from theano import tensor
from blocks.bricks import application, MLP, Initializable, Tanh
from blocks.bricks.base import lazy
from blocks.bricks.recurrent import LSTM, recurrent
from blocks.utils import shared_floatx_zeros
from fuel.transformers import Batch, Padding
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 Model(Initializable):
@lazy()
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
@application
def initial_state(self, *args, **kwargs):
return self.rec.initial_state(*args, **kwargs)
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'], sequences=['latitude', 'longitude', 'latitude_mask'])
def predict_all(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude - data.train_gps_mean[1]) / data.train_gps_std[1]
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
latitude = tensor.shape_padright(latitude)
longitude = tensor.shape_padright(longitude)
itr = self.input_to_rec.apply(tensor.concatenate(pre_emb + (latitude, longitude), axis=1))
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=latitude_mask, iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = (rto * data.train_gps_std) + data.train_gps_mean
return (rto, next_states, next_cells)
@predict_all.property('contexts')
def predict_all_inputs(self):
return self.context
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
return res[-1]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(latitude.shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(latitude.shape)
return ce * latitude_mask
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
# Only works when batch_size is 1.
return self.cost_matrix(**kwargs)[-1,0]
@valid_cost.property('inputs')
def valid_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):
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')
stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples))
stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
stream = transformers.TaxiExcludeEmptyTrips(stream)
stream = transformers.taxi_add_datetime(stream)
stream = transformers.add_destination(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)
return stream
def valid(self, req_vars):
stream = TaxiStream(self.config.valid_set, 'valid.hdf5')
stream = transformers.taxi_add_datetime(stream)
stream = transformers.add_destination(stream)
stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask')))
stream = Batch(stream, iteration_scheme=ConstantScheme(1))
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(1))
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'),
'week_of_year': tensor.bvector('week_of_year'),
'day_of_week': tensor.bvector('day_of_week'),
'qhour_of_day': tensor.bvector('qhour_of_day'),
'destination_latitude': tensor.vector('destination_latitude'),
'destination_longitude': tensor.vector('destination_longitude')}
