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Fix docs for seed arg
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@clbarnes
clbarnes committed Sep 8, 2022
1 parent 7d229a3 commit 151db9d
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11 changes: 5 additions & 6 deletions bct/algorithms/clustering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,7 +250,7 @@ def clustering_coef_wu_sign(W, coef_type='default'):
'''
Returns the weighted clustering coefficient generalized or separated
for positive and negative weights.
Three Algorithms are supported; herefore referred to as default, zhang,
and costantini.
Expand Down Expand Up @@ -384,9 +384,8 @@ def consensus_und(D, tau, reps=1000, seed=None):
reps : int
number of times the clustering algorithm is reapplied. default value
is 1000.
seed : hashable, optional
If None (default), use the np.random's global random state to generate random numbers.
Otherwise, use a new np.random.RandomState instance seeded with the given value.
seed : None, int, or numpy.random.Generator
Seed (or RNG itself) used to generate random numbers.
Returns
-------
Expand Down Expand Up @@ -485,7 +484,7 @@ def get_components(A, no_depend=False):
if not np.all(A == A.T): # ensure matrix is undirected
raise BCTParamError('get_components can only be computed for undirected'
' matrices. If your matrix is noisy, correct it with np.around')

A = binarize(A, copy=True)
n = len(A)
np.fill_diagonal(A, 1)
Expand All @@ -503,7 +502,7 @@ def get_components(A, no_depend=False):
temp.append(item)
union_sets = temp

comps = np.array([i+1 for v in range(n) for i in
comps = np.array([i+1 for v in range(n) for i in
range(len(union_sets)) if v in union_sets[i]])
comp_sizes = np.array([len(s) for s in union_sets])

Expand Down
5 changes: 2 additions & 3 deletions bct/algorithms/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,9 +166,8 @@ def core_periphery_dir(W, gamma=1, C0=None, seed=None):
0 < gamma < 1 detects large core, small periphery
C0 : NxN np.ndarray
Initial core structure
seed : hashable, optional
If None (default), use the np.random's global random state to generate random numbers.
Otherwise, use a new np.random.RandomState instance seeded with the given value.
seed : None, int, or numpy.random.Generator
Seed (or RNG itself) used to generate random numbers.
'''
rng = get_rng(seed)
n = len(W)
Expand Down
73 changes: 36 additions & 37 deletions bct/algorithms/generative.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@


@due.dcite(BibTeX(BETZEL2016), description="Generative models")
def generative_model(A, D, m, eta, gamma=None, model_type='matching',
def generative_model(A, D, m, eta, gamma=None, model_type='matching',
model_var='powerlaw', epsilon=1e-6, copy=True, seed=None):
'''
Generates synthetic networks using the models described in
Expand All @@ -32,10 +32,10 @@ def generative_model(A, D, m, eta, gamma=None, model_type='matching',
D : np.ndarray
Matrix of euclidean distances or other distances between nodes
m : int
Number of connections that should be present in the final synthetic
Number of connections that should be present in the final synthetic
network
eta : np.ndarray
A vector describing a range of values to estimate for eta, the
A vector describing a range of values to estimate for eta, the
hyperparameter describing exponential weighting of the euclidean
distance.
gamma : np.ndarray
Expand All @@ -44,7 +44,7 @@ def generative_model(A, D, m, eta, gamma=None, model_type='matching',
algorithm. If model_type='euclidean' or another distance metric,
this can be None.
model_type : Enum(str)
euclidean : Uses only euclidean distances to generate connection
euclidean : Uses only euclidean distances to generate connection
probabilities
neighbors : count of common neighbors
matching : matching index, the normalized overlap in neighborhoods
Expand All @@ -67,16 +67,15 @@ def generative_model(A, D, m, eta, gamma=None, model_type='matching',
copy : bool
Some algorithms add edges directly to the input matrix. Set this flag
to make a copy of the input matrix instead. Defaults to True.
seed : hashable, optional
If None (default), use the np.random's global random state to generate random numbers.
Otherwise, use a new np.random.RandomState instance seeded with the given value.
seed : None, int, or numpy.random.Generator
Seed (or RNG itself) used to generate random numbers.
'''
rng = get_rng(seed)
if copy:
A = A.copy()

n = len(D)

#These parameters don't do any of the voronoi narrowing.
#Its a list of eta values paired with gamma values.
#To try 3 eta and 3 gamma pairs, should use 9 list values.
Expand All @@ -93,7 +92,7 @@ def k_avg(K):
epsilon)

def k_diff(K):
return np.abs(np.tile(K, (n, 1)) -
return np.abs(np.tile(K, (n, 1)) -
np.transpose(np.tile(K, (n, 1)))) + epsilon

def k_max(K):
Expand All @@ -117,7 +116,7 @@ def s_diff(K, sc):

def s_min(K, sc):
return np.where(K < sc, K + epsilon, sc + epsilon)

def s_max(K, sc):
#return np.max((K, sc.T), axis=0)
return np.where(K > sc, K + epsilon, sc + epsilon)
Expand Down Expand Up @@ -169,12 +168,12 @@ def clu_gen(A, K, D, m, eta, gamma, model_var, x_fun):
if mv1 in ('powerlaw', 'power_law'):
Fd = D**eta
elif mv1 in ('exponential',):
Fd = np.exp(eta*D)
Fd = np.exp(eta*D)

if mv2 in ('powerlaw', 'power_law'):
Fk = K**gamma
elif mv2 in ('exponential',):
Fk = np.exp(gamma*K)
Fk = np.exp(gamma*K)

c = clustering_coef_bu(A)
k = np.sum(A, axis=1)
Expand Down Expand Up @@ -204,7 +203,7 @@ def clu_gen(A, K, D, m, eta, gamma, model_var, x_fun):
c[k<=1] = 0
bth[uu] = 1
bth[vv] = 1

k_result = x_fun(c, bth)

#print(np.shape(k_result))
Expand Down Expand Up @@ -239,12 +238,12 @@ def deg_gen(A, K, D, m, eta, gamma, model_var, s_fun):
if mv1 in ('powerlaw', 'power_law'):
Fd = D**eta
elif mv1 in ('exponential',):
Fd = np.exp(eta*D)
Fd = np.exp(eta*D)

if mv2 in ('powerlaw', 'power_law'):
Fk = K**gamma
elif mv2 in ('exponential',):
Fk = np.exp(gamma*K)
Fk = np.exp(gamma*K)

P = Fd * Fk * np.logical_not(A)
u,v = np.where(np.triu(np.ones((n,n)), 1))
Expand All @@ -258,7 +257,7 @@ def deg_gen(A, K, D, m, eta, gamma, model_var, s_fun):
# print(np.shape(np.where(A[u,v])), 'sqishy')
# print(np.shape(P), 'squnnaq')

#b[:mseed] = np.where(A[np.ix_(u,v)])
#b[:mseed] = np.where(A[np.ix_(u,v)])
b[:mseed] = np.squeeze(np.where(A[u,v]))
#print(mseed, m)
for i in range(mseed, m):
Expand Down Expand Up @@ -318,16 +317,16 @@ def matching_gen(A, K, D, m, eta, gamma, model_var):
if mv1 in ('powerlaw', 'power_law'):
Fd = D**eta
elif mv1 in ('exponential',):
Fd = np.exp(eta*D)
Fd = np.exp(eta*D)

if mv2 in ('powerlaw', 'power_law'):
Fk = K**gamma
elif mv2 in ('exponential',):
Fk = np.exp(gamma*K)
Fk = np.exp(gamma*K)

Ff = Fd * Fk * np.logical_not(A)
u,v = np.where(np.triu(np.ones((n,n)), 1))

for ii in range(mseed, m):
C = np.append(0, np.cumsum(Ff[u,v]))
r = np.sum(rng.random_sample()*C[-1] >= C)
Expand All @@ -343,7 +342,7 @@ def matching_gen(A, K, D, m, eta, gamma, model_var):
for i in range(len(updateuu)):
j = updateuu[i]
c2 = np.append(A[:,j], A[j,:])

use = np.logical_or(c1, c2)
use[uu] = use[uu+n] = use[j] = use[j+n] = 0
ncon = np.sum(c1[use]) + np.sum(c2[use])
Expand All @@ -356,12 +355,12 @@ def matching_gen(A, K, D, m, eta, gamma, model_var):
updatevv, = np.where(np.inner(A, A[:,vv]))
np.delete(updatevv, np.where(updatevv == uu))
np.delete(updatevv, np.where(updatevv == vv))

c1 = np.append(A[:,vv], A[vv,:])
for i in range(len(updatevv)):
j = updatevv[i]
c2 = np.append(A[:,j], A[j,:])

use = np.logical_or(c1, c2)
use[vv] = use[vv+n] = use[j] = use[j+n] = 0
ncon = np.sum(c1[use]) + np.sum(c2[use])
Expand All @@ -374,7 +373,7 @@ def matching_gen(A, K, D, m, eta, gamma, model_var):
Ff = Fd * Fk * np.logical_not(A)

return A

def neighbors_gen(A, K, D, m, eta, gamma, model_var):
K += epsilon

Expand All @@ -388,16 +387,16 @@ def neighbors_gen(A, K, D, m, eta, gamma, model_var):
if mv1 in ('powerlaw', 'power_law'):
Fd = D**eta
elif mv1 in ('exponential',):
Fd = np.exp(eta*D)
Fd = np.exp(eta*D)

if mv2 in ('powerlaw', 'power_law'):
Fk = K**gamma
elif mv2 in ('exponential',):
Fk = np.exp(gamma*K)
Fk = np.exp(gamma*K)

Ff = Fd * Fk * np.logical_not(A)
u,v = np.where(np.triu(np.ones((n,n)), 1))

for ii in range(mseed, m):
C = np.append(0, np.cumsum(Ff[u,v]))
r = np.sum(rng.random_sample()*C[-1] >= C)
Expand All @@ -407,7 +406,7 @@ def neighbors_gen(A, K, D, m, eta, gamma, model_var):

x = A[uu, :].astype(int)
y = A[:, vv].astype(int)

K[uu, y] += 1
K[y, uu] += 1
K[vv, x] += 1
Expand All @@ -416,7 +415,7 @@ def neighbors_gen(A, K, D, m, eta, gamma, model_var):
if mv2 in ('powerlaw', 'power_law'):
Fk = K**gamma
elif mv2 in ('exponential',):
Fk = np.exp(gamma*K)
Fk = np.exp(gamma*K)

if mv2 in ('powerlaw', 'power_law'):
Ff[uu, y] = Ff[y, uu] = Fd[uu, y] * (K[uu, y] ** gamma)
Expand Down Expand Up @@ -474,12 +473,12 @@ def euclidean_gen(A, D, m, eta, model_var):
elif model_type in ('clu-max', 'clu_max'):
Kseed = k_max(clustering_coef_bu(A))
for j, (ep, gp) in enumerate(zip(eta, gamma)):
B[:,:,j] = clu_gen(A, Kseed, D, m, ep, gp, model_var, x_max)
B[:,:,j] = clu_gen(A, Kseed, D, m, ep, gp, model_var, x_max)

elif model_type in ('clu-min', 'clu_min'):
Kseed = k_min(clustering_coef_bu(A))
for j, (ep, gp) in enumerate(zip(eta, gamma)):
B[:,:,j] = clu_gen(A, Kseed, D, m, ep, gp, model_var, x_min)
B[:,:,j] = clu_gen(A, Kseed, D, m, ep, gp, model_var, x_min)

elif model_type in ('clu-prod', 'clu_prod'):
Kseed = k_prod(clustering_coef_bu(A))
Expand All @@ -495,7 +494,7 @@ def euclidean_gen(A, D, m, eta, model_var):
Kseed = k_diff(np.sum(A, axis=1))
for j, (ep, gp) in enumerate(zip(eta, gamma)):
B[:,:,j] = deg_gen(A, Kseed, D, m, ep, gp, model_var, s_diff)

elif model_type in ('deg-max', 'deg_max'):
Kseed = k_max(np.sum(A, axis=1))
for j, (ep, gp) in enumerate(zip(eta, gamma)):
Expand Down Expand Up @@ -525,19 +524,19 @@ def euclidean_gen(A, D, m, eta, model_var):

elif model_type in ('spatial', 'geometric', 'euclidean'):
for j, ep in enumerate(eta):
B[:,:,j] = euclidean_gen(A, D, m, ep, model_var)
B[:,:,j] = euclidean_gen(A, D, m, ep, model_var)

return np.squeeze(B)

def evaluate_generative_model(A, Atgt, D, eta, gamma=None,
def evaluate_generative_model(A, Atgt, D, eta, gamma=None,
model_type='matching', model_var='powerlaw', epsilon=1e-6, seed=None):
'''
Generates synthetic networks with parameters provided and evaluates their
energy function. The energy function is defined as in Betzel et al. 2016.
Basically it takes the Kolmogorov-Smirnov statistics of 4 network
measures; comparing the degree distributions, clustering coefficients,
betweenness centrality, and Euclidean distances between connected regions.
The energy is globally low if the synthetic network matches the target.
Energy is defined as the maximum difference across the four statistics.
'''
Expand All @@ -548,11 +547,11 @@ def evaluate_generative_model(A, Atgt, D, eta, gamma=None,
xb = betweenness_bin(Atgt)
xe = D[np.triu(Atgt, 1) > 0]

B = generative_model(A, D, m, eta, gamma, model_type=model_type,
B = generative_model(A, D, m, eta, gamma, model_type=model_type,
model_var=model_var, epsilon=epsilon, copy=True, seed=seed)

#if eta != gamma then an error is thrown within generative model

nB = len(eta)

if nB == 1:
Expand All @@ -562,7 +561,7 @@ def evaluate_generative_model(A, Atgt, D, eta, gamma=None,

def kstats(x, y):
bin_edges = np.concatenate([[-np.inf],
np.sort(np.concatenate((x, y))),
np.sort(np.concatenate((x, y))),
[np.inf]])

bin_x,_ = np.histogram(x, bin_edges)
Expand Down
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