pyrregular documentation
CI/CD |
|
Code |
|
Community |
|
Paper |
Installation
You can install via pip with:
pip install pyrregular
For third party models use:
pip install pyrregular[models]
Quick Guide
List datasets
If you want to see all the datasets available, you can use the list_datasets
function:
from pyrregular import list_datasets
df = list_datasets()
Load a dataset
To load a dataset, you can use the load_dataset
function. For example, to load the “Garment” dataset, you can do:
from pyrregular import load_dataset
df = load_dataset("Garment.h5")
The dataset is saved in the default os cache directory, which can be found with:
import pooch
print(pooch.os_cache("pyrregular"))
The repository is hosted at: https://huggingface.co/datasets/splandi/pyrregular/
Downstream tasks
Classification
To use the dataset for classification, you can just “densify” it:
from pyrregular import load_dataset
df = load_dataset("Garment.h5")
X, _ = df.irr.to_dense()
y, split = df.irr.get_task_target_and_split()
X_train, X_test = X[split != "test"], X[split == "test"]
y_train, y_test = y[split != "test"], y[split == "test"]
# We have ready-to-go models from various libraries:
from pyrregular.models.rocket import rocket_pipeline
model = rocket_pipeline
model.fit(X_train, y_train)
model.score(X_test, y_test)
Available Datasets
📈 Dataset |
📖 Source |
---|---|
Alembics Bowls Flasks |
Spinnato & Landi, 2025 |
AllGestureWiimoteX |
Guna et al., 2014 |
AllGestureWiimoteY |
Guna et al., 2014 |
AllGestureWiimoteZ |
Guna et al., 2014 |
Animals |
Ferrero et al., 2018 |
AsphaltObstaclesCoordinates |
Souza, 2018 |
AsphaltPavementTypeCoordinates |
Souza, 2018 |
AsphaltRegularityCoordinates |
Souza, 2018 |
CharacterTrajectories |
Williams et al., 2006 |
DodgerLoopDay |
Ihler et al., 2006 |
DodgerLoopGame |
Ihler et al., 2006 |
DodgerLoopWeekend |
Ihler et al., 2006 |
Geolife |
Zheng et al., 2009; Zheng et al., 2008; Zheng et al., 2010 |
GestureMidAirD1 |
Caputo et al., 2018 |
GestureMidAirD2 |
Caputo et al., 2018 |
GestureMidAirD3 |
Caputo et al., 2018 |
GesturePebbleZ1 |
Mezari & Maglogiannis, 2018 |
GesturePebbleZ2 |
Mezari & Maglogiannis, 2018 |
GPS Data of Seabirds |
Browning et al., 2018 |
InsectWingbeat |
Chen et al., 2014 |
JapaneseVowels |
Kudo et al., 1999 |
Localization Data for Person Activity |
Vidulin et al., 2010 |
MelbournePedestrian |
City of Melbourne, 2019 |
MIMIC-III Clinical Database (Demo) |
Johnson et al., 2016; Johnson et al., 2019; Goldberger et al., 2000 |
PAMAP2 Physical Activity Monitoring |
Reiss & Stricker, 2012 |
PhysioNet 2012 |
Silva et al., 2012 |
PhysioNet 2019 |
Reyna et al., 2020 |
PickupGestureWiimoteZ |
Guna et al., 2014 |
PLAID |
Gao et al., 2014 |
Productivity Prediction of Garment Employees |
Imran et al., 2021 |
ShakeGestureWiimoteZ |
Guna et al., 2014 |
SpokenArabicDigits |
Hammami & Bedda, 2010 |
Taxi |
Moreira-Matias et al., 2013 |
Vehicles |
Chorochronos Archive, 2019 |
Citation
If you use this package in your research, please cite the following paper:
@misc{spinnato2025pyrregular,
title={PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks},
author={Francesco Spinnato and Cristiano Landi},
year={2025},
eprint={2505.06047},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.06047},
}