def visualize(model, graphs, res_dir, data_name, class_values, num=5):
model.eval()
model.to(device)
preds = []
graph_loader = DataLoader(graphs, 50, shuffle=False)
for data in tqdm(graph_loader):
data = data.to(device)
pred = model(data)
preds.extend(pred.view(-1).tolist())
order = np.argsort(preds).tolist()
highest = [PyGGraph_to_nx(graphs[i]) for i in order[-num:][::-1]]
lowest = [PyGGraph_to_nx(graphs[i]) for i in order[:num]]
highest_scores = [preds[i] for i in order[-num:][::-1]]
lowest_scores = [preds[i] for i in order[:num]]
scores = highest_scores + lowest_scores
type_to_label = {0: 'u0', 1: 'v0', 2: 'u1', 3: 'v1', 4: 'u2', 5: 'v2'}
#type_to_color = {0: 'r', 1: 'r', 2: 'k', 3: 'k', 4: 'k', 5: 'k'}
#type_to_color = {0: 'xkcd:orangered', 1: 'xkcd:azure', 2: 'xkcd:orange', 3: 'xkcd:lightblue', 4: 'y', 5: 'g'}
type_to_color = {0: 'xkcd:red', 1: 'xkcd:blue', 2: 'xkcd:orange', 3: 'xkcd:lightblue', 4: 'y', 5: 'g'}
plt.axis('off')
f = plt.figure(figsize=(20, 10))
axs = f.subplots(2, num)
#cmap = plt.cm.coolwarm
cmap = plt.cm.get_cmap('rainbow')
vmin, vmax = min(class_values), max(class_values)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm.set_array([])
for i, g in enumerate(highest + lowest):
u_nodes = [x for x, y in g.nodes(data=True) if y['type'] % 2 == 0]
u0, v0 = 0, len(u_nodes)
pos = nx.drawing.layout.bipartite_layout(g, u_nodes)
bottom_u_node = min(pos, key=lambda x: (pos[x][0], pos[x][1]))
bottom_v_node = min(pos, key=lambda x: (-pos[x][0], pos[x][1]))
# swap u0 and v0 with bottom nodes if they are not already
if u0 != bottom_u_node:
pos[u0], pos[bottom_u_node] = pos[bottom_u_node], pos[u0]
if v0 != bottom_v_node:
pos[v0], pos[bottom_v_node] = pos[bottom_v_node], pos[v0]
labels = {x: type_to_label[y] for x, y in nx.get_node_attributes(g, 'type').items()}
node_colors = [type_to_color[y] for x, y in nx.get_node_attributes(g, 'type').items()]
edge_types = nx.get_edge_attributes(g, 'type')
edge_types = [edge_types[x] for x in g.edges()]
#f.add_subplot(2, num, i+1)
axs[i//num, i%num].axis('off')
nx.draw_networkx(g, pos,
#labels=labels,
with_labels=False,
node_size=150,
node_color=node_colors, edge_color=edge_types,
ax=axs[i//num, i%num], edge_cmap=cmap, edge_vmin=vmin, edge_vmax=vmax,
)
# make u0 v0 on top of other nodes
nx.draw_networkx_nodes(g, {u0: pos[u0]}, nodelist=[u0], node_size=150,
node_color='xkcd:red', ax=axs[i//num, i%num])
nx.draw_networkx_nodes(g, {v0: pos[v0]}, nodelist=[v0], node_size=150,
node_color='xkcd:blue', ax=axs[i//num, i%num])
axs[i//num, i%num].set_title('{:.4f}'.format(scores[i]), x=0.5, y=-0.05, fontsize=20)
f.subplots_adjust(right=0.85)
cbar_ax = f.add_axes([0.88, 0.15, 0.02, 0.7])
if len(class_values) > 20:
class_values = np.linspace(min(class_values), max(class_values), 20, dtype=int).tolist()
cbar = plt.colorbar(sm, cax=cbar_ax, ticks=class_values)
cbar.ax.tick_params(labelsize=22)
f.savefig(os.path.join(res_dir, "visualization_{}.pdf".format(data_name)),
interpolation='nearest', bbox_inches='tight')
def visualize(
model, graphs, res_dir, data_name, class_values, num=5, sort_by="prediction"
):
model.eval()
model.to(device)
R = []
Y = []
graph_loader = DataLoader(graphs, 50, shuffle=False)
for data in tqdm(graph_loader):
data = data.to(device)
r = model(data).detach()
y = data.y
R.extend(r.view(-1).tolist())
Y.extend(y.view(-1).tolist())
if sort_by == "true": # sort graphs by their true ratings
order = np.argsort(Y).tolist()
elif sort_by == "prediction":
order = np.argsort(R).tolist()
elif sort_by == "random": # randomly select graphs to visualize
order = np.random.permutation(range(len(R))).tolist()
highest = [PyGGraph_to_nx(graphs[i]) for i in order[-num:][::-1]]
lowest = [PyGGraph_to_nx(graphs[i]) for i in order[:num]]
highest_scores = [R[i] for i in order[-num:][::-1]]
lowest_scores = [R[i] for i in order[:num]]
highest_ys = [Y[i] for i in order[-num:][::-1]]
lowest_ys = [Y[i] for i in order[:num]]
scores = highest_scores + lowest_scores
ys = highest_ys + lowest_ys
type_to_label = {0: "u0", 1: "v0", 2: "u1", 3: "v1", 4: "u2", 5: "v2"}
type_to_color = {
0: "xkcd:red",
1: "xkcd:blue",
2: "xkcd:orange",
3: "xkcd:lightblue",
4: "y",
5: "g",
}
plt.axis("off")
f = plt.figure(figsize=(20, 10))
axs = f.subplots(2, num)
cmap = plt.cm.get_cmap("rainbow")
vmin, vmax = min(class_values), max(class_values)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm.set_array([])
for i, g in enumerate(highest + lowest):
u_nodes = [x for x, y in g.nodes(data=True) if y["type"] % 2 == 0]
u0, v0 = 0, len(u_nodes)
pos = nx.drawing.layout.bipartite_layout(g, u_nodes)
bottom_u_node = min(pos, key=lambda x: (pos[x][0], pos[x][1]))
bottom_v_node = min(pos, key=lambda x: (-pos[x][0], pos[x][1]))
# swap u0 and v0 with bottom nodes if they are not already
if u0 != bottom_u_node:
pos[u0], pos[bottom_u_node] = pos[bottom_u_node], pos[u0]
if v0 != bottom_v_node:
pos[v0], pos[bottom_v_node] = pos[bottom_v_node], pos[v0]
labels = {
x: type_to_label[y] for x, y in nx.get_node_attributes(g, "type").items()
}
node_colors = [
type_to_color[y] for x, y in nx.get_node_attributes(g, "type").items()
]
edge_types = nx.get_edge_attributes(g, "type")
edge_types = [class_values[edge_types[x]] for x in g.edges()]
axs[i // num, i % num].axis("off")
nx.draw_networkx(
g,
pos,
# labels=labels,
with_labels=False,
node_size=150,
node_color=node_colors,
edge_color=edge_types,
ax=axs[i // num, i % num],
edge_cmap=cmap,
edge_vmin=vmin,
edge_vmax=vmax,
)
# make u0 v0 on top of other nodes
nx.draw_networkx_nodes(
g,
{u0: pos[u0]},
nodelist=[u0],
node_size=150,
node_color="xkcd:red",
ax=axs[i // num, i % num],
)
nx.draw_networkx_nodes(
g,
{v0: pos[v0]},
nodelist=[v0],
node_size=150,
node_color="xkcd:blue",
ax=axs[i // num, i % num],
)
axs[i // num, i % num].set_title(
"{:.4f} ({:})".format(scores[i], ys[i]), x=0.5, y=-0.05, fontsize=20
)
f.subplots_adjust(right=0.85)
cbar_ax = f.add_axes([0.88, 0.15, 0.02, 0.7])
if len(class_values) > 20:
class_values = np.linspace(
min(class_values), max(class_values), 20, dtype=int
).tolist()
cbar = plt.colorbar(sm, cax=cbar_ax, ticks=class_values)
cbar.ax.tick_params(labelsize=22)
f.savefig(
os.path.join(res_dir, "visualization_{}_{}.pdf".format(data_name, sort_by)),
interpolation="nearest",
bbox_inches="tight",
)