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train_EdgeOnly.py
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import os
import numpy as np
import pandas as pd
from scipy import sparse
from tqdm import tqdm
import torch
import torch.optim as optim
from datasets import *
from utils import *
from model.EdgeOnly import *
import argparse
##################################################################################################
parser = argparse.ArgumentParser()
parser.add_argument("-g", "--gpunum", help="GPU number to train the model.")
parser.add_argument("-d", "--dataset", help="Name of the dataset.")
parser.add_argument("-b", "--nbits", help="Number of bits of the embedded vector.", type=int)
parser.add_argument("-w", "--walk", default="Immedidate-1", help="Graph traversal strategy (BFS, DFS, Random), followed the maximum neighbors. E.g. BFS-20 we perform BFS upto 20 nodes.")
parser.add_argument("--edge_weight", default=1.0, type=float)
parser.add_argument("--dropout", help="Dropout probability (0 means no dropout)", default=0.1, type=float)
parser.add_argument("--train_batch_size", default=100, type=int)
parser.add_argument("--test_batch_size", default=100, type=int)
parser.add_argument("--transform_batch_size", default=100, type=int)
parser.add_argument("-e", "--num_epochs", default=30, type=int)
parser.add_argument("-T", "--num_samples", default=1, type=int, help="number of samples from Q(z|x).")
parser.add_argument("--lr", default=0.001, type=float)
args = parser.parse_args()
if not args.gpunum:
parser.error("Need to provide the GPU number.")
if not args.dataset:
parser.error("Need to provide the dataset.")
if not args.nbits:
parser.error("Need to provide the number of bits.")
if not args.walk:
parser.error("Need to provide the graph traversal method.")
##################################################################################################
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpunum
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#########################################################################################################
dataset_name = args.dataset
data_dir = os.path.join('dataset/clean', dataset_name)
train_set = TextDataset(dataset_name, data_dir, subset='train')
test_set = TextDataset(dataset_name, data_dir, subset='test')
train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=args.train_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=args.test_batch_size, shuffle=True)
#########################################################################################################
y_dim = train_set.num_classes()
num_bits = args.nbits
num_features = train_set[0][1].size(0)
num_nodes = len(train_set)
edge_weight = args.edge_weight
print("Train node2hash model ...")
print("dataset: {}".format(args.dataset))
print("numbits: {}".format(args.nbits))
print("T: {}".format(args.num_samples))
print("gpu id: {}".format(args.gpunum))
print("dropout probability: {}".format(args.dropout))
print("num epochs: {}".format(args.num_epochs))
print("learning rate: {}".format(args.lr))
print("num train: {} num test: {}".format(len(train_set), len(test_set)))
#########################################################################################################
walk_type, max_nodes = args.walk.split('-')
max_nodes = int(max_nodes)
print("Walk type: {} with maximum nodes of: {}".format(walk_type, max_nodes))
if walk_type == 'BFS':
neighbor_sample_func = BFS_walk
elif walk_type == 'DFS':
neighbor_sample_func = DFS_walk
elif walk_type == 'Random':
neighbor_sample_func = Random_walk
else:
neighbor_sample_func = None
print("The model will only takes the immediate neighbors.")
#assert(False), "unknown walk type (has to be one of the following: BFS, DFS, Random)"
def get_neighbors(ids, df, max_nodes, batch_size, traversal_func):
cols = []
rows = []
for idx, node_id in enumerate(ids):
col = traversal_func(df, node_id.item(), max_nodes)
rows += [idx] * len(col)
cols += col
data = [1] * len(cols)
connections = sparse.csr_matrix((data, (rows, cols)), shape=(batch_size, len(df)))
return torch.from_numpy(connections.toarray()).type(torch.FloatTensor)
#########################################################################################################
print("number of samples (T) = {}".format(args.num_samples))
model = EdgeRegOnly(dataset_name, num_features, num_nodes, num_bits, dropoutProb=args.dropout, device=device, T=args.num_samples)
model.to(device)
num_epochs = args.num_epochs
optimizer = optim.Adam(model.parameters(), lr=args.lr)
kl_weight = 0.
kl_step = 1 / 5000.
best_precision = 0
best_precision_epoch = 0
for epoch in tqdm(range(num_epochs), ncols=80):
avg_loss = []
for step, (ids, xb, yb, nb) in enumerate(train_loader):
xb = xb.to(device)
yb = yb.to(device)
if neighbor_sample_func is not None:
nb = get_neighbors(ids, train_set.df, max_nodes, xb.size(0), neighbor_sample_func)
nb = nb.to(device)
logprob_nn, mu, logvar = model(xb)
kl_loss = EdgeRegOnly.calculate_KL_loss(mu, logvar)
nn_reconstr_loss = EdgeRegOnly.compute_edge_reconstr_loss(logprob_nn, nb)
loss = edge_weight * nn_reconstr_loss + kl_weight * kl_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
kl_weight = min(kl_weight + kl_step, 1.)
avg_loss.append(loss.item())
#print('{} epoch:{} loss:{:.4f} Best Precision:({}){:.4f}'.format(model.get_name(), epoch+1, np.mean(avg_loss), best_precision_epoch, best_precision))
with torch.no_grad():
train_b, test_b, train_y, test_y = model.get_binary_code(train_loader, test_loader)
retrieved_indices = retrieve_topk(test_b.to(device), train_b.to(device), topK=100)
prec = compute_precision_at_k(retrieved_indices, test_y.to(device), train_y.to(device), topK=100)
#print("precision at 100: {:.4f}".format(prec.item()))
if prec.item() > best_precision:
best_precision = prec.item()
best_precision_epoch = epoch + 1
saved_model_file = '{}.{}.T{}.bit{}.pth'.format(model.get_name(), args.dataset, args.num_samples, args.nbits)
torch.save(model.state_dict(), 'saved_models/{}'.format(saved_model_file))
#########################################################################################################
with open('logs/EdgeOnly/result_nn.txt', 'a') as handle:
handle.write('{},{},{},{},{},{}\n'.format(dataset_name, args.nbits, walk_type, max_nodes, best_precision_epoch, best_precision))
# with open('logs/T_experiment.{}.txt'.format(args.dataset), 'a') as handle:
# #handle.write('dataset: {} bits:{} model:{} T={} Best Precision:({}){:.4f}\n'.format(args.dataset, args.nbits, model.get_name(), args.num_samples, best_precision_epoch, best_precision))
# handle.write('{}\t{}\t{}\t{}\n'.format(args.dataset, args.nbits, args.num_samples, best_precision))
print('dataset: {} bits:{} model:{} T={} Best Precision:({}){:.4f}'.format(args.dataset, args.nbits, model.get_name(), args.num_samples, best_precision_epoch, best_precision))