commit 0fd3b1497ffa1bb625bf593c845e28901bc640b7
parent 4cfba3efb678bc83c9528d603244310fec66c0c0
Author: AdeB <adbrebs@gmail.com>
Date: Sun, 21 Jun 2015 17:00:25 -0400
Model with only embeddings.
Diffstat:
5 files changed, 160 insertions(+), 1 deletion(-)
diff --git a/config/dest_simple_mlp_emb_only.py b/config/dest_simple_mlp_emb_only.py
@@ -0,0 +1,31 @@
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+from model.mlp_emb import Model, Stream
+
+use_cuts_for_training = True
+
+dim_embeddings = [
+ ('origin_call', data.origin_call_train_size, 10),
+ ('origin_stand', data.stands_size, 10),
+ ('week_of_year', 52, 10),
+ ('day_of_week', 7, 10),
+ ('qhour_of_day', 24 * 4, 10),
+ ('day_type', 3, 10),
+]
+
+dim_input = sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [200, 100]
+output_mode = "destination"
+dim_output = 2
+
+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 = 32
+
+valid_set = 'cuts/test_times_0'
+max_splits = 100
diff --git a/data/__init__.py b/data/__init__.py
@@ -5,6 +5,7 @@ import numpy
path = os.environ.get('TAXI_PATH', '/data/lisatmp3/auvolat/taxikaggle')
+path = os.environ.get('TAXI_PATH', '/Users/adeb/data/taxi')
Polyline = h5py.special_dtype(vlen=numpy.float32)
diff --git a/data/transformers.py b/data/transformers.py
@@ -94,7 +94,7 @@ class TaxiGenerateSplits(Transformer):
class _taxi_add_first_last_len_helper(object):
- def __init__(self, k, latitude, longitude):
+ def __init__(self, k, id_latitude, id_longitude):
self.k = k
self.id_latitude = id_latitude
self.id_longitude = id_longitude
diff --git a/model/mlp.py b/model/mlp.py
@@ -63,6 +63,7 @@ class Stream(object):
stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits)
+ stream = transformers.add_destination(stream)
stream = transformers.taxi_add_datetime(stream)
stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
diff --git a/model/mlp_emb.py b/model/mlp_emb.py
@@ -0,0 +1,126 @@
+from theano import tensor
+
+from fuel.transformers import Batch, MultiProcessing
+from fuel.streams import DataStream
+from fuel.schemes import ConstantScheme, ShuffledExampleScheme
+from blocks.bricks import application, MLP, Rectifier, Initializable, Identity
+
+import error
+import data
+from data import transformers
+from data.hdf5 import TaxiDataset, TaxiStream
+from data.cut import TaxiTimeCutScheme
+from model import ContextEmbedder
+
+
+class Model(Initializable):
+ def __init__(self, config, **kwargs):
+ super(Model, self).__init__(**kwargs)
+ self.config = config
+
+ self.context_embedder = ContextEmbedder(config)
+ self.mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
+ dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
+
+ 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=['destination'])
+ def predict(self, **kwargs):
+ embeddings = tuple(self.context_embedder.apply(**{k: kwargs[k] for k in self.context_embedder.inputs }))
+
+ inputs = tensor.concatenate(embeddings, axis=1)
+ outputs = self.mlp.apply(inputs)
+
+ if self.config.output_mode == "destination":
+ return data.train_gps_std * outputs + data.train_gps_mean
+ elif self.config.dim_output == "clusters":
+ return tensor.dot(outputs, self.classes)
+
+ @predict.property('inputs')
+ def predict_inputs(self):
+ return self.inputs
+
+ @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)
+
+ return error.erdist(y_hat, y).mean()
+
+ @cost.property('inputs')
+ def cost_inputs(self):
+ return self.inputs + ['destination_latitude', 'destination_longitude']
+
+
+class Stream(object):
+ def __init__(self, config):
+ self.config = config
+
+ def train(self, req_vars):
+ 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 = TaxiDataset('train')
+
+ if hasattr(self.config, 'use_cuts_for_training') and self.config.use_cuts_for_training:
+ stream = DataStream(stream, iteration_scheme=TaxiTimeCutScheme())
+ else:
+ stream = DataStream(stream, iteration_scheme=ShuffledExampleScheme(stream.num_examples))
+
+ stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids)
+ stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits)
+
+ stream = transformers.taxi_add_datetime(stream)
+ # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
+ stream = transformers.Select(stream, tuple(req_vars))
+
+ stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size))
+
+ stream = MultiProcessing(stream)
+
+ return stream
+
+ def valid(self, req_vars):
+ stream = TaxiStream(self.config.valid_set, 'valid.hdf5')
+
+ stream = transformers.taxi_add_datetime(stream)
+ # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
+ stream = transformers.Select(stream, tuple(req_vars))
+ return Batch(stream, iteration_scheme=ConstantScheme(1000))
+
+ def test(self, req_vars):
+ stream = TaxiStream('test')
+
+ stream = transformers.taxi_add_datetime(stream)
+ # stream = transformers.taxi_add_first_last_len(stream, self.config.n_begin_end_pts)
+ stream = transformers.taxi_remove_test_only_clients(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')}
