks and datasets while preserving
API consistency.*
```python
import numpy as np
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
X = [[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)
[[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length,
[[8, 7, 6, 5, 4, 4]]]
y = ['low', 'low', 'high'] # class labels for each sample
X = np.array(X)
y = np.array(y)
clf = KNeighborsTimeSeriesClassifier(distance="dtw")
clf.fit(X, y) # fit the classifier on train data
>>> KNeighborsTimeSeriesClassifier()
X_test = np.array(
[[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
y_pred = clf.predict(X_test) # make class predictions on new data
>>> ['low' 'high' 'high']
```
### Clustering
```python
import numpy as np
from aeon.clustering import TimeSeriesKMeans
X = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)
[[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length,
[[8, 7, 6, 5, 4, 4]]])
clu = TimeSeriesKMeans(distance="dtw", n_clusters=2)
clu.fit(X) # fit the clusterer on train data
>>> TimeSeriesKMeans(distance='dtw', n_clusters=2)
clu.labels_ # get training cluster labels
>>> array([0, 0, 1])
X_test = np.array(
[[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
clu.predict(X_test) # Assign clusters to new data
>>> array([1, 0, 0])
```
## 💬 Where to ask questions
| Type | Platforms |
|-------------------------------------|----------------------------------|
| 🐛 **Bug Reports** | [GitHub Issue Tracker] |
| ✨ **Feature Requests & Ideas** | [GitHub Issue Tracker] & [Slack] |
| 💻 **Usage Questions** | [GitHub Discussions] & [Slack] |
| 💬 **General Discussion** | [GitHub Discussions] & [Slack] |
| 🏭 **Contribution & Development** | [Slack] |
[GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues
[GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions
[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA
Raw data
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"description": "<p align=\"center\">\n <a href=\"https://aeon-toolkit.org\"><img src=\"https://raw.githubusercontent.com/aeon-toolkit/aeon/main/docs/images/logo/aeon-logo-blue-compact.png\" width=\"50%\" alt=\"aeon logo\" /></a>\n</p>\n\n# \u231b Welcome to aeon\n\n`aeon` is an open-source toolkit for learning from time series. It is compatible with\n[scikit-learn](https://scikit-learn.org) and provides access to the very latest\nalgorithms for time series machine learning, in addition to a range of classical\ntechniques for learning tasks such as forecasting and classification.\n\nWe strive to provide a broad library of time series algorithms including the\nlatest advances, offer efficient implementations using numba, and interfaces with other\ntime series packages to provide a single framework for algorithm comparison.\n\nThe latest `aeon` release is `v0.11.1`. You can view the full changelog\n[here](https://www.aeon-toolkit.org/en/stable/changelog.html).\n\nOur webpage and documentation is available at https://aeon-toolkit.org.\n\nThe following modules are still considered experimental, and the [deprecation policy](https://www.aeon-toolkit.org/en/stable/developer_guide/deprecation.html)\ndoes not apply:\n\n`anomaly_detection`, `benchmarking`, `segmentation`, `similarity_search`,\n`testing`, `transformations/series`, `visualisation`\n\n| Overview | |\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **CI/CD** | [](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [](https://www.aeon-toolkit.org/en/stable/) [](https://www.aeon-toolkit.org/en/latest/) [](https://codecov.io/gh/aeon-toolkit/aeon) [](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat)|\n| **Code** | [](https://pypi.org/project/aeon/) [](https://anaconda.org/conda-forge/aeon) [](https://www.python.org/) [](https://github.com/psf/black) [](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) |\n| **Community** | [](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [](https://www.linkedin.com/company/aeon-toolkit/) [](https://twitter.com/aeon_toolkit) |\n\n## \u2699\ufe0f Installation\n\n`aeon` requires a Python version of 3.9 or greater. Our full installation guide is\navailable in our [documentation](https://www.aeon-toolkit.org/en/stable/installation.html).\n\nThe easiest way to install `aeon` is via pip:\n\n```bash\npip install aeon\n```\n\nSome estimators require additional packages to be installed. If you want to install\nthe full package with all optional dependencies, you can use:\n\n```bash\npip install aeon[all_extras]\n```\n\nInstructions for installation from the [GitHub source](https://github.com/aeon-toolkit/aeon)\ncan be found [here](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html).\n\n## \u23f2\ufe0f Getting started\n\nThe best place to get started for all `aeon` packages is our [getting started guide](https://www.aeon-toolkit.org/en/stable/getting_started.html).\n\nBelow we provide a quick example of how to use `aeon` for forecasting,\nclassification and clustering.\n\n### Classification\n\n*It's worth mentioning that the classifier used in the example can easily be\nswapped out for a regressor, and the labels for numeric targets. This flexibility\nallowing for seamless adaptation to different tasks and datasets while preserving\nAPI consistency.*\n\n```python\nimport numpy as np\nfrom aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n\nX = [[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)\n [[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length,\n [[8, 7, 6, 5, 4, 4]]]\ny = ['low', 'low', 'high'] # class labels for each sample\nX = np.array(X)\ny = np.array(y)\n\nclf = KNeighborsTimeSeriesClassifier(distance=\"dtw\")\nclf.fit(X, y) # fit the classifier on train data\n>>> KNeighborsTimeSeriesClassifier()\n\nX_test = np.array(\n [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]\n)\ny_pred = clf.predict(X_test) # make class predictions on new data\n>>> ['low' 'high' 'high']\n```\n\n### Clustering\n\n```python\nimport numpy as np\nfrom aeon.clustering import TimeSeriesKMeans\n\nX = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)\n [[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length,\n [[8, 7, 6, 5, 4, 4]]])\n\nclu = TimeSeriesKMeans(distance=\"dtw\", n_clusters=2)\nclu.fit(X) # fit the clusterer on train data\n>>> TimeSeriesKMeans(distance='dtw', n_clusters=2)\n\nclu.labels_ # get training cluster labels\n>>> array([0, 0, 1])\n\nX_test = np.array(\n [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]\n)\nclu.predict(X_test) # Assign clusters to new data\n>>> array([1, 0, 0])\n```\n\n## \ud83d\udcac Where to ask questions\n\n| Type | Platforms |\n|-------------------------------------|----------------------------------|\n| \ud83d\udc1b **Bug Reports** | [GitHub Issue Tracker] |\n| \u2728 **Feature Requests & Ideas** | [GitHub Issue Tracker] & [Slack] |\n| \ud83d\udcbb **Usage Questions** | [GitHub Discussions] & [Slack] |\n| \ud83d\udcac **General Discussion** | [GitHub Discussions] & [Slack] |\n| \ud83c\udfed **Contribution & Development** | [Slack] |\n\n[GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues\n[GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions\n[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA\n",
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