def main():
parser = argparse.ArgumentParser(
description="Convert a directory of CSC graphs to graphml format")
parser.add_argument(
"directory",
action="store",
nargs="+",
help=
"The directory(ies) we should run on. *Don't add '/' to end of dir name"
)
parser.add_argument("-w",
"--weighted",
action="store_true",
help="Pass flag if the graphs are weighted")
result = parser.parse_args()
for dircty in result.directory:
new_dir = dircty + "_graphml"
if not os.path.exists(new_dir):
os.makedirs(new_dir)
print "Making dir %s ..." % new_dir
else:
print "Dir %s already exists ..." % new_dir
for fn in glob(os.path.join(dircty, "*.mat")):
print "Converting %s ..." % fn
new_fn = os.path.join(
new_dir,
os.path.splitext(os.path.basename(fn))[0] + ".graphml")
print "Creating %s ..." % new_fn
graphml_adapter.csc_to_graphml(loadAnyMat(fn),
is_weighted=result.weighted,
desikan=True,
is_directed=False,
save_fn=new_fn)
def convert_graph(gfn, informat, save_dir, *outformats):
"""
Convert between igraph supported formats. No conversion to MAT or NPY available.
Positional arguments:
====================
gfn - the graph file name
informat - the input format of the graph
save_dir - the directory where we save result to
outformat - a list of output formats
"""
try:
if informat in ["graphml", "ncol", "edgelist", "lgl", "pajek", "graphdb"]:
g = igraph.read(gfn, None)
elif informat == "mat":
g = csc_to_igraph(loadAnyMat(gfn))
elif informat == "npy":
g = csc_to_igraph(np.load(gfn).item())
else:
err_msg = "[ERROR]: Unknown format '%s'. Please check format and re-try!" % informat
print err_msg
return err_msg
except Exception, err_msg:
print err_msg
return "[ERROR]: "+str(err_msg)
def loadAdjMat(G_fn, lcc_fn):
"""
Load adjacency matrix given lcc_fn & G_fn. lcc has z-indicies corresponding to the lcc.
positional args:
================
G_fn - the .mat file holding graph
lcc_fn - the largest connected component .npy z-ordering
returns:
=======
G_lcc - The largest connected component of a graph
"""
start = time()
print "Loading adjacency matrix..."
try:
vcc = lcc.ConnectedComponent(fn = lcc_fn) # creates conn_comp array
G_full = loadAnyMat(G_fn) # sio.loadmat(G_fn)['fibergraph'] # load the full sparse graph
G_lcc = vcc.induced_subgraph(G_full) # sparse graph of LCC
G_lcc = G_lcc + G_lcc.T # Symmetrize
except Exception:
if not os.path.exists(lcc_fn):
print "[IOError]: Lcc: %s Doesn't exist" % lcc_fn
sys.exit(-1)
if not os.path.exists(G_fn):
print "[IOError]: Graph: %s Doesn't exist" % G_fn
sys.exit(-1)
print "Time to load: %s secs" % (time()-start)
return G_lcc
def convert_graph(gfn, informat, save_dir, *outformats):
"""
Convert between igraph supported formats. No conversion to MAT or NPY available.
Positional arguments:
====================
gfn - the graph file name
informat - the input format of the graph
save_dir - the directory where we save result to
outformat - a list of output formats
"""
try:
if informat in [
"graphml", "ncol", "edgelist", "lgl", "pajek", "graphdb"
]:
g = igraph.read(gfn, None)
elif informat == "mat":
g = csc_to_igraph(loadAnyMat(gfn))
elif informat == "npy":
g = csc_to_igraph(np.load(gfn).item())
elif informat == "attredge":
g = attredge_to_igraph(gfn)
else:
err_msg = "[ERROR]: Unknown format '%s'. Please check format and retry!" % informat
print err_msg
return (None, err_msg)
except Exception, err_msg:
print err_msg
return (None, "[ERROR]: " + str(err_msg))
def compute(inv_dict, sep_save=True, gformat="graphml"):
"""
Actual function that computes invariants and saves them to a location
positional arguments:
=====================
inv_dict: is a dict that must contain:
- inv_dict["graph_fn"]
inv_dict: optional arguments:
- inv_dict["edge"]: boolean for global edge count
- inv_dict["ver"]: boolean for global vertex number
- inv_dict["tri"]: boolean for local triangle count
- inv_dict["eig"]: boolean for eigenvalues and eigenvectors
- inv_dict["deg"]: boolean for local degree count
- inv_dict["ss1"]: boolean for scan 1 statistic
- inv_dict["cc"]: boolean for clustering coefficient
- inv_dict["mad"]: boolean for maximum average degree
- inv_dict["k]: the number of eigenvalues to compute
- inv_dict["save_dir"]: the base path where all invariants will create sub-dirs & be should be saved
gformat - INPUT format of the graph
optional arguments:
===================
sep_save: boolean for auto save or not
"""
if inv_dict.get("save_dir", None) is None:
inv_dict["save_dir"] = os.path.join(os.path.dirname(inv_dict["graph_fn"]), "graphInvariants")
if not os.path.exists(inv_dict["save_dir"]):
os.makedirs(inv_dict["save_dir"])
if inv_dict.has_key("G"):
if inv_dict["G"] is not None:
G = inv_dict["G"]
else:
try:
if gformat in [
"edgelist",
"pajek",
"ncol",
"lgl",
"graphml",
"gml",
"dot",
"leda",
]: # All igraph supported formats
G = r_igraph_load_graph(inv_dict["graph_fn"], gformat)
elif gformat == "mat":
G = csc_to_r_igraph(loadAnyMat(inv_dict["graph_fn"]))
if isinstance(G, str):
return G # There was a loading error
except Exception, err_msg:
return str(err_msg)
def compute(inv_dict, sep_save=True, gformat="graphml"):
"""
Actual function that computes invariants and saves them to a location
positional arguments:
=====================
inv_dict: is a dict that must contain:
- inv_dict["graph_fn"]
inv_dict: optional arguments:
- inv_dict["edge"]: boolean for global edge count
- inv_dict["ver"]: boolean for global vertex number
- inv_dict["tri"]: boolean for local triangle count
- inv_dict["eig"]: boolean for eigenvalues and eigenvectors
- inv_dict["deg"]: boolean for local degree count
- inv_dict["ss1"]: boolean for scan 1 statistic
- inv_dict["cc"]: boolean for clustering coefficient
- inv_dict["mad"]: boolean for maximum average degree
- inv_dict["k]: the number of eigenvalues to compute
- inv_dict["save_dir"]: the base path where all invariants will create sub-dirs & be should be saved
gformat - INPUT format of the graph
optional arguments:
===================
sep_save: boolean for auto save or not
"""
if inv_dict.get("save_dir", None) is None:
inv_dict["save_dir"] = os.path.join(
os.path.dirname(inv_dict["graph_fn"]), "graphInvariants")
if not os.path.exists(inv_dict["save_dir"]):
os.makedirs(inv_dict["save_dir"])
if inv_dict.has_key("G"):
if inv_dict["G"] is not None:
G = inv_dict["G"]
else:
try:
if gformat in [
"edgelist", "pajek", "ncol", "lgl", "graphml", "gml",
"dot", "leda"
]: # All igraph supported formats
G = r_igraph_load_graph(inv_dict["graph_fn"], gformat)
elif gformat == "mat":
G = csc_to_r_igraph(loadAnyMat(inv_dict['graph_fn']))
if isinstance(G, str):
return G # There was a loading error
except Exception, err_msg:
return str(err_msg)
def main():
parser = argparse.ArgumentParser(description="Convert a directory of CSC graphs to graphml format")
parser.add_argument("directory", action="store", nargs="+", help="The directory(ies) we should run on. *Don't add '/' to end of dir name")
parser.add_argument("-w", "--weighted", action="store_true", help="Pass flag if the graphs are weighted")
result = parser.parse_args()
for dircty in result.directory:
new_dir = dircty+"_graphml"
if not os.path.exists(new_dir): os.makedirs(new_dir); print "Making dir %s ..." % new_dir
else: print "Dir %s already exists ..." % new_dir
for fn in glob(os.path.join(dircty, "*.mat")):
print "Converting %s ..." % fn
new_fn = os.path.join(new_dir, os.path.splitext(os.path.basename(fn))[0]+".graphml")
print "Creating %s ..." % new_fn
graphml_adapter.csc_to_graphml(loadAnyMat(fn), is_weighted=result.weighted, desikan=True, is_directed=False, save_fn=new_fn)
def check_graph(g_fn, savedir):
limits = (182, 218, 182) # HARD-CODED BOUNDS
g = loadAnyMat(g_fn)
vset = set()
fset = set()
nonzero = g.nonzero()
LEN_NNZ = nonzero[1].shape[0]
print "Adding nnz vertices to set ..."
for idx in xrange(LEN_NNZ):
vset.add(nonzero[0][idx])
vset.add(nonzero[1][idx])
if idx % 100000 == 0:
print "Processed %d/%d ..." % (idx, LEN_NNZ)
print "Checking if vertices are outside labels ..."
for cnt, vertex in enumerate(vset):
x, y, z = MortonXYZ(vertex)
if x > limits[0] or y > limits[1] or z > limits[2]:
print "Vertex %d has fibers and is outside the label mask ..." % vertex
fset.add(vertex)
if cnt % 300000 == 0:
print "Processed %d/%d ..." % (cnt, len(vset))
if fset:
print "Writing %d ill-placed vertices to disk as json..." % len(fset)
f = open(
os.path.join(savedir,
os.path.splitext(os.path.basename(g_fn))[0] +
".json"), "wb")
f.write(str(list(fset)))
f.close()
else:
print "No vertices outside the label mask!"
def loadAdjMat(G_fn, lcc_fn):
"""
Load adjacency matrix given lcc_fn & G_fn. lcc has z-indicies corresponding to the lcc.
positional args:
================
G_fn - the .mat file holding graph
lcc_fn - the largest connected component .npy z-ordering
returns:
=======
G_lcc - The largest connected component of a graph
"""
start = time()
print "Loading adjacency matrix..."
try:
vcc = lcc.ConnectedComponent(fn = lcc_fn) # creates conn_comp array
G_full = loadAnyMat(G_fn) # sio.loadmat(G_fn)['fibergraph'] # load the full sparse graph
G_lcc = vcc.induced_subgraph(G_full) # sparse graph of LCC
G_lcc = G_lcc + G_lcc.T # Symmetrize
print "Time to load Symmetrized LCC: %s secs" % (time()-start)
return G_lcc
except Exception, err:
if not os.path.exists(lcc_fn):
print "[IOError]: Lcc: %s Doesn't exist" % lcc_fn
sys.exit(-1)
if not os.path.exists(G_fn):
print "[IOError]: Graph: %s Doesn't exist" % G_fn
sys.exit(-1)
else:
print Exception, err
raise Exception
def check_graph(g_fn, savedir):
limits = (182, 218, 182) # HARD-CODED BOUNDS
g = loadAnyMat(g_fn)
vset = set()
fset = set()
nonzero = g.nonzero()
LEN_NNZ = nonzero[1].shape[0]
print "Adding nnz vertices to set ..."
for idx in xrange(LEN_NNZ):
vset.add(nonzero[0][idx])
vset.add(nonzero[1][idx])
if idx%100000==0:
print "Processed %d/%d ..." % (idx, LEN_NNZ)
print "Checking if vertices are outside labels ..."
for cnt, vertex in enumerate(vset):
x, y, z = MortonXYZ(vertex)
if x > limits[0] or y > limits[1] or z > limits[2]:
print "Vertex %d has fibers and is outside the label mask ..." % vertex
fset.add(vertex)
if cnt%300000==0:
print "Processed %d/%d ..." % (cnt, len(vset))
if fset:
print "Writing %d ill-placed vertices to disk as json..." % len(fset)
f = open( os.path.join(savedir, os.path.splitext(os.path.basename(g_fn))[0]+".json"), "wb" )
f.write(str(list(fset)))
f.close()
else:
print "No vertices outside the label mask!"
def compute(inv_dict, save=True):
'''
@param inv_dict: is a dict optinally containing any of these:
- inv_dict['edge']: boolean for global edge count
- inv_dict['ver']: boolean for global vertex number
- inv_dict['tri']: boolean for local triangle count
- inv_dict['tri_fn']: the path of a precomputed triangle count (.npy)
- inv_dict['eig']: boolean for eigenvalues and eigenvectors
- inv_dict['eigvl_fn']: the path of a precomputed eigenvalues (.npy)
- inv_dict['eigvect_fn']: the path of a precomputed eigenvectors (.npy)
- inv_dict['deg']: boolean for local degree count
- inv_dict['deg_fn']: the path of a precomputed triangle count (.npy)
- inv_dict['ss1']: boolean for scan 1 statistic
- inv_dict['cc']: boolean for clustering coefficient
- inv_dict['mad']: boolean for maximum average degree
- inv_dict['save_dir']: the base path where all invariants will create sub-dirs & be should be saved
@param save: boolean for auto save or not. TODO: use this
'''
# Popualate inv_dict
inv_dict = populate_inv_dict(inv_dict)
if inv_dict['save_dir'] is None:
inv_dict['save_dir'] = os.path.dirname(inv_dict['graph_fn'])
if (inv_dict.has_key('G')):
if inv_dict['G'] is not None:
G = inv_dict['G']
elif (inv_dict['graphsize'] == 'b' or inv_dict['graphsize'] == 'big'):
G = loadAdjMat(inv_dict['graph_fn'], inv_dict['lcc_fn']) # TODO: test
# small graphs
else:
G = loadAnyMat(inv_dict['graph_fn'], inv_dict['data_elem'])
if isinstance(G, str):
print G
return G # Error message
num_nodes = G.shape[0] # number of nodes
# CC requires deg_fn and tri_fn. Load if available
if inv_dict['cc']:
# if either #tri or deg is undefined
if not inv_dict['tri_fn']:
inv_dict['tri'] = True
if not inv_dict['deg_fn']:
inv_dict['deg'] = True
cc_array = np.zeros(num_nodes)
# All invariants that require eigenvalues
if ((inv_dict['tri'] and not inv_dict['tri_fn'])
or (inv_dict['mad'])):
if not inv_dict['eigvl_fn']:
inv_dict['eig'] = True
# Only create arrays if the computation will be done
if inv_dict['tri']:
if inv_dict['tri_fn']:
tri_array = np.load(inv_dict['tri_fn']) # load if precomputed
else:
tri_array = np.zeros(num_nodes) # local triangle count
if inv_dict['deg'] or inv_dict['edge']: # edge is global number of edges
inv_dict['deg'] = True
if inv_dict['deg_fn']:
deg_array = np.load(inv_dict['deg_fn'])
else:
deg_array = np.zeros(num_nodes) # Vertex degrees of all vertices
if (inv_dict['ss1']):
ss1_array = np.zeros(num_nodes) # Induced subgraph edge number i.e scan statistic
if (not inv_dict['k'] or inv_dict['k'] > 100 or inv_dict['k'] > G.shape[0] - 2):
k = 100 if G.shape[0]-2 > 101 else G.shape[0] - 2 # Maximum of 100 eigenvalues
start = time()
# Calculate Eigenvalues & Eigen vectors
if inv_dict['eig']:
if not (inv_dict['eigvl_fn'] or inv_dict['eigvect_fn']):
l, u = arpack.eigs(G, k=k, which='LM') # LanczosMethod(A,0)
print 'Time taken to calc Eigenvalues: %f secs\n' % (time() - start)
else:
try:
l = np.load(inv_dict['eigvl_fn'])
u = l = np.load(inv_dict['eigvect_fn'])
except Exception:
return "[IOERROR: ]Eigenvalues failed to load"
# All other invariants
start = time()
#### For loop ####
if (inv_dict['cc'] or inv_dict['ss1'] or (inv_dict['tri'] and not inv_dict['tri_fn'])\
or (inv_dict['deg'] and not inv_dict['deg_fn']) ): # one of the others
for j in range(num_nodes):
# tri
if not inv_dict['tri_fn'] and inv_dict['tri']: # if this is still None we need to compute it
tri_array[j] = abs(round((sum( np.power(l.real,3) * (u[j][:].real**2)) ) / 6.0)) # Divide by six because we count locally
# ss1 & deg
if inv_dict['ss1'] or (not inv_dict['deg_fn'] and inv_dict['deg']):
nbors = G[:,j].nonzero()[0]
# deg
if (not inv_dict['deg_fn'] and inv_dict['deg']):
deg_array[j] = nbors.shape[0]
# ss1
if inv_dict['ss1']:
if (nbors.shape[0] > 0):
nbors_mat = G[:,nbors][nbors,:]
ss1_array[j] = nbors.shape[0] + (nbors_mat.nnz/2.0) # scan stat 1 # Divide by two because of symmetric matrix
else:
ss1_array[j] = 0 # zero neighbors hence zero cardinality enduced subgraph
# cc
if inv_dict['cc']:
if (deg_array[j] > 2):
cc_array[j] = (2.0 * tri_array[j]) / ( deg_array[j] * (deg_array[j] - 1) ) # Jari et al
else:
cc_array[j] = 0
print 'Time taken to compute loop dependent invariants: %f secs\n' % (time() - start)
### End For ###
# global edge
if inv_dict['edge']:
edge_count = deg_array.sum()
# global vertices is num_nodes
''' MAD '''
if (inv_dict['mad']):
max_ave_deg = np.max(l.real)
# Computation complete - handle the saving now ...
''' Top eigenvalues & eigenvectors '''
if not inv_dict['eigvl_fn'] and inv_dict['eig'] :
eigvDir = os.path.join(inv_dict['save_dir'], "Eigen") #if eigvDir is None else eigvDir
# Immediately write eigs to file
inv_dict['eigvl_fn'] = os.path.join(eigvDir, getBaseName(inv_dict['graph_fn']) + '_eigvl.npy')
inv_dict['eigvect_fn'] = os.path.join(eigvDir, getBaseName(inv_dict['graph_fn']) + '_eigvect.npy')
createSave(inv_dict['eigvl_fn'], l.real) # eigenvalues
createSave(inv_dict['eigvect_fn'], u) # eigenvectors
print 'Eigenvalues and eigenvectors saved ...'
''' Triangle count '''
if not inv_dict['tri_fn'] and inv_dict['tri']:
triDir = os.path.join(inv_dict['save_dir'], "Triangle") #if triDir is None else triDir
inv_dict['tri_fn'] = os.path.join(triDir, getBaseName(inv_dict['graph_fn']) + '_triangles.npy') # TODO HERE
createSave(inv_dict['tri_fn'], tri_array)
print 'Triangle Count saved ...'
''' Degree count'''
if not inv_dict['deg_fn'] and inv_dict['deg']:
degDir = os.path.join(inv_dict['save_dir'], "Degree") #if degDir is None else degDir
inv_dict['deg_fn'] = os.path.join(degDir, getBaseName(inv_dict['graph_fn']) + '_degree.npy')
createSave(inv_dict['deg_fn'], deg_array)
print 'Degree saved ...'
''' MAD '''
if inv_dict['mad']:
MADdir = os.path.join(inv_dict['save_dir'], "MAD") #if MADdir is None else MADdir
inv_dict['mad_fn'] = os.path.join(MADdir, getBaseName(inv_dict['graph_fn']) + '_mad.npy')
createSave(inv_dict['mad_fn'], max_ave_deg)
print 'Maximum average Degree saved ...'
''' Scan Statistic 1'''
if inv_dict['ss1']:
ss1Dir = os.path.join(inv_dict['save_dir'], "SS1") #if ss1Dir is None else ss1Dir
inv_dict['ss1_fn'] = os.path.join(ss1Dir, getBaseName(inv_dict['graph_fn']) + '_scanstat1.npy')
createSave(inv_dict['ss1_fn'], ss1_array) # save it
print 'Scan 1 statistic saved ...'
''' Clustering coefficient '''
if inv_dict['cc']:
ccDir = os.path.join(inv_dict['save_dir'], "ClustCoeff") #if ccDir is None else ccDir
inv_dict['cc_fn'] = os.path.join(ccDir, getBaseName(inv_dict['graph_fn']) + '_clustcoeff.npy')
createSave(inv_dict['cc_fn'], cc_array) # save it
print 'Clustering coefficient saved ...'
''' Global Vertices '''
if inv_dict['ver']:
vertDir = os.path.join(inv_dict['save_dir'], "Globals") #if vertDir is None else vertDir
inv_dict['ver_fn'] = os.path.join(vertDir, getBaseName(inv_dict['graph_fn']) + '_numvert.npy')
createSave(inv_dict['ver_fn'], num_nodes) # save it
print 'Global vertices number saved ...'
''' Global number of edges '''
if inv_dict['edge']:
edgeDir = os.path.join(inv_dict['save_dir'], "Globals") #if edgeDir is None else edgeDir
inv_dict['edge_fn'] = os.path.join(edgeDir, getBaseName(inv_dict['graph_fn']) + '_numedges.npy')
createSave(inv_dict['edge_fn'], edge_count) # save it
print 'Global edge number saved ...'
#if test: # bench test
# tri_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_triangles.npy')
# eigvl_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_eigvl.npy')
# eigvect_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_eigvect.npy')
# MAD_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_MAD.npy')
return inv_dict # TODO: Fix code this breaks. Originally was [tri_fn, deg_fn, MAD_fn, eigvl_fn, eigvect_fn]
def compute(inv_dict, save=True):
'''
@param inv_dict: is a dict optinally containing any of these:
- inv_dict['edge']: boolean for global edge count
- inv_dict['ver']: boolean for global vertex number
- inv_dict['tri']: boolean for local triangle count
- inv_dict['tri_fn']: the path of a precomputed triangle count (.npy)
- inv_dict['eig']: boolean for eigenvalues and eigenvectors
- inv_dict['eigvl_fn']: the path of a precomputed eigenvalues (.npy)
- inv_dict['eigvect_fn']: the path of a precomputed eigenvectors (.npy)
- inv_dict['deg']: boolean for local degree count
- inv_dict['deg_fn']: the path of a precomputed triangle count (.npy)
- inv_dict['ss1']: boolean for scan 1 statistic
- inv_dict['cc']: boolean for clustering coefficient
- inv_dict['mad']: boolean for maximum average degree
- inv_dict['save_dir']: the base path where all invariants will create sub-dirs & be should be saved
@param save: boolean for auto save or not. TODO: use this
'''
# Popualate inv_dict
inv_dict = populate_inv_dict(inv_dict)
if inv_dict['save_dir'] is None:
inv_dict['save_dir'] = os.path.dirname(inv_dict['graph_fn'])
if (inv_dict.has_key('G')):
if inv_dict['G'] is not None:
G = inv_dict['G']
elif (inv_dict['graphsize'] == 'b' or inv_dict['graphsize'] == 'big'):
G = loadAdjMat(inv_dict['graph_fn'], inv_dict['lcc_fn']) # TODO: test
# small graphs
else:
G = loadAnyMat(inv_dict['graph_fn'], inv_dict['data_elem'])
if isinstance(G, str):
print G
return G # Error message
num_nodes = G.shape[0] # number of nodes
# CC requires deg_fn and tri_fn. Load if available
if inv_dict['cc']:
# if either #tri or deg is undefined
if not inv_dict['tri_fn']:
inv_dict['tri'] = True
if not inv_dict['deg_fn']:
inv_dict['deg'] = True
cc_array = np.zeros(num_nodes)
# All invariants that require eigenvalues
if ((inv_dict['tri'] and not inv_dict['tri_fn']) or (inv_dict['mad'])):
if not inv_dict['eigvl_fn']:
inv_dict['eig'] = True
# Only create arrays if the computation will be done
if inv_dict['tri']:
if inv_dict['tri_fn']:
tri_array = np.load(inv_dict['tri_fn']) # load if precomputed
else:
tri_array = np.zeros(num_nodes) # local triangle count
if inv_dict['deg'] or inv_dict['edge']: # edge is global number of edges
inv_dict['deg'] = True
if inv_dict['deg_fn']:
deg_array = np.load(inv_dict['deg_fn'])
else:
deg_array = np.zeros(num_nodes) # Vertex degrees of all vertices
if (inv_dict['ss1']):
ss1_array = np.zeros(
num_nodes) # Induced subgraph edge number i.e scan statistic
if (not inv_dict['k'] or inv_dict['k'] > 100
or inv_dict['k'] > G.shape[0] - 2):
k = 100 if G.shape[0] - 2 > 101 else G.shape[
0] - 2 # Maximum of 100 eigenvalues
start = time()
# Calculate Eigenvalues & Eigen vectors
if inv_dict['eig']:
if not (inv_dict['eigvl_fn'] or inv_dict['eigvect_fn']):
l, u = arpack.eigs(G, k=k, which='LM') # LanczosMethod(A,0)
print 'Time taken to calc Eigenvalues: %f secs\n' % (time() -
start)
else:
try:
l = np.load(inv_dict['eigvl_fn'])
u = l = np.load(inv_dict['eigvect_fn'])
except Exception:
return "[IOERROR: ]Eigenvalues failed to load"
# All other invariants
start = time()
#### For loop ####
if (inv_dict['cc'] or inv_dict['ss1'] or (inv_dict['tri'] and not inv_dict['tri_fn'])\
or (inv_dict['deg'] and not inv_dict['deg_fn']) ): # one of the others
for j in range(num_nodes):
# tri
if not inv_dict['tri_fn'] and inv_dict[
'tri']: # if this is still None we need to compute it
tri_array[j] = abs(
round((sum(np.power(l.real, 3) * (u[j][:].real**2))) /
6.0)) # Divide by six because we count locally
# ss1 & deg
if inv_dict['ss1'] or (not inv_dict['deg_fn'] and inv_dict['deg']):
nbors = G[:, j].nonzero()[0]
# deg
if (not inv_dict['deg_fn'] and inv_dict['deg']):
deg_array[j] = nbors.shape[0]
# ss1
if inv_dict['ss1']:
if (nbors.shape[0] > 0):
nbors_mat = G[:, nbors][nbors, :]
ss1_array[j] = nbors.shape[0] + (
nbors_mat.nnz / 2.0
) # scan stat 1 # Divide by two because of symmetric matrix
else:
ss1_array[
j] = 0 # zero neighbors hence zero cardinality enduced subgraph
# cc
if inv_dict['cc']:
if (deg_array[j] > 2):
cc_array[j] = (2.0 * tri_array[j]) / (
deg_array[j] * (deg_array[j] - 1)) # Jari et al
else:
cc_array[j] = 0
print 'Time taken to compute loop dependent invariants: %f secs\n' % (
time() - start)
### End For ###
# global edge
if inv_dict['edge']:
edge_count = deg_array.sum()
# global vertices is num_nodes
''' MAD '''
if (inv_dict['mad']):
max_ave_deg = np.max(l.real)
# Computation complete - handle the saving now ...
''' Top eigenvalues & eigenvectors '''
if not inv_dict['eigvl_fn'] and inv_dict['eig']:
eigvDir = os.path.join(inv_dict['save_dir'],
"Eigen") #if eigvDir is None else eigvDir
# Immediately write eigs to file
inv_dict['eigvl_fn'] = os.path.join(
eigvDir,
getBaseName(inv_dict['graph_fn']) + '_eigvl.npy')
inv_dict['eigvect_fn'] = os.path.join(
eigvDir,
getBaseName(inv_dict['graph_fn']) + '_eigvect.npy')
createSave(inv_dict['eigvl_fn'], l.real) # eigenvalues
createSave(inv_dict['eigvect_fn'], u) # eigenvectors
print 'Eigenvalues and eigenvectors saved ...'
''' Triangle count '''
if not inv_dict['tri_fn'] and inv_dict['tri']:
triDir = os.path.join(inv_dict['save_dir'],
"Triangle") #if triDir is None else triDir
inv_dict['tri_fn'] = os.path.join(triDir,
getBaseName(inv_dict['graph_fn']) +
'_triangles.npy') # TODO HERE
createSave(inv_dict['tri_fn'], tri_array)
print 'Triangle Count saved ...'
''' Degree count'''
if not inv_dict['deg_fn'] and inv_dict['deg']:
degDir = os.path.join(inv_dict['save_dir'],
"Degree") #if degDir is None else degDir
inv_dict['deg_fn'] = os.path.join(
degDir,
getBaseName(inv_dict['graph_fn']) + '_degree.npy')
createSave(inv_dict['deg_fn'], deg_array)
print 'Degree saved ...'
''' MAD '''
if inv_dict['mad']:
MADdir = os.path.join(inv_dict['save_dir'],
"MAD") #if MADdir is None else MADdir
inv_dict['mad_fn'] = os.path.join(
MADdir,
getBaseName(inv_dict['graph_fn']) + '_mad.npy')
createSave(inv_dict['mad_fn'], max_ave_deg)
print 'Maximum average Degree saved ...'
''' Scan Statistic 1'''
if inv_dict['ss1']:
ss1Dir = os.path.join(inv_dict['save_dir'],
"SS1") #if ss1Dir is None else ss1Dir
inv_dict['ss1_fn'] = os.path.join(
ss1Dir,
getBaseName(inv_dict['graph_fn']) + '_scanstat1.npy')
createSave(inv_dict['ss1_fn'], ss1_array) # save it
print 'Scan 1 statistic saved ...'
''' Clustering coefficient '''
if inv_dict['cc']:
ccDir = os.path.join(inv_dict['save_dir'],
"ClustCoeff") #if ccDir is None else ccDir
inv_dict['cc_fn'] = os.path.join(
ccDir,
getBaseName(inv_dict['graph_fn']) + '_clustcoeff.npy')
createSave(inv_dict['cc_fn'], cc_array) # save it
print 'Clustering coefficient saved ...'
''' Global Vertices '''
if inv_dict['ver']:
vertDir = os.path.join(inv_dict['save_dir'],
"Globals") #if vertDir is None else vertDir
inv_dict['ver_fn'] = os.path.join(
vertDir,
getBaseName(inv_dict['graph_fn']) + '_numvert.npy')
createSave(inv_dict['ver_fn'], num_nodes) # save it
print 'Global vertices number saved ...'
''' Global number of edges '''
if inv_dict['edge']:
edgeDir = os.path.join(inv_dict['save_dir'],
"Globals") #if edgeDir is None else edgeDir
inv_dict['edge_fn'] = os.path.join(
edgeDir,
getBaseName(inv_dict['graph_fn']) + '_numedges.npy')
createSave(inv_dict['edge_fn'], edge_count) # save it
print 'Global edge number saved ...'
#if test: # bench test
# tri_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_triangles.npy')
# eigvl_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_eigvl.npy')
# eigvect_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_eigvect.npy')
# MAD_fn = os.path.join('bench', str(G.shape[0]), getBaseName(inv_dict['graph_fn']) + '_MAD.npy')
return inv_dict # TODO: Fix code this breaks. Originally was [tri_fn, deg_fn, MAD_fn, eigvl_fn, eigvect_fn]
