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
|
from theano import tensor
from fuel.transformers import Batch, MultiProcessing, Merge
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme, SequentialExampleScheme
from blocks.bricks import application, MLP, Rectifier, Initializable, Softmax
import data
from data import transformers
from data.cut import TaxiTimeCutScheme
from data.hdf5 import TaxiDataset, TaxiStream
import error
from model import ContextEmbedder
class Model(Initializable):
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
self.prefix_encoder = MLP(activations=[Rectifier() for _ in config.prefix_encoder.dim_hidden],
dims=[config.prefix_encoder.dim_input] + config.prefix_encoder.dim_hidden,
name='prefix_encoder')
self.candidate_encoder = MLP(activations=[Rectifier() for _ in config.candidate_encoder.dim_hidden],
dims=[config.candidate_encoder.dim_input] + config.candidate_encoder.dim_hidden,
name='candidate_encoder')
self.softmax = Softmax()
self.prefix_extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]}
self.candidate_extremities = {'candidate_%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]}
self.inputs = self.context_embedder.inputs + ['candidate_%s'%k for k in self.context_embedder.inputs] + self.prefix_extremities.keys() + self.candidate_extremities.keys()
self.children = [ self.context_embedder, self.prefix_encoder, self.candidate_encoder, self.softmax ]
def _push_initialization_config(self):
for (mlp, config) in [[self.prefix_encoder, self.config.prefix_encoder], [self.candidate_encoder, self.config.candidate_encoder]]:
mlp.weights_init = config.weights_init
mlp.biases_init = config.biases_init
@application(outputs=['destination'])
def predict(self, **kwargs):
prefix_embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs }))
prefix_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.prefix_extremities.items())
prefix_inputs = tensor.concatenate(prefix_extremities + prefix_embeddings, axis=1)
prefix_representation = self.prefix_encoder.apply(prefix_inputs)
candidate_embeddings = tuple(self.context_embedder.apply(**{k: kwargs['candidate_%s'%k] for k in self.context_embedder.inputs }))
candidate_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.candidate_extremities.items())
candidate_inputs = tensor.concatenate(candidate_extremities + candidate_embeddings, axis=1)
candidate_representation = self.candidate_encoder.apply(candidate_inputs)
similarity_score = tensor.dot(prefix_representation, candidate_representation)
similarity = self.softmax.apply(similarity_score)
candidate_destination = tensor.concatenate(
(tensor.shape_padright(kwargs['candidate_last_k_latitude'][:,-1]),
tensor.shape_padright(kwargs['candidate_last_k_longitude'][:,-1])),
axis=1)
return tensor.dot(similarity, candidate_destination)
@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):
valid = TaxiDataset(self.config.valid_set, 'valid.hdf5', sources=('trip_id',))
valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
dataset = TaxiDataset('train')
prefix_stream = DataStream(dataset, iteration_scheme=TaxiTimeCutScheme())
prefix_stream = transformers.TaxiExcludeTrips(prefix_stream, valid_trips_ids)
prefix_stream = transformers.TaxiGenerateSplits(prefix_stream, max_splits=self.config.max_splits)
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.taxi_add_first_last_len(prefix_stream, self.config.n_begin_end_pts)
prefix_stream = Batch(prefix_stream, iteration_scheme=ConstantScheme(self.config.batch_size))
candidate_stream = DataStream(dataset, iteration_scheme=ShuffledExampleScheme(dataset.num_examples))
candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, valid_trips_ids)
candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream)
candidate_stream = transformers.taxi_add_datetime(candidate_stream)
candidate_stream = transformers.taxi_add_first_last_len(candidate_stream, self.config.n_begin_end_pts)
candidate_stream = Batch(candidate_stream, iteration_scheme=ConstantScheme(self.config.train_candidate_size))
sources = prefix_stream.sources + tuple('candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
stream = MultiProcessing(stream)
return stream
def valid(self, req_vars):
valid_dataset = TaxiDataset(self.config.valid_set, 'valid.hdf5')
train_dataset = TaxiDataset('train')
valid_trips_ids = valid_dataset.get_data(None, slice(0, valid_dataset.num_examples))[valid_dataset.sources.index('trip_id')]
prefix_stream = DataStream(valid_dataset, iteration_scheme=SequentialExampleScheme(valid_dataset.num_examples))
prefix_stream = transformers.taxi_add_datetime(prefix_stream)
prefix_stream = transformers.taxi_add_first_last_len(prefix_stream, self.config.n_begin_end_pts)
prefix_stream = Batch(prefix_stream, iteration_scheme=ConstantScheme(self.config.batch_size))
candidate_stream = DataStream(train_dataset, iteration_scheme=ShuffledExampleScheme(train_dataset.num_examples))
candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, valid_trips_ids)
candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream)
candidate_stream = transformers.taxi_add_datetime(candidate_stream)
candidate_stream = transformers.taxi_add_first_last_len(candidate_stream, self.config.n_begin_end_pts)
candidate_stream = Batch(candidate_stream, iteration_scheme=ConstantScheme(self.config.valid_candidate_size))
sources = prefix_stream.sources + tuple('candidate_%s' % k for k in candidate_stream.sources)
stream = Merge((prefix_stream, candidate_stream), sources)
stream = transformers.Select(stream, tuple(req_vars))
stream = MultiProcessing(stream)
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'),
'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'),
'candidate_call_type': tensor.bvector('candidate_call_type'),
'candidate_origin_call': tensor.ivector('candidate_origin_call'),
'candidate_origin_stand': tensor.bvector('candidate_origin_stand'),
'candidate_taxi_id': tensor.wvector('candidate_taxi_id'),
'candidate_timestamp': tensor.ivector('candidate_timestamp'),
'candidate_day_type': tensor.bvector('candidate_day_type'),
'candidate_missing_data': tensor.bvector('candidate_missing_data'),
'candidate_latitude': tensor.matrix('candidate_latitude'),
'candidate_longitude': tensor.matrix('candidate_longitude'),
'candidate_destination_latitude': tensor.vector('candidate_destination_latitude'),
'candidate_destination_longitude': tensor.vector('candidate_destination_longitude'),
'candidate_travel_time': tensor.ivector('candidate_travel_time'),
'candidate_first_k_latitude': tensor.matrix('candidate_first_k_latitude'),
'candidate_first_k_longitude': tensor.matrix('candidate_first_k_longitude'),
'candidate_last_k_latitude': tensor.matrix('candidate_last_k_latitude'),
'candidate_last_k_longitude': tensor.matrix('candidate_last_k_longitude'),
'candidate_input_time': tensor.ivector('candidate_input_time'),
'candidate_week_of_year': tensor.bvector('candidate_week_of_year'),
'candidate_day_of_week': tensor.bvector('candidate_day_of_week'),
'candidate_qhour_of_day': tensor.bvector('candidate_qhour_of_day')}
|
