def new_descriptor_performance_plot(fig, max_overview, selection, method, glomeruli=[],
descriptor_plot_type='scatterplot'):
"""compare performance of different descriptors for several glomeruli"""
desc_names = max_overview[method][selection]['desc_names']
if 'p_selection' in max_overview[method][selection] and \
np.sum(max_overview[method][selection]['p_selection']) != 0.0:
print('plotting param selection instead of maximum')
data = max_overview[method][selection]['p_selection']
else:
data = max_overview[method][selection]['max']
# use only selected glomeruli
avail_glomeruli = max_overview[method][selection]['glomeruli']
if glomeruli:
glom_idx = [i for i, g in enumerate(avail_glomeruli) if g in glomeruli]
data = data[:, glom_idx]
# compute colors to color all glomeruli according to their all performance
all_idx = max_overview[method][selection]['desc_names'].index('all')
all_values = data[all_idx]
n_val = float(len(all_values)-1)
clist_all = [sorted(all_values,reverse=True).index(i) / n_val for i in all_values]
ax = fig.add_subplot(111)
if descriptor_plot_type == 'boxplot':
_descriptor_boxplot(ax, data, desc_names)
elif descriptor_plot_type == 'scatterplot':
_descriptor_scatterplot(ax, data, clist_all, desc_names)
elif descriptor_plot_type == 'curveplot':
_descriptor_curveplot(ax, data, desc_names)
if i_meth == 1:
ax.legend(prop={'size':5})
elif descriptor_plot_type == 'violinplot':
_violin_boxplot(ax, data, desc_names)
else:
assert False
utils.simple_axis(ax)
ax.set_ylabel('q2 score')
desc_idx1 = max_overview['svr']['linear']['desc_names'].index(desc)
desc_idx2 = max_overview['forest']['forest']['desc_names'].index(desc)
desc1_collect.extend(max_overview['svr']['linear']['p_selection'][desc_idx1, :])
desc2_collect.extend(max_overview['forest']['forest']['p_selection'][desc_idx2, :])
ax.plot(max_overview['svr']['linear']['p_selection'][desc_idx1, :],
max_overview['forest']['forest']['p_selection'][desc_idx2, :],
'o', mfc=markers[i],
label=desc,
markersize=5)
ax.plot([0, 0.8], [0, 0.8], color='0.5')
plt.axis('scaled')
ax.set_xlim([0, .9])
ax.set_ylim([0, .9])
ax.set_xlabel('SVR (q2)')
ax.set_ylabel('RFR (q2)')
utils.simple_axis(ax)
ax.legend(loc='upper left', numpoints=1, frameon=False, prop={'size': 'small'}, bbox_to_anchor=(0.01, 1))
ticks = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
ticklabels = ['0', '', '.2', '', '.4', '', '.6', '', '.8', '']
ax.set_yticks(ticks)
ax.set_yticklabels(ticklabels)
ax.set_xticks(ticks)
ax.set_xticklabels(ticklabels)
fig.subplots_adjust(bottom=0.2)
fig.tight_layout()
fig.savefig(os.path.join(outpath, 'best_method_comparison.' + config['format']), dpi=600)
assert len(desc1_collect) == len(desc2_collect)
svr_better = np.sum([1 for d1, d2 in zip(desc1_collect, desc2_collect) if d1 > d2])
rfr_better = np.sum([1 for d1, d2 in zip(desc1_collect, desc2_collect) if d1 < d2])
ratio = float(svr_better) / (np.sum(rfr_better) + np.sum(svr_better))
