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from model.bidirectional import SegregatedBidirectional
class Model(Initializable):
@lazy()
def __init__(self, config, output_dim=2, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
act = config.rec_activation() if hasattr(config, 'rec_activation') else None
self.rec = SegregatedBidirectional(LSTM(dim=config.hidden_state_dim, activation=act,
name='recurrent'))
self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='fwd_fork')
self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='bkwd_fork')
rto_in = config.hidden_state_dim * 2 + sum(x[2] for x in config.dim_embeddings)
self.rec_to_output = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
dims=[rto_in] + config.dim_hidden + [output_dim])
self.softmax = Softmax()
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.inputs = self.sequences + self.context_embedder.inputs
self.children = [ self.context_embedder, self.fwd_fork, self.bkwd_fork,
self.rec, self.rec_to_output, self.softmax ]
self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX),
name='classes')
def _push_allocation_config(self):
for i, fork in enumerate([self.fwd_fork, self.bkwd_fork]):
fork.input_dim = 2 * self.config.window_size
fork.output_dims = [ self.rec.children[i].get_dim(name)
for name in fork.output_names ]
def _push_initialization_config(self):
for brick in [self.fwd_fork, self.bkwd_fork, self.rec, self.rec_to_output]:
brick.weights_init = self.config.weights_init
brick.biases_init = self.config.biases_init
def process_outputs(self, outputs):
return tensor.dot(self.softmax.apply(outputs), self.classes)
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude.dimshuffle(1, 0, 2) - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude.dimshuffle(1, 0, 2) - data.train_gps_mean[1]) / data.train_gps_std[1]
latitude_mask = latitude_mask.T
rec_in = tensor.concatenate((latitude, longitude), axis=2)
last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64')
path = self.rec.apply(merge(self.fwd_fork.apply(rec_in, as_dict=True),
{'mask': latitude_mask}),
merge(self.bkwd_fork.apply(rec_in, as_dict=True),
{'mask': latitude_mask}))[0]
path_representation = (path[0][:, -self.config.hidden_state_dim:],
path[last_id - 1, tensor.arange(latitude_mask.shape[1])]
[:, :self.config.hidden_state_dim])
embeddings = tuple(self.context_embedder.apply(
**{k: kwargs[k] for k in self.context_embedder.inputs }))
inputs = tensor.concatenate(path_representation + embeddings, axis=1)
outputs = self.rec_to_output.apply(inputs)
return self.process_outputs(outputs)
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@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()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
class Stream(object):
def __init__(self, config):
self.config = config
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.window(stream, config.window_size)
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.window(stream, config.window_size)
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 test(self, req_vars):
stream = TaxiStream('test', data.traintest_ds)
stream = transformers.window(stream, config.window_size)
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(self.config.batch_size))
stream = Padding(stream, mask_sources=['latitude', 'longitude'])
stream = transformers.Select(stream, req_vars)
return stream
def inputs(self):
return {'call_type': tensor.bvector('call_type'),
'origin_call': tensor.ivector('origin_call'),
'origin_stand': tensor.bvector('origin_stand'),
'taxi_id': tensor.wvector('taxi_id'),
'timestamp': tensor.ivector('timestamp'),
'day_type': tensor.bvector('day_type'),
'missing_data': tensor.bvector('missing_data'),
'latitude': tensor.tensor('latitude'),
'longitude': tensor.tensor('longitude'),
'latitude_mask': tensor.matrix('latitude_mask'),
'longitude_mask': tensor.matrix('longitude_mask'),
'destination_latitude': tensor.vector('destination_latitude'),
'destination_longitude': tensor.vector('destination_longitude'),
'travel_time': tensor.ivector('travel_time'),
'input_time': tensor.ivector('input_time'),
'week_of_year': tensor.bvector('week_of_year'),
'day_of_week': tensor.bvector('day_of_week'),
'qhour_of_day': tensor.bvector('qhour_of_day')}
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