Add function for applying model

1 files changed, 31 insertions, 9 deletions

diff --git a/model.py b/model.py
index e104990..b9cc1a8 100644
--- a/model.py
+++ b/model.py
@@ -2,6 +2,8 @@ import logging
 import os
 from argparse import ArgumentParser
 
+import csv
+
 import numpy
 
 import theano
@@ -31,6 +33,7 @@ from blocks.extensions.monitoring import DataStreamMonitoring
 import data
 import transformers
 import hdist
+import apply_model
 
 n_dow = 7       # number of division for dayofweek/dayofmonth/hourofday
 n_dom = 31
@@ -43,7 +46,9 @@ n_begin_end_pts = 5     # how many points we consider at the beginning and end o
 n_end_pts = 5
 
 dim_embed = 50
-dim_hidden = 200
+dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
+dim_hidden = [200]
+dim_output = 2
 
 learning_rate = 0.002
 momentum = 0.9
@@ -68,16 +73,15 @@ def main():
     # Define the model
     client_embed_table = LookupTable(length=n_clients+1, dim=dim_embed, name='client_lookup')
     stand_embed_table = LookupTable(length=n_stands+1, dim=dim_embed, name='stand_lookup')
-    hidden_layer = MLP(activations=[Rectifier()],
-                       dims=[n_begin_end_pts * 2 * 2 + dim_embed + dim_embed, dim_hidden])
-    output_layer = Linear(input_dim=dim_hidden, output_dim=2)
+    hidden_layer = MLP(activations=[Rectifier() for _ in dim_hidden],
+                       dims=[dim_input] + dim_hidden)
+    output_layer = Linear(input_dim=dim_hidden[-1], output_dim=dim_output)
 
     # Create the Theano variables
 
     client_embed = client_embed_table.apply(x_client).flatten(ndim=2)
     stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2)
-    inputs = tensor.concatenate([x_firstk, x_lastk,
-                                 client_embed.zeros_like(), stand_embed.zeros_like()],
+    inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed],
                                 axis=1)
     # inputs = theano.printing.Print("inputs")(inputs)
     hidden = hidden_layer.apply(inputs)
@@ -86,6 +90,7 @@ def main():
 
     # Normalize & Center
     outputs = data.data_std * outputs + data.porto_center
+    outputs.name = 'outputs'
 
     # Calculate the cost
     cost = (outputs - y).norm(2, axis=1).mean()
@@ -121,7 +126,7 @@ def main():
     valid = transformers.add_last_k(n_begin_end_pts, valid)
     valid = transformers.concat_destination_xy(valid)
     valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k', 'last_k', 'destination'))
-    valid_stream = Batch(valid, iteration_scheme=ConstantScheme(batch_size))
+    valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000))
 
 
     # Training
@@ -135,8 +140,8 @@ def main():
 
     extensions=[DataStreamMonitoring([cost, hcost], valid_stream,
                                      prefix='valid',
-                                     every_n_batches=1000),
-                Printing(every_n_batches=1000),
+                                     every_n_batches=1),
+                Printing(every_n_batches=1),
                 # Dump('taxi_model', every_n_batches=100),
                 # LoadFromDump('taxi_model'),
                 ]
@@ -148,6 +153,23 @@ def main():
         extensions=extensions)
     main_loop.run()
 
+    # Produce an output on the test data
+    test = data.test_data
+    test = DataStream(test)
+    test = transformers.add_first_k(n_begin_end_pts, test)
+    test = transformers.add_last_k(n_begin_end_pts, test)
+    test = transformers.Select(test, ('trip_id', 'origin_stand', 'origin_call', 'first_k', 'last_k'))
+    test_stream = Batch(test, iteration_scheme=ConstantScheme(1000))
+
+    outfile = open("test-output.csv", "w")
+    outcsv = csv.writer(outfile)
+    for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
+        dest = out['outputs']
+        for i, trip in enumerate(out['trip_id']):
+            outcsv.writerow([trip, repr(dest[i, 1]), repr(dest[i, 0])])
+    outfile.close()
+
+
 if __name__ == "__main__":
     logging.basicConfig(level=logging.INFO)
     main()