mlots.transformation package¶
Module contents¶
-
class
mlots.transformation.
MINIROCKET
(num_kernels=10000, max_dilations_per_kernel=32, ts_type='univariate', random_seed=1992)¶ Bases:
object
NAME: MINIROCKET
This is a class that represents MINIROCKET by et al [1].
- Parameters
num_kernels (int (default 10,000)) – The number of random convolution kernels to be used.
max_dilations_per_kernel (int (default 32)) – The maximum dilation allowed per kernel.
ts_type (str (default "univariate")) – The type of time-series data; either univariate or multivariate.
random_seed (int (default 1992)) – The initial seed to be used by random function.
- Returns
object (self) – MINIROCKET class with the parameters supplied.
- Raises
ValueError – If the ts_type supplied is incompatible, i.e. not univariate or multivariate.
Examples
>>> from mlots.transformation import MINIROCKET #ts_type denotes if we are using univariate or multivariate version of the algorithm #depending upon time-series being univariate or multivariate; choose the ts_type accordingly. >>> minirocket = MINIROCKET(ts_type="univariate") >>> minirocket.fit(X_train) >>> X_train_transformed = minirocket.transform(X_train) >>> X_test_transformed = minirocket.transform(X_test)
Notes
- [1] A. Dempster, D. F. Schmidt, and G. I. Webb. MINIROCKET: A very fast (almost) deterministic transform
for time series classification. arXiv:2012.08791, 2020.
-
fit
(X=None)¶ - Parameters
X (ndarray (default None)) – The time-series data to be used to derive the kernels.
- Returns
object (self) – MINIROCKET class with the the fitted kernels.
-
transform
(X=None)¶ - Parameters
X (ndarray (default None)) – The time-series data to be transformed.
- Returns
X (ndarray) – The time-series data after transformation.
-
class
mlots.transformation.
ROCKET
(num_kernels=10000, random_seed=1992)¶ Bases:
object
NAME: ROCKET
This is a class that represents ROCKET by Dempster et al. [1].
- Parameters
num_kernels (int (default 10,000)) – The number of random convolution kernels to be used.
random_seed (int (default 1992)) – The initial seed to be used by random function.
- Returns
object (self) – ROCKET class with the parameters supplied.
Examples
>>> from mlots.transformation import ROCKET >>> rocket = ROCKET() >>> rocket.fit(X_train) >>> X_train_transformed = rocket.transform(X_train) >>> X_test_transformed = rocket.transform(X_test)
Notes
- [1] A. Dempster, F. Petitjean, and G. I. Webb. Rocket: Exceptionally fast and accuratetime
classification using random convolutional kernels. Data Mining and Knowledge Discovery, 2020.
-
fit
(X=None)¶ - Parameters
X (ndarray (default None)) – The time-series data to be used to derive the kernels.
- Returns
object (self) – ROCKET class with the the fitted kernels.
-
transform
(X=None)¶ - Parameters
X (ndarray (default None)) – The time-series data to be transformed.
- Returns
X (ndarray) – The time-series data after transformation.