# Requires nltk, nltk data
from vol import Vol
from net import Net
from trainers import Trainer
from nltk import FreqDist
from nltk.corpus import nps_chat as corpus
from string import punctuation
from random import shuffle
training_data = None
testing_data = None
network = None
t = None
N = 0
words = None
labels = None
# This list of English stop words is taken from the "Glasgow Information
# Retrieval Group". The original list can be found at
# http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words
ENGLISH_STOP_WORDS = frozenset([
"a", "about", "above", "across", "after", "afterwards", "again", "against",
"all", "almost", "alone", "along", "already", "also", "although", "always",
"am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
"any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
"around", "as", "at", "back", "be", "became", "because", "become",
"becomes", "becoming", "been", "before", "beforehand", "behind", "being",
"below", "beside", "besides", "between", "beyond", "bill", "both",
"bottom", "but", "by", "call", "can", "cannot", "cant", "co", "con",
"could", "couldnt", "cry", "de", "describe", "detail", "do", "done",
"down", "due", "during", "each", "eg", "eight", "either", "eleven", "else",
"elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone",
"everything", "everywhere", "except", "few", "fifteen", "fify", "fill",
"find", "fire", "first", "five", "for", "former", "formerly", "forty",
"found", "four", "from", "front", "full", "further", "get", "give", "go",
"had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter",
"hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his",
"how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed",
"interest", "into", "is", "it", "its", "itself", "keep", "last", "latter",
"latterly", "least", "less", "ltd", "made", "many", "may", "me",
"meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly",
"move", "much", "must", "my", "myself", "name", "namely", "neither",
"never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone",
"nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on",
"once", "one", "only", "onto", "or", "other", "others", "otherwise", "our",
"ours", "ourselves", "out", "over", "own", "part", "per", "perhaps",
"please", "put", "rather", "re", "same", "see", "seem", "seemed",
"seeming", "seems", "serious", "several", "she", "should", "show", "side",
"since", "sincere", "six", "sixty", "so", "some", "somehow", "someone",
"something", "sometime", "sometimes", "somewhere", "still", "such",
"system", "take", "ten", "than", "that", "the", "their", "them",
"themselves", "then", "thence", "there", "thereafter", "thereby",
"therefore", "therein", "thereupon", "these", "they", "thick", "thin",
"third", "this", "those", "though", "three", "through", "throughout",
"thru", "thus", "to", "together", "too", "top", "toward", "towards",
"twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us",
"very", "via", "was", "we", "well", "were", "what", "whatever", "when",
"whence", "whenever", "where", "whereafter", "whereas", "whereby",
"wherein", "whereupon", "wherever", "whether", "which", "while", "whither",
"who", "whoever", "whole", "whom", "whose", "why", "will", "with",
"within", "without", "would", "yet", "you", "your", "yours", "yourself",
"yourselves"
])
def load_data():
global N, words, labels
posts = corpus.xml_posts()[:10000]
freqs = [ FreqDist(post.text) for post in posts ]
words = list(set(word
for dist in freqs
for word in dist.keys()
if word not in ENGLISH_STOP_WORDS and
word not in punctuation))
labels = list(set([ post.get('class') for post in posts ]))
data = []
N = len(words)
for post, dist in zip(posts, freqs):
V = Vol(1, 1, N, 0.0)
for i, word in enumerate(words):
V.w[i] = dist.freq(word)
data.append((V, labels.index(post.get('class'))))
return data
def start():
global training_data, testing_data, network, t, N, labels
data = load_data()
shuffle(data)
size = int(len(data) * 0.01)
training_data, testing_data = data[size:], data[:size]
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 1, 'out_sy': 1, 'out_depth': N})
layers.append({'type': 'fc', 'num_neurons': 10, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': len(labels)})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 10, 'l2_decay': 0.0001});
def train():
global training_data, network, t
print 'In training...'
print 'k', 'time\t\t ', 'loss\t ', 'training accuracy'
print '----------------------------------------------------'
#while True:
try:
for x, y in training_data:
stats = t.train(x, y)
print stats['k'], stats['time'], stats['loss'], stats['accuracy']
except KeyboardInterrupt:
return
def test():
global testing_data, network
print 'In testing...'
right = 0
for x, y in testing_data:
network.forward(x)
right += network.getPrediction() == y
accuracy = float(right) / len(testing_data)
print accuracy
