pyprg 0.1

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 from pyprg import prg. The example usage is as follows:

from pyprg 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, create_crossing_points=True)
auprg = prg.calc_auprg(prg_curve)
print(auprg)
prg.plot_prg(prg_curve)

Authors

This package has been written by Meelis Kull, Telmo de Menezes e Silva Filho, Miquel Perello Nieto, based on work by Peter Flach and Meelis Kull, see http://www.cs.bris.ac.uk/~flach/PRGcurves/.