def print_evaluate(A_matrix,sampled_graph): stats = utils.compute_graph_statistics(sampled_graph) sps_truth = utils.sp(A_matrix) sps_gen = utils.sp(sampled_graph) stats['sp_emd']= utils.emd(sps_truth,sps_gen) s = utils.specGap(A_matrix) stats['spec_gap']=(s-utils.specGap(sampled_graph))**2 print(stats)
def gen_graphs(X_from_walks, target):
X = genExpected_fromWalks(X_from_walks, target.sum())
print(X)
sps_truth = utils.sp(target)
sp_emds = []
sgl2s = []
for i in range(20):
sampled_graph = gnp(X)
sps_gen = utils.sp(sampled_graph)
sp_emd = utils.emd(sps_truth, sps_gen)
sgl2 = (utils.specGap(target) - utils.specGap(sampled_graph))**2
sp_emds.append(sp_emd)
sgl2s.append(sgl2)
return sp_emds, sgl2s
def global_comp(A, X, sps_truth, spec):
sp_emds = []
specs = []
ccs = []
for i in range(100):
sampled_graph = gnp(X)
sps_gen = utils.sp(sampled_graph)
sp_emds.append(utils.emd(sps_truth, sps_gen))
specs.append((spec - utils.specGap(sampled_graph))**2)
ccs.append(3 * utils.statistics_triangle_count(sampled_graph) /
utils.statistics_claw_count(sampled_graph))
stats = {}
stats['shortest path emds'] = sp_emds
stats['spec gaps'] = specs
stats['clustering coefficients'] = ccs
print('Shortest path')
print(np.mean(sp_emds))
print(np.std(sp_emds))
print('Specs')
print(np.mean(specs))
print(np.std(specs))
print('Clusterig Coefficient')
print(np.mean(ccs))
print(np.std(ccs))
print('True CC')
tc = 3 * utils.statistics_triangle_count(
np.array(A)) / utils.statistics_claw_count(np.array(A))
print(tc)
print('ABS diff')
print(tc - np.mean(ccs))
return stats
def global_comp(A,X,sps_truth,spec):
sp_emds = []
specs = []
for i in range(100):
sampled_graph = gnp(X)
sps_gen = utils.sp(sampled_graph)
sp_emds.append(utils.emd(sps_truth,sps_gen))
specs.append((spec-utils.specGap(sampled_graph))**2)
return np.mean(sp_emds), np.std(sp_emds), np.mean(specs), np.std(specs)
def spec_gap_largest_component(A):
G = nx.from_numpy_matrix(A)
Gc = max(nx.connected_component_subgraphs(G), key=len)
A = np.asarray(nx.adjacency_matrix(Gc).todense())
return utils.specGap(A)
test = test_edges + test_nonEdges
G = nx.from_numpy_matrix(A_full)
L = nx.normalized_laplacian_matrix(G).todense()
eig_vals, eig_vecs = linalg.eig(L)
eig_list = zip(eig_vals, np.transpose(eig_vecs))
eig_list.sort(key=lambda x: x[0])
u = np.asarray([u_i.real for u_i in eig_list[-2][1]])[0][0]
truth = utils.compute_graph_statistics(np.asarray(A_full))
f = open('plots/truth.txt', "w")
f.write(str(truth))
f.close()
truth_spec = utils.specGap(A_full)
#train_spec = utils.specGap(A_matrix)
spec_gaps_w = []
spec_gaps_e = []
spec_gaps_w_raw = []
spec_gaps_e_raw = []
spec_gaps_a_raw = []
spec_gaps_a = []
spec_gaps_w_std = []
spec_gaps_e_std = []
spec_gaps_a_std = []
spec_gaps_truth = []
spec_gaps_train = []
spec_gaps_w_emp = []
trueScores.append(A[int(u)][int(v)])
predScores.append(X[int(u)][int(v)])
auc = roc_auc_score(trueScores, predScores)
ap = average_precision_score(trueScores, predScores)
return auc, ap
G = nx.read_gml('../data/football.gml')
_A_obs = nx.adjacency_matrix(G)
A_matrix = _A_obs.todense()
valid_edges = np.loadtxt('plots/football_val_edges.txt').tolist()
valid_nonEdges = np.loadtxt('plots/football_val_non_edges.txt').tolist()
valid = valid_edges + valid_nonEdges
sps_truth = utils.sp(A_matrix)
spec_truth = utils.specGap(A_matrix)
X = np.loadtxt(
'plots/football_walk_em/trainingIteration_1200_expectedGraph.txt')
print('Random Walks Size 16')
global_comp(A_matrix, X, sps_truth, spec_truth)
print(auc_p(X, np.asarray(A_matrix), valid))
X = np.loadtxt(
'plots/football_edge_em_nbs/trainingIteration_3200_expectedGraph.txt')
print('Random Walks Size 2 No BS adj')
global_comp(A_matrix, X, sps_truth, spec_truth)
print(auc_p(X, np.asarray(A_matrix), valid))
X = np.loadtxt(
'plots/football_edge_em_adj_bs/trainingIteration_1200_expectedGraph.txt')
print('Random Walks Size 2 BS adj')
global_comp(A_matrix, X, sps_truth, spec_truth)
def evaluate_expected(X, valid, test, A_full):
spec = utils.specGap(X)
auc_valid, ap_valid = auc_p(X, np.asarray(A_full), valid)
auc_test, ap_test = auc_p(X, np.asarray(A_full), test)
return auc_test, auc_valid, ap_test, ap_valid, spec
start = 1
train_path = 'plots/' + name + '_training.txt'
target_path = 'plots/' + name + '.txt'
A_matrix = np.loadtxt(train_path)
print('verifying correct barbell edge should be one')
print(A_matrix[3][99])
print(A_matrix[80][130])
A_full = np.loadtxt(target_path)
N = A_full.shape[0]
G = nx.from_numpy_matrix(A_full)
truth_spec = utils.specGap(A_full)
start = 1
step = 400
k = maxIter / step
#initialize all params
barbell_edge_density_1 = []
barbell_edge_density_2 = []
num_edges_crossing_1 = []
num_edges_crossing_2 = []
num_edges_crossing_3 = []
target_path = 'plots/'+name+'.txt'
A_matrix = np.loadtxt(train_path)
A_full = np.loadtxt(target_path)
N = A_full.shape[0]
valid_edges = np.loadtxt('plots/'+name+'_val_edges.txt').tolist()
valid_nonEdges = np.loadtxt('plots/'+name+'_val_non_edges.txt').tolist()
valid = valid_edges+valid_nonEdges
test_edges = np.loadtxt('plots/'+name+'_test_edges.txt').tolist()
test_nonEdges = np.loadtxt('plots/'+name+'_test_non_edges.txt').tolist()
test = test_edges+test_nonEdges
G = nx.from_numpy_matrix(A_full)
truth_spec = utils.specGap(A_full)
start = 1
step = 400
k=int(maxIter)/step
max_patience = 5
patience_spec = max_patience
patience_auc = max_patience
patience_ep = max_patience
patience_vc = max_patience
best_spec = -10
best_auc = 0
best_ep = 0
best_vc = 0
valid = valid_edges + valid_nonEdges
G = nx.from_numpy_matrix(A_matrix)
L = nx.normalized_laplacian_matrix(G).todense()
eig_vals, eig_vecs = linalg.eig(L)
eig_list = zip(eig_vals, np.transpose(eig_vecs))
eig_list.sort(key=lambda x: x[0])
u = np.asarray([u_i.real for u_i in eig_list[-2][1]])[0][0]
truth = utils.compute_graph_statistics(np.asarray(A_matrix))
f = open('plots/truth.txt', "w")
f.write(str(truth))
f.close()
truth_spec = utils.specGap(A_full)
train_spec = utils.specGap(A_matrix)
truth_cc = utils.cc(A_full)
cc_emd_combo = []
cc_emd_reg = []
cc_emd_fmm = []
cc_emd_combo_std = []
cc_emd_reg_std = []
cc_emd_fmm_std = []
k = 11
for i in range(1, k):
print(i)
X_c = np.loadtxt(
'plots/barbell_sameDensity/barbell_combo_mixed/trainingIteration_{}_expectedGraph.txt'
A_full = np.loadtxt(target_path)
N = A_full.shape[0]
valid_edges = np.loadtxt('plots/' + name + '_val_edges.txt').tolist()
valid_nonEdges = np.loadtxt('plots/' + name +
'_val_non_edges.txt').tolist()
valid = valid_edges + valid_nonEdges
test_edges = np.loadtxt('plots/' + name + '_test_edges.txt').tolist()
test_nonEdges = np.loadtxt('plots/' + name +
'_test_non_edges.txt').tolist()
test = test_edges + test_nonEdges
G_true = nx.from_numpy_matrix(A_full)
truth_spec = utils.spectrum(A_full)
truth_spec_gap = utils.specGap(A_full)
start = 5
step = 400
k = maxIter / step
#initialize all params
aucs_test_1 = []
aucs_valid_1 = []
aps_test_1 = []
aps_valid_1 = []
specs_1 = []
aucs_test_2 = []
