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authorAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 11:36:59 -0400
committerAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 11:36:59 -0400
commitab1076e00d6a92120e46d4a0085911b4425a0d60 (patch)
tree83bf857eb61b2df332362b95600686f8d90f508c /config/simple_mlp_tgtcls_1_cswdt.py
parent5b496677ea1db59a6718e5c9b2958177c76cb25f (diff)
downloadtaxi-ab1076e00d6a92120e46d4a0085911b4425a0d60.tar.gz
taxi-ab1076e00d6a92120e46d4a0085911b4425a0d60.zip
Add date/time transformer and new model that uses it
Diffstat (limited to 'config/simple_mlp_tgtcls_1_cswdt.py')
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diff --git a/config/simple_mlp_tgtcls_1_cswdt.py b/config/simple_mlp_tgtcls_1_cswdt.py
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+import cPickle
+
+import data
+
+import model.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
+
+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', 53, 10),
+ ('day_of_week', 7, 10),
+ ('qhour_of_day', 24 * 4, 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 = 0.0001
+momentum = 0.99
+batch_size = 32