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multitask.py
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import os, random
import copy
import numpy as np
from argparse import ArgumentParser
import torch
import torch.nn as nn
import torch.nn.functional as F
from chemprop.parsing import add_train_args, modify_train_args
from chemprop.models import MoleculeModel
from chemprop.data import MoleculeDataset
from chemprop.data.utils import get_data, split_data
from chemprop.nn_utils import get_activation_function, initialize_weights, compute_gnorm
from chemprop.utils import build_optimizer, build_lr_scheduler, get_loss_func, get_metric_func, load_checkpoint, makedirs, save_checkpoint
from chemprop.train import evaluate, evaluate_predictions, predict
def prepare_data(args):
data = get_data(path=args.data_path, args=args)
source_data = get_data(path=args.source_data_path, args=args)
# split train, val, test
train_data, val_data, test_data = split_data(data=data, split_type=args.split_type, sizes=args.split_sizes, seed=args.seed, args=args)
args.num_tasks = train_data.num_tasks()
args.features_size = train_data.features_size()
args.train_data_size = len(train_data)
print('source data:', len(source_data))
print('target data:', len(data))
return train_data, val_data, test_data, source_data
def prepare_model(args):
args.output_size = args.num_tasks
inv_model = MoleculeModel(classification=args.dataset_type == 'classification', multiclass=args.dataset_type == 'multiclass')
inv_model.create_encoder(args) # phi(x), shared across source and target domain
inv_model.create_ffn(args) # source function
inv_model.src_ffn = inv_model.ffn
inv_model.create_ffn(args) # target function
initialize_weights(inv_model)
return inv_model.cuda()
def forward(inv_model, mol_batch, loss_func, is_source):
smiles_batch, target_batch = mol_batch.smiles(), mol_batch.targets()
mask = torch.Tensor([[x is not None for x in tb] for tb in target_batch]).cuda()
targets = torch.Tensor([[0 if x is None else x for x in tb] for tb in target_batch]).cuda()
phi_x = inv_model.encoder(smiles_batch)
if is_source:
inv_preds = inv_model.src_ffn(phi_x)
else:
inv_preds = inv_model.ffn(phi_x)
inv_pred_loss = loss_func(inv_preds, targets) * mask
return inv_pred_loss.sum() / mask.sum()
def train(inv_model, src_data, tgt_data, loss_func, inv_opt, args):
inv_model.train()
src_data.shuffle()
new_size = len(tgt_data) / args.batch_size * args.src_batch_size
new_size = int(new_size)
src_pos_data = [d for d in src_data if d.targets[0] == 1]
src_neg_data = [d for d in src_data if d.targets[0] == 0]
print(len(tgt_data))
print(len(src_pos_data), len(src_neg_data), new_size)
src_data = MoleculeDataset(src_pos_data + src_neg_data[:new_size])
src_data.shuffle()
tgt_data.shuffle()
src_iter = range(0, len(src_data), args.src_batch_size)
tgt_iter = range(0, len(tgt_data), args.batch_size)
for i, j in zip(src_iter, tgt_iter):
inv_model.zero_grad()
src_batch = src_data[i:i + args.src_batch_size]
src_batch = MoleculeDataset(src_batch)
src_loss = forward(inv_model, src_batch, loss_func, is_source=True)
tgt_batch = tgt_data[j:j + args.batch_size]
tgt_batch = MoleculeDataset(tgt_batch)
tgt_loss = forward(inv_model, tgt_batch, loss_func, is_source=False)
loss = (src_loss + tgt_loss) / 2
loss.backward()
inv_opt[0].step()
inv_opt[1].step()
lr = inv_opt[1].get_lr()[0]
ignorm = compute_gnorm(inv_model)
print(f'lr: {lr:.5f}, loss: {loss:.4f}, gnorm: {ignorm:.4f}')
def run_training(args, save_dir):
tgt_data, val_data, test_data, src_data = prepare_data(args)
inv_model = prepare_model(args)
print('invariant', inv_model)
optimizer = build_optimizer(inv_model, args)
scheduler = build_lr_scheduler(optimizer, args)
inv_opt = (optimizer, scheduler)
loss_func = get_loss_func(args)
metric_func = get_metric_func(metric=args.metric)
best_score = float('inf') if args.minimize_score else -float('inf')
best_epoch = 0
for epoch in range(args.epochs):
print(f'Epoch {epoch}')
train(inv_model, src_data, tgt_data, loss_func, inv_opt, args)
val_scores = evaluate(inv_model, val_data, args.num_tasks, metric_func, args.batch_size, args.dataset_type)
avg_val_score = np.nanmean(val_scores)
print(f'Validation {args.metric} = {avg_val_score:.4f}')
if args.minimize_score and avg_val_score < best_score or not args.minimize_score and avg_val_score > best_score:
best_score, best_epoch = avg_val_score, epoch
save_checkpoint(os.path.join(save_dir, 'model.pt'), inv_model, args=args)
print(f'Loading model checkpoint from epoch {best_epoch}')
model = load_checkpoint(os.path.join(save_dir, 'model.pt'), cuda=args.cuda)
test_smiles, test_targets = test_data.smiles(), test_data.targets()
test_preds = predict(model, test_data, args.batch_size)
test_scores = evaluate_predictions(test_preds, test_targets, args.num_tasks, metric_func, args.dataset_type)
avg_test_score = np.nanmean(test_scores)
print(f'Test {args.metric} = {avg_test_score:.4f}')
return avg_test_score
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--source_data_path', required=True)
parser.add_argument('--src_batch_size', type=int, default=100)
parser.add_argument('--lambda_e', type=float, default=0.1)
add_train_args(parser)
args = parser.parse_args()
modify_train_args(args)
all_test_score = np.zeros((args.num_folds,))
for i in range(args.num_folds):
fold_dir = os.path.join(args.save_dir, f'fold_{i}')
makedirs(fold_dir)
all_test_score[i] = run_training(args, fold_dir)
mean, std = np.mean(all_test_score), np.std(all_test_score)
print(f'{args.num_folds} fold average: {mean:.4f} +/- {std:.4f}')