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b/script.py new file mode 100644 index 0000000..c5ee85f --- /dev/null +++ b/script.py @@ -0,0 +1,114 @@ +import gensim.downloader +from nltk.tokenize import word_tokenize +import numpy as np +import pandas as pd +import torch + + +word2vec = gensim.downloader.load("word2vec-google-news-300") + + +def get_word2vec(document): + return np.mean( + [word2vec[token] for token in document if token in word2vec] or [np.zeros(300)], + axis=0, + ) + + +class LogNetwork(torch.nn.Module): + def __init__(self, input_size, hidden_size, num_classes): + super(LogNetwork, self).__init__() + self.fc1 = torch.nn.Linear(input_size, hidden_size) + self.fc2 = torch.nn.Linear(hidden_size, num_classes) + + def forward(self, x): + x = self.fc1(x) + x = torch.relu(x) + x = self.fc2(x) + x = torch.sigmoid(x) + return x + + +train_x = pd.read_table( + "train/in.tsv.xz", + error_bad_lines=False, + header=None, + quoting=3, + usecols=["content"], + names=["content", "id"], + nrows=225000, +) +train_y = pd.read_table( + "train/expected.tsv", + error_bad_lines=False, + header=None, + quoting=3, + usecols=["label"], + names=["label"], + nrows=225000, +) +dev_x = pd.read_table( + "dev-0/in.tsv.xz", + error_bad_lines=False, + header=None, + quoting=3, + usecols=["content"], + names=["content", "id"], +) +test_x = pd.read_table( + "test-A/in.tsv.xz", + error_bad_lines=False, + header=None, + quoting=3, + usecols=["content"], + names=["content", "id"], +) + +train_x = [word_tokenize(row) for row in train_x.content.str.lower()] +dev_x = [word_tokenize(row) for row in dev_x.content.str.lower()] +test_x = [word_tokenize(row) for row in test_x.content.str.lower()] + +train_x = [get_word2vec(document) for document in train_x] +dev_x = [get_word2vec(document) for document in dev_x] +test_x = [get_word2vec(document) for document in test_x] + +network = LogNetwork(300, 600, 1) +criterion = torch.nn.BCELoss() +optim = torch.optim.SGD(network.parameters(), lr=0.01) +epochs = 7 +batch_size = 15 + +for _ in range(epochs): + network.train() + for i in range(0, train_y.shape[0], batch_size): + x = train_x[i : i + batch_size] + x = torch.tensor(x) + y = train_y[i : i + batch_size] + y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1) + outputs = network(x.float()) + loss = criterion(outputs, y) + optim.zero_grad() + loss.backward() + optim.step() + + +dev_pred = [] +test_pred = [] +with torch.no_grad(): + for i in range(0, len(dev_x), batch_size): + x = dev_x[i : i + batch_size] + x = torch.tensor(x) + outputs = network(x.float()) + prediction = outputs >= 0.5 + dev_pred += prediction.tolist() + for i in range(0, len(test_x), batch_size): + x = test_x[i : i + batch_size] + x = torch.tensor(x) + outputs = network(x.float()) + prediction = outputs >= 0.5 + test_pred += prediction.tolist() + +np.asarray(dev_pred, dtype=np.int32).tofile("./dev-0/out.tsv", sep="\n") +np.asarray(test_pred, dtype=np.int32).tofile("./test-A/out.tsv", sep="\n") + +print("Saved") diff --git 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