lstm

run.py

@@ -20,8 +20,9 @@ import csv
 import itertools
 from os.path import exists
 
-vocab_size = 30000
+vocab_size = 15000
-embed_size = 150
+embed_size = 128
+lstm_size = 128
 
 # funkcje pomocnicze
 def clean(text):
@@ -46,17 +47,29 @@ def get_word_lines_from_data(d):
         yield get_words_from_line(line)
 
 class Model(torch.nn.Module):
-    def __init__(self, vocabulary_size, embedding_size):
+    def __init__(self, vocabulary_size, embedding_size, lstm_size):
         super(Model, self).__init__()
-        self.model = torch.nn.Sequential(
+        self.lstm_size = lstm_size
-            torch.nn.Embedding(vocabulary_size, embedding_size),
+        self.embedding_dim = embedding_size
-            torch.nn.Linear(embedding_size, vocabulary_size),
+        self.num_layers = 3
-            torch.nn.Softmax()
+
+        self.embedding = torch.nn.Embedding(
+            num_embeddings=vocab_size,
+            embedding_dim=self.embedding_dim,
         )
+        self.lstm = torch.nn.LSTM(
+            input_size=self.lstm_size,
+            hidden_size=self.lstm_size,
+            num_layers=self.num_layers,
+            dropout=0.2,
+        )
+        self.fc = torch.nn.Linear(self.lstm_size, vocab_size)
 
-    def forward(self, x):
+    def forward(self, x, prev_state = None):
-        return self.model(x)
+        embed = self.embedding(x)
-
+        output, state = self.lstm(embed, prev_state)
+        logits = self.fc(output)
+        return logits, state
 
 class Trigrams(torch.utils.data.IterableDataset):
     def __init__(self, data, vocabulary_size):
@@ -82,37 +95,41 @@ class Trigrams(torch.utils.data.IterableDataset):
 
 
 # ładowanie danych treningowych
-train_in = pd.read_csv("gdrive/MyDrive/train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)[[6, 7]]
+train_in = pd.read_csv("gdrive/MyDrive/train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=20000)[[6, 7]]
-train_expected = pd.read_csv("gdrive/MyDrive/train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)
+train_expected = pd.read_csv("gdrive/MyDrive/train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=20000)
 train_data = pd.concat([train_in, train_expected], axis=1)
 train_data = train_data[6] + train_data[0] + train_data[7]
 train_data = train_data.apply(clean)
 train_dataset = Trigrams(train_data, vocab_size)
+train_dataset_rev = Trigrams(train_data.iloc[::-1], vocab_size)
 
 # trenowanie/wczytywanie modelu
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
-model = Model(vocab_size, embed_size).to(device)
+model = Model(vocab_size, embed_size, lstm_size).to(device)
 print(device)
+
 if(not exists('model1.bin')):
     data = DataLoader(train_dataset, batch_size=8000)
-    optimizer = torch.optim.Adam(model.parameters())
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
-    criterion = torch.nn.NLLLoss()
+    criterion =  torch.nn.CrossEntropyLoss()
 
     model.train()
     step = 0
-    for i in range(2):
+    for i in range(1):
       print(f"EPOCH {i}=========================")
       for x, y in data:
+          optimizer.zero_grad()
           x = x.to(device)
           y = y.to(device)
-          optimizer.zero_grad()
+
-          ypredicted = model(x)
+          y_pred, state_h = model(x)
-          loss = criterion(torch.log(ypredicted), y)
+          loss = criterion(y_pred, y)
+
+          loss.backward()
+          optimizer.step()
           if step % 100 == 0:
               print(step, loss)
           step += 1
-          loss.backward()
-          optimizer.step()
 
     torch.save(model.state_dict(), 'model1.bin')
 else:
@@ -122,47 +139,115 @@ else:
 
 vocab = train_dataset.vocab
 
-def predict(tokens):
+# trenowanie/wczytywanie modelu
-    ixs = torch.tensor(vocab.forward(tokens)).to(device)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model_b = Model(vocab_size, embed_size, lstm_size).to(device)
+print(device)
+
+if(not exists('model1_b.bin')):
+    data_b = DataLoader(train_dataset_rev, batch_size=8000)
+    optimizer = torch.optim.Adam(model_b.parameters(), lr=0.001)
+    criterion =  torch.nn.CrossEntropyLoss()
+
+    model_b.train()
+    step = 0
+    for i in range(1):
+      print(f"EPOCH {i}=========================")
+      for x, y in data:
+          optimizer.zero_grad()
+          x = x.to(device)
+          y = y.to(device)
+
+          y_pred, state_h = model_b(x)
+          loss = criterion(y_pred, y)
+
+          loss.backward()
+          optimizer.step()
+          if step % 100 == 0:
+              print(step, loss)
+          step += 1
+
+    torch.save(model_b.state_dict(), 'model1_b.bin')
+else:
+    print("Loading model1")
+    model_b.load_state_dict(torch.load('model1_b.bin'))
+
+import numpy as np 
+
+def predict(tokens_left, tokens_right):
+    ixs = torch.tensor(vocab.forward(tokens_left)).to(device)
+    ixs_r = torch.tensor(vocab.forward(tokens_right)).to(device)
+
     out = model(ixs)
+    out_b = model_b(ixs_r)
+    
     top = torch.topk(out[0], 8)
-    top_indices = top.indices.tolist()
+    top_b = torch.topk(out_b[0], 8)
-    top_probs = top.values.tolist()
+    top_indices = top.indices.tolist()[0]
-    top_words = vocab.lookup_tokens(top_indices)
+    top_probs = top.values.tolist()[0]
+    top_indices_b = top_b.indices.tolist()[0]
+    top_probs_b = top_b.values.tolist()[0]
+
+
+    raw_result = []
+    for ind in set(top_indices + top_indices_b):
+      prob = 0
+      if(ind in top_indices):
+        prob += top_probs[top_indices.index(ind)]
+      if(ind in top_indices_b):
+        prob += top_probs_b[top_indices_b.index(ind)]
+      raw_result += [[vocab.lookup_token(ind), prob]]
+    raw_result = list(filter(lambda x: x[0] != "<unk>", raw_result))
+    raw_result = sorted(raw_result, key=lambda x: -x[1])[:8]
+
+    words = [x[0] for x in raw_result]
+    probs = [x[1] for x in raw_result]
+
+    probs_x = np.exp(probs)/sum(np.exp(probs))
     result = ""
-    for word, prob in list(zip(top_words, top_probs)):
+    for word, prob in list(zip(words,probs_x)):
-                result += f"{word}:{prob} "
+        result += f"{word}:{prob} "
-    # result  += f':0.01'
+    result += ":0.3"
+    result = result.rstrip()
     return result
 
 from nltk import word_tokenize
 def predict_file(result_path, data):
     with open(result_path, "w+", encoding="UTF-8") as f:
-        for row in data:
+        for index, row in data.iterrows():
             result = {}
             before = None
-            for before in get_words_from_line(clean(str(row)), False):
+            after = None
+            for after in get_words_from_line(clean(str(row[7])), False):
+              after = [after]
+              break
+            for before in get_words_from_line(clean(str(row[6])), False):
               pass
             before = [before]
-            print(before)
+            if(len(before) < 1 and len(after) < 1):
-            if(len(before) < 1):
+              result = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
-                result = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
             else:
-                result = predict(before)
+                result = predict(before, after)
             result = result.strip()
-            f.write(result + "\n")
             print(result)
+            f.write(result + "\n")
 
 
-dev_data = pd.read_csv("gdrive/MyDrive/dev-0/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6]
+dev_data = pd.read_csv("gdrive/MyDrive/dev-0/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)
-dev_data = dev_data.apply(clean)
+dev_data[6] = dev_data[6].apply(clean)
+dev_data[7] = dev_data[7].apply(clean)
+
 predict_file("gdrive/MyDrive/dev-0/out.tsv", dev_data)
 
-test_data = pd.read_csv("gdrive/MyDrive/test-A/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6]
+test_data = pd.read_csv("gdrive/MyDrive/test-A/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)
-test_data = test_data.apply(clean)
+test_data[6] = test_data[6].apply(clean)
+test_data[7] = test_data[7].apply(clean)
 predict_file("gdrive/MyDrive/test-A/out.tsv", test_data)
 
+# !wget https://gonito.net/get/bin/geval
+# !chmod 777 geval
+
+!rm -r dev-0
+
 !cp -r gdrive/MyDrive/dev-0 dev-0
 !./geval -t dev-0 --metric PerplexityHashed
-
-!rm -r dev-0