def setup_module ():
global weights_nb
global alltags
global allwords
counters = most_common.get_tags(TRAIN_FILE)
for counts in counters.values():
allwords.update(set(counts.keys()))
class_counts = most_common.get_class_counts(counters)
weights_nb = naivebayes.learnNBWeights(counters,class_counts,allwords)
alltags = preproc.getAllTags(TRAIN_FILE)
def setup_module():
global weights_nb
global alltags
global allwords
counters = most_common.get_tags(TRAIN_FILE)
for counts in counters.values():
allwords.update(set(counts.keys()))
class_counts = most_common.get_class_counts(counters)
weights_nb = naivebayes.learnNBWeights(counters, class_counts, allwords)
alltags = preproc.getAllTags(TRAIN_FILE)
def get_HMM_weights(trainfile):
"""Train a set of of log-prob weights using HMM transition model
Parameters:
trainfile -- The name of the file to train weights
Returns:
weights -- Weights dict with log-prob of transition and emit features
"""
# compute naive bayes weights
# convert nb weights to hmm weights
counters = most_common.get_tags(trainfile)
allwords = set()
for counts in counters.values():
allwords.update(set(counts.keys()))
class_counts = most_common.get_class_counts(counters)
nb_weights = naivebayes.learnNBWeights(counters, class_counts, allwords,
0.001)
trans_cnt = defaultdict(Counter)
with open(trainfile) as instances:
prev_tag = START_TAG
for line in instances:
if len(line.rstrip()) == 0:
trans_cnt[prev_tag][END_TAG] += 1
prev_tag = START_TAG
continue
parts = line.rstrip().split()
if len(parts) > 1:
cur_tag = parts[1]
else:
cur_tag = UNKNOWN
trans_cnt[prev_tag][cur_tag] += 1
prev_tag = cur_tag
if prev_tag != START_TAG:
trans_cnt[prev_tag][END_TAG] += 1
hmm_weights = defaultdict(lambda: -1000.)
for key in nb_weights:
tag = key[0]
word = key[1]
hmm_weights[(tag, word, EMIT)] = nb_weights[key]
for prev_tag in trans_cnt:
cnt = trans_cnt[prev_tag]
total_pairs = sum(cnt.values())
for cur_tag in cnt:
hmm_weights[(cur_tag, prev_tag,
TRANS)] = np.log(cnt[cur_tag]) - np.log(total_pairs)
return hmm_weights
def get_HMM_weights(trainfile):
"""Train a set of of log-prob weights using HMM transition model
Parameters:
trainfile -- The name of the file to train weights
Returns:
weights -- Weights dict with log-prob of transition and emit features
"""
# compute naive bayes weights
# convert nb weights to hmm weights
counters = most_common.get_tags(trainfile)
allwords = set()
for counts in counters.values():
allwords.update(set(counts.keys()))
class_counts = most_common.get_class_counts(counters)
nb_weights = naivebayes.learnNBWeights(counters,class_counts,allwords,0.001)
trans_cnt = defaultdict(Counter)
with open(trainfile) as instances:
prev_tag = START_TAG
for line in instances:
if len(line.rstrip()) == 0:
trans_cnt[prev_tag][END_TAG] += 1
prev_tag = START_TAG
continue
parts = line.rstrip().split()
if len(parts) >1:
cur_tag = parts[1]
else:
cur_tag = UNKNOWN
trans_cnt[prev_tag][cur_tag] += 1
prev_tag = cur_tag
if prev_tag != START_TAG:
trans_cnt[prev_tag][END_TAG] += 1
hmm_weights = defaultdict(lambda : -1000.)
for key in nb_weights:
tag = key[0]
word = key[1]
hmm_weights[(tag, word, EMIT)] = nb_weights[key]
for prev_tag in trans_cnt:
cnt = trans_cnt[prev_tag]
total_pairs = sum(cnt.values())
for cur_tag in cnt:
hmm_weights[(cur_tag, prev_tag, TRANS)] = np.log(cnt[cur_tag]) - np.log(total_pairs)
return hmm_weights
def get_HMM_weights(trainfile):
"""Train a set of of log-prob weights using HMM transition model
Parameters:
trainfile -- The name of the file to train weights
Returns:
weights -- Weights dict with log-prob of transition and emit features
ngrams("I really like", 2)
(I, really)
(really, like)
(end_tag,'N',trans) => q(stop/N) => (N, end_tag)
q(stop/N) = count(stop, N) / count(N)
"""
# compute naive bayes weights
counters = most_common.get_tags(trainfile)
class_counts = most_common.get_class_counts(counters)
allwords = set()
for counts in counters.values():
allwords.update(set(counts.keys()))
nb_weights = naivebayes.learnNBWeights(counters, class_counts, allwords, alpha=0.001)
# convert nb weights to hmm weights
hmm_weights = defaultdict(lambda: -1000.0)
for (tag, word), weight in nb_weights.iteritems():
hmm_weights[(tag, word, EMIT)] = weight
unigram = preproc.getNgrams(trainfile)
bigram = preproc.getNgrams(trainfile, 2)
unigramCount = preproc.getAllCounts(unigram)
bigramCount = preproc.getAllCounts(bigram)
for (tag1, tag2) in bigramCount.keys():
hmm_weights[(tag2, tag1, TRANS)] = np.log(1.0 * bigramCount.get((tag1, tag2), 0)) - np.log(
unigramCount.get(tag1, 0)
)
return hmm_weights
