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 |
WDTW | FeatureTransform |
|||
RocketForestClassifier |
RocketForestRegressor |
MSM | MatrixProfile | |||
IntervalTreeClassifier |
IntervalTreeRegressor |
TWE | Segmentation | |||
IntervalForestClassifier |
IntervalForestRegressor |
LCSS | Motif discovery | |||
ProximityTreeClassifier |
ERP | SAX |
||||
ProximityForestClassifier |
EDR | PAA |
||||
MatrixProfileTransform |
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 GNU/Linux and Python 3.8, 3.9, 3.10
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 fomatted using Black
- add relevant tests
- add relevant documentation
Source code
You can check the latest sources with the command:
git clone https://github.com/isaksamsten/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
-
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
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Source Distribution
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