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plot.py
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import scipy.sparse as sp
import scipy.sparse.linalg as slinalg
from numpy import linalg
import scipy.misc
from sklearn.preprocessing import normalize
from gcn.utils import Test21, absorption_probability, smooth, load_data, taubin_smoothing
import gcn.utils
from config import configuration
import matplotlib.pyplot as plt
import numpy as np
import json
def preprocess_features(features, feature_type):
# """Row-normalize feature matrix and convert to tuple representation"""
rowsum = np.array(features.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
features = r_mat_inv.dot(features)
def normalize_adj(adj):
"""Symmetrically normalize adjacency matrix."""
# adj = sp.coo_matrix(adj)
rowsum = np.array(adj.sum(1))
# d_inv_sqrt = np.power(rowsum, -0.5)
# d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
# return adj*d_inv_sqrt*d_inv_sqrt.flatten()
d_inv_sqrt = np.power(rowsum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)
def laplacian_smooth(features, adj, lam, mu, repeat=1):
n = adj.shape[0]
smoothor = sp.diags(np.ones(n)) * (1-lam) + lam * adj
inflator = sp.diags(np.ones(n)) * (1-mu) + mu * adj
transformor = smoothor*inflator
for i in range(repeat):
features = transformor * features
return features
if __name__ == '__main__':
# model_config = configuration['model_list'][0]
# adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, size_of_each_class, triplet = \
# load_data(model_config['dataset'], train_size=model_config['train_size'],
# validation_size=model_config['validation_size'],
# model_config=model_config, shuffle=model_config['shuffle'])
#
# stored = model_config['dataset']
# n = adj.shape[0]
# adj_I = adj + sp.diags(np.ones(n))
# adj_I_sym = normalize_adj(adj_I)
#
# try:
# # raise Exception('DEBUG')
# vals, vecs, vec_inv = np.load(stored+'_I_vals.npy'), \
# np.load(stored+'_I_vecs.npy'), \
# np.load(stored + '_I_vec_inv.npy')
# print('load vals, vecs, vec_inv from files')
# except:
# # vals, vecs = slinalg.eigsh(adj_I_sym.toarray(), k=adj_I_sym.shape[0]-1)
# vals, vecs = linalg.eigh(adj_I_sym.toarray())
# vec_inv = vecs.T
#
# np.save(stored+'_I_vals.npy', vals)
# np.save(stored+'_I_vecs.npy', vecs)
# np.save(stored+'_I_vec_inv.npy', vec_inv)
# c = vec_inv.dot(gcn.utils.all_labels)
# c_abs = np.abs(c)
# sorted = np.sort(c_abs.flatten())
# # acc = np.add.accumulate(sorted)
# import matplotlib.pyplot as plt
# # plt.plot(acc)
# for i in range(c.shape[1]):
# plt.plot(vals, c_abs[:,i], 'o')
# plt.show()
dpi = 300
plt.figure(figsize=(3, 4))
axes = plt.gca()
axes.set_ylim([0,1])
plt.yticks(np.arange(0.,1.1,0.1))
plt.grid()
labels = [r'AP, $\alpha=10$',
r'RNM, $k=6$',
r'RW, $k=12$',
r'Raw features']
files = ['run-cnn_ap_appro_0.1_Model23_eval-tag-{}.json',
'run-cnn_taubin1_0_6_Model23_eval-tag-{}.json',
'run-cnn_taubin0.5_0_12_Model23_eval-tag-{}.json',
'run-cnn_Model23_eval-tag-{}.json']
for i in range(4):
xy = np.array(json.load(open(files[i].format('accuracy'))))[0:100]
plt.plot(xy[:,1], xy[:,2], label=labels[i])
legend = plt.legend(loc='lower right', shadow=True, fontsize=10)
plt.xlabel(r'Train steps', fontsize=14)
plt.ylabel(r'Test accuracy', rotation='horizontal', fontsize=14)
axes.yaxis.set_label_coords(0.05,1.05)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/train/test_acc.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3, 4))
axes = plt.gca()
plt.grid()
for i in range(4):
xy = np.array(json.load(open(files[i].format('loss'))))[0:100]
plt.plot(xy[:,1], xy[:,2], label=labels[i])
legend = plt.legend(loc='upper right', shadow=True, fontsize=10)
plt.xlabel(r'Train steps', fontsize=14)
plt.ylabel(r'Test loss', rotation='horizontal', fontsize=14)
axes.yaxis.set_label_coords(0,1.05)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/train/test_loss.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(2.5, 2.5))
axes = plt.gca()
# axes.set_ylim(0.90,0.955)
axes.set_xlim(0,21)
plt.grid()
alpha = [20, 12.5, 10, 5, 3.3, ]
acc1 = [94.47, 94.7, 94.72, 94.46, 94.44, ]
plt.plot(alpha, acc1, '-o', label='AP')
plt.plot([3,20], [92.05, 92.05], '--',label='LP')
plt.plot([3,20], [91.33, 91.33], '--',label='GCN')
plt.plot([3,20], [91.60, 91.60], '--',label='CNN')
legend = plt.legend(loc='lower left', shadow=True, fontsize=12)
plt.xlabel(r'$\alpha$', fontsize=14)
plt.ylabel(r'Test accuracy', rotation='horizontal', fontsize=14)
axes.yaxis.set_label_coords(0.1,1.05)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/parameters/parameters_AP.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(2.5, 2.5))
axes = plt.gca()
# axes.set_ylim(0.90,0.955)
axes.set_xlim(0,21)
plt.grid()
k1 = [4, 6, 8, 10, 12, 14, 16, 18, 20]
acc2 = [94.64, 94.95, 94.95, 94.97, 95.09, 94.65, 94.82, 94.94, 94.96]
plt.plot(k1, acc2, '-o', label='RW')
plt.plot([3,20], [92.05, 92.05], '--',label='LP')
plt.plot([3,20], [91.33, 91.33], '--',label='GCN')
plt.plot([3,20], [91.60, 91.60], '--',label='CNN')
legend = plt.legend(loc='lower left', shadow=True, fontsize=12)
plt.xlabel(r'$k$', fontsize=14)
plt.ylabel(r'Test accuracy', rotation='horizontal', fontsize=14)
axes.yaxis.set_label_coords(0.1,1.05)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/parameters/parameters_RW.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(2.5, 2.5))
axes = plt.gca()
# axes.set_ylim(0.90,0.955)
axes.set_xlim(0,11)
plt.grid()
k2 = [3, 4, 5, 6, 7, 8, 9, 10, ]
acc3 = [94.48, 94.53, 94.94, 94.76, 94.85, 94.95, 95.17, 94.81, ]
plt.plot(k2, acc3, '-o', label='RNM')
plt.plot([3,10], [92.05, 92.05], '--',label='LP')
plt.plot([3,10], [91.33, 91.33], '--',label='GCN')
plt.plot([3,10], [91.60, 91.60], '--',label='CNN')
legend = plt.legend(loc='lower left', shadow=True, fontsize=12)
plt.xlabel(r'$k$', fontsize=14)
plt.ylabel(r'Test accuracy', rotation='horizontal', fontsize=14)
axes.yaxis.set_label_coords(0.1,1.05)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/parameters/parameters_RNM.pdf', dpi=dpi, bbox_inches="tight")
model_config = {
'dataset' : 'cora', # 'Dataset string. (cora | citeseer | pubmed | CIFAR-Fea | Cifar_10000_fea | Cifar_R10000_fea | USPS-Fea | MNIST-Fea | MNIST-10000)'
'shuffle' : False,
'train_size' : 10, # if train_size is a number, then use TRAIN_SIZE labels per class.
# 'train_size' : [20 for i in range(10)], # if train_size is a list of numbers, then it specifies training labels for each class.
'validation_size' : 500, # 'Use VALIDATION_SIZE data to train model'
'validate' : False, # Whether use validation set
'loss_func' :'default', #'imbalance', 'triplet'
'ws_beta' : 20,
'max_triplet':1000, #for triplet, 1000 for cora to get all tripets
'feature' : 'bow',
'test_size' : None,
'smoothing': 'taubin',
'taubin_lambda': 0.5,
'taubin_mu': 0,
'taubin_repeat': 10,
}
# Load data
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, size_of_each_class, triplet = \
load_data(model_config['dataset'], train_size=model_config['train_size'],
validation_size=model_config['validation_size'],
model_config=model_config, shuffle=model_config['shuffle'])
# train_size = model_config['train_size']
# order = np.argsort(labels[train_mask])
# digits = features[train_mask][order].reshape(10,train_size,28,28)
# digits = np.vstack([np.hstack([digits[i,j,:,:] for j in range(digits.shape[1])]) for i in range(digits.shape[0])])
# scipy.misc.imsave('image/digits/digits.pdf', digits)
#
# for repeat in [20]:
# smoothed_digits = taubin_smoothing(adj, model_config['taubin_lambda'], model_config['taubin_mu'], repeat, features)
# smoothed_digits = smoothed_digits[train_mask][order].reshape(10,train_size,28,28)
# smoothed_digits = np.vstack([np.hstack([smoothed_digits[i,j,:,:] for j in range(smoothed_digits.shape[1])]) for i in range(smoothed_digits.shape[0])])
# scipy.misc.imsave('image/digits/smoothed_digits_{}.pdf'.format(repeat), smoothed_digits)
dataset = model_config['dataset']
y=np.argmax(gcn.utils.all_labels, axis=1)
n = adj.shape[0]
adj_I = adj + sp.diags(np.ones(n))
# d_I = np.diag(np.array(adj.sum(1)))
adj_rw = normalize(adj, norm='l1', axis=1)
adj_sym = normalize_adj(adj)
adj_I_rw = normalize(adj, norm='l1', axis=1)
adj_I_sym = normalize_adj(adj_I) #
d_I = np.array(adj_I.sum(1)).reshape(-1)
d_I_inv_sqrt = np.power(d_I, -0.5)
d_I = np.diag(d_I)
d_I_inv_sqrt = np.diag(d_I_inv_sqrt)
try:
vals, vecs, vec_inv = np.load(dataset+'_I_vals.npy'), \
np.load(dataset+'_I_vecs.npy'), \
np.load(dataset + '_I_vec_inv.npy')
print('load vals, vecs, vec_inv from files')
except:
vals, vecs = slinalg.eigsh(adj_I_sym, k=adj_I_sym.shape[0]-1)
vecs = d_I_inv_sqrt.dot(vecs)
# vec_inv = np.linalg.inv(vecs)
vec_inv = vecs.T
# vals = vals.astype(np.float64)
# vec_inv = vec_inv.astype(np.float64)
np.save(dataset+'_I_vals.npy', vals)
np.save(dataset+'_I_vecs.npy', vecs)
np.save(dataset+'_I_vec_inv.npy', vec_inv)
x_I = 1-vals
try:
vals, vecs, vec_inv = np.load(dataset+'_vals.npy'), \
np.load(dataset+'_vecs.npy'), \
np.load(dataset + '_vec_inv.npy')
print('load vals, vecs, vec_inv from files')
except:
vals, vecs = slinalg.eigsh(adj_sym, k=adj_I_sym.shape[0]-1)
vecs = d_I_inv_sqrt.dot(vecs)
# vec_inv = np.linalg.inv(vecs)
vec_inv = vecs.T
# vals = vals.astype(np.float64)
# vec_inv = vec_inv.astype(np.float64)
np.save(dataset+'_vals.npy', vals)
np.save(dataset+'_vecs.npy', vecs)
np.save(dataset+'_vec_inv.npy', vec_inv)
x = 1-vals
# plt.figure()
# plt.grid()
# axes = plt.gca()
# alpha = 0.1
# A = absorption_probability(adj_sym, alpha, stored_A=dataset+'_A_sym')
# A_acc = np.add.accumulate(-np.sort(-A, axis=None))
# A_acc /= A_acc[-1]
# l = A.shape[0]*A.shape[1]
# plt.plot(np.arange(l)/n, A_acc)
# beta = 20
# y = A_acc[beta*n]
# plt.scatter(beta, y, c='C1')
# axes.text(beta+50, y, "({:.0f}, {:.2f})".format(beta, y))
# beta = 200
# y = A_acc[beta*n]
# plt.scatter(beta, y, c='C2')
# axes.text(beta+50, y, "({:.0f}, {:.2f})".format(beta, y))
# beta = 530
# y = A_acc[beta*n]
# plt.scatter(beta, y, c='C3')
# axes.text(beta+50, y+0.01, "({:.0f}, {:.2f})".format(beta, y))
# plt.xlabel(r'$\beta$')
# plt.ylabel('percentage of sum')
# plt.savefig('image/energy_entries.pdf', dpi=dpi)
area = 2
alpha = 1
plt.figure()
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
plt.plot(x, 1 / (1 + 5 * x), label=r'$(1+\alpha\lambda)^{-1}, \alpha=5$', alpha=alpha, linewidth=area)
plt.plot(x, 1 - x, label=r'$1-\lambda$', alpha=alpha, linewidth=area)
plt.plot(x, (1 - x) ** 2, label=r'$(1-\lambda)^2$', alpha=alpha, linewidth=area)
plt.xlabel(r'$\lambda$')
legend = plt.legend(loc='lower left', shadow=True)
plt.savefig('image/response_function.pdf', dpi=dpi)
# plt.show()
color = ['C0','C4','C2','C3']
plt.figure(figsize=(3.2, 2.5))
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1.1, 1.1])
plt.grid()
for i in [0.9, 0.7, 0.5, 0.3]:
plt.plot([0, i, i, 2], [1, 1, 0, 0], label=r'$\lambda_k={}$'.format(i), alpha=alpha, linewidth=area)
legend = plt.legend(loc='lower left', shadow=True, fontsize=8)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/filters/ideal_filters.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
plt.plot(x, 1 / (1 + 3 * x), label=r'$(1+3\lambda)^{-1}$', alpha=alpha, linewidth=area, c=color[0])
plt.plot(x, 1 / (1 + 5 * x), label=r'$(1+5\lambda)^{-1}$', alpha=alpha, linewidth=area, c=color[1])
plt.plot(x, 1 / (1 + 10 * x), label=r'$(1+10\lambda)^{-1}$', alpha=alpha, linewidth=area, c=color[2])
plt.plot(x, 1 / (1 + 20 * x), label=r'$(1+20\lambda)^{-1}$', alpha=alpha, linewidth=area, c=color[3])
legend = plt.legend(loc='lower left', shadow=True, fontsize=8)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/filters/lp-like_filters.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
plt.plot(x, 1 - x, label=r'$1-\lambda$', alpha=alpha, linewidth=area, c=color[0])
plt.plot(x, (1 - x) ** 2, label=r'$(1-\lambda)^2$', alpha=alpha, linewidth=area, c=color[1])
plt.plot(x, (1 - x) ** 3, label=r'$(1-\lambda)^3$', alpha=alpha, linewidth=area, c=color[2])
plt.plot(x, (1 - x) ** 4, label=r'$(1-\lambda)^4$', alpha=alpha, linewidth=area, c=color[3])
legend = plt.legend(loc='lower left', shadow=True, fontsize=8)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/filters/gcn-like_filters_1.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
plt.plot(x, 1 - 0.5*x, label=r'$1-\frac{1}{2}\lambda$', alpha=alpha, linewidth=area, c=color[0])
plt.plot(x, (1 - 0.5*x) ** 2, label=r'$(1-0.5\lambda)^2$', alpha=alpha, linewidth=area, c=color[1])
plt.plot(x, (1 - 0.5*x) ** 3, label=r'$(1-0.5\lambda)^3$', alpha=alpha, linewidth=area, c=color[2])
plt.plot(x, (1 - 0.5*x) ** 4, label=r'$(1-0.5\lambda)^4$', alpha=alpha, linewidth=area, c=color[3])
legend = plt.legend(loc='lower left', shadow=True, fontsize=8)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/filters/gcn-like_filters_2.pdf',dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
plt.plot(x, (1 - 0.5*x) ** 2, label=r'$(1-0.5\lambda)^2$', alpha=alpha, linewidth=area, c=color[0])
plt.plot(x, (1 - x) ** 2, label=r'$(1-\lambda)^2$', alpha=alpha, linewidth=area, c=color[2])
plt.plot(x, (1 - 0.5*x) ** 4, label=r'$(1-0.5\lambda)^4$', alpha=alpha, linewidth=area, c=color[3])
# plt.plot(x, 1 / (1 + 6 * x), label=r'$(1+6\lambda)^{-1}$', alpha=alpha, linewidth=area)
# plt.plot(x, (1 - x) ** 3, label=r'$(1-\lambda)^3$', alpha=alpha, linewidth=area)
# plt.plot(x, (1 - 0.5*x) ** 6, label=r'$(1-0.5\lambda)^6$', alpha=alpha, linewidth=area)
legend = plt.legend(loc='lower left', shadow=True, fontsize=8)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/filters/filter-comparison.pdf',dpi=dpi, bbox_inches="tight")
sample = np.random.random_sample(x.shape)
sample_rate=0.2
x = x[sample<sample_rate]
x_I = x_I[sample<sample_rate]
area = 4
plt.figure(figsize=(3.2, 2.5))
plt.scatter(x, 1 - x, alpha=alpha, s=area)
# plt.title(r'$(\lambda, 1-\lambda)$')
# plt.ylabel(r'$1-\lambda$', rotation='horizontal')
# plt.xlabel(r'$\lambda$')
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.yaxis.set_label_coords(-0.1, 1.05)
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
legend = plt.legend(loc='upper center', shadow=True)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/compressing_effect/1_lambda.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.scatter(x, (1 - x) ** 2, alpha=alpha, s=area)
# plt.title(r'$\left(\lambda, (1-\lambda)^2\right)$')
# plt.xlabel(r'$\lambda$')
# plt.ylabel(r'$(1-\lambda)^2$', rotation='horizontal')
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.yaxis.set_label_coords(-0.1, 1.05)
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
legend = plt.legend(loc='upper center', shadow=True)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/compressing_effect/1_lambda_2.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.scatter(x_I, 1 - x_I, s=area, alpha=alpha)
# plt.title(r'$(\tilde{\;\lambda}, 1-\tilde{\;\lambda})$')
# plt.xlabel(r'$\tilde{\;\lambda}$')
# plt.ylabel(r'$1-\tilde{\;\lambda}$', rotation='horizontal')
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.yaxis.set_label_coords(-0.1, 1.05)
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
legend = plt.legend(loc='upper center', shadow=True)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/compressing_effect/1_tilde_lambda.pdf', dpi=dpi, bbox_inches="tight")
plt.figure(figsize=(3.2, 2.5))
plt.scatter(x_I, (1 - x_I) ** 2, s=area, alpha=alpha)
# plt.title(r'$(\tilde{\;\lambda},(1-\tilde{\;\lambda})^2)$')
# plt.xlabel(r'$\tilde{\;\lambda}$')
# plt.ylabel(r'$(1-\tilde{\;\lambda})^2$', rotation='horizontal')
plt.plot([-1, 3], [0, 0], c='black', linewidth=0.5)
axes = plt.gca()
axes.yaxis.set_label_coords(-0.1, 1.05)
axes.set_xlim([0, 2])
axes.set_ylim([-1, 1])
plt.grid()
legend = plt.legend(loc='upper center', shadow=True)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.savefig('image/compressing_effect/1_tilde_lambda_2.pdf', dpi=dpi, bbox_inches="tight")
# colors = np.array(['C0','C1', 'C2', 'C3', 'C4', 'C5', 'C6']).repeat(len(x))
# y = np.hstack([1 - x,
# 1 - x_I,
# (1 - x) ** 2,
# (1 - x_I) ** 2,
# 1 / (1 + 5 * x),
# (2 - x) / 2,
# ((2 - x) ** 2) / 4
# ])
# labels = [r'$1-\lambda$',
# r'$1-\lambda$ with self-loop',
# r'$(1-\lambda)^2$',
# r'$(1-\lambda)^2$ with self-loop',
# r'$(1+\alpha\lambda)^{-1}, \alpha=5$',
# r'$\frac{(2-\lambda)}{2}$',
# r'$\frac{(2-\lambda)^2}{4}$']
# x = np.hstack([x, x_I, x, x_I, x, x, x])
# points = np.array([x,y,colors]).T
# np.random.shuffle(points)
# plt.scatter(points[:, 0], points[:, 1], c=points[:, 2], s=area, alpha=alpha)