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Diffstat (limited to 'model/mlp.py')
| -rw-r--r-- | model/mlp.py | 103 |
1 files changed, 103 insertions, 0 deletions
diff --git a/model/mlp.py b/model/mlp.py new file mode 100644 index 0000000..9c84ef9 --- /dev/null +++ b/model/mlp.py @@ -0,0 +1,103 @@ +from theano import tensor + +from fuel.transformers import Batch +from fuel.streams import DataStream +from fuel.schemes import ConstantScheme, ShuffledExampleScheme +from blocks.bricks import application, MLP, Rectifier, Initializable + +import data +from data import transformers +from data.hdf5 import TaxiDataset, TaxiStream +from model import ContextEmbedder + + +class FFMLP(Initializable): + def __init__(self, config, output_layer=None, **kwargs): + super(FFMLP, self).__init__(**kwargs) + self.config = config + + self.context_embedder = ContextEmbedder(config) + + output_activation = [] if output_layer is None else [output_layer()] + output_dim = [] if output_layer is None else [config.dim_output] + self.mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + output_activation, + dims=[config.dim_input] + config.dim_hidden + output_dim) + + self.extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]} + 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=['prediction']) + def predict(self, **kwargs): + 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.extremities.items()) + + inputs = tensor.concatenate(extremities + embeddings, axis=1) + outputs = self.mlp.apply(inputs) + + return outputs + + @predict.property('inputs') + def predict_inputs(self): + return self.inputs + +class Stream(object): + def __init__(self, config): + self.config = config + + def train(self, req_vars): + stream = TaxiDataset('train') + stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples)) + + 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 = transformers.TaxiExcludeTrips(valid_trips_ids, stream) + stream = transformers.TaxiGenerateSplits(stream, max_splits=100) + + stream = transformers.TaxiAddDateTime(stream) + stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, stream) + stream = transformers.Select(stream, tuple(req_vars)) + return Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size)) + + def valid(self, req_vars): + stream = TaxiStream(self.config.valid_set, 'valid.hdf5') + + stream = transformers.TaxiAddDateTime(stream) + stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, stream) + stream = transformers.Select(stream, tuple(req_vars)) + return Batch(stream, iteration_scheme=ConstantScheme(1000)) + + def test(self, req_vars): + stream = TaxiStream('test') + + stream = transformers.TaxiAddDateTime(stream) + stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, 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')} |