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from theano import tensor
from fuel.transformers import Batch, MultiProcessing, Merge, Padding
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme, SequentialExampleScheme
from blocks.bricks import application, MLP, Rectifier, Initializable, Softmax
import data
from data import transformers
from data.cut import TaxiTimeCutScheme
from data.hdf5 import TaxiDataset, TaxiStream
import error
from model import ContextEmbedder
class MemoryNetworkBase(Initializable):
def __init__(self, config, prefix_encoder, candidate_encoder, **kwargs):
super(MemoryNetworkBase, self).__init__(**kwargs)
self.prefix_encoder = prefix_encoder
self.candidate_encoder = candidate_encoder
self.config = config
self.softmax = Softmax()
self.children = [ self.softmax, prefix_encoder, candidate_encoder ]
self.inputs = self.prefix_encoder.apply.inputs \
+ ['candidate_%s'%x for x in self.candidate_encoder.apply.inputs] \
+ ['candidate_destination_latitude', 'candidate_destination_longitude']
def candidate_destination(self, **kwargs):
return tensor.concatenate(
(tensor.shape_padright(kwargs['candidate_destination_latitude']),
tensor.shape_padright(kwargs['candidate_destination_longitude'])),
axis=1)
@application(outputs=['cost'])
def cost(self, **kwargs):
y_hat = self.predict(**kwargs)
y = tensor.concatenate((kwargs['destination_latitude'][:, None],
kwargs['destination_longitude'][:, None]), axis=1)
return error.erdist(y_hat, y).mean()
@application(outputs=['destination'])
def predict(self, **kwargs):
prefix_representation = self.prefix_encoder.apply(**{ x: kwargs[x] for x in self.prefix_encoder.apply.inputs })
candidate_representation = self.candidate_encoder.apply(**{ x: kwargs['candidate_'+x] for x in self.candidate_encoder.apply.inputs })
if self.config.normalize_representation:
prefix_representation = prefix_representation \
/ tensor.sqrt((prefix_representation ** 2).sum(axis=1, keepdims=True))
candidate_representation = candidate_representation \
/ tensor.sqrt((candidate_representation ** 2).sum(axis=1, keepdims=True))
similarity_score = tensor.dot(prefix_representation, candidate_representation.T)
similarity = self.softmax.apply(similarity_score)
return tensor.dot(similarity, self.candidate_destination(**kwargs))
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
class StreamBase(object):
def __init__(self, config):
self.config = config
self.prefix_inputs = [
('call_type', tensor.bvector),
('origin_call', tensor.ivector),
('origin_stand', tensor.bvector),
('taxi_id', tensor.wvector),
('timestamp', tensor.ivector),
('day_type', tensor.bvector),
('missing_data', tensor.bvector),
('latitude', tensor.matrix),
('longitude', tensor.matrix),
('destination_latitude', tensor.vector),
('destination_longitude', tensor.vector),
('travel_time', tensor.ivector),
('input_time', tensor.ivector),
('week_of_year', tensor.bvector),
('day_of_week', tensor.bvector),
('qhour_of_day', tensor.bvector)
]
self.candidate_inputs = self.prefix_inputs
def inputs(self):
prefix_inputs = { name: constructor(name)
for name, constructor in self.prefix_inputs }
candidate_inputs = { 'candidate_'+name: constructor('candidate_'+name)
for name, constructor in self.candidate_inputs }
return dict(prefix_inputs.items() + candidate_inputs.items())
@property
def valid_dataset(self):
return TaxiDataset(data.valid_set, data.valid_ds)
@property
def valid_trips_ids(self):
valid = TaxiDataset(data.valid_set, data.valid_ds, sources=('trip_id',))
return valid.get_data(None, slice(0, valid.num_examples))[0]
@property
def train_dataset(self):
return TaxiDataset('train', data.traintest_ds)
@property
def test_dataset(self):
return TaxiDataset('test', data.traintest_ds)
class StreamSimple(StreamBase):
def __init__(self, config):
super(StreamSimple, self).__init__(config)
self.prefix_inputs += [
('first_k_latitude', tensor.matrix),
('first_k_longitude', tensor.matrix),
('last_k_latitude', tensor.matrix),
('last_k_longitude', tensor.matrix),
]
self.candidate_inputs = self.prefix_inputs
def candidate_stream(self, n_candidates):
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)
candidate_stream = transformers.taxi_add_first_last_len(candidate_stream,
self.config.n_begin_end_pts)
if not data.tvt:
candidate_stream = transformers.add_destination(candidate_stream)
return Batch(candidate_stream,
iteration_scheme=ConstantScheme(n_candidates))
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 valid(self, req_vars):
prefix_stream = DataStream(
self.valid_dataset,
iteration_scheme=SequentialExampleScheme(self.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 = 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
class StreamRecurrent(StreamBase):
def __init__(self, config):
super(StreamRecurrent, self).__init__(config)
self.prefix_inputs += [
('latitude_mask', tensor.matrix),
('longitude_mask', tensor.matrix),
]
self.candidate_inputs = self.prefix_inputs
def candidate_stream(self, n_candidates):
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)
candidate_stream = Batch(candidate_stream,
iteration_scheme=ConstantScheme(n_candidates))
candidate_stream = Padding(candidate_stream,
mask_sources=['latitude', 'longitude'])
return candidate_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 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 = 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.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)
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)
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
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