diff options
| -rw-r--r-- | config/memory_network_mlp.py | 54 | ||||
| -rw-r--r-- | data/transformers.py | 17 | ||||
| -rw-r--r-- | model/memory_network.py | 58 | ||||
| -rw-r--r-- | model/memory_network_bidir.py | 17 | ||||
| -rw-r--r-- | model/memory_network_mlp.py | 28 |
5 files changed, 124 insertions, 50 deletions
diff --git a/config/memory_network_mlp.py b/config/memory_network_mlp.py new file mode 100644 index 0000000..8c7b445 --- /dev/null +++ b/config/memory_network_mlp.py @@ -0,0 +1,54 @@ +from blocks.initialization import IsotropicGaussian, Constant + +from blocks.bricks import Tanh + +import data +from model.memory_network_mlp import Model, Stream + +n_begin_end_pts = 5 + +dim_embeddings = [ + ('origin_call', data.origin_call_train_size, 10), + ('origin_stand', data.stands_size, 10), + ('week_of_year', 52, 10), + ('day_of_week', 7, 10), + ('qhour_of_day', 24 * 4, 10), + ('day_type', 3, 10), +] + +embed_weights_init = IsotropicGaussian(0.001) + +class MLPConfig(object): + __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init', 'embed_weights_init', 'dim_embeddings') + +prefix_encoder = MLPConfig() +prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) +prefix_encoder.dim_hidden = [100, 100] +prefix_encoder.weights_init = IsotropicGaussian(0.01) +prefix_encoder.biases_init = Constant(0.001) +prefix_encoder.embed_weights_init = embed_weights_init +prefix_encoder.dim_embeddings = dim_embeddings + +candidate_encoder = MLPConfig() +candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) +candidate_encoder.dim_hidden = [100, 100] +candidate_encoder.weights_init = IsotropicGaussian(0.01) +candidate_encoder.biases_init = Constant(0.001) +candidate_encoder.embed_weights_init = embed_weights_init +candidate_encoder.dim_embeddings = dim_embeddings + +representation_size = 100 +representation_activation = Tanh + +normalize_representation = True + + +batch_size = 32 +batch_sort_size = 20 + +max_splits = 100 +num_cuts = 1000 + +train_candidate_size = 100 +valid_candidate_size = 100 +test_candidate_size = 100 diff --git a/data/transformers.py b/data/transformers.py index b3a8486..88fdcf6 100644 --- a/data/transformers.py +++ b/data/transformers.py @@ -6,7 +6,7 @@ import theano import fuel from fuel.schemes import ConstantScheme -from fuel.transformers import Batch, Mapping, SortMapping, Transformer, Unpack +from fuel.transformers import Batch, Mapping, SortMapping, Transformer, Unpack, FilterSources import data @@ -22,20 +22,7 @@ def at_least_k(k, v, pad_at_begin, is_longitude): v = numpy.concatenate((v, numpy.full((k - len(v),), v[-1]))) return v - -class Select(Transformer): - produces_examples = True - - def __init__(self, data_stream, sources): - super(Select, self).__init__(data_stream) - self.ids = [data_stream.sources.index(source) for source in sources] - self.sources=sources - - def get_data(self, request=None): - if request is not None: - raise ValueError - data=next(self.child_epoch_iterator) - return [data[id] for id in self.ids] +Select = FilterSources class TaxiExcludeTrips(Transformer): produces_examples = True diff --git a/model/memory_network.py b/model/memory_network.py index 84a8edf..7ced8c0 100644 --- a/model/memory_network.py +++ b/model/memory_network.py @@ -25,12 +25,13 @@ class MemoryNetworkBase(Initializable): self.children = [ self.softmax, prefix_encoder, candidate_encoder ] self.inputs = self.prefix_encoder.apply.inputs \ - + ['candidate_%s'%x for x in self.candidate_encoder.apply.inputs] + + ['candidate_%s'%x for x in self.candidate_encoder.apply.inputs] \ + + ['candidate_destination_latitude', 'candidate_destination_longitude'] - def candidate_destination(**kwargs): + def candidate_destination(self, **kwargs): return tensor.concatenate( - (tensor.shape_padright(kwargs['candidate_last_k_latitude'][:,-1]), - tensor.shape_padright(kwargs['candidate_last_k_longitude'][:,-1])), + (tensor.shape_padright(kwargs['candidate_destination_latitude']), + tensor.shape_padright(kwargs['candidate_destination_longitude'])), axis=1) @application(outputs=['cost']) @@ -43,10 +44,8 @@ class MemoryNetworkBase(Initializable): @application(outputs=['destination']) def predict(self, **kwargs): - prefix_representation = self.prefix_encoder.apply( - { x: kwargs[x] for x in self.prefix_encoder.apply.inputs }) - candidate_representatin = self.candidate_encoder.apply( - { x: kwargs['candidate_'+x] for x in self.candidate_encoder.apply.inputs }) + prefix_representation = self.prefix_encoder.apply(**{ x: kwargs[x] for x in self.prefix_encoder.apply.inputs }) + candidate_representation = self.candidate_encoder.apply(**{ x: kwargs['candidate_'+x] for x in self.candidate_encoder.apply.inputs }) if self.config.normalize_representation: prefix_representation = prefix_representation \ @@ -130,12 +129,16 @@ class StreamSimple(StreamBase): def candidate_stream(self, n_candidates): candidate_stream = DataStream(self.train_dataset, - iteration_scheme=ShuffledExampleScheme(dataset.num_examples)) - candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, self.valid_trips_ids) + iteration_scheme=ShuffledExampleScheme(self.train_dataset.num_examples)) + if not data.tvt: + candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, self.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) + if not data.tvt: + candidate_stream = transformers.add_destination(candidate_stream) + return Batch(candidate_stream, iteration_scheme=ConstantScheme(n_candidates)) @@ -180,6 +183,27 @@ class StreamSimple(StreamBase): stream = MultiProcessing(stream) return stream + def test(self, req_vars): + prefix_stream = DataStream( + self.test_dataset, + iteration_scheme=SequentialExampleScheme(self.test_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) + + if not data.tvt: + prefix_stream = transformers.taxi_remove_test_only_clients(prefix_stream) + + prefix_stream = Batch(prefix_stream, + iteration_scheme=ConstantScheme(self.config.batch_size)) + + candidate_stream = self.candidate_stream(self.config.test_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 class StreamRecurrent(StreamBase): def __init__(self, config): @@ -194,10 +218,14 @@ class StreamRecurrent(StreamBase): def candidate_stream(self, n_candidates): candidate_stream = DataStream(self.train_dataset, iteration_scheme=ShuffledExampleScheme(self.train_dataset.num_examples)) - candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, self.valid_trips_ids) + if not data.tvt: + candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, self.valid_trips_ids) candidate_stream = transformers.TaxiExcludeEmptyTrips(candidate_stream) candidate_stream = transformers.taxi_add_datetime(candidate_stream) + if not data.tvt: + candidate_stream = transformers.add_destination(candidate_stream) + candidate_stream = Batch(candidate_stream, iteration_scheme=ConstantScheme(n_candidates)) @@ -210,7 +238,8 @@ class StreamRecurrent(StreamBase): prefix_stream = DataStream(self.train_dataset, iteration_scheme=ShuffledExampleScheme(self.train_dataset.num_examples)) - prefix_stream = transformers.TaxiExcludeTrips(prefix_stream, self.valid_trips_ids) + if not data.tvt: + prefix_stream = transformers.TaxiExcludeTrips(prefix_stream, self.valid_trips_ids) prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream) prefix_stream = transformers.TaxiGenerateSplits(prefix_stream, max_splits=self.config.max_splits) @@ -238,7 +267,7 @@ class StreamRecurrent(StreamBase): self.valid_dataset, iteration_scheme=SequentialExampleScheme(self.valid_dataset.num_examples)) - prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream) + #prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream) prefix_stream = transformers.taxi_add_datetime(prefix_stream) @@ -262,7 +291,8 @@ class StreamRecurrent(StreamBase): iteration_scheme=SequentialExampleScheme(self.test_dataset.num_examples)) prefix_stream = transformers.taxi_add_datetime(prefix_stream) - prefix_stream = transformers.taxi_remove_test_only_clients(prefix_stream) + if not data.tvt: + prefix_stream = transformers.taxi_remove_test_only_clients(prefix_stream) prefix_stream = Batch(prefix_stream, iteration_scheme=ConstantScheme(self.config.batch_size)) diff --git a/model/memory_network_bidir.py b/model/memory_network_bidir.py index cc99312..81e6440 100644 --- a/model/memory_network_bidir.py +++ b/model/memory_network_bidir.py @@ -72,22 +72,25 @@ class RecurrentEncoder(Initializable): return outputs + @apply.property('inputs') + def apply_inputs(self): + return self.inputs + class Model(MemoryNetworkBase): def __init__(self, config, **kwargs): # Build prefix encoder : recurrent then MLP - prefix_encoder = RecurrentEncoder(self.config.prefix_encoder, - self.config.representation_size, - self.config.representation_activation(), + prefix_encoder = RecurrentEncoder(config.prefix_encoder, + config.representation_size, + config.representation_activation(), name='prefix_encoder') # Build candidate encoder - candidate_encoder = RecurrentEncoder(self.config.candidate_encoder, - self.config.representation_size, - self.config.representation_activation(), + candidate_encoder = RecurrentEncoder(config.candidate_encoder, + config.representation_size, + config.representation_activation(), name='candidate_encoder') # And... that's it! super(Model, self).__init__(config, prefix_encoder, candidate_encoder, **kwargs) - diff --git a/model/memory_network_mlp.py b/model/memory_network_mlp.py index de07e60..fc897d5 100644 --- a/model/memory_network_mlp.py +++ b/model/memory_network_mlp.py @@ -18,17 +18,17 @@ from memory_network import MemoryNetworkBase class MLPEncoder(Initializable): def __init__(self, config, output_dim, activation, **kwargs): - super(RecurrentEncoder, self).__init__(**kwargs) + super(MLPEncoder, self).__init__(**kwargs) self.config = config self.context_embedder = ContextEmbedder(self.config) - self.encoder_mlp = MLP(activations=[Rectifier() for _ in config.prefix_encoder.dim_hidden] - + [config.representation_activation()], - dims=[config.prefix_encoder.dim_input] - + config.prefix_encoder.dim_hidden - + [config.representation_size], - name='prefix_encoder') + self.encoder_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + + [activation()], + dims=[config.dim_input] + + config.dim_hidden + + [output_dim], + name='encoder') self.extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]} @@ -37,7 +37,7 @@ class MLPEncoder(Initializable): self.encoder_mlp ] def _push_initialization_config(self): - for brick in [self.contex_encoder, self.encoder_mlp]: + for brick in [self.context_embedder, self.encoder_mlp]: brick.weights_init = self.config.weights_init brick.biases_init = self.config.biases_init @@ -46,7 +46,7 @@ class MLPEncoder(Initializable): 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.prefix_extremities.items()) + for k, v in self.extremities.items()) inputs = tensor.concatenate(extremities + embeddings, axis=1) return self.encoder_mlp.apply(inputs) @@ -60,12 +60,12 @@ class Model(MemoryNetworkBase): def __init__(self, config, **kwargs): prefix_encoder = MLPEncoder(config.prefix_encoder, config.representation_size, - config.representation_activation()) + config.representation_activation, + name='prefix_encoder') - candidate_encoer = MLPEncoder(config.candidate_encoder, + candidate_encoder = MLPEncoder(config.candidate_encoder, config.representation_size, - config.representation_activation()) + config.representation_activation, + name='candidate_encoder') super(Model, self).__init__(config, prefix_encoder, candidate_encoder, **kwargs) - - |