:rocket: **Version 2.11.0 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/skpro/blob/main/LICENSE) [](https://gc-os-ai.github.io/) | | **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, y_test = 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://github.com/sktime/skpro/blob/main/CONTRIBUTING.md [donate]: https://opencollective.com/sktime [developer guides]: https://skpro.readthedocs.io/en/latest/developer_guide.html [contributors]: https://github.com/sktime/skpro/graphs/contributors [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).