final version

main.py

@@ -0,0 +1,102 @@
+import pandas as pd
+import numpy as np
+import torch
+import csv
+from nltk.tokenize import word_tokenize
+from gensim.models import Word2Vec
+import gensim.downloader
+
+class NeuralNetwork(torch.nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNetwork, self).__init__()
+        self.l1 = torch.nn.Linear(input_size, hidden_size)
+        self.l2 = torch.nn.Linear(hidden_size, num_classes)
+
+    def forward(self, x):
+        x = self.l1(x)
+        x = torch.relu(x)
+        x = self.l2(x)
+        x = torch.sigmoid(x)
+        return x
+
+col_names = ['content', 'id', 'label']
+
+print('Wczytanie danych...')
+# loading dataset
+train_set_features = pd.read_table('train/in.tsv.xz', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[:2])
+train_set_labels = pd.read_table('train/expected.tsv', error_bad_lines=False, quoting=csv.QUOTE_NONE, header=None, names=col_names[2:])
+dev_set = pd.read_table('dev-0/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])
+test_set = pd.read_table('test-A/in.tsv.xz', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, names=col_names[:2])
+
+print('Preprocessing danych...')
+# lowercase
+X_train = train_set_features['content'].str.lower()
+y_train = train_set_labels['label']
+
+X_dev = dev_set['content'].str.lower()
+X_test = test_set['content'].str.lower()
+
+# tokenize
+X_train = [word_tokenize(content) for content in X_train]
+X_dev = [word_tokenize(content) for content in X_dev]
+X_test = [word_tokenize(content) for content in X_test]
+
+# word2vec
+word2vec = gensim.downloader.load('word2vec-google-news-300')
+X_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_train]
+X_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_dev]
+X_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in X_test]
+
+model = NeuralNetwork(300, 600, 1)
+
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
+
+batch_size = 10
+
+print('Trenowanie modelu...')
+for epoch in range(6):
+    model.train()
+    for i in range(0, y_train.shape[0], batch_size):
+        X = X_train[i:i+batch_size]
+        X = torch.tensor(X)
+        y = y_train[i:i+batch_size]
+        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
+
+        outputs = model(X.float())
+        loss = criterion(outputs, y)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+
+print('Predykcje...')
+dev_prediction = []
+test_prediction = []
+
+model.eval()
+with torch.no_grad():
+    for i in range(0, len(X_dev), batch_size):
+        X = X_dev[i:i+batch_size]
+        X = torch.tensor(X)
+
+        outputs = model(X.float())
+
+        prediction = (outputs > 0.5)
+        dev_prediction = dev_prediction + prediction.tolist()
+
+    for i in range(0, len(X_test), batch_size):
+        X = X_test[i:i+batch_size]
+        X = torch.tensor(X)
+
+        outputs = model(X.float())
+
+        prediction = (outputs > 0.5)
+        test_prediction = test_prediction + prediction.tolist()
+
+dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
+test_prediction = np.asarray(test_prediction, dtype=np.int32)
+
+dev_prediction.tofile('./dev-0/out.tsv', sep='\n')
+test_prediction.tofile('./test-A/out.tsv', sep='\n')