Time series learning with Python.
Project description
wildboar
wildboar is a Python module for temporal machine learning and fast distance computations built on top of scikit-learn and numpy distributed under the BSD 3-Clause license.
It is currently maintained by Isak Samsten
Features
Data | Classification | Regression | Explainability | Metric | Unsupervised | Outlier |
---|---|---|---|---|---|---|
Repositories | ShapeletForestClassifier |
ShapeletForestRegressor |
ShapeletForestCounterfactual |
UCR-suite | ShapeletForestTransform |
IsolationShapeletForest |
Classification (wildboar/ucr ) |
ExtraShapeletTreesClassifier |
ExtraShapeletTreesRegressor |
KNearestCounterfactual |
MASS | RandomShapeletEmbedding |
|
Regression (wildboar/tsereg ) |
RocketTreeClassifier |
RocketRegressor |
PrototypeCounterfactual |
DTW | RocketTransform |
|
Outlier detection (wildboar/outlier:easy ) |
RocketClassifier |
RandomShapeletRegressor |
IntervalImportance |
DDTW | IntervalTransform |
|
RandomShapeletClassifier |
RocketTreeRegressor |
ShapeletImportance |
WDTW | FeatureTransform |
||
RocketForestClassifier |
RocketForestRegressor |
MSM | MatrixProfileTransform |
|||
IntervalTreeClassifier |
IntervalTreeRegressor |
TWE | Segmentation | |||
IntervalForestClassifier |
IntervalForestRegressor |
LCSS | Motif discovery | |||
ProximityTreeClassifier |
ERP | SAX |
||||
ProximityForestClassifier |
EDR | PAA |
||||
HydraClassifier |
ADTW | MatrixProfileTransform |
||||
KNeighborsClassifier |
HydraTransform |
|||||
ElasticEnsembleClassifier |
KMeans with (W)DTW support |
|||||
DilatedShapeletClassifier |
KMedoids |
|||||
DilatedShapeletTransform |
See the documentation for examples.
Installation
Binaries
wildboar
is available through pip
and can be installed with:
pip install wildboar
Universal binaries are compiled for Python 3.8, 3.9, 3.10 and 3.11 running on GNU/Linux, Windows and macOS.
Compilation
If you already have a working installation of numpy, scikit-learn, scipy and cython, compiling and installing wildboar is as simple as:
pip install .
To install the requirements, use:
pip install -r requirements.txt
For complete instructions see the documentation
Usage
from wildboar.ensemble import ShapeletForestClassifier
from wildboar.datasets import load_dataset
x_train, x_test, y_train, y_test = load_dataset("GunPoint", merge_train_test=False)
c = ShapeletForestClassifier()
c.fit(x_train, y_train)
c.score(x_test, y_test)
The User guide includes more detailed usage instructions.
Changelog
The changelog records a
history of notable changes to wildboar
.
Development
Contributions are welcome! The developer's guide has detailed information about contributing code and more!
In short, pull requests should:
- be well motivated
- be formatted using Black
- add relevant tests
- add relevant documentation
Source code
You can check the latest sources with the command:
git clone https://github.com/wildboar-foundation/wildboar
Documentation
- HTML documentation: https://wildboar.dev
Citation
If you use wildboar
in a scientific publication, I would appreciate
citations to the paper:
-
Karlsson, I., Papapetrou, P. Boström, H., 2016. Generalized Random Shapelet Forests. In the Data Mining and Knowledge Discovery Journal
ShapeletForestClassifier
-
Isak Samsten, 2020. isaksamsten/wildboar: wildboar. Zenodo. doi:10.5281/zenodo.4264063
-
Karlsson, I., Rebane, J., Papapetrou, P. et al. Locally and globally explainable time series tweaking. Knowl Inf Syst 62, 1671–1700 (2020)
ShapeletForestCounterfactual
KNearestCounterfactual
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for wildboar-1.2.0b2-cp312-cp312-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 408d7af465779a8b2c3973ce9cc4491437aa4ae5fb64d20fc81a16d6c72798eb |
|
MD5 | 7317824016592e09ca158321414ef4ba |
|
BLAKE2b-256 | beeb42165585f4e620caca844de422395e226e3910e00bbd04495d055d9d9067 |
Hashes for wildboar-1.2.0b2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f5dfdfd10a908570c5bbbd39ae3312f41cbe87487f3686aa3c2604a4b5685561 |
|
MD5 | 0034a474729389d4af752b6b119f184d |
|
BLAKE2b-256 | 8be538430c9ab5c601231b8d4914ecf80d4d5206e1b0a113c9675da259d02c8c |
Hashes for wildboar-1.2.0b2-cp312-cp312-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 89ff5c0ec758af061d2e37737a9447e667abe098a969aab76c5383dd454c2d40 |
|
MD5 | 9822f6e8167c9689fda6a20f3d84a1ff |
|
BLAKE2b-256 | b2a065d4d3101b868bcb2fe42ca1044f8eeaff737267bb88da845af75f6fd3e9 |
Hashes for wildboar-1.2.0b2-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c9eef26c484db1037c985821c7553027442b4f4cfb010a1373aad648904c23a5 |
|
MD5 | 1a9852fedc37ac43d248898f767d929e |
|
BLAKE2b-256 | aa1d8fd4f58dad2c5ec8b96262c77b8eee6dbb8f914ddbed9327000f779542c8 |
Hashes for wildboar-1.2.0b2-cp312-cp312-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fa05e1671a57b78749c9e8a5ab3c8ea296a4bf5e217975190952d1d0c798bd15 |
|
MD5 | 6fefb918d5d513aa86416db4e842b4c4 |
|
BLAKE2b-256 | 2a6ac9cb5ff8c0ca7cb4dd344c2b1725342d521a4935c7f9d95a3c35a1676f96 |
Hashes for wildboar-1.2.0b2-cp311-cp311-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e7cd99462a42fafc7de4d74498c4c6ae1b28a03913200d49b9cbc6e0015898c9 |
|
MD5 | 97ad5c88809efcac23451d5b08146fc5 |
|
BLAKE2b-256 | 51cf6a86fb35693694ae3b97ed5f092896a88816f54a6193ed66be277d226a01 |
Hashes for wildboar-1.2.0b2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0b119cb3fb37c478af274f2c71d81e9e4edf632f1c20837ddcf2756b16ae2e9f |
|
MD5 | 64997c612cf696e137671f927b47ea2f |
|
BLAKE2b-256 | 59553460709a6672f73c60335c9cd3e6e34eeecae1c88a29b31b23768d72797c |
Hashes for wildboar-1.2.0b2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5b0dd8de5242d0ac25df192aa71ab3057400d4f4c73b0eb9f5ed6015fd4e51b3 |
|
MD5 | c9da4b0689df63a682d32d696b1fc753 |
|
BLAKE2b-256 | 99ee108c50c5b2375cf28fa341e067eb76a4f2c5492ed85a62b08590d537ff82 |
Hashes for wildboar-1.2.0b2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3a92535757f00614a2a9f7605eceb590c9b78b9320fdcab695144d501fcc46a6 |
|
MD5 | 896252deb5bac39f2fd39df3642fa96e |
|
BLAKE2b-256 | a816268cdd838e85c5022e7146cd171dfb95489ea56200ea73ba75140d7b9289 |
Hashes for wildboar-1.2.0b2-cp311-cp311-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 14f314910e26c7d9335c7a5dd725cdd90e06196f8bb633e6462922cc9a01ebec |
|
MD5 | 271b490705b76e5d28babb1c3622085c |
|
BLAKE2b-256 | 704730468d8c230bc9f7cdc7ac9719fea7f512000049d3e9ae1d91b54d39a602 |
Hashes for wildboar-1.2.0b2-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2cd7451b8700c5c1bf92a1cc5009cc38964f8feb3663db8964d5b3e940e98ff8 |
|
MD5 | eb7ec8543d7aa9214be87fadaca86552 |
|
BLAKE2b-256 | fbf8f16c4ceef7cb0cc3fe0587c002fc7e7a36d92c5efa3aaad6fa0c375f0c1d |
Hashes for wildboar-1.2.0b2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05d4b7abe4f56c1d51c0f615f3768fd36a8db780031257a26223e5a36a8995d6 |
|
MD5 | e1a13b84717baf711fc85db9785e7ac9 |
|
BLAKE2b-256 | 96b0f05249405e1a59f3c095905aa9396e2640031a421672447c4596be2c6bec |
Hashes for wildboar-1.2.0b2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5efbcf7e7f07c513e075c0c430806468748ac4679710290f4650ce953e263bcb |
|
MD5 | f95f60f2417cc0967f34aef96b661d1a |
|
BLAKE2b-256 | d62ce4e0c3103a4215d78113fbfe882834ac80fa938c529bb8f115a81df24e7c |
Hashes for wildboar-1.2.0b2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b80675053ba2d749c302f55565162a5296bd35db482c73b5f6d58d9cc87bb017 |
|
MD5 | c46c52d8cd8ca8fef49ff2a7c8b74772 |
|
BLAKE2b-256 | 75f36f7cc83899b47a389c2675b228e58681dc6b4b8f02c6f8d5891441b84800 |
Hashes for wildboar-1.2.0b2-cp310-cp310-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d1aa0e127e1b948b67b46be7e0b74217ffe949f8ca98adcec433ecda032a176c |
|
MD5 | 2bd9b021031639e6dc97992f532a7d33 |
|
BLAKE2b-256 | 22fa2a05504429534484e6be2be0c2757c7f305de13e82ffb7ee236378478edb |
Hashes for wildboar-1.2.0b2-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a6eb31d690fafea8be63e71048fe79df4fcbf2aca5596105d0a0b997f5c82d5 |
|
MD5 | 840658f746589aff088e4dfe49a83d22 |
|
BLAKE2b-256 | 1c9204000d90590fbc4e51d33763991b8d14a73e78d60b13e71b839a10273ac1 |
Hashes for wildboar-1.2.0b2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4568a2c380f26c9e1edada10e99a05391dfcca7dfb8c2ebfe0048c8bce61a7c3 |
|
MD5 | c0d093599fea9ce9de2b035d74e0f108 |
|
BLAKE2b-256 | 66bfee48c714b07c28a1ec60c829042098fb6e4a8108850acd5b78bcfc82df77 |
Hashes for wildboar-1.2.0b2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fb0c16655a7621f8bbd78e19642166d2fdbd04cf3d6bd236855368e6f0e090e5 |
|
MD5 | 3b6a323b7d76a2d8370e13e1be4b7566 |
|
BLAKE2b-256 | 73b89b15abc4010aff045b721883305246174b673b003b4758b36c7354c17973 |
Hashes for wildboar-1.2.0b2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a662cfb424c930e1a2314c57b115b8028ef0fc3383d934568a3aba9daa58ab28 |
|
MD5 | b87d500afd3bfe2006da2142e2b7bddc |
|
BLAKE2b-256 | ef84dac99169e9063f5667bbb578135ff828cc0acc372c1740148651b4482400 |
Hashes for wildboar-1.2.0b2-cp39-cp39-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 21a2ff650d32a1feaad890e12011683fc215b3b996b3a19dd84e22e38d61a637 |
|
MD5 | 76aab7576cd962cdae26d05dd52964f1 |
|
BLAKE2b-256 | 809b512fc592d1baeb5ce2c615b17d6ed1e7571a22944270992245ce21b0d90c |
Hashes for wildboar-1.2.0b2-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cc747ae2da305be11849352d4eea4b9cb751424c61300efce4ccfcbc34f96072 |
|
MD5 | 74cf1ae5977f7cfe463fa40fcfda0613 |
|
BLAKE2b-256 | a5bca69ec3512f55d0c7578cb33d77aae38d8051862b61de697baf0395b0d46c |
Hashes for wildboar-1.2.0b2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7195e4d7732edf2b109f2f1a0bf97b88a9314e808f812bfa1cd14e53f3768199 |
|
MD5 | 7bd34675d33ed87f926244144d8c83e5 |
|
BLAKE2b-256 | 3879445029ede6b960cc15b0124a96dec6ff05ee5589f71b39e9e0631b30fec4 |
Hashes for wildboar-1.2.0b2-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 651c5e7fb79511ed86b463be2c1730cc736dec0db730ebb01d8d46b44c1c54db |
|
MD5 | 3e5b29ade8fd0cf547eb6246f9d454af |
|
BLAKE2b-256 | 8509a11b7903d0d46db4b1776ae3fe6896acc07d15099a16098060e22c16deb1 |
Hashes for wildboar-1.2.0b2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ab0102193f02a20855b98facd92ee132f8137bbb123ca29001f6ecdf77920622 |
|
MD5 | c53e81352559e76201fb8b5b15bc3962 |
|
BLAKE2b-256 | 6c421e5edaf6858becf0a25bbfe18cb4fcc34643b23c6f326dadfafa11ee3513 |
Hashes for wildboar-1.2.0b2-cp38-cp38-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 890355d87d15ebc5eaec50a568532ae492b81b463695ee53921688ae9e715d19 |
|
MD5 | 63efe345e32a35c6f8f9dcf52a458ffd |
|
BLAKE2b-256 | 1ecbc32d68ffeef84ce1b0d7bf94ec6788e3d15ae8371b79635f3e504a0ea7da |