Issue
I calculated results of the Friedman-Nemenyi test for 12 datasets and 11 classifiers and I want to plot the results like the following figure
I computed all the requirement CD is single number (shown as CD in the figure), list of classifiers (C4.5+m+cf, C4.5+m and so) and the value of mean ranked (which on x axis)
Many thanks in advance
2: enter link description here
Solution
In order to reproduce the above plot, you may just set 3 of the spines of a normal plot to invisible and then add the respective elements to the plot.
import matplotlib.pyplot as plt
# input data
cd = 1.2
c = 3.2
ccf = 2.8
cmcf = 1.9
cm = 2.05
limits=(4,1)
fig, ax = plt.subplots(figsize=(5,1.8))
plt.subplots_adjust(left=0.2, right=0.8)
# set up plot
ax.set_xlim(limits)
ax.set_ylim(0,1)
ax.spines['top'].set_position(('axes', 0.6))
#ax.xaxis.tick_top()
ax.xaxis.set_ticks_position('top')
ax.yaxis.set_visible(False)
for pos in ["bottom", "left", "right"]:
ax.spines[pos].set_visible(False)
# CD bar
ax.plot([limits[0],limits[0]-cd], [.9,.9], color="k")
ax.plot([limits[0],limits[0]], [.9-0.03,.9+0.03], color="k")
ax.plot([limits[0]-cd,limits[0]-cd], [.9-0.03,.9+0.03], color="k")
ax.text(limits[0]-cd/2., 0.92, "CD", ha="center", va="bottom")
# annotations
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
arrowprops=dict(arrowstyle="-",connectionstyle="angle,angleA=0,angleB=90")
kw = dict(xycoords='data',textcoords="axes fraction",
arrowprops=arrowprops, bbox=bbox_props, va="center")
ax.annotate("C4.5", xy=(c, 0.6), xytext=(0,0.25),ha="right", **kw)
ax.annotate("C4.5+cf", xy=(ccf, 0.6), xytext=(0,0),ha="right", **kw)
ax.annotate("C4.5+m+cf", xy=(cmcf, 0.6), xytext=(1.,0.25),ha="left", **kw)
ax.annotate("C4.5+m", xy=(cm, 0.6), xytext=(1.,0),ha="left", **kw)
#bars
ax.plot([ccf,c],[0.55,0.55], color="k", lw=3)
ax.plot([ccf,cmcf],[0.48,0.48], color="k", lw=3)
plt.show()
Answered By - ImportanceOfBeingErnest
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