out fix

dev-0/out.tsv

@@ -0,0 +1,471 @@
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guessword.py

@@ -0,0 +1,232 @@
+import os
+import numpy as np
+import torch
+from sklearn.model_selection import train_test_split
+import nltk
+from nltk.tokenize import word_tokenize 
+nltk.download('punkt')
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+NGRAMS = 5
+BATCH_SIZE = 128
+EPOCHS = 15
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+with open(dir_path + "\\train\\train.tsv", "r", encoding="utf8") as lalka_path:
+    lines = lalka_path.readlines()
+
+train, test = train_test_split(lines, test_size = 0.2)
+
+with open(dir_path + "\\train\\train_train.tsv", "w", encoding="utf8") as out_train_file:
+    for i in train:
+        out_train_file.write(i)
+
+with open(dir_path + "\\train\\train_test.tsv", "w", encoding="utf8") as out_test_file:
+    for i in test:
+        out_test_file.write(i)
+
+lalka_path_train= dir_path + '\\train\\train_train.tsv'
+lalka_path_valid= dir_path + '\\train\\train_test.tsv'
+
+corpora_train = open(lalka_path_train, encoding="utf8").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>"
+
+vocab_stoi = dict()
+
+for i, token in enumerate(vocab_itos):
+    vocab_stoi[token] = i
+
+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
+
+def get_samples(dataset):
+    samples = []
+    for i in range(len(dataset)-NGRAMS):
+        samples.append(dataset[i:i+NGRAMS])
+    return samples
+
+train_ids = get_token_id(corpora_train_tokenized)
+
+train_ids = get_samples(train_ids)
+
+train_ids = torch.tensor(train_ids, device = device)
+
+corpora_valid = open(lalka_path_valid, encoding="utf8").read()
+
+corpora_valid_tokenized = list(word_tokenize(corpora_valid))
+corpora_valid_tokenized = [token.lower() for token in corpora_valid_tokenized]
+
+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)
+
+def get_ppl(dataset_ids):
+    lm.eval()
+
+    batches = 0
+    loss_sum =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
+    lm.train()
+    total = len(train_ids)-BATCH_SIZE+1
+    for i in range(0, total, BATCH_SIZE):
+        print('batches: ' + str(batches))
+
+        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)
+    
+    print('epoch: ', epoch)
+    print('train ppl: ', ppl_train)
+    print('valid ppl: ', ppl_valid)
+    print()
+
+tokenized = list(word_tokenize('Gości innych nie widział oprócz spółleśników'))
+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>'])
+
+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)
+
+print('starting outs...')
+
+with open(dir_path + "\\dev-0\\in.tsv", "r", encoding="UTF-8") as dev_path:
+    nr_of_dev_lines = len(dev_path.readlines())
+
+with open(dir_path + "\\test-A\\in.tsv", "r", encoding="UTF-8") as test_a_path:
+    nr_of_test_a_lines = len(test_a_path.readlines())
+
+with open(dir_path + "\\dev-0\\out.tsv", "w", encoding="UTF-8") as out_dev_file:
+    for i in range(nr_of_dev_lines):
+        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)]
+        candidate2 = 15001
+        while candidate2 > 15000 or candidate2 == candidate:
+            candidate2 = candidates[np.random.randint(candidates_number)]
+        candidate3 = 15001
+        while candidate3 > 15000 or candidate2 == candidate:
+            candidate3 = candidates[np.random.randint(candidates_number)]
+        ids =  torch.cat((ids, torch.tensor([[candidate]], device = device)), 1)
+        out_dev_file.write(vocab_itos[candidate]+ ':0.3 ' + vocab_itos[candidate2]
+        + ':0.2' + vocab_itos[candidate3] + '0.1' + ' :0.4\n')
+
+with open(dir_path + "\\test-A\\out.tsv", "w", encoding="UTF-8") as out_test_file:
+    for i in range(nr_of_dev_lines):
+        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)]
+        candidate2 = 15001
+        while candidate2 > 15000 or candidate2 == candidate:
+            candidate2 = candidates[np.random.randint(candidates_number)]
+        candidate3 = 15001
+        while candidate3 > 15000 or candidate2 == candidate:
+            candidate3 = candidates[np.random.randint(candidates_number)]
+        ids =  torch.cat((ids, torch.tensor([[candidate]], device = device)), 1)
+        out_test_file.write(vocab_itos[candidate]+ ':0.3 ' + vocab_itos[candidate2]
+        + ':0.2' + vocab_itos[candidate3] + '0.1' + ' :0.4\n')

test-A/out.tsv

@@ -0,0 +1,471 @@
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