commit c912ef9424be973b11b4c7b7dbb2d32a8f3a9ab9
parent 80d3ea67a845484d119cb88f0a0412f981ab344c
Author: Alex Auvolat <alex.auvolat@ens.fr>
Date: Mon, 4 May 2015 16:58:17 -0400
Restructure model & config
Diffstat:
6 files changed, 227 insertions(+), 213 deletions(-)
diff --git a/config/model_0.py b/config/model_0.py
@@ -1,17 +0,0 @@
-n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday
-n_dom = 31
-n_hour = 24
-
-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
-
-dim_embed = 10
-dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
-dim_hidden = [200, 100]
-dim_output = 2
-
-learning_rate = 0.0001
-momentum = 0.99
-batch_size = 32
diff --git a/config/simple_mlp_0.py b/config/simple_mlp_0.py
@@ -0,0 +1,19 @@
+import model.simple_mlp as model
+
+n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday
+n_dom = 31
+n_hour = 24
+
+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
+
+dim_embed = 10
+dim_input = n_begin_end_pts * 2 * 2 + dim_embed + dim_embed
+dim_hidden = [200, 100]
+dim_output = 2
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 32
diff --git a/model.py b/model.py
@@ -1,196 +0,0 @@
-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_train_stream():
- # Load the training and test data
- train = H5PYDataset(data.H5DATA_PATH,
- which_set='train',
- subset=slice(0, data.dataset_size),
- load_in_memory=True)
- train = DataStream(train, iteration_scheme=SequentialExampleScheme(data.dataset_size - config.n_valid))
- train = transformers.filter_out_trips(data.valid_trips, train)
- train = transformers.TaxiGenerateSplits(train, max_splits=100)
- train = transformers.add_first_k(config.n_begin_end_pts, train)
- train = transformers.add_last_k(config.n_begin_end_pts, 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))
-
- return train_stream
-
-def setup_valid_stream():
- valid = DataStream(data.valid_data)
- valid = transformers.add_first_k(config.n_begin_end_pts, valid)
- valid = transformers.add_last_k(config.n_begin_end_pts, 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 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_train_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 = setup_train_stream()
- valid_stream = setup_valid_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=5)
- ]
-
- 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()
-
diff --git a/model/__init__.py b/model/__init__.py
diff --git a/model/simple_mlp.py b/model/simple_mlp.py
@@ -0,0 +1,69 @@
+from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
+from blocks.bricks.lookup import LookupTable
+
+from blocks.initialization import IsotropicGaussian, Constant
+
+from theano import tensor
+
+import data
+import hdist
+
+class Model(object):
+ def __init__(self, config):
+ # 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)
+
+ # Define the model
+ client_embed_table = LookupTable(length=data.n_train_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 = hdist.erdist(outputs, y).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()
+
+ self.cost = cost
+ self.hcost = hcost
+ self.outputs = outputs
diff --git a/train.py b/train.py
@@ -0,0 +1,139 @@
+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.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_train_stream():
+ # Load the training and test data
+ train = H5PYDataset(data.H5DATA_PATH,
+ which_set='train',
+ subset=slice(0, data.dataset_size),
+ load_in_memory=True)
+ train = DataStream(train, iteration_scheme=SequentialExampleScheme(data.dataset_size - config.n_valid))
+ train = transformers.filter_out_trips(data.valid_trips, train)
+ train = transformers.TaxiGenerateSplits(train, max_splits=100)
+ train = transformers.add_first_k(config.n_begin_end_pts, train)
+ train = transformers.add_last_k(config.n_begin_end_pts, 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))
+
+ return train_stream
+
+def setup_valid_stream():
+ valid = DataStream(data.valid_data)
+ valid = transformers.add_first_k(config.n_begin_end_pts, valid)
+ valid = transformers.add_last_k(config.n_begin_end_pts, 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 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():
+ model = config.model.Model(config)
+
+ cost = model.cost
+ hcost = model.hcost
+ outputs = model.outputs
+
+ train_stream = setup_train_stream()
+ valid_stream = setup_valid_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=5)
+ ]
+
+ 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()
+
