1 files changed, 71 insertions, 0 deletions
diff --git a/model/joint_mlp_tgtcls.py b/model/joint_mlp_tgtcls.py
new file mode 100644
index 0000000..d6d4e49
--- /dev/null
+++ b/model/joint_mlp_tgtcls.py
@@ -0,0 +1,71 @@
+import numpy
+import theano
+from theano import tensor
+from blocks import roles
+from blocks.bricks import application, MLP, Rectifier, Softmax
+
+import error
+from model.mlp import FFMLP, Stream
+
+
+class Model(FFMLP):
+    def __init__(self, config, **kwargs):
+        super(Model, self).__init__(config, **kwargs)
+        
+        self.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')
+        self.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')
+
+        self.dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes')
+        self.time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes')
+
+        self.inputs.append('input_time')
+        self.children.extend([self.dest_mlp, self.time_mlp])
+
+    def _push_initialization_config(self):
+        super(Model, self)._push_initialization_config()
+        for mlp in [self.dest_mlp, self.time_mlp]:
+            mlp.weights_init = self.config.mlp_weights_init
+            mlp.biases_init = self.config.mlp_biases_init
+
+    @application(outputs=['destination', 'duration'])
+    def predict(self, **kwargs):
+        hidden = super(Model, self).predict(**kwargs)
+
+        dest_cls_probas = self.dest_mlp.apply(hidden)
+        dest_outputs = tensor.dot(dest_cls_probas, self.dest_classes)
+
+        time_cls_probas = self.time_mlp.apply(hidden)
+        time_outputs = kwargs['input_time'] + tensor.dot(time_cls_probas, self.time_classes)
+
+        self.add_auxiliary_variable(dest_cls_probas, name='destination classes ponderations')
+        self.add_auxiliary_variable(time_cls_probas, name='time classes ponderations')
+
+        return (dest_outputs, time_outputs)
+
+    @predict.property('inputs')
+    def predict_inputs(self):
+        return self.inputs
+
+    @application(outputs=['cost'])
+    def cost(self, **kwargs):
+        (destination_hat, time_hat) = self.predict(**kwargs)
+
+        destination = tensor.concatenate((kwargs['destination_latitude'][:, None],
+                                          kwargs['destination_longitude'][:, None]), axis=1)
+        time = kwargs['travel_time']
+
+        destination_cost = error.erdist(destination_hat, destination).mean()
+        time_cost = error.rmsle(time_hat.flatten(), time.flatten())
+
+        self.add_auxiliary_variable(destination_cost, [roles.COST], 'destination_cost')
+        self.add_auxiliary_variable(time_cost, [roles.COST], 'time_cost')
+
+        return destination_cost + self.config.time_cost_factor * time_cost
+
+    @cost.property('inputs')
+    def cost_inputs(self):
+        return self.inputs + ['destination_latitude', 'destination_longitude', 'travel_time']