"""Code for Bag-of-SFA Symbols."""
# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause
import numpy as np
from math import ceil
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.utils.validation import check_array, check_is_fitted
from sklearn.utils.multiclass import check_classification_targets
from ..approximation import SymbolicFourierApproximation
from ..base import UnivariateTransformerMixin
from ..utils.utils import _windowed_view
[docs]class BOSS(BaseEstimator, UnivariateTransformerMixin):
"""Bag of Symbolic Fourier Approximation Symbols.
For each time series, subseries are extracted using a sliding window.
Then the subseries are transformed into a word using the Symbolic
Fourier Approximation (SFA) algorithm. For each time series, the words
are grouped together and a histogram counting the occurences of each
word is created.
Parameters
----------
word_size : int (default = 4)
Size of each word.
n_bins : int (default = 4)
The number of bins to produce. It must be between 2 and 26.
strategy : str (default = 'quantile')
Strategy used to define the widths of the bins:
- 'uniform': All bins in each sample have identical widths
- 'quantile': All bins in each sample have the same number of points
- 'normal': Bin edges are quantiles from a standard normal distribution
- 'entropy': Bin edges are computed using information gain
window_size : int or float (default = 10)
Size of the sliding window. If float, it represents the percentage of
the size of each time series and must be between 0 and 1. The window
size will be computed as ``ceil(window_size * n_timestamps)``.
window_step : int or float (default = 1)
Step of the sliding window. If float, it represents the percentage of
the size of each time series and must be between 0 and 1. The window
size will be computed as ``ceil(window_step * n_timestamps)``.
anova : bool (default = False)
If True, the Fourier coefficient selection is done via a one-way
ANOVA test. If False, the first Fourier coefficients are selected.
drop_sum : bool (default = False)
If True, the first Fourier coefficient (i.e. the sum of the subseries)
is dropped. Otherwise, it is kept.
norm_mean : bool (default = False)
If True, center each subseries before scaling.
norm_std : bool (default = False)
If True, scale each subseries to unit variance.
numerosity_reduction : bool (default = True)
If True, delete sample-wise all but one occurence of back to back
identical occurences of the same words.
sparse : bool (default = True)
Return a sparse matrix if True, else return an array.
alphabet : None, 'ordinal' or array-like, shape = (n_bins,)
Alphabet to use. If None, the first `n_bins` letters of the Latin
alphabet are used.
Attributes
----------
vocabulary_ : dict
A mapping of feature indices to terms.
References
----------
.. [1] P. Schäfer, "The BOSS is concerned with time series classification
in the presence of noise". Data Mining and Knowledge Discovery,
29(6), 1505-1530 (2015).
Examples
--------
>>> from pyts.datasets import load_gunpoint
>>> from pyts.transformation import BOSS
>>> X_train, X_test, _, _ = load_gunpoint(return_X_y=True)
>>> boss = BOSS(word_size=2, n_bins=2, sparse=False)
>>> boss.fit(X_train)
BOSS(...)
>>> sorted(boss.vocabulary_.values())
['aa', 'ab', 'ba', 'bb']
>>> boss.transform(X_test)
array(...)
"""
[docs] def __init__(self, word_size=4, n_bins=4, strategy='quantile',
window_size=10, window_step=1, anova=False, drop_sum=False,
norm_mean=False, norm_std=False, numerosity_reduction=True,
sparse=True, alphabet=None):
self.word_size = word_size
self.n_bins = n_bins
self.strategy = strategy
self.window_size = window_size
self.window_step = window_step
self.anova = anova
self.drop_sum = drop_sum
self.norm_mean = norm_mean
self.norm_std = norm_std
self.numerosity_reduction = numerosity_reduction
self.sparse = sparse
self.alphabet = alphabet
[docs] def fit(self, X, y=None):
"""Fit the model according to the given training data.
Parameters
----------
X : array-like, shape = (n_samples, n_timestamps)
Training vector.
y : None or array-like, shape = (n_samples,)
Class labels for each data sample.
Returns
-------
self : object
"""
X = check_array(X)
n_samples, n_timestamps = X.shape
if y is not None:
check_classification_targets(y)
window_size, window_step = self._check_params(n_timestamps)
n_windows = (n_timestamps - window_size + window_step) // window_step
X_windowed = _windowed_view(
X, n_samples, n_timestamps, window_size, window_step
)
X_windowed = X_windowed.reshape(n_samples * n_windows, window_size)
sfa = SymbolicFourierApproximation(
n_coefs=self.word_size, drop_sum=self.drop_sum, anova=self.anova,
norm_mean=self.norm_mean, norm_std=self.norm_std,
n_bins=self.n_bins, strategy=self.strategy, alphabet=self.alphabet
)
if y is None:
y_repeated = None
else:
y_repeated = np.repeat(y, n_windows)
X_sfa = sfa.fit_transform(X_windowed, y_repeated)
X_word = np.asarray([''.join(X_sfa[i])
for i in range(n_samples * n_windows)])
X_word = X_word.reshape(n_samples, n_windows)
if self.numerosity_reduction:
not_equal = np.c_[X_word[:, 1:] != X_word[:, :-1],
np.full(n_samples, True)]
X_bow = np.asarray([' '.join(X_word[i, not_equal[i]])
for i in range(n_samples)])
else:
X_bow = np.asarray([' '.join(X_word[i]) for i in range(n_samples)])
vectorizer = CountVectorizer()
vectorizer.fit(X_bow)
self.vocabulary_ = {value: key for key, value in
vectorizer.vocabulary_.items()}
self._window_size = window_size
self._window_step = window_step
self._n_windows = n_windows
self._sfa = sfa
self._vectorizer = vectorizer
return self
def _check_params(self, n_timestamps):
if not isinstance(self.word_size, (int, np.integer)):
raise TypeError("'word_size' must be an integer.")
if not self.word_size >= 1:
raise ValueError("'word_size' must be a positive integer.")
if not isinstance(self.window_size,
(int, np.integer, float, np.floating)):
raise TypeError("'window_size' must be an integer or a float.")
if isinstance(self.window_size, (int, np.integer)):
if self.drop_sum:
if not 1 <= self.window_size <= (n_timestamps - 1):
raise ValueError(
"If 'window_size' is an integer, it must be greater "
"than or equal to 1 and lower than or equal to "
"(n_timestamps - 1) if 'drop_sum=True'."
)
else:
if not 1 <= self.window_size <= n_timestamps:
raise ValueError(
"If 'window_size' is an integer, it must be greater "
"than or equal to 1 and lower than or equal to "
"n_timestamps if 'drop_sum=False'."
)
window_size = self.window_size
else:
if not 0 < self.window_size <= 1:
raise ValueError(
"If 'window_size' is a float, it must be greater "
"than 0 and lower than or equal to 1."
)
window_size = ceil(self.window_size * n_timestamps)
if not isinstance(self.window_step,
(int, np.integer, float, np.floating)):
raise TypeError("'window_step' must be an integer or a float.")
if isinstance(self.window_step, (int, np.integer)):
if not 1 <= self.window_step <= n_timestamps:
raise ValueError(
"If 'window_step' is an integer, it must be greater "
"than or equal to 1 and lower than or equal to "
"n_timestamps."
)
window_step = self.window_step
else:
if not 0 < self.window_step <= 1:
raise ValueError(
"If 'window_step' is a float, it must be greater "
"than 0 and lower than or equal to 1."
)
window_step = ceil(self.window_step * n_timestamps)
if self.drop_sum:
if not self.word_size <= (window_size - 1):
raise ValueError(
"'word_size' must be lower than or equal to "
"(window_size - 1) if 'drop_sum=True'."
)
else:
if not self.word_size <= window_size:
raise ValueError(
"'word_size' must be lower than or equal to "
"window_size if 'drop_sum=False'."
)
return window_size, window_step
