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 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')
if hasattr(
self.config,
'use_cuts_for_training') and self.config.use_cuts_for_training:
stream = DataStream(stream, iteration_scheme=TaxiTimeCutScheme())
else:
stream = DataStream(stream,
iteration_scheme=ShuffledExampleScheme(
stream.num_examples))
stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
stream = transformers.TaxiGenerateSplits(
stream, max_splits=self.config.max_splits)
stream = transformers.taxi_add_datetime(stream)
# stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
stream = transformers.Select(stream, tuple(req_vars))
stream = Batch(stream,
iteration_scheme=ConstantScheme(self.config.batch_size))
stream = MultiProcessing(stream)
return 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 train(self, req_vars):
stream = TaxiDataset('train', data.traintest_ds)
if hasattr(
self.config,
'use_cuts_for_training') and self.config.use_cuts_for_training:
stream = DataStream(stream, iteration_scheme=TaxiTimeCutScheme())
else:
stream = DataStream(stream,
iteration_scheme=ShuffledExampleScheme(
stream.num_examples))
if not data.tvt:
valid = TaxiDataset(data.valid_set,
data.valid_ds,
sources=('trip_id', ))
valid_trips_ids = valid.get_data(None,
slice(0, valid.num_examples))[0]
stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
if hasattr(self.config, 'max_splits'):
stream = transformers.TaxiGenerateSplits(
stream, max_splits=self.config.max_splits)
elif not data.tvt:
stream = transformers.add_destination(stream)
if hasattr(self.config, 'train_max_len'):
idx = stream.sources.index('latitude')
def max_len_filter(x):
return len(x[idx]) <= self.config.train_max_len
stream = Filter(stream, max_len_filter)
stream = transformers.TaxiExcludeEmptyTrips(stream)
stream = transformers.taxi_add_datetime(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)
stream = MultiProcessing(stream)
return stream
def valid(self, req_vars):
stream = TaxiStream(data.valid_set, data.valid_ds)
stream = transformers.taxi_add_datetime(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)
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]
dataset = TaxiDataset('train')
prefix_stream = DataStream(dataset,
iteration_scheme=TaxiTimeCutScheme(
self.config.num_cuts))
prefix_stream = transformers.TaxiExcludeTrips(prefix_stream,
valid_trips_ids)
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 = DataStream(dataset,
iteration_scheme=ShuffledExampleScheme(
dataset.num_examples))
candidate_stream = transformers.TaxiExcludeTrips(
candidate_stream, valid_trips_ids)
candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream)
candidate_stream = transformers.taxi_add_datetime(candidate_stream)
candidate_stream = transformers.taxi_add_first_last_len(
candidate_stream, self.config.n_begin_end_pts)
candidate_stream = Batch(candidate_stream,
iteration_scheme=ConstantScheme(
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 valid(self, req_vars):
valid_dataset = TaxiDataset(self.config.valid_set, 'valid.hdf5')
train_dataset = TaxiDataset('train')
valid_trips_ids = valid_dataset.get_data(
None, slice(0, valid_dataset.num_examples))[
valid_dataset.sources.index('trip_id')]
prefix_stream = DataStream(valid_dataset,
iteration_scheme=SequentialExampleScheme(
valid_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)
prefix_stream = Batch(prefix_stream,
iteration_scheme=ConstantScheme(
self.config.batch_size))
candidate_stream = DataStream(train_dataset,
iteration_scheme=ShuffledExampleScheme(
train_dataset.num_examples))
candidate_stream = transformers.TaxiExcludeTrips(
candidate_stream, valid_trips_ids)
candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream)
candidate_stream = transformers.taxi_add_datetime(candidate_stream)
candidate_stream = transformers.taxi_add_first_last_len(
candidate_stream, self.config.n_begin_end_pts)
candidate_stream = Batch(candidate_stream,
iteration_scheme=ConstantScheme(
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_value_error_on_request(self):
background = MultiProcessing(self.transformer)
assert_raises(ValueError, background.get_data, [0, 1])
def test_multiprocessing(self):
background = MultiProcessing(self.transformer)
assert_equal(list(background.get_epoch_iterator()),
list(zip(range(1, 101))))
def test_multiprocessing():
stream = IterableDataset(range(100)).get_example_stream()
plus_one = Mapping(stream, lambda x: (x[0] + 1, ))
background = MultiProcessing(plus_one)
for a, b in zip(background.get_epoch_iterator(), range(1, 101)):
assert a == (b, )
def test_multiprocessing():
stream = IterableDataset(range(100)).get_example_stream()
plus_one = Mapping(stream, lambda x: (x[0] + 1,))
background = MultiProcessing(plus_one)
for a, b in zip(background.get_epoch_iterator(), range(1, 101)):
assert a == (b,)
def test_axis_labels_passed_on_by_default(self):
self.transformer.axis_labels = {'features': ('batch', 'index')}
background = MultiProcessing(self.transformer)
assert_equal(background.axis_labels, self.transformer.axis_labels)
def test_multiprocessing(self):
background = MultiProcessing(self.transformer)
assert_equal(list(background.get_epoch_iterator()),
list(zip(range(1, 101))))
print(args)
rsync = Rsync(args.tmpdir)
rsync.sync(args.data_path)
args.data_path = os.path.join(args.tmpdir,
os.path.basename(args.data_path))
train_ds = fuel_utils.get_datastream(
path=args.data_path,
which_set=args.train_dataset,
batch_size=args.batch_size,
use_ivectors=args.use_ivectors,
truncate_ivectors=args.truncate_ivectors,
ivector_dim=args.ivector_dim)
train_ds = MultiProcessing(train_ds, max_store=200)
valid_ds = fuel_utils.get_datastream(
path=args.data_path,
which_set=args.valid_dataset,
batch_size=args.batch_size,
use_ivectors=args.use_ivectors,
truncate_ivectors=args.truncate_ivectors,
ivector_dim=args.ivector_dim)
test_ds = fuel_utils.get_datastream(
path=args.data_path,
which_set=args.test_dataset,
batch_size=args.batch_size,
use_ivectors=args.use_ivectors,
truncate_ivectors=args.truncate_ivectors,
ivector_dim=args.ivector_dim)