1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
|
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'])
def cost(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 *= latitude_mask.flatten()
return ce.sum() / latitude_mask.sum()
@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):
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(1000))
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')}
|
