1.1

main.py

@@ -0,0 +1,79 @@
+import torch
+import numpy as np 
+import torch.nn as nn
+import torch.optim as optim
+from torch.autograd import Variable
+from torch.nn import functional as F
+import itertools as itertools
+
+data = []
+data_out = []
+input_dim = 2
+epochs = 200
+label_to_ix = {0: 0, 1: 1}
+
+
+for sent in open('train/in.tsv', 'r').readlines()[0:1000]:
+  data.append(sent.split())
+for sent in open('train/expected.tsv', 'r').readlines()[0:1000]:
+  data_out.append(int(sent))
+
+word_to_ix = {}
+for x in data:
+    for y in x:
+        if y not in word_to_ix:
+            word_to_ix[y] = len(word_to_ix)
+
+NUM_LABELS = 2
+output_dim = len(word_to_ix)
+
+class LogisticRegression(torch.nn.Module):
+     def __init__(self, NUM_LABELS, output_dim):
+        super(LogisticRegression, self).__init__()
+        self.linear = torch.nn.Linear(output_dim, NUM_LABELS)     
+     def forward(self, x):
+        return F.log_softmax(self.linear(x), dim=1)
+
+model = LogisticRegression(NUM_LABELS, output_dim)
+loss_function = nn.NLLLoss()     
+optimizer = optim.SGD(model.parameters(), lr=0.1)
+
+def make_target(label, label_to_ix):
+    return torch.LongTensor([label_to_ix[label]])
+
+def make_bow_vector(sentence, word_to_ix):
+    vec = torch.zeros(len(word_to_ix))
+    for word in sentence:
+#        if word in word_to_ix:
+         vec[word_to_ix[word]] += 1
+    return vec.view(1, -1)
+
+for epoch in range(int(epochs)):
+    for instance, label in itertools.zip_longest(data, data_out):
+        bow_vec = make_bow_vector(instance, word_to_ix)
+        target = make_target(label, {0: 0, 1: 1})
+#        target = torch.LongTensor([label])
+        model.zero_grad()
+        log_probs = model(bow_vec)
+        loss = loss_function(log_probs, target)
+        loss.backward()
+        optimizer.step()
+        
+
+inputf = open('test-A/in.tsv', 'r')
+outputf = open('test-A/out.tsv', 'w')
+data = []
+
+
+with torch.no_grad():
+    for line in inputf.readlines():
+        bow_vector = make_bow_vector(x, word_to_ix)
+        log_probs = model(bow_vector)
+        if log_probs[0][0]> log_probs[0][1]:
+            outputf.write("0\n")
+        else:
+            outputf.write("1\n")
+
+
+
+