fABBA 0.0.8

An efficient aggregation based symbolic representation

fABBA

An efficient aggregation based symbolic representation for temporal data

fABBA is a fast and accurate symbolic representation methods, which allows for data compression and mining. By replacing the k-means clustering used in ABBA with a sorting-based aggregation technique, fABBA thereby avoid repeated within-cluster-sum-of-squares computations, and the computational complexity is significantly reduced. Also, in contrast to the ABBA, fABBA does not require the number of time series symbols to be specified in advance while achieves competing performance against ABBA and other symbolic methods.

Install

To install the current release

pip install fABBA

Apply series compression

>>> import numpy as np
>>> from fABBA.symbolic_representation import fabba_model
>>> np.random.seed(1)
>>> N = 100
>>> ts = np.random.rand(N)
>>> fabba = fabba_model(tol=0.1, alpha=0.5, sorting='lexi', scl=1, verbose=1, max_len=np.inf, string_form=True)
>>> print(fabba)
fABBA({'_alpha': 0.5, '_sorting': '2-norm', '_tol': 0.1, '_scl': 1, '_verbose': 1, '_max_len': inf, '_string_form': True, '_n_jobs': 1})

>>> string = fabba.fit_transform(ts)
>>> print(string)
&"+"!'$#(",!")*$")%!"$-!%&#!#$#(#$."&!%!#/%!#!*0'!1!

>>> inverse_ts = fabba.inverse_transform(symbolic_tsf, ts[0]) # reconstructed time series

Apply adaptively polygonal chian approximation

>>> from fABBA.chainApproximation import compress
>>> from fABBA.chainApproximation import inverse_compress
>>> np.random.seed(1)
>>> N = 100
>>> ts = np.random.rand(N)
>>> pieces = compress(ts, tol=0.1)
>>> inverse_ts = inverse_compress(pieces, ts[0])

Apply aggregated digitization

>>> from fABBA.digitization import digitize
>>> from fABBA.digitization import inverse_digitize
>>> string, parameters = digitize(pieces, alpha=0.1, sorting='2-norm', scl=1) # pieces from aforementioned compression
>>> print(''.join(string))
,"-#!.%&/#0'"12(#34$&%5!67)$*(+8*9:";!<'+=>!)$?@A!B!

>>> inverse_pieces = inverse_digitize(string, parameters)
>>> inverse_ts = inverse_compress(inverse_pieces, ts[0])

Image compression

>>> from fABBA.load_datasets import load_images
>>> from fABBA.symbolic_representation import image_compress
>>> from fABBA.symbolic_representation import image_decompress
>>> from fABBA.symbolic_representation import fabba_model
>>> from cv2 import resize
>>> img_samples = load_images(shape=(100,100)) # load fABBA image test samples
>>> img = resize(img_samples[0], (100, 100)) # select the first image for test
>>> fabba = fabba_model(tol=0.1, alpha=0.01, sorting='2-norm', scl=1, verbose=1, max_len=np.inf, string_form=True)
>>> strings = image_compress(fabba, img)
>>> inverse_img = image_decompress(fabba, strings)

Authors

Stefan Guettel [e-mail]

Xinye Chen [e-mail]