diff options
| -rw-r--r-- | config/rnn_1.py | 33 | ||||
| -rw-r--r-- | model/rnn.py | 176 |
2 files changed, 209 insertions, 0 deletions
diff --git a/config/rnn_1.py b/config/rnn_1.py new file mode 100644 index 0000000..5bc62d8 --- /dev/null +++ b/config/rnn_1.py @@ -0,0 +1,33 @@ +from blocks.initialization import IsotropicGaussian, Constant + +import data +from model.rnn import Model, Stream + +class EmbedderConfig(object): + __slots__ = ('dim_embeddings', 'embed_weights_init') + +pre_embedder = EmbedderConfig() +pre_embedder.embed_weights_init = IsotropicGaussian(0.001) +pre_embedder.dim_embeddings = [ + ('week_of_year', 52, 10), + ('day_of_week', 7, 10), + ('qhour_of_day', 24 * 4, 10), + ('day_type', 3, 10), + ('taxi_id', 448, 10), +] + +post_embedder = EmbedderConfig() +post_embedder.embed_weights_init = IsotropicGaussian(0.001) +post_embedder.dim_embeddings = [ + ('origin_call', data.origin_call_train_size, 10), + ('origin_stand', data.stands_size, 10), +] + + +hidden_state_dim = 100 +weights_init = IsotropicGaussian(0.01) +biases_init = Constant(0.001) + +batch_size = 10 +batch_sort_size = 10 +valid_set = 'cuts/test_times_0' diff --git a/model/rnn.py b/model/rnn.py new file mode 100644 index 0000000..af35414 --- /dev/null +++ b/model/rnn.py @@ -0,0 +1,176 @@ +from theano import tensor + +from blocks.bricks import application, MLP, Initializable, Tanh +from blocks.bricks.base import lazy +from blocks.bricks.recurrent import LSTM, recurrent +from blocks.utils import shared_floatx_zeros + +from fuel.transformers import Batch, Padding +from fuel.streams import DataStream +from fuel.schemes import ConstantScheme, ShuffledExampleScheme + +from model import ContextEmbedder +import data +from data import transformers +from data.hdf5 import TaxiDataset, TaxiStream +import error + + +class Model(Initializable): + @lazy() + def __init__(self, config, **kwargs): + super(Model, self).__init__(**kwargs) + self.config = config + + self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder') + self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder') + + in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings) + self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec') + + self.rec = LSTM( + dim = config.hidden_state_dim, + name = 'recurrent' + ) + + in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings) + self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output') + + self.sequences = ['latitude', 'latitude_mask', 'longitude'] + self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs + self.inputs = self.sequences + self.context + self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ] + + self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,), + name="initial_state") + self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,), + name="initial_cells") + + def _push_initialization_config(self): + for mlp in [self.input_to_rec, self.rec_to_output]: + mlp.weights_init = self.config.weights_init + mlp.biases_init = self.config.biases_init + self.rec.weights_init = self.config.weights_init + + def get_dim(self, name): + return self.rec.get_dim(name) + + @application + def initial_state(self, *args, **kwargs): + return self.rec.initial_state(*args, **kwargs) + + @recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'], sequences=['latitude', 'longitude', 'latitude_mask']) + def predict_all(self, latitude, longitude, latitude_mask, **kwargs): + latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0] + longitude = (longitude - data.train_gps_mean[1]) / data.train_gps_std[1] + + pre_emb = tuple(self.pre_context_embedder.apply(**kwargs)) + latitude = tensor.shape_padright(latitude) + longitude = tensor.shape_padright(longitude) + itr = self.input_to_rec.apply(tensor.concatenate(pre_emb + (latitude, longitude), axis=1)) + itr = itr.repeat(4, axis=1) + (next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=latitude_mask, iterate=False) + + post_emb = tuple(self.post_context_embedder.apply(**kwargs)) + rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1)) + + rto = (rto * data.train_gps_std) + data.train_gps_mean + return (rto, next_states, next_cells) + + @predict_all.property('contexts') + def predict_all_inputs(self): + return self.context + + @application(outputs=['destination']) + def predict(self, latitude, longitude, latitude_mask, **kwargs): + latitude = latitude.T + longitude = longitude.T + latitude_mask = latitude_mask.T + res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0] + return res[-1] + + @predict.property('inputs') + def predict_inputs(self): + return self.inputs + + @application(outputs=['cost']) + def cost(self, latitude, longitude, latitude_mask, **kwargs): + latitude = latitude.T + longitude = longitude.T + latitude_mask = latitude_mask.T + + res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0] + target = tensor.concatenate( + (kwargs['destination_latitude'].dimshuffle('x', 0, 'x'), + kwargs['destination_longitude'].dimshuffle('x', 0, 'x')), + axis=2) + target = target.repeat(latitude.shape[0], axis=0) + ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2))) + ce *= latitude_mask.flatten() + return ce.sum() / latitude_mask.sum() + + @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') + stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples)) + stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids) + stream = transformers.TaxiExcludeEmptyTrips(stream) + stream = transformers.taxi_add_datetime(stream) + stream = transformers.add_destination(stream) + stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) + + stream = transformers.balanced_batch(stream, key='latitude', batch_size=self.config.batch_size, batch_sort_size=self.config.batch_sort_size) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + return stream + + def valid(self, req_vars): + stream = TaxiStream(self.config.valid_set, 'valid.hdf5') + stream = transformers.taxi_add_datetime(stream) + stream = transformers.add_destination(stream) + stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) + + stream = Batch(stream, iteration_scheme=ConstantScheme(1000)) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + return stream + + def test(self, req_vars): + stream = TaxiStream('test') + stream = transformers.taxi_add_datetime(stream) + stream = transformers.taxi_remove_test_only_clients(stream) + stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) + + stream = Batch(stream, iteration_scheme=ConstantScheme(1)) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + 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'), + 'latitude_mask': tensor.matrix('latitude_mask'), + 'longitude_mask': tensor.matrix('longitude_mask'), + 'week_of_year': tensor.bvector('week_of_year'), + 'day_of_week': tensor.bvector('day_of_week'), + 'qhour_of_day': tensor.bvector('qhour_of_day'), + 'destination_latitude': tensor.vector('destination_latitude'), + 'destination_longitude': tensor.vector('destination_longitude')} |