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rnn_baseline.py
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import numpy as np
import torch
from gru_entitiylibrary import LSTMClassifier
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
import torch_preprocess_word as function
import math
import read_files as read
np.random.seed(123)
torch.manual_seed(123)
## parameter setting
def iterate_minibatches(inputs, targets, sentence_len,batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt],sentence_len[excerpt]
### parameter setting
### training procedure
def train(vocab_dict, label_dict, train_x,train_y,train_sentence_len,valid_x,valid_y , valid_sentence_len, dataset,folder):
embedding_dim = 300
hidden_dim = 100
epochs = 60
batch_size = 50
learning_rate = 1.0
model = LSTMClassifier(embedding_dim=embedding_dim, hidden_dim=hidden_dim,
vocab_size=len(vocab_dict), label_size=len(label_dict), batch_size=batch_size)
train_acc_ = []
valid_acc_ = []
optimizer = optim.Adadelta(model.parameters(), lr=learning_rate, weight_decay=1e-6)
loss_function = nn.CrossEntropyLoss()
train_loss_ = []
valid_loss_ = []
val_acc = -math.inf
for epoch in range(epochs):
## training epoch
total_acc = 0.0
total_loss = 0.0
total = 0.0
for batch in iterate_minibatches(train_x, train_y,train_sentence_len, batchsize=batch_size, shuffle=True):
input_batch, target_batch, seq_lens_batch = batch
train_input_batch = torch.LongTensor(input_batch)
train_target_batch = torch.LongTensor(target_batch)
train_seq_lens_batch = torch.LongTensor(seq_lens_batch)
train_seq_lens_batch, perm_index = train_seq_lens_batch.sort(0, descending=True)
train_x_batch= train_input_batch[perm_index]
train_y_batch = train_target_batch[perm_index]
train_x_varaible = Variable(train_x_batch)
model.zero_grad()
model.batch_size = len(train_x_batch)
model.hidden = model.init_hidden()
output = model(train_x_batch,train_seq_lens_batch)
loss = loss_function(output, Variable(train_y_batch))
loss.backward()
optimizer.step()
# calc training acc
_, predicted = torch.max(output.data, 1)
total_acc += np.float((predicted == train_y_batch).sum().item())
total += len(train_y_batch)
total_loss += loss.item()
train_loss_.append(total_loss / total)
train_acc_.append(total_acc / total)
## validing epoch
total_acc = 0.0
total_loss = 0.0
total = 0.0
for batch in iterate_minibatches(valid_x, valid_y,valid_sentence_len, batchsize=batch_size, shuffle=False):
input_batch, target_batch, seq_lens_batch= batch
valid_input = torch.LongTensor(input_batch)
valid_label = torch.LongTensor(target_batch)
valid_seq_lens_batch = torch.LongTensor(seq_lens_batch)
valid_seq_lens_batch, perm_index = valid_seq_lens_batch.sort(0, descending=True)
valid_x_batch = valid_input[perm_index]
valid_y_batch = valid_label[perm_index]
valid_x_batch = Variable(valid_x_batch)
model.batch_size = len(valid_x_batch)
model.hidden = model.init_hidden()
output = model(valid_x_batch,valid_seq_lens_batch)
loss = loss_function(output, Variable(valid_y_batch))
# calc testing acc
_, predicted = torch.max(output.data, 1)
total_acc += np.float((predicted == valid_y_batch).sum().item())
total += len(valid_y_batch)
total_loss += loss.item()
if (total_acc/total > val_acc):
torch.save(model, "data/model/model_" + dataset + "_folder_" + str(folder) + ".pkl")
val_acc = total_acc/total
valid_loss_.append(total_loss / total)
valid_acc_.append(total_acc / total)
print(
'[Epoch: %3d/%3d] Training Loss: %.3f, Validating Loss: %.3f, Training Acc: %.3f, Validing Acc: %.3f'
% (epoch, epochs, train_loss_[epoch], valid_loss_[epoch], train_acc_[epoch],
valid_acc_[epoch]))
#train()
def eval(folder, model,test_x,test_y , test_sentence_len,mode):
## testing epoch
test_loss_ = []
test_acc_ = []
total_acc = 0.0
total_loss = 0.0
total = 0.0
batch_size = 50
for batch in iterate_minibatches(test_x, test_y,test_sentence_len, batchsize=batch_size, shuffle=False):
input_batch, target_batch, seq_lens_batch= batch
test_input = torch.LongTensor(input_batch)
test_label = torch.LongTensor(target_batch)
test_seq_lens_batch = torch.LongTensor(seq_lens_batch)
test_seq_lens_batch, perm_index = test_seq_lens_batch.sort(0, descending=True)
test_x_batch = test_input[perm_index]
test_y_batch = test_label[perm_index]
test_x_batch = Variable(test_x_batch)
model.batch_size = len(test_x_batch)
model.hidden = model.init_hidden()
output = model(test_x_batch,test_seq_lens_batch)
loss_function = nn.CrossEntropyLoss()
loss = loss_function(output, Variable(test_y_batch))
# calc testing acc
_, predicted = torch.max(output.data, 1)
total_acc += np.float((predicted == test_y_batch).sum().item())
total += len(test_y_batch)
total_loss += loss.item()
test_loss_.append(total_loss / total)
test_acc_.append(total_acc / total)
# print('%s Loss for folder %s: %.3f, %s Acc: %.3f'
# % (mode ,str(folder),test_loss_[0], mode, test_acc_[0]))
return test_acc_[0]
#test()
def rnn_baseline(dataset,train_model):
avg_test_acc = 0.0
avg_dev_acc = 0.0
print("Run RNN Baseline for %s: ..." %(dataset))
texts, label_texts, labels = function.load_data(
"data/" + dataset + "/" + dataset + ".fold-" + str(0) + ".train.txt",
"data/" + dataset + "/" + dataset + ".fold-" + str(0) + ".validation.txt",
"data/" + dataset + "/" + dataset + ".fold-" + str(0) + ".test.txt")
vocab_dict, label_dict = function.get_vocab(texts, label_texts, labels)
vocab_dict["UNK"] = len(vocab_dict) + 1
vocab_dict["PAD"] = 0
for i in range(10):
texts, label_texts, labels = function.load_data("data/"+dataset + "/"+dataset+".fold-"+ str(i) +".train.txt",
"data/"+dataset + "/"+dataset+".fold-"+ str(i) +".validation.txt",
"data/"+dataset + "/"+dataset+".fold-"+ str(i) +".test.txt")
max_length = 56
train_x,train_y,train_sentence_len = function.dataset_preprocess(texts[0],labels[0], vocab_dict,label_dict,max_length)
valid_x,valid_y , valid_sentence_len = function.dataset_preprocess(texts[1],labels[1], vocab_dict,label_dict,max_length)
test_x,test_y , test_sentence_len = function.dataset_preprocess(texts[2],labels[2], vocab_dict,label_dict,max_length)
if train_model == True:
train(vocab_dict, label_dict, train_x, train_y, train_sentence_len, valid_x, valid_y, valid_sentence_len,dataset,i)
model = torch.load("data/model/model_" + dataset + "_folder_" + str(i) + ".pkl")
dev_acc = eval(i,model, valid_x,valid_y , valid_sentence_len,mode = "Dev")
test_acc = eval(i,model, test_x, test_y, test_sentence_len,mode = "Test")
else:
model = torch.load("data/model/model_" + dataset + "_folder_" + str(i) + ".pkl")
dev_acc = eval(i, model, valid_x, valid_y, valid_sentence_len, mode="Dev")
print(dev_acc)
test_acc = eval(i, model, test_x, test_y, test_sentence_len, mode = "Test")
avg_test_acc += test_acc
avg_dev_acc +=dev_acc
print('Average Dev Acc for %s: %.3f'
% (dataset, avg_dev_acc/10.0))
print('Average Testing Acc for %s: %.3f'
% (dataset, avg_test_acc/10.0))
#import term_matching_baseline
#term_matching_baseline.term_matching_baseline("AskAPatient")
#term_matching_baseline.term_matching_baseline("TwADR-L")
rnn_baseline("AskAPatient",train_model=False)
rnn_baseline("TwADR-L",train_model=False)