Skpro

<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 2.11.0 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 | |

: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. |

: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 |

: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.

skpro curates libraries of components of the following types:

| Module | Status | Links | |---|---|---| | Probabilistic tabular regression | maturing | Tutorial · API Reference · Extension Template | | Time-to-event (survival) prediction | maturing | Tutorial · API Reference · Extension Template | | Performance metrics | maturing | API Reference | | Probability distributions | maturing | Tutorial · API Reference · Extension Template |

:hourglass_flowing_sand: 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.

:zap: 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, 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 mod