<a href="https://skpro.readthedocs.io/en/latest"><img src="https://github.com/sktime/skpro/blob/main/docs/source/images/skpro-banner.png" width="500" align="right" /></a>
:rocket: **Version 2.6.0 out now!** [Read the release notes here.](https://skpro.readthedocs.io/en/latest/changelog.html).
`skpro` is a library for supervised probabilistic prediction in python.
It provides `scikit-learn`-like, `scikit-base` compatible interfaces to:
* tabular **supervised regressors for probabilistic prediction** - interval, quantile and distribution predictions
* tabular **probabilistic time-to-event and survival prediction** - instance-individual survival distributions
* **metrics to evaluate probabilistic predictions**, e.g., pinball loss, empirical coverage, CRPS, survival losses
* **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal
* building **pipelines and composite models**, including tuning via probabilistic performance metrics
* symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface
| Overview | |
|---|---|
| **Open Source** | [](https://github.com/sktime/sktime/blob/main/LICENSE) |
| **Tutorials** | [](https://mybinder.org/v2/gh/sktime/skpro/main?filepath=examples) [](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
| **Community** | [](https://discord.com/invite/54ACzaFsn7) [](https://www.linkedin.com/company/scikit-time/) |
| **CI/CD** | [](https://github.com/sktime/skpro/actions/workflows/wheels.yml) [](https://codecov.io/gh/sktime/skpro) [](https://skpro.readthedocs.io/en/latest/) [](https://github.com/sktime/skpro) |
| **Code** | [](https://pypi.org/project/skpro/) [](https://anaconda.org/conda-forge/skpro) [](https://www.python.org/) [](https://github.com/psf/black) |
| **Downloads** |   [)](https://pepy.tech/project/skpro) |
| **Citation** | [](https://zenodo.org/doi/10.5281/zenodo.11002671) |
## :books: Documentation
| Documentation | |
| -------------------------- | -------------------------------------------------------------- |
| :star: **[Tutorials]** | New to skpro? Here's everything you need to know! |
| :clipboard: **[Binder Notebooks]** | Example notebooks to play with in your browser. |
| :woman_technologist: **[User Guides]** | How to use skpro and its features. |
| :scissors: **[Extension Templates]** | How to build your own estimator using skpro's API. |
| :control_knobs: **[API Reference]** | The detailed reference for skpro's API. |
| :hammer_and_wrench: **[Changelog]** | Changes and version history. |
| :deciduous_tree: **[Roadmap]** | skpro's software and community development plan. |
| :pencil: **[Related Software]** | A list of related software. |
[tutorials]: https://skpro.readthedocs.io/en/latest/tutorials.html
[binder notebooks]: https://mybinder.org/v2/gh/sktime/skpro/main?filepath=examples
[user guides]: https://skpro.readthedocs.io/en/latest/user_guide.html
[extension templates]: https://github.com/sktime/skpro/tree/main/extension_templates
[api reference]: https://skpro.readthedocs.io/en/latest/api_reference.html
[changelog]: https://skpro.readthedocs.io/en/latest/changelog.html
[roadmap]: https://skpro.readthedocs.io/en/latest/roadmap.html
[related software]: https://skpro.readthedocs.io/en/latest/related_software.html
## :speech_balloon: Where to ask questions
Questions and feedback are extremely welcome!
We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.
`skpro` is maintained by the `sktime` community, we use the same social channels.
| Type | Platforms |
| ------------------------------- | --------------------------------------- |
| :bug: **Bug Reports** | [GitHub Issue Tracker] |
| :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker] |
| :woman_technologist: **Usage Questions** | [GitHub Discussions] · [Stack Overflow] |
| :speech_balloon: **General Discussion** | [GitHub Discussions] |
| :factory: **Contribution & Development** | `dev-chat` channel · [Discord] |
| :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 13 UTC, dev/meet-ups channel |
[github issue tracker]: https://github.com/sktime/skpro/issues
[github discussions]: https://github.com/sktime/skpro/discussions
[stack overflow]: https://stackoverflow.com/questions/tagged/sktime
[discord]: https://discord.com/invite/54ACzaFsn7
## :dizzy: Features
Our objective is to enhance the interoperability and usability of the AI model ecosystem:
* ``skpro`` is compatible with [scikit-learn] and [sktime], e.g., an ``sktime`` proba forecaster can
be built with an ``skpro`` proba regressor which in an ``sklearn`` regressor with proba mode added by ``skpro``
* ``skpro`` provides a mini-package management framework for first-party implementations,
and for interfacing popular second- and third-party components,
such as [cyclic-boosting], [MAPIE], or [ngboost] packages.
[scikit-learn]: https://scikit-learn.org/stable/
[sktime]: https://www.sktime.net
[MAPIE]: https://mapie.readthedocs.io/en/latest/
[cyclic-boosting]: https://cyclic-boosting.readthedocs.io/en/latest/
[ngboost]: https://stanfordmlgroup.github.io/projects/ngboost/
``skpro`` curates libraries of components of the following types:
| Module | Status | Links |
|---|---|---|
| **[Probabilistic tabular regression]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/01_skpro_intro.ipynb) · [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/regression.html) · [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/regression.py) |
| **[Time-to-event (survival) prediction]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/02_skpro_survival.ipynb) · [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/survival.html) · [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/survival.py) |
| **[Performance metrics]** | maturing | [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/metrics.html) |
| **[Probability distributions]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/03_skpro_distributions.ipynb) · [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/distributions.html) · [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/distributions.py) |
[Probabilistic tabular regression]: https://github.com/sktime/skpro/tree/main/skpro/regression
[Time-to-event (survival) prediction]: https://github.com/sktime/skpro/tree/main/skpro/survival
[Performance metrics]: https://github.com/sktime/skpro/tree/main/skpro/metrics
[Probability distributions]: https://github.com/sktime/skpro/tree/main/skpro/distributions
## :hourglass_flowing_sand: Installing `skpro`
To install `skpro`, use `pip`:
```bash
pip install skpro
```
or, with maximum dependencies,
```bash
pip install skpro[all_extras]
```
Releases are available as source packages and binary wheels. You can see all available wheels [here](https://pypi.org/simple/skpro/).
## :zap: Quickstart
### Making probabilistic predictions
``` python
from sklearn.datasets import load_diabetes
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from skpro.regression.residual import ResidualDouble
# step 1: data specification
X, y = load_diabetes(return_X_y=True, as_frame=True)
X_train, X_new, y_train, _ = train_test_split(X, y)
# step 2: specifying the regressor - any compatible regressor is valid!
# example - "squaring residuals" regressor
# random forest for mean prediction
# linear regression for variance prediction
reg_mean = RandomForestRegressor()
reg_resid = LinearRegression()
reg_proba = ResidualDouble(reg_mean, reg_resid)
# step 3: fitting the model to training data
reg_proba.fit(X_train, y_train)
# step 4: predicting labels on new data
# probabilistic prediction modes - pick any or multiple
# full distribution prediction
y_pred_proba = reg_proba.predict_proba(X_new)
# interval prediction
y_pred_interval = reg_proba.predict_interval(X_new, coverage=0.9)
# quantile prediction
y_pred_quantiles = reg_proba.predict_quantiles(X_new, alpha=[0.05, 0.5, 0.95])
# variance prediction
y_pred_var = reg_proba.predict_var(X_new)
# mean prediction is same as "classical" sklearn predict, also available
y_pred_mean = reg_proba.predict(X_new)
```
### Evaluating predictions
``` python
# step 5: specifying evaluation metric
from skpro.metrics import CRPS
metric = CRPS() # continuous rank probability score - any skpro metric works!
# step 6: evaluat metric, compare predictions to actuals
metric(y_test, y_pred_proba)
>>> 32.19
```
## :wave: How to get involved
There are many ways to get involved with development of `skpro`, which is
developed by the `sktime` community.
We follow the [all-contributors](https://github.com/all-contributors/all-contributors)
specification: all kinds of contributions are welcome - not just code.
| Documentation | |
| -------------------------- | -------------------------------------------------------------- |
| :gift_heart: **[Contribute]** | How to contribute to skpro. |
| :school_satchel: **[Mentoring]** | New to open source? Apply to our mentoring program! |
| :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! |
| :woman_mechanic: **[Developer Guides]** | How to further develop the skpro code base. |
| :medal_sports: **[Contributors]** | A list of all contributors. |
| :raising_hand: **[Roles]** | An overview of our core community roles. |
| :money_with_wings: **[Donate]** | Fund sktime and skpro maintenance and development. |
| :classical_building: **[Governance]** | How and by whom decisions are made in the sktime community. |
[contribute]: https://skpro.readthedocs.io/en/latest/get_involved/contributing.html
[donate]: https://opencollective.com/sktime
[developer guides]: https://skpro.readthedocs.io/en/latest/developer_guide.html
[contributors]: https://github.com/sktime/skpro/blob/main/CONTRIBUTORS.md
[governance]: https://www.sktime.net/en/latest/get_involved/governance.html
[mentoring]: https://github.com/sktime/mentoring
[meetings]: https://calendar.google.com/calendar/u/0/embed?src=sktime.toolbox@gmail.com&ctz=UTC
[roles]: https://www.sktime.net/en/latest/about/team.html
## :wave: Citation
To cite `skpro` in a scientific publication, see [citations](CITATION.rst).
Raw data
{
"_id": null,
"home_page": null,
"name": "skpro",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.9",
"maintainer_email": "skpro developers <sktime.toolbox@gmail.com>",
"keywords": "data-science, machine-learning, data-mining, time-series, scikit-learn, regression",
"author": "Franz Kir\u00e1ly, Frithjof Gressmann, Vitaly Davydov",
"author_email": "skpro developers <sktime.toolbox@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/89/d1/481b951d4aa2da094b08064a05f6f2586bc75b083560d5d3aa3f6d6e7116/skpro-2.6.0.tar.gz",
"platform": null,
"description": "<a href=\"https://skpro.readthedocs.io/en/latest\"><img src=\"https://github.com/sktime/skpro/blob/main/docs/source/images/skpro-banner.png\" width=\"500\" align=\"right\" /></a>\n\n:rocket: **Version 2.6.0 out now!** [Read the release notes here.](https://skpro.readthedocs.io/en/latest/changelog.html).\n\n`skpro` is a library for supervised probabilistic prediction in python.\nIt provides `scikit-learn`-like, `scikit-base` compatible interfaces to:\n\n* tabular **supervised regressors for probabilistic prediction** - interval, quantile and distribution predictions\n* tabular **probabilistic time-to-event and survival prediction** - instance-individual survival distributions\n* **metrics to evaluate probabilistic predictions**, e.g., pinball loss, empirical coverage, CRPS, survival losses\n* **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal\n* building **pipelines and composite models**, including tuning via probabilistic performance metrics\n* symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface\n\n| Overview | |\n|---|---|\n| **Open Source** | [](https://github.com/sktime/sktime/blob/main/LICENSE) |\n| **Tutorials** | [](https://mybinder.org/v2/gh/sktime/skpro/main?filepath=examples) [](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |\n| **Community** | [](https://discord.com/invite/54ACzaFsn7) [](https://www.linkedin.com/company/scikit-time/) |\n| **CI/CD** | [](https://github.com/sktime/skpro/actions/workflows/wheels.yml) [](https://codecov.io/gh/sktime/skpro) [](https://skpro.readthedocs.io/en/latest/) [](https://github.com/sktime/skpro) |\n| **Code** | [](https://pypi.org/project/skpro/) [](https://anaconda.org/conda-forge/skpro) [](https://www.python.org/) [](https://github.com/psf/black) |\n| **Downloads** |   [)](https://pepy.tech/project/skpro) |\n| **Citation** | [](https://zenodo.org/doi/10.5281/zenodo.11002671) |\n\n## :books: Documentation\n\n| Documentation | |\n| -------------------------- | -------------------------------------------------------------- |\n| :star: **[Tutorials]** | New to skpro? Here's everything you need to know! |\n| :clipboard: **[Binder Notebooks]** | Example notebooks to play with in your browser. |\n| :woman_technologist: **[User Guides]** | How to use skpro and its features. |\n| :scissors: **[Extension Templates]** | How to build your own estimator using skpro's API. |\n| :control_knobs: **[API Reference]** | The detailed reference for skpro's API. |\n| :hammer_and_wrench: **[Changelog]** | Changes and version history. |\n| :deciduous_tree: **[Roadmap]** | skpro's software and community development plan. |\n| :pencil: **[Related Software]** | A list of related software. |\n\n[tutorials]: https://skpro.readthedocs.io/en/latest/tutorials.html\n[binder notebooks]: https://mybinder.org/v2/gh/sktime/skpro/main?filepath=examples\n[user guides]: https://skpro.readthedocs.io/en/latest/user_guide.html\n[extension templates]: https://github.com/sktime/skpro/tree/main/extension_templates\n[api reference]: https://skpro.readthedocs.io/en/latest/api_reference.html\n[changelog]: https://skpro.readthedocs.io/en/latest/changelog.html\n[roadmap]: https://skpro.readthedocs.io/en/latest/roadmap.html\n[related software]: https://skpro.readthedocs.io/en/latest/related_software.html\n\n\n## :speech_balloon: Where to ask questions\n\nQuestions and feedback are extremely welcome!\nWe strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.\n\n`skpro` is maintained by the `sktime` community, we use the same social channels.\n\n| Type | Platforms |\n| ------------------------------- | --------------------------------------- |\n| :bug: **Bug Reports** | [GitHub Issue Tracker] |\n| :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker] |\n| :woman_technologist: **Usage Questions** | [GitHub Discussions] \u00b7 [Stack Overflow] |\n| :speech_balloon: **General Discussion** | [GitHub Discussions] |\n| :factory: **Contribution & Development** | `dev-chat` channel \u00b7 [Discord] |\n| :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 13 UTC, dev/meet-ups channel |\n\n[github issue tracker]: https://github.com/sktime/skpro/issues\n[github discussions]: https://github.com/sktime/skpro/discussions\n[stack overflow]: https://stackoverflow.com/questions/tagged/sktime\n[discord]: https://discord.com/invite/54ACzaFsn7\n\n\n## :dizzy: Features\n\nOur objective is to enhance the interoperability and usability of the AI model ecosystem:\n\n* ``skpro`` is compatible with [scikit-learn] and [sktime], e.g., an ``sktime`` proba forecaster can\nbe built with an ``skpro`` proba regressor which in an ``sklearn`` regressor with proba mode added by ``skpro``\n\n* ``skpro`` provides a mini-package management framework for first-party implementations,\nand for interfacing popular second- and third-party components,\nsuch as [cyclic-boosting], [MAPIE], or [ngboost] packages.\n\n[scikit-learn]: https://scikit-learn.org/stable/\n[sktime]: https://www.sktime.net\n[MAPIE]: https://mapie.readthedocs.io/en/latest/\n[cyclic-boosting]: https://cyclic-boosting.readthedocs.io/en/latest/\n[ngboost]: https://stanfordmlgroup.github.io/projects/ngboost/\n\n``skpro`` curates libraries of components of the following types:\n\n| Module | Status | Links |\n|---|---|---|\n| **[Probabilistic tabular regression]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/01_skpro_intro.ipynb) \u00b7 [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/regression.html) \u00b7 [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/regression.py) |\n| **[Time-to-event (survival) prediction]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/02_skpro_survival.ipynb) \u00b7 [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/survival.html) \u00b7 [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/survival.py) |\n| **[Performance metrics]** | maturing | [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/metrics.html) |\n| **[Probability distributions]** | maturing | [Tutorial](https://github.com/sktime/skpro/blob/main/examples/03_skpro_distributions.ipynb) \u00b7 [API Reference](https://skpro.readthedocs.io/en/latest/api_reference/distributions.html) \u00b7 [Extension Template](https://github.com/sktime/skpro/blob/main/extension_templates/distributions.py) |\n\n[Probabilistic tabular regression]: https://github.com/sktime/skpro/tree/main/skpro/regression\n[Time-to-event (survival) prediction]: https://github.com/sktime/skpro/tree/main/skpro/survival\n[Performance metrics]: https://github.com/sktime/skpro/tree/main/skpro/metrics\n[Probability distributions]: https://github.com/sktime/skpro/tree/main/skpro/distributions\n\n\n## :hourglass_flowing_sand: Installing `skpro`\n\nTo install `skpro`, use `pip`:\n\n```bash\npip install skpro\n```\n\nor, with maximum dependencies,\n\n```bash\npip install skpro[all_extras]\n```\n\nReleases are available as source packages and binary wheels. You can see all available wheels [here](https://pypi.org/simple/skpro/).\n\n## :zap: Quickstart\n\n### Making probabilistic predictions\n\n``` python\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\n\nfrom skpro.regression.residual import ResidualDouble\n\n# step 1: data specification\nX, y = load_diabetes(return_X_y=True, as_frame=True)\nX_train, X_new, y_train, _ = train_test_split(X, y)\n\n# step 2: specifying the regressor - any compatible regressor is valid!\n# example - \"squaring residuals\" regressor\n# random forest for mean prediction\n# linear regression for variance prediction\nreg_mean = RandomForestRegressor()\nreg_resid = LinearRegression()\nreg_proba = ResidualDouble(reg_mean, reg_resid)\n\n# step 3: fitting the model to training data\nreg_proba.fit(X_train, y_train)\n\n# step 4: predicting labels on new data\n\n# probabilistic prediction modes - pick any or multiple\n\n# full distribution prediction\ny_pred_proba = reg_proba.predict_proba(X_new)\n\n# interval prediction\ny_pred_interval = reg_proba.predict_interval(X_new, coverage=0.9)\n\n# quantile prediction\ny_pred_quantiles = reg_proba.predict_quantiles(X_new, alpha=[0.05, 0.5, 0.95])\n\n# variance prediction\ny_pred_var = reg_proba.predict_var(X_new)\n\n# mean prediction is same as \"classical\" sklearn predict, also available\ny_pred_mean = reg_proba.predict(X_new)\n```\n\n### Evaluating predictions\n\n``` python\n# step 5: specifying evaluation metric\nfrom skpro.metrics import CRPS\n\nmetric = CRPS() # continuous rank probability score - any skpro metric works!\n\n# step 6: evaluat metric, compare predictions to actuals\nmetric(y_test, y_pred_proba)\n>>> 32.19\n```\n\n## :wave: How to get involved\n\nThere are many ways to get involved with development of `skpro`, which is\ndeveloped by the `sktime` community.\nWe follow the [all-contributors](https://github.com/all-contributors/all-contributors)\nspecification: all kinds of contributions are welcome - not just code.\n\n| Documentation | |\n| -------------------------- | -------------------------------------------------------------- |\n| :gift_heart: **[Contribute]** | How to contribute to skpro. |\n| :school_satchel: **[Mentoring]** | New to open source? Apply to our mentoring program! |\n| :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! |\n| :woman_mechanic: **[Developer Guides]** | How to further develop the skpro code base. |\n| :medal_sports: **[Contributors]** | A list of all contributors. |\n| :raising_hand: **[Roles]** | An overview of our core community roles. |\n| :money_with_wings: **[Donate]** | Fund sktime and skpro maintenance and development. |\n| :classical_building: **[Governance]** | How and by whom decisions are made in the sktime community. |\n\n[contribute]: https://skpro.readthedocs.io/en/latest/get_involved/contributing.html\n[donate]: https://opencollective.com/sktime\n[developer guides]: https://skpro.readthedocs.io/en/latest/developer_guide.html\n[contributors]: https://github.com/sktime/skpro/blob/main/CONTRIBUTORS.md\n[governance]: https://www.sktime.net/en/latest/get_involved/governance.html\n[mentoring]: https://github.com/sktime/mentoring\n[meetings]: https://calendar.google.com/calendar/u/0/embed?src=sktime.toolbox@gmail.com&ctz=UTC\n[roles]: https://www.sktime.net/en/latest/about/team.html\n\n\n## :wave: Citation\n\nTo cite `skpro` in a scientific publication, see [citations](CITATION.rst).\n",
"bugtrack_url": null,
"license": null,
"summary": "A unified framework for probability distributions and probabilistic supervised regression",
"version": "2.6.0",
"project_urls": {
"API Reference": "https://github.com/sktime/skpro",
"Documentation": "https://github.com/sktime/skpro",
"Download": "https://pypi.org/project/skpro/#files",
"Homepage": "https://github.com/sktime/skpro",
"Release Notes": "https://github.com/sktime/skpro",
"Repository": "https://github.com/sktime/skpro"
},
"split_keywords": [
"data-science",
" machine-learning",
" data-mining",
" time-series",
" scikit-learn",
" regression"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "9d0e6102701e4d8582c3420c42dd675d5019b8909a3f41cd151fbce82ca9d4e9",
"md5": "0605ca97423776a4a9252873e4f64e6b",
"sha256": "542bb6047217146b79918cf3666dfe4f15949e00c494ec26e9730831153078ed"
},
"downloads": -1,
"filename": "skpro-2.6.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0605ca97423776a4a9252873e4f64e6b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.13,>=3.9",
"size": 381825,
"upload_time": "2024-09-09T08:42:49",
"upload_time_iso_8601": "2024-09-09T08:42:49.771352Z",
"url": "https://files.pythonhosted.org/packages/9d/0e/6102701e4d8582c3420c42dd675d5019b8909a3f41cd151fbce82ca9d4e9/skpro-2.6.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "89d1481b951d4aa2da094b08064a05f6f2586bc75b083560d5d3aa3f6d6e7116",
"md5": "204ce41755cf3ca63aee7c727bdc3d43",
"sha256": "bb8ccbd6c7a3650be21a5940ba6ae167133331d0224264f298c93ccee2b3653e"
},
"downloads": -1,
"filename": "skpro-2.6.0.tar.gz",
"has_sig": false,
"md5_digest": "204ce41755cf3ca63aee7c727bdc3d43",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.13,>=3.9",
"size": 262037,
"upload_time": "2024-09-09T08:42:51",
"upload_time_iso_8601": "2024-09-09T08:42:51.212970Z",
"url": "https://files.pythonhosted.org/packages/89/d1/481b951d4aa2da094b08064a05f6f2586bc75b083560d5d3aa3f6d6e7116/skpro-2.6.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-09 08:42:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "sktime",
"github_project": "skpro",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"lcname": "skpro"
}
JSON
