Add model with predefined target classes

1 files changed, 74 insertions, 0 deletions

diff --git a/model/simple_mlp_tgtcls.py b/model/simple_mlp_tgtcls.py
new file mode 100644
index 0000000..99a459a
--- /dev/null
+++ b/model/simple_mlp_tgtcls.py
@@ -0,0 +1,74 @@
+import numpy 
+
+import theano
+from theano import tensor
+
+from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
+from blocks.bricks.lookup import LookupTable
+
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+import hdist
+
+class Model(object):
+    def __init__(self, config):
+        # The input and the targets
+        x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.porto_center[0]) / data.data_std[0]
+        x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.porto_center[1]) / data.data_std[1]
+
+        x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
+        x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
+
+        x_client = tensor.lvector('origin_call')
+        x_stand = tensor.lvector('origin_stand')
+
+        y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
+                                tensor.vector('destination_longitude')[:, None]), axis=1)
+
+        # Define the model
+        client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup')
+        stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup')
+        mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Softmax()],
+                           dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
+        classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
+
+        # Create the Theano variables
+        client_embed = client_embed_table.apply(x_client)
+        stand_embed = stand_embed_table.apply(x_stand)
+        inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude,
+                                     x_lastk_latitude, x_lastk_longitude,
+                                     client_embed, stand_embed],
+                                    axis=1)
+        # inputs = theano.printing.Print("inputs")(inputs)
+        cls_probas = mlp.apply(inputs)
+        outputs = tensor.dot(cls_probas, classes)
+
+        # Normalize & Center
+        # outputs = theano.printing.Print("normal_outputs")(outputs)
+        outputs = data.data_std * outputs + data.porto_center
+
+        # outputs = theano.printing.Print("outputs")(outputs)
+        # y = theano.printing.Print("y")(y)
+
+        outputs.name = 'outputs'
+
+        # Calculate the cost
+        cost = hdist.erdist(outputs, y).mean()
+        cost.name = 'cost'
+        hcost = hdist.hdist(outputs, y).mean()
+        hcost.name = 'hcost'
+
+        # Initialization
+        client_embed_table.weights_init = IsotropicGaussian(0.001)
+        stand_embed_table.weights_init = IsotropicGaussian(0.001)
+        mlp.weights_init = IsotropicGaussian(0.01)
+        mlp.biases_init = Constant(0.001)
+
+        client_embed_table.initialize()
+        stand_embed_table.initialize()
+        mlp.initialize()
+
+        self.cost = cost
+        self.hcost = hcost
+        self.outputs = outputs