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utils.py
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utils.py
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'''
Miscellaneous functions.
'''
import numpy as np
import cPickle as pkl
from nltk.tokenize import wordpunct_tokenize
import parameters as prm
from random import randint
import math
import re
from collections import OrderedDict
from sklearn.decomposition import PCA
from theano import config
from time import time
# only print four decimals on float arrays.
np.set_printoptions(linewidth=150, formatter={'float': lambda x: "{0:0.4f}".format(x)})
# Set the random number generators' seeds for consistency
SEED = 123
np.random.seed(SEED)
def clean(txt):
'''
#remove most of Wikipedia and AQUAINT markups, such as '[[', and ']]'.
'''
txt = re.sub(r'\|.*?\]\]', '', txt) # remove link anchor
txt = txt.replace('&', ' ').replace('<',' ').replace('>',' ').replace('"', ' ').replace('\'', ' ').replace('(', ' ').replace(')', ' ').replace('.', ' ').replace('"',' ').replace(',',' ').replace(';',' ').replace(':',' ').replace('<93>', ' ').replace('<98>', ' ').replace('<99>',' ').replace('<9f>',' ').replace('<80>',' ').replace('<82>',' ').replace('<83>', ' ').replace('<84>', ' ').replace('<85>', ' ').replace('<89>', ' ').replace('=', ' ').replace('*', ' ').replace('\n', ' ').replace('!', ' ').replace('-',' ').replace('[[', ' ').replace(']]', ' ')
return txt
def BOW(words, vocab):
'''
Convert a list of words to the BoW representation.
'''
bow = {} # BoW densely represented as <vocab word idx: quantity>
for word in words:
if word in vocab:
if vocab[word] not in bow:
bow[vocab[word]] = 0.
bow[vocab[word]] += 1.
bow_v = np.asarray(bow.values())
sumw = float(bow_v.sum())
if sumw == 0.:
sumw = 1.
bow_v /= sumw
return [bow.keys(), bow_v]
def BOW2(texts, vocab, dim):
'''
Convert a list of texts to the BoW dense representation.
'''
out = np.zeros((len(texts), dim), dtype=np.int32)
mask = np.zeros((len(texts), dim), dtype=np.float32)
for i, text in enumerate(texts):
bow = BOW(wordpunct_tokenize(text), vocab)
out[i,:len(bow[0])] = bow[0]
mask[i,:len(bow[1])] = bow[1]
return out, mask
def Word2Vec_encode(texts, wemb):
out = np.zeros((len(texts), prm.dim_emb), dtype=np.float32)
for i, text in enumerate(texts):
words = wordpunct_tokenize(text)
n = 0.
for word in words:
if word in wemb:
out[i,:] += wemb[word]
n += 1.
out[i,:] /= max(1.,n)
return out
def text2idx(texts, vocab, dim, use_mask=False):
'''
Convert a list of texts to their corresponding vocabulary indexes.
'''
if use_mask:
out = -np.ones((len(texts), dim), dtype=np.int32)
mask = np.zeros((len(texts), dim), dtype=np.float32)
else:
out = -2 * np.ones((len(texts), dim), dtype=np.int32)
for i, text in enumerate(texts):
for j, symbol in enumerate(text[:dim]):
if symbol in vocab:
out[i,j] = vocab[symbol]
else:
out[i,j] = -1 # for UNKnown symbols
if use_mask:
mask[i,:j] = 1.
if use_mask:
return out, mask
else:
return out
def text2idx2(texts, vocab, dim, use_mask=False):
'''
Convert a list of texts to their corresponding vocabulary indexes.
'''
if use_mask:
out = -np.ones((len(texts), dim), dtype=np.int32)
mask = np.zeros((len(texts), dim), dtype=np.float32)
# this is both padding and unk as -1
else:
out = -2 * np.ones((len(texts), dim), dtype=np.int32)
# this is padding with -2
out_lst = []
for i, text in enumerate(texts):
words = wordpunct_tokenize(text)[:dim]
#print i, text, words
for j, word in enumerate(words):
if word in vocab:
out[i,j] = vocab[word]
else:
out[i,j] = -1 # Unknown words
out_lst.append(words)
if use_mask:
mask[i,:j] = 1.
if use_mask:
return out, mask, out_lst
else:
return out, out_lst
def idx2text(idxs, vocabinv, max_words=-1, char=False, output_unk=True):
'''
Convert list of vocabulary indexes to text.
'''
out = []
for i in idxs:
if i >= 0:
out.append(vocabinv[i])
elif i == -1:
if output_unk:
out.append('<UNK>')
else:
break
if max_words > -1:
if len(out) >= max_words:
break
if char:
return ''.join(out)
else:
return ' '.join(out)
def n_words(words, vocab):
'''
Counts the number of words that have an entry in the vocabulary.
'''
c = 0
for word in words:
if word in vocab:
c += 1
return c
def load_vocab(path, n_words=None):
t0 = time()
dic = pkl.load(open(path, "rb"))
print("Loading pickled vobac in {}".format(time()-t0))
vocab = {}
if not n_words:
n_words = len(dic.keys())
for i, word in enumerate(dic.keys()[:n_words]):
vocab[word] = i
return vocab
def numpy_floatX(data):
return np.asarray(data, dtype=config.floatX)
def np_floatX(data):
return np.asarray(data, dtype=config.floatX)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
def get_one_random_batch_idx(n, batch_size, shuffle=True):
idx_list = np.arange(n, dtype="int32")
if batch_size > n:
batch_size = n
if shuffle:
np.random.shuffle(idx_list)
idx_list = idx_list[:batch_size]
return idx_list
def get_minibatches_idx(n, minibatch_size, shuffle=False, max_samples=None):
"""
Used to shuffle the dataset at each iteration.
"""
idx_list = np.arange(n, dtype="int32")
if shuffle:
np.random.shuffle(idx_list)
if max_samples:
idx_list = idx_list[:max_samples]
n = max_samples
minibatches = []
minibatch_start = 0
for i in range(n // minibatch_size):
minibatches.append(idx_list[minibatch_start:
minibatch_start + minibatch_size])
minibatch_start += minibatch_size
if (minibatch_start != n):
# Make a minibatch out of what is left
minibatches.append(idx_list[minibatch_start:])
return zip(range(len(minibatches)), minibatches)
def zipp(params, tparams):
"""
When we reload the model. Needed for the GPU stuff.
"""
for kk, vv in params.iteritems():
tparams[kk].set_value(vv)
def unzip(zipped):
"""
When we pickle the model. Needed for the GPU stuff.
"""
new_params = OrderedDict()
for kk, vv in zipped.iteritems():
new_params[kk] = vv.get_value()
return new_params
def lst2matrix(lst):
maxdim = len(max(lst, key=len))
out = -np.ones((len(lst), maxdim), dtype=np.int32)
for i, item in enumerate(lst):
out[i, :min(len(item), maxdim)] = item[:maxdim]
return out
def load_params(path, params):
pp = np.load(path)
for kk, vv in params.iteritems():
if kk in pp:
if params[kk].shape == pp[kk].shape:
params[kk] = pp[kk]
else:
print 'The shape of layer', kk, params[kk].shape, 'is different from shape of the stored layer with the same name', pp[kk].shape, '.'
else:
print '%s is not in the archive' % kk
return params
def load_wemb(params, vocab):
wemb = pkl.load(open(prm.wordemb_path, 'rb'))
dim_emb_orig = wemb.values()[0].shape[0]
W = 0.01 * np.random.randn(prm.n_words, dim_emb_orig).astype(config.floatX)
for word, pos in vocab.items():
if word in wemb:
W[pos, :] = wemb[word]
if prm.dim_emb < dim_emb_orig:
pca = PCA(n_components=prm.dim_emb, copy=False, whiten=True)
W = pca.fit_transform(W)
params['W'] = W
return params
def itemlist(tparams):
return [vv for kk, vv in tparams.iteritems()]
def init_tparams(params):
tparams = OrderedDict()
for kk, pp in params.iteritems():
tparams[kk] = params[kk]
return tparams
def ortho_weight(ndim):
W = np.random.randn(ndim, ndim)
u, s, v = np.linalg.svd(W)
return u.astype(config.floatX)
def matrix(dim):
return np.concatenate([ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim)], axis=1)
def init_params(options):
params = OrderedDict()
exclude_params = {}
params['W'] = 0.01 * np.random.randn(prm.n_words, prm.dim_emb).astype(config.floatX) # vocab to word embeddings
params['UNK'] = 0.01 * np.random.randn(1, prm.dim_emb).astype(config.floatX) # vector for unknown words.
n_features = [prm.dim_emb, ] + prm.filters_query
for i in range(len(prm.filters_query)):
params['Ww_att_q' + str(i)] = 0.01 * np.random.randn(n_features[i + 1], n_features[i], 1,
prm.window_query[i]).astype(config.floatX)
params['bw_att_q' + str(i)] = np.zeros((n_features[i + 1],)).astype(config.floatX) # bias score
params['Aq'] = 0.01 * np.random.randn(n_features[-1], prm.dim_proj).astype(config.floatX) # score
n_hidden_actor = [prm.dim_proj] + prm.n_hidden_actor + [2]
for i in range(len(n_hidden_actor) - 1):
params['V' + str(i)] = 0.01 * np.random.randn(n_hidden_actor[i], n_hidden_actor[i + 1]).astype(
config.floatX) # score
params['bV' + str(i)] = np.zeros((n_hidden_actor[i + 1],)).astype(config.floatX) # bias score
# set initial bias towards not selecting words.
params['bV' + str(i)] = np.array([10., 0.]).astype(config.floatX) # bias score
n_hidden_critic = [prm.dim_proj] + prm.n_hidden_critic + [1]
for i in range(len(n_hidden_critic) - 1):
params['C' + str(i)] = 0.01 * np.random.randn(n_hidden_critic[i], n_hidden_critic[i + 1]).astype(
config.floatX) # score
params['bC' + str(i)] = np.zeros((n_hidden_critic[i + 1],)).astype(config.floatX) # bias score
n_features = [prm.dim_emb, ] + prm.filters_cand
for i in range(len(prm.filters_cand)):
params['Ww_att_c_0_' + str(i)] = 0.01 * np.random.randn(n_features[i + 1], n_features[i], 1,
prm.window_cand[i]).astype(config.floatX)
params['bw_att_c_0_' + str(i)] = np.zeros((n_features[i + 1],)).astype(config.floatX) # bias score
params['Ad'] = 0.01 * np.random.randn(n_features[-1], prm.dim_proj).astype(config.floatX) # score
params['bAd'] = np.zeros((prm.dim_proj,)).astype(config.floatX) # bias score
if prm.fixed_wemb:
exclude_params['W'] = True
return params, exclude_params