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 GNU General Public License Version 3.
It is currently maintained by Isak Samsten
Features
Data | Classification | Regression | Explainability | Metric | Unsupervised | Outlier |
---|---|---|---|---|---|---|
Repositories | ShapeletForestClassifier |
ShapeletForestRegressor |
ShapeletForestCounterfactual |
UCR-suite | ShapeletForestEmbedding |
IsolationShapeletForest |
ExtraShapeletTreesClassifier |
ExtraShapeletTreesRegressor |
KNearestCounterfactual |
||||
PrototypeCounterfactual |
Installation
Dependencies
wildboar requires:
- python>=3.7
- numpy>=1.17.4
- scikit-learn>=0.21.3
- scipy>=1.3.2
Some parts of wildboar is implemented using Cython. Hence, compilation requires:
- cython (>= 0.29.14)
Binaries
wildboar
is available through pip
and can be installed with:
pip install wildboar
Universal binaries are compiled for GNU/Linux and Python 3.7, 3.8 and 3.9.
Compilation
If you already have a working installation of numpy, scikit-learn, scipy and cython, compiling and installing wildboar is as simple as:
python setup.py install
To install the requirements, use:
pip install -r requirements.txt
Development
Contributions are welcome. Pull requests should be formatted using Black.
Usage
from wildboar.ensemble import ShapeletForestClassifier
from wildboar.datasets import load_two_lead_ecg
x_train, x_test, y_train, y_test = load_two_lead_ecg(merge_train_test=False)
c = ShapeletForestClassifier()
c.fit(x_train, y_train)
c.score(x_test, y_test)
See the tutorial for more examples.
Source code
You can check the latest sources with the command:
git clone https://github.com/isakkarlsson/wildboar
Documentation
- HTML documentation: https://isaksamsten.github.io/wildboar
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 (Version 1.0.3). Zenodo. doi:10.5281/zenodo.4264063
ShapeletForestRegressor
ExtraShapeletForestClassifier
ExtraShapeletForestRegressor
IsolationShapeletForest
ShapeletForestEmbedding
PrototypeCounterfactual
-
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-1.0.9-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cec99a39fab2878baf805ba85fe9e04f8ea96c45409bdf492c4b4914da9cc45 |
|
MD5 | 8c567be8b6b6b1d44a04b084ef78a3fd |
|
BLAKE2b-256 | 214d36dbdc63487d80454e2ebab80d263f87e5bc6377d3187474e8ba5cb070e4 |
Hashes for wildboar-1.0.9-cp39-cp39-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7d56e23d6dfe87775af180d4cba111c8c34d296b9b48933df4d20f8240075599 |
|
MD5 | 97e4273b0f83a000ded774f818edaddb |
|
BLAKE2b-256 | 9e5bde3689ebfbd9952ba104cfd58503aaa1c638267a2ad8f8700ebcf8e2ab21 |
Hashes for wildboar-1.0.9-cp39-cp39-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 01bd7b71700de7332479486aa6bbfe52934d90b1379c845c624efae18c1c068e |
|
MD5 | ce1c2219a7ab4e3481a14bc20c15029e |
|
BLAKE2b-256 | 44fac7c9ab738641e994dd402ad448265c87231f630bfe48d5d798092fa0ee09 |
Hashes for wildboar-1.0.9-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e9d8ca32d580120c012f646d833058567ad55c1f097901dae12f748b55afa3de |
|
MD5 | 06b14c03b58cebb081e0c3326b959193 |
|
BLAKE2b-256 | 6bb30c7758c654d0503486a6ccc3c1da47d511b3a3bde4aa88fff4649e799a0b |
Hashes for wildboar-1.0.9-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | aeceb186b90a4d87e08a772d6b5198886d0efb2c01be9e5d2abf001f977fd906 |
|
MD5 | b1c834a775f0205994b46dc396412abf |
|
BLAKE2b-256 | d97e50b065fb262794f0afb05363ec2635dc0bb387925d5fad2e3a80ca54073c |
Hashes for wildboar-1.0.9-cp38-cp38-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f13688603af2c1ee00c5ac78a0dc671d59b8abfd0c84bd1c809489ebdb154351 |
|
MD5 | d9d1c2efa52068945e9268159e0619c5 |
|
BLAKE2b-256 | 91965e476198fa4392099779e17772187ce69425e20adc27e1cb1083e4370840 |
Hashes for wildboar-1.0.9-cp38-cp38-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2ab1d96b01a0df550442e8865f3193e286b5c59b7d3f31ee5478699da642be9f |
|
MD5 | 3b6a21cb535dc62f6c178ab9a40bb45d |
|
BLAKE2b-256 | 98beb64d591168f9373a08e8b71fb0940c266eae2b88f1c582afaebc96a2237d |
Hashes for wildboar-1.0.9-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cbc9fbf42a856a83f6d31e36c535f2aaa41fff841bfb3cb6e96221bd79970723 |
|
MD5 | 018ceb1f6db1627ed8ab0a23a641c5c5 |
|
BLAKE2b-256 | 86c8f6324673486388c6e89d524d25bb80b23448cbe2f36896ddeb0e9841d8d3 |
Hashes for wildboar-1.0.9-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b421aa5a36ad8f5d587933e165e5e52cd78a4e8751441cd0a6f583de8934ea3c |
|
MD5 | 7bed0adc5bc7982fd4d0786bf41a0435 |
|
BLAKE2b-256 | afc4a0886b2e625731194f5e41901c3ce902555b73ee483bba108b1209810ce0 |
Hashes for wildboar-1.0.9-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1800a85f806440c13c4910ccd614d5c856f2f7ffe3ae3541a5739559642f33b3 |
|
MD5 | d6e9f9197780d26c35a950881f149b12 |
|
BLAKE2b-256 | 3ae5307813931e5ad58dcd2151c65fcc0d4cfe076eb0c168d521676e726a510a |
Hashes for wildboar-1.0.9-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ac960654b8b351f4319cfc97412e8eabe3aafeafced9d358486ddc0727d576f4 |
|
MD5 | 206ebfdc6e8638e3eef1c02cccd6c6c8 |
|
BLAKE2b-256 | 8da6fadfa18121c0786742f724a26fbe3f0f3575c57a8dd7d5385656a001e6c9 |
Hashes for wildboar-1.0.9-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1f21b4c46c48014fe4f7d4bf7efc0c6e89b42a8d9ffa88e650dd8705ece9d726 |
|
MD5 | fa10c9c75c3a5f674c4520f297dbeade |
|
BLAKE2b-256 | 2b10023d973a81fd92ac9df76b65a4e68092504f4049c5d4781eb02d66e3dc63 |