Add models for time predictioAdd models for time prediction

9 files changed, 52 insertions, 7 deletions

diff --git a/config/simple_mlp_2_cs.py b/config/dest_simple_mlp_2_cs.py
index fa2f4c1..2cec78d 100644
--- a/config/simple_mlp_2_cs.py
+++ b/config/dest_simple_mlp_2_cs.py
@@ -1,4 +1,4 @@
-import model.simple_mlp as model
+import model.dest_simple_mlp as model
 
 import data
 
diff --git a/config/simple_mlp_2_cswdt.py b/config/dest_simple_mlp_2_cswdt.py
index 05c9450..f6ddf34 100644
--- a/config/simple_mlp_2_cswdt.py
+++ b/config/dest_simple_mlp_2_cswdt.py
@@ -1,4 +1,4 @@
-import model.simple_mlp as model
+import model.dest_simple_mlp as model
 
 import data
 
diff --git a/config/simple_mlp_2_noembed.py b/config/dest_simple_mlp_2_noembed.py
index 2f45f63..3832146 100644
--- a/config/simple_mlp_2_noembed.py
+++ b/config/dest_simple_mlp_2_noembed.py
@@ -1,4 +1,4 @@
-import model.simple_mlp as model
+import model.dest_simple_mlp as model
 
 import data
 
diff --git a/config/simple_mlp_tgtcls_0_cs.py b/config/dest_simple_mlp_tgtcls_0_cs.py
index 96faca0..a8a5a0e 100644
--- a/config/simple_mlp_tgtcls_0_cs.py
+++ b/config/dest_simple_mlp_tgtcls_0_cs.py
@@ -2,7 +2,7 @@ import cPickle
 
 import data
 
-import model.simple_mlp_tgtcls 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
diff --git a/config/simple_mlp_tgtcls_1_cs.py b/config/dest_simple_mlp_tgtcls_1_cs.py
index 293a0ab..8136f10 100644
--- a/config/simple_mlp_tgtcls_1_cs.py
+++ b/config/dest_simple_mlp_tgtcls_1_cs.py
@@ -2,7 +2,7 @@ import cPickle
 
 import data
 
-import model.simple_mlp_tgtcls 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
diff --git a/config/simple_mlp_tgtcls_1_cswdt.py b/config/dest_simple_mlp_tgtcls_1_cswdt.py
index 45bd39e..af7b2a3 100644
--- a/config/simple_mlp_tgtcls_1_cswdt.py
+++ b/config/dest_simple_mlp_tgtcls_1_cswdt.py
@@ -2,7 +2,7 @@ import cPickle
 
 import data
 
-import model.simple_mlp_tgtcls 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
diff --git a/config/simple_mlp_tgtcls_1_cswdtx.py b/config/dest_simple_mlp_tgtcls_1_cswdtx.py
index d51ddde..b9832df 100644
--- a/config/simple_mlp_tgtcls_1_cswdtx.py
+++ b/config/dest_simple_mlp_tgtcls_1_cswdtx.py
@@ -2,7 +2,7 @@ import cPickle
 
 import data
 
-import model.simple_mlp_tgtcls 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
diff --git a/config/time_simple_mlp_1.py b/config/time_simple_mlp_1.py
new file mode 100644
index 0000000..eea4159
--- /dev/null
+++ b/config/time_simple_mlp_1.py
@@ -0,0 +1,19 @@
+import model.time_simple_mlp as model
+
+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
+
+dim_embeddings = [
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [200]
+dim_output = 1
+
+learning_rate = 0.00001
+momentum = 0.99
+batch_size = 32
diff --git a/config/time_simple_mlp_2_cswdtx.py b/config/time_simple_mlp_2_cswdtx.py
new file mode 100644
index 0000000..ceb66e8
--- /dev/null
+++ b/config/time_simple_mlp_2_cswdtx.py
@@ -0,0 +1,26 @@
+import model.time_simple_mlp as model
+
+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
+
+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, 100]
+dim_output = 1
+
+learning_rate = 0.00001
+momentum = 0.99
+batch_size = 32