Refactor architecture so that embedding sizes can be easily changed.

4 files changed, 42 insertions, 6 deletions

diff --git a/config/simple_mlp_2_cs.py b/config/simple_mlp_2_cs.py
new file mode 100644
index 0000000..692d325
--- /dev/null
+++ b/config/simple_mlp_2_cs.py
@@ -0,0 +1,25 @@
+import model.simple_mlp as model
+
+import data
+
+n_dow = 7       # number of division for dayofweek/dayofmonth/hourofday
+n_dom = 31
+n_hour = 24
+
+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)
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [200, 100]
+dim_output = 2
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 32
diff --git a/config/simple_mlp_0.py b/config/simple_mlp_2_noembed.py
index 61ddbfd..bc300e7 100644
--- a/config/simple_mlp_0.py
+++ b/config/simple_mlp_2_noembed.py
@@ -1,5 +1,7 @@
 import model.simple_mlp as model
 
+import data
+
 n_dow = 7       # number of division for dayofweek/dayofmonth/hourofday
 n_dom = 31
 n_hour = 24
@@ -9,8 +11,9 @@ n_end_pts = 5
 
 n_valid = 1000
 
-dim_embed = 10
-dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
+dim_embeddings = []   # do not use embeddings
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [200, 100]
 dim_output = 2
 
diff --git a/config/simple_mlp_tgtcls_0.py b/config/simple_mlp_tgtcls_0_cs.py
index 91770c2..b174517 100644
--- a/config/simple_mlp_tgtcls_0.py
+++ b/config/simple_mlp_tgtcls_0_cs.py
@@ -15,8 +15,12 @@ n_valid = 1000
 
 with open(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
 
-dim_embed = 10
-dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
+dim_embeddings = [
+    ('origin_call', data.n_train_clients+1, 10),
+    ('origin_stand', data.n_stands+1, 10)
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = []
 dim_output = tgtcls.shape[0]
 
diff --git a/config/simple_mlp_tgtcls_1.py b/config/simple_mlp_tgtcls_1_cs.py
index 8d6c37b..6bf82e1 100644
--- a/config/simple_mlp_tgtcls_1.py
+++ b/config/simple_mlp_tgtcls_1_cs.py
@@ -15,8 +15,12 @@ n_valid = 1000
 
 with open(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
 
-dim_embed = 10
-dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
+dim_embeddings = [
+    ('origin_call', data.n_train_clients+1, 10),
+    ('origin_stand', data.n_stands+1, 10)
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
 dim_hidden = [500]
 dim_output = tgtcls.shape[0]