Bag-of-SFA Symbols (BOSS)

Bag-of-words approaches are common in time series classification. The Bag-of-SFA Symbols (BOSS) algorithm extracts words from time series and builds features representing frequencies of each word for each time series. This example illustrates the words and the frequencies of these words that have been learned by this algorithm. It is implemented as pyts.transformation.BOSS.

# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause

import numpy as np
import matplotlib.pyplot as plt
from pyts.datasets import load_gunpoint
from pyts.transformation import BOSS

# Toy dataset
X_train, _, y_train, _ = load_gunpoint(return_X_y=True)

# BOSS transformation
boss = BOSS(word_size=2, n_bins=4, window_size=12, sparse=False)
X_boss = boss.fit_transform(X_train)

# Visualize the transformation for the first time series
plt.figure(figsize=(6, 4))
vocabulary_length = len(boss.vocabulary_)
width = 0.3
plt.bar(np.arange(vocabulary_length) - width / 2, X_boss[y_train == 1][0],
        width=width, label='First time series in class 1')
plt.bar(np.arange(vocabulary_length) + width / 2, X_boss[y_train == 2][0],
        width=width, label='First time series in class 2')
plt.xticks(np.arange(vocabulary_length),
           np.vectorize(boss.vocabulary_.get)(np.arange(X_boss[0].size)),
           fontsize=12)
y_max = np.max(np.concatenate([X_boss[y_train == 1][0],
                               X_boss[y_train == 2][0]]))
plt.yticks(np.arange(y_max + 1), fontsize=12)
plt.xlabel("Words", fontsize=14)
plt.ylabel("Frequencies", fontsize=14)
plt.title("BOSS transformation", fontsize=16)
plt.legend(loc='best', fontsize=10)
plt.tight_layout()
plt.show()