https://github.com/corels/pycorels
Public home of pycorels, the python binding to CORELS
https://github.com/corels/pycorels
c-plus-plus machine-learning-algorithms python
Last synced: over 1 year ago
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Public home of pycorels, the python binding to CORELS
- Host: GitHub
- URL: https://github.com/corels/pycorels
- Owner: corels
- License: gpl-3.0
- Created: 2018-06-13T19:59:47.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-06-25T00:50:24.000Z (about 6 years ago)
- Last Synced: 2025-04-13T23:27:07.166Z (over 1 year ago)
- Topics: c-plus-plus, machine-learning-algorithms, python
- Language: Python
- Size: 17.1 MB
- Stars: 78
- Watchers: 5
- Forks: 14
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pycorels
[](https://travis-ci.org/fingoldin/pycorels)
[](https://pycorels.readthedocs.io/en/latest/?badge=latest)
Welcome to the python binding of the Certifiably Optimal RulE ListS (CORELS) algorithm!
## Overview
CORELS (Certifiably Optimal RulE ListS) is a custom discrete optimization technique for building rule lists over a categorical feature space. Using algorithmic bounds and efficient data structures, our approach produces optimal rule lists on practical problems in seconds.
The CORELS pipeline is simple. Given a dataset matrix of size `n_samples x n_features` and a labels vector of size `n_samples`, it will compute a rulelist (similar to a series of if-then statements) to predict the labels with the highest accuracy.
Here's an example:
More information about the algorithm [can be found here](https://corels.eecs.harvard.edu/corels)
## Dependencies
CORELS uses [Python](https://www.python.org), [Numpy](https://www.numpy.org), [GMP](https://gmplib.org).
GMP (GNU Multiple Precision library) is not required, but it is *highly recommended*, as it improves performance. If it is not installed, CORELS will run slower.
## Installation
CORELS exists on PyPI, and can be downloaded with
`pip install corels`
To install from this repo, simply run `pip install .` or `python setup.py install` from the `corels/` directory.
Here are some detailed examples of how to install all the dependencies needed, followed by corels itself:
#### Ubuntu
```
sudo apt install libgmp-dev
pip install corels
```
#### Mac
```
# Install g++ and gmp
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew install g++ gmp
pip install corels
```
#### Windows
Note: Python 2 is currently NOT supported on Windows.
```
pip install corels
```
#### Troubleshooting
- If you come across an error saying Python version >=3.5 is required, try running `pip install numpy` before again running `pip install corels`.
- If `pip` does not successfully install corels, try using `pip3`
## Documentation
The docs for this package are hosted on here: https://pycorels.readthedocs.io/
## Tests
After installing corels, run `pytest` (you may have to install it with `pip install pytest` first) from the `tests/` folder, where the tests are located.
## Examples
### Large dataset, loaded from [this file](https://raw.githubusercontent.com/fingoldin/pycorels/master/examples/data/compas.csv)
```python
from corels import *
# Load the dataset
X, y, _, _ = load_from_csv("data/compas.csv")
# Create the model, with 10000 as the maximum number of iterations
c = CorelsClassifier(n_iter=10000)
# Fit, and score the model on the training set
a = c.fit(X, y).score(X, y)
# Print the model's accuracy on the training set
print(a)
```
### Toy dataset (See picture example above)
```python
from corels import CorelsClassifier
# ["loud", "samples"] is the most verbose setting possible
C = CorelsClassifier(max_card=2, c=0.0, verbosity=["loud", "samples"])
# 4 samples, 3 features
X = [[1, 0, 1], [0, 0, 0], [1, 1, 0], [0, 1, 0]]
y = [1, 0, 0, 1]
# Feature names
features = ["Mac User", "Likes Pie", "Age < 20"]
# Fit the model
C.fit(X, y, features=features, prediction_name="Has a dirty computer")
# Print the resulting rulelist
print(C.rl())
# Predict on the training set
print(C.predict(X))
```
More examples are in the `examples/` directory
### Questions?
Email the maintainer at: vassilioskaxiras@gmail.com