/
run_seq2tree.py
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run_seq2tree.py
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# coding: utf-8
import os
from src.train_and_evaluate import *
from src.models import *
import time
import torch.optim
from src.expressions_transfer import *
batch_size = 64
embedding_size = 128
hidden_size = 512
n_epochs = 80
learning_rate = 1e-3
weight_decay = 1e-5
beam_size = 5
n_layers = 2
os.makedirs("models", exist_ok=True)
data = load_raw_data("data/Math_23K.json")
pairs, generate_nums, copy_nums = transfer_num(data)
temp_pairs = []
for p in pairs:
temp_pairs.append((p[0], from_infix_to_prefix(p[1]), p[2], p[3]))
pairs = temp_pairs
fold_size = int(len(pairs) * 0.2)
fold_pairs = []
for split_fold in range(4):
fold_start = fold_size * split_fold
fold_end = fold_size * (split_fold + 1)
fold_pairs.append(pairs[fold_start:fold_end])
fold_pairs.append(pairs[(fold_size * 4):])
best_acc_fold = []
for fold in range(5):
pairs_tested = []
pairs_trained = []
for fold_t in range(5):
if fold_t == fold:
pairs_tested += fold_pairs[fold_t]
else:
pairs_trained += fold_pairs[fold_t]
input_lang, output_lang, train_pairs, test_pairs = prepare_data(pairs_trained, pairs_tested, 5, generate_nums,
copy_nums, tree=True)
# Initialize models
encoder = EncoderSeq(input_size=input_lang.n_words, embedding_size=embedding_size, hidden_size=hidden_size,
n_layers=n_layers)
predict = Prediction(hidden_size=hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums),
input_size=len(generate_nums))
generate = GenerateNode(hidden_size=hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums),
embedding_size=embedding_size)
merge = Merge(hidden_size=hidden_size, embedding_size=embedding_size)
# the embedding layer is only for generated number embeddings, operators, and paddings
encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=learning_rate, weight_decay=weight_decay)
predict_optimizer = torch.optim.Adam(predict.parameters(), lr=learning_rate, weight_decay=weight_decay)
generate_optimizer = torch.optim.Adam(generate.parameters(), lr=learning_rate, weight_decay=weight_decay)
merge_optimizer = torch.optim.Adam(merge.parameters(), lr=learning_rate, weight_decay=weight_decay)
encoder_scheduler = torch.optim.lr_scheduler.StepLR(encoder_optimizer, step_size=20, gamma=0.5)
predict_scheduler = torch.optim.lr_scheduler.StepLR(predict_optimizer, step_size=20, gamma=0.5)
generate_scheduler = torch.optim.lr_scheduler.StepLR(generate_optimizer, step_size=20, gamma=0.5)
merge_scheduler = torch.optim.lr_scheduler.StepLR(merge_optimizer, step_size=20, gamma=0.5)
# Move models to GPU
if USE_CUDA:
encoder.cuda()
predict.cuda()
generate.cuda()
merge.cuda()
generate_num_ids = []
for num in generate_nums:
generate_num_ids.append(output_lang.word2index[num])
for epoch in range(n_epochs):
encoder_scheduler.step()
predict_scheduler.step()
generate_scheduler.step()
merge_scheduler.step()
loss_total = 0
input_batches, input_lengths, output_batches, output_lengths, nums_batches, num_stack_batches, num_pos_batches, num_size_batches = prepare_train_batch(train_pairs, batch_size)
print("fold:", fold + 1)
print("epoch:", epoch + 1)
start = time.time()
for idx in range(len(input_lengths)):
loss = train_tree(
input_batches[idx], input_lengths[idx], output_batches[idx], output_lengths[idx],
num_stack_batches[idx], num_size_batches[idx], generate_num_ids, encoder, predict, generate, merge,
encoder_optimizer, predict_optimizer, generate_optimizer, merge_optimizer, output_lang, num_pos_batches[idx])
loss_total += loss
print("loss:", loss_total / len(input_lengths))
print("training time", time_since(time.time() - start))
print("--------------------------------")
if epoch % 10 == 0 or epoch > n_epochs - 5:
value_ac = 0
equation_ac = 0
eval_total = 0
start = time.time()
for test_batch in test_pairs:
test_res = evaluate_tree(test_batch[0], test_batch[1], generate_num_ids, encoder, predict, generate,
merge, output_lang, test_batch[5], beam_size=beam_size)
val_ac, equ_ac, _, _ = compute_prefix_tree_result(test_res, test_batch[2], output_lang, test_batch[4], test_batch[6])
if val_ac:
value_ac += 1
if equ_ac:
equation_ac += 1
eval_total += 1
print(equation_ac, value_ac, eval_total)
print("test_answer_acc", float(equation_ac) / eval_total, float(value_ac) / eval_total)
print("testing time", time_since(time.time() - start))
print("------------------------------------------------------")
torch.save(encoder.state_dict(), "models/encoder")
torch.save(predict.state_dict(), "models/predict")
torch.save(generate.state_dict(), "models/generate")
torch.save(merge.state_dict(), "models/merge")
if epoch == n_epochs - 1:
best_acc_fold.append((equation_ac, value_ac, eval_total))
a, b, c = 0, 0, 0
for bl in range(len(best_acc_fold)):
a += best_acc_fold[bl][0]
b += best_acc_fold[bl][1]
c += best_acc_fold[bl][2]
print(best_acc_fold[bl])
print(a / float(c), b / float(c))