diff options
Diffstat (limited to 'model/memory_network.py')
| -rw-r--r-- | model/memory_network.py | 321 |
1 files changed, 187 insertions, 134 deletions
diff --git a/model/memory_network.py b/model/memory_network.py index 1afc9cb..14d1e07 100644 --- a/model/memory_network.py +++ b/model/memory_network.py @@ -1,7 +1,7 @@ from theano import tensor -from fuel.transformers import Batch, MultiProcessing, Merge +from fuel.transformers import Batch, MultiProcessing, Merge, Padding from fuel.streams import DataStream from fuel.schemes import ConstantScheme, ShuffledExampleScheme, SequentialExampleScheme from blocks.bricks import application, MLP, Rectifier, Initializable, Softmax @@ -13,98 +13,100 @@ from data.hdf5 import TaxiDataset, TaxiStream import error from model import ContextEmbedder - -class Model(Initializable): +class MemoryNetworkBase(Initializable): def __init__(self, config, **kwargs): - super(Model, self).__init__(**kwargs) + super(MemoryNetworkBase, self).__init__(**kwargs) + self.config = config - self.context_embedder = ContextEmbedder(config) - - self.prefix_encoder = 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.candidate_encoder = MLP(activations=[Rectifier() for _ in config.candidate_encoder.dim_hidden] + [config.representation_activation()], - dims=[config.candidate_encoder.dim_input] + config.candidate_encoder.dim_hidden + [config.representation_size], - 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) - if self.config.normalize_representation: - prefix_representation = prefix_representation / tensor.sqrt((prefix_representation ** 2).sum(axis=1, keepdims=True)) - - 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) - if self.config.normalize_representation: - candidate_representation = candidate_representation / tensor.sqrt((candidate_representation ** 2).sum(axis=1, keepdims=True)) - - similarity_score = tensor.dot(prefix_representation, candidate_representation.T) - 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): + +class StreamBase(object): def __init__(self, config): self.config = config - def train(self, req_vars): + self.prefix_inputs = [ + ('call_type', tensor.bvector), + ('origin_call', tensor.ivector), + ('origin_stand', tensor.bvector), + ('taxi_id', tensor.wvector), + ('timestamp', tensor.ivector), + ('day_type', tensor.bvector), + ('missing_data', tensor.bvector), + ('latitude', tensor.matrix), + ('longitude', tensor.matrix), + ('destination_latitude', tensor.vector), + ('destination_longitude', tensor.vector), + ('travel_time', tensor.ivector), + ('input_time', tensor.ivector), + ('week_of_year', tensor.bvector), + ('day_of_week', tensor.bvector), + ('qhour_of_day', tensor.bvector) + ] + self.candidate_inputs = self.prefix_inputs + + def inputs(self): + prefix_inputs = { name: constructor(name) + for name, constructor in self.prefix_inputs } + candidate_inputs = { 'candidate_'+name: constructor('candidate_'+name) + for name, constructor in self.candidate_inputs } + return dict(prefix_inputs.items() + candidate_inputs.items()) + + @property + def valid_dataset(self): + return TaxiDataset(self.config.valid_set, 'valid.hdf5') + + @property + def valid_trips_ids(self): valid = TaxiDataset(self.config.valid_set, 'valid.hdf5', sources=('trip_id',)) - valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0] + return valid.get_data(None, slice(0, valid.num_examples))[0] - dataset = TaxiDataset('train') + @property + def train_dataset(self): + return TaxiDataset('train') + + @property + def test_dataset(self): + return TaxiDataset('test') - prefix_stream = DataStream(dataset, iteration_scheme=TaxiTimeCutScheme(self.config.num_cuts)) - 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) +class StreamSimple(StreamBase): + def __init__(self, config): + super(StreamSimple, self).__init__(config) + + self.prefix_inputs += [ + ('first_k_latitude', tensor.matrix), + ('first_k_longitude', tensor.matrix), + ('last_k_latitude', tensor.matrix), + ('last_k_longitude', tensor.matrix), + ] + self.candidate_inputs = self.prefix_inputs + + 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) 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)) + candidate_stream = transformers.taxi_add_first_last_len(candidate_stream, + self.config.n_begin_end_pts) + return Batch(candidate_stream, + iteration_scheme=ConstantScheme(n_candidates)) + + def train(self, req_vars): + prefix_stream = DataStream(self.train_dataset, + iteration_scheme=ShuffledExampleScheme(self.train_dataset.num_examples)) + + 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) + 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 = self.candidate_stream(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) @@ -113,66 +115,117 @@ class Stream(object): 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 = DataStream( + self.valid_dataset, + iteration_scheme=SequentialExampleScheme(self.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)) + 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 = self.candidate_stream(self.config.valid_candidate_size) - candidate_stream = DataStream(train_dataset, iteration_scheme=ShuffledExampleScheme(train_dataset.num_examples)) - candidate_stream = transformers.TaxiExcludeTrips(candidate_stream, valid_trips_ids) + 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): + super(StreamRecurrent, self).__init__(config) + + self.prefix_inputs += [ + ('latitude_mask', tensor.matrix), + ('longitude_mask', tensor.matrix), + ] + self.candidate_inputs = self.prefix_inputs + + 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) 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)) + + candidate_stream = Batch(candidate_stream, + iteration_scheme=ConstantScheme(n_candidates)) + + candidate_stream = Padding(candidate_stream, + mask_sources=['latitude', 'longitude']) + + return candidate_stream + + def train(self, req_vars): + prefix_stream = DataStream(self.train_dataset, + iteration_scheme=ShuffledExampleScheme(self.train_dataset.num_examples)) + + 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) + + prefix_stream = transformers.taxi_add_datetime(prefix_stream) + + prefix_stream = transformers.balanced_batch(prefix_stream, + key='latitude', + batch_size=self.config.batch_size, + batch_sort_size=self.config.batch_sort_size) + + prefix_stream = Padding(prefix_stream, mask_sources=['latitude', 'longitude']) + + candidate_stream = self.candidate_stream(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) + # 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')} + def valid(self, req_vars): + prefix_stream = DataStream( + self.valid_dataset, + iteration_scheme=SequentialExampleScheme(self.valid_dataset.num_examples)) + + prefix_stream = transformers.TaxiExcludeEmptyTrips(prefix_stream) + + prefix_stream = transformers.taxi_add_datetime(prefix_stream) + + prefix_stream = Batch(prefix_stream, + iteration_scheme=ConstantScheme(self.config.batch_size)) + prefix_stream = Padding(prefix_stream, mask_sources=['latitude', 'longitude']) + + candidate_stream = self.candidate_stream(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 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_remove_test_only_clients(prefix_stream) + + prefix_stream = Batch(prefix_stream, + iteration_scheme=ConstantScheme(self.config.batch_size)) + prefix_stream = Padding(prefix_stream, mask_sources=['latitude', 'longitude']) + + 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 |