def filter_useless_nodes(args_dataset,
args_model,
args_explainers,
args_hops,
args_num_samples,
args_test_samples,
args_K,
args_prop_noise_nodes,
args_connectedness,
node_indices,
info,
args_hv,
args_feat,
args_coal,
args_g,
args_multiclass,
args_regu,
args_gpu,
args_fullempty,
args_S,
seed):
""" Add noisy neighbours to dataset and check how many are included in explanations
The fewest, the better the explainer.
Args:
Arguments defined in argument parser of script_eval.py
"""
# Define dataset
data = prepare_data(args_dataset, seed=seed)
# Select a random subset of nodes to eval the explainer on.
if not node_indices:
node_indices = extract_test_nodes(data, args_test_samples, seed)
# Define number of noisy nodes according to dataset size
args_num_noise_nodes = int(args_prop_noise_nodes * data.x.size(0))
args_c = eval('EVAL1_' + data.name)['args_c']
args_p = eval('EVAL1_' + data.name)['args_p']
args_binary = eval('EVAL1_' + data.name)['args_binary']
# Add noisy neighbours to the graph, with random features
data = add_noise_neighbours(data, args_num_noise_nodes, node_indices,
binary=args_binary, p=args_p,
connectedness=args_connectedness, c=args_c)
# Define training parameters depending on (model-dataset) couple
hyperparam = ''.join(['hparams_', args_dataset, '_', args_model])
param = ''.join(['params_', args_dataset, '_', args_model])
# Define the model
if args_model == 'GCN':
model = GCN(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
else:
model = GAT(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
# Re-train the model on dataset with noisy features
train_and_val(model, data, **eval(param))
# Evaluate model
model.eval()
with torch.no_grad():
log_logits = model(x=data.x, edge_index=data.edge_index) # [2708, 7]
test_acc = accuracy(log_logits[data.test_mask], data.y[data.test_mask])
print('Test accuracy is {:.4f}'.format(test_acc))
# Derive predicted class for each test node
with torch.no_grad():
true_confs, predicted_classes = log_logits.exp()[node_indices].max(dim=1)
del log_logits
if args_regu == 1:
args_regu = 0
# Study attention weights of noisy nodes in GAT model - compare attention with explanations
if str(type(model)) == "<class 'src.models.GAT'>":
study_attention_weights(data, model, args_test_samples)
# Do for several explainers
for c, explainer_name in enumerate(args_explainers):
print('EXPLAINER: ', explainer_name)
# Define the explainer
explainer = eval(explainer_name)(data, model, args_gpu)
# Loop on each test sample and store how many times do noisy nodes appear among
# K most influential features in our explanations
# count number of noisy nodes in explanations
pred_class_num_noise_neis = []
# count number of noisy nodes in subgraph
total_num_noisy_nei = []
# Number of neigbours of v in subgraph
total_neigbours = []
# Stores number of most important neighbours we look at, for each node
K = []
# To retrieve the predicted class
j = 0
for node_idx in tqdm(node_indices, desc='explain node', leave=False):
# Look only at coefficients for nodes (not node features)
if explainer_name == 'Greedy':
coefs = explainer.explain_nei(node_idx,
args_hops,
args_num_samples)
elif explainer_name == 'GNNExplainer':
_ = explainer.explain(node_idx,
args_hops,
args_num_samples)
coefs = explainer.coefs
else:
# Explanations via GraphSVX
coefs = explainer.explain([node_idx],
args_hops,
args_num_samples,
info,
args_multiclass,
args_fullempty,
args_S,
args_hv,
args_feat,
args_coal,
args_g,
args_regu)
coefs = coefs[0].T[explainer.F:]
# Number of noisy nodes in the subgraph of node_idx
num_noisy_nodes = len(
[n_idx for n_idx in explainer.neighbours if n_idx >= data.x.size(0)-args_num_noise_nodes])
# Number of neighbours in the subgraph
total_neigbours.append(len(explainer.neighbours))
# Adaptable K - vary according to number of nodes in the subgraph
if len(explainer.neighbours) > 100:
K.append(int(args_K * 100))
else:
K.append( max(1, int(args_K * len(explainer.neighbours))) )
# Store indexes of K most important features, for each class
nei_indices = coefs.argsort()[-K[j]:].tolist()
# Number of noisy features that appear in explanations - use index to spot them
noise_nei = [idx for idx in nei_indices if idx > (explainer.neighbours.shape[0] - num_noisy_nodes)]
# If node importance of top K neighbours is unsignificant, discard
# Possible as we have importance informative measure, unlike others.
if explainer_name == 'GraphSVX':
explainable_part = true_confs[c] - \
explainer.base_values[c]
noise_nei = [idx for idx in noise_nei if np.abs(coefs[idx]) > 0.05*np.abs(explainable_part)]
num_noise_nei = len(noise_nei)
pred_class_num_noise_neis.append(num_noise_nei)
# Return number of noisy nodes adjacent to node of interest
total_num_noisy_nei.append(num_noisy_nodes)
j += 1
print('Noisy neighbours included in explanations: ',
pred_class_num_noise_neis)
print('There are {} noise neighbours found in the explanations of {} test samples, an average of {} per sample'
.format(sum(pred_class_num_noise_neis), args_test_samples, sum(pred_class_num_noise_neis)/args_test_samples))
print('Proportion of explanations showing noisy neighbours: {:.2f}%'.format(
100 * sum(pred_class_num_noise_neis) / sum(K)))
perc = 100 * sum(pred_class_num_noise_neis) / (sum(total_num_noisy_nei))
perc2 = 100 * (sum(K) - sum(pred_class_num_noise_neis)) \
/ (sum(total_neigbours) - sum(total_num_noisy_nei))
print('Proportion of noisy neighbours found in explanations vs normal neighbours (in subgraph): {:.2f}% vs {:.2f}'.format(
perc, perc2))
print('Proportion of nodes in subgraph that are noisy: {:.2f}%'.format(
100 * sum(total_num_noisy_nei) / sum(total_neigbours)))
print('Proportion of noisy neighbours found in explanations (entire graph): {:.2f}%'.format(
100 * sum(pred_class_num_noise_neis) / (args_test_samples * args_num_noise_nodes)))
print('------------------------------------')
# Plot of kernel density estimates of number of noisy features included in explanation
# Do for all benchmarks (with diff colors) and plt.show() to get on the same graph
plot_dist(pred_class_num_noise_neis,
label=explainer_name, color=COLOURS[c])
# Random explainer - plot estimated kernel density
total_num_noise_neis = noise_nodes_for_random(
data, model, K, node_indices, total_num_noisy_nei, total_neigbours)
plot_dist(total_num_noise_neis, label='Random',
color='y')
# Store graph - with key params and time
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
plt.savefig('results/eval1_node_{}_{}_{}_{}_{}.pdf'.format(data.name,
args_coal,
args_feat,
args_hv,
current_time))
plt.close()
#plt.show()
return total_num_noise_neis
def filter_useless_nodes_multiclass(args_dataset,
args_model,
args_explainers,
args_hops,
args_num_samples,
args_test_samples,
args_prop_noise_nodes,
args_connectedness,
node_indices,
args_K,
info,
args_hv,
args_feat,
args_coal,
args_g,
args_multiclass,
args_regu,
args_gpu,
args_fullempty,
args_S,
seed):
""" Add noisy neighbours to dataset and check how many are included in explanations
The fewest, the better the explainer.
Args:
Arguments defined in argument parser of script_eval.py
"""
# Define dataset
data = prepare_data(args_dataset, seed=10)
args_num_noise_nodes = int(args_prop_noise_nodes * data.x.size(0))
args_c = eval('EVAL1_' + data.name)['args_c']
args_p = eval('EVAL1_' + data.name)['args_p']
args_binary = eval('EVAL1_' + data.name)['args_binary']
# Select a random subset of nodes to eval the explainer on.
if not node_indices:
node_indices = extract_test_nodes(data, args_test_samples, seed)
# Add noisy neighbours to the graph, with random features
data = add_noise_neighbours(data, args_num_noise_nodes, node_indices,
binary=args_binary, p=args_p, connectedness=args_connectedness)
# Define training parameters depending on (model-dataset) couple
hyperparam = ''.join(['hparams_', args_dataset, '_', args_model])
param = ''.join(['params_', args_dataset, '_', args_model])
# Define the model
if args_model == 'GCN':
model = GCN(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
else:
model = GAT(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
# Re-train the model on dataset with noisy features
train_and_val(model, data, **eval(param))
model.eval()
with torch.no_grad():
log_logits = model(x=data.x, edge_index=data.edge_index) # [2708, 7]
test_acc = accuracy(log_logits[data.test_mask], data.y[data.test_mask])
print('Test accuracy is {:.4f}'.format(test_acc))
del log_logits
# Study attention weights of noisy nodes in GAT model - compare attention with explanations
if str(type(model)) == "<class 'src.models.GAT'>":
study_attention_weights(data, model, args_test_samples)
# Adaptable K - top k explanations we look at for each node
# Depends on number of existing features/neighbours considered for GraphSVX
# if 'GraphSVX' in args_explainers:
# K = []
# else:
# K = [5]*len(node_indices)
# Do for several explainers
for c, explainer_name in enumerate(args_explainers):
print('EXPLAINER: ', explainer_name)
# Define the explainer
explainer = eval(explainer_name)(data, model, args_gpu)
# Loop on each test sample and store how many times do noisy nodes appear among
# K most influential features in our explanations
# 1 el per test sample - count number of noisy nodes in explanations
total_num_noise_neis = []
# 1 el per test sample - count number of noisy nodes in explanations for 1 class
pred_class_num_noise_neis = []
# 1 el per test sample - count number of noisy nodes in subgraph
total_num_noisy_nei = []
total_neigbours = [] # 1 el per test samples - number of neigbours of v in subgraph
M = [] # 1 el per test sample - number of non zero features
for node_idx in tqdm(node_indices, desc='explain node', leave=False):
# Look only at coefficients for nodes (not node features)
if explainer_name == 'Greedy':
coefs = explainer.explain_nei(node_index=node_idx,
hops=args_hops,
num_samples=args_num_samples,
info=False,
multiclass=True)
elif explainer_name == 'GNNExplainer':
_ = explainer.explain(node_index=node_idx,
hops=args_hops,
num_samples=args_num_samples,
info=False,
multiclass=True)
coefs = explainer.coefs
else:
# Explanations via GraphSVX
coefs = explainer.explain([node_idx],
args_hops,
args_num_samples,
info,
args_multiclass,
args_fullempty,
args_S,
args_hv,
args_feat,
args_coal,
args_g,
args_regu)
coefs = coefs[0].T[explainer.F:]
# if explainer.F > 50:
# K.append(10)
# else:
# K.append(int(explainer.F * args_K))
# Check how many non zero features
M.append(explainer.M)
# Number of noisy nodes in the subgraph of node_idx
num_noisy_nodes = len(
[n_idx for n_idx in explainer.neighbours if n_idx >= data.x.size(0)-args_num_noise_nodes])
# Number of neighbours in the subgraph
total_neigbours.append(len(explainer.neighbours))
# Multilabel classification - consider all classes instead of focusing on the
# class that is predicted by our model
num_noise_neis = [] # one element for each class of a test sample
true_conf, predicted_class = model(x=data.x, edge_index=data.edge_index).exp()[
node_idx].max(dim=0)
for i in range(data.num_classes):
# Store indexes of K most important features, for each class
nei_indices = np.abs(coefs[:, i]).argsort()[-args_K:].tolist()
# Number of noisy features that appear in explanations - use index to spot them
num_noise_nei = sum(
idx >= (explainer.neighbours.shape[0] - num_noisy_nodes) for idx in nei_indices)
num_noise_neis.append(num_noise_nei)
if i == predicted_class:
#nei_indices = coefs[:,i].argsort()[-args_K:].tolist()
#num_noise_nei = sum(idx >= (explainer.neighbours.shape[0] - num_noisy_nodes) for idx in nei_indices)
pred_class_num_noise_neis.append(num_noise_nei)
# Return this number => number of times noisy neighbours are provided as explanations
total_num_noise_neis.append(sum(num_noise_neis))
# Return number of noisy nodes adjacent to node of interest
total_num_noisy_nei.append(num_noisy_nodes)
if info:
print('Noisy neighbours included in explanations: ',
total_num_noise_neis)
print('There are {} noise neighbours found in the explanations of {} test samples, an average of {} per sample'
.format(sum(total_num_noise_neis), args_test_samples, sum(total_num_noise_neis)/args_test_samples))
print(np.sum(pred_class_num_noise_neis) /
args_test_samples, 'for the predicted class only')
print('Proportion of explanations showing noisy neighbours: {:.2f}%'.format(
100 * sum(total_num_noise_neis) / (args_K * args_test_samples * data.num_classes)))
perc = 100 * sum(total_num_noise_neis) / (args_test_samples *
args_num_noise_nodes * data.num_classes)
perc2 = 100 * ((args_K * args_test_samples * data.num_classes) -
sum(total_num_noise_neis)) / (np.sum(M) - sum(total_num_noisy_nei))
print('Proportion of noisy neighbours found in explanations vs normal features: {:.2f}% vs {:.2f}'.format(
perc, perc2))
print('Proportion of nodes in subgraph that are noisy: {:.2f}%'.format(
100 * sum(total_num_noisy_nei) / sum(total_neigbours)))
print('Proportion of noisy neighbours in subgraph found in explanations: {:.2f}%'.format(
100 * sum(total_num_noise_neis) / (sum(total_num_noisy_nei) * data.num_classes)))
# Plot of kernel density estimates of number of noisy features included in explanation
# Do for all benchmarks (with diff colors) and plt.show() to get on the same graph
total_num_noise_neis = [item/data.num_classes for item in total_num_noise_neis]
plot_dist(total_num_noise_neis,
label=explainer_name, color=COLOURS[c])
# else: # consider only predicted class
# plot_dist(pred_class_num_noise_neis,
# label=explainer_name, color=COLOURS[c])
# Random explainer - plot estimated kernel density
total_num_noise_neis = noise_nodes_for_random(
data, model, args_K, args_num_noise_nodes, node_indices)
total_num_noise_neis= [item/data.num_classes for item in total_num_noise_neis]
plot_dist(total_num_noise_neis, label='Random',
color='y')
plt.savefig('results/eval1_node_{}'.format(data.name))
#plt.show()
return total_num_noise_neis
def filter_useless_features(args_dataset,
args_model,
args_explainers,
args_hops,
args_num_samples,
args_test_samples,
args_K,
args_prop_noise_feat,
node_indices,
info,
args_hv,
args_feat,
args_coal,
args_g,
args_multiclass,
args_regu,
args_gpu,
args_fullempty,
args_S,
seed):
""" Add noisy features to dataset and check how many are included in explanations
The fewest, the better the explainer.
Args:
Arguments defined in argument parser of script_eval.py
"""
# Define dataset
data = prepare_data(args_dataset, seed=seed)
args_num_noise_feat = int(data.x.size(1) * args_prop_noise_feat)
args_p = eval('EVAL1_' + data.name)['args_p']
args_binary = eval('EVAL1_' + data.name)['args_binary']
# Include noisy neighbours
data, noise_feat = add_noise_features(
data, num_noise=args_num_noise_feat, binary=args_binary, p=args_p)
# Define training parameters depending on (model-dataset) couple
hyperparam = ''.join(['hparams_', args_dataset, '_', args_model])
param = ''.join(['params_', args_dataset, '_', args_model])
# Define the model
if args_model == 'GCN':
model = GCN(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
else:
model = GAT(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
# Re-train the model on dataset with noisy features
train_and_val(model, data, **eval(param))
# Select random subset of nodes to eval the explainer on.
if not node_indices:
node_indices = extract_test_nodes(data, args_test_samples, seed)
# Evaluate the model on test set
model.eval()
with torch.no_grad():
log_logits = model(x=data.x, edge_index=data.edge_index)
test_acc = accuracy(log_logits[data.test_mask], data.y[data.test_mask])
print('Test accuracy is {:.4f}'.format(test_acc))
# Derive predicted class for each test sample
with torch.no_grad():
true_confs, predicted_classes = log_logits.exp()[node_indices].max(dim=1)
del log_logits
# Adaptable K - top k explanations we look at for each node
# Depends on number of existing features considered for GraphSVX
if 'GraphSVX' in args_explainers:
K = []
else:
K = [10]*len(node_indices)
#for node_idx in node_indices:
# K.append(int(data.x[node_idx].nonzero().shape[0] * args_K))
if args_regu == 0:
args_regu = 1
# Loop on the different explainers selected
for c, explainer_name in enumerate(args_explainers):
# Define explainer
explainer = eval(explainer_name)(data, model, args_gpu)
print('EXPLAINER: ', explainer_name)
# count noisy features found in explanations
pred_class_num_noise_feats = []
# count number of noisy features considered
total_num_noise_feat_considered = []
# count number of features
F = []
# Loop on each test sample and store how many times do noise features appear among
# K most influential features in our explanations
j=0
for node_idx in tqdm(node_indices, desc='explain node', leave=False):
# Explanations via GraphSVX
if explainer_name == 'GraphSVX':
coefs = explainer.explain(
[node_idx],
args_hops,
args_num_samples,
info,
args_multiclass,
args_fullempty,
args_S,
args_hv,
args_feat,
args_coal,
args_g,
args_regu,
)
# Look only at features coefficients
# Neighbours are irrelevant here
coefs = coefs[0][:explainer.F]
# Adaptable K
if explainer.F > 100:
K.append(int(args_K * 100))
else:
K.append( max(1, int(explainer.F * args_K)) )
# Num_features_considered
if args_feat == 'Null':
feat_idx = noise_feat[explainer.neighbours, :].mean(axis=0).nonzero()
num_noise_feat_considered = feat_idx.size()[0]
# Consider all features (+ use expectation like below)
elif args_feat == 'All':
num_noise_feat_considered = args_num_noise_feat
# Consider only features whose aggregated value is different from expected one
else:
# Stats dataset
var = noise_feat.std(axis=0)
mean = noise_feat.mean(axis=0)
# Feature intermediate rep
mean_subgraph = noise_feat[explainer.neighbours, :].mean(axis=0)
# Select relevant features only - (E-e,E+e)
mean_subgraph = torch.where(mean_subgraph > mean - 0.25*var, mean_subgraph,
torch.ones_like(mean_subgraph)*100)
mean_subgraph = torch.where(mean_subgraph < mean + 0.25*var, mean_subgraph,
torch.ones_like(mean_subgraph)*100)
feat_idx = (mean_subgraph == 100).nonzero()
num_noise_feat_considered = feat_idx.shape[0]
del mean, mean_subgraph, var
else:
coefs = explainer.explain(node_idx,
args_hops,
args_num_samples,
info=False,
multiclass=False
)[:explainer.F]
# All features are considered
num_noise_feat_considered = args_num_noise_feat
# Features considered
F.append(explainer.F)
# Store indexes of K most important node features, for each class
feat_indices = coefs.argsort()[-K[j]:].tolist()
# Number of noisy features that appear in explanations - use index to spot them
num_noise_feat = [idx for idx in feat_indices if idx > (explainer.F - num_noise_feat_considered)]
# If node importance of top K features is unsignificant, discard
# Possible as we have importance informative measure, unlike others.
if explainer_name == 'GraphSVX':
explainable_part = true_confs[c] - \
explainer.base_values[c]
num_noise_feat = [idx for idx in num_noise_feat if np.abs(coefs[idx]) > 0.05*np.abs(explainable_part)]
# Count number of noisy that appear in explanations
num_noise_feat = len(num_noise_feat)
pred_class_num_noise_feats.append(num_noise_feat)
# Return number of noisy features considered in this test sample
total_num_noise_feat_considered.append(num_noise_feat_considered)
j+=1
print('Noisy features included in explanations: ',
sum(pred_class_num_noise_feats) )
print('For the predicted class, there are {} noise features found in the explanations of {} test samples, an average of {} per sample'
.format(sum(pred_class_num_noise_feats), args_test_samples, sum(pred_class_num_noise_feats)/args_test_samples))
print(pred_class_num_noise_feats)
if sum(F) != 0:
perc = 100 * sum(total_num_noise_feat_considered) / sum(F)
print(
'Proportion of considered noisy features among features: {:.2f}%'.format(perc))
if sum(K) != 0:
perc = 100 * sum(pred_class_num_noise_feats) / sum(K)
print('Proportion of explanations showing noisy features: {:.2f}%'.format(perc))
if sum(total_num_noise_feat_considered) != 0:
perc = 100 * sum(pred_class_num_noise_feats) / (sum(total_num_noise_feat_considered))
perc2 = 100 * (sum(K) - sum(pred_class_num_noise_feats)) / (sum(F) - sum(total_num_noise_feat_considered))
print('Proportion of noisy features found in explanations vs proportion of normal features (among considered ones): {:.2f}% vs {:.2f}%, over considered features only'.format(
perc, perc2))
print('------------------------------------')
# Plot of kernel density estimates of number of noisy features included in explanation
# Do for all benchmarks (with diff colors) and plt.show() to get on the same graph
plot_dist(pred_class_num_noise_feats,
label=explainer_name, color=COLOURS[c])
# Random explainer - plot estimated kernel density
total_num_noise_feats = noise_feats_for_random(
data, model, K, args_num_noise_feat, node_indices)
save_path = 'results/eval1_feat'
plot_dist(total_num_noise_feats, label='Random', color='y')
# Store graph - with key params and time
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
plt.savefig('results/eval1_feat_{}_{}_{}_{}_{}.pdf'.format(data.name,
args_coal,
args_feat,
args_hv,
current_time))
plt.close()
def filter_useless_features_multiclass(args_dataset,
args_model,
args_explainers,
args_hops,
args_num_samples,
args_test_samples,
args_prop_noise_nodes,
args_connectedness,
node_indices,
args_K,
info,
args_hv,
args_feat,
args_coal,
args_g,
args_multiclass,
args_regu,
args_gpu,
args_fullempty,
args_S,
seed):
""" Add noisy features to dataset and check how many are included in explanations
The fewest, the better the explainer.
Args:
Arguments defined in argument parser of script_eval.py
"""
# Define dataset - include noisy features
data = prepare_data(args_dataset, seed=10)
args_num_noise_feat = int(data.x.size(1) * args_prop_noise_feat)
args_p = eval('EVAL1_' + data.name)['args_p']
args_binary = eval('EVAL1_' + data.name)['args_binary']
data, noise_feat = add_noise_features(
data, num_noise=args_num_noise_feat, binary=args_binary, p=args_p)
# Define training parameters depending on (model-dataset) couple
hyperparam = ''.join(['hparams_', args_dataset, '_', args_model])
param = ''.join(['params_', args_dataset, '_', args_model])
# Define the model
if args_model == 'GCN':
model = GCN(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
else:
model = GAT(input_dim=data.x.size(
1), output_dim=data.num_classes, **eval(hyperparam))
# Re-train the model on dataset with noisy features
train_and_val(model, data, **eval(param))
# Select random subset of nodes to eval the explainer on.
if not node_indices:
node_indices = extract_test_nodes(data, args_test_samples, seed)
# Evaluate the model - test set
model.eval()
with torch.no_grad():
log_logits = model(x=data.x, edge_index=data.edge_index) # [2708, 7]
test_acc = accuracy(log_logits[data.test_mask], data.y[data.test_mask])
print('Test accuracy is {:.4f}'.format(test_acc))
del log_logits
# Loop on different explainers selected
for c, explainer_name in enumerate(args_explainers):
# Define explainer
explainer = eval(explainer_name)(data, model, args_gpu)
# count noisy features found in explanations for each test sample (for each class)
total_num_noise_feats = []
# count noisy features found in explanations for each test sample for class of interest
pred_class_num_noise_feats = []
# count number of noisy features considered for each test sample (for each class)
total_num_noise_feat_considered = []
F = [] # count number of non zero features for each test sample
# Loop on each test sample and store how many times do noise features appear among
# K most influential features in our explanations
for node_idx in tqdm(node_indices, desc='explain node', leave=False):
if explainer_name == 'GraphSVX':
coefs = explainer.explain(
[node_idx],
args_hops,
args_num_samples,
info,
args_multiclass,
args_fullempty,
args_S,
args_hv,
args_feat,
args_coal,
args_g,
args_regu)
coefs = coefs[0].T[:explainer.F]
# Explanations via GraphSVX
else:
coefs = explainer.explain(node_index=node_idx,
hops=args_hops,
num_samples=args_num_samples,
info=False,
multiclass=True)
# Check how many non zero features
F.append(explainer.F)
# Number of non zero noisy features
# Dfferent for explainers with all features considered vs non zero features only (shap,graphshap)
# if explainer.F != data.x.size(1)
if explainer_name == 'GraphSVX' or explainer_name == 'SHAP':
num_noise_feat_considered = len(
[val for val in noise_feat[node_idx] if val != 0])
else:
num_noise_feat_considered = args_num_noise_feat
# Multilabel classification - consider all classes instead of focusing on the
# class that is predicted by our model
num_noise_feats = []
true_conf, predicted_class = model(x=data.x, edge_index=data.edge_index).exp()[
node_idx].max(dim=0)
for i in range(data.num_classes):
# Store indexes of K most important node features, for each class
feat_indices = np.abs(
coefs[:explainer.F, i]).argsort()[-args_K:].tolist()
# Number of noisy features that appear in explanations - use index to spot them
num_noise_feat = sum(
idx < num_noise_feat_considered for idx in feat_indices)
num_noise_feats.append(num_noise_feat)
# For predicted class only
if i == predicted_class:
pred_class_num_noise_feats.append(num_noise_feat)
# Return number of times noisy features are provided as explanations
total_num_noise_feats.append(sum(num_noise_feats))
# Return number of noisy features considered in this test sample
total_num_noise_feat_considered.append(num_noise_feat_considered)
if info:
print('Noise features included in explanations: ',
total_num_noise_feats)
print('There are {} noise features found in the explanations of {} test samples, an average of {} per sample'
.format(sum(total_num_noise_feats), args_test_samples, sum(total_num_noise_feats)/args_test_samples))
# Number of noisy features found in explanation for the predicted class
print(np.sum(pred_class_num_noise_feats) /
args_test_samples, 'for the predicted class only')
perc = 100 * sum(total_num_noise_feat_considered) / np.sum(F)
print(
'Proportion of non-zero noisy features among non-zero features: {:.2f}%'.format(perc))
perc = 100 * sum(total_num_noise_feats) / \
(args_K * args_test_samples * data.num_classes)
print(
'Proportion of explanations showing noisy features: {:.2f}%'.format(perc))
if sum(total_num_noise_feat_considered) != 0:
perc = 100 * sum(total_num_noise_feats) / \
(sum(total_num_noise_feat_considered)*data.num_classes)
perc2 = 100 * (args_K * args_test_samples * data.num_classes - sum(total_num_noise_feats)) / (
data.num_classes * (sum(F) - sum(total_num_noise_feat_considered)))
print('Proportion of noisy features found in explanations vs normal features (among considered ones): {:.2f}% vs {:.2f}%, over considered features only'.format(
perc, perc2))
print('------------------------------------')
# Plot of kernel density estimates of number of noisy features included in explanation
# Do for all benchmarks (with diff colors) and plt.show() to get on the same graph
# plot_dist(total_num_noise_feats, label=explainer_name, color=COLOURS[c])
plot_dist(total_num_noise_feats,
label=explainer_name, color=COLOURS[c])
# Random explainer - plot estimated kernel density
total_num_noise_feats = noise_feats_for_random(
data, model, args_K, args_num_noise_feat, node_indices)
save_path = 'results/eval1_feat'
plot_dist(total_num_noise_feats, label='Random', color='y')
plt.savefig('results/eval1_feat_{}'.format(data.name))