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utils.py
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utils.py
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import torch
import numpy as np
import networkx as nx
import rdkit.Chem as Chem
import matplotlib.pyplot as plt
import matplotlib
from torch_geometric.utils.num_nodes import maybe_num_nodes
from textwrap import wrap
def find_closest_node_result(results, max_nodes):
""" return the highest reward graph node constraining to the subgraph size """
results = sorted(results, key=lambda x: x.P, reverse=True)
results = sorted(results, key=lambda x: len(x.coalition))
result_node = results[0]
for result_idx in range(len(results)):
x = results[result_idx]
if len(x.coalition) <= max_nodes and x.P > result_node.P:
result_node = x
return result_node
class PlotUtils():
def __init__(self, dataset_name):
self.dataset_name = dataset_name
def plot(self, graph, nodelist, figname, **kwargs):
""" plot function for different dataset """
if self.dataset_name.lower() == 'BA_2motifs'.lower():
self.plot_ba2motifs(graph, nodelist, figname=figname)
elif self.dataset_name.lower() in ['bbbp', 'mutag', 'clintox']:
x = kwargs.get('x')
self.plot_molecule(graph, nodelist, x, figname=figname)
elif self.dataset_name.lower() == 'ba_shapes':
y = kwargs.get('y')
node_idx = kwargs.get('node_idx')
self.plot_bashapes(graph, nodelist, y, node_idx, figname=figname)
elif self.dataset_name.lower() in ['grt_sst2_BERT_Identity'.lower(), 'graph_sst2', 'graph_twitter']:
words = kwargs.get('words')
self.plot_sentence(graph, nodelist, words=words, figname=figname)
else:
raise NotImplementedError
def plot_subgraph(self, graph, nodelist, colors='#FFA500', labels=None, edge_color='gray',
edgelist=None, subgraph_edge_color='black', title_sentence=None, figname=None):
if edgelist is None:
edgelist = [(n_frm, n_to) for (n_frm, n_to) in graph.edges() if
n_frm in nodelist and n_to in nodelist]
pos = nx.kamada_kawai_layout(graph)
pos_nodelist = {k: v for k, v in pos.items() if k in nodelist}
nx.draw_networkx_nodes(graph, pos,
nodelist=list(graph.nodes()),
node_color=colors,
node_size=300)
nx.draw_networkx_edges(graph, pos, width=3, edge_color=edge_color, arrows=False)
nx.draw_networkx_edges(graph, pos=pos_nodelist,
edgelist=edgelist, width=6,
edge_color=subgraph_edge_color,
arrows=False)
if labels is not None:
nx.draw_networkx_labels(graph, pos, labels)
plt.axis('off')
if title_sentence is not None:
plt.title('\n'.join(wrap(title_sentence, width=60)))
if figname is not None:
plt.savefig(figname)
# plt.show()
plt.close('all')
def plot_subgraph_with_nodes(self, graph, nodelist, node_idx, colors='#FFA500', labels=None, edge_color='gray',
edgelist=None, subgraph_edge_color='black', title_sentence=None, figname=None):
node_idx = int(node_idx)
if edgelist is None:
edgelist = [(n_frm, n_to) for (n_frm, n_to) in graph.edges() if
n_frm in nodelist and n_to in nodelist]
pos = nx.kamada_kawai_layout(graph) # calculate according to graph.nodes()
pos_nodelist = {k: v for k, v in pos.items() if k in nodelist}
nx.draw_networkx_nodes(graph, pos,
nodelist=list(graph.nodes()),
node_color=colors,
node_size=300)
if isinstance(colors, list):
list_indices = int(np.where(np.array(graph.nodes()) == node_idx)[0])
node_idx_color = colors[list_indices]
else:
node_idx_color = colors
nx.draw_networkx_nodes(graph, pos=pos,
nodelist=[node_idx],
node_color=node_idx_color,
node_size=600)
nx.draw_networkx_edges(graph, pos, width=3, edge_color=edge_color, arrows=False)
nx.draw_networkx_edges(graph, pos=pos_nodelist,
edgelist=edgelist, width=3,
edge_color=subgraph_edge_color,
arrows=False)
if labels is not None:
nx.draw_networkx_labels(graph, pos, labels)
plt.axis('off')
if title_sentence is not None:
plt.title('\n'.join(wrap(title_sentence, width=60)))
if figname is not None:
plt.savefig(figname)
plt.close('all')
def plot_ba2motifs(self, graph, nodelist, edgelist=None, figname=None):
return self.plot_subgraph(graph, nodelist, edgelist=edgelist, figname=figname)
def plot_molecule(self, graph, nodelist, x, edgelist=None, figname=None):
# collect the text information and node color
if self.dataset_name == 'mutag':
node_dict = {0: 'C', 1: 'N', 2: 'O', 3: 'F', 4: 'I', 5: 'Cl', 6: 'Br'}
node_idxs = {k: int(v) for k, v in enumerate(np.where(x.cpu().numpy() == 1)[1])}
node_labels = {k: node_dict[v] for k, v in node_idxs.items()}
node_color = ['#E49D1C', '#4970C6', '#FF5357', '#29A329', 'brown', 'darkslategray', '#F0EA00']
colors = [node_color[v % len(node_color)] for k, v in node_idxs.items()]
elif self.dataset_name == 'bbbp' or 'clintox':
element_idxs = {k: int(v) for k, v in enumerate(x[:, 0])}
node_idxs = element_idxs
node_labels = {k: Chem.PeriodicTable.GetElementSymbol(Chem.GetPeriodicTable(), int(v))
for k, v in element_idxs.items()}
node_color = ['#29A329', 'lime', '#F0EA00', 'maroon', 'brown', '#E49D1C', '#4970C6', '#FF5357']
colors = [node_color[(v - 1) % len(node_color)] for k, v in node_idxs.items()]
else:
raise NotImplementedError
self.plot_subgraph(graph, nodelist, colors=colors, labels=node_labels,
edgelist=edgelist, edge_color='gray',
subgraph_edge_color='black',
title_sentence=None, figname=figname)
def plot_sentence(self, graph, nodelist, words, edgelist=None, figname=None):
pos = nx.kamada_kawai_layout(graph)
words_dict = {i: words[i] for i in graph.nodes}
if nodelist is not None:
pos_coalition = {k: v for k, v in pos.items() if k in nodelist}
nx.draw_networkx_nodes(graph, pos_coalition,
nodelist=nodelist,
node_color='darkorange',
node_shape='o',
node_size=800)
if edgelist is None:
edgelist = [(n_frm, n_to) for (n_frm, n_to) in graph.edges()
if n_frm in nodelist and n_to in nodelist]
nx.draw_networkx_edges(graph, pos=pos_coalition, edgelist=edgelist, width=5, edge_color='darkorange')
nx.draw_networkx_nodes(graph, pos, nodelist=list(graph.nodes()), node_color='powderblue', node_size=300)
nx.draw_networkx_edges(graph, pos, width=2, edge_color='grey')
nx.draw_networkx_labels(graph, pos, words_dict, font_size=16)
plt.axis('off')
matplotlib.rcParams['figure.figsize'] = 6, 6
plt.title('\n'.join(wrap(' '.join(words), width=40)), fontsize=17)
if figname is not None:
plt.savefig(figname, dpi=300, bbox_inches='tight')
plt.close('all')
def plot_bashapes(self, graph, nodelist, y, node_idx, edgelist=None, figname=None):
node_idxs = {k: int(v) for k, v in enumerate(y.reshape(-1).tolist())}
node_color = ['#FFA500', '#4970C6', '#FE0000', 'green']
colors = [node_color[v % len(node_color)] for k, v in node_idxs.items()]
self.plot_subgraph_with_nodes(graph, nodelist, node_idx, colors, edgelist=edgelist, figname=figname,
subgraph_edge_color='black')
def get_topk_edges_subgraph(self, edge_index, edge_mask, top_k, un_directed=False):
if un_directed:
top_k = 2 * top_k
edge_mask = edge_mask.reshape(-1)
thres_index = max(edge_mask.shape[0] - top_k, 0)
threshold = float(edge_mask.reshape(-1).sort().values[thres_index])
hard_edge_mask = (edge_mask >= threshold)
selected_edge_idx = np.where(hard_edge_mask == 1)[0].tolist()
nodelist = []
edgelist = []
for edge_idx in selected_edge_idx:
edges = edge_index[:, edge_idx].tolist()
nodelist += [int(edges[0]), int(edges[1])]
edgelist.append((edges[0], edges[1]))
nodelist = list(set(nodelist))
return nodelist, edgelist
def plot_soft_edge_mask(self, graph, edge_index, edge_mask, top_k, un_directed, figname, **kwargs):
#edge_index = torch.tensor(list(graph.edges())).T
edge_mask = torch.tensor(edge_mask)
if self.dataset_name.lower() == 'BA_2motifs'.lower():
nodelist, edgelist = self.get_topk_edges_subgraph(edge_index, edge_mask, top_k, un_directed)
self.plot_ba2motifs(graph, nodelist, edgelist, figname=figname)
elif self.dataset_name.lower() in ['bbbp', 'mutag']:
x = kwargs.get('x')
nodelist, edgelist = self.get_topk_edges_subgraph(edge_index, edge_mask, top_k, un_directed)
self.plot_molecule(graph, nodelist, x, edgelist, figname=figname)
elif self.dataset_name.lower() == 'ba_shapes':
y = kwargs.get('y')
node_idx = kwargs.get('node_idx')
nodelist, edgelist = self.get_topk_edges_subgraph(edge_index, edge_mask, top_k, un_directed)
self.plot_bashapes(graph, nodelist, y, node_idx, edgelist, figname=figname)
elif self.dataset_name.lower() in ['Graph_SST2'.lower(), 'graph_twitter']:
words = kwargs.get('words')
nodelist, edgelist = self.get_topk_edges_subgraph(edge_index, edge_mask, top_k, un_directed)
self.plot_sentence(graph, nodelist, words=words, edgelist=edgelist, figname=figname)
else:
raise NotImplementedError