From 9a779f7328a712a20dd393bdf32c6a84bf9fbe52 Mon Sep 17 00:00:00 2001 From: Alex Auvolat Date: Thu, 21 May 2015 10:46:05 -0400 Subject: Model changes --- .../joint_simple_mlp_tgtcls_111_cswdtx_bigger.py | 56 ++++++++++++++++++++ ..._simple_mlp_tgtcls_111_cswdtx_bigger_dropout.py | 59 ++++++++++++++++++++++ config/joint_simple_mlp_tgtcls_1_cswdtx_bigger.py | 55 ++++++++++++++++++++ 3 files changed, 170 insertions(+) create mode 100644 config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py create mode 100644 config/joint_simple_mlp_tgtcls_111_cswdtx_bigger_dropout.py create mode 100644 config/joint_simple_mlp_tgtcls_1_cswdtx_bigger.py (limited to 'config') diff --git a/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py b/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py new file mode 100644 index 0000000..8adb6e7 --- /dev/null +++ b/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py @@ -0,0 +1,56 @@ +import cPickle + +import model.joint_simple_mlp_tgtcls as model + +from blocks.initialization import IsotropicGaussian, Constant + +import data + +n_begin_end_pts = 10 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 10 + +n_valid = 1000 + +with open("%s/arrival-clusters.pkl" % data.path) as f: + dest_tgtcls = cPickle.load(f) + +# generate target classes for time prediction as a Fibonacci sequence +time_tgtcls = [1, 2] +for i in range(21): + time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2]) + +dim_embeddings = [ + ('origin_call', data.origin_call_size+1, 15), + ('origin_stand', data.stands_size+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), +] + +# Common network part +dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) +dim_hidden = [1000] + +# Destination prediction part +dim_hidden_dest = [400] +dim_output_dest = dest_tgtcls.shape[0] + +# Time prediction part +dim_hidden_time = [400] +dim_output_time = len(time_tgtcls) + +# Cost ratio between distance cost and time cost +time_cost_factor = 4 + +embed_weights_init = IsotropicGaussian(0.01) +mlp_weights_init = IsotropicGaussian(0.1) +mlp_biases_init = Constant(0.01) + +learning_rate = 0.000001 +momentum = 0.99 +batch_size = 200 + +valid_set = 'cuts/test_times_0' + diff --git a/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger_dropout.py b/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger_dropout.py new file mode 100644 index 0000000..02c8bd8 --- /dev/null +++ b/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger_dropout.py @@ -0,0 +1,59 @@ +import cPickle + +import model.joint_simple_mlp_tgtcls as model + +from blocks.initialization import IsotropicGaussian, Constant + +import data + +n_begin_end_pts = 10 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 10 + +n_valid = 1000 + +with open("%s/arrival-clusters.pkl" % data.path) as f: + dest_tgtcls = cPickle.load(f) + +# generate target classes for time prediction as a Fibonacci sequence +time_tgtcls = [1, 2] +for i in range(21): + time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2]) + +dim_embeddings = [ + ('origin_call', data.origin_call_size+1, 15), + ('origin_stand', data.stands_size+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), +] + +# Common network part +dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) +dim_hidden = [5000] + +# Destination prediction part +dim_hidden_dest = [1000] +dim_output_dest = dest_tgtcls.shape[0] + +# Time prediction part +dim_hidden_time = [500] +dim_output_time = len(time_tgtcls) + +# Cost ratio between distance cost and time cost +time_cost_factor = 4 + +embed_weights_init = IsotropicGaussian(0.01) +mlp_weights_init = IsotropicGaussian(0.1) +mlp_biases_init = Constant(0.01) + +# apply_dropout = True +# dropout_p = 0.5 + +learning_rate = 0.001 +momentum = 0.9 +batch_size = 200 + +valid_set = 'cuts/test_times_0' + diff --git a/config/joint_simple_mlp_tgtcls_1_cswdtx_bigger.py b/config/joint_simple_mlp_tgtcls_1_cswdtx_bigger.py new file mode 100644 index 0000000..995f858 --- /dev/null +++ b/config/joint_simple_mlp_tgtcls_1_cswdtx_bigger.py @@ -0,0 +1,55 @@ +import cPickle + +import model.joint_simple_mlp_tgtcls as model + +from blocks.initialization import IsotropicGaussian, Constant + +import data + +n_begin_end_pts = 7 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 7 + +n_valid = 1000 + +with open("%s/arrival-clusters.pkl" % data.path) as f: + dest_tgtcls = cPickle.load(f) + +# generate target classes for time prediction as a Fibonacci sequence +time_tgtcls = [1, 2] +for i in range(21): + time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2]) + +dim_embeddings = [ + ('origin_call', data.origin_call_size+1, 15), + ('origin_stand', data.stands_size+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), +] + +# Common network part +dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) +dim_hidden = [5000] + +# Destination prediction part +dim_hidden_dest = [] +dim_output_dest = dest_tgtcls.shape[0] + +# Time prediction part +dim_hidden_time = [] +dim_output_time = len(time_tgtcls) + +# Cost ratio between distance cost and time cost +time_cost_factor = 4 + +embed_weights_init = IsotropicGaussian(0.01) +mlp_weights_init = IsotropicGaussian(0.1) +mlp_biases_init = Constant(0.01) + +learning_rate = 0.0001 +momentum = 0.99 +batch_size = 200 + +valid_set = 'cuts/test_times_0' -- cgit v1.2.3