A Julia implementation of the Rocket method of using random feature kernels for time series classification.
This project is not programmed by the original authors of the original paper; please see the Attribution section for more details on the original paper and software.
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For detailed usage instructions, please see the Documentation.
To use the package, you must:
- Load
Rocketeer
, - Create a
RocketModule
object (with optionally specifiedinput_length
andn_kernels
hyperparameters), apply_kernels
to your dataset to extract the Rocket features,- Optionally
save_rocket
andload_rocket
if you intend to utilize the exact same kernels in future experiments.
For example:
# Load the module
using Rocketeer
# Set some parameters of the example
filepath = "my_rocket" # Point to a save file
input_length = 10 # The length of the input window
n_kernels = 200 # The number of kernels to generate
# Create a rocket module
my_rocket = RocketModule(input_length, n_kernels)
# Save it for future use
save_rocket(my_rocket, filepath)
# Load the module back into a new object
my_new_rocket = load_rocket(filepath)
# Create some random data
X = rand(input_length)
# Apply the kernels to get features
features = apply_kernels(my_new_rocket, X)
This Julia package is programmed by:
- Sasha Petrenko petrenkos@mst.edu @AP6YC
The original paper is authored by:
- Angus Dempster
- Francois Petitjean
- Geoff Webb
The links for the original work are:
- Papers:
- Software:
- rocket (Python)
The bibtex entry for the original paper is:
@article{dempster_etal_2020,
author = {Dempster, Angus and Petitjean, Francois and Webb, Geoffrey I},
title = {ROCKET: Exceptionally fast and accurate time classification using random convolutional kernels},
year = {2020},
journal = {Data Mining and Knowledge Discovery},
doi = {https://doi.org/10.1007/s10618-020-00701-z}
}
The icon used for the project logo is from the following: