RNN & Bidir RNN refactoring (& fixes, maybe)

1 files changed, 2 insertions, 61 deletions

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')}