s443930

neurotic.py

@@ -1,53 +0,0 @@
-import torch, numpy as np
-from gensim.models import Word2Vec
-import inout as io
-from nnModel import NeuralNetworkModel, trainModel, predict
-
-
-def getX(train, dev, test):
-    Xs = []
-    for file in [train, dev, test]:
-        X = io.read(file)
-        Xs.append([x[0].split() for x in X])
-    return Xs
-
-def getY(dir):
-    return [np.array(io.read(file)) for file in dir] 
-
-def vectorize(word2vec, documents):
-    vectorized = []
-    for d in documents:
-        vectorized.append(np.mean([word2vec.wv[word] if word in word2vec.wv else np.zeros(100, dtype=float) for word in d], axis=0))
-    return np.array(vectorized)
-
-
-if __name__ == '__main__':
-    trainX, devX, testX = getX('train/in.tsv.xz', 'dev-0/in.tsv.xz', 'test-A/in.tsv.xz')
-    trainY, devY = getY(['train/expected.tsv', 'dev-0/expected.tsv'])
-
-    word2vec = Word2Vec(trainX, vector_size=100, min_count=2)
-
-    trainX = vectorize(word2vec, trainX)
-    devX = vectorize(word2vec, devX)
-    testX = vectorize(word2vec, testX)
-
-    nnModel = NeuralNetworkModel()
-    optimizer = torch.optim.SGD(nnModel.parameters(), lr = 0.1)
-
-    trainModel(nnModel, trainX, trainY, devX, devY, optimizer)
-
-    io.write(predict(nnModel, trainX), 'train/out.tsv')
-    io.write(predict(nnModel, devX), 'dev-0/out.tsv')
-    io.write(predict(nnModel, testX), 'test-A/out.tsv')
-
-    
-
-
-
-    
-    
-    
-
-
-
-   

nnModel.py

@@ -1,67 +0,0 @@
-import torch, numpy as np
-
-class NeuralNetworkModel(torch.nn.Module):
-    def __init__(self, features=100):
-        super(NeuralNetworkModel, self).__init__()
-        self.fc1 = torch.nn.Linear(features, 500)
-        self.fc2 = torch.nn.Linear(500, 1)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = torch.relu(x)
-        x = self.fc2(x)
-        x = torch.sigmoid(x)
-        return x
-
-def getMetrics(model, X_dataset, Y_dataset, criterion, batchSize):
-    loss_score = 0
-    acc_score = 0
-    items_total = 0
-    model.eval()
-    for i in range(0, Y_dataset.shape[0], batchSize):
-        X = X_dataset[i:i+batchSize]
-        X = torch.tensor(X.astype(np.float32))
-        Y = Y_dataset[i:i+batchSize]
-        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
-        Y_predictions = model(X)
-        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
-        items_total += Y.shape[0] 
-
-        loss = criterion(Y_predictions, Y)
-
-        loss_score += loss.item() * Y.shape[0] 
-    return (loss_score / items_total), (acc_score / items_total)
-
-def trainModel(model, trainX, trainY, devX, devY, optimizer, criterion=torch.nn.BCELoss(), epochs=5, batchSize=256):
-    for epoch in range(epochs):
-        loss_score = 0
-        acc_score = 0
-        items_total = 0
-        model.train()
-        for i in range(0, trainY.shape[0], batchSize):
-            X = trainX[i:i+batchSize]
-            X = torch.tensor(X.astype(np.float32))
-            Y = trainY[i:i+batchSize]
-            Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
-            Y_predictions = model(X)
-            acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
-            items_total += Y.shape[0] 
-
-            optimizer.zero_grad()
-            loss = criterion(Y_predictions, Y)
-            loss.backward()
-            optimizer.step()
-
-            loss_score += loss.item() * Y.shape[0] 
-            
-        print(f'Epoch {epoch+1}/{epochs}')
-        loss, accuracy = getMetrics(model, trainX, trainY, criterion, batchSize)
-        print(f'Train set\nloss = {loss}, accuracy = {accuracy}')
-
-def flatten(t):
-    return [str(int(item)) for sublist in t for item in sublist]
-
-def predict(model, testX):
-    testX = torch.tensor(testX.astype(np.float32))
-    with torch.no_grad():
-        return flatten(model(testX).round().tolist())