Add model for a network that predicts both time and destination.

3 files changed, 165 insertions, 17 deletions

diff --git a/config/joint_simple_mlp_tgtcls_1_cswdtx.py b/config/joint_simple_mlp_tgtcls_1_cswdtx.py
new file mode 100644
index 0000000..f3de40b
--- /dev/null
+++ b/config/joint_simple_mlp_tgtcls_1_cswdtx.py
@@ -0,0 +1,52 @@
+import cPickle
+
+import model.joint_simple_mlp_tgtcls as model
+
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+
+n_begin_end_pts = 5     # how many points we consider at the beginning and end of the known trajectory
+n_end_pts = 5
+
+n_valid = 1000
+
+with open("%s/arrival-clusters.pkl" % data.path) as f:
+    dest_tgtcls = cPickle.load(f)
+
+# generate target classes for time prediction as a Fibonacci sequence
+time_tgtcls = [1, 2]
+for i in range(22):
+    time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2])
+
+dim_embeddings = [
+    ('origin_call', data.origin_call_size+1, 10),
+    ('origin_stand', data.stands_size+1, 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),
+]
+
+# Common network part
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [500]
+
+# Destination prediction part
+dim_hidden_dest = []
+dim_output_dest = len(dest_tgtcls)
+
+# Time prediction part
+dim_hidden_time = []
+dim_output_time = len(time_tgtcls)
+
+embed_weights_init = IsotropicGaussian(0.001)
+mlp_weights_init = IsotropicGaussian(0.01)
+mlp_biases_init = Constant(0.001)
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 200
+
+valid_set = 'cuts/test_times_0'
diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py
new file mode 100644
index 0000000..0a38e06
--- /dev/null
+++ b/model/joint_simple_mlp_tgtcls.py
@@ -0,0 +1,90 @@
+from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
+from blocks.bricks.lookup import LookupTable
+
+import numpy
+import theano
+from theano import tensor
+
+import data
+import error
+
+class Model(object):
+    def __init__(self, config):
+        # The input and the targets
+        x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0]
+        x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1]
+
+        x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0]
+        x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1]
+
+        x_input_time = tensor.lvector('input_time')
+
+        input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude]
+        embed_tables = []
+
+        self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude', 'input_time']
+
+        for (varname, num, dim) in config.dim_embeddings:
+            self.require_inputs.append(varname)
+            vardata = tensor.lvector(varname)
+            tbl = LookupTable(length=num, dim=dim, name='%s_lookup'%varname)
+            embed_tables.append(tbl)
+            input_list.append(tbl.apply(vardata))
+
+        y_dest = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
+                                     tensor.vector('destination_longitude')[:, None]), axis=1)
+        y_time = tensor.lvector('travel_time')
+
+        # Define the model
+        common_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden],
+                           dims=[config.dim_input] + config.dim_hidden)
+
+        dest_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_dest] + [Softmax()],
+                       dims=[config.dim_hidden[-1]] + config.dim_hidden_dest + [config.dim_output_dest],
+                       name='dest_mlp')
+        dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes')
+
+        time_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_time] + [Softmax()],
+                       dims=[config.dim_hidden[-1]] + config.dim_hidden_time + [config.dim_output_time],
+                       name='time_mlp')
+        time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes')
+
+        # Create the Theano variables
+        inputs = tensor.concatenate(input_list, axis=1)
+        # inputs = theano.printing.Print("inputs")(inputs)
+        hidden = common_mlp.apply(inputs)
+
+        dest_cls_probas = dest_mlp.apply(hidden)
+        dest_outputs = tensor.dot(dest_cls_probas, dest_classes)
+        dest_outputs.name = 'dest_outputs'
+
+        time_cls_probas = time_mlp.apply(hidden)
+        time_outputs = tensor.dot(time_cls_probas, time_classes) + x_input_time
+        time_outputs.name = 'time_outputs'
+
+        # Calculate the cost
+        dest_cost = error.erdist(dest_outputs, y_dest).mean()
+        dest_cost.name = 'dest_cost'
+        dest_hcost = error.hdist(dest_outputs, y_dest).mean()
+        dest_hcost.name = 'dest_hcost'
+        time_cost = error.rmsle(time_outputs.flatten(), y_time.flatten())
+        time_cost.name = 'time_cost'
+        cost = dest_cost + time_cost
+        cost.name = 'cost'
+
+        # Initialization
+        for tbl in embed_tables:
+            tbl.weights_init = config.embed_weights_init
+            tbl.initialize()
+
+        for mlp in [common_mlp, dest_mlp, time_mlp]:
+            mlp.weights_init = config.mlp_weights_init
+            mlp.biases_init = config.mlp_biases_init
+            mlp.initialize()
+
+        self.cost = cost
+        self.monitor = [cost, dest_cost, dest_hcost, time_cost]
+        self.outputs = tensor.concatenate([dest_outputs, time_outputs[:, None]], axis=1)
+        self.outputs.name = 'outputs'
+        self.pred_vars = ['destination_longitude', 'destination_latitude', 'travel_time']
+
diff --git a/train.py b/train.py
index 8d9f4ad..a70bb90 100755
--- a/train.py
+++ b/train.py
@@ -67,7 +67,6 @@ def setup_test_stream(req_vars):
     
     test = transformers.TaxiAddDateTime(test)
     test = transformers.TaxiAddFirstLastLen(config.n_begin_end_pts, test)
-    test = transformers.TaxiAddLast(config.n_begin_end_pts, test)
     test = transformers.Select(test, tuple(req_vars))
 
     test_stream = Batch(test, iteration_scheme=ConstantScheme(1000))
@@ -96,13 +95,17 @@ def main():
         # step_rule=AdaDelta(decay_rate=0.5),
         step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
         params=params)
+    
+    plot_vars = [['valid_' + x.name for x in model.monitor]]
+    # plot_vars = ['valid_cost']
+    print "Plot: ", plot_vars
 
     extensions=[TrainingDataMonitoring(model.monitor, prefix='train', every_n_batches=1000),
                 DataStreamMonitoring(model.monitor, valid_stream,
                                      prefix='valid',
                                      every_n_batches=1000),
                 Printing(every_n_batches=1000),
-                Plot(model_name, channels=[['valid_cost']], every_n_batches=1000),
+                Plot(model_name, channels=plot_vars, every_n_batches=1000),
                 # Checkpoint('model.pkl', every_n_batches=100),
                 Dump('model_data/' + model_name, every_n_batches=1000),
                 LoadFromDump('model_data/' + model_name),
@@ -120,21 +123,24 @@ def main():
     # Produce an output on the test data
     test_stream = setup_test_stream(req_vars_test)
 
-    outfile = open("output/test-output-%s.csv" % model_name, "w")
-    outcsv = csv.writer(outfile)
-    if model.pred_vars == ['travel_time']:
-        outcsv.writerow(["TRIP_ID", "TRAVEL_TIME"])
-        for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
-            time = out['outputs']
-            for i, trip in enumerate(out['trip_id']):
-                outcsv.writerow([trip, int(time[i])])
-    else:
-        outcsv.writerow(["TRIP_ID", "LATITUDE", "LONGITUDE"])
-        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, 0]), repr(dest[i, 1])])
-    outfile.close()
+    if 'destination_longitude' in model.pred_vars:
+        dest_outfile = open("output/test-dest-output-%s.csv" % model_name, "w")
+        dest_outcsv = csv.writer(dest_outfile)
+        dest_outcsv.writerow(["TRIP_ID", "LATITUDE", "LONGITUDE"])
+    if 'travel_time' in model.pred_vars:
+        time_outfile = open("output/test-time-output-%s.csv" % model_name, "w")
+        time_outcsv = csv.writer(time_outfile)
+        time_outcsv.writerow(["TRIP_ID", "TRAVEL_TIME"])
+
+    for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
+        outputs = out['outputs']
+        for i, trip in enumerate(out['trip_id']):
+            if model.pred_vars == ['travel_time']:
+                time_outcsv.writerow([trip, int(outputs[i])])
+            else:
+                dest_outcsv.writerow([trip, repr(outputs[i, 0]), repr(outputs[i, 1])])
+                if 'travel_time' in model.pred_vars:
+                    time_outcsv.writerow([trip, int(outputs[i, 2])])
 
 
 if __name__ == "__main__":