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https://github.com/sktime/skpro
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
https://github.com/sktime/skpro
ai data-science distributional-regression distributions failure-prediction framework machine-learning prediction probability-distributions python regression sklearn sktime survival-analysis survival-models survival-prediction time-to-event
Last synced: 5 days ago
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A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
- Host: GitHub
- URL: https://github.com/sktime/skpro
- Owner: sktime
- License: bsd-3-clause
- Created: 2017-09-11T08:03:54.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2025-01-12T16:35:05.000Z (10 days ago)
- Last Synced: 2025-01-14T14:04:22.645Z (8 days ago)
- Topics: ai, data-science, distributional-regression, distributions, failure-prediction, framework, machine-learning, prediction, probability-distributions, python, regression, sklearn, sktime, survival-analysis, survival-models, survival-prediction, time-to-event
- Language: Python
- Homepage: https://skpro.readthedocs.io/en/latest
- Size: 11.7 MB
- Stars: 254
- Watchers: 12
- Forks: 51
- Open Issues: 84
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.rst
- Codeowners: CODEOWNERS
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
:rocket: **Version 2.8.0 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/[email protected]&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).