Awesome
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.