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 reg_omni(adjs):
adjs = [a + 1 / (len(lp_inds)**2) for a in adjs]
adjs = [augment_diagonal(a) for a in adjs]
omni = OmnibusEmbed(n_components=4, check_lcc=False, n_iter=10)
embed = omni.fit_transform(adjs)
embed = np.concatenate(embed, axis=-1)
embed = embed[2:] # TODO
embed = np.concatenate(embed, axis=0)
return embed
def _embed(self, adj=None):
if adj is None:
adj = self.adj
# TODO look into PTR at this level as well
# lp_inds, rp_inds = get_paired_inds(self.meta)
lp_inds = self.left_pair_inds
rp_inds = self.right_pair_inds
embed_adj = pass_to_ranks(adj)
if self.embed == "ase":
embedder = AdjacencySpectralEmbed(
n_components=self.n_components, n_elbows=self.n_elbows
)
embed = embedder.fit_transform(embed_adj)
elif self.embed == "lse":
embedder = LaplacianSpectralEmbed(
n_components=self.n_components,
n_elbows=self.n_elbows,
regularizer=self.regularizer,
)
embed = embedder.fit_transform(embed_adj)
elif self.embed == "unscaled_ase":
embed_adj = pass_to_ranks(adj)
embed_adj = augment_diagonal(embed_adj)
embed = selectSVD(
embed_adj, n_components=self.n_components, n_elbows=self.n_elbows
)
embed = (embed[0], embed[2].T)
X = np.concatenate(embed, axis=1)
fraction_paired = (len(lp_inds) + len(rp_inds)) / len(self.root_inds)
print(f"Learning transformation with {fraction_paired} neurons paired")
R, _ = orthogonal_procrustes(X[lp_inds], X[rp_inds])
X[self.left_inds] = X[self.left_inds] @ R
if self.normalize:
row_sums = np.sum(X, axis=1)
X /= row_sums[:, None]
return X
def _embed(self, adj=None):
if adj is None:
adj = self.adj
lp_inds = self.left_pair_inds
rp_inds = self.right_pair_inds
embed_adj = pass_to_ranks(adj) # TODO PTR here?
if self.plus_c:
embed_adj += 1 / adj.size
if self.embed == "ase":
embedder = AdjacencySpectralEmbed(n_components=self.n_components,
n_elbows=self.n_elbows)
embed = embedder.fit_transform(embed_adj)
elif self.embed == "lse":
embedder = LaplacianSpectralEmbed(
n_components=self.n_components,
n_elbows=self.n_elbows,
regularizer=self.regularizer,
)
embed = embedder.fit_transform(embed_adj)
elif self.embed == "unscaled_ase":
embed_adj = augment_diagonal(embed_adj)
embed = selectSVD(embed_adj,
n_components=self.n_components,
n_elbows=self.n_elbows)
embed = (embed[0], embed[2].T)
X = np.concatenate(embed, axis=1)
fraction_paired = (len(lp_inds) + len(rp_inds)) / len(self.root_inds)
print(f"Learning transformation with {fraction_paired} neurons paired")
X = self._procrustes(X)
if self.normalize:
row_norms = np.linalg.norm(X, axis=1)
X /= row_norms[:, None]
return X
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
def preprocess_adjs(adjs):
adjs = [pass_to_ranks(a) for a in adjs]
adjs = [a + 1 / a.size for a in adjs]
adjs = [augment_diagonal(a) for a in adjs]
return adjs
lp_inds, rp_inds = get_paired_inds(meta)
left_inds = meta[meta["left"]]["inds"]
right_inds = meta[meta["right"]]["inds"]
# %% Load and preprocess all graphs
graph_types = ["Gad", "Gaa", "Gdd", "Gda"]
adjs = []
for g in graph_types:
temp_mg = load_metagraph(g, version="2020-04-01")
temp_mg.reindex(mg.meta.index, use_ids=True)
temp_adj = temp_mg.adj
adjs.append(temp_adj)
embed_adjs = [pass_to_ranks(a) for a in adjs]
embed_adjs = [a + 1 / a.size for a in embed_adjs]
embed_adjs = [augment_diagonal(a) for a in embed_adjs]
# %% [markdown]
# ##
def omni_procrust_svd(embed_adjs):
omni = OmnibusEmbed(n_components=None, check_lcc=False)
joint_embed = omni.fit_transform(embed_adjs)
cat_embed = np.concatenate(joint_embed, axis=-1)
# print(f"Omni concatenated embedding shape: {cat_embed.shape}")
for e in cat_embed:
e[left_inds] = e[left_inds] @ orthogonal_procrustes(
e[lp_inds], e[rp_inds])[0]
cat_embed = np.concatenate(cat_embed, axis=-1)
U, S, Vt = selectSVD(cat_embed, n_elbows=3)