def draw_clustered_mlp(weights_path,
clustering_result,
n_clusters=4,
is_first_square=True,
ax=None):
"""Draw MLP with its spectral clustering."""
weights = load_weights(weights_path)
layer_widths = extact_layer_widths(weights)
labels, metrics = clustering_result
G = nx.from_scipy_sparse_matrix(weights_to_graph(weights))
pos = set_nodes_positions(G.nodes, layer_widths, labels, is_first_square)
color_mapper = get_color_mapper(n_clusters)
color_map = [color_mapper[label] for label in labels]
if ax is None:
_, ax = plt.subplots(1)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
nx.draw(G, pos=pos, node_color=color_map, width=0, node_size=10, ax=ax)
draw_metrics(metrics, ax)
return ax, labels, metrics
def plot_eigenvalues_old(weights_path, n_eigenvalues=None, ax=None, **kwargs):
warnings.warn('deprecated', DeprecationWarning)
loaded_weights = load_weights(weights_path)
G = nx.from_scipy_sparse_matrix(weights_to_graph(loaded_weights))
G_nn = G.subgraph(max(nx.connected_components(G), key=len))
assert nx.is_connected(G_nn)
nrom_laplacian_matrics = nx.normalized_laplacian_matrix(G_nn)
eigen_values = np.sort(np.linalg.eigvals(nrom_laplacian_matrics.A))
if n_eigenvalues == None:
start, end = 0, len(G_nn)
elif isinstance(n_eigenvalues, int):
start, end = 0, n_eigenvalues
elif isinstance(n_eigenvalues, tuple):
start, end = n_eigenvalues
else:
raise TypeError(
'n_eigenvalues should be either None or int or tuple or slice.')
eigen_values = eigen_values[start:end]
if ax is None:
_, ax = plt.subplots(1)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
if 'linestyle' not in kwargs:
kwargs['linestyle'] = 'none'
kwargs['marker'] = '*'
kwargs['markersize'] = 5
return ax.plot(range(start + 1, end + 1), eigen_values, **kwargs)
def plot_eigenvalues(weights_path, n_eigenvalues=None, ax=None, **kwargs):
weights = load_weights(weights_path)
if 'cnn' in str(weights_path):
weights, _ = extract_cnn_weights(
weights, with_avg=True) #(max_weight_convention=='one_on_n'))
print([w.shape for w in weights])
# TODO: take simpler solution from delete_isolated_ccs_refactored
adj_mat = weights_to_graph(weights)
_, components = sparse.csgraph.connected_components(adj_mat)
most_common_component_counts = Counter(components).most_common(2)
main_component_id = most_common_component_counts[0][0]
assert (len(most_common_component_counts) == 1
or most_common_component_counts[1][1] == 1)
main_component_mask = (components == main_component_id)
selected_adj_mat = adj_mat[main_component_mask, :][:, main_component_mask]
nrom_laplacian_matrix = sparse.csgraph.laplacian(selected_adj_mat,
normed=True)
if n_eigenvalues == None:
start, end = 0, selected_adj_mat.shape[0] - 2
elif isinstance(n_eigenvalues, int):
start, end = 0, n_eigenvalues
elif isinstance(n_eigenvalues, tuple):
start, end = n_eigenvalues
else:
raise TypeError(
'n_eigenvalues should be either None or int or tuple or slice.')
"""
eigen_values, _ = sparse.linalg.eigs(nrom_laplacian_matrix, k=end,
which='SM')
"""
sigma = 1
OP = nrom_laplacian_matrix - sigma * sparse.eye(
nrom_laplacian_matrix.shape[0])
OPinv = sparse.linalg.LinearOperator(
matvec=lambda v: sparse.linalg.minres(OP, v, tol=1e-5)[0],
shape=nrom_laplacian_matrix.shape,
dtype=nrom_laplacian_matrix.dtype)
eigen_values, _ = sparse.linalg.eigsh(nrom_laplacian_matrix,
sigma=sigma,
k=end,
which='LM',
tol=1e-5,
OPinv=OPinv)
eigen_values = np.sort(eigen_values)
eigen_values = eigen_values[start:end]
if ax is None:
_, ax = plt.subplots(1)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
if 'linestyle' not in kwargs:
kwargs['linestyle'] = 'none'
kwargs['marker'] = '*'
kwargs['markersize'] = 5
return ax.plot(range(start + 1, end + 1), eigen_values, **kwargs)
from visualization import run_spectral_cluster, build_cluster_graph, plot_eigenvalues
dataset = "Cora"
file_constraited = dataset + "Convergence"
for epoch in range(0, 200, 40):
weights_path0 = dataset + "Convergence/WeightChanges-Cora-GCN-param_512_2_0.99_0.2-monte_0-" + str(
epoch) + ".pt"
weights_array0 = torch.load(weights_path0)
weights_array = []
for (i, weight) in enumerate(weights_array0):
weight = weight[:800, :800].cpu().detach().numpy()
weights_array.append(weight)
weights_path = dataset + "Convergence/WeightChanges-Cora-GCN-param_512_2_0.99_0.2-monte_0-" + str(
epoch) + ".pckl"
pickle.dump(weights_array, open(weights_path, 'wb'))
adj_mat = weights_to_graph(weights_array)
new_weight_array, new_adj_mat = delete_isolated_ccs(weights_array, adj_mat)
num_clusters = 6
assign_labels = 'kmeans'
eigen_solver = 'amg'
epsilon = 1e-8
#[ncut_val, clustering_labels]=weights_array_to_cluster_quality(weights_array, adj_mat, num_clusters,eigen_solver, assign_labels, epsilon,is_testing=False)
# weights_array_to_cluster_quality()
#[labels, metrics]=run_spectral_cluster(weights_path)
plot_eigenvalues(weights_path)
#labels=cluster_net(num_clusters, adj_mat, eigen_solver, assign_labels)
#build_cluster_graph(weights_path,labels,normalize_in_out=True)