skpro 2.0.1

NOTE: for historical reasons, version numbers of maturing versions start at 2.0.0.

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
• metrics to evaluate probabilistic predictions, e.g., pinball loss, empirical coverage, CRPS
• 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

:books: Documentation

: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.

: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, _ = 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

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

:wave: Citation

To cite skpro in a scientific publication, see citations.