def test_ER_score(self):
p_mat = self.p_mat
graph = self.graph
estimator = EREstimator(directed=False)
_test_score(estimator, p_mat, graph)
with pytest.raises(ValueError):
estimator.score_samples(graph=er_np(500, 0.5))
def test_ER_sample(self):
with pytest.raises(ValueError):
self.estimator.sample(n_samples=-1)
with pytest.raises(TypeError):
self.estimator.sample(n_samples="nope")
g = er_np(100, 0.5)
estimator = EREstimator(directed=True, loops=False)
estimator.fit(g)
p_mat = np.full((100, 100), 0.5)
p_mat -= np.diag(np.diag(p_mat))
_test_sample(estimator, p_mat)
def evaluate_models(graph,
labels=None,
title=None,
plot_graphs=False,
min_comp=0,
max_comp=1,
n_comp=5):
if plot_graphs:
heatmap(graph, inner_hier_labels=cell_labels)
## Set up models to test
non_rdpg_models = [
EREstimator(fit_degrees=False),
SBEstimator(fit_degrees=False),
SBEstimator(fit_degrees=True),
]
d = [6]
rdpg_models = [RDPGEstimator(n_components=i) for i in d]
models = non_rdpg_models + rdpg_models
names_nonRDPG = ["ER", "SBM", "DCSBM"]
names_RDPG = ["RDPGrank{}".format(i) for i in d]
names = names_nonRDPG + names_RDPG
bics = []
log_likelihoods = []
## Test models
for model, name in zip(models, names):
m = model.fit(graph, y=labels)
if plot_graphs:
heatmap(m.p_mat_,
inner_hier_labels=labels,
title=(name + "P matrix"))
heatmap(m.sample(),
inner_hier_labels=labels,
title=(name + "sample"))
bic = m.bic(graph)
log_likelihoods.append(m.score(graph))
bics.append(bic)
plt.show()
ase = AdjacencySpectralEmbed(n_components=2)
latent = ase.fit_transform(m.p_mat_)
# if type(latent) is tuple:
# pairplot(np.concatenate((latent[0], latent[1]), axis=1))
# plt.show()
# else:
print("here")
# plt.figure(figsize=(20, 20))
ax = scatterplot(latent,
labels=cell_labels,
height=4,
alpha=0.6,
font_scale=1.25)
# plt.suptitle(name, y=0.94, x=0.1, fontsize=30, horizontalalignment="left")
plt.savefig(name + "latent.png", format="png", dpi=1000)
plt.close()
def setup_class(cls):
np.random.seed(8888)
cls.graph = er_np(1000, 0.5)
cls.p = 0.5
cls.p_mat = np.full((1000, 1000), 0.5)
cls.estimator = EREstimator(directed=True, loops=False)
cls.estimator.fit(cls.graph)
cls.p_hat = cls.estimator.p_
def test_ER_inputs(self):
ere = EREstimator()
with pytest.raises(TypeError):
EREstimator(directed="hey")
with pytest.raises(TypeError):
EREstimator(loops=6)
with pytest.raises(ValueError):
ere.fit(self.graph[:, :99])
with pytest.raises(ValueError):
ere.fit(self.graph[..., np.newaxis])
**params)
plotHeatmap(adj, "Drosophila right MB")
# ---------------------------------------
# Erdos-Reyni (ER) model
# ---------------------------------------
'''The Erdos-Reyni (ER) model is the simplest random graph model one could
write down. We are interested in modeling the probability of an edge existing
between any two nodes, π and π. We denote this probability πππ. For the ER model:
πππ = π, for any combination of π and π. This means that the one parameter π is the
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')
# With only that, you can design all the graphs you want, except RDPG because it uses some latent positions (see notes),
# and that can only be generated by ASE.
adj, labels = load_drosophila_right(return_labels=True)
# makes the adjacency matrix contain only 0 and 1 as float values
adj = binarize(adj)
info = heatmap(adj,
inner_hier_labels=labels,
title='Drosophila right MB',
font_scale=1.5,
sort_nodes=True);
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",
def test_ER_score(self):
p_mat = self.p_mat
graph = self.graph
estimator = EREstimator(directed=False)
_test_score(estimator, p_mat, graph)