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: 6 days 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 8 years ago)
• Default Branch: master
• Last Pushed: 2019-11-09T07:59:49.000Z (almost 5 years ago)
• Last Synced: 2024-05-21T13:32:22.565Z (6 months ago)
• Language: Python
• Homepage:
• Size: 367 KB
• Stars: 217
• Watchers: 12
• Forks: 58
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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• 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.

![Network diagram](lts-diag.png)

## 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 [email protected]: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.

![sample_shapelets](sample_shapelets.png)