commit 6d946f29f7548c75e97f30c4356dbac200ee6cce
parent 1e6d08b0c9ac5983691b182631c71e9d46ee71cc
Author: Étienne Simon <esimon@esimon.eu>
Date: Mon, 18 May 2015 16:22:00 -0400
Refactor models, clean the code and separate training from testing.
Diffstat:
25 files changed, 475 insertions(+), 469 deletions(-)
diff --git a/config/dest_simple_mlp_2_cs.py b/config/dest_simple_mlp_2_cs.py
@@ -1,8 +1,8 @@
from blocks.initialization import IsotropicGaussian, Constant
-import model.dest_simple_mlp as model
-
import data
+from model.dest_simple_mlp import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_2_cswdt.py b/config/dest_simple_mlp_2_cswdt.py
@@ -1,8 +1,8 @@
-import model.dest_simple_mlp as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_2_noembed.py b/config/dest_simple_mlp_2_noembed.py
@@ -1,8 +1,8 @@
-import model.dest_simple_mlp as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_tgtcls_0_cs.py b/config/dest_simple_mlp_tgtcls_0_cs.py
@@ -3,8 +3,8 @@ import cPickle
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp_tgtcls import Model, Stream
-import model.dest_simple_mlp_tgtcls as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_tgtcls_1_cs.py b/config/dest_simple_mlp_tgtcls_1_cs.py
@@ -3,8 +3,8 @@ import cPickle
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp_tgtcls import Model, Stream
-import model.dest_simple_mlp_tgtcls as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdt.py b/config/dest_simple_mlp_tgtcls_1_cswdt.py
@@ -3,8 +3,8 @@ import cPickle
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp_tgtcls import Model, Stream
-import model.dest_simple_mlp_tgtcls as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx.py b/config/dest_simple_mlp_tgtcls_1_cswdtx.py
@@ -3,8 +3,8 @@ import cPickle
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp_tgtcls import Model, Stream
-import model.dest_simple_mlp_tgtcls as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
@@ -3,8 +3,8 @@ import cPickle
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.dest_simple_mlp_tgtcls import Model, Stream
-import model.dest_simple_mlp_tgtcls as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/joint_simple_mlp_tgtcls_111_cswdtx.py b/config/joint_simple_mlp_tgtcls_111_cswdtx.py
@@ -1,10 +1,10 @@
import cPickle
-import model.joint_simple_mlp_tgtcls as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.joint_simple_mlp_tgtcls import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/joint_simple_mlp_tgtcls_111_cswdtx_noise_dout.py b/config/joint_simple_mlp_tgtcls_111_cswdtx_noise_dout.py
@@ -0,0 +1,62 @@
+import cPickle
+
+from blocks import roles
+from blocks.bricks import Rectifier
+from blocks.filter import VariableFilter
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+from model.joint_simple_mlp_tgtcls import Model, Stream
+
+
+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 = [100]
+dim_output_dest = len(dest_tgtcls)
+
+# Time prediction part
+dim_hidden_time = [100]
+dim_output_time = len(time_tgtcls)
+
+# Cost ratio between distance cost and time cost
+time_cost_factor = 4
+
+embed_weights_init = IsotropicGaussian(0.001)
+mlp_weights_init = IsotropicGaussian(0.01)
+mlp_biases_init = Constant(0.001)
+
+batch_size = 200
+
+dropout = 0.5
+dropout_inputs = VariableFilter(bricks=[Rectifier], name='output')
+
+noise = 0.01
+noise_inputs = VariableFilter(roles=[roles.PARAMETER])
+
+valid_set = 'cuts/test_times_0'
diff --git a/config/joint_simple_mlp_tgtcls_1_cswdtx.py b/config/joint_simple_mlp_tgtcls_1_cswdtx.py
@@ -1,10 +1,10 @@
import cPickle
-import model.joint_simple_mlp_tgtcls as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.joint_simple_mlp_tgtcls import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/time_simple_mlp_1.py b/config/time_simple_mlp_1.py
@@ -1,8 +1,8 @@
-import model.time_simple_mlp as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.time_simple_mlp import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/time_simple_mlp_2_cswdtx.py b/config/time_simple_mlp_2_cswdtx.py
@@ -1,8 +1,8 @@
-import model.time_simple_mlp as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.time_simple_mlp import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/config/time_simple_mlp_tgtcls_2_cswdtx.py b/config/time_simple_mlp_tgtcls_2_cswdtx.py
@@ -1,8 +1,8 @@
-import model.time_simple_mlp_tgtcls as model
-
from blocks.initialization import IsotropicGaussian, Constant
import data
+from model.time_simple_mlp_tgtcls import Model, Stream
+
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
diff --git a/data/transformers.py b/data/transformers.py
@@ -64,8 +64,9 @@ class TaxiGenerateSplits(Transformer):
dlat = numpy.float32(self.data[self.id_latitude][-1])
dlon = numpy.float32(self.data[self.id_longitude][-1])
+ ttime = numpy.int32(15 * (len(self.data[self.id_longitude]) - 1))
- return tuple(r + [dlat, dlon, 15 * (len(self.data[self.id_longitude]) - 1)])
+ return tuple(r + [dlat, dlon, ttime])
class TaxiAddFirstLastLen(Transformer):
def __init__(self, k, stream):
@@ -87,7 +88,7 @@ class TaxiAddFirstLastLen(Transformer):
dtype=theano.config.floatX),
numpy.array(at_least_k(self.k, data[self.id_longitude], True, True)[-self.k:],
dtype=theano.config.floatX))
- input_time = (15 * (len(data[self.id_latitude]) - 1),)
+ input_time = (numpy.int32(15 * (len(data[self.id_latitude]) - 1)),)
return data + first_k + last_k + input_time
class TaxiAddDateTime(Transformer):
@@ -101,7 +102,9 @@ class TaxiAddDateTime(Transformer):
ts = data[self.id_timestamp]
date = datetime.datetime.utcfromtimestamp(ts)
yearweek = date.isocalendar()[1] - 1
- info = ((51 if yearweek == 52 else yearweek), date.weekday(), date.hour * 4 + date.minute / 15)
+ info = (numpy.int8(51 if yearweek == 52 else yearweek),
+ numpy.int8(date.weekday()),
+ numpy.int8(date.hour * 4 + date.minute / 15))
return data + info
class TaxiExcludeTrips(Transformer):
diff --git a/model/__init__.py b/model/__init__.py
@@ -0,0 +1,36 @@
+from blocks.bricks import application, Initializable
+from blocks.bricks.lookup import LookupTable
+
+
+class ContextEmbedder(Initializable):
+ def __init__(self, config, **kwargs):
+ super(ContextEmbedder, self).__init__(**kwargs)
+ self.dim_embeddings = config.dim_embeddings
+ self.embed_weights_init = config.embed_weights_init
+
+ self.inputs = [ name for (name, _, _) in self.dim_embeddings ]
+ self.outputs = [ '%s_embedded' % name for name in self.inputs ]
+
+ self.lookups = { name: LookupTable(name='%s_lookup' % name) for name in self.inputs }
+ self.children = self.lookups.values()
+
+ def _push_allocation_config(self):
+ for (name, num, dim) in self.dim_embeddings:
+ self.lookups[name].length = num
+ self.lookups[name].dim = dim
+
+ def _push_initialization_config(self):
+ for name in self.inputs:
+ self.lookups[name].weights_init = self.embed_weights_init
+
+ @application
+ def apply(self, **kwargs):
+ return tuple(self.lookups[name].apply(kwargs[name]) for name in self.inputs)
+
+ @apply.property('inputs')
+ def apply_inputs(self):
+ return self.inputs
+
+ @apply.property('outputs')
+ def apply_outputs(self):
+ return self.outputs
diff --git a/model/dest_simple_mlp.py b/model/dest_simple_mlp.py
@@ -1,71 +1,32 @@
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
-from blocks.bricks.lookup import LookupTable
-
from theano import tensor
+from blocks.bricks import application, Identity
import data
import error
+from model.mlp import FFMLP, Stream
-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]
-
- 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']
-
- 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 = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
- tensor.vector('destination_longitude')[:, None]), axis=1)
-
- # Define the model
- mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
- dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
-
- # Create the Theano variables
- inputs = tensor.concatenate(input_list, axis=1)
- # inputs = theano.printing.Print("inputs")(inputs)
- outputs = mlp.apply(inputs)
-
- # Normalize & Center
- # outputs = theano.printing.Print("normal_outputs")(outputs)
- outputs = data.train_gps_std * outputs + data.train_gps_mean
-
- # outputs = theano.printing.Print("outputs")(outputs)
- # y = theano.printing.Print("y")(y)
- outputs.name = 'outputs'
+class Model(FFMLP):
+ def __init__(self, config, **kwargs):
+ super(Model, self).__init__(config, output_layer=Identity, **kwargs)
- # Calculate the cost
- cost = error.erdist(outputs, y).mean()
- cost.name = 'cost'
- hcost = error.hdist(outputs, y).mean()
- hcost.name = 'hcost'
+ @application(outputs=['destination'])
+ def predict(self, **kwargs):
+ outputs = super(Model, self).predict(**kwargs)
+ return data.train_gps_std * outputs + data.train_gps_mean
- # Initialization
- for tbl in embed_tables:
- tbl.weights_init = config.embed_weights_init
- mlp.weights_init = config.mlp_weights_init
- mlp.biases_init = config.mlp_biases_init
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
- for tbl in embed_tables:
- tbl.initialize()
- mlp.initialize()
+ @application(outputs=['cost'])
+ def cost(self, **kwargs):
+ y_hat = self.predict(**kwargs)
+ y = tensor.concatenate((kwargs['destination_latitude'][:, None],
+ kwargs['destination_longitude'][:, None]), axis=1)
- self.cost = cost
- self.monitor = [cost, hcost]
- self.outputs = outputs
- self.pred_vars = ['destination_latitude', 'destination_longitude']
+ return error.erdist(y_hat, y).mean()
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['destination_latitude', 'destination_longitude']
diff --git a/model/dest_simple_mlp_tgtcls.py b/model/dest_simple_mlp_tgtcls.py
@@ -1,73 +1,34 @@
-import numpy
-
+import numpy
import theano
from theano import tensor
+from blocks.bricks import application, Softmax
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
-from blocks.bricks.lookup import LookupTable
-
-import data
import error
+from model.mlp import FFMLP, Stream
-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]
-
- 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']
-
- 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 = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
- tensor.vector('destination_longitude')[:, None]), axis=1)
-
- # Define the model
- mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Softmax()],
- dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
- classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
-
- # Create the Theano variables
- inputs = tensor.concatenate(input_list, axis=1)
-
- # inputs = theano.printing.Print("inputs")(inputs)
- cls_probas = mlp.apply(inputs)
- outputs = tensor.dot(cls_probas, classes)
-
- # outputs = theano.printing.Print("outputs")(outputs)
- # y = theano.printing.Print("y")(y)
- outputs.name = 'outputs'
+class Model(FFMLP):
+ def __init__(self, config, **kwargs):
+ super(Model, self, output_layer=Softmax).__init__(config, **kwargs)
+ self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
- # Calculate the cost
- cost = error.erdist(outputs, y).mean()
- cost.name = 'cost'
- hcost = error.hdist(outputs, y).mean()
- hcost.name = 'hcost'
+ @application(outputs=['destination'])
+ def predict(self, **kwargs):
+ cls_probas = super(Model, self).predict(**kwargs)
+ return tensor.dot(cls_probas, self.classes)
- # Initialization
- for tbl in embed_tables:
- tbl.weights_init = config.embed_weights_init
- mlp.weights_init = config.mlp_weights_init
- mlp.biases_init = config.mlp_biases_init
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
- for tbl in embed_tables:
- tbl.initialize()
- mlp.initialize()
+ @application(outputs=['cost'])
+ def cost(self, **kwargs):
+ y_hat = self.predict(**kwargs)
+ y = tensor.concatenate((kwargs['destination_latitude'][:, None],
+ kwargs['destination_longitude'][:, None]), axis=1)
- self.cost = cost
- self.monitor = [cost, hcost]
- self.outputs = outputs
- self.pred_vars = ['destination_latitude', 'destination_longitude']
+ return error.erdist(y_hat, y).mean()
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['destination_latitude', 'destination_longitude']
diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py
@@ -1,109 +1,71 @@
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
-from blocks.bricks.lookup import LookupTable
-
-from blocks.filter import VariableFilter
-from blocks.graph import ComputationGraph, apply_dropout
-
import numpy
import theano
from theano import tensor
+from blocks import roles
+from blocks.bricks import application, MLP, Rectifier, Softmax
-import data
import error
+from model.mlp import FFMLP, Stream
-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]
+class Model(FFMLP):
+ def __init__(self, config, **kwargs):
+ super(Model, self).__init__(config, **kwargs)
+
+ self.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')
+ self.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')
- x_input_time = tensor.lvector('input_time')
+ self.dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes')
+ self.time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes')
- input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude]
- embed_tables = []
+ self.inputs.append('input_time')
+ self.children.extend([self.dest_mlp, self.time_mlp])
- self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude', 'input_time']
+ def _push_initialization_config(self):
+ super(Model, self)._push_initialization_config()
+ for mlp in [self.dest_mlp, self.time_mlp]:
+ mlp.weights_init = self.config.mlp_weights_init
+ mlp.biases_init = self.config.mlp_biases_init
- 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))
+ @application(outputs=['destination', 'duration'])
+ def predict(self, **kwargs):
+ hidden = super(Model, self).predict(**kwargs)
- y_dest = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
- tensor.vector('destination_longitude')[:, None]), axis=1)
- y_time = tensor.lvector('travel_time')
+ dest_cls_probas = self.dest_mlp.apply(hidden)
+ dest_outputs = tensor.dot(dest_cls_probas, self.dest_classes)
- # Define the model
- common_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden],
- dims=[config.dim_input] + config.dim_hidden)
+ time_cls_probas = self.time_mlp.apply(hidden)
+ time_outputs = kwargs['input_time'] + tensor.dot(time_cls_probas, self.time_classes)
- 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')
+ self.add_auxiliary_variable(dest_cls_probas, name='destination classes ponderations')
+ self.add_auxiliary_variable(time_cls_probas, name='time classes ponderations')
- 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'
- time_scost = config.time_cost_factor * time_cost
- time_scost.name = 'time_scost'
-
- cost = dest_cost + time_scost
-
- if hasattr(config, 'dropout_p'):
- cg = ComputationGraph(cost)
- dropout_inputs = VariableFilter(
- bricks=[b for b in list(common_mlp.children) +
- list(dest_mlp.children) +
- list(time_mlp.children)
- if isinstance(b, Rectifier)],
- name='output')(cg)
- cg = apply_dropout(cg, dropout_inputs, config.dropout_p)
- cost = cg.outputs[0]
-
- 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, time_scost]
- self.outputs = tensor.concatenate([dest_outputs, time_outputs[:, None]], axis=1)
- self.outputs.name = 'outputs'
- self.pred_vars = ['destination_longitude', 'destination_latitude', 'travel_time']
+ return (dest_outputs, time_outputs)
+
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
+
+ @application(outputs=['cost'])
+ def cost(self, **kwargs):
+ (destination_hat, time_hat) = self.predict(**kwargs)
+
+ destination = tensor.concatenate((kwargs['destination_latitude'][:, None],
+ kwargs['destination_longitude'][:, None]), axis=1)
+ time = kwargs['travel_time']
+
+ destination_cost = error.erdist(destination_hat, destination).mean()
+ time_cost = error.rmsle(time_hat.flatten(), time.flatten())
+
+ self.add_auxiliary_variable(destination_cost, [roles.COST], 'destination_cost')
+ self.add_auxiliary_variable(time_cost, [roles.COST], 'time_cost')
+
+ return destination_cost + self.config.time_cost_factor * time_cost
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['destination_latitude', 'destination_longitude', 'travel_time']
diff --git a/model/mlp.py b/model/mlp.py
@@ -0,0 +1,103 @@
+from theano import tensor
+
+from fuel.transformers import Batch
+from fuel.streams import DataStream
+from fuel.schemes import ConstantScheme, ShuffledExampleScheme
+from blocks.bricks import application, MLP, Rectifier, Initializable
+
+import data
+from data import transformers
+from data.hdf5 import TaxiDataset, TaxiStream
+from model import ContextEmbedder
+
+
+class FFMLP(Initializable):
+ def __init__(self, config, output_layer=None, **kwargs):
+ super(FFMLP, self).__init__(**kwargs)
+ self.config = config
+
+ self.context_embedder = ContextEmbedder(config)
+
+ output_activation = [] if output_layer is None else [output_layer()]
+ output_dim = [] if output_layer is None else [config.dim_output]
+ self.mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + output_activation,
+ dims=[config.dim_input] + config.dim_hidden + output_dim)
+
+ self.extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis for side in ['first', 'last'] for axis in [0, 1]}
+ self.inputs = self.context_embedder.inputs + self.extremities.keys()
+ self.children = [ self.context_embedder, self.mlp ]
+
+ def _push_initialization_config(self):
+ self.mlp.weights_init = self.config.mlp_weights_init
+ self.mlp.biases_init = self.config.mlp_biases_init
+
+ @application(outputs=['prediction'])
+ def predict(self, **kwargs):
+ embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs }))
+ extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.extremities.items())
+
+ inputs = tensor.concatenate(extremities + embeddings, axis=1)
+ outputs = self.mlp.apply(inputs)
+
+ return outputs
+
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
+
+class Stream(object):
+ def __init__(self, config):
+ self.config = config
+
+ def train(self, req_vars):
+ stream = TaxiDataset('train')
+ stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples))
+
+ valid = TaxiDataset(self.config.valid_set, 'valid.hdf5', sources=('trip_id',))
+ valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0]
+
+ stream = transformers.TaxiExcludeTrips(valid_trips_ids, stream)
+ stream = transformers.TaxiGenerateSplits(stream, max_splits=100)
+
+ stream = transformers.TaxiAddDateTime(stream)
+ stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, stream)
+ stream = transformers.Select(stream, tuple(req_vars))
+ return Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
+
+ def valid(self, req_vars):
+ stream = TaxiStream(self.config.valid_set, 'valid.hdf5')
+
+ stream = transformers.TaxiAddDateTime(stream)
+ stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, stream)
+ stream = transformers.Select(stream, tuple(req_vars))
+ return Batch(stream, iteration_scheme=ConstantScheme(1000))
+
+ def test(self, req_vars):
+ stream = TaxiStream('test')
+
+ stream = transformers.TaxiAddDateTime(stream)
+ stream = transformers.TaxiAddFirstLastLen(self.config.n_begin_end_pts, stream)
+
+ return Batch(stream, iteration_scheme=ConstantScheme(1))
+
+ def inputs(self):
+ return {'call_type': tensor.bvector('call_type'),
+ 'origin_call': tensor.ivector('origin_call'),
+ 'origin_stand': tensor.bvector('origin_stand'),
+ 'taxi_id': tensor.wvector('taxi_id'),
+ 'timestamp': tensor.ivector('timestamp'),
+ 'day_type': tensor.bvector('day_type'),
+ 'missing_data': tensor.bvector('missing_data'),
+ 'latitude': tensor.matrix('latitude'),
+ 'longitude': tensor.matrix('longitude'),
+ 'destination_latitude': tensor.vector('destination_latitude'),
+ 'destination_longitude': tensor.vector('destination_longitude'),
+ 'travel_time': tensor.ivector('travel_time'),
+ 'first_k_latitude': tensor.matrix('first_k_latitude'),
+ 'first_k_longitude': tensor.matrix('first_k_longitude'),
+ 'last_k_latitude': tensor.matrix('last_k_latitude'),
+ 'last_k_longitude': tensor.matrix('last_k_longitude'),
+ 'input_time': tensor.ivector('input_time'),
+ 'week_of_year': tensor.bvector('week_of_year'),
+ 'day_of_week': tensor.bvector('day_of_week'),
+ 'qhour_of_day': tensor.bvector('qhour_of_day')}
diff --git a/model/time_simple_mlp.py b/model/time_simple_mlp.py
@@ -1,63 +1,29 @@
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
-from blocks.bricks.lookup import LookupTable
+from blocks.bricks import application, Identity
-from theano import tensor
-
-import data
import error
+from model.mlp import FFMLP, Stream
-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]
-
- 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']
-
- 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 = tensor.lvector('travel_time')
-
- # Define the model
- mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
- dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
-
- # Create the Theano variables
- inputs = tensor.concatenate(input_list, axis=1)
- # inputs = theano.printing.Print("inputs")(inputs)
- outputs = config.exp_base ** mlp.apply(inputs)
-
- # outputs = theano.printing.Print("outputs")(outputs)
- # y = theano.printing.Print("y")(y)
- outputs.name = 'outputs'
+class Model(FFMLP):
+ def __init__(self, config, **kwargs):
+ super(Model, self).__init__(config, output_layer=Identity, **kwargs)
+ self.inputs.append('input_time')
- # Calculate the cost
- cost = error.rmsle(outputs.flatten(), y.flatten())
- cost.name = 'cost'
+ @application(outputs=['duration'])
+ def predict(self, **kwargs):
+ outputs = super(Model, self).predict(**kwargs)
+ return kwargs['input_time'] + self.config.exp_base ** outputs
- # Initialization
- for tbl in embed_tables:
- tbl.weights_init = config.embed_weights_init
- mlp.weights_init = config.mlp_weights_init
- mlp.biases_init = config.mlp_biases_init
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
- for tbl in embed_tables:
- tbl.initialize()
- mlp.initialize()
+ @application(outputs=['cost'])
+ def cost(self, **kwargs):
+ y_hat = self.predict(**kwargs)
+ y = kwargs['travel_time']
+ return error.rmsle(y_hat.flatten(), y.flatten())
- self.cost = cost
- self.monitor = [cost]
- self.outputs = outputs
- self.pred_vars = ['travel_time']
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['travel_time']
diff --git a/model/time_simple_mlp_tgtcls.py b/model/time_simple_mlp_tgtcls.py
@@ -1,67 +1,33 @@
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax
-from blocks.bricks.lookup import LookupTable
-
import numpy
import theano
from theano import tensor
+from blocks.bricks import application, Softmax
-import data
import error
+from model.mlp import FFMLP, Stream
-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]
-
- 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']
-
- 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 = tensor.lvector('travel_time')
-
- # Define the model
- mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Softmax()],
- dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
- classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
-
- # Create the Theano variables
- inputs = tensor.concatenate(input_list, axis=1)
- # inputs = theano.printing.Print("inputs")(inputs)
- cls_probas = mlp.apply(inputs)
- outputs = tensor.dot(cls_probas, classes)
-
- # outputs = theano.printing.Print("outputs")(outputs)
- # y = theano.printing.Print("y")(y)
- outputs.name = 'outputs'
+class Model(FFMLP):
+ def __init__(self, config, **kwargs):
+ super(Model, self, output_layer=Softmax).__init__(config, **kwargs)
+ self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
+ self.inputs.append('input_time')
- # Calculate the cost
- cost = error.rmsle(outputs.flatten(), y.flatten())
- cost.name = 'cost'
+ @application(outputs=['duration'])
+ def predict(self, **kwargs):
+ cls_probas = super(Model, self).predict(**kwargs)
+ return kwargs['input_time'] + tensor.dot(cls_probas, self.classes)
- # Initialization
- for tbl in embed_tables:
- tbl.weights_init = config.embed_weights_init
- mlp.weights_init = config.mlp_weights_init
- mlp.biases_init = config.mlp_biases_init
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
- for tbl in embed_tables:
- tbl.initialize()
- mlp.initialize()
+ @application(outputs=['cost'])
+ def cost(self, **kwargs):
+ y_hat = self.predict(**kwargs)
+ y = kwargs['travel_time']
+ return error.rmsle(y_hat.flatten(), y.flatten())
- self.cost = cost
- self.monitor = [cost]
- self.outputs = outputs
- self.pred_vars = ['travel_time']
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['travel_time']
diff --git a/test.py b/test.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+
+import sys
+import os
+import importlib
+import csv
+
+from blocks.dump import load_parameter_values
+from blocks.model import Model
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 2:
+ print >> sys.stderr, 'Usage: %s config' % sys.argv[0]
+ sys.exit(1)
+ model_name = sys.argv[1]
+ config = importlib.import_module('.%s' % model_name, 'config')
+ model_config = config.Model(config)
+
+ stream = config.Stream(config)
+ inputs = stream.inputs()
+ outputs = model_config.predict.outputs
+ req_vars_test = model_config.predict.inputs + ['trip_id']
+ test_stream = stream.test(req_vars_test)
+
+ model = Model(model_config.predict(**inputs))
+ parameters = load_parameter_values(os.path.join('model_data', model_name, 'params.npz'))
+ model.set_param_values(parameters)
+
+ if 'destination' in outputs:
+ 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 'duration' in outputs:
+ 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"])
+
+ function = model.get_theano_function()
+ for d in test_stream.get_epoch_iterator(as_dict=True):
+ input_values = [d[k.name] for k in model.inputs]
+ output_values = function(*input_values)
+ if 'destination' in outputs:
+ destination = output_values[outputs.index('destination')]
+ dest_outcsv.writerow([d['trip_id'][0], destination[0, 0], destination[0, 1]])
+ if 'duration' in outputs:
+ duration = output_values[outputs.index('duration')]
+ time_outcsv.writerow([d['trip_id'][0], duration[0]])
diff --git a/train.py b/train.py
@@ -1,32 +1,25 @@
#!/usr/bin/env python
-import sys
-import logging
import importlib
+import logging
+import operator
+import os
+import sys
+from functools import reduce
-import csv
-
-from picklable_itertools.extras import equizip
-
-from blocks.model import Model
-
-from fuel.transformers import Batch
-from fuel.streams import DataStream
-from fuel.schemes import ConstantScheme, ShuffledExampleScheme
-
-from blocks.algorithms import CompositeRule, RemoveNotFinite, GradientDescent, AdaDelta, Momentum
-from blocks.graph import ComputationGraph, apply_dropout
-from blocks.main_loop import MainLoop
+from blocks import roles
+from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite
from blocks.extensions import Printing, FinishAfter
-from blocks.extensions.saveload import Dump, LoadFromDump, Checkpoint
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.extensions.plot import Plot
+from blocks.extensions.saveload import Dump, LoadFromDump
+from blocks.filter import VariableFilter
+from blocks.graph import ComputationGraph, apply_dropout, apply_noise
+from blocks.main_loop import MainLoop
+from blocks.model import Model
-from theano import tensor
-from data import transformers
-from data.hdf5 import TaxiDataset, TaxiStream
-import apply_model
+logger = logging.getLogger(__name__)
if __name__ == "__main__":
if len(sys.argv) != 2:
@@ -35,123 +28,70 @@ if __name__ == "__main__":
model_name = sys.argv[1]
config = importlib.import_module('.%s' % model_name, 'config')
-def compile_valid_trip_ids():
- valid = TaxiDataset(config.valid_set, 'valid.hdf5', sources=('trip_id',))
- ids = valid.get_data(None, slice(0, valid.num_examples))
- return set(ids[0])
-
-def setup_train_stream(req_vars, valid_trips_ids):
- train = TaxiDataset('train')
- train = DataStream(train, iteration_scheme=ShuffledExampleScheme(train.num_examples))
-
- train = transformers.TaxiExcludeTrips(valid_trips_ids, train)
- train = transformers.TaxiGenerateSplits(train, max_splits=100)
-
- train = transformers.TaxiAddDateTime(train)
- train = transformers.TaxiAddFirstLastLen(config.n_begin_end_pts, train)
- train = transformers.Select(train, tuple(req_vars))
-
- train_stream = Batch(train, iteration_scheme=ConstantScheme(config.batch_size))
-
- return train_stream
-
-def setup_valid_stream(req_vars):
- valid = TaxiStream(config.valid_set, 'valid.hdf5')
+ logger.info('# Configuration: %s' % config.__name__)
+ for key in dir(config):
+ if not key.startswith('__') and isinstance(getattr(config, key), (int, str, list, tuple)):
+ logger.info(' %20s %s' % (key, str(getattr(config, key))))
- valid = transformers.TaxiAddDateTime(valid)
- valid = transformers.TaxiAddFirstLastLen(config.n_begin_end_pts, valid)
- valid = transformers.Select(valid, tuple(req_vars))
+ model = config.Model(config)
+ model.initialize()
- valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000))
-
- return valid_stream
-
-def setup_test_stream(req_vars):
- test = TaxiStream('test')
-
- test = transformers.TaxiAddDateTime(test)
- test = transformers.TaxiAddFirstLastLen(config.n_begin_end_pts, test)
- test = transformers.Select(test, tuple(req_vars))
-
- test_stream = Batch(test, iteration_scheme=ConstantScheme(1000))
-
- return test_stream
-
-
-def main():
- model = config.model.Model(config)
-
- cost = model.cost
- outputs = model.outputs
-
- req_vars = model.require_inputs + model.pred_vars
- req_vars_test = model.require_inputs + [ 'trip_id' ]
+ stream = config.Stream(config)
+ inputs = stream.inputs()
+ req_vars = model.cost.inputs
- valid_trips_ids = compile_valid_trip_ids()
- train_stream = setup_train_stream(req_vars, valid_trips_ids)
- valid_stream = setup_valid_stream(req_vars)
+ train_stream = stream.train(req_vars)
+ valid_stream = stream.valid(req_vars)
- # Training
+ cost = model.cost(**inputs)
cg = ComputationGraph(cost)
+ unmonitor = set()
+ if hasattr(config, 'dropout') and config.dropout < 1.0:
+ unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables))
+ cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
+ if hasattr(config, 'noise') and config.noise > 0.0:
+ unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables))
+ cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
+ cost = cg.outputs[0]
+ cg = Model(cost)
+
+ logger.info('# Parameter shapes:')
+ parameters_size = 0
+ for key, value in cg.get_params().iteritems():
+ logger.info(' %20s %s' % (value.get_value().shape, key))
+ parameters_size += reduce(operator.mul, value.get_value().shape, 1)
+ logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.get_params())))
params = cg.parameters
-
algorithm = GradientDescent(
cost=cost,
step_rule=CompositeRule([
RemoveNotFinite(),
- #AdaDelta(decay_rate=0.95),
- Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
- ]),
+ AdaDelta(),
+ ]),
params=params)
- plot_vars = [['valid_' + x.name for x in model.monitor]]
- print "Plot: ", plot_vars
-
- extensions=[TrainingDataMonitoring(model.monitor, prefix='train', every_n_batches=1000),
- DataStreamMonitoring(model.monitor, valid_stream,
+ monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables)) - unmonitor
+ plot_vars = [['valid_' + x.name for x in monitored]]
+ logger.info('Plotted variables: %s' % str(plot_vars))
+
+ dump_path = os.path.join('model_data', model_name)
+ logger.info('Dump path: %s' % dump_path)
+ extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000),
+ DataStreamMonitoring(monitored, valid_stream,
prefix='valid',
- every_n_batches=500),
- Printing(every_n_batches=500),
+ every_n_batches=1000),
+ Printing(every_n_batches=1000),
Plot(model_name, channels=plot_vars, every_n_batches=500),
- # Checkpoint('model.pkl', every_n_batches=100),
- Dump('model_data/' + model_name, every_n_batches=500),
- LoadFromDump('model_data/' + model_name),
- # FinishAfter(after_epoch=4),
+ Dump(dump_path, every_n_batches=5000),
+ LoadFromDump(dump_path),
+ #FinishAfter(after_n_batches=2),
]
main_loop = MainLoop(
- model=Model([cost]),
+ model=cg,
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(req_vars_test)
-
- 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__":
- logging.basicConfig(level=logging.INFO)
- main()
-
diff --git a/visualizer/HTTPServer.py b/visualizer/HTTPServer.py
@@ -7,8 +7,6 @@ import SimpleHTTPServer
import SocketServer
from cStringIO import StringIO
-import h5py
-
import data
from data.hdf5 import TaxiDataset
from visualizer import Vlist, Path
