Add basic RNN

1 files changed, 176 insertions, 0 deletions

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')}