def test_as_dict(self):
transformer = Merge(self.streams, ('english', 'french'))
assert_equal(next(transformer.get_epoch_iterator(as_dict=True)),
({
'english': 'Hello world!',
'french': 'Bonjour le monde!'
}))
def test_reset_calls_reset_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.reset()
assert all(stream.reset_called for stream in streams)
def test_reset_calls_reset_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.reset()
assert all(stream.reset_called for stream in streams)
def getFinalStream(X, Y, sources, sources_k, batch_size=128, embedding_dim=300,
shuffle=False):
"""Despite horrible variable names, this method
gives back the final stream for both train or test data
batch_size:
embedding_dim: for glove vects
min_df and max_features: for Tokenizer
Returns
-------
merged stream with sources = sources + sources_k
"""
trX, trY = (X, Y)
trX_k, trY_k = (X, Y)
# Transforms
trXt=lambda x: floatX(x)
Yt=lambda y: intX(SeqPadded(vect.transform(sampleCaptions(y)), 'back'))
# Foxhound Iterators
# RCL: Write own iterator to sample positive examples/captions, since there are 5 for each image.
train_iterator = iterators.Linear(
trXt=trXt, trYt=Yt, size=batch_size, shuffle=shuffle
)
train_iterator_k = iterators.Linear(
trXt=trXt, trYt=Yt, size=batch_size, shuffle=shuffle
)
# FoxyDataStreams
train_stream = FoxyDataStream(
(trX, trY)
, sources
, train_iterator
, FoxyIterationScheme(len(trX), batch_size)
)
train_stream_k = FoxyDataStream(
(trX_k, trY_k)
, sources_k
, train_iterator_k
, FoxyIterationScheme(len(trX), batch_size)
)
glove_version = "glove.6B.%sd.txt.gz" % embedding_dim
train_transformer = GloveTransformer(
glove_version, data_stream=train_stream, vectorizer=vect
)
train_transformer_k = GloveTransformer(
glove_version, data_stream=train_stream_k, vectorizer=vect
)
# Final Data Streams w/ contrastive examples
final_train_stream = Merge(
(train_transformer, ShuffleBatch(train_transformer_k))
, sources + sources_k
)
final_train_stream.iteration_scheme = FoxyIterationScheme(len(trX), batch_size)
return final_train_stream
def test_merge():
english = IterableDataset(['Hello world!'])
french = IterableDataset(['Bonjour le monde!'])
streams = (english.get_example_stream(), french.get_example_stream())
merged_stream = Merge(streams, ('english', 'french'))
assert merged_stream.sources == ('english', 'french')
assert (next(merged_stream.get_epoch_iterator()) == ('Hello world!',
'Bonjour le monde!'))
class TestMerge(object):
def setUp(self):
self.streams = (DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.transformer = Merge(self.streams, ('english', 'french'))
def test_sources(self):
assert_equal(self.transformer.sources, ('english', 'french'))
def test_merge(self):
assert_equal(next(self.transformer.get_epoch_iterator()),
('Hello world!', 'Bonjour le monde!'))
def test_as_dict(self):
assert_equal(next(self.transformer.get_epoch_iterator(as_dict=True)),
({
'english': 'Hello world!',
'french': 'Bonjour le monde!'
}))
def test_error_on_wrong_number_of_sources(self):
assert_raises(ValueError, Merge, self.streams, ('english', ))
def test_value_error_on_different_stream_output_type(self):
spanish_stream = DataStream(IndexableDataset(['Hola mundo!']),
iteration_scheme=SequentialScheme(2, 2))
assert_raises(ValueError, Merge, self.streams + (spanish_stream, ),
('english', 'french', 'spanish'))
def test_close_calls_close_on_all_streams(self):
streams = [
FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))
]
transformer = Merge(streams, ('1', '2', '3'))
transformer.close()
assert all(stream.close_called for stream in streams)
def test_next_epoch_calls_next_epoch_on_all_streams(self):
streams = [
FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))
]
transformer = Merge(streams, ('1', '2', '3'))
transformer.next_epoch()
assert all(stream.next_epoch_called for stream in streams)
def test_reset_calls_reset_on_all_streams(self):
streams = [
FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))
]
transformer = Merge(streams, ('1', '2', '3'))
transformer.reset()
assert all(stream.reset_called for stream in streams)
def test_merge():
english = IterableDataset(['Hello world!'])
french = IterableDataset(['Bonjour le monde!'])
streams = (english.get_example_stream(),
french.get_example_stream())
merged_stream = Merge(streams, ('english', 'french'))
assert merged_stream.sources == ('english', 'french')
assert (next(merged_stream.get_epoch_iterator()) ==
('Hello world!', 'Bonjour le monde!'))
def getFinalStream(X,
Y,
sources,
sources_k,
batch_size=128,
embedding_dim=300,
shuffle=False):
"""
Returns
-------
merged stream with sources = sources + sources_k
"""
trX, trY = (X, Y)
trX_k, trY_k = (X, Y)
# Transforms
trXt = lambda x: floatX(x)
Yt = lambda y: intX(
SeqPadded(vect.transform(sampleCaptions(y)), 'back'))
# Foxhound Iterators
# RCL: Write own iterator to sample positive examples/captions, since there are 5 for each image.
train_iterator = iterators.Linear(trXt=trXt,
trYt=Yt,
size=batch_size,
shuffle=shuffle)
train_iterator_k = iterators.Linear(trXt=trXt,
trYt=Yt,
size=batch_size,
shuffle=True)
# FoxyDataStreams
train_stream = FoxyDataStream(
(trX, trY), sources, train_iterator,
FoxyIterationScheme(len(trX), batch_size))
train_stream_k = FoxyDataStream(
(trX_k, trY_k), sources_k, train_iterator_k,
FoxyIterationScheme(len(trX), batch_size))
glove_version = "glove.6B.%sd.txt.gz" % embedding_dim
train_transformer = GloveTransformer(glove_version,
data_stream=train_stream,
vectorizer=vect)
train_transformer_k = GloveTransformer(glove_version,
data_stream=train_stream_k,
vectorizer=vect)
# Final Data Streams w/ contrastive examples
final_train_stream = Merge(
(train_transformer, ShuffleBatch(train_transformer_k)),
sources + sources_k)
final_train_stream.iteration_scheme = FoxyIterationScheme(
len(trX), batch_size)
return final_train_stream
def setUp(self):
self.streams = (
DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.batch_streams = (
Batch(DataStream(IterableDataset(['Hello world!', 'Hi!'])),
iteration_scheme=ConstantScheme(2)),
Batch(DataStream(IterableDataset(['Bonjour le monde!', 'Salut!'])),
iteration_scheme=ConstantScheme(2)))
self.transformer = Merge(
self.streams, ('english', 'french'))
self.batch_transformer = Merge(
self.batch_streams, ('english', 'french'))
def setUp(self):
self.streams = (
DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.batch_streams = (
Batch(DataStream(IterableDataset(['Hello world!', 'Hi!'])),
iteration_scheme=ConstantScheme(2)),
Batch(DataStream(IterableDataset(['Bonjour le monde!', 'Salut!'])),
iteration_scheme=ConstantScheme(2)))
self.transformer = Merge(
self.streams, ('english', 'french'))
self.batch_transformer = Merge(
self.batch_streams, ('english', 'french'))
class TestMerge(object):
def setUp(self):
self.streams = (
DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.transformer = Merge(self.streams, ('english', 'french'))
def test_sources(self):
assert_equal(self.transformer.sources, ('english', 'french'))
def test_merge(self):
assert_equal(next(self.transformer.get_epoch_iterator()),
('Hello world!', 'Bonjour le monde!'))
def test_as_dict(self):
assert_equal(
next(self.transformer.get_epoch_iterator(as_dict=True)),
({'english': 'Hello world!', 'french': 'Bonjour le monde!'}))
def test_error_on_wrong_number_of_sources(self):
assert_raises(ValueError, Merge, self.streams, ('english',))
def test_value_error_on_different_stream_output_type(self):
spanish_stream = DataStream(IndexableDataset(['Hola mundo!']),
iteration_scheme=SequentialScheme(2, 2))
assert_raises(ValueError, Merge, self.streams + (spanish_stream,),
('english', 'french', 'spanish'))
def test_close_calls_close_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.close()
assert all(stream.close_called for stream in streams)
def test_next_epoch_calls_next_epoch_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.next_epoch()
assert all(stream.next_epoch_called for stream in streams)
def test_reset_calls_reset_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.reset()
assert all(stream.reset_called for stream in streams)
def get_tr_stream(src_vocab, trg_vocab, src_data, trg_data,
src_vocab_size=30000, trg_vocab_size=30000,
unk_id=0, eos_id=1, bos_id=2, train_noise=0,
seq_len=50, batch_size=80, sort_k_batches=12, **kwargs):
src_stream = get_stream(src_vocab, src_data, src_vocab_size, unk_id, eos_id, bos_id, train_noise)
trg_stream = get_stream(trg_vocab, trg_data, trg_vocab_size, unk_id, eos_id, bos_id, 0)
# Merge them to get a source, target pair
stream = Merge([src_stream, trg_stream], ('source', 'target'))
# Filter sequences that are too long
stream = Filter(stream, predicate=_not_too_long(seq_len))
# Build a batched version of stream to read k batches ahead
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size * sort_k_batches))
# Sort all samples in the read-ahead batch
stream = Mapping(stream, SortMapping(_length))
# Convert it into a stream again
stream = Unpack(stream)
# Construct batches from the stream with specified batch size
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
# Pad sequences that are short
return PaddingWithEOS(stream, [eos_id, eos_id])
def train(self, req_vars):
prefix_stream = DataStream(self.train_dataset,
iteration_scheme=ShuffledExampleScheme(
self.train_dataset.num_examples))
if not data.tvt:
prefix_stream = transformers.TaxiExcludeTrips(
prefix_stream, self.valid_trips_ids)
prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream)
prefix_stream = transformers.TaxiGenerateSplits(
prefix_stream, max_splits=self.config.max_splits)
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.balanced_batch(
prefix_stream,
key='latitude',
batch_size=self.config.batch_size,
batch_sort_size=self.config.batch_sort_size)
prefix_stream = Padding(prefix_stream,
mask_sources=['latitude', 'longitude'])
candidate_stream = self.candidate_stream(
self.config.train_candidate_size)
sources = prefix_stream.sources + tuple(
'candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
# stream = MultiProcessing(stream)
return stream
def get_comb_stream(fea2obj,
which_set,
batch_size=None,
shuffle=True,
num_examples=None):
streams = []
for fea in fea2obj:
obj = fea2obj[fea]
dataset = H5PYDataset(obj.fuelfile,
which_sets=(which_set, ),
load_in_memory=True)
if batch_size == None: batch_size = dataset.num_examples
if num_examples == None: num_examples = dataset.num_examples
if shuffle:
iterschema = ShuffledScheme(examples=num_examples,
batch_size=batch_size,
rng=numpy.random.RandomState(seed))
else:
iterschema = SequentialScheme(examples=num_examples,
batch_size=batch_size)
stream = DataStream(dataset=dataset, iteration_scheme=iterschema)
if fea in seq_features:
stream = CutInput(stream, obj.max_len)
if obj.rec == True:
logger.info('transforming data for recursive input')
stream = LettersTransposer(
stream, which_sources=fea
) # Required because Recurrent last_hid receive as input [sequence, batch,# features]
streams.append(stream)
stream = Merge(streams, tuple(fea2obj.keys()))
return stream, num_examples
def valid(self, req_vars):
prefix_stream = DataStream(self.valid_dataset,
iteration_scheme=SequentialExampleScheme(
self.valid_dataset.num_examples))
#prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream)
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.balanced_batch(
prefix_stream,
key='latitude',
batch_size=self.config.batch_size,
batch_sort_size=self.config.batch_sort_size)
prefix_stream = Padding(prefix_stream,
mask_sources=['latitude', 'longitude'])
candidate_stream = self.candidate_stream(
self.config.valid_candidate_size)
sources = prefix_stream.sources + tuple(
'candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
# stream = MultiProcessing(stream)
return stream
def test(self, req_vars):
prefix_stream = DataStream(self.test_dataset,
iteration_scheme=SequentialExampleScheme(
self.test_dataset.num_examples))
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.taxi_add_first_last_len(
prefix_stream, self.config.n_begin_end_pts)
if not data.tvt:
prefix_stream = transformers.taxi_remove_test_only_clients(
prefix_stream)
prefix_stream = Batch(prefix_stream,
iteration_scheme=ConstantScheme(
self.config.batch_size))
candidate_stream = self.candidate_stream(
self.config.test_candidate_size)
sources = prefix_stream.sources + tuple(
'candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
stream = MultiProcessing(stream)
return stream
def get_train_stream(configuration, sfiles, tfiles, svocab_dict, tvocab_dict):
s_dataset = TextFile(sfiles, svocab_dict, bos_token=None, eos_token=None,\
unk_token='<unk>', level='word', preprocess=None, encoding='utf8')
t_dataset = TextFile(tfiles, tvocab_dict, bos_token=None, eos_token=None,\
unk_token='<unk>', level='word', preprocess=None, encoding='utf8')
# Merge
stream = Merge([s_dataset.get_example_stream(),
t_dataset.get_example_stream()],
('source', 'target'))
# Filter -- TODO
stream = Filter(stream, predicate=_too_long(seq_len=configuration['seq_len']))
# Map - no need
# Batch - Sort
stream = Batch(stream,
iteration_scheme=ConstantScheme(
configuration['batch_size']*configuration['sort_k_batches']))
stream = Mapping(stream, SortMapping(_length))
stream = Unpack(stream)
stream = Batch(
stream, iteration_scheme=ConstantScheme(configuration['batch_size']))
# Pad
# Note that </s>=0. Fuel only allows padding 0 by default
masked_stream = Padding(stream)
return masked_stream
def get_dev_stream_with_context_features(val_context_features=None, val_set=None, src_vocab=None,
src_vocab_size=30000, unk_id=1, **kwargs):
"""Setup development set stream if necessary."""
def _get_np_array(filename):
return numpy.load(filename)['arr_0']
dev_stream = None
if val_set is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(
src_vocab if isinstance(src_vocab, dict) else
cPickle.load(open(src_vocab)),
bos_idx=0, eos_idx=src_vocab_size - 1, unk_idx=unk_id)
dev_dataset = TextFile([val_set], src_vocab, None)
# now add the source with the image features
# create the image datastream (iterate over a file line-by-line)
con_features = _get_np_array(val_context_features)
con_feature_dataset = IterableDataset(con_features)
valid_image_stream = DataStream(con_feature_dataset)
# dev_stream = DataStream(dev_dataset)
dev_stream = Merge([dev_dataset.get_example_stream(),
valid_image_stream], ('source', 'initial_context'))
# dev_stream = dev_stream.get_example_stream()
return dev_stream
def train(self, req_vars):
prefix_stream = DataStream(self.train_dataset,
iteration_scheme=ShuffledExampleScheme(
self.train_dataset.num_examples))
if not data.tvt:
prefix_stream = transformers.TaxiExcludeTrips(
prefix_stream, self.valid_trips_ids)
prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream)
prefix_stream = transformers.TaxiGenerateSplits(
prefix_stream, max_splits=self.config.max_splits)
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.taxi_add_first_last_len(
prefix_stream, self.config.n_begin_end_pts)
prefix_stream = Batch(prefix_stream,
iteration_scheme=ConstantScheme(
self.config.batch_size))
candidate_stream = self.candidate_stream(
self.config.train_candidate_size)
sources = prefix_stream.sources + tuple(
'candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
stream = MultiProcessing(stream)
return stream
def _get_align_stream(src_data, trg_data, src_vocab_size, trg_vocab_size,
seq_len, **kwargs):
"""Creates the stream which is used for the main loop.
Args:
src_data (string): Path to the source sentences
trg_data (string): Path to the target sentences
src_vocab_size (int): Size of the source vocabulary in the NMT
model
trg_vocab_size (int): Size of the target vocabulary in the NMT
model
seq_len (int): Maximum length of any source or target sentence
Returns:
ExplicitNext. Alignment data stream which can be iterated
explicitly
"""
# Build dummy vocabulary to make TextFile happy
src_vocab = _add_special_ids({str(i): i for i in xrange(src_vocab_size)})
trg_vocab = _add_special_ids({str(i): i for i in xrange(trg_vocab_size)})
# Get text files from both source and target
src_dataset = TextFile([src_data], src_vocab, None)
trg_dataset = TextFile([trg_data], trg_vocab, None)
# Merge them to get a source, target pair
s = Merge(
[src_dataset.get_example_stream(),
trg_dataset.get_example_stream()], ('source', 'target'))
s = Filter(s, predicate=stream._too_long(seq_len=seq_len))
s = Batch(s, iteration_scheme=ConstantScheme(1))
masked_stream = stream.PaddingWithEOS(s, [utils.EOS_ID, utils.EOS_ID])
return ExplicitNext(masked_stream)
def get_dev_stream(val_set=None,
valid_sent_dict=None,
src_vocab=None,
trg_vocab=None,
src_vocab_size=30000,
trg_vocab_size=30000,
unk_id=1,
**kwargs):
"""Setup development set stream if necessary."""
dev_stream = None
if val_set is not None and src_vocab is not None:
# Load dictionaries and ensure special tokens exist
src_vocab = ensure_special_tokens(src_vocab if isinstance(
src_vocab, dict) else cPickle.load(open(src_vocab)),
bos_idx=0,
eos_idx=src_vocab_size - 1,
unk_idx=unk_id)
trg_vocab = ensure_special_tokens(trg_vocab if isinstance(
trg_vocab, dict) else cPickle.load(open(trg_vocab)),
bos_idx=0,
eos_idx=trg_vocab_size - 1,
unk_idx=unk_id)
dev_dataset = TextFile([val_set], src_vocab, None)
dev_dictset = TextFile([valid_sent_dict], trg_vocab, None)
#dev_stream = DataStream(dev_dataset)
# Merge them to get a source, target pair
dev_stream = Merge([
dev_dataset.get_example_stream(),
dev_dictset.get_example_stream()
], ('source', 'valid_sent_trg_dict'))
return dev_stream
def get_dev_stream_with_topicalq(test_set=None,
src_vocab=None,
src_vocab_size=30000,
topical_test_set=None,
topical_vocab=None,
topical_vocab_size=2000,
unk_id=1,
**kwargs):
"""Setup development set stream if necessary."""
dev_stream = None
if test_set is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(src_vocab if isinstance(
src_vocab, dict) else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0,
eos_idx=src_vocab_size - 1,
unk_idx=unk_id)
print test_set, type(src_vocab)
topical_vocab = cPickle.load(open(topical_vocab, 'rb'))
#not ensure special token.
topical_dataset = TextFile([topical_test_set], topical_vocab, None,
None, '10')
dev_dataset = TextFile([test_set], src_vocab, None)
#dev_stream = DataStream(dev_dataset)
# Merge them to get a source, target pair
dev_stream = Merge([
dev_dataset.get_example_stream(),
topical_dataset.get_example_stream()
], ('source', 'source_topical'))
return dev_stream
def test(self, req_vars):
prefix_stream = DataStream(self.test_dataset,
iteration_scheme=SequentialExampleScheme(
self.test_dataset.num_examples))
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
if not data.tvt:
prefix_stream = transformers.taxi_remove_test_only_clients(
prefix_stream)
prefix_stream = Batch(prefix_stream,
iteration_scheme=ConstantScheme(
self.config.batch_size))
prefix_stream = Padding(prefix_stream,
mask_sources=['latitude', 'longitude'])
candidate_stream = self.candidate_stream(
self.config.test_candidate_size, False)
sources = prefix_stream.sources + tuple(
'candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
# stream = MultiProcessing(stream)
return stream
def get_test_stream(sfiles, svocab_dict):
dataset = TextFile(sfiles, svocab_dict, bos_token=None, eos_token=None,\
unk_token='<unk>', level='word', preprocess=None, encoding='utf8')
stream = Merge([dataset.get_example_stream(),], ('source', ))
stream = Batch(
stream, iteration_scheme=ConstantScheme(10))
stream = Padding(stream)
return stream
def get_tr_stream(src_vocab, trg_vocab, src_data, trg_data,
src_vocab_size=30000, trg_vocab_size=30000, unk_id=1,
seq_len=50, batch_size=80, sort_k_batches=12, **kwargs):
"""Prepares the training data stream."""
# Load dictionaries and ensure special tokens exist
src_vocab = _ensure_special_tokens(
src_vocab if isinstance(src_vocab, dict)
else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0, eos_idx=src_vocab_size - 1, unk_idx=unk_id)
trg_vocab = _ensure_special_tokens(
trg_vocab if isinstance(trg_vocab, dict) else
cPickle.load(open(trg_vocab, 'rb')),
bos_idx=0, eos_idx=trg_vocab_size - 1, unk_idx=unk_id)
# Get text files from both source and target
src_dataset = TextFile([src_data], src_vocab, None)
trg_dataset = TextFile([trg_data], trg_vocab, None)
# Merge them to get a source, target pair
stream = Merge([src_dataset.get_example_stream(),
trg_dataset.get_example_stream()],
('source', 'target'))
# Filter sequences that are too long
stream = Filter(stream,
predicate=_too_long(seq_len=seq_len))
# Replace out of vocabulary tokens with unk token
stream = Mapping(stream,
_oov_to_unk(src_vocab_size=src_vocab_size,
trg_vocab_size=trg_vocab_size,
unk_id=unk_id))
# Build a batched version of stream to read k batches ahead
stream = Batch(stream,
iteration_scheme=ConstantScheme(
batch_size*sort_k_batches))
# Sort all samples in the read-ahead batch
stream = Mapping(stream, SortMapping(_length))
# Convert it into a stream again
stream = Unpack(stream)
# Construct batches from the stream with specified batch size
stream = Batch(
stream, iteration_scheme=ConstantScheme(batch_size))
# Pad sequences that are short
masked_stream = PaddingWithEOS(
stream, [src_vocab_size - 1, trg_vocab_size - 1])
return masked_stream
def get_sgnmt_tr_stream(src_data,
trg_data,
src_vocab_size=30000,
trg_vocab_size=30000,
unk_id=1,
seq_len=50,
batch_size=80,
sort_k_batches=12,
**kwargs):
"""Prepares the unshuffled training data stream. This corresponds
to ``get_sgnmt_tr_stream`` in ``machine_translation/stream`` in the
blocks examples."""
# Build dummy vocabulary to make TextFile happy
src_vocab = add_special_ids({str(i): i for i in xrange(src_vocab_size)})
trg_vocab = add_special_ids({str(i): i for i in xrange(trg_vocab_size)})
# Get text files from both source and target
src_dataset = TextFile([src_data], src_vocab, None)
trg_dataset = TextFile([trg_data], trg_vocab, None)
# Merge them to get a source, target pair
s = Merge(
[src_dataset.get_example_stream(),
trg_dataset.get_example_stream()], ('source', 'target'))
# Filter sequences that are too long
s = Filter(s, predicate=stream._too_long(seq_len=seq_len))
# Replace out of vocabulary tokens with unk token
s = Mapping(
s,
stream._oov_to_unk(src_vocab_size=src_vocab_size,
trg_vocab_size=trg_vocab_size,
unk_id=utils.UNK_ID))
# Build a batched version of stream to read k batches ahead
s = Batch(s, iteration_scheme=ConstantScheme(batch_size * sort_k_batches))
# Sort all samples in the read-ahead batch
s = Mapping(s, SortMapping(stream._length))
# Convert it into a stream again
s = Unpack(s)
# Construct batches from the stream with specified batch size
s = Batch(s, iteration_scheme=ConstantScheme(batch_size))
# Pad sequences that are short
masked_stream = stream.PaddingWithEOS(s, [utils.EOS_ID, utils.EOS_ID])
return masked_stream
def get_test_stream_withContext_grdTruth(test_ctx_datas=None,
test_set_source=None,
test_set_target=None,
src_vocab=None,
src_vocab_size=30000,
trg_vocab=None,
trg_vocab_size=30000,
batch_size=128,
unk_id=1,
ctx_num=3,
**kwargs):
"""Setup development set stream if necessary."""
masked_stream = None
if test_set_source is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(src_vocab if isinstance(
src_vocab, dict) else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0,
eos_idx=src_vocab_size - 1,
unk_idx=unk_id)
trg_vocab = _ensure_special_tokens(trg_vocab if isinstance(
trg_vocab, dict) else cPickle.load(open(trg_vocab, 'rb')),
bos_idx=0,
eos_idx=trg_vocab_size - 1,
unk_idx=unk_id)
print test_set_source, type(src_vocab)
# Get text files from both source and target
ctx_datasets = []
for i in range(ctx_num):
ctx_datasets.append(TextFile([test_ctx_datas[i]], src_vocab, None))
dev_dataset = TextFile([test_set_source], src_vocab, None)
dev_target = TextFile([test_set_target], trg_vocab, None)
dev_stream = Merge([i.get_example_stream() for i in ctx_datasets] + [
dev_dataset.get_example_stream(),
dev_target.get_example_stream()
],
tuple('context_' + str(i) for i in range(ctx_num)) +
('source', 'target'))
stream = Mapping(
dev_stream,
_oov_to_unk(ctx_num=ctx_num,
src_vocab_size=src_vocab_size,
trg_vocab_size=trg_vocab_size,
unk_id=unk_id))
# Build a batched version of stream to read k batches ahead
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
masked_stream = PaddingWithEOSContext(
stream, [src_vocab_size - 1
for i in range(ctx_num + 1)] + [trg_vocab_size - 1])
return masked_stream
def pair_data_stream(dataset, batch_size):
data_streams = [
Rename(_data_stream(dataset=dataset, batch_size=batch_size),
names={
source: '{}_{}'.format(source, i)
for source in dataset.sources
}) for i in [1, 2]
]
data_stream = Merge(data_streams=data_streams,
sources=data_streams[0].sources +
data_streams[1].sources)
_ = data_streams[0].get_epoch_iterator() # make sure not same order
return data_stream
def get_dev_stream_withContext_withPosTag(test_ctx_datas=None,
test_posTag_datas=None,
test_set_source=None,
src_vocab=None,
src_vocab_size=30000,
unk_id=1,
ctx_num=3,
**kwargs):
"""Setup development set stream if necessary."""
dev_stream = None
if test_set_source is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(src_vocab if isinstance(
src_vocab, dict) else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0,
eos_idx=src_vocab_size - 1,
unk_idx=unk_id)
print test_set_source, type(src_vocab)
# Get text files from both source and target
ctx_datasets = []
posTag_datasets = []
for i in range(ctx_num):
ctx_datasets.append(TextFile([test_ctx_datas[i]], src_vocab, None))
posTag_datasets.append(
TextFile([test_posTag_datas[i]], src_vocab, None))
posTag_datasets.append(
TextFile([test_posTag_datas[ctx_num]], src_vocab, None))
src_dataset = TextFile([test_set_source], src_vocab, None)
# Merge them to get a source, target pair
dev_stream = Merge(
[i.get_example_stream() for i in ctx_datasets] +
[i.get_example_stream()
for i in posTag_datasets] + [src_dataset.get_example_stream()],
tuple('context_' + str(i) for i in range(ctx_num)) +
tuple('context_posTag_' + str(i)
for i in range(ctx_num)) + ('source_posTag', 'source'))
stream = Mapping(
dev_stream,
_oov_to_unk_posTag_dev(ctx_num=ctx_num,
src_vocab_size=src_vocab_size,
unk_id=unk_id))
# Build a batched version of stream to read k batches ahead
stream = Batch(stream, iteration_scheme=ConstantScheme(1))
masked_stream = PaddingWithEOSContext(
stream, [src_vocab_size - 1 for i in range(2 * ctx_num + 2)])
return masked_stream
def get_dev_stream_with_grdTruth(val_set_source=None,
val_set_target=None,
src_vocab=None,
src_vocab_size=30000,
trg_vocab=None,
trg_vocab_size=30000,
batch_size=128,
unk_id=1,
seq_len=50,
**kwargs):
"""Setup development set stream if necessary."""
dev_stream = None
if val_set_source is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(src_vocab if isinstance(
src_vocab, dict) else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0,
eos_idx=src_vocab_size - 1,
unk_idx=unk_id)
trg_vocab = _ensure_special_tokens(trg_vocab if isinstance(
trg_vocab, dict) else cPickle.load(open(trg_vocab, 'rb')),
bos_idx=0,
eos_idx=trg_vocab_size - 1,
unk_idx=unk_id)
print val_set_source, type(src_vocab)
dev_dataset = TextFile([val_set_source], src_vocab, None)
trg_dataset = TextFile([val_set_target], trg_vocab, None)
# Merge them to get a source, target pair
dev_stream = Merge([
dev_dataset.get_example_stream(),
trg_dataset.get_example_stream()
], ('dev_source', 'dev_target'))
# Filter sequences that are too long
stream = Filter(dev_stream, predicate=_too_long(seq_len=seq_len))
# Replace out of vocabulary tokens with unk token
stream = Mapping(
stream,
_oov_to_unk(src_vocab_size=src_vocab_size,
trg_vocab_size=trg_vocab_size,
unk_id=unk_id))
# Build a batched version of stream to read k batches ahead
stream = Batch(stream, iteration_scheme=ConstantScheme(1))
# Pad sequences that are short
masked_stream = PaddingWithEOS(
stream, [src_vocab_size - 1, trg_vocab_size - 1])
return masked_stream
def get_log_prob_stream(cg, config):
eid, did = p_(cg)
dataset = config['log_prob_sets'][cg]
# Prepare source vocabs and files, make sure special tokens are there
src_vocab = cPickle.load(open(config['src_vocabs'][eid]))
src_vocab['<S>'] = 0
src_vocab['</S>'] = config['src_eos_idxs'][eid]
src_vocab['<UNK>'] = config['unk_id']
# Prepare target vocabs and files, make sure special tokens are there
trg_vocab = cPickle.load(open(config['trg_vocabs'][did]))
trg_vocab['<S>'] = 0
trg_vocab['</S>'] = config['trg_eos_idxs'][did]
trg_vocab['<UNK>'] = config['unk_id']
# Build the preprocessing pipeline for individual streams
logger.info('Building logprob stream for cg:[{}]'.format(cg))
src_dataset = TextFile([dataset[0]], src_vocab, None)
trg_dataset = TextFile([dataset[1]], trg_vocab, None)
stream = Merge(
[src_dataset.get_example_stream(),
trg_dataset.get_example_stream()], ('source', 'target'))
stream = Mapping(
stream,
_oov_to_unk(src_vocab_size=config['src_vocab_sizes'][eid],
trg_vocab_size=config['trg_vocab_sizes'][did],
unk_id=config['unk_id']))
bs = 100
if 'log_prob_bs' in config:
if isinstance(config['log_prob_bs'], dict):
bs = config['log_prob_bs'][cg]
else:
bs = config['log_prob_bs']
stream = Batch(stream,
iteration_scheme=ConstantScheme(bs,
num_examples=get_num_lines(
dataset[0])))
masked_stream = Padding(stream)
masked_stream = Mapping(
masked_stream,
_remapWordIdx([(0, 0, config['src_eos_idxs'][eid]),
(2, 0, config['trg_eos_idxs'][did])]))
return masked_stream
def get_dev_stream_with_prefix_file(val_set=None, val_set_grndtruth=None, val_set_prefixes=None, val_set_suffixes=None,
src_vocab=None, src_vocab_size=30000, trg_vocab=None, trg_vocab_size=30000, unk_id=1,
return_vocab=False, **kwargs):
"""Setup development stream with user-provided source, target, prefixes, and suffixes"""
dev_stream = None
if val_set is not None and val_set_grndtruth is not None and val_set_prefixes is not None and val_set_suffixes is not None:
src_vocab = _ensure_special_tokens(
src_vocab if isinstance(src_vocab, dict) else
cPickle.load(open(src_vocab)),
bos_idx=0, eos_idx=src_vocab_size - 1, unk_idx=unk_id)
trg_vocab = _ensure_special_tokens(
trg_vocab if isinstance(trg_vocab, dict) else
cPickle.load(open(trg_vocab)),
bos_idx=0, eos_idx=trg_vocab_size - 1, unk_idx=unk_id)
# Note: user should have already provided the EOS token in the data representation for the suffix
# Note: The reason that we need EOS tokens in the reference file is that IMT systems need to evaluate metrics
# Note: which count prediction of the </S> token, and evaluation scripts are called on the files
dev_source_dataset = TextFile([val_set], src_vocab,
bos_token='<S>',
eos_token='</S>',
unk_token='<UNK>')
dev_target_dataset = TextFile([val_set_grndtruth], trg_vocab,
bos_token='<S>',
eos_token='</S>',
unk_token='<UNK>')
dev_prefix_dataset = TextFile([val_set_prefixes], trg_vocab,
bos_token='<S>',
eos_token=None,
unk_token='<UNK>')
dev_suffix_dataset = TextFile([val_set_suffixes], trg_vocab,
bos_token=None,
eos_token=None,
unk_token='<UNK>')
dev_stream = Merge([dev_source_dataset.get_example_stream(),
dev_target_dataset.get_example_stream(),
dev_prefix_dataset.get_example_stream(),
dev_suffix_dataset.get_example_stream()],
('source', 'target','target_prefix','target_suffix'))
if return_vocab:
return dev_stream, src_vocab, trg_vocab
else:
return dev_stream
def load_parallel_data(src_file,
tgt_file,
batch_size,
sort_k_batches,
dictionary,
training=False):
def preproc(s):
s = s.replace('``', '"')
s = s.replace('\'\'', '"')
return s
enc_dset = TextFile(files=[src_file],
dictionary=dictionary,
bos_token=None,
eos_token=None,
unk_token=CHAR_UNK_TOK,
level='character',
preprocess=preproc)
dec_dset = TextFile(files=[tgt_file],
dictionary=dictionary,
bos_token=CHAR_SOS_TOK,
eos_token=CHAR_EOS_TOK,
unk_token=CHAR_UNK_TOK,
level='character',
preprocess=preproc)
# NOTE merge encoder and decoder setup together
stream = Merge(
[enc_dset.get_example_stream(),
dec_dset.get_example_stream()], ('source', 'target'))
if training:
# filter sequences that are too long
stream = Filter(stream, predicate=TooLong(seq_len=CHAR_MAX_SEQ_LEN))
# batch and read k batches ahead
stream = Batch(stream,
iteration_scheme=ConstantScheme(batch_size *
sort_k_batches))
# sort all samples in read-ahead batch
stream = Mapping(stream, SortMapping(lambda x: len(x[1])))
# turn back into stream
stream = Unpack(stream)
# batch again
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
masked_stream = Padding(stream)
return masked_stream
def get_dev_tr_stream_with_topic_target(val_set_source=None,val_set_target=None, src_vocab=None,trg_vocab=None, src_vocab_size=30000,trg_vocab_size=30000,
trg_topic_vocab_size=2000,source_topic_vocab_size=2000,
topical_dev_set=None,topic_vocab_input=None,topic_vocab_output=None,topical_vocab_size=2000,
unk_id=1, **kwargs):
"""Prepares the training data stream."""
dev_stream = None
if val_set_source is not None and src_vocab is not None:
src_vocab = _ensure_special_tokens(
src_vocab if isinstance(src_vocab, dict)
else cPickle.load(open(src_vocab, 'rb')),
bos_idx=0, eos_idx=src_vocab_size - 1, unk_idx=unk_id)
trg_vocab = _ensure_special_tokens(
trg_vocab if isinstance(trg_vocab, dict) else
cPickle.load(open(trg_vocab, 'rb')),
bos_idx=0, eos_idx=trg_vocab_size - 1, unk_idx=unk_id)
topic_vocab_input=cPickle.load(open(topic_vocab_input,'rb'));
topic_vocab_output=cPickle.load(open(topic_vocab_output, 'rb'));#already has <UNK> and </S> in it
topic_binary_vocab={};
for k,v in topic_vocab_output.items():
if k=='<UNK>':
topic_binary_vocab[k]=0;
else:
topic_binary_vocab[k]=1;
# Get text files from both source and target
src_dataset = TextFile([val_set_source], src_vocab, None)
trg_dataset = TextFile([val_set_target], trg_vocab, None)
src_topic_input=TextFile([topical_dev_set],topic_vocab_input,None,None,'rt')
trg_topic_dataset = TextFile([val_set_target],topic_vocab_output,None);
trg_topic_binary_dataset= TextFile([val_set_target],topic_binary_vocab,None);
# Merge them to get a source, target pair
dev_stream = Merge([src_dataset.get_example_stream(),
trg_dataset.get_example_stream(),
src_topic_input.get_example_stream(),
trg_topic_dataset.get_example_stream(),
trg_topic_binary_dataset.get_example_stream()],
('source', 'target','source_topical','target_topic','target_binary_topic'))
stream = Batch(
dev_stream, iteration_scheme=ConstantScheme(1))
masked_stream = PaddingWithEOS(
stream, [src_vocab_size - 1,trg_vocab_size - 1, source_topic_vocab_size-1,trg_topic_vocab_size - 1,trg_topic_vocab_size-1])
return masked_stream
def get_dev_stream(sfiles, tfiles, svocab_dict, tvocab_dict):
s_dataset = TextFile(sfiles, svocab_dict, bos_token=None, eos_token=None,\
unk_token='<unk>', level='word', preprocess=None, encoding='utf8')
t_dataset = TextFile(tfiles, tvocab_dict, bos_token=None, eos_token=None,\
unk_token='<unk>', level='word', preprocess=None, encoding='utf8')
# Merge
stream = Merge([s_dataset.get_example_stream(),
t_dataset.get_example_stream()],
('source', 'target'))
# Batch - Sort
stream = Batch(stream,
iteration_scheme=ConstantScheme(1006))
# Pad
# Note that </s>=0. Fuel only allows padding 0 by default
masked_stream = Padding(stream)
return masked_stream
class TestMerge(object):
def setUp(self):
self.streams = (
DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.batch_streams = (
Batch(DataStream(IterableDataset(['Hello world!', 'Hi!'])),
iteration_scheme=ConstantScheme(2)),
Batch(DataStream(IterableDataset(['Bonjour le monde!', 'Salut!'])),
iteration_scheme=ConstantScheme(2)))
self.transformer = Merge(
self.streams, ('english', 'french'))
self.batch_transformer = Merge(
self.batch_streams, ('english', 'french'))
def test_sources(self):
assert_equal(self.transformer.sources, ('english', 'french'))
def test_merge(self):
it = self.transformer.get_epoch_iterator()
assert_equal(next(it), ('Hello world!', 'Bonjour le monde!'))
assert_raises(StopIteration, next, it)
# There used to be problems with reseting Merge, for which
# reason we regression-test it as follows:
it = self.transformer.get_epoch_iterator()
assert_equal(next(it), ('Hello world!', 'Bonjour le monde!'))
assert_raises(StopIteration, next, it)
def test_batch_merge(self):
it = self.batch_transformer.get_epoch_iterator()
assert_equal(next(it),
(('Hello world!', 'Hi!'),
('Bonjour le monde!', 'Salut!')))
assert_raises(StopIteration, next, it)
# There used to be problems with reseting Merge, for which
# reason we regression-test it as follows:
it = self.batch_transformer.get_epoch_iterator()
assert_equal(next(it),
(('Hello world!', 'Hi!'),
('Bonjour le monde!', 'Salut!')))
assert_raises(StopIteration, next, it)
def test_merge_batch_streams(self):
it = self.transformer.get_epoch_iterator()
assert_equal(next(it), ('Hello world!', 'Bonjour le monde!'))
assert_raises(StopIteration, next, it)
# There used to be problems with reseting Merge, for which
# reason we regression-test it as follows:
it = self.transformer.get_epoch_iterator()
assert_equal(next(it), ('Hello world!', 'Bonjour le monde!'))
assert_raises(StopIteration, next, it)
def test_as_dict(self):
assert_equal(
next(self.transformer.get_epoch_iterator(as_dict=True)),
({'english': 'Hello world!', 'french': 'Bonjour le monde!'}))
def test_error_on_wrong_number_of_sources(self):
assert_raises(ValueError, Merge, self.streams, ('english',))
def test_value_error_on_different_stream_output_type(self):
spanish_stream = DataStream(IndexableDataset(['Hola mundo!']),
iteration_scheme=SequentialScheme(2, 2))
assert_raises(ValueError, Merge, self.streams + (spanish_stream,),
('english', 'french', 'spanish'))
def test_close_calls_close_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.close()
assert all(stream.close_called for stream in streams)
def test_next_epoch_calls_next_epoch_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.next_epoch()
assert all(stream.next_epoch_called for stream in streams)
def test_reset_calls_reset_on_all_streams(self):
streams = [FlagDataStream(IterableDataset([1, 2, 3])),
FlagDataStream(IterableDataset([4, 5, 6])),
FlagDataStream(IterableDataset([7, 8, 9]))]
transformer = Merge(streams, ('1', '2', '3'))
transformer.reset()
assert all(stream.reset_called for stream in streams)
def test_merge(self):
transformer = Merge(self.streams, ("english", "french"))
assert_equal(next(transformer.get_epoch_iterator()), ("Hello world!", "Bonjour le monde!"))
def setUp(self):
self.streams = (
DataStream(IterableDataset(['Hello world!'])),
DataStream(IterableDataset(['Bonjour le monde!'])))
self.transformer = Merge(self.streams, ('english', 'french'))
def test_as_dict(self):
transformer = Merge(self.streams, ("english", "french"))
assert_equal(
next(transformer.get_epoch_iterator(as_dict=True)),
({"english": "Hello world!", "french": "Bonjour le monde!"}),
)
def test_merge(self):
transformer = Merge(self.streams, ('english', 'french'))
assert_equal(next(transformer.get_epoch_iterator()),
('Hello world!', 'Bonjour le monde!'))
def test_as_dict(self):
transformer = Merge(self.streams, ('english', 'french'))
assert_equal(
next(transformer.get_epoch_iterator(as_dict=True)),
({'english': 'Hello world!', 'french': 'Bonjour le monde!'}))
