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
- 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
git clone email@example.com:mohaseeb/shaplets-python.git cd shaplets-python pip install . # or, for dev # pip install .[dev]
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, make sure all the features in dataset are standardized (i.e. each has zero mean and unit variance).