shaplets

Python implementation of the Learning Time-Series Shapelets method by Josif Grabocka et al., 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

git clone git@github.com:mohaseeb/shaplets-python.git
cd shaplets-python
pip install .
# or, for dev
# pip install .[dev]

Usage

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.