sktime.transformations.panel.rocket¶
-
class
sktime.transformations.panel.rocket.
MiniRocket
(num_features=10000, max_dilations_per_kernel=32, random_state=None)[source]¶ Bases:
sktime.transformations.base._PanelToTabularTransformer
MINIROCKET
MINImally RandOm Convolutional KErnel Transform
Univariate
Unviariate input only. Use class MiniRocketMultivariate for multivariate input.
- @article{dempster_etal_2020,
author = {Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I}, title = {{MINIROCKET}: A Very Fast (Almost) Deterministic Transform for
Time Series Classification},
year = {2020}, journal = {arXiv:2012.08791}
}
- Parameters
-
class
sktime.transformations.panel.rocket.
MiniRocketMultivariate
(num_features=10000, max_dilations_per_kernel=32, random_state=None)[source]¶ Bases:
sktime.transformations.base._PanelToTabularTransformer
MINIROCKET (Multivariate)
MINImally RandOm Convolutional KErnel Transform
Multivariate
A provisional and naive extension of MINIROCKET to multivariate input. Use class MiniRocket for univariate input.
- @article{dempster_etal_2020,
author = {Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I}, title = {{MINIROCKET}: A Very Fast (Almost) Deterministic Transform for
Time Series Classification},
year = {2020}, journal = {arXiv:2012.08791}
}
- Parameters
-
class
sktime.transformations.panel.rocket.
Rocket
(num_kernels=10000, normalise=True, random_state=None)[source]¶ Bases:
sktime.transformations.base._PanelToTabularTransformer
ROCKET
RandOm Convolutional KErnel Transform
- @article{dempster_etal_2019,
author = {Dempster, Angus and Petitjean, Francois and Webb, Geoffrey I}, title = {ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels}, year = {2019}, journal = {arXiv:1910.13051}
}
- Parameters
num_kernels (int, number of random convolutional kernels (default 10,000)) –
normalise (boolean, whether or not to normalise the input time) –
per instance (default True) (series) –
random_state (int (ignored unless int due to compatability with Numba),) –
seed (optional (random) –
None) (default) –
-
fit
(X, y=None)[source]¶ Infers time series length and number of channels / dimensions ( for multivariate time series) from input pandas DataFrame, and generates random kernels.
- Parameters
X (pandas DataFrame, input time series (sktime format)) –
y (array_like, target values (optional, ignored as irrelevant)) –
- Returns
- Return type
self