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import logging
import os
import sys
import importlib
from argparse import ArgumentParser
import csv
import numpy
import theano
from theano import printing
from theano import tensor
from theano.ifelse import ifelse
from blocks.filter import VariableFilter
from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
from blocks.bricks.lookup import LookupTable
from blocks.initialization import IsotropicGaussian, Constant
from blocks.model import Model
from fuel.datasets.hdf5 import H5PYDataset
from fuel.transformers import Batch
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, SequentialExampleScheme
from blocks.algorithms import GradientDescent, Scale, AdaDelta, Momentum
from blocks.graph import ComputationGraph
from blocks.main_loop import MainLoop
from blocks.extensions import Printing, FinishAfter
from blocks.extensions.saveload import Dump, LoadFromDump, Checkpoint
from blocks.extensions.monitoring import DataStreamMonitoring
import data
import transformers
import hdist
import apply_model
if __name__ == "__main__":
if len(sys.argv) != 2:
print >> sys.stderr, 'Usage: %s config' % sys.argv[0]
sys.exit(1)
config = importlib.import_module(sys.argv[1])
def setup_stream():
# Load the training and test data
train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5',
which_set='train',
subset=slice(0, data.dataset_size - config.n_valid),
load_in_memory=True)
train = DataStream(train, iteration_scheme=SequentialExampleScheme(data.dataset_size - config.n_valid))
train = transformers.add_first_k(config.n_begin_end_pts, train)
train = transformers.add_random_k(config.n_begin_end_pts, train)
train = transformers.add_destination(train)
train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k_latitude',
'last_k_latitude', 'first_k_longitude', 'last_k_longitude',
'destination_latitude', 'destination_longitude'))
train_stream = Batch(train, iteration_scheme=ConstantScheme(config.batch_size))
valid = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5',
which_set='train',
subset=slice(data.dataset_size - config.n_valid, data.dataset_size),
load_in_memory=True)
valid = DataStream(valid, iteration_scheme=SequentialExampleScheme(config.n_valid))
valid = transformers.add_first_k(config.n_begin_end_pts, valid)
valid = transformers.add_random_k(config.n_begin_end_pts, valid)
valid = transformers.add_destination(valid)
valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude',
'last_k_latitude', 'first_k_longitude', 'last_k_longitude',
'destination_latitude', 'destination_longitude'))
valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000))
return (train_stream, valid_stream)
def setup_test_stream():
test = data.test_data
test = DataStream(test)
test = transformers.add_first_k(config.n_begin_end_pts, test)
test = transformers.add_last_k(config.n_begin_end_pts, test)
test = transformers.Select(test, ('trip_id', 'origin_stand', 'origin_call', 'first_k_latitude',
'last_k_latitude', 'first_k_longitude', 'last_k_longitude'))
test_stream = Batch(test, iteration_scheme=ConstantScheme(1000))
return test_stream
def main():
# The input and the targets
x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.porto_center[0]) / data.data_std[0]
x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.porto_center[1]) / data.data_std[1]
x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
x_client = tensor.lvector('origin_call')
x_stand = tensor.lvector('origin_stand')
y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
tensor.vector('destination_longitude')[:, None]), axis=1)
# x_firstk_latitude = theano.printing.Print("x_firstk_latitude")(x_firstk_latitude)
# x_firstk_longitude = theano.printing.Print("x_firstk_longitude")(x_firstk_longitude)
# x_lastk_latitude = theano.printing.Print("x_lastk_latitude")(x_lastk_latitude)
# x_lastk_longitude = theano.printing.Print("x_lastk_longitude")(x_lastk_longitude)
# Define the model
client_embed_table = LookupTable(length=data.n_clients+1, dim=config.dim_embed, name='client_lookup')
stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup')
mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
# Create the Theano variables
client_embed = client_embed_table.apply(x_client)
stand_embed = stand_embed_table.apply(x_stand)
inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude,
x_lastk_latitude, x_lastk_longitude,
client_embed, stand_embed],
axis=1)
# inputs = theano.printing.Print("inputs")(inputs)
outputs = mlp.apply(inputs)
# Normalize & Center
# outputs = theano.printing.Print("normal_outputs")(outputs)
outputs = data.data_std * outputs + data.porto_center
# outputs = theano.printing.Print("outputs")(outputs)
# y = theano.printing.Print("y")(y)
outputs.name = 'outputs'
# Calculate the cost
cost = (outputs - y).norm(2, axis=1).mean()
cost.name = 'cost'
hcost = hdist.hdist(outputs, y).mean()
hcost.name = 'hcost'
# Initialization
client_embed_table.weights_init = IsotropicGaussian(0.001)
stand_embed_table.weights_init = IsotropicGaussian(0.001)
mlp.weights_init = IsotropicGaussian(0.01)
mlp.biases_init = Constant(0.001)
client_embed_table.initialize()
stand_embed_table.initialize()
mlp.initialize()
(train_stream, valid_stream) = setup_stream()
# Training
cg = ComputationGraph(cost)
params = cg.parameters # VariableFilter(bricks=[Linear])(cg.parameters)
algorithm = GradientDescent(
cost=cost,
# step_rule=AdaDelta(decay_rate=0.5),
step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
params=params)
extensions=[DataStreamMonitoring([cost, hcost], valid_stream,
prefix='valid',
every_n_batches=1000),
Printing(every_n_batches=1000),
# Checkpoint('model.pkl', every_n_batches=100),
Dump('taxi_model', every_n_batches=1000),
LoadFromDump('taxi_model'),
FinishAfter(after_epoch=1)
]
main_loop = MainLoop(
model=Model([cost]),
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
main_loop.profile.report()
# Produce an output on the test data
test_stream = setup_test_stream()
outfile = open("test-output.csv", "w")
outcsv = csv.writer(outfile)
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 __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
main()
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