80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
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import torch
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import numpy as np
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import torch.nn as nn
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import torch.optim as optim
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from torch.autograd import Variable
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from torch.nn import functional as F
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import itertools as itertools
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data = []
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data_out = []
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input_dim = 2
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epochs = 200
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label_to_ix = {0: 0, 1: 1}
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for sent in open('train/in.tsv', 'r').readlines()[0:1000]:
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data.append(sent.split())
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for sent in open('train/expected.tsv', 'r').readlines()[0:1000]:
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data_out.append(int(sent))
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word_to_ix = {}
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for x in data:
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for y in x:
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if y not in word_to_ix:
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word_to_ix[y] = len(word_to_ix)
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NUM_LABELS = 2
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output_dim = len(word_to_ix)
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class LogisticRegression(torch.nn.Module):
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def __init__(self, NUM_LABELS, output_dim):
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super(LogisticRegression, self).__init__()
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self.linear = torch.nn.Linear(output_dim, NUM_LABELS)
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def forward(self, x):
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return F.log_softmax(self.linear(x), dim=1)
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model = LogisticRegression(NUM_LABELS, output_dim)
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loss_function = nn.NLLLoss()
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optimizer = optim.SGD(model.parameters(), lr=0.1)
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def make_target(label, label_to_ix):
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return torch.LongTensor([label_to_ix[label]])
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def make_bow_vector(sentence, word_to_ix):
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vec = torch.zeros(len(word_to_ix))
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for word in sentence:
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# if word in word_to_ix:
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vec[word_to_ix[word]] += 1
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return vec.view(1, -1)
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for epoch in range(int(epochs)):
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for instance, label in itertools.zip_longest(data, data_out):
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bow_vec = make_bow_vector(instance, word_to_ix)
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target = make_target(label, {0: 0, 1: 1})
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# target = torch.LongTensor([label])
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model.zero_grad()
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log_probs = model(bow_vec)
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loss = loss_function(log_probs, target)
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loss.backward()
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optimizer.step()
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inputf = open('test-A/in.tsv', 'r')
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outputf = open('test-A/out.tsv', 'w')
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data = []
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with torch.no_grad():
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for line in inputf.readlines():
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bow_vector = make_bow_vector(x, word_to_ix)
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log_probs = model(bow_vector)
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if log_probs[0][0]> log_probs[0][1]:
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outputf.write("0\n")
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else:
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outputf.write("1\n")
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