105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# noinspection PyUnresolvedReferences
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import csv
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import torch
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import numpy as np
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import pandas as pd
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from nltk.util import pr
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from gensim import downloader
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from nltk.tokenize import word_tokenize
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BATCH_SIZE = 5
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self):
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dim = 200
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super(NeuralNetworkModel, self).__init__()
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self.one = torch.nn.Linear(dim, 500)
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self.two = torch.nn.Linear(500, 1)
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def forward(self, x):
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x = self.one(x)
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x = torch.relu(x)
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x = self.two(x)
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x = torch.sigmoid(x)
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return x
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def read_data():
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x_labels = (pd.read_csv('in-header.tsv', sep='\t')).columns
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y_labels = (pd.read_csv('out-header.tsv', sep='\t')).columns
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x_train = pd.read_table('train/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
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y_train = pd.read_table('train/expected.tsv', header=None, quoting=csv.QUOTE_NONE, names=y_labels)
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x_dev = pd.read_table('dev-0/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
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x_test = pd.read_table('test-A/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
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# remove some rows for faster development
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remove_n = 200000
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drop_indices = np.random.choice(x_train.index, remove_n, replace=False)
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x_train = x_train.drop(drop_indices)
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y_train = y_train.drop(drop_indices)
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return x_labels, y_labels, x_train, y_train, x_dev, x_test
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def process_data(x_labels, y_labels, x_train, y_train, x_dev, x_test):
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x_train = x_train[x_labels[0]].str.lower()
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x_dev = x_dev[x_labels[0]].str.lower()
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x_test = x_test[x_labels[0]].str.lower()
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y_train = y_train[y_labels[0]]
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x_train = [word_tokenize(x) for x in x_train]
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x_dev = [word_tokenize(x) for x in x_dev]
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x_test = [word_tokenize(x) for x in x_test]
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w2v = downloader.load('glove-wiki-gigaword-200')
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x_train = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_train]
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x_dev = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_dev]
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x_test = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_test]
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return x_train, y_train, x_dev, x_test
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def predict(model, x_data, out_path):
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y_out = []
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model.eval()
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with torch.no_grad():
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for i in range(0, len(x_data), BATCH_SIZE):
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x = x_data[i:i + BATCH_SIZE]
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x = torch.tensor(x)
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pred = nn_model(x.float())
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y_pred = (pred > 0.5)
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y_out.extend(y_pred)
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y_data = np.asarray(y_out, dtype=np.int32)
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pd.DataFrame(y_data).to_csv(out_path, sep='\t', index=False, header=False)
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if __name__ == "__main__":
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x_labels, y_labels, x_train, y_train, x_dev, x_test = read_data()
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x_train, y_train, x_dev, x_test = process_data(x_labels, y_labels, x_train, y_train, x_dev, x_test)
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nn_model = NeuralNetworkModel()
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(nn_model.parameters(), lr=0.1)
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for epoch in range(5):
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nn_model.train()
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for i in range(0, y_train.shape[0], BATCH_SIZE):
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X = x_train[i:i + BATCH_SIZE]
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X = torch.tensor(X)
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Y = y_train[i:i + BATCH_SIZE]
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Y = torch.tensor(Y.astype(np.float32).to_numpy()).reshape(-1, 1)
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Y_predictions = nn_model(X.float())
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loss = criterion(Y_predictions, Y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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predict(nn_model, x_dev, 'dev-0/out.tsv')
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predict(nn_model, x_test, 'test-A/out.tsv') |