# coding=utf-8
import os
from tf_geometric.utils.graph_utils import RandomNeighborSampler
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from tf_geometric.datasets import CoraDataset
import time
graph, (train_index, valid_index, test_index) = CoraDataset().load_data()
neighbor_sampler = RandomNeighborSampler(graph.edge_index)
for _ in range(100):
start = time.time()
print(neighbor_sampler.sample(ratio=0.5))
print(time.time() - start)
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import tf_geometric as tfg
from tf_geometric.datasets.ppi import PPIDataset
from tf_geometric.utils.graph_utils import RandomNeighborSampler
import tensorflow as tf
from tensorflow import keras
import numpy as np
from sklearn.metrics import f1_score
from tqdm import tqdm
train_graphs, valid_graphs, test_graphs = PPIDataset().load_data()
# traverse all graphs
for graph in train_graphs + valid_graphs + test_graphs:
neighbor_sampler = RandomNeighborSampler(graph.edge_index)
graph.cache["sampler"] = neighbor_sampler
num_classes = train_graphs[0].y.shape[1]
graph_sages = [
tfg.layers.MaxPoolGraphSage(units=128, activation=tf.nn.relu),
tfg.layers.MaxPoolGraphSage(units=128, activation=tf.nn.relu)
]
fc = tf.keras.Sequential(
[keras.layers.Dropout(0.3),
tf.keras.layers.Dense(num_classes)])
num_sampled_neighbors_list = [25, 10]
# coding=utf-8
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from tf_geometric.utils.graph_utils import reindex_sampled_edge_index, RandomNeighborSampler
from tf_geometric.datasets import CoraDataset
import time
graph, (train_index, valid_index, test_index) = CoraDataset().load_data()
neighbor_sampler = RandomNeighborSampler(graph.edge_index)
for _ in range(100):
start = time.time()
sampled_edge_index, sampled_edge_weight = neighbor_sampler.sample(ratio=0.5)
print(sampled_edge_index, sampled_edge_weight)
print(time.time() - start)
for _ in range(100):
start = time.time()
print("sample for central nodes: ")
central_node_index = [0, 1, 5, 24]
(sampled_edge_index, sampled_edge_weight), (sampled_node_index, central_node_index, non_central_node_index) = \
neighbor_sampler.sample(ratio=0.5, central_node_index=central_node_index)
print("sampled_node_index: ", sampled_node_index)
print("central_node_index: ", central_node_index)
print("non_central_node_index: ", non_central_node_index)
print("sampled_edge_index: \n", sampled_edge_index)
print("sampled_edge_weight: ", sampled_edge_weight)
print("reindex sampled nodes and edges to construct edges for a subgraph: ")
reindexed_edge_index = reindex_sampled_edge_index(sampled_edge_index, sampled_node_index)
print("reindexed_edge_index: \n", reindexed_edge_index)