def preprocess_adjs(adjs, method="ase"):
adjs = [pass_to_ranks(a) for a in adjs]
adjs = [a + 1 / a.size for a in adjs]
if method == "ase":
adjs = [augment_diagonal(a) for a in adjs]
elif method == "lse":
adjs = [to_laplace(a) for a in adjs]
return adjs
def preprocess_adjs(adjs, method="ase"):
"""Preprocessing necessary prior to embedding a graph, opetates on a list
Parameters
----------
adjs : list of adjacency matrices
[description]
method : str, optional
[description], by default "ase"
Returns
-------
[type]
[description]
"""
adjs = [pass_to_ranks(a) for a in adjs]
adjs = [a + 1 / a.size for a in adjs]
if method == "ase":
adjs = [augment_diagonal(a) for a in adjs]
elif method == "lse": # haven't really used much. a few params to look at here
adjs = [to_laplace(a) for a in adjs]
return adjs
ax=ax,
legend=False,
palette=palette,
s=10,
alpha=0.8,
linewidth=0,
)
ax.set(xticks=[], yticks=[], xlabel="")
fig.suptitle(method, y=0.93)
#%% [markdown]
# ## Appendix: double checking some assumptions
# %% try with the actual eigendecomp
L = to_laplace(adj, form="I-DAD")
evals, evecs = eigh(L)
method = "lse"
embedding = evecs[:, :k]
plot_df = make_plot_df(embedding)
plot_dfs[method] = plot_df
fig, axs = plt.subplots(2, 2, figsize=(16, 8))
for i, ax in enumerate(axs.ravel()):
sns.scatterplot(
data=plot_df,
x="ind",
n_per_comm = [100, 100, 100]
n_verts = np.sum(n_per_comm)
block_probs = np.array([[0.4, 0.1, 0.1], [0.1, 0.4, 0.1], [0.1, 0.1, 0.4]])
adj, labels = sbm(n_per_comm, block_probs, return_labels=True)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
sns.heatmap(
block_probs, annot=True, cmap="RdBu_r", center=0, square=True, ax=axs[0], cbar=False
)
heatmap(adj, inner_hier_labels=labels, ax=axs[1], cbar=False)
#%%
I_DAD = to_laplace(adj, form="I-DAD")
DAD = to_laplace(adj, form="DAD")
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
heatmap_kws = dict(inner_hier_labels=labels, cbar=False)
heatmap(I_DAD, ax=axs[0], **heatmap_kws)
heatmap(DAD, ax=axs[1], **heatmap_kws)
def eig(A):
evals, evecs = np.linalg.eig(A)
sort_inds = np.argsort(evals)[::-1]
evals = evals[sort_inds]
evecs = evecs[:, sort_inds]
return evals, evecs
pairplot(latent, labels=labels, title="LSE" + base_title)
save("LSE")
#%% MASE
embed_graphs = []
for graph in split_graph_list:
graph = preprocess(graph)
embed_graphs.append(graph)
mase = MultipleASE(n_components=n_components, scaled=True)
shared_latent = mase.fit_transform(embed_graphs)
shared_latent = np.concatenate(shared_latent, axis=-1)
labels = get_simple(split_graph_list[0])
pairplot(shared_latent, labels=labels, title="MASE" + base_title)
save("MASE")
#%% MLSE
regularizer = 1
embed_graphs = []
for graph in split_graph_list:
graph = preprocess(graph)
laplacian = to_laplace(graph, "R-DAD", regularizer=regularizer)
embed_graphs.append(laplacian)
mase = MultipleASE(n_components=n_components, scaled=False)
shared_latent = mase.fit_transform(embed_graphs)
shared_latent = np.concatenate(shared_latent, axis=-1)
labels = get_simple(split_graph_list[0])
pairplot(shared_latent, labels=labels, title="MLSE" + base_title)
save("MLSE")
#%%
print("Embedding graph")
n_components = 4
regularizer = 2
ptr = True
binary = False
embed_graph = lcc_graph_t
if ptr:
embed_graph = pass_to_ranks(embed_graph)
if binary:
embed_graph = binarize(embed_graph)
# lse = LaplacianSpectralEmbed(
# n_components=n_components, form="R-DAD", regularizer=regularizer
# )
embed_graph = to_laplace(embed_graph, form="R-DAD", regularizer=regularizer)
heatmap(
embed_graph,
figsize=(20, 20),
inner_hier_labels=lcc_simple_classes,
outer_hier_labels=lcc_hemisphere,
hier_label_fontsize=10,
sort_nodes=True,
)
gridplot(
[embed_graph],
height=15,
inner_hier_labels=lcc_simple_classes,
outer_hier_labels=lcc_hemisphere,
hier_label_fontsize=10,
Gn_adj = import_graph(Gn)
Gn_adj = remove_loops(Gn_adj)
# Gn = pass_to_ranks(Gn)
Gn_adj = binarize(Gn_adj)
# plot graph
heatmap(
Gn_adj,
inner_hier_labels=simple_classes,
sort_nodes=True,
figsize=(15, 15),
title=use_graph,
)
# compute and plot directed normalized laplacian
L = to_laplace(Gn_adj, form="R-DAD", regularizer=1)
heatmap(
L,
inner_hier_labels=simple_classes,
sort_nodes=True,
figsize=(15, 15),
title="Normalized Laplacian",
)
# compute lse, plot with simple class labels
lse = AdjacencySpectralEmbed(n_components=n_components)
latent = lse.fit_transform(L)
latent = np.concatenate(latent, axis=-1)
pairplot(latent, labels=simple_classes)