taxi

commit 9313c8bb7f62b14f8c15d2119ae678641f751dbd
parent dd8ae5ea8ed0c7cb1a7880b1e1887c6e23cdf910
Author: Alex Auvolat <alex.auvolat@ens.fr>
Date:   Thu, 23 Jul 2015 10:13:28 -0400

Can do test with batches of size >1

Diffstat:
M
ext_test.py
 | 
13
+++++++------
M
model/mlp.py
 | 
2
+-

2 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/ext_test.py b/ext_test.py
@@ -61,12 +61,13 @@ class RunOnTest(SimpleExtension):
         for d in self.test_stream.get_epoch_iterator(as_dict=True):
             input_values = [d[k.name] for k in self.inputs]
             output_values = self.function(*input_values)
-            if 'destination' in self.outputs:
-                destination = output_values[self.outputs.index('destination')]
-                dest_outcsv.writerow([d['trip_id'][0], destination[0, 0], destination[0, 1]])
-            if 'duration' in self.outputs:
-                duration = output_values[self.outputs.index('duration')]
-                time_outcsv.writerow([d['trip_id'][0], int(round(duration[0]))])
+            for i in range(d['trip_id'].shape[0]):
+                if 'destination' in self.outputs:
+                    destination = output_values[self.outputs.index('destination')]
+                    dest_outcsv.writerow([d['trip_id'][i], destination[i, 0], destination[i, 1]])
+                if 'duration' in self.outputs:
+                    duration = output_values[self.outputs.index('duration')]
+                    time_outcsv.writerow([d['trip_id'][i], int(round(duration[i]))])
 
         if 'destination' in self.outputs:
             dest_outfile.close()
diff --git a/model/mlp.py b/model/mlp.py
@@ -106,7 +106,7 @@ class Stream(object):
         stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
         stream = transformers.taxi_remove_test_only_clients(stream)
 
-        return Batch(stream, iteration_scheme=ConstantScheme(1))
+        return Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
 
     def inputs(self):
         return {'call_type': tensor.bvector('call_type'),