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] + [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): 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(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) 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')}