neuralNetwork

run_nn.py

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+import gensim.downloader
+import torch.optim as optim
+import torch.nn as nn
+import torch
+import numpy as np
+from torch import relu, sigmoid
+
+#from timeit import default_timer as timer
+
+class NNet(nn.Module):
+    def __init__(self):
+        super(NNet, self).__init__()
+        self.ll1 = nn.Linear(100, 1000)
+        self.ll2 = nn.Linear(1000, 400)
+        self.ll3 = nn.Linear(400, 1)
+
+    def forward(self, x):
+        x = relu(self.ll1(x))
+        x = relu(self.ll2(x))
+        x = sigmoid(self.ll3(x))
+        return x
+
+def read_data(folder_name):
+    with open(f'{folder_name}/in.tsv', encoding='utf-8') as file:
+        x = [line.lower().split()[:-2] for line in file.readlines()]
+
+    with open(f'{folder_name}/expected.tsv', encoding='utf-8') as file:
+        y = [int(line.split()[0]) for line in file.readlines()]
+    return x, y
+
+
+def process_data(data, word2vec):
+    processed_data = []
+    for reddit in data:
+        words_sim = [word2vec[word] for word in reddit if word in word2vec]
+        processed_data.append(np.mean(words_sim or [np.zeros(100)], axis=0))
+    return processed_data
+
+
+def predict(folder_name, model, word_vec):
+    with open(f'{folder_name}/in.tsv', encoding='utf-8') as file:
+        x_data = [line.lower().split()[:-2] for line in file.readlines()]
+
+    x_train = process_data(x_data, word_vec)
+
+    y_predictions = []
+    with torch.no_grad():
+        for i, inputs in enumerate(x_train):
+            inputs = torch.tensor(inputs.astype(np.float32)).to(device)
+
+            y_predicted = model(inputs)
+            y_predictions.append(y_predicted > 0.5)
+    return y_predictions
+
+
+def save_predictions(folder_name, predicted_labels):
+    predictions = []
+    for pred in predicted_labels:
+        predictions.append(pred.int()[0].item())
+
+    with open(f"{folder_name}/out.tsv", "w", encoding="UTF-8") as file_out:
+        for pred in predictions:
+            file_out.writelines(f"{str(pred)}\n")
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(device)   #gpu is a bit faster here
+
+word_vectors = gensim.downloader.load("glove-wiki-gigaword-100")
+
+x_data, y_train = read_data('train')
+x_train = process_data(x_data, word_vectors)
+
+model = NNet().to(device)
+
+criterion = nn.BCELoss()
+optimizer = optim.SGD(model.parameters(), lr=0.005) #, momentum=0.9)
+
+for epoch in range(2):
+    running_loss = 0.0
+    correct = 0.
+    total = 0.
+    for i, (inputs, label) in enumerate(zip(x_train, y_train)):
+        inputs = torch.tensor(inputs.astype(np.float32)).to(device)
+        label = torch.tensor(np.array(label).astype(np.float32)).reshape(1).to(device)
+
+        # zero the parameter gradients
+        optimizer.zero_grad()
+
+        # forward + backward + optimize
+        y_predicted = model(inputs)
+        loss = criterion(y_predicted, label)
+        loss.backward()
+        optimizer.step()
+
+        # print statistics
+        running_loss += loss.item()
+        correct += ((y_predicted > 0.5) == label).type(torch.float).sum().item()
+        total += label.size(0)
+
+        if i % 10000 == 9999:  # print every 10000 mini-batches
+            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 10000:.3f}')
+            print(f'Accuracy score: {100 * correct / total} %')
+            running_loss = 0.0
+
+predicted = predict('dev-0', model, word_vectors)
+save_predictions('dev-0', predicted)
+
+predicted = predict('test-A', model, word_vectors)
+save_predictions('test-A', predicted)