tslearn.piecewise
.SymbolicAggregateApproximation¶

class
tslearn.piecewise.
SymbolicAggregateApproximation
(n_segments=1, alphabet_size_avg=5, scale=False)[source]¶ Symbolic Aggregate approXimation (SAX) transformation.
SAX was originally presented in [1].
Parameters:  n_segments : int (default: 1)
Number of PAA segments to compute
 alphabet_size_avg : int (default: 5)
Number of SAX symbols to use
 scale: bool (default: False)
Whether input data should be scaled for each feature to have zero mean and unit variance across the dataset passed at fit time. Default for this parameter is set to False in version 0.4 to ensure backward compatibility, but is likely to change in a future version.
Attributes:  breakpoints_avg_ : numpy.ndarray of shape (alphabet_size  1, )
List of breakpoints used to generate SAX symbols
Notes
This method requires a dataset of equalsized time series.
References
[1] J. Lin, E. Keogh, L. Wei, et al. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 2007. vol. 15(107) Examples
>>> sax = SymbolicAggregateApproximation(n_segments=3, alphabet_size_avg=2) >>> data = [[1., 2., 0.1, 1., 1., 1.], [1., 3.2, 1., 3., 1., 1.]] >>> sax_data = sax.fit_transform(data) >>> sax_data.shape (2, 3, 1) >>> sax_data array([[[1], [0], [1]], <BLANKLINE> [[1], [0], [1]]]) >>> sax.distance_sax(sax_data[0], sax_data[1]) # doctest: +ELLIPSIS 0.0 >>> sax.distance(data[0], data[1]) # doctest: +ELLIPSIS 0.0 >>> sax.inverse_transform(sax_data) array([[[ 0.67448975], [ 0.67448975], [0.67448975], [0.67448975], [ 0.67448975], [ 0.67448975]], <BLANKLINE> [[ 0.67448975], [ 0.67448975], [0.67448975], [0.67448975], [ 0.67448975], [ 0.67448975]]])
Methods
distance
(self, ts1, ts2)Compute distance between SAX representations as defined in [1]. distance_paa
(self, paa1, paa2)Compute distance between PAA representations as defined in [1]. distance_sax
(self, sax1, sax2)Compute distance between SAX representations as defined in [1]. fit
(self, X[, y])Fit a SAX representation. fit_transform
(self, X[, y])Fit a SAX representation and transform the data accordingly. from_hdf5
(path)Load model from a HDF5 file. from_json
(path)Load model from a JSON file. from_pickle
(path)Load model from a pickle file. get_params
(self[, deep])Get parameters for this estimator. inverse_transform
(self, X)Compute time series corresponding to given SAX representations. set_params
(self, **params)Set the parameters of this estimator. to_hdf5
(self, path)Save model to a HDF5 file. to_json
(self, path)Save model to a JSON file. to_pickle
(self, path)Save model to a pickle file. transform
(self, X[, y])Transform a dataset of time series into its SAX representation. 
distance
(self, ts1, ts2)[source]¶ Compute distance between SAX representations as defined in [1].
Parameters:  ts1 : arraylike
A time series
 ts2 : arraylike
Another time series
Returns:  float
SAX distance
References
[1] (1, 2) J. Lin, E. Keogh, L. Wei, et al. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 2007. vol. 15(107)

distance_paa
(self, paa1, paa2)[source]¶ Compute distance between PAA representations as defined in [1].
Parameters:  paa1 : arraylike
PAA representation of a time series
 paa2 : arraylike
PAA representation of another time series
Returns:  float
PAA distance
References
[1] (1, 2) E. Keogh & M. Pazzani. Scaling up dynamic time warping for datamining applications. SIGKDD 2000, pp. 285–289.

distance_sax
(self, sax1, sax2)[source]¶ Compute distance between SAX representations as defined in [1].
Parameters:  sax1 : arraylike
SAX representation of a time series
 sax2 : arraylike
SAX representation of another time series
Returns:  float
SAX distance
References
[1] (1, 2) J. Lin, E. Keogh, L. Wei, et al. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 2007. vol. 15(107)

fit
(self, X, y=None)[source]¶ Fit a SAX representation.
Parameters:  X : arraylike of shape (n_ts, sz, d)
Time series dataset
Returns:  SymbolicAggregateApproximation
self

fit_transform
(self, X, y=None, **fit_params)[source]¶ Fit a SAX representation and transform the data accordingly.
Parameters:  X : arraylike of shape (n_ts, sz, d)
Time series dataset
Returns:  numpy.ndarray of integers with shape (n_ts, n_segments, d)
SAXTransformed dataset

classmethod
from_hdf5
(path)[source]¶ Load model from a HDF5 file. Requires
h5py
http://docs.h5py.org/Parameters:  path : str
Full path to file.
Returns:  Model instance

classmethod
from_json
(path)[source]¶ Load model from a JSON file.
Parameters:  path : str
Full path to file.
Returns:  Model instance

classmethod
from_pickle
(path)[source]¶ Load model from a pickle file.
Parameters:  path : str
Full path to file.
Returns:  Model instance

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

inverse_transform
(self, X)[source]¶ Compute time series corresponding to given SAX representations.
Parameters:  X : arraylike of shape (n_ts, sz_sax, d)
A dataset of SAX series.
Returns:  numpy.ndarray of shape (n_ts, sz_original_ts, d)
A dataset of time series corresponding to the provided representation.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters:  **params : dict
Estimator parameters.
Returns:  self : object
Estimator instance.

to_hdf5
(self, path)[source]¶ Save model to a HDF5 file. Requires
h5py
http://docs.h5py.org/Parameters:  path : str
Full file path. File must not already exist.
Raises:  FileExistsError
If a file with the same path already exists.