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Diffstat (limited to 'model/mlp_emb.py')
-rw-r--r-- | model/mlp_emb.py | 126 |
1 files changed, 126 insertions, 0 deletions
diff --git a/model/mlp_emb.py b/model/mlp_emb.py new file mode 100644 index 0000000..f34541b --- /dev/null +++ b/model/mlp_emb.py @@ -0,0 +1,126 @@ +from theano import tensor + +from fuel.transformers import Batch, MultiProcessing +from fuel.streams import DataStream +from fuel.schemes import ConstantScheme, ShuffledExampleScheme +from blocks.bricks import application, MLP, Rectifier, Initializable, Identity + +import error +import data +from data import transformers +from data.hdf5 import TaxiDataset, TaxiStream +from data.cut import TaxiTimeCutScheme +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.mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], + dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) + + self.inputs = self.context_embedder.inputs # + self.extremities.keys() + self.children = [ self.context_embedder, self.mlp ] + + def _push_initialization_config(self): + self.mlp.weights_init = self.config.mlp_weights_init + self.mlp.biases_init = self.config.mlp_biases_init + + @application(outputs=['destination']) + def predict(self, **kwargs): + embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs })) + + inputs = tensor.concatenate(embeddings, axis=1) + outputs = self.mlp.apply(inputs) + + if self.config.output_mode == "destination": + return data.train_gps_std * outputs + data.train_gps_mean + elif self.config.dim_output == "clusters": + return tensor.dot(outputs, self.classes) + + @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] + + stream = TaxiDataset('train') + + if hasattr(self.config, 'use_cuts_for_training') and self.config.use_cuts_for_training: + stream = DataStream(stream, iteration_scheme=TaxiTimeCutScheme()) + else: + stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples)) + + stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids) + stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits) + + stream = transformers.taxi_add_datetime(stream) + # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts) + stream = transformers.Select(stream, tuple(req_vars)) + + stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size)) + + stream = MultiProcessing(stream) + + return stream + + def valid(self, req_vars): + stream = TaxiStream(self.config.valid_set, 'valid.hdf5') + + stream = transformers.taxi_add_datetime(stream) + # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts) + stream = transformers.Select(stream, tuple(req_vars)) + return Batch(stream, iteration_scheme=ConstantScheme(1000)) + + def test(self, req_vars): + stream = TaxiStream('test') + + stream = transformers.taxi_add_datetime(stream) + # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts) + stream = transformers.taxi_remove_test_only_clients(stream) + + return Batch(stream, iteration_scheme=ConstantScheme(1)) + + 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')} |