overall probability of connection for any two nodes'''
from graspy.models import EREstimator
er = EREstimator(directed=True, loops=False) # Create Erdos-Reyni estimator
er.fit(adj) # Fit Erdos-Reyni model
# Show results
fig, ax = plt.subplots(1, 2)
plotHeatmap(er.p_mat_,
"ER probability matrix",
params={
'vmin': 0,
'vmax': 1,
'ax': ax[0]
})
plotHeatmap(er.sample()[0], "ER sample", params={'ax': ax[1]})
fig.suptitle('Erdos-Reyni (ER) model', fontsize=16)
print(f"ER \"p\" parameter: {er.p_}")
plt.savefig('figure.png')
# ---------------------------------------
# Degree-corrected Erdos-Reyni (DCER) model
# ---------------------------------------
'''A slightly more complicated variant of the ER model is the degree-corrected
Erdos-Reyni model (DCER). Here, there is still a global parameter π to specify
relative connection probability between all edges. However, we add a promiscuity
parameter ππ for each node π which specifies its expected degree relative to other
nodes: πππ=πππππ, so the probility of an edge from π to π is a function of the two nodes'
degree-correction parameters, and the overall probability of an edge in the graph'''
from graspy.models import DCEREstimator
plt.savefig("DrosophilaRightMB", bbox_inches='tight')
er = EREstimator(directed=True,loops=False)
er.fit(adj)
print(f"ER \"p\" parameter: {er.p_}")
heatmap(er.p_mat_,
inner_hier_labels=labels,
font_scale=1.5,
title="ER probability matrix",
vmin=0, vmax=1,
sort_nodes=True)
plt.savefig("ERProbabilityMatrix", bbox_inches='tight')
heatmap(er.sample()[0],
inner_hier_labels=labels,
font_scale=1.5,
title="ER sample",
sort_nodes=True);
plt.savefig("ERSample", bbox_inches='tight')
dcer = DCEREstimator(directed=True,loops=False)
dcer.fit(adj)
print(f"DCER \"p\" parameter: {dcer.p_}")
heatmap(dcer.p_mat_,
inner_hier_labels=labels,
vmin=0,
vmax=1,
font_scale=1.5,