https://github.com/Nikolay-Lysenko/dsawl
A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).
https://github.com/Nikolay-Lysenko/dsawl
active-learning categorical-features epsilon-greedy out-of-fold stacking target-encoding
Last synced: about 1 month ago
JSON representation
A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).
- Host: GitHub
- URL: https://github.com/Nikolay-Lysenko/dsawl
- Owner: Nikolay-Lysenko
- License: mit
- Archived: true
- Created: 2017-09-01T18:58:26.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-08-27T13:57:44.000Z (almost 2 years ago)
- Last Synced: 2025-04-09T20:51:51.555Z (2 months ago)
- Topics: active-learning, categorical-features, epsilon-greedy, out-of-fold, stacking, target-encoding
- Language: Python
- Homepage:
- Size: 194 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://travis-ci.org/Nikolay-Lysenko/dsawl)
[](https://codecov.io/gh/Nikolay-Lysenko/dsawl)
[](https://codeclimate.com/github/Nikolay-Lysenko/dsawl/maintainability)
[](https://badge.fury.io/py/dsawl)
# dsawl
## What is it?
This is a set of tools for machine learning. Provided by the package utilities are described in the below table:
Subject | Description | Docs
:-----: | :---------: | :--:
Active Learning | Highly-modular system that recommends which previously unlabelled examples should be labelled in order to increase model quality quickly and significantly. Special features: various options for both exploitation and exploration. | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/active_learning_demo.ipynb)
Stacking | A method that applies machine learning algorithm to out-of-fold predictions or transformations made by other machine learning models. Special features: support of any `sklearn`-compatible estimators (in particular, pipelines). | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/stacking_demo.ipynb)
Target Encoding | An alternative to one-hot encoding and hashing trick that attempts to have both memory efficiency and incorporation of all useful information from initial features. Special features: `sklearn`-compatible wrapper that can transform data out-of-fold and apply an estimator to the result.| [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/target_encoding_demo.ipynb)
Repository name is a combination of three words: DS, saw, and awl. DS is as an abbreviation for Data Science and the latter two words represent useful tools.
## How to install the package?
The package is compatible with Python 3.5 or newer. A virtual environment where it is guaranteed that the package works can be created based on [the file](https://github.com/Nikolay-Lysenko/dsawl/blob/master/requirements.txt) named `requirements.txt`.
To install a stable release of the package, run this command:
```
pip install dsawl
```
To install the latest version from sources, execute this from your terminal:
```
cd path/to/your/destination
git clone https://github.com/Nikolay-Lysenko/dsawl
cd dsawl
pip install -e .
```
If you have any troubles with installation, your questions are welcome.