class EMStep(MRJob):
INTERNAL_PROTOCOL = PickleProtocol
def __init__(self, *args, **kwargs):
MRJob.__init__(self, *args, **kwargs)
# Create HMM object.
self.hmm = HMM(word_dict, tag_dict)
from os import path
filename = 'hmm.txt'
if path.exists(filename):
# Load the HMM parameters from a text file.
load_parameters(filename, self.hmm, smoothing=0.1)
else:
# Initialize the HMM parameters randomly.
self.hmm.initialize_random()
self.log_likelihood = 0
self.initial_counts = 0
self.emission_counts = 0
self.transition_counts = 0
self.final_counts = 0
def mapper(self, key, s):
seq = load_sequence(s, self.hmm.observation_labels, self.hmm.state_labels)
log_likelihood, initial_counts, transition_counts, final_counts, emission_counts = predict_sequence(
seq, self.hmm)
self.log_likelihood += log_likelihood
self.initial_counts += initial_counts
self.emission_counts += emission_counts
self.transition_counts += transition_counts
self.final_counts += final_counts
def mapper_final(self):
num_states = self.hmm.get_num_states() # Number of states.
num_observations = self.hmm.get_num_observations() # Number of observation symbols.
yield 'log-likelihood', self.log_likelihood
for y in xrange(num_states):
name_y = self.hmm.state_labels.get_label_name(y)
for s in xrange(num_states):
name_s = self.hmm.state_labels.get_label_name(s)
yield 'transition %s %s' % (name_y, name_s), self.transition_counts[y, s]
yield 'final '+name_y, self.final_counts[y]
yield 'initial '+name_y, self.initial_counts[y]
for w in xrange(num_observations):
name_w = self.hmm.observation_labels.get_label_name(w)
if self.emission_counts[w].any():
for s in xrange(num_states):
name_s = self.hmm.state_labels.get_label_name(s)
if self.emission_counts[w, s]:
yield 'emission %s %s' % (name_w, name_s), self.emission_counts[w, s]
def reducer(self, key, counts):
yield key, sum(counts)
def __init__(self, *args, **kwargs):
MRJob.__init__(self, *args, **kwargs)
from os import path
filename = 'hmm.pkl'
if path.exists(filename):
self.hmm = pickle.loads(open(filename).read().decode('string-escape'))
else:
# Initialize the HMM parameters randomly.
self.hmm = HMM(word_dict, tag_dict)
self.hmm.initialize_random()
self.log_likelihood = 0
self.initial_counts = 0
self.emission_counts = 0
self.transition_counts = 0
self.final_counts = 0
class EMStep(MRJob):
INTERNAL_PROTOCOL = PickleProtocol
OUTPUT_PROTOCOL = PickleValueProtocol
def __init__(self, *args, **kwargs):
MRJob.__init__(self, *args, **kwargs)
from os import path
filename = 'hmm.pkl'
if path.exists(filename):
self.hmm = pickle.loads(open(filename).read().decode('string-escape'))
else:
# Initialize the HMM parameters randomly.
self.hmm = HMM(word_dict, tag_dict)
self.hmm.initialize_random()
self.log_likelihood = 0
self.initial_counts = 0
self.emission_counts = 0
self.transition_counts = 0
self.final_counts = 0
def mapper(self, key, s):
seq = load_sequence(s, self.hmm.observation_labels, self.hmm.state_labels)
log_likelihood, initial_counts, transition_counts, final_counts,\
emission_counts = predict_sequence(seq, self.hmm)
self.log_likelihood += log_likelihood
self.initial_counts += initial_counts
self.emission_counts += emission_counts
self.transition_counts += transition_counts
self.final_counts += final_counts
def mapper_final(self):
yield 'result', (self.log_likelihood,
self.initial_counts,
self.transition_counts,
self.final_counts,
self.emission_counts)
def reducer(self, key, counts):
combine_partials(counts, self.hmm)
self.hmm.compute_parameters()
yield 'hmm', self.hmm
class EMStep(MRJob):
INTERNAL_PROTOCOL = PickleProtocol
OUTPUT_PROTOCOL = PickleValueProtocol
def __init__(self, *args, **kwargs):
MRJob.__init__(self, *args, **kwargs)
from os import path
filename = 'hmm.pkl'
if path.exists(filename):
self.hmm = pickle.loads(
open(filename).read().decode('string-escape'))
else:
# Initialize the HMM parameters randomly.
self.hmm = HMM(word_dict, tag_dict)
self.hmm.initialize_random()
self.log_likelihood = 0
self.initial_counts = 0
self.emission_counts = 0
self.transition_counts = 0
self.final_counts = 0
def mapper(self, key, s):
seq = load_sequence(s, self.hmm.observation_labels,
self.hmm.state_labels)
log_likelihood, initial_counts, transition_counts, final_counts,\
emission_counts = predict_sequence(seq, self.hmm)
self.log_likelihood += log_likelihood
self.initial_counts += initial_counts
self.emission_counts += emission_counts
self.transition_counts += transition_counts
self.final_counts += final_counts
def mapper_final(self):
yield 'result', (self.log_likelihood, self.initial_counts,
self.transition_counts, self.final_counts,
self.emission_counts)
def reducer(self, key, counts):
combine_partials(counts, self.hmm)
self.hmm.compute_parameters()
yield 'hmm', self.hmm
def __init__(self, *args, **kwargs):
MRJob.__init__(self, *args, **kwargs)
# Create HMM object.
self.hmm = HMM(word_dict, tag_dict)
from os import path
filename = 'hmm.txt'
if path.exists(filename):
# Load the HMM parameters from a text file.
load_parameters(filename, self.hmm, smoothing=0.1)
else:
# Initialize the HMM parameters randomly.
self.hmm.initialize_random()
self.log_likelihood = 0
self.initial_counts = 0
self.emission_counts = 0
self.transition_counts = 0
self.final_counts = 0
