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-1.0.12-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5f7130724ad2605590288f5c5517548bd4d9a73007f9ebd0f5772d48fff20938 |
|
MD5 | d5ae77aaaac2515053f77707ee99725e |
|
BLAKE2b-256 | 41fa207cdf45796f51ee1eaedf5109a24dcbbc74e1ff11aef4e039d6980c33d0 |
Hashes for wildboar-1.0.12-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 151eb0a30857ec84392c1b2dc2941e2e9246d6f2d250a525077ca87334b50408 |
|
MD5 | c9a67102b2a360107e693e43bd183e8c |
|
BLAKE2b-256 | ce2c2d6e43a14adb26dfed4d100ce7f15cfb81a50e05326863ed4ffed2ef2c2c |
Hashes for wildboar-1.0.12-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 70c591d4986e5ec8228dbe3657e7a259d261f2de7eda449a03cfacfbe01dd4af |
|
MD5 | df8a6f5b0861a333902ba64c4e03f104 |
|
BLAKE2b-256 | e1c36e744eaa1dfd4fef11d1ba0a83891a9620c17812be9835e3db7a3a089034 |
Hashes for wildboar-1.0.12-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e515977ab0cbf2ede52ea55daec8537df9e9674d73654b789313c780a7a7d493 |
|
MD5 | 9010dbc87a3ea19f0829f64d4548265e |
|
BLAKE2b-256 | f1fbd6cd87e5b2a7720a2f1c5bf085e421c16b373a4e1e65baeac9004397f0f2 |
Hashes for wildboar-1.0.12-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6cf9d5009e487a64ece3cdc24bf9897f2bf68de356b07236ad597367443fd5db |
|
MD5 | f94835c9917df843109d81dec1650acb |
|
BLAKE2b-256 | 48c6e8d1873a6a4da320d595e9d4d6d6cbbdb6ff00439e2113273febaf4dfeaa |
Hashes for wildboar-1.0.12-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a1b2c2c91ef07bd6ea9593c4f29bf2bafcd5e1bf6043eace79fb40b7de8f865a |
|
MD5 | 055b9187468fb27f3a6df6262d5d9422 |
|
BLAKE2b-256 | 8a3098093894d50c39d75f344b67c2ada553d618565ff1aa890fca056da8ea2a |
Hashes for wildboar-1.0.12-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4b4f20675c70dedf4204df96e24c74382265e8566a6308068586d57e1686f644 |
|
MD5 | a2b8f220e8b562080cb904bab0699539 |
|
BLAKE2b-256 | d830d977476f3bd341c0ca53881ad785ea873adeaccefc2ae28d2cac75e6869a |
Hashes for wildboar-1.0.12-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5e2d37624e3626ae72b4632a615441757364901e82f60b88122693ac7b7df78f |
|
MD5 | 37415199ccfa971a0a4cd4e284411e79 |
|
BLAKE2b-256 | e742b11b6d60fdac10679627cd2a3c3ab1c1dcbc7fdc0fad09c7ce1f02aa3926 |
Hashes for wildboar-1.0.12-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d4e381442718a4ecf1d0bd4d06bb1d91ab332afa0b82213346fbcbb10d44e124 |
|
MD5 | 8f2ee3b56a436b7b5bae418836b5f4cd |
|
BLAKE2b-256 | 8027c54fc14155b17fc9cb6d4779defe27514b20956c621a2ed2c9e223274f41 |