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play_ground.py
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from math import ceil
import json
import os
import sys
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import roc_auc_score, roc_curve, classification_report
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from datasets import link_prediction
from layers import MeanAggregator, LSTMAggregator, MaxPoolAggregator, MeanPoolAggregator
from models import DGNN, AAGNN
from models_variants import EAAGNN, EAACGNN
import utils
# Set up arguments for datasets, models and training.
config = utils.parse_args()
print(config)
print(config['classifier'])
# config['num_layers'] = len(config['hidden_dims']) + 1
# if config['cuda'] and torch.cuda.is_available():
# device = 'cuda:0'
# else:
# device = 'cpu'
# config['device'] = device
# print(f"val: {config['val']}")
# print(f"test: {config['test']}")
# # Get the dataset, dataloader and model.
# if not config['val'] and not config['test']:
# dataset_args = ('train', config['num_layers'])
# if config['val']:
# dataset_args = ('val', config['num_layers'])
# if config['test']:
# dataset_args = ('test', config['num_layers'])
# datasets = utils.get_dataset_gcn(dataset_args, config['dataset_folder'], is_debug=True)
# loader = DataLoader(dataset=datasets[0], batch_size=config['batch_size'],
# shuffle=False, collate_fn=datasets[0].collate_wrapper)
# loaders = []
# for i in range(len(datasets)):
# loaders.append(DataLoader(dataset=datasets[i], batch_size=config['batch_size'],
# shuffle=True, collate_fn=datasets[i].collate_wrapper))
# input_dim, output_dim = datasets[0].get_dims()
# model = AAGNN(input_dim, config['hidden_dims'][0], output_dim,
# config['dropout'], config['device'])
# model.to(config['device'])
# sigmoid = nn.Sigmoid()
# criterion = utils.get_criterion(config['task'])
# optimizer = optim.Adam(model.parameters(), lr=config['lr'],
# weight_decay=config['weight_decay'])
# epochs = config['epochs']
# epochs = 10
# #scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1500, gamma=0.8)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 10, 15, 20, 25], gamma=0.5) # Epoch decay
# model.train()
# print('--------------------------------')
# print('Training.')
# for epoch in range(epochs):
# print('Epoch {} / {}'.format(epoch+1, epochs))
# epoch_loss = 0.0
# epoch_roc = 0.0
# epoch_batches = 0
# shuffle = list(range(len(loaders)))
# random.shuffle(shuffle) # Shuffle order of graphs
# for i in shuffle:
# num_batches = int(ceil(len(datasets[i]) / config['batch_size']))
# epoch_batches += num_batches
# graph_roc = 0.0
# running_loss = 0.0
# for (idx, batch) in enumerate(loaders[i]):
# adj, adj_list, features, coords, edges, labels, dist, node_layers = batch
# labels = labels.to(device)
# optimizer.zero_grad()
# adj_relative_cos = utils.get_relative_cos_list(adj_list, coords, device)
# adj, features = adj.to(device), features.to(device)
# out = model(features, adj, adj_relative_cos)
# all_pairs = torch.mm(out, out.t())
# all_pairs = sigmoid(all_pairs)
# scores = all_pairs[edges.T]
# loss = criterion(scores, labels.float())
# loss.backward()
# optimizer.step()
# with torch.no_grad():
# running_loss += loss.item()
# epoch_loss += loss.item()
# if (torch.sum(labels.long() == 0).item() > 0) and (torch.sum(labels.long() == 1).item() > 0):
# area = roc_auc_score(labels.detach().cpu().numpy(), scores.detach().cpu().numpy())
# epoch_roc += area
# graph_roc += area
# running_loss /= num_batches
# print(' Graph {} / {}: loss {:.4f}'.format(
# i+1, len(datasets), running_loss))
# print(' ROC-AUC score: {:.4f}'.format(graph_roc/num_batches))
# scheduler.step()
# print("Epoch avg loss: {}".format(epoch_loss / epoch_batches))
# print("Epoch avg ROC_AUC score: {}".format(epoch_roc / epoch_batches))
# print('Finished training.')
# print('--------------------------------')
# y_true, y_scores = [], []
# for batch in loader:
# adj, adj_list, features, coords, edges, labels, dist, node_layers = batch
# labels = labels.to(device)
# adj_relative_cos = utils.get_relative_cos_list(adj_list, coords, device)
# adj, features = adj.to(device), features.to(device)
# out = model(features, adj, adj_relative_cos)
# all_pairs = torch.mm(out, out.t())
# all_pairs = sigmoid(all_pairs)
# scores = all_pairs[edges.T]
# y_true.extend(labels.detach().cpu().numpy())
# y_scores.extend(scores.detach().cpu().numpy())
# y_true = np.array(y_true).flatten()
# y_scores = np.array(y_scores).flatten()
# area = roc_auc_score(y_true, y_scores)
# print(y_scores)
# print(y_scores[np.isnan(y_scores)])
# print(area)