lalka-lm/main.py

import numpy as np
import torch
from sklearn.model_selection import train_test_split
from nltk.tokenize import word_tokenize


lalka_path_train= 'train/train_train.tsv'

lalka_path_valid= 'train/train_test.tsv'

corpora_train = open(lalka_path_train).read()

corpora_train_tokenized = list(word_tokenize(corpora_train))

corpora_train_tokenized = [token.lower() for token in corpora_train_tokenized]

vocab_itos = sorted(set(corpora_train_tokenized))


vocab_itos = vocab_itos[:15005]
vocab_itos[15001] = "<UNK>"
vocab_itos[15002] = "<BOS>"
vocab_itos[15003] = "<EOS>"
vocab_itos[15004] = "<PAD>"


with open("train/train.tsv", "r") as lalka:
    lines = lalka.readlines()
with open("train/train_train.tsv", "w") as out_train:
    for i in train:
        out_train.write(i)
with open("train/train_test.tsv", "w") as out_test:
    for i in test:
        out_test.write(i)



vocab_stoi = dict()
for i, token in enumerate(vocab_itos):
    vocab_stoi[token] = i

NGRAMS = 5

def get_token_id(dataset):
    token_ids = [vocab_stoi['<PAD>']] * (NGRAMS-1) + [vocab_stoi['<BOS>']]
    for token in dataset:
        try:
            token_ids.append(vocab_stoi[token])
        except KeyError:
            token_ids.append(vocab_stoi['<UNK>'])
    token_ids.append(vocab_stoi['<EOS>'])
    return token_ids

train_ids = get_token_id(corpora_train_tokenized)

def get_samples(dataset):
    samples = []
    for i in range(len(dataset)-NGRAMS):
        samples.append(dataset[i:i+NGRAMS])
    return samples

train_ids = get_samples(train_ids)

train_ids = torch.tensor(train_ids, device = device)

corpora_valid = open(lalka_path_valid).read()

corpora_valid_tokenized = list(word_tokenize(corpora_valid))

valid_ids = get_token_id(corpora_valid_tokenized)

valid_ids = torch.tensor(get_samples(valid_ids), dtype = torch.long, device = device)

class GRU(torch.nn.Module):

    def __init__(self):
        super(GRU, self).__init__()
        self.emb = torch.nn.Embedding(len(vocab_itos),100)
        self.rec = torch.nn.GRU(100, 256, 1, batch_first = True)
        self.fc1 = torch.nn.Linear( 256 ,len(vocab_itos))

    def forward(self, x):
        emb = self.emb(x)
        output, h_n = self.rec(emb)
        hidden = h_n.squeeze(0)
        out = self.fc1(hidden)
        return out

lm = GRU().to(device)

criterion = torch.nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(lm.parameters(),lr=0.0001)

BATCH_SIZE = 128
EPOCHS = 15

def get_ppl(dataset_ids):
    lm.eval()

    batches = 0
    loss_sum =0
    acc_score = 0

    for i in range(0, len(dataset_ids)-BATCH_SIZE+1, BATCH_SIZE):
        X = dataset_ids[i:i+BATCH_SIZE,:NGRAMS-1]
        Y = dataset_ids[i:i+BATCH_SIZE,NGRAMS-1]
        predictions = lm(X)
        
        loss = criterion(predictions,Y)

        loss_sum += loss.item()
        batches += 1

    return np.exp(loss_sum / batches)

history_ppl_train  = []
history_ppl_valid  = []
for epoch in range(EPOCHS):
    
    batches = 0
    loss_sum =0
    acc_score = 0
    lm.train()
    #for i in range(0, len(train_ids)-BATCH_SIZE+1, BATCH_SIZE):
    for i in range(0, len(train_ids)-BATCH_SIZE+1, BATCH_SIZE):
        X = train_ids[i:i+BATCH_SIZE,:NGRAMS-1]
        Y = train_ids[i:i+BATCH_SIZE,NGRAMS-1]
        predictions = lm(X)
        loss = criterion(predictions,Y)
    
    
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        loss_sum += loss.item()
        batches += 1
        
    ppl_train = get_ppl(train_ids)
    ppl_valid = get_ppl(valid_ids)
    
    history_ppl_train.append(ppl_train)
    history_ppl_valid.append(ppl_valid)
    
tokenized = list(word_tokenize('Gości innych nie widział oprócz spółleśników'))
tokenized = tokenized[-NGRAMS  :-1 ] 

ids = []
for word in tokenized:
    if word in vocab_stoi:
        ids.append(vocab_stoi[word])
    else:
        ids.append(vocab_stoi['<UNK>'])

lm.eval()

ids = torch.tensor(ids, dtype = torch.long, device = device)

preds= lm(ids.unsqueeze(0))

vocab_itos[torch.argmax(torch.softmax(preds,1),1).item()]

tokenized = list(word_tokenize('Lalka'))
tokenized = [token.lower() for token in tokenized]

ids = []
for word in tokenized:
    if word in vocab_stoi:
        ids.append(vocab_stoi[word])
    else:
        ids.append(vocab_stoi['<UNK>'])
ids = torch.tensor([ids], dtype = torch.long, device = device)

candidates_number = 10
for i in range(30):
    preds= lm(ids)
    candidates = torch.topk(torch.softmax(preds,1),candidates_number)[1][0].cpu().numpy()
    candidate = 15001
    while candidate > 15000:
        candidate = candidates[np.random.randint(candidates_number)]
    print(vocab_itos[candidate])
    ids =  torch.cat((ids, torch.tensor([[candidate]], device = device)), 1)

dev= open("dev-0/in.tsv", "r")
dev_ = len(dev.readlines())
a = open("test-A/in.tsv", "r")
a_ = len(a.readlines())



with open("dev-0/out.tsv", "w") as out_dev:
    for i in range(dev_):
        prediction = lm(id_list)
        candidates = torch.topk(torch.softmax(prediction, 1), candidates_number)[1][0].cpu().numpy()
        
        candidate = 15001
        
        while candidate > 15000:
            candidate = candidates[np.random.randint(candidates_number)]
        id_list = torch.cat((id_list, torch.tensor([[candidate]], device=device)), 1)
        out_dev_file.write(vocab_itos[candidate] + '\n')
with open("test-A/out.tsv", "w") as out_test:
    for i in range(dev_):
        preds = lm(id_list)
        candidates = torch.topk(torch.softmax(preds, 1), candidates_number)[1][0].cpu().numpy()
        
        candidate = 15001
        
        while candidate > 15000:
            candidate = candidates[np.random.randint(candidates_number)]
        id_list = torch.cat((id_list, torch.tensor([[candidate]], device=device)), 1)
        out_test_file.write(vocab_itos[candidate] + '\n')