Creates the Precision-Recall-Gain curve and calculates the area under the curve
Project description
What are the Precision-Recall-Gain curves?
Please see http://www.cs.bris.ac.uk/~flach/PRGcurves/.
Contents
This package provides the following 6 functions:
precision_gain(TP,FN,FP,TN) recall_gain(TP,FN,FP,TN) create_prg_curve(labels,pos_scores) calc_auprg(prg_curve) prg_convex_hull(prg_curve) plot_prg(prg_curve)
Installation
This package can be installed using pip from command line:
pip install pyprg
Usage
Detailed information about the usage can be seen in the manual pages of the individual functions, e.g. by typing ?prg.create_prg_curve after importing with import prg. The example usage is as follows:
from prg import prg
import numpy as np
labels = np.array([1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,0,0,1,0,0,0,1,0,1], dtype='int')
scores = np.arange(1,26)[::-1]
prg_curve = prg.create_prg_curve(labels, scores)
auprg = prg.calc_auprg(prg_curve)
print(auprg)
prg.plot_prg(prg_curve)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyprg-0.1.1b6.tar.gz
(6.1 kB
view hashes)
Built Distribution
Close
Hashes for pyprg-0.1.1b6-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b74c6498753907dbefda27cced375f5629cca034e63348abf75495a25b2bbf57 |
|
MD5 | 59a29475da525469a80b922c0898d7b7 |
|
BLAKE2b-256 | ec4ad846ee8c65ac74adcbb649bd7277e77b944d360832d888778911fcd452c1 |