def _feedforward(self, x):
x_in = x
for layer in self.net:
x_out = []
for node in layer:
active = activation(node['func'])
node["a"] = active(dotprod(node['weights'], x_in))
x_out.append(node["a"])
x_in = x_out # set output as next input
return x_in
def gdraw(bgraph,cgraphs, plotname = 'default_name', measure = 'cosine'):
#pos = nx.graphviz_layout(cgraphs['kg'])
nodelist = bgraph.nodes()
adjs = [ array(nx.adj_matrix(g, nodelist = nodelist)) for g in cgraphs.values() ]
badj = array(nx.adj_matrix(bgraph, nodelist = nodelist))
bnrm = badj / sqrt(sum(badj**2))
if measure == 'cosine':
nrms = []
bnrm = badj / sqrt(sum(badj**2))
for a in adjs:
n = sqrt(sum(a**2))
nrms.append(a / n)
sims = array([round(nfu.cosine_adj(a1,bnrm),8) for a1 in nrms])
elif measure =='jaccard':
sims = array([round(nfu.dotprod(a1,badj),8)/ (sum(a1) + sum(badj))
for a1 in adjs])
elif measure =='specificity':
sims = array([round(nfu.dotprod(a1,badj),8)/sum(a1) for a1 in adjs])
elif measure =='sensitivity':
sims = array([round(nfu.dotprod(a1,badj),8)/sum(badj) for a1 in adjs])
else:
raise Exception()
srto = argsort(cgraphs.keys())
#XVALs give ranks of each key index.
xvals = argsort(srto)
cols = map(lambda x:
('flt' in x and x.count('thr') > 1) and 'orange' or
('flt' in x) and 'red' or
('thr' in x) and 'yellow' or
('fg' in x) and 'green' or
('su' in x) and 'blue' or
'black', cgraphs.keys())
yvals = sims
f = plt.gcf()
f = myplots.fignum(3, (.25 * len(sims),10))
f.clear()
ax = f.add_subplot(111)
myplots.padded_limits(ax,xvals,yvals + [0.], margin = [.02,.02])
ax.scatter(xvals,yvals,100, color = cols)
ax.set_ylabel('red fly similarity ({0})'.format(measure))
ax.set_xlabel('networks')
ax.set_xticklabels([])
ax.set_xticks([])
mark_ys = [0, median(sims), mean(sims), sort(sims)[::-1][1],1]
ax.hlines(mark_ys, *ax.get_xlim(), linestyle = ':',alpha = .2)
f.savefig(cfg.dataPath('figs/meAWG/filter_{0}_meth_{1}_nolabels.pdf'.\
format(plotname,measure)))
ax.set_xticks(range(len(srto)))
cols_added = []
annotes = []
for z in zip(cgraphs.keys(),cols):
if not z[1] in cols_added:
annotes.append( ' '.join(z))
cols_added.append(z[1])
ax.annotate('\n'.join(annotes),
[1,1],xycoords = 'axes fraction', va = 'top', ha = 'right')
ax.set_xticklabels([cgraphs.keys()[i] for i in srto],
rotation = 90, va = 'bottom', size = 'xx-small')
f.savefig(cfg.dataPath('figs/meAWG/filter_{0}_meth_{1}_labels.pdf'.\
format(plotname,measure)))
def show_mcmc(graphs,nc_base =12,
weighted = True, module_type = 'doubles',
measure = 'cosine'):
nodelist = graphs.values()[0].nodes()
print 'using module type: {0}'.format(module_type)
adjs = [ array(nx.adj_matrix(g, nodelist = nodelist)) for g in graphs.values() ]
nrms = []
for a in adjs:
n = sqrt(sum(a**2))
nrms.append(a / n)
idxs_allowed =[ i for i, k in enumerate(graphs.keys()) if 'mcmc' in k]
belt = graphs.keys().index('network_flt{0}'.format(nc_base))
if measure == 'cosine':
sims = array([round(nfu.cosine_adj(a1,nrms[belt]),8)
for i, a1 in enumerate(nrms) if i in idxs_allowed])
elif measure == 'dotprod':
sims = array([nfu.dotprod(a1,adjs[belt])
for i, a1 in enumerate(adfs) if i in idxs_allowed])
else:
raise Exception()
keys_allowed = [k for i, k in enumerate(graphs.keys())
if i in idxs_allowed]
srto = argsort([k for i, k in enumerate(graphs.keys())
if i in idxs_allowed])
#XVALs give ranks of each key index.
xvals = argsort(srto)
cols = map(lambda x:
('flt' in x and x.count('thr') > 1) and 'orange' or
('flt' in x) and 'red' or
('thr' in x) and 'yellow' or
('fg' in x) and 'green' or
('su' in x) and 'blue' or
'black', keys_allowed)
yvals = sims
f = plt.gcf()
f = myplots.fignum(3, (.25 * len(sims),10))
f.clear()
ax = f.add_subplot(111)
myplots.padded_limits(ax,xvals,yvals + [0.], margin = [.02,.02])
ax.scatter(xvals,yvals,100, color = cols)
ax.set_ylabel('red fly similarity ({0})'.format(measure))
ax.set_xlabel('networks')
ax.set_xticklabels([])
ax.set_xticks([])
mark_ys = [0, median(sims), mean(sims), sort(sims)[::-1][1],1]
ax.hlines(mark_ys, *ax.get_xlim(), linestyle = ':',alpha = .2)
ax.vlines(range(len(xvals))[::10], *ax.get_ylim(), linestyle = ':',alpha = .1)
figtitle = 'mcmc_net_comparisons_{0}_{1}net_comps{2}_sim_{3}_nolabels'.\
format(module_type,nc_base ,'' if weighted else 'unweighted',measure)
f.savefig(figtemplate.format(figtitle))
ax.set_xticks(range(len(srto)))
#ax.annotate('\n'.join(' '.join(z) for z in zip(graphs.keys())),
# [0,1],xycoords = 'axes fraction', va = 'top')
ax.set_xticklabels([keys_allowed[i] for i in srto],
rotation = 90, size = 'xx-small',
)#color = cols)
figtitle = 'mcmc_net_comparisons_{0}_{1}net_comps{2}_sim_{3}_labels'.\
format(module_type,nc_base ,'' if weighted else 'unweighted', measure)
f.savefig(figtemplate.format(figtitle))
