Move weights init to config files ; fix s/time/travel_time

15 files changed, 86 insertions, 99 deletions

diff --git a/config/dest_simple_mlp_2_cs.py b/config/dest_simple_mlp_2_cs.py
index 0dd2704..accb611 100644
--- a/config/dest_simple_mlp_2_cs.py
+++ b/config/dest_simple_mlp_2_cs.py
@@ -1,3 +1,5 @@
+from blocks.initialization import IsotropicGaussian, Constant
+
 import model.dest_simple_mlp as model
 
 import data
@@ -16,6 +18,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [200, 100]
 dim_output = 2
 
+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 = 32
diff --git a/config/dest_simple_mlp_2_cswdt.py b/config/dest_simple_mlp_2_cswdt.py
index 1011488..62d0db4 100644
--- a/config/dest_simple_mlp_2_cswdt.py
+++ b/config/dest_simple_mlp_2_cswdt.py
@@ -1,5 +1,7 @@
 import model.dest_simple_mlp 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
@@ -20,6 +22,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [200, 100]
 dim_output = 2
 
+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 = 32
diff --git a/config/dest_simple_mlp_2_noembed.py b/config/dest_simple_mlp_2_noembed.py
index 3cddcb9..bbe7798 100644
--- a/config/dest_simple_mlp_2_noembed.py
+++ b/config/dest_simple_mlp_2_noembed.py
@@ -1,5 +1,7 @@
 import model.dest_simple_mlp 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
@@ -13,6 +15,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [200, 100]
 dim_output = 2
 
+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 = 32
diff --git a/config/dest_simple_mlp_tgtcls_0_cs.py b/config/dest_simple_mlp_tgtcls_0_cs.py
index 031cd12..704e62c 100644
--- a/config/dest_simple_mlp_tgtcls_0_cs.py
+++ b/config/dest_simple_mlp_tgtcls_0_cs.py
@@ -1,5 +1,7 @@
 import cPickle
 
+from blocks.initialization import IsotropicGaussian, Constant
+
 import data
 
 import model.dest_simple_mlp_tgtcls as model
@@ -20,6 +22,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = []
 dim_output = tgtcls.shape[0]
 
+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 = 32
diff --git a/config/dest_simple_mlp_tgtcls_1_cs.py b/config/dest_simple_mlp_tgtcls_1_cs.py
index 48d9fa0..f2a22a5 100644
--- a/config/dest_simple_mlp_tgtcls_1_cs.py
+++ b/config/dest_simple_mlp_tgtcls_1_cs.py
@@ -1,5 +1,7 @@
 import cPickle
 
+from blocks.initialization import IsotropicGaussian, Constant
+
 import data
 
 import model.dest_simple_mlp_tgtcls as model
@@ -20,6 +22,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500]
 dim_output = tgtcls.shape[0]
 
+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 = 32
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdt.py b/config/dest_simple_mlp_tgtcls_1_cswdt.py
index 6aa2a03..a3ae654 100644
--- a/config/dest_simple_mlp_tgtcls_1_cswdt.py
+++ b/config/dest_simple_mlp_tgtcls_1_cswdt.py
@@ -1,5 +1,7 @@
 import cPickle
 
+from blocks.initialization import IsotropicGaussian, Constant
+
 import data
 
 import model.dest_simple_mlp_tgtcls as model
@@ -24,6 +26,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500]
 dim_output = tgtcls.shape[0]
 
+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 = 32
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx.py b/config/dest_simple_mlp_tgtcls_1_cswdtx.py
index 7918242..6306c15 100644
--- a/config/dest_simple_mlp_tgtcls_1_cswdtx.py
+++ b/config/dest_simple_mlp_tgtcls_1_cswdtx.py
@@ -1,5 +1,7 @@
 import cPickle
 
+from blocks.initialization import IsotropicGaussian, Constant
+
 import data
 
 import model.dest_simple_mlp_tgtcls as model
@@ -25,6 +27,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500]
 dim_output = tgtcls.shape[0]
 
+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 = 32
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
index 5642f27..8c090c7 100644
--- a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
+++ b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
@@ -1,8 +1,10 @@
 import cPickle
 
+from blocks.initialization import IsotropicGaussian, Constant
+
 import data
 
-import model.dest_simple_mlp_tgtcls_alexandre as model
+import model.dest_simple_mlp_tgtcls 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
@@ -25,6 +27,10 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500]
 dim_output = tgtcls.shape[0]
 
+embed_weights_init = IsotropicGaussian(0.01)
+mlp_weights_init = IsotropicGaussian(0.1) 
+mlp_biases_init = Constant(0.01)
+
 learning_rate = 0.01
 momentum = 0.9
 batch_size = 200
diff --git a/config/time_simple_mlp_1.py b/config/time_simple_mlp_1.py
index eea4159..bf3699d 100644
--- a/config/time_simple_mlp_1.py
+++ b/config/time_simple_mlp_1.py
@@ -1,5 +1,7 @@
 import model.time_simple_mlp 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
@@ -14,6 +16,14 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [200]
 dim_output = 1
 
+embed_weights_init = IsotropicGaussian(0.001)
+mlp_weights_init = IsotropicGaussian(0.01)
+mlp_biases_init = Constant(0.001)
+
+exp_base = 1.5
+
 learning_rate = 0.00001
 momentum = 0.99
 batch_size = 32
+
+valid_set = 'cuts/test_times_0'
diff --git a/config/time_simple_mlp_2_cswdtx.py b/config/time_simple_mlp_2_cswdtx.py
index ceb66e8..98467e3 100644
--- a/config/time_simple_mlp_2_cswdtx.py
+++ b/config/time_simple_mlp_2_cswdtx.py
@@ -1,5 +1,7 @@
 import model.time_simple_mlp 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
@@ -21,6 +23,14 @@ dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500, 100]
 dim_output = 1
 
+embed_weights_init = IsotropicGaussian(0.001)
+mlp_weights_init = IsotropicGaussian(0.01)
+mlp_biases_init = Constant(0.001)
+
+exp_base = 1.5
+
 learning_rate = 0.00001
 momentum = 0.99
 batch_size = 32
+
+valid_set = 'cuts/test_times_0'
diff --git a/data/transformers.py b/data/transformers.py
index 1cc4834..e3ff7b1 100644
--- a/data/transformers.py
+++ b/data/transformers.py
@@ -34,7 +34,7 @@ class Select(Transformer):
 class TaxiGenerateSplits(Transformer):
     def __init__(self, data_stream, max_splits=-1):
         super(TaxiGenerateSplits, self).__init__(data_stream)
-        self.sources = data_stream.sources + ('destination_latitude', 'destination_longitude', 'time')
+        self.sources = data_stream.sources + ('destination_latitude', 'destination_longitude', 'travel_time')
         self.max_splits = max_splits
         self.data = None
         self.splits = []
diff --git a/model/dest_simple_mlp.py b/model/dest_simple_mlp.py
index f422f11..a9e97cb 100644
--- a/model/dest_simple_mlp.py
+++ b/model/dest_simple_mlp.py
@@ -1,8 +1,6 @@
 from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
 from blocks.bricks.lookup import LookupTable
 
-from blocks.initialization import IsotropicGaussian, Constant
-
 from theano import tensor
 
 import data
@@ -58,9 +56,9 @@ class Model(object):
 
         # Initialization
         for tbl in embed_tables:
-            tbl.weights_init = IsotropicGaussian(0.001)
-        mlp.weights_init = IsotropicGaussian(0.01)
-        mlp.biases_init = Constant(0.001)
+            tbl.weights_init = config.embed_weights_init
+        mlp.weights_init = config.mlp_weights_init
+        mlp.biases_init = config.mlp_biases_init
 
         for tbl in embed_tables:
             tbl.initialize()
diff --git a/model/dest_simple_mlp_tgtcls.py b/model/dest_simple_mlp_tgtcls.py
index a7b6f9b..1381d4c 100644
--- a/model/dest_simple_mlp_tgtcls.py
+++ b/model/dest_simple_mlp_tgtcls.py
@@ -6,8 +6,6 @@ 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
 
@@ -60,9 +58,9 @@ class Model(object):
 
         # Initialization
         for tbl in embed_tables:
-            tbl.weights_init = IsotropicGaussian(0.001)
-        mlp.weights_init = IsotropicGaussian(0.01)
-        mlp.biases_init = Constant(0.001)
+            tbl.weights_init = config.embed_weights_init
+        mlp.weights_init = config.mlp_weights_init
+        mlp.biases_init = config.mlp_biases_init
 
         for tbl in embed_tables:
             tbl.initialize()
diff --git a/model/dest_simple_mlp_tgtcls_alexandre.py b/model/dest_simple_mlp_tgtcls_alexandre.py
deleted file mode 100644
index 825bf80..0000000
--- a/model/dest_simple_mlp_tgtcls_alexandre.py
+++ /dev/null
@@ -1,75 +0,0 @@
-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.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]
-
-        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/model/time_simple_mlp.py b/model/time_simple_mlp.py
index 1568ed3..9e1b10a 100644
--- a/model/time_simple_mlp.py
+++ b/model/time_simple_mlp.py
@@ -1,8 +1,6 @@
 from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
 from blocks.bricks.lookup import LookupTable
 
-from blocks.initialization import IsotropicGaussian, Constant
-
 from theano import tensor
 
 import data
@@ -11,11 +9,11 @@ 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_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.porto_center[0]) / data.data_std[0]
-        x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_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]
 
         input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude]
         embed_tables = []
@@ -29,7 +27,7 @@ class Model(object):
             embed_tables.append(tbl)
             input_list.append(tbl.apply(vardata))
 
-        y = tensor.lvector('time')
+        y = tensor.lvector('travel_time')
 
         # Define the model
         mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
@@ -38,7 +36,7 @@ class Model(object):
         # Create the Theano variables
         inputs = tensor.concatenate(input_list, axis=1)
         # inputs = theano.printing.Print("inputs")(inputs)
-        outputs = tensor.exp(mlp.apply(inputs) + 2)
+        outputs = config.exp_base ** mlp.apply(inputs)
 
         # outputs = theano.printing.Print("outputs")(outputs)
         # y = theano.printing.Print("y")(y)
@@ -51,9 +49,9 @@ class Model(object):
 
         # Initialization
         for tbl in embed_tables:
-            tbl.weights_init = IsotropicGaussian(0.001)
-        mlp.weights_init = IsotropicGaussian(0.01)
-        mlp.biases_init = Constant(0.001)
+            tbl.weights_init = config.embed_weights_init
+        mlp.weights_init = config.mlp_weights_init
+        mlp.biases_init = config.mlp_biases_init
 
         for tbl in embed_tables:
             tbl.initialize()
@@ -62,4 +60,4 @@ class Model(object):
         self.cost = cost
         self.monitor = [cost]
         self.outputs = outputs
-        self.pred_vars = ['time']
+        self.pred_vars = ['travel_time']