commit 20a1a01cef9d61ce9dd09995f2c811ab5aca2a9d
parent 0ecac7973fd02f44af9c8bc5765f7c159c94b23a
Author: Alex Auvolat <alex.auvolat@ens.fr>
Date: Fri, 8 May 2015 14:59:44 -0400
Add model for a network that predicts both time and destination.
Diffstat:
3 files changed, 165 insertions(+), 17 deletions(-)
diff --git a/config/joint_simple_mlp_tgtcls_1_cswdtx.py b/config/joint_simple_mlp_tgtcls_1_cswdtx.py
@@ -0,0 +1,52 @@
+import cPickle
+
+import model.joint_simple_mlp_tgtcls 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
+n_end_pts = 5
+
+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(22):
+ time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2])
+
+dim_embeddings = [
+ ('origin_call', data.origin_call_size+1, 10),
+ ('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 = [500]
+
+# Destination prediction part
+dim_hidden_dest = []
+dim_output_dest = len(dest_tgtcls)
+
+# Time prediction part
+dim_hidden_time = []
+dim_output_time = len(time_tgtcls)
+
+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 = 200
+
+valid_set = 'cuts/test_times_0'
diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py
@@ -0,0 +1,90 @@
+from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
+from blocks.bricks.lookup import LookupTable
+
+import numpy
+import theano
+from theano import tensor
+
+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]
+
+ x_input_time = tensor.lvector('input_time')
+
+ 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', 'input_time']
+
+ 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_dest = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
+ tensor.vector('destination_longitude')[:, None]), axis=1)
+ y_time = tensor.lvector('travel_time')
+
+ # Define the model
+ common_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden],
+ dims=[config.dim_input] + config.dim_hidden)
+
+ dest_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_dest] + [Softmax()],
+ dims=[config.dim_hidden[-1]] + config.dim_hidden_dest + [config.dim_output_dest],
+ name='dest_mlp')
+ dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes')
+
+ time_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_time] + [Softmax()],
+ dims=[config.dim_hidden[-1]] + config.dim_hidden_time + [config.dim_output_time],
+ name='time_mlp')
+ time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes')
+
+ # Create the Theano variables
+ inputs = tensor.concatenate(input_list, axis=1)
+ # inputs = theano.printing.Print("inputs")(inputs)
+ hidden = common_mlp.apply(inputs)
+
+ dest_cls_probas = dest_mlp.apply(hidden)
+ dest_outputs = tensor.dot(dest_cls_probas, dest_classes)
+ dest_outputs.name = 'dest_outputs'
+
+ time_cls_probas = time_mlp.apply(hidden)
+ time_outputs = tensor.dot(time_cls_probas, time_classes) + x_input_time
+ time_outputs.name = 'time_outputs'
+
+ # Calculate the cost
+ dest_cost = error.erdist(dest_outputs, y_dest).mean()
+ dest_cost.name = 'dest_cost'
+ dest_hcost = error.hdist(dest_outputs, y_dest).mean()
+ dest_hcost.name = 'dest_hcost'
+ time_cost = error.rmsle(time_outputs.flatten(), y_time.flatten())
+ time_cost.name = 'time_cost'
+ cost = dest_cost + time_cost
+ cost.name = 'cost'
+
+ # Initialization
+ for tbl in embed_tables:
+ tbl.weights_init = config.embed_weights_init
+ tbl.initialize()
+
+ for mlp in [common_mlp, dest_mlp, time_mlp]:
+ mlp.weights_init = config.mlp_weights_init
+ mlp.biases_init = config.mlp_biases_init
+ mlp.initialize()
+
+ self.cost = cost
+ self.monitor = [cost, dest_cost, dest_hcost, time_cost]
+ self.outputs = tensor.concatenate([dest_outputs, time_outputs[:, None]], axis=1)
+ self.outputs.name = 'outputs'
+ self.pred_vars = ['destination_longitude', 'destination_latitude', 'travel_time']
+
diff --git a/train.py b/train.py
@@ -67,7 +67,6 @@ def setup_test_stream(req_vars):
test = transformers.TaxiAddDateTime(test)
test = transformers.TaxiAddFirstLastLen(config.n_begin_end_pts, test)
- test = transformers.TaxiAddLast(config.n_begin_end_pts, test)
test = transformers.Select(test, tuple(req_vars))
test_stream = Batch(test, iteration_scheme=ConstantScheme(1000))
@@ -96,13 +95,17 @@ def main():
# step_rule=AdaDelta(decay_rate=0.5),
step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
params=params)
+
+ plot_vars = [['valid_' + x.name for x in model.monitor]]
+ # plot_vars = ['valid_cost']
+ print "Plot: ", plot_vars
extensions=[TrainingDataMonitoring(model.monitor, prefix='train', every_n_batches=1000),
DataStreamMonitoring(model.monitor, valid_stream,
prefix='valid',
every_n_batches=1000),
Printing(every_n_batches=1000),
- Plot(model_name, channels=[['valid_cost']], every_n_batches=1000),
+ Plot(model_name, channels=plot_vars, every_n_batches=1000),
# Checkpoint('model.pkl', every_n_batches=100),
Dump('model_data/' + model_name, every_n_batches=1000),
LoadFromDump('model_data/' + model_name),
@@ -120,21 +123,24 @@ def main():
# Produce an output on the test data
test_stream = setup_test_stream(req_vars_test)
- outfile = open("output/test-output-%s.csv" % model_name, "w")
- outcsv = csv.writer(outfile)
- if model.pred_vars == ['travel_time']:
- outcsv.writerow(["TRIP_ID", "TRAVEL_TIME"])
- for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
- time = out['outputs']
- for i, trip in enumerate(out['trip_id']):
- outcsv.writerow([trip, int(time[i])])
- else:
- outcsv.writerow(["TRIP_ID", "LATITUDE", "LONGITUDE"])
- for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
- dest = out['outputs']
- for i, trip in enumerate(out['trip_id']):
- outcsv.writerow([trip, repr(dest[i, 0]), repr(dest[i, 1])])
- outfile.close()
+ if 'destination_longitude' in model.pred_vars:
+ dest_outfile = open("output/test-dest-output-%s.csv" % model_name, "w")
+ dest_outcsv = csv.writer(dest_outfile)
+ dest_outcsv.writerow(["TRIP_ID", "LATITUDE", "LONGITUDE"])
+ if 'travel_time' in model.pred_vars:
+ time_outfile = open("output/test-time-output-%s.csv" % model_name, "w")
+ time_outcsv = csv.writer(time_outfile)
+ time_outcsv.writerow(["TRIP_ID", "TRAVEL_TIME"])
+
+ for out in apply_model.Apply(outputs=outputs, stream=test_stream, return_vars=['trip_id', 'outputs']):
+ outputs = out['outputs']
+ for i, trip in enumerate(out['trip_id']):
+ if model.pred_vars == ['travel_time']:
+ time_outcsv.writerow([trip, int(outputs[i])])
+ else:
+ dest_outcsv.writerow([trip, repr(outputs[i, 0]), repr(outputs[i, 1])])
+ if 'travel_time' in model.pred_vars:
+ time_outcsv.writerow([trip, int(outputs[i, 2])])
if __name__ == "__main__":
