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plot recall precision diagrams with f-measure height lines
# -*- coding: utf-8 -*-
Script to plot recall-precision values with f-measure equi-potential lines.
Created on Dec 16, 2009
@author: Jörn Hees
import scipy as sc
import pylab as pl
import itertools as it
def fmeasure(p, r):
"""Calculates the fmeasure for precision p and recall r."""
return 2*p*r / (p+r)
def _fmeasureCurve(f, p):
"""For a given f1 value and precision get the recall value.
The f1 measure is defined as: f(p,r) = 2*p*r / (p + r).
If you want to plot "equipotential-lines" into a precision/recall diagramm
(recall (y) over precision (x)), for a given fixed f value we get this
function by solving for r:
return f * p / (2 * p - f)
def _plotFMeasures(fstepsize=.1, stepsize=0.001):
"""Plots 10 fmeasure Curves into the current canvas."""
p = sc.arange(0., 1., stepsize)[1:]
for f in sc.arange(0., 1., fstepsize)[1:]:
points = [(x, _fmeasureCurve(f, x)) for x in p
if 0 < _fmeasureCurve(f, x) <= 1.5]
xs, ys = zip(*points)
curve, = pl.plot(xs, ys, "--", color="gray", linewidth=0.5) # , label=r"$f=%.1f$"%f) # exclude labels, for legend
# bad hack:
# gets the 10th last datapoint, from that goes a bit to the left, and a bit down
pl.annotate(r"$f=%.1f$" % f, xy=(xs[-10], ys[-10]), xytext=(xs[-10] - 0.05, ys[-10] - 0.035), size="small", color="gray")
# def _contourPlotFMeasure():
# delta = 0.01
# x = sc.arange(0.,1.,delta)
# y = sc.arange(0.,1.,delta)
# X,Y = sc.meshgrid(x,y)
# cs = pl.contour(X,Y,fmeasure,sc.arange(0.1,1.0,0.1)) # FIXME: make an array out of fmeasure first
# pl.clabel(cs, inline=1, fontsize=10)
colors = "bgrcmyk" # 7 is a prime, so we'll loop over all combinations of colors and markers, when zipping their cycles
markers = "so^>v<dph8" # +x taken out, as no color.
# # if you don't believe the prime loop:
# icons = set()
# for i,j in it.izip(it.cycle(colors),it.cycle(markers)):
# if (i,j) in icons: break
# icons.add((i,j))
# print len(icons), len(colors)*len(markers)
def plotPrecisionRecallDiagram(title="title", points=None, labels=None, loc="center right"):
"""Plot (precision,recall) values with 10 f-Measure equipotential lines.
Plots into the current canvas.
Points is a list of (precision,recall) pairs.
Optionally you can also provide labels (list of strings), which will be
used to create a legend, which is located at loc.
if labels:
ax = pl.axes([0.1, 0.1, 0.7, 0.8]) # llc_x, llc_y, width, height
ax = pl.gca()
# _contourPlotFMeasure()
if points:
getColor = it.cycle(colors).next
getMarker = it.cycle(markers).next
scps = [] # scatter points
for i, (x, y) in enumerate(points):
label = None
if labels:
label = labels[i]
print i, x, y, label
scp = ax.scatter(x, y, label=label, s=50, linewidths=0.75,
facecolor=getColor(), alpha=0.75, marker=getMarker())
# pl.plot(x,y, label=label, marker=getMarker(), markeredgewidth=0.75, markerfacecolor=getColor())
# if labels: pl.text(x, y, label, fontsize="x-small")
if labels:
# pl.legend(scps, labels, loc=loc, scatterpoints=1, numpoints=1, fancybox=True) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot
pl.legend(scps, labels, loc=(1.01, 0), scatterpoints=1, numpoints=1, fancybox=True) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot
pl.axis([-0.02, 1.02, -0.02, 1.02]) # xmin, xmax, ymin, ymax
if __name__ == '__main__':
# plotPrecisionRecallDiagram(points=[(0.9,0.95), (0.9,0.6), (0.7,0.9), (0.25,0.9)], labels=["foaf 0.5", "foaf 0.75", "foaf 0.25", "bar"])
plotPrecisionRecallDiagram("footitle", sc.rand(15, 2), ["item " + str(i) for i in range(15)])
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