class_docstr: 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')
  >>> clf.fit(X_train, y_train)
  SAXVSM(...)
  >>> clf.score(X_test, y_test)
  1.0
class_docstr_tok: ["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","'",")",">",">",">","clf.fit","(","X_train",",","y_train",")","SAXVSM","(","...",")",">",">",">","clf.score","(","X_test",",","y_test",")","1.0"]
docstr: 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
docstr_tok: ["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"]
code: 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
  
file_path: pyts/pyts/classification/saxvsm.py
imports: 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
label_desc: Use image node_id 2 for calling the SAXVSM obj's underlying member method code with example usage: obj.fit(X, y) and returns: self