diff options
| -rw-r--r-- | config/__init__.py | 0 | ||||
| -rw-r--r-- | config/model_0.py | 21 | ||||
| -rw-r--r-- | data.py | 4 | ||||
| -rw-r--r-- | model.py | 114 | ||||
| -rw-r--r-- | transformers.py | 69 |
5 files changed, 108 insertions, 100 deletions
diff --git a/config/__init__.py b/config/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/config/__init__.py diff --git a/config/model_0.py b/config/model_0.py new file mode 100644 index 0000000..ba04a15 --- /dev/null +++ b/config/model_0.py @@ -0,0 +1,21 @@ +n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday +n_dom = 31 +n_hour = 24 + +n_clients = 57124 #57105 +n_stands = 63 + +n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 5 + +train_size = 1710670 +n_valid = 1000 + +dim_embed = 50 +dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed +dim_hidden = [200] +dim_output = 2 + +learning_rate = 0.002 +momentum = 0.9 +batch_size = 32 @@ -21,8 +21,8 @@ def get_client_id(n): else: return 0 -porto_center = numpy.array([[ -8.61612, 41.1573]], dtype=theano.config.floatX) -data_std = numpy.sqrt(numpy.array([[ 0.00333233, 0.00549598]], dtype=theano.config.floatX)) +porto_center = numpy.array([41.1573, -8.61612], dtype=theano.config.floatX) +data_std = numpy.sqrt(numpy.array([0.00549598, 0.00333233], dtype=theano.config.floatX)) class CallType(Enum): CENTRAL = 0 @@ -1,5 +1,7 @@ import logging import os +import sys +import importlib from argparse import ArgumentParser import csv @@ -19,9 +21,10 @@ 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 +from fuel.schemes import ConstantScheme, SequentialExampleScheme from blocks.algorithms import GradientDescent, Scale, AdaDelta, Momentum from blocks.graph import ComputationGraph @@ -35,58 +38,59 @@ import transformers import hdist import apply_model -n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday -n_dom = 31 -n_hour = 24 - -n_clients = 57105 -n_stands = 63 - -n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory -n_end_pts = 5 - -dim_embed = 50 -dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed -dim_hidden = [200] -dim_output = 2 +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]) -learning_rate = 0.002 -momentum = 0.9 -batch_size = 32 +def setup_stream(): + # Load the training and test data + train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', which_set='train', subset=slice(0, config.train_size - config.n_valid), load_in_memory=True) + train = DataStream(train, iteration_scheme=SequentialExampleScheme(config.train_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(config.train_size - config.n_valid, config.train_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_last_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 main(): # The input and the targets - x_firstk = tensor.matrix('first_k') - n = x_firstk.shape[0] - x_firstk = (x_firstk.reshape((n, n_begin_end_pts, 2)) - data.porto_center[None, None, :]) / data.data_std[None, None, :] - x_firstk = x_firstk.reshape((n, 2 * n_begin_end_pts)) + 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_firstk = tensor.concatenate((x_firstk_latitude, x_firstk_longitude), axis=1) - x_lastk = tensor.matrix('last_k') - n = x_lastk.shape[0] - x_lastk = (x_lastk.reshape((n, n_begin_end_pts, 2)) - data.porto_center[None, None, :]) / data.data_std[None, None, :] - x_lastk = x_lastk.reshape((n, 2 * n_begin_end_pts)) + 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_lastk = tensor.concatenate((x_lastk_latitude, x_lastk_longitude), axis=1) x_client = tensor.lvector('origin_call') x_stand = tensor.lvector('origin_stand') - y = tensor.matrix('destination') + y = tensor.concatenate((tensor.vector('destination_latitude')[:, None], tensor.vector('destination_longitude')[:, None]), axis=1) # Define the model - client_embed_table = LookupTable(length=n_clients+1, dim=dim_embed, name='client_lookup') - stand_embed_table = LookupTable(length=n_stands+1, dim=dim_embed, name='stand_lookup') - hidden_layer = MLP(activations=[Rectifier() for _ in dim_hidden], - dims=[dim_input] + dim_hidden) - output_layer = Linear(input_dim=dim_hidden[-1], output_dim=dim_output) + client_embed_table = LookupTable(length=config.n_clients+1, dim=config.dim_embed, name='client_lookup') + stand_embed_table = LookupTable(length=config.n_stands+1, dim=config.dim_embed, name='stand_lookup') + mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [None], + dims=[config.dim_input] + config.dim_hidden + [config.dim_output]) # Create the Theano variables - client_embed = client_embed_table.apply(x_client).flatten(ndim=2) stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2) inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed], axis=1) # inputs = theano.printing.Print("inputs")(inputs) - hidden = hidden_layer.apply(inputs) - # hidden = theano.printing.Print("hidden")(hidden) - outputs = output_layer.apply(hidden) + outputs = mlp.apply(inputs) # Normalize & Center outputs = data.data_std * outputs + data.porto_center @@ -101,42 +105,22 @@ def main(): # Initialization client_embed_table.weights_init = IsotropicGaussian(0.001) stand_embed_table.weights_init = IsotropicGaussian(0.001) - hidden_layer.weights_init = IsotropicGaussian(0.01) - hidden_layer.biases_init = Constant(0.001) - output_layer.weights_init = IsotropicGaussian(0.01) - output_layer.biases_init = Constant(0.001) + mlp.weights_init = IsotropicGaussian(0.01) + mlp.biases_init = Constant(0.001) client_embed_table.initialize() stand_embed_table.initialize() - hidden_layer.initialize() - output_layer.initialize() - - # Load the training and test data - train = data.train_data - train = DataStream(train) - train = transformers.add_first_k(n_begin_end_pts, train) - train = transformers.add_random_k(n_begin_end_pts, train) - train = transformers.add_destination(train) - train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k', 'last_k', 'destination')) - train_stream = Batch(train, iteration_scheme=ConstantScheme(batch_size)) - - valid = data.valid_data - valid = DataStream(valid) - valid = transformers.add_first_k(n_begin_end_pts, valid) - valid = transformers.add_last_k(n_begin_end_pts, valid) - valid = transformers.concat_destination_xy(valid) - valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k', 'last_k', 'destination')) - valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000)) + mlp.initialize() + (train_stream, valid_stream) = setup_stream() # Training cg = ComputationGraph(cost) - params = VariableFilter(bricks=[Linear])(cg.parameters) algorithm = GradientDescent( cost=cost, # step_rule=AdaDelta(decay_rate=0.5), - step_rule=Momentum(learning_rate=learning_rate, momentum=momentum), - params=params) + step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum), + params=cg.parameters) extensions=[DataStreamMonitoring([cost, hcost], valid_stream, prefix='valid', @@ -154,10 +138,11 @@ def main(): main_loop.run() # Produce an output on the test data + ''' test = data.test_data test = DataStream(test) - test = transformers.add_first_k(n_begin_end_pts, test) - test = transformers.add_last_k(n_begin_end_pts, test) + test = transformers.add_first_k(conifg.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', 'last_k')) test_stream = Batch(test, iteration_scheme=ConstantScheme(1000)) @@ -169,6 +154,7 @@ def main(): for i, trip in enumerate(out['trip_id']): outcsv.writerow([trip, repr(dest[i, 1]), repr(dest[i, 0])]) outfile.close() + ''' if __name__ == "__main__": diff --git a/transformers.py b/transformers.py index c60d362..13852ac 100644 --- a/transformers.py +++ b/transformers.py @@ -3,15 +3,15 @@ import numpy import theano import random -def at_least_k(k, pl, pad_at_begin): - if len(pl) == 0: - pl = [[ -8.61612, 41.1573]] - if len(pl) < k: +def at_least_k(k, v, pad_at_begin, is_longitude): + if len(v) == 0: + v = numpy.array([41.1573 if is_longitude else -8.61612], dtype=theano.config.floatX) + if len(v) < k: if pad_at_begin: - pl = [pl[0]] * (k - len(pl)) + pl + v = numpy.concatenate((numpy.full((k - len(v),), v[0]), v)) else: - pl = pl + [pl[-1]] * (k - len(pl)) - return pl + v = numpy.concatenate((v, numpy.full((k - len(v),), v[-1]))) + return v class Select(Transformer): @@ -27,38 +27,39 @@ class Select(Transformer): return [data[id] for id in self.ids] def add_first_k(k, stream): - id_polyline=stream.sources.index('polyline') - def first_k(x): - pl = at_least_k(k, x[id_polyline], False) - return (numpy.array(pl[:k], dtype=theano.config.floatX).flatten(),) - stream = Mapping(stream, first_k, ('first_k',)) - return stream + id_latitude = stream.sources.index('latitude') + id_longitude = stream.sources.index('longitude') + return Mapping(stream, + lambda data: + (numpy.array(at_least_k(k, data[id_latitude], False, False)[:k], dtype=theano.config.floatX), + numpy.array(at_least_k(k, data[id_longitude], False, True)[:k], dtype=theano.config.floatX)), + ('first_k_latitude', 'first_k_longitude')) def add_random_k(k, stream): - id_polyline=stream.sources.index('polyline') + id_latitude = stream.sources.index('latitude') + id_longitude = stream.sources.index('longitude') def random_k(x): - pl = at_least_k(k, x[id_polyline], True) - loc = random.randrange(len(pl)-k+1) - return (numpy.array(pl[loc:loc+k], dtype=theano.config.floatX).flatten(),) - stream = Mapping(stream, random_k, ('last_k',)) - return stream + lat = at_least_k(k, x[id_latitude], True, False) + lon = at_least_k(k, x[id_latitude], True, True) + loc = random.randrange(len(lat)-k+1) + return (numpy.array(lat[loc:loc+k], dtype=theano.config.floatX), + numpy.array(lon[loc:loc+k], dtype=theano.config.floatX)), + return Mapping(stream, random_k, ('last_k_latitude', 'last_k_longitude')) def add_last_k(k, stream): - id_polyline=stream.sources.index('polyline') - def last_k(x): - pl = at_least_k(k, x[id_polyline], True) - return (numpy.array(pl[-k:], dtype=theano.config.floatX).flatten(),) - stream = Mapping(stream, last_k, ('last_k',)) - return stream + id_latitude = stream.sources.index('latitude') + id_longitude = stream.sources.index('longitude') + return Mapping(stream, + lambda data: + (numpy.array(at_least_k(k, data[id_latitude], True, False)[-k:], dtype=theano.config.floatX), + numpy.array(at_least_k(k, data[id_longitude], True, True)[-k:], dtype=theano.config.floatX)), + ('last_k_latitude', 'last_k_longitude')) def add_destination(stream): - id_polyline=stream.sources.index('polyline') + id_latitude = stream.sources.index('latitude') + id_longitude = stream.sources.index('longitude') return Mapping(stream, - lambda x: - (numpy.array(at_least_k(1, x[id_polyline], True)[-1], dtype=theano.config.floatX),), - ('destination',)) - -def concat_destination_xy(stream): - id_dx=stream.sources.index('destination_x') - id_dy=stream.sources.index('destination_y') - return Mapping(stream, lambda x: (numpy.array([x[id_dx], x[id_dy]], dtype=theano.config.floatX),), ('destination',)) + lambda data: + (numpy.array(at_least_k(1, data[id_latitude], True, False)[-1], dtype=theano.config.floatX), + numpy.array(at_least_k(1, data[id_longitude], True, True)[-1], dtype=theano.config.floatX)), + ('destination_latitude', 'destination_longitude')) |