start = time()
if inv_dict.get("eig", False) != False:
if inv_dict.get("k") is None:
inv_dict["k"] = min(100,
r_igraph_vcount(G, False) -
3) # Max of 100 eigenvalues
# Test if graph is too big for invariants
print "Computing eigen decompositon ..."
lcc = True if r_igraph_ecount(G, False) > 500000 else False
if sep_save:
inv_dict["eigvl_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvl.npy")
inv_dict["eigvect_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvect.npy")
G = r_igraph_eigs(G,
inv_dict['k'],
save_fn=(inv_dict["eigvl_fn"],
inv_dict["eigvect_fn"]),
lcc=lcc)
else:
G = r_igraph_eigs(G, inv_dict['k'], save_fn=None, lcc=lcc)
#else: G = r_igraph_eigs(G, 4, save_fn=None, lcc=True)
if inv_dict.get("mad", False) != False:
if r_igraph_vcount(
G, False) < 1000000: # Cannot compute eigs on very big graphs
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]
# ============================ Call to invariants ============================#
start = time()
if inv_dict.get("eig", False) != False:
if inv_dict.get("k") is None:
inv_dict["k"] = min(100, r_igraph_vcount(G, False) - 3) # Max of 100 eigenvalues
# Test if graph is too big for invariants
print "Computing eigen decompositon ..."
lcc = True if r_igraph_ecount(G, False) > 500000 else False
if sep_save:
inv_dict["eigvl_fn"] = os.path.join(
inv_dict["save_dir"], "Eigen", getBaseName(inv_dict["graph_fn"]) + "_eigvl.npy"
)
inv_dict["eigvect_fn"] = os.path.join(
inv_dict["save_dir"], "Eigen", getBaseName(inv_dict["graph_fn"]) + "_eigvect.npy"
)
G = r_igraph_eigs(G, inv_dict["k"], save_fn=(inv_dict["eigvl_fn"], inv_dict["eigvect_fn"]), lcc=lcc)
else:
G = r_igraph_eigs(G, inv_dict["k"], save_fn=None, lcc=lcc)
# else: G = r_igraph_eigs(G, 4, save_fn=None, lcc=True)
if inv_dict.get("mad", False) != False:
if r_igraph_vcount(G, False) < 1000000: # Cannot compute eigs on very big graphs
if sep_save:
inv_dict["mad_fn"] = os.path.join(
inv_dict["save_dir"], "MAD", getBaseName(inv_dict["graph_fn"]) + "_mad.npy"
#============================ Call to invariants ============================#
start = time()
if inv_dict.get("eig", False) != False:
if inv_dict.get("k") is None:
inv_dict["k"] = min(100, r_igraph_vcount(G, False)-3) # Max of 100 eigenvalues
# Test if graph is too big for invariants
print "Computing eigen decompositon ..."
lcc = True if r_igraph_ecount(G, False) > 500000 else False
if sep_save:
inv_dict["eigvl_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvl.npy")
inv_dict["eigvect_fn"] = os.path.join(inv_dict["save_dir"], "Eigen", \
getBaseName(inv_dict["graph_fn"]) + "_eigvect.npy")
G = r_igraph_eigs(G, inv_dict['k'], save_fn=(inv_dict["eigvl_fn"], inv_dict["eigvect_fn"]), lcc=lcc)
else: G = r_igraph_eigs(G, inv_dict['k'], save_fn=None, lcc=lcc)
#else: G = r_igraph_eigs(G, 4, save_fn=None, lcc=True)
if inv_dict.get("mad", False) != False:
if r_igraph_vcount(G, False) < 1000000: # Cannot compute eigs on very big graphs
if sep_save:
inv_dict["mad_fn"] = os.path.join(inv_dict["save_dir"], "MAD", \
getBaseName(inv_dict["graph_fn"]) + "_mad.npy")
print "Computing MAD ..."
G = r_igraph_max_ave_degree(G)
else:
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]
