WEASELMUSE stand for Word ExtrAction for time SEries cLassification plus Multivariate Unsupervised Symbols and dErivatives. This example shows how the WEASELMUSE algorithm transforms multivariate time series of real numbers into a sequence of frequencies of words, and illustrates the features obtained for two time series. It is implemented as pyts.multivariate.transformation.WEASELMUSE.

WEASEL+MUSE transformation
# 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_basic_motions
from pyts.multivariate.transformation import WEASELMUSE
from sklearn.preprocessing import LabelEncoder

# Toy dataset
X_train, _, y_train, _ = load_basic_motions(return_X_y=True)
y_train = LabelEncoder().fit_transform(y_train)

# WEASEL+MUSE transformation
transformer = WEASELMUSE(word_size=2, n_bins=2, window_sizes=[12, 36],
                         chi2_threshold=15, sparse=False)
X_weasel = transformer.fit_transform(X_train, y_train)

# Visualize the transformation for the first time series
plt.figure(figsize=(6, 4))
vocabulary_length = len(transformer.vocabulary_)
width = 0.3
plt.bar(np.arange(vocabulary_length) - width / 2, X_weasel[y_train == 0][0],
        width=width, label='First time series in class 0')
plt.bar(np.arange(vocabulary_length) + width / 2, X_weasel[y_train == 1][0],
        width=width, label='First time series in class 1')
           fontsize=12, rotation=60, ha='right')
y_max = np.max(np.concatenate([X_weasel[y_train == 0][0],
                               X_weasel[y_train == 1][0]]))
plt.yticks(np.arange(y_max + 1), fontsize=12)
plt.xlabel("Words", fontsize=14)
plt.ylabel("Frequencies", fontsize=14)
plt.title("WEASEL+MUSE transformation", fontsize=16)
plt.legend(loc='best', fontsize=10)


Total running time of the script: ( 0 minutes 1.130 seconds)

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