Close-k Classifier
This repository contains code accompanying
Minimizing Close-k Aggregate Loss Improves Classification
Bryan He, James Zou.
We provide a Python 3 implementation using the scikit-learn API, and provide code to reproduce the figures and tables from the paper.
Installation
Our package is available on PyPy, and can be installed using
pip install -i https://pypi.org/project/ closek
You can also install this package by cloning the Github repository, and running
pip install closek
If you want directly use the implementation in your package, you can also copy closek/closek.py into your code.
Usage
An example of how to use our package is shown in test.py.
Generating Results from Paper
The code used for the paper is in experiments. See the README there for more details.
1 maintainer
Metadata
1 maintainer
Release files for closek 1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| closek-1.1.tar.gz | 3.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| closek-1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.2 kB
Release files / closek-1.1.tar.gz
| Download URL | closek-1.1.tar.gz |
|---|---|
| Size | 3.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.0
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Release files / closek-1.1-py3-none-any.whl
| Download URL | closek-1.1-py3-none-any.whl |
|---|---|
| Size | 4.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.0
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