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 Lesser 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 |
Classification (wildboar/ucr ) |
ExtraShapeletTreesClassifier |
ExtraShapeletTreesRegressor |
KNearestCounterfactual |
RandomShapeletEmbedding |
||
Regression (wildboar/tsereg ) |
RocketTreeClassifier |
RocketRegressor |
PrototypeCounterfactual |
RocketEmbedding |
||
Outlier detection (wildboar/outlier:easy ) |
RocketClassifier |
RandomShapeletRegressor |
IntervalEmbedding |
|||
RandomShapeletClassifier |
RocketTreeRegressor |
FeatureEmbedding |
||||
RockestClassifier |
RockestRegressor |
|||||
IntervalTreeClassifier |
IntervalTreeRegressor |
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. Zenodo. doi:10.5281/zenodo.4264063
ShapeletForestRegressor
ExtraShapeletForestClassifier
ExtraShapeletForestRegressor
IsolationShapeletForest
ShapeletForestEmbedding
PrototypeCounterfactual
RocketTreeClassifier
RocketTreeRegressor
RockestClassifier
RockestRegressor
-
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
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Source Distribution
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