from theano import tensor
from blocks.bricks import application, MLP, Initializable, Linear, Rectifier, Identity
from blocks.bricks.base import lazy
from blocks.bricks.recurrent import Bidirectional, LSTM
from blocks.utils import shared_floatx_zeros
from blocks.bricks.parallel import Fork
from fuel.transformers import Batch, Padding, Mapping, SortMapping, Unpack, MultiProcessing
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme
from model import ContextEmbedder
import data
from data import transformers
from data.hdf5 import TaxiDataset, TaxiStream
import error
class BidiRNN(Initializable):
@lazy()
def __init__(self, config, output_dim=2, **kwargs):
super(BidiRNN, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
self.rec = Bidirectional(LSTM(dim = config.hidden_state_dim, name = 'recurrent'))
self.fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], prototype=Linear())
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.sequences = ['latitude', 'latitude_mask', 'longitude']
self.inputs = self.sequences + self.context_embedder.inputs
self.children = [ self.context_embedder, self.fork, self.rec, self.rec_to_output ]
def _push_allocation_config(self):
self.fork.input_dim = 2
self.fork.output_dims = [ self.rec.children[0].get_dim(name) for name in self.fork.output_names ]
self.fork.weights_init = self.config.fork_weights_init
self.fork.biases_init = self.config.fork_biases_init
self.rec.weights_init = self.config.rec_weights_init
self.rec_to_output.weights_init = self.config.mlp_weights_init
self.rec_to_output.biases_init = self.config.mlp_biases_init
def process_outputs(self, outputs):
return outputs
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude.T - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude.T - data.train_gps_mean[1]) / data.train_gps_std[1]
latitude_mask = latitude_mask.T
latitude = tensor.shape_padright(latitude)
longitude = tensor.shape_padright(longitude)
rec_in = tensor.concatenate((latitude, longitude), axis=2)
last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64')
path = self.rec.apply(self.fork.apply(rec_in), 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 UniformGenerator(object):
def __init__(self):
self.rng = numpy.random.RandomState(123)
def __call__(self, *args):
return float(self.rng.uniform())
class Stream(object):
def __init__(self, config):
self.config = config
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)
if hasattr(self.config, 'shuffle_batch_size'):
stream = transformers.Batch(stream, iteration_scheme=ConstantScheme(self.config.shuffle_batch_size))
stream = Mapping(stream, SortMapping(key=UniformGenerator()))
stream = Unpack(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(self.config.valid_set, 'valid.hdf5')
stream = transformers.taxi_add_datetime(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 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(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.matrix('latitude'),
'longitude': tensor.matrix('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')}
