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https://github.com/kailashbuki/pycute
Information-Theoretic Causal Inference on Event Sequences
https://github.com/kailashbuki/pycute
Last synced: 12 days ago
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Information-Theoretic Causal Inference on Event Sequences
- Host: GitHub
- URL: https://github.com/kailashbuki/pycute
- Owner: kailashbuki
- License: mit
- Created: 2019-10-31T16:36:21.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-27T15:36:32.000Z (almost 2 years ago)
- Last Synced: 2024-11-28T23:40:59.880Z (26 days ago)
- Language: Python
- Size: 7.81 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Pycute
-------
Pycute is a infromation-theoretic causal inference method for event sequences based on Granger-causality.
Pycute Module Installation
----------------------------
The recommended way to install the `pycute` module is to simply use `pip`:
```console
$ pip install pycute
```
Pycute officially supports Python >= 3.6.
How to use pycute?
------------------
```pycon
>>> X = [1] * 1000
>>> Y = [-1] * 1000
>>> from pycute import cute, tent, simulations
>>> cute.cute(X, Y) # CUTE
(0.0, 0.0)
>>> tent.tent(X, Y) # TENT
(0.0, 0.0)
>>> simulations.simulate_decision_rate_against_data_type('/results/dir/')
# for decision rate vs causal relationship type plots
...
```
How to cite the paper?
----------------------
Todo: Add the citation to thesis.