RNN & Bidir RNN refactoring (& fixes, maybe)

4 files changed, 149 insertions, 170 deletions

diff --git a/model/bidirectional.py b/model/bidirectional.py
index ba6440a..4c4ffb0 100644
--- a/model/bidirectional.py
+++ b/model/bidirectional.py
@@ -1,21 +1,33 @@
 from theano import tensor
 
+from toolz import merge
+
 from blocks.bricks import application, MLP, Initializable, Linear, Rectifier, Identity
 from blocks.bricks.base import lazy
 from blocks.bricks.recurrent import Bidirectional, LSTM
 from blocks.utils import shared_floatx_zeros
 from blocks.bricks.parallel import Fork
 
-from fuel.transformers import Batch, Padding, Mapping, SortMapping, Unpack, MultiProcessing
-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
 
+import data
+
+from model.stream import StreamRec as Stream
+
+class SegregatedBidirectional(Bidirectional):
+    @application
+    def apply(self, forward_dict, backward_dict):
+        """Applies forward and backward networks and concatenates outputs."""
+
+        forward = self.children[0].apply(as_list=True, **forward_dict)
+        backward = [x[::-1] for x in
+
+        self.children[1].apply(reverse=True, as_list=True,
+                **backward_dict)]
+
+        return [tensor.concatenate([f, b], axis=2)
+                for f, b in zip(forward, backward)]
 
 class BidiRNN(Initializable):
     @lazy()
@@ -25,29 +37,36 @@ class BidiRNN(Initializable):
 
         self.context_embedder = ContextEmbedder(config)
         
-        self.rec = Bidirectional(LSTM(dim = config.hidden_state_dim, name = 'recurrent'))
+        self.rec = SegregatedBidirectional(LSTM(dim=config.hidden_state_dim, name='recurrent'))
 
-        self.fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], prototype=Linear())
+        self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
+                             prototype=Linear(), name='fwd_fork')
+        self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
+                              prototype=Linear(), name='bkwd_fork')
 
         rto_in = config.hidden_state_dim * 2 + sum(x[2] for x in config.dim_embeddings)
-        self.rec_to_output = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], dims=[rto_in] + config.dim_hidden + [output_dim])
+        self.rec_to_output = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], 
+                                 dims=[rto_in] + config.dim_hidden + [output_dim])
 
         self.sequences = ['latitude', 'latitude_mask', 'longitude']
         self.inputs = self.sequences + self.context_embedder.inputs
 
-        self.children = [ self.context_embedder, self.fork, self.rec, self.rec_to_output ]
+        self.children = [ self.context_embedder, self.fwd_fork, self.bkwd_fork,
+                          self.rec, self.rec_to_output ]
 
     def _push_allocation_config(self):
-        self.fork.input_dim = 2
-        self.fork.output_dims = [ self.rec.children[0].get_dim(name) for name in self.fork.output_names ]
-        self.fork.weights_init = self.config.fork_weights_init
-        self.fork.biases_init = self.config.fork_biases_init
-        self.rec.weights_init = self.config.rec_weights_init
-        self.rec_to_output.weights_init = self.config.mlp_weights_init
-        self.rec_to_output.biases_init = self.config.mlp_biases_init
+        for i, fork in enumerate([self.fwd_fork, self.bkwd_fork]):
+            fork.input_dim = 2
+            fork.output_dims = [ self.rec.children[i].get_dim(name)
+                                 for name in fork.output_names ]
+
+    def _push_initialization_config(self):
+        for brick in [self.fwd_fork, self.bkwd_fork, self.rec, self.rec_to_output]:
+            brick.weights_init = self.config.weights_init
+            brick.biases_init = self.config.biases_init
 
     def process_outputs(self, outputs):
-        return outputs
+        pass # must be implemented in child class
 
     @application(outputs=['destination'])
     def predict(self, latitude, longitude, latitude_mask, **kwargs):
@@ -55,16 +74,21 @@ class BidiRNN(Initializable):
         longitude = (longitude.T - data.train_gps_mean[1]) / data.train_gps_std[1]
         latitude_mask = latitude_mask.T
 
-        latitude = tensor.shape_padright(latitude)
-        longitude = tensor.shape_padright(longitude)
-        rec_in = tensor.concatenate((latitude, longitude), axis=2)
+        rec_in = tensor.concatenate((latitude[:, :, None], longitude[:, :, None]), axis=2)
 
         last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64')
-        path = self.rec.apply(self.fork.apply(rec_in), mask=latitude_mask)[0]
+
+        path = self.rec.apply(merge(self.fwd_fork.apply(rec_in, as_dict=True),
+                                    {'mask': latitude_mask}),
+                              merge(self.bkwd_fork.apply(rec_in, as_dict=True),
+                                    {'mask': latitude_mask}))[0]
+
         path_representation = (path[0][:, -self.config.hidden_state_dim:],
-                path[last_id - 1, tensor.arange(latitude_mask.shape[1])][:, :self.config.hidden_state_dim])
+                               path[last_id - 1, tensor.arange(latitude_mask.shape[1])]
+                                   [:, :self.config.hidden_state_dim])
 
-        embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs }))
+        embeddings = tuple(self.context_embedder.apply(
+                        **{k: kwargs[k] for k in self.context_embedder.inputs }))
 
         inputs = tensor.concatenate(path_representation + embeddings, axis=1)
         outputs = self.rec_to_output.apply(inputs)
@@ -87,87 +111,4 @@ class BidiRNN(Initializable):
     def cost_inputs(self):
         return self.inputs + ['destination_latitude', 'destination_longitude']
 
-class UniformGenerator(object):
-    def __init__(self):
-        self.rng = numpy.random.RandomState(123)
-    def __call__(self, *args):
-        return float(self.rng.uniform())
-
-class Stream(object):
-    def __init__(self, config):
-        self.config = config
 
-    def train(self, req_vars):
-        stream = TaxiDataset('train', data.traintest_ds)
-
-        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))
-
-        if not data.tvt:
-            valid = TaxiDataset(data.valid_set, data.valid_ds, sources=('trip_id',))
-            valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
-            stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
-
-        stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits)
-
-        if hasattr(self.config, 'shuffle_batch_size'):
-            stream = transformers.Batch(stream, iteration_scheme=ConstantScheme(self.config.shuffle_batch_size))
-            stream = Mapping(stream, SortMapping(key=UniformGenerator()))
-            stream = Unpack(stream)
-
-        stream = transformers.taxi_add_datetime(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)
-        stream = MultiProcessing(stream)
-
-        return stream
-
-    def valid(self, req_vars):
-        stream = TaxiStream(data.valid_set, data.valid_ds)
-
-        stream = transformers.taxi_add_datetime(stream)
-        stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask')))
-
-        stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
-        stream = Padding(stream, mask_sources=['latitude', 'longitude'])
-        stream = transformers.Select(stream, req_vars)
-        return stream
-
-    def test(self, req_vars):
-        stream = TaxiStream('test', data.traintest_ds)
-        
-        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(self.config.batch_size))
-        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'),
-                'destination_latitude': tensor.vector('destination_latitude'),
-                'destination_longitude': tensor.vector('destination_longitude'),
-                'travel_time': tensor.ivector('travel_time'),
-                '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')}
diff --git a/model/bidirectional_tgtcls.py b/model/bidirectional_tgtcls.py
index 36120f7..4dfbad5 100644
--- a/model/bidirectional_tgtcls.py
+++ b/model/bidirectional_tgtcls.py
@@ -11,9 +11,12 @@ class Model(BidiRNN):
     @lazy()
     def __init__(self, config, **kwargs):
         super(Model, self).__init__(config, output_dim=config.tgtcls.shape[0], **kwargs)
-        self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
+
+        self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX),
+                                     name='classes')
         self.softmax = Softmax()
         self.children.append(self.softmax)
 
     def process_outputs(self, outputs):
         return tensor.dot(self.softmax.apply(outputs), self.classes)
+
diff --git a/model/rnn.py b/model/rnn.py
index b4c6550..1b192ae 100644
--- a/model/rnn.py
+++ b/model/rnn.py
@@ -16,6 +16,8 @@ from data.hdf5 import TaxiDataset, TaxiStream
 import error
 
 
+from model.stream import StreamRec as Stream
+
 class RNN(Initializable):
     @lazy()
     def __init__(self, config, rec_input_len=2, output_dim=2, **kwargs):
@@ -141,64 +143,3 @@ class RNN(Initializable):
         return self.inputs + ['destination_latitude', 'destination_longitude']
 
 
-class Stream(object):
-    def __init__(self, config):
-        self.config = config
-
-    def train(self, req_vars):
-        stream = TaxiDataset('train', data.traintest_ds)
-        stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples))
-        if not data.tvt:
-            valid = TaxiDataset(data.valid_set, data.valid_ds, sources=('trip_id',))
-            valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
-            stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
-
-        stream = transformers.TaxiExcludeEmptyTrips(stream)
-        stream = transformers.taxi_add_datetime(stream)
-        if not data.tvt:
-            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(data.valid_set, data.valid_ds)
-        stream = transformers.taxi_add_datetime(stream)
-        stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask')))
-
-        stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
-        stream = Padding(stream, mask_sources=['latitude', 'longitude'])
-        stream = transformers.Select(stream, req_vars)
-        return stream
-
-    def test(self, req_vars):
-        stream = TaxiStream('test', data.traintest_ds)
-        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(self.config.batch_size))
-        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')}
diff --git a/model/stream.py b/model/stream.py
new file mode 100644
index 0000000..88b1d7f
--- /dev/null
+++ b/model/stream.py
@@ -0,0 +1,94 @@
+from fuel.transformers import Batch, Padding, Mapping, SortMapping, Unpack, MultiProcessing
+from fuel.streams import DataStream
+from fuel.schemes import ConstantScheme, ShuffledExampleScheme
+
+from theano import tensor
+
+import data
+from data import transformers
+from data.hdf5 import TaxiDataset, TaxiStream
+
+
+class StreamRec(object):
+    def __init__(self, config):
+        self.config = config
+
+    def train(self, req_vars):
+        stream = TaxiDataset('train', data.traintest_ds)
+
+        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))
+
+        if not data.tvt:
+            valid = TaxiDataset(data.valid_set, data.valid_ds, sources=('trip_id',))
+            valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
+            stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
+
+        if hasattr(self.config, 'max_splits'):
+            stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits)
+        elif not data.tvt:
+            stream = transformers.add_destination(stream)
+
+        stream = transformers.TaxiExcludeEmptyTrips(stream)
+        stream = transformers.taxi_add_datetime(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)
+
+        stream = MultiProcessing(stream)
+
+        return stream
+
+    def valid(self, req_vars):
+        stream = TaxiStream(data.valid_set, data.valid_ds)
+
+        stream = transformers.taxi_add_datetime(stream)
+        stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask')))
+
+        stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
+        stream = Padding(stream, mask_sources=['latitude', 'longitude'])
+        stream = transformers.Select(stream, req_vars)
+        return stream
+
+    def test(self, req_vars):
+        stream = TaxiStream('test', data.traintest_ds)
+        
+        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(self.config.batch_size))
+        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'),
+                'destination_latitude': tensor.vector('destination_latitude'),
+                'destination_longitude': tensor.vector('destination_longitude'),
+                'travel_time': tensor.ivector('travel_time'),
+                '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')}
+