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from theano import tensor
import numpy
import fuel
import blocks
from fuel.transformers import Batch, MultiProcessing, Mapping, SortMapping, Unpack
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme
from blocks.bricks import application, MLP, Rectifier, Initializable
import data
from data import transformers
from data.hdf5 import TaxiDataset, TaxiStream
from data.cut import TaxiTimeCutScheme
from model import ContextEmbedder
blocks.config.default_seed = 123
fuel.config.default_seed = 123
class FFMLP(Initializable):
def __init__(self, config, output_layer=None, **kwargs):
super(FFMLP, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
output_activation = [] if output_layer is None else [output_layer()]
output_dim = [] if output_layer is None else [config.dim_output]
self.mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + output_activation,
dims=[config.dim_input] + config.dim_hidden + output_dim)
self.extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]}
self.inputs = self.context_embedder.inputs + self.extremities.keys()
self.children = [ self.context_embedder, self.mlp ]
def _push_initialization_config(self):
self.mlp.weights_init = self.config.mlp_weights_init
self.mlp.biases_init = self.config.mlp_biases_init
@application(outputs=['prediction'])
def predict(self, **kwargs):
embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs }))
extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.extremities.items())
inputs = tensor.concatenate(extremities + embeddings, axis=1)
outputs = self.mlp.apply(inputs)
return outputs
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
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.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 valid(self, req_vars):
stream = TaxiStream(self.config.valid_set, 'valid.hdf5')
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))
return Batch(stream, iteration_scheme=ConstantScheme(1000))
def test(self, req_vars):
stream = TaxiStream('test')
stream = transformers.taxi_add_datetime(stream)
stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
stream = transformers.taxi_remove_test_only_clients(stream)
return Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
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'),
'destination_latitude': tensor.vector('destination_latitude'),
'destination_longitude': tensor.vector('destination_longitude'),
'travel_time': tensor.ivector('travel_time'),
'first_k_latitude': tensor.matrix('first_k_latitude'),
'first_k_longitude': tensor.matrix('first_k_longitude'),
'last_k_latitude': tensor.matrix('last_k_latitude'),
'last_k_longitude': tensor.matrix('last_k_longitude'),
'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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