commit 2a6980fdac3f6c3987d92882368bf413b50dee36
parent 0fd3b1497ffa1bb625bf593c845e28901bc640b7
Author: Étienne Simon <esimon@esimon.eu>
Date: Sun, 21 Jun 2015 17:01:59 -0400
Add bugged memory networks
Diffstat:
2 files changed, 217 insertions(+), 0 deletions(-)
diff --git a/config/memory_network_1.py b/config/memory_network_1.py
@@ -0,0 +1,43 @@
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+from model.memory_network import Model, Stream
+
+
+n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
+
+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),
+]
+
+
+class MLPConfig(object):
+ __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init')
+
+prefix_encoder = MLPConfig()
+prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+prefix_encoder.dim_hidden = [50]
+prefix_encoder.weights_init = IsotropicGaussian(0.01)
+prefix_encoder.biases_init = Constant(0.001)
+
+candidate_encoder = MLPConfig()
+candidate_encoder.dim_input = n_begin_end_pts * 2 + sum(x for (_, _, x) in dim_embeddings)
+candidate_encoder.dim_hidden = [50]
+candidate_encoder.weights_init = IsotropicGaussian(0.01)
+candidate_encoder.biases_init = Constant(0.001)
+
+
+embed_weights_init = IsotropicGaussian(0.001)
+
+batch_size = 32
+
+valid_set = 'cuts/test_times_0'
+max_splits = 1
+
+train_candidate_size = 1000
+valid_candidate_size = 10000
diff --git a/model/memory_network.py b/model/memory_network.py
@@ -0,0 +1,174 @@
+
+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')}
