def check_aperiodic(g):
a = gt.adjacency(g)
b = a * a
diag_two_sum = b.diagonal().sum()
# print '\tA*A diag sum:', int(diag_two_sum)
b *= a
diag_three_sum = b.diagonal().sum()
return bool(diag_two_sum) and bool(diag_three_sum)
def feat_diffusion(cls, X, g=None, D_inv=None, A=None, iter=10):
if iter != 0:
if g is None and D is None and A is None:
print('input at least either g or D & A')
return None
if A is None:
A = gt.adjacency(g)
if D_inv is None:
# TODO: maybe need to change here when using undirected graph
D_inv = np.diag(1.0 / g.get_in_degrees(g.get_vertices()))
for i in range(iter):
X = D_inv.dot(A.dot(X))
def _gen(self, gname: str, gen_id: int) -> nx.Graph:
import graph_tool.all as gt # local import
assert 'state' in self.params, 'missing parameter: state for SBM'
state = self.params['state']
gen_gt_g = gt.generate_sbm(
state.b.a,
gt.adjacency(state.get_bg(),
state.get_ers()).T) # returns a graphtool graph
g = graphtool_to_networkx(gen_gt_g)
g.name = gname
g.gen_id = gen_id
return g
def get_modified_adjacency_matrix(g, k):
# Get regular adjacency matrix
adj = gt.adjacency(g)
# Initialize the modified adjacency matrix
X = np.zeros(adj.shape)
# Loop over nonzero elements
for i, j in zip(*adj.nonzero()):
X[i, j] = 1 / adj[i, j]
adj_max = adj.max()
# Loop over zero elements
for i, j in set(itertools.product(range(adj.shape[0]), range(adj.shape[1]))).difference(zip(*adj.nonzero())):
X[i, j] = k * adj_max
return X
def adjacency(gdf: gpd.GeoDataFrame, directed: bool=False, weight: str=None, include_vertices_position: bool=False) -> (csr_matrix, list):
"""create weighted adjacency matrix from graph.
Adjacency matrix is a square matrix of |vertices| x |vertices| indicating edges as an integer.
Parameters
----------
gdf : :class:`~geopandas.GeoDataFrame`
A geopandas dataframe with the list of edges
directed : bool (default to False)
is the graph directed
weight : string (default to None)
column of the geodataframe to use as a weight
include_vertices_position :boolean (default to False)
also return a dictionary of attributes for the vertices ({v:(x,y)}
Returns
-------
adj : :class:`~scipy.sparse.csr_matrix`
"""
g = gt.Graph(directed=directed)
if weight is not None:
if weight not in gdf.columns:
logging.error('{0} is not in geodataframe columns {1}'.format(weight,gdf.columns))
return None, None
data_type_obj = gdf.dtypes[weight]
if data_type_obj == np.int:
weight_prop = g.new_edge_property('int')
else:
weight_prop = g.new_edge_property('float')
edgelist = gdf.reset_index()[['u','v',weight]].values
nodes_id = g.add_edge_list(edgelist, hashed=True, eprops=[weight,])
else:
edgelist = gdf.reset_index()[['u','v']].values
nodes_id = g.add_edge_list(edgelist, hashed=True)
if not include_vertices_position:
return gt.adjacency(g,weight_prop), None
if
e5 = G.add_edge(v3, v5)
e6 = G.add_edge(v5, v3)
e7 = G.add_edge(v1, v3)
e8 = G.add_edge(v3, v1)
vlen = len(G.get_vertices())
#Figure 1
gt.graph_draw(G,
vertex_text=G.vertex_index,
vertex_font_size=18,
output_size=(800, 800),
output="example2.pdf")
#A will be the adjacency matrix of G
A = gt.adjacency(G)
with open('log.txt', 'w') as file_w:
file_w.write("A_G=\n")
file_w.write(str(A.todense().transpose()))
file_w.write("\n")
L = gt.laplacian(G, deg='total', normalized=False, weight=None,
index=None) # unnormalized Laplace
L = L - A.transpose() #L
with open('log.txt', 'ab') as file_w:
file_w.write("L=\n")
file_w.write(str(L.todense()))
file_w.write("\n")
vcc_path.remove(num_vert)
g.remove_vertex(num_vert+1)
g.remove_vertex(num_vert)
plane = [i for i in range(Lx*Ly,2*Lx*Ly+1) if i in g.vertex(i-Lx*Ly).all_neighbours() and i in vcc_path]
top = range(Lx*Ly)
bot = range(l-Lx*Ly,l)
others = vcc_path-set(top)-set(bot)
fixed = set(range(l))-others
adj = gt.adjacency(g)
del g
resnet.csr_rows_set_nz_to_val(adj,fixed) # nodes not in vcc_path or those on the top/bottom
diag = adj.sum(axis=1)
log.info("creating PETSc structures")
b = PETSc.Vec().createSeq(l)
x = PETSc.Vec().createSeq(l)
#A = PETSc.Mat().createAIJ(size=adj.shape,nnz=7,csr=(adj.indptr,adj.indices,-adj.data))
A = PETSc.Mat().createAIJ(size=adj.shape,nnz=7)
# the gm factor here and below doesn't actually matter
# scaling each resistor by the same constant still produces
# the same distribution of voltages
def weisfeiler_lehman_subtree_kernel(graph_db, hashed_attributes, *kwargs):
iterations = kwargs[0]
compute_gram_matrix = kwargs[1]
normalize_gram_matrix = kwargs[2]
use_labels = kwargs[3]
# Create one empty feature vector for each graph
feature_vectors = []
for _ in graph_db:
feature_vectors.append(np.zeros(0, dtype=np.float64))
# Construct block diagonal matrix of all adjacency matrices
adjacency_matrices = []
for g in graph_db:
adjacency_matrices.append(gt.adjacency(g))
M = sp.sparse.block_diag(tuple(adjacency_matrices),
dtype=np.float64,
format="csr")
num_vertices = M.shape[0]
# Load list of precalculated logarithms of prime numbers
log_primes = log_pl.log_primes[0:num_vertices]
# Color vector representing labels
colors_0 = np.zeros(num_vertices, dtype=np.float64)
# Color vector representing hashed attributes
colors_1 = hashed_attributes
# Get labels (colors) from all graph instances
offset = 0
graph_indices = []
for g in graph_db:
if use_labels == 1:
for i, v in enumerate(g.vertices()):
colors_0[i + offset] = g.vp.nl[v]
if use_labels == 2:
for i, v in enumerate(g.vertices()):
colors_0[i + offset] = v.out_degree()
graph_indices.append((offset, offset + g.num_vertices() - 1))
offset += g.num_vertices()
# Map labels to [0, number_of_colors)
if use_labels:
_, colors_0 = np.unique(colors_0, return_inverse=True)
for it in range(0, iterations + 1):
if use_labels:
# Map colors into a single color vector
if len(colors_1) > 0:
colors_all = np.array([colors_0, colors_1])
else:
colors_all = np.array([colors_0])
colors_all = [hash(tuple(row)) for row in colors_all.T]
_, colors_all = np.unique(colors_all, return_inverse=True)
max_all = int(np.amax(colors_all) + 1)
# max_all = int(np.amax(colors_0) + 1)
feature_vectors = [
np.concatenate((feature_vectors[i],
np.bincount(colors_all[index[0]:index[1] + 1],
minlength=max_all)))
for i, index in enumerate(graph_indices)
]
# Avoid coloring computation in last iteration
if it < iterations:
colors_0 = compute_coloring(M, colors_0,
log_primes[0:len(colors_0)])
if len(colors_1) > 0:
colors_1 = compute_coloring(M, colors_1,
log_primes[0:len(colors_1)])
else:
max_1 = int(np.amax(colors_1) + 1)
feature_vectors = [
np.concatenate((feature_vectors[i],
np.bincount(colors_1[index[0]:index[1] + 1],
minlength=max_1)))
for i, index in enumerate(graph_indices)
]
# Avoid coloring computation in last iteration
if it < iterations:
colors_1 = compute_coloring(M, colors_1,
log_primes[0:len(colors_1)])
if not compute_gram_matrix:
return feature_vectors
#return lil.lil_matrix(feature_vectors, dtype=np.float64)
else:
# Make feature vectors sparse
gram_matrix = csr.csr_matrix(feature_vectors, dtype=np.float64)
# Compute gram matrix
gram_matrix = gram_matrix.dot(gram_matrix.T)
gram_matrix = gram_matrix.toarray()
if normalize_gram_matrix:
return aux.normalize_gram_matrix(gram_matrix)
else:
return gram_matrix
def generate_power_law_bipartite_net(N, frac_left_node, gamma, ave_deg,
min_deg_left, min_deg_right, node_class):
"""
Generate power law bipartite network.
Params
------
N : int
Number of nodes
frac_left_node : float
Fraction of nodes on the left part.
gamma : float
Power-law exponent (same expoent for both sides)
ave_deg : float
Average degree
min_deg_left : int
Minimum degree for nodes on the left part
min_deg_right : int
Minimum degree for nodes on the right part
node_class : list of str
Name of the class for the left and right nodes
node_class[0] : str for left nodes.
node_class[1] : str for right nodes.
Return
------
G : networkx.Graph
"""
def zipf(a, min, max, size=None):
"""
Generate Zipf-like random variables,
but in inclusive [min...max] interval
"""
v = np.arange(min, max + 1) # values to sample
p = 1.0 / np.power(v, a) # probabilities
p /= np.sum(p) # normalized
return np.random.choice(v, size=size, replace=True, p=p)
def add_n_stabs(deg, n):
"""
Add n stabs to degree sequence
"""
stubs = np.random.choice(np.arange(len(deg)),
size=int(n),
replace=True,
p=deg / np.sum(deg))
for s in stubs:
deg[s] += 1
return deg
def to_graphical_deg_seq(deg_left, deg_right):
"""
Make the degree sequence to be graphical
by adding edges
"""
deg_left_sum = np.sum(deg_left)
deg_right_sum = np.sum(deg_right)
if deg_left_sum < deg_right_sum:
deg_left = add_n_stabs(deg_left, deg_right_sum - deg_left_sum)
elif deg_left_sum > deg_right_sum:
deg_right = add_n_stabs(deg_right, deg_left_sum - deg_right_sum)
return deg_left, deg_right
# Compute the number of nodes
N_left = int(N * frac_left_node)
N_right = N - N_left
# Generate degree sequence
deg_left = zipf(3, min_deg_left, N_right, size=N_left)
deg_right = zipf(3, min_deg_right, N_left, size=N_right)
# Rescale such that the average degree is the prescribed average degree
E = ave_deg * (N_left + N_right)
deg_left = np.clip(np.round(deg_left * E / np.sum(deg_left)), min_deg_left,
N_right)
deg_right = np.clip(np.round(deg_right * E / np.sum(deg_right)),
min_deg_right, N_left)
# Make them graphical degree sequences
deg_left, deg_right = to_graphical_deg_seq(deg_left, deg_right)
# Prepare parameters for graph-tool
E = np.sum(deg_right)
gt_params = {
"out_degs":
np.concatenate([np.zeros_like(deg_left), deg_right]).astype(int),
"in_degs":
np.concatenate([deg_left, np.zeros_like(deg_right)]).astype(int),
"b":
np.concatenate([np.zeros(N_left), np.ones(N_right)]),
"probs":
np.array([[0, 0], [E, 0]]),
"directed":
True,
"micro_degs":
True,
}
# Generate the network until the degree sequence
# satisfied the thresholds
while True:
g = gt.generate_sbm(**gt_params)
A = gt.adjacency(g).T
A.data = np.ones_like(A.data)
outdeg = np.array(A.sum(axis=1)).reshape(-1)[N_left:]
indeg = np.array(A.sum(axis=0)).reshape(-1)[:N_left]
if (np.min(indeg) >= min_deg_left) and (np.min(outdeg) >=
min_deg_right):
break
# Convert to the networkx objet
G = nx.from_scipy_sparse_matrix(A, create_using=nx.Graph)
# Add attributes to the nodes
node_attr = {i: node_class[int(i > N_left)] for i in range(N)}
nx.set_node_attributes(G, node_attr, "class")
return G
def main():
import sys
import os.path
import glob
import itertools
from argparse import ArgumentParser
parser = ArgumentParser(
description='Read a graph, and produce a layout with t-SNE.')
# Input
parser.add_argument(
'graphs',
nargs='+',
help='(List of) input graph(s). Or a folder with graphs.')
# Output
parser.add_argument('-o',
default='./output',
help='Folder to write output to. Default: ./output')
parser.add_argument('--save_every',
type=int,
help='Save a jpg snapshot ever x epochs.')
parser.add_argument(
'--render_video',
action='store_true',
help=
'Render a video of the layout evolution. Needs ImageMagick and ffmpeg.'
)
parser.add_argument(
'--retain_snaps',
action='store_true',
help=
'Retain the snapshots. This argument is ignored if no video is rendered.'
)
parser.add_argument(
'--save_layout_data',
action='store_true',
help='Save all layout coordinates in a .pickle file and a .txt file.')
parser.add_argument('--opacity',
type=float,
default=0.3,
help='Edge opacity.')
# Manipulations to graph
parser.add_argument(
'--strip_graph',
action='store_true',
help='Retain only the largest connected component in the graph.')
parser.add_argument('--rnd_seed',
'-r',
type=int,
nargs='+',
default=[None],
help='Seed for random state. (Default: Random seed)')
parser.add_argument(
'--pre_sfdp',
action='store_true',
help=
'If this flag is given, the vertices will be pre-initialized with SFDP.'
)
parser.add_argument('--only_sfdp',
action='store_true',
help='If this flag is given, only SFDP will be done.')
parser.add_argument(
'--accept_all_sfdp',
action='store_true',
help=
'If this flag is given, no confirmation is asked for the SFDP layouts.'
)
parser.add_argument(
'--remove_rnd_edges',
nargs='+',
type=float,
default=[0],
help=
'Mutate the graph by removing random edges. If this is used without a random seed, a random random seed will be generated. The value given to this argument is the fraction of edges that will be removed.'
)
# Hyperparameters
parser.add_argument('--n_epochs',
'-e',
nargs='+',
type=int,
default=[1000],
help='One or more numbers of t-SNE epochs.')
parser.add_argument('--lr_init',
nargs='+',
type=float,
default=[80],
help='One or more initial learning rates.')
parser.add_argument(
'--lr_final',
nargs='+',
type=float,
default=[None],
help='One or more final learning rates. Default: Same as lr_init.')
parser.add_argument('--lr_switch',
nargs='+',
type=int,
default=[None],
help='One or more learning rate switch-points.')
parser.add_argument('--momentum_init',
nargs='+',
type=float,
default=[0.5],
help='One or more initial momenta.')
parser.add_argument('--momentum_final',
nargs='+',
type=float,
default=[0.5],
help='One or more initial momenta.')
parser.add_argument('--momentum_switch',
nargs='+',
type=int,
default=[None],
help='One or more momentum switch-points.')
# Distance metric parameters
parser.add_argument(
'--distance_metric',
'-d',
choices=['shortest_path', 'spdm', 'modified_adjacency', 'mam'],
default='spdm',
help='The distance metric that is used for the pairwise distances.')
parser.add_argument('-k',
nargs='+',
type=float,
default=[1],
help='Exponent for transfer function.')
# Cost function parameters
# Kullback-Leibler
parser.add_argument('--perplexity',
'-p',
nargs='+',
type=float,
default=[80],
help='One or more perplexities.')
parser.add_argument('--l_kl_init',
nargs='+',
type=float,
default=[1],
help='One or more KL factors.')
parser.add_argument('--l_kl_final',
nargs='+',
type=float,
default=[1],
help='One or more KL factors.')
parser.add_argument('--l_kl_switch',
nargs='+',
type=int,
default=[None],
help='One or more KL switch-points')
# Edge contraction
parser.add_argument('--l_e_init',
nargs='+',
type=float,
default=[0],
help='One or more edge contraction factors.')
parser.add_argument('--l_e_final',
nargs='+',
type=float,
default=[0],
help='One or more edge contraction factors.')
parser.add_argument('--l_e_switch',
nargs='+',
type=int,
default=[None],
help='One or more edge contraction switch-points')
# Compression
parser.add_argument('--l_c_init',
nargs='+',
type=float,
default=[1.2],
help='One or more compression factors.')
parser.add_argument('--l_c_final',
nargs='+',
type=float,
default=[0],
help='One or more compression factors.')
parser.add_argument('--l_c_switch',
nargs='+',
type=int,
default=[None],
help='One or more compression switch-points')
# Repulsion
parser.add_argument('--l_r_init',
nargs='+',
type=float,
default=[0],
help='One or more repulsion factors.')
parser.add_argument('--l_r_final',
nargs='+',
type=float,
default=[0.5],
help='One or more repulsion factors.')
parser.add_argument('--l_r_switch',
nargs='+',
type=int,
default=[None],
help='One or more repulsion switch-points')
parser.add_argument(
'--r_eps',
nargs='+',
type=float,
default=[0.2],
help='Additional term in denominator to prevent near-singularities.')
args = parser.parse_args()
# Retrieve a list of all files in the directory, if args.graphs[0] is a directory.
if len(args.graphs) == 1 and os.path.isdir(args.graphs[0]):
args.graphs = glob.glob(args.graphs[0] + '/*')
# Check graph input
for g_file in args.graphs:
if not os.path.isfile(g_file):
raise FileNotFoundError(g_file + ' is not a file.')
# Generate random random seed if none is given.
if args.rnd_seed == [None]:
args.rnd_seed = [np.random.randint(1e8)]
# Ignore retain_snaps argument if no video is rendered.
if not args.render_video:
args.retain_snaps = True
# Get names of the graphs (by splitting of path and extension)
names = [
os.path.split(os.path.splitext(file)[0])[1] for file in args.graphs
]
# Determine output folders. One is created in the specified output folder
# for every graph that is supplied.
output_folders = [args.o + '/' + name for name in names]
# Check (and possibly create) output folders
for folder in [args.o] + output_folders:
if not os.path.exists(folder):
os.makedirs(folder)
# At least everything is fine for now.
there_were_exceptions = False
# Loop over all graphs (and their respective output folders)
for g_file, g_name, output_folder in zip(args.graphs, names,
output_folders):
# Load the graph
g = graph_io.load_graph(g_file)
print(
'[tsnetwork] Loaded graph {0} (|V| = {1}, |E| = {2}) into memory.'.
format(g_name, g.num_vertices(), g.num_edges()))
# Add graph name as propery in the internal representation
g.graph_properties['name'] = g.new_graph_property('string', g_name)
# Usually this loop has just one iteration, with only 0 as the value
# for rmv_edge_frac (that is, no edges are removed).
for rmv_edge_frac in args.remove_rnd_edges:
print(
'[tsnetwork] Original graph: (|V|, |E|) = ({0}, {1}).'.format(
g.num_vertices(), g.num_edges()))
# Create a temporary copy of the graph that will be manipulated.
gv = gt.GraphView(g)
# Remove rmv_edge_frac of the graphs edges from gv.
gv.clear_filters()
gv.reindex_edges()
edge_list = list(gv.edges())
not_here_ep = gv.new_edge_property('bool', val=True)
n_remove_edges = int(rmv_edge_frac * gv.num_edges())
for e in np.random.randint(0, gv.num_edges(), n_remove_edges):
not_here_ep[edge_list[e]] = False
gv.set_edge_filter(not_here_ep)
if n_remove_edges > 0:
print(
'[tsnetwork] Removed {2} random edges: (|V|, |E|) = ({0}, {1}).'
.format(gv.num_vertices(), gv.num_edges(), n_remove_edges))
# Filter the graph s.t. only the largest connected component
# remains.
if args.strip_graph:
largest_connected_component = gt.label_largest_component(gv)
gv.set_vertex_filter(largest_connected_component)
gv.purge_vertices()
print(
'[tsnetwork] Filtered largest component: (|V|, |E|) = ({0}, {1}).'
.format(gv.num_vertices(), gv.num_edges()))
if args.pre_sfdp or args.only_sfdp:
# Perform a SFDP layout (either as the only layout or as a
# starting point for t-SNE.)
Y_init, _ = sfdp_placement(
gv,
output_folder,
ask_for_acceptance=not args.accept_all_sfdp,
opacity=args.opacity)
if args.only_sfdp:
continue
else:
# Random positions will be generated
Y_init = None
# Compute distance matrix of this graph with the specified metric
X = distance_matrix.get_distance_matrix(gv, args.distance_metric)
# Retrieve the adjacency matrix of the graph
Adj_sparse = gt.adjacency(gv)
Adj = np.zeros(Adj_sparse.shape, dtype='float32')
for i, j in zip(*Adj_sparse.nonzero()):
Adj[i, j] = Adj_sparse[i, j]
# Make list of tsnetwork configuration objects. These are objects
# that represent a configuration for a t-SNE layout.
tsn_configs = []
for perplexity, n_epochs, initial_lr, final_lr, lr_switch, initial_momentum,\
final_momentum, momentum_switch,\
initial_l_kl, final_l_kl, l_kl_switch,\
initial_l_e, final_l_e, l_e_switch,\
initial_l_c, final_l_c, l_c_switch,\
initial_l_r, final_l_r, l_r_switch,\
r_eps, k, rnd_seed in itertools.product(
args.perplexity, args.n_epochs, args.lr_init, args.lr_final,
args.lr_switch, args.momentum_init, args.momentum_final,
args.momentum_switch,
args.l_kl_init, args.l_kl_final, args.l_kl_switch,
args.l_e_init, args.l_e_final, args.l_e_switch,
args.l_c_init, args.l_c_final, args.l_c_switch,
args.l_r_init, args.l_r_final, args.l_r_switch,
args.r_eps, args.k, args.rnd_seed):
# Use 50% for the switching points if no argument is given
if lr_switch is None:
lr_switch = int(n_epochs * 0.5)
if momentum_switch is None:
momentum_switch = int(n_epochs * 0.5)
if l_kl_switch is None:
l_kl_switch = int(n_epochs * 0.5)
if l_e_switch is None:
l_e_switch = int(n_epochs * 0.5)
if l_c_switch is None:
l_c_switch = int(n_epochs * 0.5)
if l_r_switch is None:
l_r_switch = int(n_epochs * 0.5)
if final_lr is None:
final_lr = initial_lr
cfg = TsnConfig(perplexity=perplexity,
n_epochs=n_epochs,
initial_lr=initial_lr,
final_lr=final_lr,
lr_switch=lr_switch,
initial_momentum=initial_momentum,
final_momentum=final_momentum,
momentum_switch=momentum_switch,
initial_l_kl=initial_l_kl,
final_l_kl=final_l_kl,
l_kl_switch=l_kl_switch,
initial_l_e=initial_l_e,
final_l_e=final_l_e,
l_e_switch=l_e_switch,
initial_l_c=initial_l_c,
final_l_c=final_l_c,
l_c_switch=l_c_switch,
initial_l_r=initial_l_r,
final_l_r=final_l_r,
l_r_switch=l_r_switch,
r_eps=r_eps,
k=k,
pre_sfdp=args.pre_sfdp,
rmv_edge_frac=rmv_edge_frac,
rnd_seed=rnd_seed,
distance_matrix=args.distance_metric)
# Do no add the configurations that already have files matching
# the description, unless the user confirms to overwrite.
if any([
file.startswith(cfg.get_description() + '.')
for file in os.listdir(output_folder)
]):
if not usr_input.confirm('[tsnetwork] ' +
cfg.get_description() +
' files exists! Overwrite?'):
continue
tsn_configs.append(cfg)
# Loop over the t-SNE configurations for a single graph
for cfg in tsn_configs:
print('[tsnetwork] Processing: ' + cfg.get_description())
# String that has the path to the directory where the snapshots
# will come. (If --save_every is given)
snaps_dir = output_folder + '/snaps_' + cfg.get_description()
# Clean out existing snaps directory if it exists.
if args.save_every is not None and os.path.exists(snaps_dir):
if usr_input.confirm('[tsnetwork] ' + snaps_dir +
' exists. Delete contents?'):
for file in os.listdir(snaps_dir):
file_path = os.path.join(snaps_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(e)
elif args.save_every is not None and not os.path.exists(
snaps_dir):
# Make folder for snaps, if it is necessary and it doesn't
# exist yet.
os.makedirs(snaps_dir)
# Apply the transfer function
X_transfered = X**cfg.k
# Try to do the tsne layout.
try:
Y, costs = thesne.tsne(
X_transfered,
random_state=cfg.rnd_seed,
perplexity=cfg.perplexity,
n_epochs=cfg.n_epochs,
Y=Y_init,
initial_lr=cfg.initial_lr,
final_lr=cfg.final_lr,
lr_switch=cfg.lr_switch,
initial_momentum=cfg.initial_momentum,
final_momentum=cfg.final_momentum,
momentum_switch=cfg.momentum_switch,
initial_l_kl=cfg.initial_l_kl,
final_l_kl=cfg.final_l_kl,
l_kl_switch=cfg.l_kl_switch,
initial_l_e=cfg.initial_l_e,
final_l_e=cfg.final_l_e,
l_e_switch=cfg.l_e_switch,
initial_l_c=cfg.initial_l_c,
final_l_c=cfg.final_l_c,
l_c_switch=cfg.l_c_switch,
initial_l_r=cfg.initial_l_r,
final_l_r=cfg.final_l_r,
l_r_switch=cfg.l_r_switch,
r_eps=cfg.r_eps,
Adj=Adj,
g=gv,
snaps_output_folder=snaps_dir,
save_every=args.save_every)
except (thesne.NaNException, thesne.SigmaTooLowException) as e:
there_were_exceptions = True
print('[exception] {0}'.format(e))
# Also write exception to a file.
with open(
output_folder + '/exception_' +
cfg.get_description() + '.out', 'w') as f:
print('{0}'.format(e), file=f)
f.close()
print('[tsnetwork] Continuing with next TsnConfig.')
continue
# Render an animation of the snapshots
if args.render_video:
animations.save_animation(snaps_dir, cfg.get_description())
# Remove the directory with snapshots.
if args.save_every is not None and not args.retain_snaps and os.path.exists(
snaps_dir):
print('[tsnetwork] Cleaning up snaps directory.')
shutil.rmtree(snaps_dir)
# Save the data (graph, vertex coordinates)
if args.save_layout_data:
layout_io.save_vna_layout(
output_folder + '/layout_' + cfg.get_description() +
'.vna', gv, Y)
layout_io.save_layout_txt(
output_folder + '/layout_edges_' +
cfg.get_description() + '.txt', gv, Y)
# Save final drawing of the layout
layout_io.save_drawing(output_folder,
gv,
Y.T,
cfg.get_description(),
formats=['jpg', 'pdf'],
edge_colors="rgb",
draw_vertices=False,
opacity=args.opacity)
if there_were_exceptions:
print('[tsnetwork] Done! However, be wary. There were exceptions.')
else:
print('[tsnetwork] Done!')
def graph_union(g, h): g = gt.Graph(g, prune=True) in_h_notin_g = (gt.adjacency(h) - gt.adjacency(g)).toarray() > 0 edges = transpose(triu(in_h_notin_g, 1).nonzero()) g.add_edge_list(edges) return g
def shortest_path_kernel(graph_db, hashed_attributes, *kwargs):
compute_gram_matrix = kwargs[0]
normalize_gram_matrix = kwargs[1]
use_labels = kwargs[2]
num_vertices = 0
for g in graph_db:
num_vertices += g.num_vertices()
offset = 0
graph_indices = []
colors_0 = np.zeros(num_vertices, dtype=np.int64)
# Get labels (colors) from all graph instances
offset = 0
for g in graph_db:
graph_indices.append((offset, offset + g.num_vertices() - 1))
if use_labels == 1:
for i, v in enumerate(g.vertices()):
colors_0[i + offset] = g.vp.nl[v]
if use_labels == 2:
for i, v in enumerate(g.vertices()):
colors_0[i + offset] = v.out_degree()
offset += g.num_vertices()
_, colors_0 = np.unique(colors_0, return_inverse=True)
colors_1 = hashed_attributes
triple_indices = []
triple_offset = 0
triples = []
# Solve APSP problem for every graphs in graph data base
for i, g in enumerate(graph_db):
a = gt.adjacency(g)
M = csg.shortest_path(a, method='J', directed=False, unweighted=True)
index = graph_indices[i]
if use_labels:
l = colors_0[index[0]:index[1] + 1]
h = colors_1[index[0]:index[1] + 1]
else:
h = colors_1[index[0]:index[1] + 1]
d = M.shape[0]
# For each pair of vertices collect labels, hashed attributes, and shortest-path distance
pairs = list(it.product(range(d), repeat=2))
if use_labels:
t = [
hash((l[k], h[k], l[j], h[j], M[k][j])) for (k, j) in pairs
if (k != j or ~np.isinf(M[k][j]))
]
else:
t = [
hash((h[k], h[j], M[k][j])) for (k, j) in pairs
if (k != j or ~np.isinf(M[k][j]))
]
triples.extend(t)
triple_indices.append((triple_offset, triple_offset + len(t) - 1))
triple_offset += len(t)
_, colors = np.unique(triples, return_inverse=True)
m = np.amax(colors) + 1
# Compute feature vectors
feature_vectors = []
for i, index in enumerate(triple_indices):
feature_vectors.append(
np.bincount(colors[index[0]:index[1] + 1], minlength=m))
if not compute_gram_matrix:
return lil.lil_matrix(feature_vectors, dtype=np.float64)
else:
# Make feature vectors sparse
gram_matrix = csr.csr_matrix(feature_vectors, dtype=np.float64)
# Compute gram matrix
gram_matrix = gram_matrix.dot(gram_matrix.T)
gram_matrix = gram_matrix.toarray()
if normalize_gram_matrix:
return aux.normalize_gram_matrix(gram_matrix)
else:
return gram_matrix
Nres = args.N # number of lattice sites on an edge
M = 2 # edge length (w.r.t. Nphys) of cubes to be removed, total vol removed is M**3
p = args.p # volume fraction
tol = 1e-3 # desired tolerance on volume fraction for generation
log.info("Nphys = %d, Nres = %d, M = %d, p = %f" % (Nphys,Nres,M,p))
flags = resnet.discrete_pore_space(Nphys,M,p,tol)
# generate a matrix with True volume fraction close to desired value
log.info("generating lattice")
g = gt.lattice([Nres,Nres,Nres])
mat = triu(gt.adjacency(g))
bonds = np.vstack([mat.row,mat.col])
r1 = 8e-9
r2 = 25e-9
log.info("warping lattice")
x,y,z = resnet.bonds_to_xyz(bonds,Nres,r1,r2)
# coordinates inside a certain radius
# inner = np.argwhere(np.sqrt(x**2 + y**2 + z**2) < (r1 + 0.2*(r2-r1))).flatten()
# fit lattice into space of 'physical matrix' indices
x = ne.evaluate('(x/r2/2+0.5)*(Nphys-1)')
y = ne.evaluate('(y/r2/2+0.5)*(Nphys-1)')
common_neighbors = np.sum(x * y)
if common_neighbors >= threshold:
return True
else:
return False
A = args.alpha
B = args.beta
def P(k, alpha=A, beta=B):
return 1 - (1 - beta) * (1 - alpha)**k
adj = gp.adjacency(graph).toarray() # matriz de adyacencia
N = graph.num_vertices()
one = np.ones_like(adj)
disjoin = one - adj # matriz de no-vecinos
pairs = 0
sameType_pairs = 0
expected_pairs = 0
for i in range(N):
for j in range(i + 1, N): # Suma sobre j>i
if disjoin[i, j] and F(
adj[i, :], adj[j, :], args.threshold
): # si no es vecino y cumple F es un par que nos interesa
pairs += 1
if graph.vp.essential[i] == graph.vp.essential[
