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train_cnn_match.py
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train_cnn_match.py
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import argparse
from os.path import join
import os
import time
import json
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_recall_curve
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
import torch.nn.functional as F
from data_loader import ProcessedCNNInputDataset
from models import CNNMatchModel
from utils import ChunkSampler
import settings
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s') # include timestamp
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.')
parser.add_argument('--matrix-size1', type=int, default=10, help='Matrix size 1.')
parser.add_argument('--matrix-size2', type=int, default=5, help='Matrix size 2.')
parser.add_argument('--mat1-channel1', type=int, default=4, help='Matrix1 number of channels1.')
parser.add_argument('--mat1-kernel-size1', type=int, default=3, help='Matrix1 kernel size1.')
parser.add_argument('--mat1-channel2', type=int, default=8, help='Matrix1 number of channel2.')
parser.add_argument('--mat1-kernel-size2', type=int, default=2, help='Matrix1 kernel size2.')
parser.add_argument('--hidden1', type=int, default=64, help='Matrix1 hidden dim.')
parser.add_argument('--mat2-channel1', type=int, default=4, help='Matrix2 number of channels1.')
parser.add_argument('--mat2-kernel-size1', type=int, default=2, help='Matrix2 kernel size1.')
parser.add_argument('--hidden2', type=int, default=16, help='Matrix2 hidden dim')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--seed-delta', type=int, default=0, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100, help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=3e-4, help='Initial learning rate.')
parser.add_argument('--weight-decay', type=float, default=1e-3, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--batch', type=int, default=32, help="Batch size")
parser.add_argument('--check-point', type=int, default=5, help="Check point")
parser.add_argument('--shuffle', action='store_true', default=True, help="Shuffle dataset")
parser.add_argument('--entity-type', type=str, default="aff", help="Types of entities to match")
parser.add_argument('--n-try', type=int, default=1, help="Repeat Times")
args = parser.parse_args()
def evaluate(epoch, loader, model, thr=None, return_best_thr=False, args=args):
model.eval()
total = 0.
loss = 0.
y_true, y_pred, y_score = [], [], []
for i_batch, batch in enumerate(loader):
X_title, X_author, Y = batch
bs = len(Y)
if args.cuda:
X_title = X_title.cuda()
X_author = X_author.cuda()
Y = Y.cuda()
output = model(X_title.float(), X_author.float())
loss_batch = F.nll_loss(output, Y.long())
loss += bs * loss_batch.item()
y_true += Y.data.tolist()
y_pred += output.max(1)[1].data.tolist()
y_score += output[:, 1].data.tolist()
total += bs
model.train()
if thr is not None:
logger.info("using threshold %.4f", thr)
y_score = np.array(y_score)
y_pred = np.zeros_like(y_score)
y_pred[y_score > thr] = 1
prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary")
auc = roc_auc_score(y_true, y_score)
logger.info("loss: %.4f AUC: %.4f Prec: %.4f Rec: %.4f F1: %.4f",
loss / total, auc, prec, rec, f1)
metrics = [loss / total, auc, prec, rec, f1]
if return_best_thr: # valid
precs, recs, thrs = precision_recall_curve(y_true, y_score)
f1s = 2 * precs * recs / (precs + recs)
f1s = f1s[:-1]
thrs = thrs[~np.isnan(f1s)]
f1s = f1s[~np.isnan(f1s)]
best_thr = thrs[np.argmax(f1s)]
logger.info("best threshold=%4f, f1=%.4f", best_thr, np.max(f1s))
return best_thr, metrics
else:
return None, metrics
def train(epoch, train_loader, valid_loader, test_loader, model, optimizer, args=args):
model.train()
loss = 0.
total = 0.
for i_batch, batch in enumerate(train_loader):
X_title, X_author, Y = batch
bs = Y.shape[0]
if args.cuda:
X_title = X_title.cuda()
X_author = X_author.cuda()
Y = Y.cuda()
optimizer.zero_grad()
output = model(X_title.float(), X_author.float())
loss_train = F.nll_loss(output, Y.long())
loss += bs * loss_train.item()
total += bs
loss_train.backward()
optimizer.step()
logger.info("train loss epoch %d: %f", epoch, loss / total)
metrics_val = None
metrics_test = None
if (epoch + 1) % args.check_point == 0:
logger.info("epoch %d, checkpoint! validation...", epoch)
best_thr, metrics_val = evaluate(epoch, valid_loader, model, return_best_thr=True, args=args)
logger.info('eval on test data!...')
_, metrics_test = evaluate(epoch, test_loader, model, thr=best_thr, args=args)
return metrics_val, metrics_test
def train_one_time(args, wf, repeat_seed):
args.cuda = not args.no_cuda and torch.cuda.is_available()
logger.info('cuda is available %s', args.cuda)
np.random.seed(args.seed + repeat_seed)
torch.manual_seed(args.seed + repeat_seed)
if args.cuda:
torch.cuda.manual_seed(args.seed + repeat_seed)
dataset = ProcessedCNNInputDataset(args.entity_type, "train")
dataset_valid = ProcessedCNNInputDataset(args.entity_type, "valid")
dataset_test = ProcessedCNNInputDataset(args.entity_type, "test")
N = len(dataset)
N_valid = len(dataset_valid)
N_test = len(dataset_test)
print("n_train", N)
train_loader = DataLoader(dataset, batch_size=args.batch, sampler=ChunkSampler(N, 0))
valid_loader = DataLoader(dataset_valid, batch_size=args.batch, sampler=ChunkSampler(N_valid, 0))
test_loader = DataLoader(dataset_test, batch_size=args.batch, sampler=ChunkSampler(N_test, 0))
model = CNNMatchModel(input_matrix_size1=args.matrix_size1, input_matrix_size2=args.matrix_size2,
mat1_channel1=args.mat1_channel1, mat1_kernel_size1=args.mat1_kernel_size1,
mat1_channel2=args.mat1_channel2, mat1_kernel_size2=args.mat1_kernel_size2,
mat2_channel1=args.mat2_channel1, mat2_kernel_size1=args.mat2_kernel_size1,
hidden1=args.hidden1, hidden2=args.hidden2)
model = model.float()
if args.cuda:
model.cuda()
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
t_total = time.time()
logger.info("training...")
model_dir = join(settings.OUT_DIR, args.entity_type, "cnn-models")
os.makedirs(model_dir, exist_ok=True)
n_paras = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("number of paras:", n_paras)
evaluate(0, test_loader, model, thr=None, args=args)
min_loss_val = None
best_test_metrics = None
best_epoch = -1
for epoch in range(args.epochs):
metrics_val, metrics_test = train(epoch, train_loader, valid_loader, test_loader, model, optimizer, args=args)
if metrics_val is not None:
if min_loss_val is None or min_loss_val > metrics_val[0]:
min_loss_val = metrics_val[0]
best_test_metrics = metrics_test
best_epoch = epoch
torch.save(model.state_dict(), join(model_dir, "cnn-match-best-now-try-{}.mdl".format(repeat_seed)))
logger.info("optimization Finished!")
logger.info("total time elapsed: {:.4f}s".format(time.time() - t_total))
print("best epoch", best_epoch)
print("min valid loss {:.4f}, best test metrics: AUC: {:.2f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}".format(
min_loss_val, best_test_metrics[1], best_test_metrics[2], best_test_metrics[3], best_test_metrics[4]
))
wf.write(
"min valid loss {:.4f}, best test metrics: AUC: {:.2f}, Prec: {:.2f}, Rec: {:.2f}, F1: {:.2f}\n\n".format(
min_loss_val, best_test_metrics[1] * 100, best_test_metrics[2] * 100, best_test_metrics[3] * 100,
best_test_metrics[4] * 100
))
def main(args):
model_dir = join(settings.OUT_DIR, args.entity_type)
os.makedirs(model_dir, exist_ok=True)
wf = open(join(model_dir, "{}_cnn_results.txt".format(args.entity_type)), "w")
for t in range(args.n_try):
train_one_time(args, wf, t)
wf.flush()
wf.write(json.dumps(vars(args)) + "\n")
wf.close()
def calc_avg_metrics(args):
model_dir = join(settings.OUT_DIR, args.entity_type)
metrics = []
with open(join(model_dir, "{}_cnn_results.txt".format(args.entity_type))) as rf:
for i, line in enumerate(rf):
line = line.strip()
if i % 2 == 0 and i < 10:
items = line.split(":")[2:]
items = [float(x.split(",")[0].strip()) for x in items]
metrics.append(items)
print(np.mean(np.array(metrics), axis=0))
if __name__ == "__main__":
print("args", args)
main(args)
calc_avg_metrics(args)