https://github.com/mohaseeb/shaplets-python
Python implementation of the Learning Time-Series Shapelets method, that learns a shapelet-based time-series classifier with gradient descent.
https://github.com/mohaseeb/shaplets-python
Last synced: 8 months ago
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Python implementation of the Learning Time-Series Shapelets method, that learns a shapelet-based time-series classifier with gradient descent.
- Host: GitHub
- URL: https://github.com/mohaseeb/shaplets-python
- Owner: mohaseeb
- Created: 2016-06-19T14:49:51.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2019-11-09T07:59:49.000Z (about 6 years ago)
- Last Synced: 2024-11-02T15:36:14.022Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 367 KB
- Stars: 218
- Watchers: 12
- Forks: 59
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome_time_series_in_python - shapelets-python
README
# shaplets
Python implementation of [the Learning Time-Series Shapelets method by Josif Grabocka et al.](http://www.ismll.uni-hildesheim.de/pub/pdfs/grabocka2014e-kdd.pdf), that learns a shapelet-based time-series classifier with gradient descent.
This implementation views the model as a layered graph, where each layer
implements a forward, backword and parameters update methods (see below
diagram). This abstraction simplifies thinking about the algorithm and implementing it.
## Differences from the paper ##
* This implmenetation employs two (LinearLayer + SigmoidLayer) pairs instead of one (LinearLayer + SigmoidLayer) pair as in the paper (and shown in above diagram). This (using two pairs) has yielded improved results on some datasets. To have a similar setup as in the paper, simply update `shapelets_lts/classification/shapelet_models.py:LtsShapeletClassifier._init_network()`.
* The loss in this implementation is an updated version of the one in the
paper to allow training a single model for all the classes in the dataset (rather than one model/class). The impact on performance was not analysed. For details check `shapelets_lts/network/cross_entropy_loss_layer.py`
## Installation ##
```bash
git clone git@github.com:mohaseeb/shaplets-python.git
cd shaplets-python
pip install .
# or, for dev
# pip install .[dev]
```
## Usage ##
```python
from shapelets_lts.classification import LtsShapeletClassifier
# create an LtsShapeletClassifier instance
classifier = LtsShapeletClassifier(
K=20,
R=3,
L_min=30,
epocs=50,
lamda=0.01,
eta=0.01,
shapelet_initialization='segments_centroids',
plot_loss=True
)
# train the classifier.
# train_data.shape -> (# train samples X time-series length)
# train_label.shape -> (# train samples)
classifier.fit(train_data, train_label, plot_loss=True)
# evaluate on test data.
# test_data.shape -> (# test samples X time-series length)
prediction = classifier.predict(test_data)
# retrieve the learnt shapelets
shapelets = classifier.get_shapelets()
# and plot sample shapelets
from shapelets_lts.util import plot_sample_shapelets
plot_sample_shapelets(shapelets=shapelets, sample_size=36)
```
Also have a look at example.py. For a stable training, the samples might need to be scaled.
Example plot from plot_sample_shapelets.
