Add batch shuffle preprocessing step

2 files changed, 48 insertions, 1 deletions

diff --git a/config/dest_mlp_tgtcls_1_cswdtx_batchshuffle.py b/config/dest_mlp_tgtcls_1_cswdtx_batchshuffle.py
new file mode 100644
index 0000000..a4db33c
--- /dev/null
+++ b/config/dest_mlp_tgtcls_1_cswdtx_batchshuffle.py
@@ -0,0 +1,40 @@
+import os
+import cPickle
+
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import Momentum
+
+import data
+from model.dest_mlp_tgtcls import Model, Stream
+
+
+n_begin_end_pts = 5     # how many points we consider at the beginning and end of the known trajectory
+
+with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f)
+
+dim_embeddings = [
+    ('origin_call', data.origin_call_train_size, 10),
+    ('origin_stand', data.stands_size, 10),
+    ('week_of_year', 52, 10),
+    ('day_of_week', 7, 10),
+    ('qhour_of_day', 24 * 4, 10),
+    ('day_type', 3, 10),
+    ('taxi_id', 448, 10),
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [1000]
+dim_output = tgtcls.shape[0]
+
+embed_weights_init = IsotropicGaussian(0.01)
+mlp_weights_init = IsotropicGaussian(0.1) 
+mlp_biases_init = Constant(0.01)
+
+step_rule = Momentum(learning_rate=0.01, momentum=0.9)
+
+batch_size = 200
+
+shuffle_batch_size = 5000
+
+valid_set = 'cuts/test_times_0'
+max_splits = 100
diff --git a/model/mlp.py b/model/mlp.py
index 7e7d092..1f53e8c 100644
--- a/model/mlp.py
+++ b/model/mlp.py
@@ -1,9 +1,10 @@
 from theano import tensor
+import numpy
 
 import fuel
 import blocks
 
-from fuel.transformers import Batch, MultiProcessing
+from fuel.transformers import Batch, MultiProcessing, Mapping, SortMapping, Unpack
 from fuel.streams import DataStream
 from fuel.schemes import ConstantScheme, ShuffledExampleScheme
 from blocks.bricks import application, MLP, Rectifier, Initializable
@@ -73,6 +74,12 @@ class Stream(object):
         stream = transformers.taxi_add_datetime(stream)
         stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
         stream = transformers.Select(stream, tuple(req_vars))
+
+        if hasattr(self.config, 'shuffle_batch_size'):
+            stream = transformers.Batch(stream, iteration_scheme=ConstantScheme(self.config.shuffle_batch_size))
+            rng = numpy.random.RandomState(123)
+            stream = Mapping(stream, SortMapping(lambda x: float(rng.uniform())))
+            stream = Unpack(stream)
         
         stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))