def initialize(self,
instances,
parallel_init=False,
max_processes=None,
**kwargs):
"""Initialize instances with the feature set. This can be optionally
used by instances to preprocess for efficiency.
"""
num_instances = len(instances)
if parallel_init:
num_processes = multiprocessing.cpu_count() \
if max_processes is None \
else min(max_processes, multiprocessing.cpu_count())
print "Using", num_processes, "processes for initialization"
pool = multiprocessing.Pool(processes=num_processes)
args = [(idx, kwargs) for idx in range(len(instances))]
self.instances = instances
for i, returned_args in enumerate(
pool.imap_unordered(self.per_instance_init, args)):
idx, instance = returned_args
self.instances[idx] = instance
sys.stdout.write("Initializing " + str(num_instances) +
" instances: " + str(i + 1) + '\r')
print
instances = self.instances
delattr(self, 'instances')
else:
with timer.AvgTimer(num_instances):
for i, instance in enumerate(instances):
instance.initialize(self.features, **kwargs)
sys.stdout.write("Initializing " + str(num_instances) +
" instances: " + str(i + 1) + '\r')
print
def train_single(self,
instances,
num_epochs=20,
num_relaxed_epochs=0,
**kwargs):
"""Trains a model with the perceptron-style algorithm following
Collins (2002). Saves model state after every epoch.
"""
# External models that are needed for instance initialization and/or
# decoding may be too large to save. We assume that these models
# will be supplied directly to train(); these are then added to the
# saved parameters for initialization and decoding.
kwargs.update(self.params)
self.initialize(instances, **kwargs)
# Initialize instances with the feature set. This can be optionally
# used by instances to preprocess for efficiency.
num_instances = len(instances)
for n in range(self.current_epoch, num_epochs):
random.shuffle(instances)
with timer.AvgTimer(num_instances):
for i, instance in enumerate(instances):
weight_update = self.decode_instance(
instance,
self.current_weights,
i,
epoch=n,
num_instances=num_instances,
relax=n < num_relaxed_epochs,
**kwargs)
self.update_weights(weight_update)
print
interrupt = self.finish_epoch(**kwargs)
if interrupt:
break
def evaluate(self,
learner,
partition='test',
debug_idxs=None,
skip_idxs=(),
decoder='ilp',
n_eval=(1, 2, 3, 4),
streaming=True,
overwritten_params=(),
eval_path=None,
output_path=None,
lm_proxy=None,
**kwargs):
"""Run the transduction model on designated test instances and report
performance metrics.
"""
# When evaluating multiple iterations of the same model over a fixed
# partition, decoding should ensure that initialization isn't
# unnecessarily repeated.
#print(kwargs)
if partition == 'test' and kwargs['subcorpus'] is not None and kwargs[
'subcorpus'] == 'final':
#if kwargs['subcorpus'] == 'final':
print("FINAL")
eval_instances = self.get_instances(partition=partition,
debug_idxs=debug_idxs,
skip_idxs=skip_idxs)
system_name = learner.name
elif learner is not None:
eval_instances = self.decode_instances(learner,
partition=partition,
debug_idxs=debug_idxs,
skip_idxs=skip_idxs,
decoder=decoder,
streaming=streaming,
overwritten_params=\
overwritten_params,
**kwargs)
system_name = learner.name
else:
eval_instances = self.get_instances(partition=partition,
debug_idxs=debug_idxs,
skip_idxs=skip_idxs)
system_name = 'baseline'
num_instances = len(eval_instances)
# Record overwritten parameters in the filenames
overwriting_str = None
if len(overwritten_params) > 0:
overwriting_str = '_OW-'
i = 0
for param_name, value in overwritten_params.iteritems():
if isinstance(value, list) or isinstance(value, tuple):
overwriting_str += '+'.join(str(v) for v in sorted(value))
else:
overwriting_str += str(value)
i += 1
if i < len(overwritten_params):
overwriting_str += '-'
if output_path is not None:
output_filename = ''.join(
(output_path, '/', '_'.join((partition, 'under', system_name)),
overwriting_str if overwriting_str is not None else '', '_',
decoder, '.out'))
outf = open(output_filename, 'wb')
# Determine the evaluations to run by looking at a representative
# instance
i = 0
while i < len(eval_instances) and \
not hasattr(eval_instances[i], 'output_sent'):
i += 1
if i == len(eval_instances):
print "WARNING: all instances failed; skipping evaluation"
sys.exit()
some_instance = eval_instances[i]
has_labels = hasattr(some_instance, 'label_sentences')
has_rasp = hasattr(some_instance.gold_sentences[0], 'relgraph')
has_outtrees = hasattr(some_instance.output_sent, 'outtree')
has_outframes = hasattr(some_instance.output_sent, 'outframes')
# FIXME TEMPORARY! MUST MAKE "False" FOR TEST!
skip_failed = False
# Initialize the evaluations
eval_obj = evaluation.Evaluation(title='TRANSDUCTION_EVAL')
output_sents = []
with timer.AvgTimer(num_instances):
for i, instance in enumerate(eval_instances):
sys.stdout.write("Evaluating " + str(num_instances) +
(" " +
partition if partition is not None else "") +
" instances: " + str(i + 1) + '\r')
# Duration and failure status
eval_obj.include(
system=system_name,
corpus='other',
decode_time=instance.decode_times[-1],
solution_time=instance.solution_times[-1] \
if len(instance.solution_times) > 0 else 0,
inputs=len(instance.input_sents),
_failed=int(not hasattr(instance, 'output_sent')),
)
if skip_failed and not hasattr(instance, 'output_sent'):
print "WARNING: Skipping failed instance", instance.idx
continue
# POS tag recall
for use_labels in set([False]) | set([has_labels]):
#for prefix in ('NN', 'VB', 'JJ', 'RB'):
# p, r, f = instance.score_content_words(
# use_labels=use_labels, prefixes=(prefix,))
# eval_obj.add_metrics(
# precision=p,
# recall=r,
# system=system_name,
# corpus=('LBLs ' + prefix) if use_labels \
# else ('GOLD ' + prefix),
# )
p, r, f = instance.score_content_words(
use_labels=use_labels, prefixes=('NN', 'VB'))
eval_obj.add_metrics(
precision=p,
recall=r,
system=system_name,
corpus=('LBLs ' + 'NN+VB') if use_labels \
else ('GOLD ' + 'NN+VB'),
)
try:
if lm_proxy is not None:
output_tokens = instance.output_sent.tokens \
if hasattr(instance, 'output_sent') else []
eval_obj.include(system=system_name,
corpus='other',
lm=lm_proxy.score_sent(output_tokens))
except jsonrpc.RPCTransportError:
print "ERROR: JSON-RPC hiccups; skipping LM scoring"
pass
if decoder.startswith('dp+'):
# Record convergence of dual decomposition or
# bisection. Will be 0 if neither are used.
eval_obj.include(
system=system_name,
corpus='other',
convergence_=int(instance.converged),
iterations=instance.num_iterations,
)
if len(instance.sentences) == 1:
# Paraphrasing or compression-specific metrics
eval_obj.include(
system=system_name,
corpus='STATS gold',
comp_=instance.get_gold_compression_rate(),
length=instance.avg_gold_len,
proj_=avg(
int(gold_sent.dparse.is_projective())
for gold_sent in instance.gold_sentences),
overlap_=avg(
instance.get_overlap(gold_sent)
for gold_sent in instance.gold_sentences),
)
eval_obj.include(
system=system_name,
corpus='STATS input',
comp_=1.0,
length=instance.avg_len,
proj_=int(
instance.sentences[0].dparse.is_projective()),
overlap_=instance.get_overlap(instance.sentences[0]))
eval_obj.include(
system=system_name,
corpus='STATS output',
comp_=instance.get_compression_rate(),
length=len(instance.output_sent.tokens) if hasattr(
instance, 'output_sent') else 0,
)
if hasattr(instance, 'output_sent') and has_outtrees:
eval_obj.include(
system=system_name,
corpus='STATS output',
proj_=int(instance.output_sent.\
outtree.is_projective())
if hasattr(instance.output_sent.outtree,\
'is_projective')
else 0,
overlap_=instance.get_overlap(
instance.output_sent,
parse_type='outtree')
)
# print "INSTANCE ", instance.idx
# crossing_edges = \
# instance.output_sent.outtree.get_crossing_edges()
# print "\n\nINPUT:",
# self.dump_parse(instance.sentences[0])
#
# for gs, gold_sent in enumerate(
# instance.gold_sentences):
# # get output indices for gold
# gold_idxs = []
# i = 0
# for token in gold_sent.tokens:
# while instance.sentences[0].tokens[i] != token:
# i += 1
# gold_idxs.append((0,i))
#
# print "\nGOLD:", gs,
# self.dump_parse(gold_sent,
# idx_mapper=gold_idxs)
#
# print "\n\nOUTPUT:",
# self.dump_parse(instance.output_sent,
# parse_type='outtree',
# crossing_edges=crossing_edges,
# idx_mapper=instance.output_idxs)
# n-gram precision and recall
for use_labels in set([False]) | set([has_labels]):
for n in n_eval:
p, r, f = instance.score_ngrams(n=n,
use_labels=use_labels)
eval_obj.add_metrics(
precision=p,
recall=r,
system=system_name,
corpus='LBLs n=' +
str(n) if use_labels else 'GOLD n=' + str(n),
)
if hasattr(instance, 'output_sent') and has_outframes:
# Precision and recall for frames
p, r, f = instance.score_frames(fes=False,
frames_type='outframes',
use_labels=use_labels)
eval_obj.add_metrics(
precision=p,
recall=r,
system=system_name,
corpus="GOLD frames",
)
# Precision and recall for frame elements
p, r, f = instance.score_frames(fes=True,
frames_type='outframes',
use_labels=use_labels)
eval_obj.add_metrics(
precision=p,
recall=r,
system=system_name,
corpus="GOLD fes",
)
# Parse output sentences for syntactic evaluation. The
# 100 token limit is intended for the Stanford parser.
if hasattr(instance, 'output_sent') and \
len(instance.output_sent.tokens) <= 100:
output_sents.append(instance.output_sent)
# Write the output to a file
if output_path is not None:
outf.write(instance.get_display_string())
# print
if output_path is not None:
outf.close()
# Parse-based evaluations
try:
parse_types = ['dparse']
if has_outtrees:
parse_types.append('outtree')
# Get annotations. Only run RASP if the inputs have RASP
# annotations since it's slow
annotations.annotate(output_sents, 'Stanford')
if has_rasp:
annotations.annotate(output_sents, 'Rasp')
parse_types.append('relgraph')
# Add dependency results to evaluations
for i, instance in enumerate(eval_instances):
if skip_failed and not hasattr(instance, 'output_sent'):
print "WARNING: Skipping failed instance",
print instance.idx, "again"
continue
for parse_type in parse_types:
for use_labels in set([False]) | set([has_labels]):
name = ('LBLs ' if use_labels else 'GOLD ') + \
parse_type
p, r, f = instance.score_dependencies(
parse_type=parse_type, use_labels=use_labels)
eval_obj.add_metrics(
precision=p,
recall=r,
system=system_name,
corpus=name,
_failed=int(not instance.has_output_parses(
parse_type=parse_type)))
except OSError:
print "Skipping parser evaluations"
print eval_obj.title
print eval_obj.table(skip_single_keys=True)
if eval_path is not None and debug_idxs is None:
eval_filename = ''.join(
(eval_path, '/', '_'.join((partition, 'under', system_name)),
overwriting_str if overwriting_str is not None else '', '_',
decoder, '.eval'))
eval_obj.save(eval_filename, append=False)
def train_master_minibatch(self,
instances,
machine_idx=0,
num_epochs=20,
num_relaxed_epochs=0,
master_oversees=False,
**kwargs):
"""Run the master process of a round-robin distributed learner.
"""
# Sanity check
assert machine_idx == 0
# Runtime parameters that are not saved with the model
kwargs.update(self.params)
# Divide instances into minibatches
num_instances = len(instances)
minibatches = self.machine_init_minibatch(
instances,
machine_idx=machine_idx,
master_oversees=master_oversees,
**kwargs)
# Initialize slave proxies
all_machines = self.master_init_slaves(len(self.current_weights),
master_oversees=master_oversees,
**kwargs)
pool = multiprocessing.Pool(processes=len(all_machines),
maxtasksperchild=1)
# manager = multiprocessing.Manager()
# instance_queue = manager.Queue() if not master_oversees else None
# instance_queue = None
for n in range(self.current_epoch, num_epochs):
if n > 0: # for comparing and debugging
random.shuffle(minibatches)
if not master_oversees:
print "WARNING: minibatching will duplicate the heap",
print "for the master."
print "Consider running with --master_oversees instead."
self.temp_instances = instances
self.temp_params = kwargs
with timer.AvgTimer(num_instances):
for b, minibatch in enumerate(minibatches):
# Print current epoch and minibatch
sys.stdout.write("[Epoch " + str(n) + "] Batch " +
str(b + 1) + "/" + str(len(minibatches)) +
"\r")
# # Put instances assigned to the master in a queue
# for master_idx in minibatch[0]:
# instance_queue.put(instances[master_idx])
args = [
(n, num_relaxed_epochs, all_machines[m], instance_idxs)
# self.current_weights, self.learning_rate.value(),
# kwargs, instance_queue)
for m, instance_idxs in enumerate(minibatch)
]
weight_update = np.zeros(len(self.current_weights))
num_minibatch_updates = 0
for returned_args in pool.imap_unordered(
self.minibatch_epoch, args):
if returned_args is None:
print "Failed to decode minibatch", b
continue
sum_weight_update, num_machine_updates = returned_args
if sum_weight_update is not None:
weight_update += sum_weight_update
num_minibatch_updates += num_machine_updates
if num_minibatch_updates > 0:
weight_update /= num_minibatch_updates
else:
weight_update = None
self.update_weights(weight_update)
print
if not master_oversees:
del self.temp_instances
del self.temp_params
interrupt = self.finish_epoch(**kwargs)
if interrupt:
break
def train_master_serial(self,
instances,
machine_idx=0,
num_epochs=20,
num_relaxed_epochs=0,
master_oversees=False,
**kwargs):
"""Run the master process of a round-robin distributed learner.
"""
# Sanity check
assert machine_idx == 0
if master_oversees:
print "WARNING: Master cannot oversee in serial mode"
# Runtime parameters that are not saved with the model
kwargs.update(self.params)
# Map out splits for each slave and generate a mapper from instances
# to slaves.
spans_per_machine = self.machine_init_serial(instances,
machine_idx=machine_idx,
**kwargs)
num_instances = len(instances)
instance_mapper = [None] * num_instances
for machine_idx, span in enumerate(spans_per_machine):
for instance_idx in range(*span):
instance_mapper[instance_idx] = machine_idx
# Initialize slave proxies
all_machines = self.master_init_slaves(len(self.current_weights),
**kwargs)
instance_idxs = range(num_instances)
for n in range(self.current_epoch, num_epochs):
random.shuffle(instance_idxs)
with timer.AvgTimer(num_instances):
prev_machine_idx = -1
for i, instance_idx in enumerate(instance_idxs):
machine_idx = instance_mapper[instance_idx]
if machine_idx == 0:
instance = instances[instance_idx]
weight_update = self.decode_instance(
instance,
self.current_weights,
i,
epoch=n,
num_instances=num_instances,
relax=n < num_relaxed_epochs,
**kwargs)
else:
# If the last instance was decoded by the same slave
# we don't need to send the weights again since it
# already has them.
weight_update, num_updates = \
self.master_decode_instance(
all_machines[machine_idx],
[instance_idx],
self.current_weights,
self.learning_rate.value(),
relax=n < num_relaxed_epochs,
slave_has_weights=(
machine_idx==prev_machine_idx),
slave_keeps_weights=True)
prev_machine_idx = machine_idx
self.update_weights(weight_update)
print
interrupt = self.finish_epoch(**kwargs)
if interrupt:
break
def train_multi(self,
instances,
num_epochs=20,
num_relaxed_epochs=0,
max_processes=None,
**kwargs):
"""Trains a model with the perceptron-style algorithm from
Collins (2002). Attempts to speed up computation by utilizing
multiple cores with iterative parameter scaling as described in
McDonald et al. (2009). Saves model state after every epoch.
"""
# External models that are needed for instance initialization may
# be too large to save. We assume that these models will be supplied
# directly to train(); these are then added to the saved parameters
# for initialization.
kwargs.update(self.params)
# TODO: These arguments are NOT passed to decoding when using
# multiple processes to avoid a deep copy of the models for each
# process. Shared memory across processes can be implemented using
# Value, Array or, more generally, shared ctypes. This is currently
# not implemented. If needed, use parallel_decoding=False.
self.initialize(instances, **kwargs)
# Determine number of parallel processes to use
num_processes = multiprocessing.cpu_count() if max_processes is None \
else min(max_processes, multiprocessing.cpu_count())
# The size of each parallel shard is taken as the floor of the number
# of instances per process in order to prevent biases in the
# parameter mixing
num_instances = len(instances)
num_shards = num_processes
shard_size = np.floor(num_instances / num_shards)
shard_spans = [[i * shard_size, (i + 1) * shard_size]
for i in range(num_shards)]
shard_spans[-1][1] = num_instances
for n in range(self.current_epoch, num_epochs):
# Randomly shuffle training instances and break them into shards
# using the indices created earlier
random.shuffle(instances)
shards = [instances[begin:end] for begin, end in shard_spans]
# Pack shard indices and current weight vector together because
# Pool doesn't support multiple function arguments for parallel
# maps. Note that local keyword arguments that are not in
# self.params will not be available for decoding.
args = [(n, num_relaxed_epochs, shard, self.current_weights)
for shard in shards]
# Initialize a process pool to run a single epoch on
# each shard asychronously
pool = multiprocessing.Pool(processes=num_processes)
next_w = np.zeros(len(self.features))
with timer.AvgTimer(num_instances):
for returned_args in pool.imap_unordered(
self.per_shard_epoch, args):
# Unpack returned values
w, sum_w, num_shard_updates = returned_args
# The final weight vector from the shard, normalized by
# the size of the shard, contributes to the input weight
# vector for the next iteration
normalizer = num_shard_updates / len(instances)
next_w += (np.array(w) * normalizer)
# Update the running sum of shard weights
# TODO: move perceptron-specific update code to subclass
self.sum_weights += sum_w
self.num_updates_seen += num_shard_updates
print
# Terminate pool (processes should already be finished)
pool.terminate()
# Set weight vector for next parallel epoch
self.current_weights = next_w[:]
interrupt = self.finish_epoch(**kwargs)
if interrupt:
break
