def test_two_sources(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))),
ConstantScheme(2)))
assert len(next(transformer.get_epoch_iterator())) == 4
def test_two_sources(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))),
ConstantScheme(2)))
assert len(next(transformer.get_epoch_iterator())) == 4
def test_mask_dtype(self):
transformer = Padding(
Batch(
DataStream(IterableDataset(dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))), ConstantScheme(2)
),
mask_dtype="uint8",
)
assert_equal(str(next(transformer.get_epoch_iterator())[1].dtype), "uint8")
def indexData(self):
labCounts = graph_helper.getLabelCounts(
self.G, self.trainNodes + self.validationNodes)
trainXY, trainIDs = encode_data_VarLen(
self.G,
self.trainNodes,
self.attrKey,
self.maxNeighbors,
usePrevWeights=self.usePrevWeights,
useActualLabs=self.useActualLabs,
onlyLabs=self.onlyLabs,
useInputX2=self.useInputX2,
labCounts=labCounts,
dataAug=self.dataAug,
pageRankOrder=self.pageRankOrder,
usePro=self.usePro,
lastH=self.lastHH,
nodeIDs=True)
validationXY, testIDs = encode_data_VarLen(
self.G,
self.validationNodes,
self.attrKey,
self.maxNeighbors,
labCounts=labCounts,
usePrevWeights=self.usePrevWeights,
useActualLabs=self.useActualLabs,
onlyLabs=self.onlyLabs,
useInputX2=self.useInputX2,
pageRankOrder=self.pageRankOrder,
usePro=self.usePro,
lastH=self.lastHH,
nodeIDs=True)
self.input_dimx1 = trainXY['x'][0].shape[1]
if 'x2' in trainXY:
self.input_dimx2 = trainXY['x2'].shape[1]
dataset_train = IndexableDataset(trainXY)
dataset_valid = IndexableDataset(validationXY)
self.num_examples_train = dataset_train.num_examples
self.num_examples_valid = dataset_valid.num_examples
if self.usePro:
transpose_stream = self.transpose_streamPro
else:
transpose_stream = self.transpose_stream
self.stream_train = DataStream(dataset=dataset_train,
iteration_scheme=ShuffledScheme(
examples=dataset_train.num_examples,
batch_size=self.batch_size))
self.stream_train = Padding(self.stream_train, mask_sources=['x'])
self.stream_train = Mapping(self.stream_train, transpose_stream)
self.stream_valid = DataStream(dataset=dataset_valid,
iteration_scheme=ShuffledScheme(
examples=dataset_valid.num_examples,
batch_size=self.batch_size))
self.stream_valid = Padding(self.stream_valid, mask_sources=['x'])
self.stream_valid = Mapping(self.stream_valid, transpose_stream)
def test_mask_sources(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
OrderedDict([('features', [[1], [2, 3]]),
('targets', [[4, 5, 6], [7]])]))),
ConstantScheme(2)),
mask_sources=('features',))
assert_equal(len(next(transformer.get_epoch_iterator())), 3)
def test_mask_dtype(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))),
ConstantScheme(2)),
mask_dtype='uint8')
assert_equal(
str(next(transformer.get_epoch_iterator())[1].dtype), 'uint8')
def test_mask_sources(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
OrderedDict([('features', [[1], [2, 3]]),
('targets', [[4, 5, 6], [7]])]))),
ConstantScheme(2)),
mask_sources=('features',))
assert_equal(len(next(transformer.get_epoch_iterator())), 3)
def test_1d_sequences(self):
stream = Batch(
DataStream(IterableDataset([[1], [2, 3], [], [4, 5, 6], [7]])),
ConstantScheme(2))
transformer = Padding(stream)
assert_equal(transformer.sources, ("data", "data_mask"))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.array([[1, 0], [2, 3]]),
numpy.array([[1, 0], [1, 1]])),
(numpy.array([[0, 0, 0], [4, 5, 6]]),
numpy.array([[0, 0, 0], [1, 1, 1]])),
(numpy.array([[7]]), numpy.array([[1]]))])
def test_1d_sequences(self):
stream = Batch(
DataStream(IterableDataset([[1], [2, 3], [], [4, 5, 6], [7]])),
ConstantScheme(2))
transformer = Padding(stream)
assert_equal(transformer.sources, ("data", "data_mask"))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.array([[1, 0], [2, 3]]),
numpy.array([[1, 0], [1, 1]])),
(numpy.array([[0, 0, 0], [4, 5, 6]]),
numpy.array([[0, 0, 0], [1, 1, 1]])),
(numpy.array([[7]]), numpy.array([[1]]))])
def setup_datastream(path, batch_size, sort_batch_count, valid=False):
A = numpy.load(
os.path.join(path,
('valid_x_raw.npy' if valid else 'train_x_raw.npy')))
B = numpy.load(
os.path.join(path, ('valid_phn.npy' if valid else 'train_phn.npy')))
C = numpy.load(
os.path.join(
path,
('valid_seq_to_phn.npy' if valid else 'train_seq_to_phn.npy')))
D = [B[x[0]:x[1], 2] for x in C]
ds = IndexableDataset({'input': A, 'output': D})
stream = DataStream(ds, iteration_scheme=ShuffledExampleScheme(len(A)))
stream = Batch(stream,
iteration_scheme=ConstantScheme(batch_size *
sort_batch_count))
comparison = _balanced_batch_helper(stream.sources.index('input'))
stream = Mapping(stream, SortMapping(comparison))
stream = Unpack(stream)
stream = Batch(stream,
iteration_scheme=ConstantScheme(batch_size,
num_examples=len(A)))
stream = Padding(stream, mask_sources=['input', 'output'])
return ds, 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 candidate_stream(self, n_candidates, sortmap=True):
candidate_stream = DataStream(self.train_dataset,
iteration_scheme=ShuffledExampleScheme(
self.train_dataset.num_examples))
if not data.tvt:
candidate_stream = transformers.TaxiExcludeTrips(
candidate_stream, self.valid_trips_ids)
candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream)
candidate_stream = transformers.taxi_add_datetime(candidate_stream)
if not data.tvt:
candidate_stream = transformers.add_destination(candidate_stream)
if sortmap:
candidate_stream = transformers.balanced_batch(
candidate_stream,
key='latitude',
batch_size=n_candidates,
batch_sort_size=self.config.batch_sort_size)
else:
candidate_stream = Batch(
candidate_stream,
iteration_scheme=ConstantScheme(n_candidates))
candidate_stream = Padding(candidate_stream,
mask_sources=['latitude', 'longitude'])
return candidate_stream
def setup_datastream(path, vocab_file, config):
ds = QADataset(path,
vocab_file,
config.n_entities,
need_sep_token=config.concat_ctx_and_question)
it = QAIterator(path, shuffle=config.shuffle_questions)
stream = DataStream(ds, iteration_scheme=it)
if config.concat_ctx_and_question:
stream = ConcatCtxAndQuestion(stream, config.concat_question_before,
ds.reverse_vocab['<SEP>'])
# Sort sets of multiple batches to make batches of similar sizes
stream = Batch(stream,
iteration_scheme=ConstantScheme(config.batch_size *
config.sort_batch_count))
comparison = _balanced_batch_helper(
stream.sources.index(
'question' if config.concat_ctx_and_question else 'context'))
stream = Mapping(stream, SortMapping(comparison))
stream = Unpack(stream)
print('sources')
print(stream.sources)
stream = Batch(stream, iteration_scheme=ConstantScheme(config.batch_size))
stream = Padding(stream,
mask_sources=['context', 'question', 'candidates'],
mask_dtype='int32')
print('sources2')
print(stream.sources)
return ds, stream
def framewise_timit_datastream(path, which_set, batch_size, local_copy=False):
# load frame-wise dataset
timit_dataset = FramewiseTimit(which_set=which_set,
path=path,
local_copy=local_copy)
# set shuffle range
shuffle_rng = numpy.random.RandomState(123)
# set iterator scheme
iterator_scheme = SequentialShuffledScheme(
num_examples=timit_dataset.num_examples,
batch_size=batch_size,
rng=shuffle_rng)
# base data stream
base_stream = DataStream(dataset=timit_dataset,
iteration_scheme=iterator_scheme)
# reshape data stream data_source, shape_source
reshape_stream = Reshape(data_source='features',
shape_source='features_shapes',
data_stream=base_stream,
iteration_scheme=iterator_scheme)
# sort data stream
sort_stream = Mapping(data_stream=reshape_stream,
mapping=SortMapping(key=lambda x: x[0].shape[0]))
# padding data stream
padded_stream = Padding(data_stream=sort_stream)
return padded_stream
def test_value_error_on_request(self):
transformer = Padding(Batch(
DataStream(
IterableDataset(
dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))),
ConstantScheme(2)))
assert_raises(ValueError, transformer.get_data, [0, 1])
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 train(self, req_vars):
valid = TaxiDataset(self.config.valid_set,
'valid.hdf5',
sources=('trip_id', ))
valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
stream = TaxiDataset('train')
stream = DataStream(stream,
iteration_scheme=ShuffledExampleScheme(
stream.num_examples))
stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
stream = transformers.TaxiExcludeEmptyTrips(stream)
stream = transformers.taxi_add_datetime(stream)
stream = transformers.add_destination(stream)
stream = transformers.Select(
stream, tuple(v for v in req_vars if not v.endswith('_mask')))
stream = transformers.balanced_batch(
stream,
key='latitude',
batch_size=self.config.batch_size,
batch_sort_size=self.config.batch_sort_size)
stream = Padding(stream, mask_sources=['latitude', 'longitude'])
stream = transformers.Select(stream, req_vars)
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)
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 setup_squad_ranker_datastream(path,
vocab_file,
config,
example_count=1836975):
ds = SQuADRankerDataset(path, vocab_file)
it = ShuffledExampleScheme(examples=example_count)
stream = DataStream(ds, iteration_scheme=it)
# Sort sets of multiple batches to make batches of similar sizes
stream = Batch(stream,
iteration_scheme=ConstantScheme(config.batch_size *
config.sort_batch_count))
comparison = _balanced_batch_helper(stream.sources.index('question'))
stream = Mapping(stream, SortMapping(comparison))
stream = Unpack(stream)
stream = Batch(stream, iteration_scheme=ConstantScheme(config.batch_size))
stream = Padding(stream,
mask_sources=[
'question', 'answer', 'better', 'worse', 'b_left',
'b_right', 'w_left', 'w_right'
],
mask_dtype='int32')
return ds, 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.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_stream(self, part, batch_size, seed=None, raw_text=False):
d = self.get_dataset(part)
print("Dataset with {} examples".format(d.num_examples))
it = ShuffledExampleScheme(d.num_examples,
rng=numpy.random.RandomState(seed))
stream = DataStream(d, iteration_scheme=it)
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
if self._retrieval:
stream = FixedMapping(
stream,
functools.partial(retrieve_and_pad_snli, self._retrieval),
add_sources=("defs", "def_mask", "sentence1_def_map",
"sentence2_def_map")
) # This is because there is bug in Fuel :( Cannot concatenate tuple and list
if not raw_text:
stream = SourcewiseMapping(stream,
functools.partial(digitize, self.vocab),
which_sources=('sentence1',
'sentence2'))
stream = Padding(
stream,
mask_sources=('sentence1',
'sentence2')) # Increases amount of outputs by x2
return stream
def get_datastream(path,
which_set,
batch_size=1,
norm_path=None,
use_ivectors=False,
truncate_ivectors=False,
ivector_dim=100,
shuffled=True):
wsj_dataset = H5PYDataset(path, which_sets=(which_set, ))
if shuffled:
iterator_scheme = ShuffledScheme(batch_size=batch_size,
examples=wsj_dataset.num_examples)
else:
iterator_scheme = SequentialScheme(batch_size=batch_size,
examples=wsj_dataset.num_examples)
base_stream = DataStream(dataset=wsj_dataset,
iteration_scheme=iterator_scheme)
if norm_path:
data_mean_std = numpy.load(norm_path)
base_stream = Normalize(data_stream=base_stream,
means=data_mean_std['mean'],
stds=data_mean_std['std'])
if use_ivectors:
fs = FilterSources(data_stream=base_stream,
sources=['features', 'ivectors', 'targets'])
if truncate_ivectors:
fs = TruncateTransformer(fs, 'ivectors', ivector_dim)
# fs = ConcatenateTransformer(fs, ['features', 'ivectors'], 'features')
else:
fs = FilterSources(data_stream=base_stream,
sources=['features', 'targets'])
return Padding(fs)
def setup_squad_datastream(path, vocab_file, config):
ds = SQuADDataset(path, vocab_file)
it = SQuADIterator(path)
stream = DataStream(ds, iteration_scheme=it)
if config.concat_ctx_and_question:
stream = ConcatCtxAndQuestion(stream, config.concat_question_before,
ds.reverse_vocab['<DUMMY>'])
# Sort sets of multiple batches to make batches of similar sizes
stream = Batch(stream,
iteration_scheme=ConstantScheme(config.batch_size *
config.sort_batch_count))
comparison = _balanced_batch_helper(stream.sources.index('context'))
stream = Mapping(stream, SortMapping(comparison))
stream = Unpack(stream)
stream = Batch(stream, iteration_scheme=ConstantScheme(config.batch_size))
stream = Padding(stream,
mask_sources=[
'context', 'question', 'answer', 'ans_indices',
'ans_boundaries'
],
mask_dtype='int32')
return ds, stream
def get_stream(self,
part,
batch_size=None,
max_length=None,
seed=None,
remove_keys=False,
add_bos_=True,
remove_n_identical_keys=True):
dataset = self.get_dataset(part, max_length)
if self._layout == 'lambada' and part == 'train':
stream = DataStream(dataset,
iteration_scheme=RandomSpanScheme(
dataset.num_examples, max_length, seed))
stream = Mapping(stream, listify)
else:
stream = dataset.get_example_stream()
if add_bos_:
stream = SourcewiseMapping(stream,
functools.partial(
add_bos, Vocabulary.BOS),
which_sources=('words'))
if max_length != None:
stream = SourcewiseMapping(stream,
functools.partial(
cut_if_too_long, max_length),
which_sources=('words'))
stream = SourcewiseMapping(stream, vectorize, which_sources=('words'))
stream = SourcewiseMapping(stream,
word_to_singleton_list,
which_sources=('keys'))
stream = SourcewiseMapping(stream, vectorize, which_sources=('keys'))
stream = Flatten(stream, which_sources=('keys'))
if self._layout == 'dict':
if remove_keys:
stream = FilterSources(
stream,
[source for source in stream.sources if source != 'keys'])
if remove_n_identical_keys:
print "remove identical keys"
stream = FilterSources(stream, [
source for source in stream.sources
if source != 'n_identical_keys'
])
if not batch_size:
return stream
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
stream = Padding(stream, mask_sources=('words'))
#stream = Flatten(stream, which_sources=('n_identical_keys'))
#if self._layout == 'dict':
# stream = FilterSources(stream, [source for source in stream.sources
# if source != 'keys_mask'])
# stream = FilterSources(stream, [source for source in stream.sources
# if source != 'n_identical_keys_mask'])
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_feat_stream(path, which_set='test_eval92', batch_size=1):
wsj_dataset = H5PYDataset(path, which_sets=(which_set, ))
print path, which_set
iterator_scheme = SequentialScheme(examples=wsj_dataset.num_examples, batch_size=batch_size)
base_stream = DataStream(dataset=wsj_dataset,
iteration_scheme=iterator_scheme)
fs = FilterSources(data_stream=base_stream, sources=['features'])
padded_stream = Padding(data_stream=fs)
return padded_stream
def get_datastream(path, which_set='train_si84', batch_size=1):
wsj_dataset = H5PYDataset(path, which_sets=(which_set, ))
print path, which_set
iterator_scheme = ShuffledScheme(batch_size=batch_size, examples=wsj_dataset.num_examples)
base_stream = DataStream(dataset=wsj_dataset,
iteration_scheme=iterator_scheme)
fs = FilterSources(data_stream=base_stream, sources=['features', 'targets'])
padded_stream = Padding(data_stream=fs)
return padded_stream
def replaceTestData(self, testNodes, maxNeighbors=1000, maskNames=['x']):
if self.batchesInferences:
batch_size = self.batch_size
else:
batch_size = 1
testing, testIDs = encode_data_VarLen(self.G,
testNodes,
self.attrKey,
maxNeighbors,
useActualLabs=self.useActualLabs,
useInputX2=self.useInputX2,
onlyLabs=self.onlyLabs,
lastH=self.lastHH,
nodeIDs=True)
dataset_test = IndexableDataset(testing)
self.stream_test = DataStream(dataset=dataset_test,
iteration_scheme=SequentialScheme(
examples=dataset_test.num_examples,
batch_size=batch_size))
#add masks, have to do individually to avoid all dimensions must be equal error
#write own padding transformer, their's sucks ...
self.stream_test = Padding(self.stream_test, mask_sources=maskNames)
#transpose them for rnn input
self.stream_test = Mapping(self.stream_test, self.transpose_streamTest)
self.num_examples_test = dataset_test.num_examples
#replace shareddata with test_all data
self.test_all, names = self.iterateShared(self.stream_test,
makeShared=False,
name="test")
#if we are doing test in batches
if self.batchesInferences:
for key in self.test_all:
totalTestBatches = len(self.test_all[key])
if key != 'nodeID':
for i in range(0, totalTestBatches):
#if test data has more batches, we add more to shared data list
#else we just reset
if i >= self.totalBatches:
newKey = key + '_myinput'
self.sharedData[key].append(
shared(self.test_all[key][i],
name=self.sharedName + '_' + newKey +
'_test_' + str(i)))
else:
self.sharedData[key][i].set_value(
self.test_all[key][i], borrow=True)
self.sharedBatch[key].set_value(
self.sharedData[key][0].get_value(borrow=True),
borrow=True)
self.stream_test_int = IntStream(0, totalTestBatches, 1,
'int_stream')
def setup_datastream(batch_size, **kwargs):
ds = ToyDataset(**kwargs)
stream = DataStream(ds,
iteration_scheme=SequentialExampleScheme(
kwargs['nb_examples']))
stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size))
stream = Padding(stream, mask_sources=['input', 'output'])
return ds, stream
def valid(self, req_vars):
stream = TaxiStream(self.config.valid_set, 'valid.hdf5')
stream = transformers.taxi_add_datetime(stream)
stream = transformers.add_destination(stream)
stream = transformers.Select(
stream, tuple(v for v in req_vars if not v.endswith('_mask')))
stream = Batch(stream, iteration_scheme=ConstantScheme(1))
stream = Padding(stream, mask_sources=['latitude', 'longitude'])
stream = transformers.Select(stream, req_vars)
return stream
def _liacl_data_stream(dataset,
rel2index,
batch_size,
word2index,
target='negative_sampling',
name="",
k=3,
shuffle=False,
neg_sample_kwargs={}):
batches_per_epoch = int(np.ceil(dataset.num_examples / float(batch_size)))
if shuffle:
iteration_scheme = ShuffledScheme(dataset.num_examples, batch_size)
else:
iteration_scheme = SequentialScheme(dataset.num_examples, batch_size)
data_stream = DataStream(dataset, iteration_scheme=iteration_scheme)
data_stream = NumberizeWords(data_stream,
word2index,
default=word2index[UNKNOWN_TOKEN],
which_sources=('head', 'tail'))
data_stream = NumberizeWords(data_stream, rel2index, which_sources=('rel'))
if target == "score":
data_stream = Rename(data_stream, {'score': 'target'})
else:
data_stream = FilterSources(data_stream,
sources=('head', 'tail', 'rel'))
data_stream = Padding(data_stream,
mask_sources=('head, tail'),
mask_dtype=np.float32)
if target == 'negative_sampling':
logger.info('target for data stream ' + str(name) +
' is negative sampling')
data_stream = NegativeSampling(data_stream, k=k)
elif target == 'filtered_negative_sampling':
logger.info('target for data stream ' + str(name) +
' is filtered negative sampling')
data_stream = FilteredNegativeSampling(data_stream,
k=k,
**neg_sample_kwargs)
elif target == 'score':
logger.info('target for data stream ' + str(name) + ' is score')
else:
raise NotImplementedError(
'target ', target,
' must be one of "score" or "negative_sampling"')
data_stream = MergeSource(data_stream,
merge_sources=('head', 'tail', 'head_mask',
'tail_mask', 'rel'),
merge_name='input')
return data_stream, batches_per_epoch
def test(self, req_vars):
stream = TaxiStream('test')
stream = transformers.taxi_add_datetime(stream)
stream = transformers.taxi_remove_test_only_clients(stream)
stream = transformers.Select(
stream, tuple(v for v in req_vars if not v.endswith('_mask')))
stream = Batch(stream, iteration_scheme=ConstantScheme(1))
stream = Padding(stream, mask_sources=['latitude', 'longitude'])
stream = transformers.Select(stream, req_vars)
return stream
def construct_stream(dataset, rng, pool_size, maximum_frames, window_features,
**kwargs):
"""Construct data stream.
Parameters:
-----------
dataset : Dataset
Dataset to use.
rng : numpy.random.RandomState
Random number generator.
pool_size : int
Pool size for TIMIT dataset.
maximum_frames : int
Maximum frames for TIMIT datset.
subsample : bool, optional
Subsample features.
pretrain_alignment : bool, optional
Use phoneme alignment for pretraining.
uniform_alignment : bool, optional
Use uniform alignment for pretraining.
"""
kwargs.setdefault('subsample', False)
kwargs.setdefault('pretrain_alignment', False)
kwargs.setdefault('uniform_alignment', False)
stream = DataStream(dataset,
iteration_scheme=SequentialShuffledScheme(
dataset.num_examples, pool_size, rng))
if kwargs['pretrain_alignment'] and kwargs['uniform_alignment']:
stream = AddUniformAlignmentMask(stream)
stream = Reshape('features', 'features_shapes', data_stream=stream)
means, stds = dataset.get_normalization_factors()
stream = Normalize(stream, means, stds)
if not window_features == 1:
stream = WindowFeatures(stream, 'features', window_features)
if kwargs['pretrain_alignment']:
stream = Reshape('alignments', 'alignments_shapes', data_stream=stream)
stream = Mapping(stream, SortMapping(key=key))
stream = MaximumFrameCache(max_frames=maximum_frames,
data_stream=stream,
rng=rng)
stream = Padding(data_stream=stream, mask_sources=['features', 'phonemes'])
if kwargs['pretrain_alignment']:
stream = AlignmentPadding(stream, 'alignments')
stream = Transpose(stream, [(1, 0, 2), (1, 0), (1, 0), (1, 0),
(2, 1, 0)])
else:
stream = Transpose(stream, [(1, 0, 2), (1, 0), (1, 0), (1, 0)])
stream = ForceFloatX(stream)
if kwargs['subsample']:
stream = Subsample(stream, 'features', 5)
stream = Subsample(stream, 'features_mask', 5)
return stream
def test_2d_sequences(self):
stream = Batch(
DataStream(
IterableDataset([numpy.ones((3, 4)), 2 * numpy.ones((2, 4))])),
ConstantScheme(2))
it = Padding(stream).get_epoch_iterator()
data, mask = next(it)
assert data.shape == (2, 3, 4)
assert (data[0, :, :] == 1).all()
assert (data[1, :2, :] == 2).all()
assert (mask == numpy.array([[1, 1, 1], [1, 1, 0]])).all()
def test_padding():
# 1-D sequences
stream = Batch(
DataStream(
IterableDataset([[1], [2, 3], [], [4, 5, 6], [7]])),
ConstantScheme(2))
mask_stream = Padding(stream)
assert mask_stream.sources == ("data", "data_mask")
it = mask_stream.get_epoch_iterator()
data, mask = next(it)
assert (data == numpy.array([[1, 0], [2, 3]])).all()
assert (mask == numpy.array([[1, 0], [1, 1]])).all()
data, mask = next(it)
assert (data == numpy.array([[0, 0, 0], [4, 5, 6]])).all()
assert (mask == numpy.array([[0, 0, 0], [1, 1, 1]])).all()
data, mask = next(it)
assert (data == numpy.array([[7]])).all()
assert (mask == numpy.array([[1]])).all()
# 2D sequences
stream2 = Batch(
DataStream(
IterableDataset([numpy.ones((3, 4)), 2 * numpy.ones((2, 4))])),
ConstantScheme(2))
it = Padding(stream2).get_epoch_iterator()
data, mask = next(it)
assert data.shape == (2, 3, 4)
assert (data[0, :, :] == 1).all()
assert (data[1, :2, :] == 2).all()
assert (mask == numpy.array([[1, 1, 1], [1, 1, 0]])).all()
# 2 sources
stream3 = Padding(Batch(
DataStream(
IterableDataset(
dict(features=[[1], [2, 3]], targets=[[4, 5, 6], [7]]))),
ConstantScheme(2)))
assert len(next(stream3.get_epoch_iterator())) == 4
batch_size = 20
frame_size = 3
k = 20
target_size = frame_size * k
hidden_size_recurrent = 400
readout_size = 6 * k + 1
lr = 3e-4
dataset = Handwriting(("train",))
data_stream = DataStream.default_stream(dataset, iteration_scheme=SequentialScheme(dataset.num_examples, batch_size))
# data_stream = FilterSources(data_stream,
# sources = ('features',))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
# data_stream = ForceFloatX(data_stream)
dataset = Handwriting(("valid",))
valid_stream = DataStream.default_stream(
dataset, iteration_scheme=SequentialScheme(dataset.num_examples, 10 * batch_size)
)
valid_stream = Padding(valid_stream)
valid_stream = Mapping(valid_stream, _transpose)
x_tr = next(data_stream.get_epoch_iterator())
x = tensor.tensor3("features")
x_mask = tensor.matrix("features_mask")
