done

x_create_vocab.py

@@ -0,0 +1,29 @@
+from itertools import islice
+import regex as re
+import sys
+from torchtext.vocab import build_vocab_from_iterator
+import lzma
+import utils
+import torch
+def get_word_lines_from_file(file_name):
+  counter=0
+  with lzma.open(file_name, 'r') as fh:
+    for line in fh:
+      counter+=1
+    #   if counter == 4000:
+    #     break
+      line = line.decode("utf-8")
+      yield utils.get_words_from_line(line)
+
+
+vocab_size = utils.vocab_size
+
+vocab = build_vocab_from_iterator(
+    get_word_lines_from_file('train/in.tsv.xz'),
+    max_tokens = vocab_size,
+    specials = ['<unk>', '<empty>'])
+
+
+import pickle
+with open("vocab.pickle", 'wb') as handle:
+    pickle.dump(vocab, handle)

x_train.py

@@ -0,0 +1,348 @@
+from torch import nn
+import torch
+from torch.utils.data import DataLoader
+import copy
+from torch.utils.data import IterableDataset
+import itertools
+import lzma
+import regex as re
+import pickle
+import scripts
+import string 
+import pdb
+import utils
+
+def divide_chunks(l, n):
+     
+    # looping till length l
+    for i in range(0, len(l), n):
+        yield l[i:i + n]
+ 
+
+with open("vocab.pickle", 'rb') as handle:
+    vocab = pickle.load( handle)
+vocab.set_default_index(vocab['<unk>'])
+
+
+
+def look_ahead_iterator(gen):
+    seq = []
+    counter = 0
+    for item in gen:
+        seq.append(item)
+        if counter % 11 == 0 and counter !=0:
+            if len(seq) == 11:
+                yield seq
+            seq = []
+        counter+=1
+
+def get_word_lines_from_file(file_name):
+  counter=0
+  with lzma.open(file_name, 'r') as fh:
+    for line in fh:
+      counter+=1
+    #   if counter == 100000:
+    #     break
+      line = line.decode("utf-8")
+      yield scripts.get_words_from_line(line)
+
+
+
+class Grams_10(IterableDataset):
+  def load_vocab(self):
+    with open("vocab.pickle", 'rb') as handle:
+        vocab = pickle.load( handle)
+    return vocab
+
+  def __init__(self, text_file, vocab):
+      self.vocab = vocab
+      self.vocab.set_default_index(self.vocab['<unk>'])
+      self.text_file = text_file
+
+  def __iter__(self):
+     return look_ahead_iterator(
+         (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
+
+vocab_size = scripts.vocab_size
+
+train_dataset = Grams_10('train/in.tsv.xz', vocab)
+BATCH_SIZE = 2048
+
+train_data = DataLoader(train_dataset, batch_size=BATCH_SIZE)
+
+
+PREFIX_TRAIN = 'train'
+PREFIX_VALID = 'dev-0'
+BATCHES = []
+# def read_train_file(folder_prefix, vocab):
+#     dataset_x = []
+#     dataset_y = []
+#     counter_lines = 0
+#     seq_len = 10
+#     with lzma.open(f'{folder_prefix}/in.tsv.xz', 'r') as train, open(f'{folder_prefix}/expected.tsv', 'r') as expected:
+#         for t_line, e_line in zip(train, expected):
+#             t_line = t_line.decode("utf-8")
+#             t_line = t_line.rstrip()
+#             e_line = e_line.rstrip()
+#             t_line = t_line.translate(str.maketrans('', '', string.punctuation))
+#             t_line_splitted_by_tab = t_line.split('\t')
+#             # t_line_cleared = t_line_splitted_by_tab[-2] + ' ' + e_line + ' ' + t_line_splitted_by_tab[-1]
+
+
+#             whole_line = t_line_splitted_by_tab[-2] + ' ' + e_line + ' ' + t_line_splitted_by_tab[-1] 
+
+#             whole_line_splitted = list(scripts.get_words_from_line(whole_line))
+
+#             whole_lines_splitted = divide_chunks(whole_line_splitted, 11)
+
+#             for chunk_line in whole_line_splitted:
+                
+
+#                 left_context_splitted = chunk_line[0:10]
+
+#                 seq_x = []
+#                 for i in range(seq_len):
+#                     index = -1 - i 
+#                     if len(left_context_splitted) < i + 1:
+#                         seq_x.insert(0, '<empty>')
+#                     else:
+#                         seq_x.insert(0, left_context_splitted[-1 -i])
+                
+#                 left_vocabed = [vocab[t] for t in seq_x]
+
+
+#                 dataset_x.append(left_vocabed )
+#                 dataset_y.append([vocab[chunk_line[10]]])
+
+#             counter_lines+=1
+#             # if counter_lines > 20000:
+#             #     break
+#     return dataset_x, dataset_y
+
+def read_dev_file(folder_prefix, vocab):
+    dataset_x = []
+    dataset_y = []
+    counter_lines = 0
+    seq_len = 10
+    with lzma.open(f'{folder_prefix}/in.tsv.xz', 'r') as train, open(f'{folder_prefix}/expected.tsv', 'r') as expected:
+        for t_line, e_line in zip(train, expected):
+            t_line = t_line.decode("utf-8")
+            t_line = t_line.rstrip()
+            e_line = e_line.rstrip()
+            t_line = t_line.translate(str.maketrans('', '', string.punctuation))
+            t_line_splitted_by_tab = t_line.split('\t')
+            # t_line_cleared = t_line_splitted_by_tab[-2] + ' ' + e_line + ' ' + t_line_splitted_by_tab[-1]
+
+            left_context = t_line_splitted_by_tab[-2]
+            left_context_splitted = list(scripts.get_words_from_line(left_context))
+            
+
+            seq_x = []
+            for i in range(seq_len):
+                index = -1 - i 
+                if len(left_context_splitted) < i + 1:
+                    seq_x.insert(0, '<empty>')
+                else:
+                    seq_x.insert(0, left_context_splitted[-1 -i])
+            
+            left_vocabed = [vocab[t] for t in seq_x]
+
+
+            dataset_x.append(left_vocabed )
+            dataset_y.append([vocab[e_line]])
+
+            counter_lines+=1
+            # if counter_lines > 20000:
+            #     break
+    return dataset_x, dataset_y
+
+def read_test_file(folder_prefix, vocab):
+    dataset_x = []
+    dataset_y = []
+    counter_lines = 0
+    seq_len = 10
+    with lzma.open(f'{folder_prefix}/in.tsv.xz', 'r') as train:
+        for t_line in train:
+            t_line = t_line.decode("utf-8")
+            t_line = t_line.rstrip()
+            t_line = t_line.translate(str.maketrans('', '', string.punctuation))
+            t_line_splitted_by_tab = t_line.split('\t')
+            # t_line_cleared = t_line_splitted_by_tab[-2] + ' ' + e_line + ' ' + t_line_splitted_by_tab[-1]
+
+            left_context = t_line_splitted_by_tab[-2]
+            left_context_splitted = list(scripts.get_words_from_line(left_context))
+            
+
+            seq_x = []
+            for i in range(seq_len):
+                index = -1 - i 
+                if len(left_context_splitted) < i + 1:
+                    seq_x.insert(0, '<empty>')
+                else:
+                    seq_x.insert(0, left_context_splitted[-1 -i])
+            
+            left_vocabed = [vocab[t] for t in seq_x]
+
+
+            dataset_x.append(left_vocabed )
+
+            counter_lines+=1
+            # if counter_lines > 20000:
+            #     break
+    return dataset_x
+
+
+
+# train_set_x, train_set_y = read_file(PREFIX_TRAIN, vocab)
+dev_set_x, dev_set_y = read_dev_file(PREFIX_VALID, vocab)
+
+test_set_x = read_test_file('test-A', vocab)
+
+# train_data_x = DataLoader(train_set_x, batch_size=4048)
+# train_data_y = DataLoader(train_set_y, batch_size=4048)
+
+# train_data_x = DataLoader(train_set_x, batch_size=4048)
+# train_data_y = DataLoader(train_set_y, batch_size=4048)
+
+
+dev_data_x = DataLoader(dev_set_x, batch_size=1)
+dev_data_y = DataLoader(dev_set_y, batch_size=1)
+
+
+
+test_set_x = DataLoader(test_set_x, batch_size=1)
+# pdb.set_trace()
+device = utils.device
+
+model = utils.LanguageModel(scripts.vocab_size, utils.embed_size).to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=utils.learning_rate)
+criterion = torch.nn.NLLLoss()
+model.train()
+
+step = 0
+last_best_acc = -1
+epochs = 3
+for epoch in range(epochs):
+    model.train()
+    for batch in train_data:
+        x = batch[:10]
+        y = [batch[10]]
+
+        x = [i.to(device) for i in x]
+        y = y[0].to(device)
+        optimizer.zero_grad()
+        ypredicted = model(x)
+        # pdb.set_trace()
+        loss = criterion(torch.log(ypredicted), y)
+        if step % 10000 == 0:
+            print('Step: ', step, loss)
+            # torch.save(model.state_dict(), f'model1_{step}.bin')
+        step += 1
+        loss.backward()
+        optimizer.step()
+
+    # evaluation
+    model.eval()
+    y_predeicted = []
+    top_50_true = 0
+    for d_x, d_y in zip(dev_data_x, dev_data_y):
+        # pdb.set_trace()
+        d_x = [i.to(device) for i in d_x]
+        # d_y = d_y.to(device)
+
+        optimizer.zero_grad()
+        ypredicted = model(d_x)
+
+        top = torch.topk(ypredicted[0], 64)
+        top_indices = top.indices.tolist()
+        if d_y[0] in top_indices:
+            top_50_true+=1
+    my_acc = top_50_true/len(dev_data_y)
+    print('My_accuracy: ', my_acc, ", epoch: ", epoch)
+    if my_acc > last_best_acc:
+        print('NEW BEST -- My_accuracy: ', my_acc, ", epoch: ", epoch)
+        last_best_acc = my_acc
+        best_model = copy.deepcopy(model)
+        torch.save(model.state_dict(), f'model_last_best_.bin')
+    if epoch % 15 == 0:
+        print('Epoch: ', epoch, step, loss)
+        # torch.save(model.state_dict(), f'model_epoch_{epoch}_.bin')
+
+
+
+# inference
+print('inference')
+inference_result = []
+for d_x, d_y in zip(dev_data_x, dev_data_y):
+    # pdb.set_trace()
+    d_x = [i.to(device) for i in d_x]
+    # d_y = d_y.to(device)
+
+    optimizer.zero_grad()
+    ypredicted = model(d_x)
+
+    top = torch.topk(ypredicted[0], 10)
+    top_indices = top.indices.tolist()
+    top_probs = top.values.tolist()
+    top_words = vocab.lookup_tokens(top_indices)
+
+    string_to_print = ''
+
+    sum_probs = 0
+    for w, p in zip(top_words, top_probs):
+        # print(top_words)
+        if '<unk>' in w:
+            continue
+        string_to_print += f"{w}:{p} "
+        sum_probs += p
+
+    if string_to_print == '':
+        inference_result.append("the:0.2 a:0.3 :0.5")
+        continue
+    unknow_prob = 1 - sum_probs
+    string_to_print += f":{unknow_prob}"
+
+    inference_result.append(string_to_print)
+
+with open('dev-0/out.tsv', 'w') as f:
+    for line in inference_result:
+        f.write(line+'\n')
+
+
+print('inference test')
+inference_result = []
+for d_x in test_set_x:
+    # pdb.set_trace()
+    d_x = [i.to(device) for i in d_x]
+    # d_y = d_y.to(device)
+
+    optimizer.zero_grad()
+    ypredicted = model(d_x)
+
+    top = torch.topk(ypredicted[0], 64)
+    top_indices = top.indices.tolist()
+    top_probs = top.values.tolist()
+    top_words = vocab.lookup_tokens(top_indices)
+
+    string_to_print = ''
+
+    sum_probs = 0
+    for w, p in zip(top_words, top_probs):
+        # print(top_words)
+        if '<unk>' in w:
+            continue
+        string_to_print += f"{w}:{p} "
+        sum_probs += p
+
+    if string_to_print == '':
+        inference_result.append("the:0.2 a:0.3 :0.5")
+        continue
+    unknow_prob = 1 - sum_probs
+    string_to_print += f":{unknow_prob}"
+
+    inference_result.append(string_to_print)
+
+with open('test-A/out.tsv', 'w') as f:
+    for line in inference_result:
+        f.write(line+'\n')
+print('All done')