Version 2.9.0 out now! Read the release notes here..

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
Tutorials
Community
CI/CD
Code
Downloads	)
Citation

Documentation

Documentation
Tutorials	New to skpro? Here’s everything you need to know!
Binder Notebooks	Example notebooks to play with in your browser.
User Guides	How to use skpro and its features.
Extension Templates	How to build your own estimator using skpro’s API.
API Reference	The detailed reference for skpro’s API.
Changelog	Changes and version history.
Roadmap	skpro’s software and community development plan.
Related Software	A list of related software.

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 Reports	GitHub Issue Tracker
Feature Requests & Ideas	GitHub Issue Tracker
Usage Questions	GitHub Discussions · Stack Overflow
General Discussion	GitHub Discussions
Contribution & Development	dev-chat channel · Discord
Community collaboration session	Discord - Fridays 13 UTC, dev/meet-ups channel

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.

skpro curates libraries of components of the following types:

Installing skpro

To install skpro, use pip:

pip install skpro

or, with maximum dependencies,

pip install skpro[all_extras]

Releases are available as source packages and binary wheels. You can see all available wheels here.

Quickstart

Making probabilistic predictions

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

# 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

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
specification: all kinds of contributions are welcome - not just code.

Documentation
Contribute	How to contribute to skpro.
Mentoring	New to open source? Apply to our mentoring program!
sktime.toolbox@gmail.com&ctz=UTC">Meetings	Join our discussions, tutorials, workshops, and sprints!
Developer Guides	How to further develop the skpro code base.
Contributors	A list of all contributors.
Roles	An overview of our core community roles.
Donate	Fund sktime and skpro maintenance and development.
Governance	How and by whom decisions are made in the sktime community.

Citation

To cite skpro in a scientific publication, see citations.