Source code for pyts.classification.saxvsm

"""Code for SAX-VSM."""

# Author: Johann Faouzi <>
# License: BSD-3-Clause

from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import check_classification_targets
from sklearn.base import BaseEstimator
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from ..bag_of_words import BagOfWords
from ..base import UnivariateClassifierMixin

[docs]class SAXVSM(BaseEstimator, UnivariateClassifierMixin): """Classifier based on SAX-VSM representation and tf-idf statistics. Time series are first transformed into bag of words using Symbolic Aggregate approXimation (SAX) algorithm followed by a bag-of-words model. Then the classes are transformed into a Vector Space Model (VSM) using term frequencies (tf) and inverse document frequencies (idf). Parameters ---------- window_size : int or float (default = 0.5) Length of the sliding window. If float, it represents a percentage of the size of each time series and must be between 0 and 1. word_size : int or float (default = 0.5) Length of the words. If float, it represents a percentage of the length of the sliding window and must be between 0. and 1. n_bins : int (default = 4) The number of bins to produce. It must be between 2 and ``min(window_size, 26)``. strategy : 'uniform', 'quantile' or 'normal' (default = 'normal') 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 numerosity_reduction : bool (default = True) If True, delete sample-wise all but one occurence of back to back identical occurences of the same words. 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 step of sliding window will be computed as ``ceil(window_step * n_timestamps)``. threshold_std: float (default = 0.01) Threshold used to determine whether a subsequence is standardized. Subsequences whose standard deviations are lower than this threshold are not standardized. norm_mean : bool (default = True) If True, center each subseries before scaling. norm_std : bool (default = True) If True, scale each subseries to unit variance. use_idf : bool (default = True) Enable inverse-document-frequency reweighting. smooth_idf : bool (default = False) Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : bool (default = True) Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). overlapping : bool (default = True) If True, time points may belong to two bins when decreasing the size of the subsequence with the Piecewise Aggregate Approximation algorithm. If False, each time point belong to one single bin, but the size of the bins may vary. alphabet : None or array-like, shape = (n_bins,) Alphabet to use. If None, the first `n_bins` letters of the Latin alphabet are used. Attributes ---------- classes_ : array, shape = (n_classes,) An array of class labels known to the classifier. idf_ : array, shape = (n_features,) , or None The learned idf vector (global term weights) when ``use_idf=True``, None otherwise. tfidf_ : array, shape = (n_classes, n_words) Term-document matrix. vocabulary_ : dict A mapping of feature indices to terms. References ---------- .. [1] P. Senin, and S. Malinchik, "SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model". International Conference on Data Mining, 13, 1175-1180 (2013). Examples -------- >>> from pyts.classification import SAXVSM >>> from pyts.datasets import load_gunpoint >>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True) >>> clf = SAXVSM(window_size=64, word_size=12, n_bins=5, strategy='normal') >>>, y_train) SAXVSM(...) >>> clf.score(X_test, y_test) 1.0 """
[docs] def __init__(self, window_size=0.5, word_size=0.5, n_bins=4, strategy='normal', numerosity_reduction=True, window_step=1, threshold_std=0.01, norm_mean=True, norm_std=True, use_idf=True, smooth_idf=False, sublinear_tf=True, overlapping=True, alphabet=None): self.window_size = window_size self.word_size = word_size self.n_bins = n_bins self.strategy = strategy self.numerosity_reduction = numerosity_reduction self.window_step = window_step self.threshold_std = threshold_std self.norm_mean = norm_mean self.norm_std = norm_std self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf self.overlapping = overlapping self.alphabet = alphabet
[docs] def fit(self, X, y): """Fit the model according to the given training data. Parameters ---------- X : array-like, shape = (n_samples, n_timestamps) Training vector. y : array-like, shape = (n_samples,) Class labels for each data sample. Returns ------- self : object """ X, y = check_X_y(X, y) check_classification_targets(y) le = LabelEncoder() y_ind = le.fit_transform(y) self.classes_ = le.classes_ n_classes = self.classes_.size # Transform each time series into a bag of words bow = BagOfWords( window_size=self.window_size, word_size=self.word_size, n_bins=self.n_bins, strategy=self.strategy, numerosity_reduction=self.numerosity_reduction, window_step=self.window_step, threshold_std=self.threshold_std, norm_mean=self.norm_mean, norm_std=self.norm_std, overlapping=self.overlapping, alphabet=self.alphabet ) X_bow = bow.fit_transform(X) X_class = [' '.join(X_bow[y_ind == classe]) for classe in range(n_classes)] tfidf = TfidfVectorizer( norm=None, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf ) self.tfidf_ = tfidf.fit_transform(X_class).A self.vocabulary_ = {value: key for key, value in tfidf.vocabulary_.items()} if self.use_idf: self.idf_ = tfidf.idf_ else: self.idf_ = None self._tfidf = tfidf self._bow = bow return self
[docs] def decision_function(self, X): """Evaluate the cosine similarity between document-term matrix and X. Parameters ---------- X : array-like, shape (n_samples, n_timestamps) Test samples. Returns ------- X : array-like, shape (n_samples, n_classes) osine similarity between the document-term matrix and X. """ check_is_fitted(self, ['vocabulary_', 'tfidf_', 'idf_', '_tfidf', 'classes_']) X = check_array(X) X_bow = self._bow.transform(X) vectorizer = CountVectorizer(vocabulary=self._tfidf.vocabulary_) X_transformed = vectorizer.transform(X_bow).toarray() return cosine_similarity(X_transformed, self.tfidf_)
[docs] def predict(self, X): """Predict the class labels for the provided data. Parameters ---------- X : array-like, shape = (n_samples, n_timestamps) Test samples. Returns ------- y_pred : array-like, shape = (n_samples,) Class labels for each data sample. """ return self.classes_[self.decision_function(X).argmax(axis=1)]