New hyperparameters. Training error is monitored.

3 files changed, 108 insertions, 2 deletions

diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
new file mode 100644
index 0000000..91ad71c
--- /dev/null
+++ b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
@@ -0,0 +1,30 @@
+import cPickle
+
+import data
+
+import model.dest_simple_mlp_tgtcls_alexandre as model
+
+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(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
+
+dim_embeddings = [
+    ('origin_call', data.n_train_clients+1, 10),
+    ('origin_stand', data.n_stands+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),
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [500]
+dim_output = tgtcls.shape[0]
+
+learning_rate = 0.01
+momentum = 0.9
+batch_size = 200
diff --git a/model/dest_simple_mlp_tgtcls_alexandre.py b/model/dest_simple_mlp_tgtcls_alexandre.py
new file mode 100644
index 0000000..87e20a3
--- /dev/null
+++ b/model/dest_simple_mlp_tgtcls_alexandre.py
@@ -0,0 +1,75 @@
+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 error
+
+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]
+
+        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']
+
+        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 = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
+                                tensor.vector('destination_longitude')[:, None]), axis=1)
+
+        # Define the model
+        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
+        inputs = tensor.concatenate(input_list, axis=1)
+
+        # inputs = theano.printing.Print("inputs")(inputs)
+        cls_probas = mlp.apply(inputs)
+        outputs = tensor.dot(cls_probas, classes)
+
+        # outputs = theano.printing.Print("outputs")(outputs)
+        # y = theano.printing.Print("y")(y)
+
+        outputs.name = 'outputs'
+
+        # Calculate the cost
+        cost = error.erdist(outputs, y).mean()
+        cost.name = 'cost'
+        hcost = error.hdist(outputs, y).mean()
+        hcost.name = 'hcost'
+
+        # Initialization
+        for tbl in embed_tables:
+            tbl.weights_init = IsotropicGaussian(0.01)
+        mlp.weights_init = IsotropicGaussian(0.1)
+        mlp.biases_init = Constant(0.01)
+
+        for tbl in embed_tables:
+            tbl.initialize()
+        mlp.initialize()
+
+        self.cost = cost
+        self.monitor = [cost, hcost]
+        self.outputs = outputs
+        self.pred_vars = ['destination_latitude', 'destination_longitude']
+
diff --git a/train.py b/train.py
index 4cbd526..f501cba 100644
--- a/train.py
+++ b/train.py
@@ -27,7 +27,7 @@ from blocks.graph import ComputationGraph
 from blocks.main_loop import MainLoop
 from blocks.extensions import Printing, FinishAfter
 from blocks.extensions.saveload import Dump, LoadFromDump, Checkpoint
-from blocks.extensions.monitoring import DataStreamMonitoring
+from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
 
 import data
 import transformers
@@ -107,7 +107,8 @@ def main():
         step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
         params=params)
 
-    extensions=[DataStreamMonitoring(model.monitor, valid_stream,
+    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),