pyts.approximation.SymbolicAggregateApproximation

class pyts.approximation.SymbolicAggregateApproximation(n_bins=4, strategy='quantile', alphabet=None)

Symbolic Aggregate approXimation.

Parameters:

n_bins : int (default = 4)

The number of bins to produce. It must be between 2 and min(n_timestamps, 26).

strategy : ‘uniform’, ‘quantile’ or ‘normal’ (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

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. If ‘ordinal’, integers are used.

References

[R2014a3481a12-1]

J. Lin, E. Keogh, L. Wei, and S. Lonardi, “Experiencing SAX: a novel symbolic representation of time series”. Data Mining and Knowledge Discovery, 15(2), 107-144 (2007).

Examples

>>> from pyts.approximation import SymbolicAggregateApproximation
>>> X = [[0, 4, 2, 1, 7, 6, 3, 5],
...      [2, 5, 4, 5, 3, 4, 2, 3]]
>>> transformer = SymbolicAggregateApproximation()
>>> print(transformer.transform(X))
[['a' 'c' 'b' 'a' 'd' 'd' 'b' 'c']
 ['a' 'd' 'c' 'd' 'b' 'c' 'a' 'b']]

Methods

__init__(self[, n_bins, strategy, alphabet])	Initialize self.
fit(self[, X, y])	Pass.
fit_transform(self, X[, y])	Fit to data, then transform it.
get_params(self[, deep])	Get parameters for this estimator.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, X)	Bin the data with the given alphabet.

__init__(self, n_bins=4, strategy='quantile', alphabet=None)

Initialize self. See help(type(self)) for accurate signature.

fit(self, X=None, y=None)

Pass.

Parameters:

X

Ignored

y

Ignored

fit_transform(self, X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

**fit_params : dict

Additional fit parameters.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

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.

set_params(self, **params)

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.

transform(self, X)

Bin the data with the given alphabet.

Parameters:

X : array-like, shape = (n_samples, n_timestamps)

Data to transform.

y

Ignored

Returns:

X_new : array, shape = (n_samples, n_timestamps)

Binned data.

Examples using pyts.approximation.SymbolicAggregateApproximation

Symbolic Aggregate approXimation