MultipleCoefficientBinning

class pyts.approximation.MultipleCoefficientBinning(n_bins: int = 4, strategy: str = 'quantile', alphabet: str | ArrayLike | None = None)

Bin continuous data into intervals column-wise.

Parameters:

n_binsint (default = 4)

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

strategystr (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

alphabetNone, ‘ordinal’ or array-like, shape = (n_bins,)

Alphabet to use. If None, the first n_bins letters of the Latin alphabet are used if n_bins is lower than 27, otherwise the alphabet will be defined to [chr(i) for i in range(n_bins)]. If ‘ordinal’, integers are used.

Attributes:

bin_edges_array, shape = (n_bins - 1,) or (n_timestamps, n_bins - 1)

Bin edges with shape = (n_bins - 1,) if strategy='normal' or (n_timestamps, n_bins - 1) otherwise.

References

[1]

P. Schäfer, and M. Högqvist, “SFA: A Symbolic Fourier Approximation and Index for Similarity Search in High Dimensional Datasets”, International Conference on Extending Database Technology, 15, 516-527 (2012).

Examples

>>> from pyts.approximation import MultipleCoefficientBinning
>>> X = [[0, 4],
...      [2, 7],
...      [1, 6],
...      [3, 5]]
>>> transformer = MultipleCoefficientBinning(n_bins=2)
>>> print(transformer.fit_transform(X))
[['a' 'a']
 ['b' 'b']
 ['a' 'b']
 ['b' 'a']]

Methods

__init__([n_bins, strategy, alphabet])
fit(X[, y])	Compute the bin edges for each feature.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Bin the data.

__init__(n_bins: int = 4, strategy: str = 'quantile', alphabet: str | ArrayLike | None = None) → None

fit(X: ArrayLike, y: ArrayLike | None = None) → Self

Compute the bin edges for each feature.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps)

Data to transform.

yNone or array-like, shape = (n_samples,)

Class labels for each sample. Only used if strategy='entropy'.

transform(X: ArrayLike) → NDArray[Any]

Bin the data.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps)

Data to transform.

Returns:

X_newarray, shape = (n_samples, n_timestamps)

Binned data.

Examples using pyts.approximation.MultipleCoefficientBinning

Multiple Coefficient Binning

Multiple Coefficient Binning