From 4b7e29dbf1ac173d2fbea52c85f127e8c28cf88e Mon Sep 17 00:00:00 2001
From: rizwan09 <rizwan.incipient@gmail.com>
Date: Mon, 17 Apr 2017 13:43:30 -0400
Subject: [PATCH] ngram

---
 nGram.py | 122 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 nGram.py

diff --git a/nGram.py b/nGram.py
new file mode 100644
index 0000000..44b2983
--- /dev/null
+++ b/nGram.py
@@ -0,0 +1,122 @@
+###############################################################################
+# Author: Md Rizwan Parvez
+# Project: LanModeledProgramGeneration
+# Date Created: 4/1/2017
+# Some codes are from Wasi Ahmad main.py
+# File Description: This is the main script from where all experimental
+# execution begins.
+###############################################################################
+import torch.nn as nn
+import util, data #, helper, train
+import torch, random
+from torch import optim
+# import model_rnd
+import torch.nn.functional as F
+import time, math
+# from encoder import EncoderRNN
+# from embedding_layer import Embedding_Drop_Layer
+from torch.autograd import Variable
+import train
+args = util.get_args()
+# args = get_args()
+# Set the random seed manually for reproducibility.
+
+print ('='*90, '\nWARNING:::: please fix  pickle_file_name, nepochs, dictionary lower case, batchify, data path, batch size, trim_data for non divisible by batch size,  Glove embedding initialization (model_rnd.py 45)!!!!!\n', '='*89)
+print ('='*90, '\nWARNING:::: if you have insatnce based lstm you need to init model for each batch!!!!!\n', '='*89)
+
+torch.manual_seed(args.seed)
+if torch.cuda.is_available():
+    if not args.cuda:
+        print('='*90, "\nWARNING: You have a CUDA device, so you should probably run with --cuda\n", '='*89)
+    else:
+        torch.cuda.manual_seed(args.seed)
+###############################################################################
+# Load data
+###############################################################################
+
+#### fix this
+# corpus = data.Corpus(args.data)
+corpus = data.Corpus(args)
+print('Train set size = ', len(corpus.train_c))
+# print('Development set size = ', len(corpus.dev))
+# print('Test set size = ', len(corpus.test))
+print('Vocabulary size = ', len(corpus.dictionary))
+
+###############################################################################
+# load_emb
+###############################################################################
+#### fix this
+#file_name = 'train_corpus_3' + 'embeddings_index.p'
+file_name = 'soft_data.p'
+embeddings_index = util.get_initial_embeddings(file_name, args.data_path, args.word_vectors_directory, args.Glove_filename, corpus.dictionary)
+#print('Number of OOV words = ', len(corpus.dictionary) - len(embeddings_index))
+
+###############################################################################
+# batchify
+###############################################################################
+#### fix this
+# train_batches = util.batchify(corpus.train, args.batch_size, args.cuda)
+
+
+trigrams = [([corpus.train_c[i], corpus.train_c[i + 1]], corpus.train_c[i + 2])
+            for i in range(len(corpus.train_c) - 2)]
+# print the first 3, just so you can see what they look like
+print(trigrams[:3])
+
+vocab = len(corpus.dictionary)
+word_to_ix = corpus.dictionary.word2idx
+EMBEDDING_DIM = 300
+CONTEXT_SIZE = 2
+class NGramLanguageModeler(nn.Module):
+
+    def __init__(self, vocab_size, embedding_dim, context_size):
+        super(NGramLanguageModeler, self).__init__()
+        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
+        self.linear1 = nn.Linear(context_size * embedding_dim, 128)
+        self.linear2 = nn.Linear(128, vocab_size)
+
+    def forward(self, inputs):
+        embeds = self.embeddings(inputs).view((1, -1))
+        out = F.relu(self.linear1(embeds))
+        out = self.linear2(out)
+        log_probs = F.log_softmax(out)
+        return log_probs
+
+losses = []
+loss_function = nn.NLLLoss()
+model = NGramLanguageModeler((vocab), EMBEDDING_DIM, CONTEXT_SIZE)
+optimizer = optim.SGD(model.parameters(), lr=0.001)
+print(len (trigrams))
+for epoch in range(10):
+    total_loss = torch.Tensor([0])
+    c= 0;
+    for context, target in trigrams:
+        c+=1;
+        # Step 1. Prepare the inputs to be passed to the model (i.e, turn the words
+        # into integer indices and wrap them in variables)
+        # context_idxs = [word_to_ix[w] for w in context]
+        context_idxs = context
+        context_var = Variable(torch.LongTensor(context_idxs))
+
+        # Step 2. Recall that torch *accumulates* gradients. Before passing in a
+        # new instance, you need to zero out the gradients from the old instance
+        model.zero_grad()
+
+        # Step 3. Run the forward pass, getting log probabilities over next
+        # words
+        log_probs = model(context_var)
+
+        # Step 4. Compute your loss function. (Again, Torch wants the target
+        # word wrapped in a variable)
+        loss = loss_function(log_probs, Variable(
+            torch.LongTensor([target])))
+
+        # Step 5. Do the backward pass and update the gradient
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.data
+        if(c/5000==0):print('epoch: ', epoch, ' step: ', c,  " loss: ", loss.data, ' total so far: ', total_loss)
+    losses.append(total_loss)
+    print('epoch: ', epoch, " loss: ", total_loss)
+print(losses)  # The loss decreased every iteration over the training data!
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