word_to_num = du.invert_dict(num_to_word)
fraction_lost = float(
sum([
vocab['count'][word] for word in vocab.index
if (not word in word_to_num) and (not word == "UUUNKKK")
]))
fraction_lost /= sum([
vocab['count'][word] for word in vocab.index if (not word == "UUUNKKK")
])
print "Retained %d words from %d (%.02f%% of all tokens)" % (
vocabsize, len(vocab), 100 * (1 - fraction_lost))
docs = du.load_dataset('data/lm/ptb-train.txt')
S_train = du.docs_to_indices(docs, word_to_num)
X_train, Y_train = du.seqs_to_lmXY(S_train)
docs = du.load_dataset('data/lm/ptb-dev.txt')
S_dev = du.docs_to_indices(docs, word_to_num)
X_dev, Y_dev = du.seqs_to_lmXY(S_dev)
docs = du.load_dataset('data/lm/ptb-test.txt')
S_test = du.docs_to_indices(docs, word_to_num)
X_test, Y_test = du.seqs_to_lmXY(S_test)
#print " ".join(d[0] for d in docs[7])
#print " ".join(num_to_word[i] for i in S_test[7])
#For random samples from N(mu, sigma^2), use:
# sigma * np.random.randn(...) + mu
num_to_word = dict(enumerate(vocab.index[:vocabsize]))
word_to_num = du.invert_dict(num_to_word)
##
# Below needed for 'adj_loss': DO NOT CHANGE
fraction_lost = float(sum([vocab['count'][word] for word in vocab.index
if (not word in word_to_num)
and (not word == "UUUNKKK")]))
fraction_lost /= sum([vocab['count'][word] for word in vocab.index
if (not word == "UUUNKKK")])
print "Retained %d words from %d (%.02f%% of all tokens)" % (vocabsize, len(vocab),
100*(1-fraction_lost))
# Load the training set
docs = du.load_dataset('data/lm/ptb-train.txt')
S_train = du.docs_to_indices(docs, word_to_num)
X_train, Y_train = du.seqs_to_lmXY(S_train)
# Load the dev set (for tuning hyperparameters)
docs = du.load_dataset('data/lm/ptb-dev.txt')
S_dev = du.docs_to_indices(docs, word_to_num)
X_dev, Y_dev = du.seqs_to_lmXY(S_dev)
# Load the test set (final evaluation only)
docs = du.load_dataset('data/lm/ptb-test.txt')
S_test = du.docs_to_indices(docs, word_to_num)
X_test, Y_test = du.seqs_to_lmXY(S_test)
# Display some sample data
#print " ".join(d[0] for d in docs[7])
#print S_test[7]
#
if __name__ == "__main__":
# Load the vocabulary
vocab = pd.read_table("data/dictionary", header=None, sep="\s+",
index_col=0, names=['count', 'freq'], )
# Choose how many top words to keep
vocabsize = len(vocab)
print 'vocabulary size %d' % vocabsize
#vocabsize = 2000
num_to_word = dict(enumerate(vocab.index[:vocabsize]))
word_to_num = du.invert_dict(num_to_word)
print 'load dictionary done'
docs = du.load_dataset('data/rnn_input_train')
S_train = du.docs_to_indices(docs, word_to_num)
X_train, Y_train = du.seqs_to_lmXY(S_train)
docs = du.load_dataset('data/rnn_input_test')
S_train = du.docs_to_indices(docs, word_to_num)
X_dev, Y_dev = du.seqs_to_lmXY(S_train)
#X_train = X_train[:3000]
#Y_train = Y_train[:3000]
print 'load data done'
print 'number of training data %d' % len(Y_train)
method = "RNNPTONE"
hdim = 40 # dimension of hidden layer = dimension of word vectors
#random.seed(10)
nepoch = 1
N = nepoch * len(Y_train)
k = 5 # minibatch size