https://github.com/borgwardtlab/wtk
A Wasserstein Subsequence Kernel for Time Series.
https://github.com/borgwardtlab/wtk
data-mining machine-learning optimal-transport time-series-classification
Last synced: 3 months ago
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A Wasserstein Subsequence Kernel for Time Series.
- Host: GitHub
- URL: https://github.com/borgwardtlab/wtk
- Owner: BorgwardtLab
- License: mit
- Created: 2019-10-08T12:00:51.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-17T23:06:46.000Z (over 1 year ago)
- Last Synced: 2024-11-08T15:05:26.300Z (11 months ago)
- Topics: data-mining, machine-learning, optimal-transport, time-series-classification
- Language: Python
- Homepage:
- Size: 208 KB
- Stars: 21
- Watchers: 5
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Wasserstein Time Series Kernel
A preprint version of the paper accepted at ICDM 2019 can be found [here](https://christian.bock.ml/publications/ICDM2019_WTK.pdf).
## Dependencies
WTK relies on the following dependencies:
- `numpy`
- `scikit-learn`
- `POT`
- `cython`
## Installation
The easiest way is to install WTK from the Python Package Index (PyPI) via
```
$ pip install cython numpy wtk
```
## Usage
The package provides functions to transform a set of `n` training time series and `o` test time series into an `n x n` distance matrix for training and an `o x n` distance matrix for testing.
Additionally, we provide a way to run a grid search for a krein space SVM. `krein_svm_grid_search` runs a `5`-fold
cross-validation on the training set to determine the best hyperparameters. Then, its classification accuracy is
computed on the test set.
```python
from wtk import transform_to_dist_matrix
from wtk.utilities import get_ucr_dataset, krein_svm_grid_search
# Read UCR data
X_train, y_train, X_test, y_test = get_ucr_dataset('../data/UCR/raw_data/', 'DistalPhalanxTW')
# Compute wasserstein distance matrices with subsequence length k=10
D_train, D_test = transform_to_dist_matrix(X_train, X_test, 10)
# Run the grid search
svm_clf = krein_svm_grid_search(D_train, D_test, y_train, y_test)
```
Alternatively, you can get the kernel matrices computed from the distance matrices and train your own classifier.
```python
from sklearn.svm import SVC
from wtk import get_kernel_matrix
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
# Get the kernel matrices
K_train = get_kernel_matrix(D_train, psd=True, gamma=0.2)
K_test = get_kernel_matrix(D_test, psd=False, gamma=0.2)
# Train your classifier
clf = SVC(C=5, kernel='precomputed')
clf.fit(K_train, y_train)
y_pred = clf.predict(K_test)
print(accuracy_score(y_test, y_pred))
```
## Examples
You can find some simple examples on our [examples
page](https://github.com/BorgwardtLab/WTK/tree/master/examples) and an
examples [jupyter
notebook](https://github.com/BorgwardtLab/WTK/blob/master/examples/example_notebook.ipynb).
In case the notebook cannot be rendered, please visit it on
[nbviewer](https://nbviewer.jupyter.org/github/BorgwardtLab/WTK/blob/master/examples/example_notebook.ipynb).
## Help
If you have questions concerning WTK or you encounter problems when
trying to build the tool under your own system, please open an issue in
[the issue tracker](https://github.com/BorgwardtLab/WTK/issues). Try to
describe the issue in sufficient detail in order to make it possible for
us to help you.
## Contributors
WTK is developed and maintained by members of the [Machine Learning and
Computational Biology Lab](https://www.bsse.ethz.ch/mlcb):
- Christian Bock ([GitHub](https://github.com/chrisby))
- Matteo Togninalli ([GitHub](https://github.com/mtog))
- Bastian Rieck ([GitHub](https://github.com/Pseudomanifold))
## Citation
Please use the following BibTeX citation when using our method or comparing to it:
```
@InProceedings{Bock19,
author = {Bock, Christian and Togninalli, Matteo and Ghisu, Elisabetta and Gumbsch, Thomas and Rieck, Bastian and Borgwardt, Karsten},
title = {A Wasserstein Subsequence Kernel for Time Series},
booktitle = {Proceedings of the 19th IEEE International Conference on Data Mining~(ICDM)},
year = {2019},
pubstate = {inpress},
}
```