BaseRIST

class BaseRIST(n_intervals=None, n_shapelets=None, series_transformers='default', use_pycatch22=False, use_pyfftw=False, estimator=None, n_jobs=1, random_state=None)

Randomised Interval-Shapelet Transformation (RIST) pipeline base.

RIST is a hybrid pipeline using the RandomIntervalTransformer using Catch22 features and summary stats, and the RandomDilatedShapeletTransformer. Both transforms extract features from different series transformations (1st Order Differences, PeriodogramTransformer, and ARCoefficientTransformer).

Parameters:

n_intervalsint, callable or None, default=None,

The number of intervals of random length, position and dimension to be extracted for the interval portion of the pipeline. Input should be an int or a function that takes a 3D np.ndarray input and returns an int. Functions may extract a different number of intervals per series_transformer output. If None, extracts int(np.sqrt(X.shape[2]) * np.sqrt(X.shape[1]) * 15 + 5) intervals where Xt is the series representation data.

n_shapeletsint, callable or None, default=None,

The number of shapelets of random dilation and position to be extracted for the shapelet portion of the pipeline. Input should be an int or a function that takes a 3D np.ndarray input and returns an int. Functions may extract a different number of shapelets per series_transformer output. If None, extracts int(np.sqrt(Xt.shape[2]) * 200 + 5) shapelets where Xt is the series representation data.

series_transformersTransformerMixin, list, tuple, or None, default=None

The transformers to apply to the series before extracting intervals and shapelets. If None, use the series as is. If “default”, use [None, 1st Order Differences, PeriodogramTransformer, and ARCoefficientTransformer].

A list or tuple of transformers will extract intervals from all transformations concatenate the output. Including None in the list or tuple will use the series as is for interval extraction.

use_pycatch22bool, optional, default=False

Wraps the C based pycatch22 implementation for aeon. (https://github.com/DynamicsAndNeuralSystems/pycatch22). This requires the pycatch22 package to be installed if True.

use_pyfftwbool, default=False

Whether to use the pyfftw library for FFT calculations. Requires the pyfftw package to be installed.

estimatorsklearn estimator, default=None

An sklearn estimator to be built using the transformed data. Defaults to an extra trees forest with 200 trees.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

Attributes:

n_cases_int

The number of train cases in the training set.

n_channels_int

The number of dimensions per case in the training set.

n_timepoints_int

The length of each series in the training set.