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utils.py
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utils.py
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from __future__ import print_function
from collections import defaultdict
import numpy as np
import json
from operator import itemgetter
from keras.layers.embeddings import Embedding
from keras.preprocessing.sequence import pad_sequences
import re
from nltk.corpus import stopwords
from collections import namedtuple
import pickle
from keras.callbacks import Callback
import pdb
class Embedder(object):
""" Generic embedding interface.
Required: attributes g and N """
def map_tokens(self, tokens, ndim=2):
""" for the given list of tokens, return a list of GloVe embeddings,
or a single plain bag-of-words average embedding if ndim=1.
Unseen words (that's actually *very* rare) are mapped to 0-vectors. """
gtokens = [self.g[t] for t in tokens if t in self.g]
if not gtokens:
return np.zeros((1, self.N)) if ndim == 2 else np.zeros(self.N)
gtokens = np.array(gtokens)
if ndim == 2:
return gtokens
else:
return gtokens.mean(axis=0)
def map_set(self, ss, ndim=2):
""" apply map_tokens on a whole set of sentences """
return [self.map_tokens(s, ndim=ndim) for s in ss]
def pad_set(self, ss, spad, N=None):
""" Given a set of sentences transformed to per-word embeddings
(using glove.map_set()), convert them to a 3D matrix with fixed
sentence sizes - padded or trimmed to spad embeddings per sentence.
Output is a tensor of shape (len(ss), spad, N).
To determine spad, use something like
np.sort([np.shape(s) for s in s0], axis=0)[-1000]
so that typically everything fits, but you don't go to absurd lengths
to accomodate outliers.
"""
ss2 = []
if N is None:
N = self.N
for s in ss:
if spad > s.shape[0]:
if s.ndim == 2:
s = np.vstack((s, np.zeros((spad - s.shape[0], N))))
else: # pad non-embeddings (e.g. toklabels) too
s = np.hstack((s, np.zeros(spad - s.shape[0])))
elif spad < s.shape[0]:
s = s[:spad]
ss2.append(s)
return np.array(ss2)
class GloVe(Embedder):
""" A GloVe dictionary and the associated N-dimensional vector space """
def __init__(self, N=300, glovepath='glove.6B.%dd.txt'):
""" Load GloVe dictionary from the standard distributed text file.
Glovepath should contain %d, which is substituted for the embedding
dimension N. """
self.N = N
self.g = dict()
self.glovepath = glovepath % (N,)
with open(self.glovepath, 'r') as f:
for line in f:
l = line.split()
word = l[0]
self.g[word] = np.array(l[1:]).astype(float)
def hash_params(pardict):
ps = json.dumps(dict([(k, str(v)) for k, v in pardict.items()]), sort_keys=True)
h = hash(ps)
return ps, h
class Vocabulary:
""" word-to-index mapping, token sequence mapping tools and
embedding matrix construction tools """
def __init__(self, sentences, count_thres=1):
""" build a vocabulary from given list of sentences, but including
only words occuring at least #count_thres times """
# Counter() is superslow :(
vocabset = defaultdict(int)
for s in sentences:
for t in s:
vocabset[t] += 1
vocab = sorted(list(map(itemgetter(0),
filter(lambda k: itemgetter(1)(k) >= count_thres,
vocabset.items() ) )))
self.word_idx = dict((w, i + 2) for i, w in enumerate(vocab))
self.word_idx['_PAD_'] = 0
self.word_idx['_OOV_'] = 1
print('Vocabulary of %d words' % (len(self.word_idx)))
self.embcache = dict()
def embmatrix(self, emb):
""" generate index-based embedding matrix from embedding class emb
(typically GloVe); pass as weights= argument of Keras' Embedding layer """
if str(emb) in self.embcache:
return self.embcache[str(emb)]
embedding_weights = np.zeros((len(self.word_idx), emb.N))
for word, index in self.word_idx.items():
if index == 0:
embedding_weights[index, :] = np.zeros(emb.N)
try:
embedding_weights[index, :] = emb.g[word]
except KeyError:
if index == 0:
embedding_weights[index, :] = np.zeros(emb.N)
else:
embedding_weights[index, :] = np.random.uniform(-0.25, 0.25, emb.N) # 0.25 is embedding SD
self.embcache[str(emb)] = embedding_weights
return embedding_weights
def size(self):
return len(self.word_idx)
def acc(pred, gt_list, qid_list, f=None, runid=None, best=None):
n_true = 0
n_false = 0
scores = []
qid = -1
pr_label = np.argmax(pred, axis=1)
gt_arr = np.array(gt_list)
ce_losses = []
m_losses = []
for i in range(len(gt_list)):
gt = gt_list[i]
pr = pred[i][gt]
ce_loss = np.log2(pr)*-1
ce_losses.append(ce_loss)
negl = []
for j in range(5):
if j==gt:
continue
else:
negl.append(pred[i][j])
neg = np.max(negl)
m_loss = np.max([0.05+neg-pr, 0])
m_losses.append(m_loss)
mean_ce_loss = np.mean(ce_losses)
mean_m_loss = np.mean(m_losses)
acc = float(np.sum(pr_label==gt_arr)) / float(gt_arr.shape[-1])
if best is not None and runid is not None and acc > best:
f = open('results/'+runid+'_val.csv', 'wb')
if f is not None:
for idx in range(gt_arr.shape[-1]):
f.write('%s,%d,%d,' % (qid_list[idx], pr_label[idx], gt_arr[idx]))
f.write('%f,%f,%f,%f,%f\n' % (pred[idx][0], pred[idx][1], pred[idx][2],
pred[idx][3], pred[idx][4]))
return acc, mean_ce_loss, mean_m_loss
def eval_QA(pred, qid_list, gt, set_name, runid):
with open('results/'+runid+'_'+set_name+'.csv', 'wb') as f:
acc_, ce_loss_, m_loss_ = acc(pred, gt, qid_list, f=f)
print('Accuracy: %f' %(acc_))
print('CE Loss: %f'%(ce_loss_))
print('Margin Loss: %f'%(m_loss_))
return acc_
"""
Task-specific callbacks for the fit() function.
"""
class AnsSelCB(Callback):
""" A callback that monitors ACC after each epoch """
def __init__(self, val_q, val_s, val_a0, val_a1, val_a2, val_a3, val_a4, val_qid, val_pred, inputs, runid):
self.val_q = val_q
self.val_s = val_s
self.val_a0 = val_a0
self.val_a1 = val_a1
self.val_a2 = val_a2
self.val_a3 = val_a3
self.val_a4 = val_a4
self.val_qid = val_qid
self.val_pred = val_pred
self.val_inputs = inputs
self.runid = runid
self.best_acc = -1
def on_epoch_end(self, epoch, logs={}):
pred = self.model.predict(self.val_inputs, batch_size=16)
acc_, ce_loss_, m_loss_ = acc(pred, self.val_pred, self.val_qid, runid=self.runid, best=self.best_acc)
print('\tval ACC %f\tval CE_LOSS %f\tMargin_LOSS %f' % (acc_, ce_loss_, m_loss_))
if acc_ > self.best_acc:
self.best_acc = acc_
logs['acc'] = acc_
class ModelCheckpointPickle(Callback):
def __init__(self, filepath, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False,
mode='auto', period=1):
super(ModelCheckpointPickle, self).__init__()
self.monitor = monitor
self.verbose = verbose
self.filepath = filepath
self.save_best_only = save_best_only
self.save_weights_only = save_weights_only
self.period = period
self.epochs_since_last_save = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('ModelCheckpoint mode %s is unknown, '
'fallback to auto mode.' % (mode),
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
self.epochs_since_last_save += 1
if self.epochs_since_last_save >= self.period:
self.epochs_since_last_save = 0
filepath = self.filepath.format(epoch=epoch + 1, **logs)
filepath = filepath.split('.h5')[0] + '.pickle'
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
warnings.warn('Can save best model only with %s available, '
'skipping.' % (self.monitor), RuntimeWarning)
else:
if self.monitor_op(current, self.best):
print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
' saving model to %s'
% (epoch + 1, self.monitor, self.best,
current, filepath))
self.best = current
if self.save_weights_only:
#self.model.save_weights(filepath, overwrite=True)
cc = self.model.get_weights()
with open(filepath, 'wb') as f:
pickle.dump(cc, f)
else:
self.model.save(filepath, overwrite=True)
else:
if self.verbose > 0:
print('\nEpoch %05d: %s did not improve' %
(epoch + 1, self.monitor))
else:
if self.verbose > 0:
print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))
if self.save_weights_only:
self.model.save_weights(filepath, overwrite=True)
else:
self.model.save(filepath, overwrite=True)