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run_graph_classification.py
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run_graph_classification.py
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from attrdict import AttrDict
from torch_geometric.datasets import TUDataset
from torch_geometric.utils import to_networkx, from_networkx, to_dense_adj
from experiments.graph_classification import Experiment
import time
import tqdm
import torch
import numpy as np
import pandas as pd
from hyperparams import get_args_from_input
from preprocessing import rewiring, sdrf, fosr, digl, borf
mutag = list(TUDataset(root="data", name="MUTAG"))
enzymes = list(TUDataset(root="data", name="ENZYMES"))
proteins = list(TUDataset(root="data", name="PROTEINS"))
imdb = list(TUDataset(root="data", name="IMDB-BINARY"))
datasets = {"mutag" : mutag, "enzymes" : enzymes, "imdb": imdb, "proteins": proteins}
for key in datasets:
if key in ["reddit", "imdb", "collab"]:
for graph in datasets[key]:
n = graph.num_nodes
graph.x = torch.ones((n,1))
def average_spectral_gap(dataset):
# computes the average spectral gap out of all graphs in a dataset
spectral_gaps = []
for graph in dataset:
G = to_networkx(graph, to_undirected=True)
spectral_gap = rewiring.spectral_gap(G)
spectral_gaps.append(spectral_gap)
return sum(spectral_gaps) / len(spectral_gaps)
def log_to_file(message, filename="results/graph_classification.txt"):
print(message)
file = open(filename, "a")
file.write(message)
file.close()
default_args = AttrDict({
"dropout": 0.5,
"num_layers": 4,
"hidden_dim": 64,
"learning_rate": 1e-3,
"layer_type": "R-GCN",
"display": True,
"num_trials": 100,
"eval_every": 1,
"rewiring": "fosr",
"num_iterations": 10,
"patience": 100,
"output_dim": 2,
"alpha": 0.1,
"eps": 0.001,
"dataset": None,
"last_layer_fa": False,
"borf_batch_add" : 4,
"borf_batch_remove" : 2,
"sdrf_remove_edges" : False
})
hyperparams = {
"mutag": AttrDict({"output_dim": 2}),
"enzymes": AttrDict({"output_dim": 6}),
"proteins": AttrDict({"output_dim": 2}),
"collab": AttrDict({"output_dim": 3}),
"imdb": AttrDict({"output_dim": 2}),
"reddit": AttrDict({"output_dim": 2})
}
results = []
args = default_args
args += get_args_from_input()
if args.dataset:
# restricts to just the given dataset if this mode is chosen
name = args.dataset
datasets = {name: datasets[name]}
for key in datasets:
args += hyperparams[key]
train_accuracies = []
validation_accuracies = []
test_accuracies = []
energies = []
print(f"TESTING: {key} ({args.rewiring} - layer {args.layer_type})")
dataset = datasets[key]
print('REWIRING STARTED...')
start = time.time()
with tqdm.tqdm(total=len(dataset)) as pbar:
if args.rewiring == "fosr":
for i in range(len(dataset)):
edge_index, edge_type, _ = fosr.edge_rewire(dataset[i].edge_index.numpy(), num_iterations=args.num_iterations)
dataset[i].edge_index = torch.tensor(edge_index)
dataset[i].edge_type = torch.tensor(edge_type)
pbar.update(1)
elif args.rewiring == "sdrf_orc":
for i in range(len(dataset)):
dataset[i].edge_index, dataset[i].edge_type = sdrf.sdrf(dataset[i], loops=args.num_iterations, remove_edges=False, is_undirected=True, curvature='orc')
pbar.update(1)
elif args.rewiring == "sdrf_bfc":
for i in range(len(dataset)):
dataset[i].edge_index, dataset[i].edge_type = sdrf.sdrf(dataset[i], loops=args.num_iterations, remove_edges=args["sdrf_remove_edges"],
is_undirected=True, curvature='bfc')
pbar.update(1)
elif args.rewiring == "borf":
print(f"[INFO] BORF hyper-parameter : num_iterations = {args.num_iterations}")
print(f"[INFO] BORF hyper-parameter : batch_add = {args.borf_batch_add}")
print(f"[INFO] BORF hyper-parameter : batch_remove = {args.borf_batch_remove}")
for i in range(len(dataset)):
dataset[i].edge_index, dataset[i].edge_type = borf.borf3(dataset[i],
loops=args.num_iterations,
remove_edges=False,
is_undirected=True,
batch_add=args.borf_batch_add,
batch_remove=args.borf_batch_remove,
dataset_name=key,
graph_index=i)
pbar.update(1)
elif args.rewiring == "digl":
for i in range(len(dataset)):
dataset[i].edge_index = digl.rewire(dataset[i], alpha=0.1, eps=0.05)
m = dataset[i].edge_index.shape[1]
dataset[i].edge_type = torch.tensor(np.zeros(m, dtype=np.int64))
pbar.update(1)
end = time.time()
rewiring_duration = end - start
#spectral_gap = average_spectral_gap(dataset)
print('TRAINING STARTED...')
start = time.time()
for trial in range(args.num_trials):
train_acc, validation_acc, test_acc, energy = Experiment(args=args, dataset=dataset).run()
train_accuracies.append(train_acc)
validation_accuracies.append(validation_acc)
test_accuracies.append(test_acc)
energies.append(energy)
end = time.time()
run_duration = end - start
train_mean = 100 * np.mean(train_accuracies)
val_mean = 100 * np.mean(validation_accuracies)
test_mean = 100 * np.mean(test_accuracies)
energy_mean = 100 * np.mean(energies)
train_ci = 2 * np.std(train_accuracies)/(args.num_trials ** 0.5)
val_ci = 2 * np.std(validation_accuracies)/(args.num_trials ** 0.5)
test_ci = 2 * np.std(test_accuracies)/(args.num_trials ** 0.5)
energy_ci = 200 * np.std(energies)/(args.num_trials ** 0.5)
log_to_file(f"RESULTS FOR {key} ({args.rewiring}), {args.num_iterations} ITERATIONS:\n")
log_to_file(f"average acc: {test_mean}\n")
log_to_file(f"plus/minus: {test_ci}\n\n")
results.append({
"dataset": key,
"rewiring": args.rewiring,
"layer_type": args.layer_type,
"num_iterations": args.num_iterations,
"borf_batch_add" : args.borf_batch_add,
"borf_batch_remove" : args.borf_batch_remove,
"sdrf_remove_edges" : args.sdrf_remove_edges,
"alpha": args.alpha,
"eps": args.eps,
"test_mean": test_mean,
"test_ci": test_ci,
"val_mean": val_mean,
"val_ci": val_ci,
"train_mean": train_mean,
"train_ci": train_ci,
"energy_mean": energy_mean,
"energy_ci": energy_ci,
"last_layer_fa": args.last_layer_fa,
"rewiring_duration" : rewiring_duration,
"run_duration" : run_duration,
})
# Log every time a dataset is completed
df = pd.DataFrame(results)
with open(f'results/graph_classification_{args.layer_type}_{args.rewiring}.csv', 'a') as f:
df.to_csv(f, mode='a', header=f.tell()==0)