diff options
| -rw-r--r-- | config/rnn_1.py | 2 | ||||
| -rw-r--r-- | config/rnn_lag_tgtcls_1.py | 49 | ||||
| -rw-r--r-- | config/rnn_tgtcls_1.py | 37 | ||||
| -rw-r--r-- | ext_test.py | 13 | ||||
| -rw-r--r-- | model/rnn.py | 82 | ||||
| -rw-r--r-- | model/rnn_direct.py | 13 | ||||
| -rw-r--r-- | model/rnn_lag_tgtcls.py | 31 | ||||
| -rw-r--r-- | model/rnn_tgtcls.py | 19 |
8 files changed, 207 insertions, 39 deletions
diff --git a/config/rnn_1.py b/config/rnn_1.py index 5bc62d8..6e148c4 100644 --- a/config/rnn_1.py +++ b/config/rnn_1.py @@ -1,7 +1,7 @@ from blocks.initialization import IsotropicGaussian, Constant import data -from model.rnn import Model, Stream +from model.rnn_direct import Model, Stream class EmbedderConfig(object): __slots__ = ('dim_embeddings', 'embed_weights_init') diff --git a/config/rnn_lag_tgtcls_1.py b/config/rnn_lag_tgtcls_1.py new file mode 100644 index 0000000..7a41b70 --- /dev/null +++ b/config/rnn_lag_tgtcls_1.py @@ -0,0 +1,49 @@ +import os +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.rnn_lag_tgtcls import Model, Stream + +class EmbedderConfig(object): + __slots__ = ('dim_embeddings', 'embed_weights_init') + +pre_embedder = EmbedderConfig() +pre_embedder.embed_weights_init = IsotropicGaussian(0.001) +pre_embedder.dim_embeddings = [ + ('week_of_year', 52, 10), + ('day_of_week', 7, 10), + ('qhour_of_day', 24 * 4, 10), + ('day_type', 3, 10), + ('taxi_id', 448, 10), +] + +post_embedder = EmbedderConfig() +post_embedder.embed_weights_init = IsotropicGaussian(0.001) +post_embedder.dim_embeddings = [ + ('origin_call', data.origin_call_train_size, 10), + ('origin_stand', data.stands_size, 10), +] + +with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f) + +hidden_state_dim = 100 +weights_init = IsotropicGaussian(0.01) +biases_init = Constant(0.001) + +rec_to_out_dims = [200, 1000] +in_to_rec_dims = [200] + +dropout = 0.5 +dropout_inputs = VariableFilter(bricks=[Rectifier], name='output') + +noise = 0.01 +noise_inputs = VariableFilter(roles=[roles.PARAMETER]) + +batch_size = 10 +batch_sort_size = 10 +valid_set = 'cuts/test_times_0' diff --git a/config/rnn_tgtcls_1.py b/config/rnn_tgtcls_1.py new file mode 100644 index 0000000..3204559 --- /dev/null +++ b/config/rnn_tgtcls_1.py @@ -0,0 +1,37 @@ +import os +import cPickle + +from blocks.initialization import IsotropicGaussian, Constant + +import data +from model.rnn_tgtcls import Model, Stream + +class EmbedderConfig(object): + __slots__ = ('dim_embeddings', 'embed_weights_init') + +pre_embedder = EmbedderConfig() +pre_embedder.embed_weights_init = IsotropicGaussian(0.001) +pre_embedder.dim_embeddings = [ + ('week_of_year', 52, 10), + ('day_of_week', 7, 10), + ('qhour_of_day', 24 * 4, 10), + ('day_type', 3, 10), + ('taxi_id', 448, 10), +] + +post_embedder = EmbedderConfig() +post_embedder.embed_weights_init = IsotropicGaussian(0.001) +post_embedder.dim_embeddings = [ + ('origin_call', data.origin_call_train_size, 10), + ('origin_stand', data.stands_size, 10), +] + +with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f) + +hidden_state_dim = 100 +weights_init = IsotropicGaussian(0.01) +biases_init = Constant(0.001) + +batch_size = 10 +batch_sort_size = 10 +valid_set = 'cuts/test_times_0' diff --git a/ext_test.py b/ext_test.py index 6a3fa0a..3af637b 100644 --- a/ext_test.py +++ b/ext_test.py @@ -29,12 +29,21 @@ class RunOnTest(SimpleExtension): iter_no = repr(self.main_loop.log.status['iterations_done']) if 'valid_destination_cost' in self.main_loop.log.current_row: dvc = self.main_loop.log.current_row['valid_destination_cost'] - else: + elif 'valid_model_cost_cost' in self.main_loop.log.current_row: dvc = self.main_loop.log.current_row['valid_model_cost_cost'] + elif 'valid_model_valid_cost_cost' in self.main_loop.log.current_row: + dvc = self.main_loop.log.current_row['valid_model_valid_cost_cost'] + else: + raise RuntimeError("Unknown model type") + if 'valid_time_cost' in self.main_loop.log.current_row: tvc = self.main_loop.log.current_row['valid_time_cost'] - else: + elif 'valid_model_cost_cost' in self.main_loop.log.current_row: tvc = self.main_loop.log.current_row['valid_model_cost_cost'] + elif 'valid_model_valid_cost_cost' in self.main_loop.log.current_row: + tvc = self.main_loop.log.current_row['valid_model_valid_cost_cost'] + else: + raise RuntimeError("Unknown model type") if 'destination' in self.outputs: dest_outname = 'test-dest-%s-it%s-cost%.3f.csv' % (self.model_name, iter_no, dvc) diff --git a/model/rnn.py b/model/rnn.py index be17a95..bfc3122 100644 --- a/model/rnn.py +++ b/model/rnn.py @@ -16,16 +16,16 @@ from data.hdf5 import TaxiDataset, TaxiStream import error -class Model(Initializable): +class RNN(Initializable): @lazy() - def __init__(self, config, **kwargs): - super(Model, self).__init__(**kwargs) + def __init__(self, config, rec_input_len=2, output_dim=2, **kwargs): + super(RNN, self).__init__(**kwargs) self.config = config self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder') self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder') - in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings) + in1 = rec_input_len + sum(x[2] for x in config.pre_embedder.dim_embeddings) self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec') self.rec = LSTM( @@ -34,7 +34,7 @@ class Model(Initializable): ) in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings) - self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output') + self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, output_dim], name='rec_to_output') self.sequences = ['latitude', 'latitude_mask', 'longitude'] self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs @@ -55,59 +55,69 @@ class Model(Initializable): def get_dim(self, name): return self.rec.get_dim(name) - @application - def initial_state(self, *args, **kwargs): - return self.rec.initial_state(*args, **kwargs) + def process_rto(self, rto): + return rto - @recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'], sequences=['latitude', 'longitude', 'latitude_mask']) - def predict_all(self, latitude, longitude, latitude_mask, **kwargs): - latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0] - longitude = (longitude - data.train_gps_mean[1]) / data.train_gps_std[1] + def rec_input(self, latitude, longitude, **kwargs): + return (tensor.shape_padright(latitude), tensor.shape_padright(longitude)) + @recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells']) + def predict_all(self, **kwargs): pre_emb = tuple(self.pre_context_embedder.apply(**kwargs)) - latitude = tensor.shape_padright(latitude) - longitude = tensor.shape_padright(longitude) - itr = self.input_to_rec.apply(tensor.concatenate(pre_emb + (latitude, longitude), axis=1)) + + itr_in = tensor.concatenate(pre_emb + self.rec_input(**kwargs), axis=1) + itr = self.input_to_rec.apply(itr_in) itr = itr.repeat(4, axis=1) - (next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=latitude_mask, iterate=False) + (next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=kwargs['latitude_mask'], iterate=False) post_emb = tuple(self.post_context_embedder.apply(**kwargs)) rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1)) - rto = (rto * data.train_gps_std) + data.train_gps_mean + rto = self.process_rto(rto) return (rto, next_states, next_cells) + @predict_all.property('sequences') + def predict_all_sequences(self): + return self.sequences + + @application(outputs=predict_all.states) + def initial_states(self, *args, **kwargs): + return self.rec.initial_states(*args, **kwargs) + @predict_all.property('contexts') - def predict_all_inputs(self): + def predict_all_context(self): return self.context + def before_predict_all(self, kwargs): + kwargs['latitude'] = (kwargs['latitude'].T - data.train_gps_mean[0]) / data.train_gps_std[0] + kwargs['longitude'] = (kwargs['longitude'].T - data.train_gps_mean[1]) / data.train_gps_std[1] + kwargs['latitude_mask'] = kwargs['latitude_mask'].T + @application(outputs=['destination']) - def predict(self, latitude, longitude, latitude_mask, **kwargs): - latitude = latitude.T - longitude = longitude.T - latitude_mask = latitude_mask.T - res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0] - return res[-1] + def predict(self, **kwargs): + self.before_predict_all(kwargs) + res = self.predict_all(**kwargs)[0] + + last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=0) - 1, dtype='int64') + return res[last_id] @predict.property('inputs') def predict_inputs(self): return self.inputs @application(outputs=['cost_matrix']) - def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs): - latitude = latitude.T - longitude = longitude.T - latitude_mask = latitude_mask.T + def cost_matrix(self, **kwargs): + self.before_predict_all(kwargs) - res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0] + res = self.predict_all(**kwargs)[0] target = tensor.concatenate( (kwargs['destination_latitude'].dimshuffle('x', 0, 'x'), kwargs['destination_longitude'].dimshuffle('x', 0, 'x')), axis=2) - target = target.repeat(latitude.shape[0], axis=0) + target = target.repeat(kwargs['latitude'].shape[0], axis=0) ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2))) - ce = ce.reshape(latitude.shape) - return ce * latitude_mask + ce = ce.reshape(kwargs['latitude'].shape) + return ce * kwargs['latitude_mask'] @cost_matrix.property('inputs') def cost_matrix_inputs(self): @@ -123,8 +133,8 @@ class Model(Initializable): @application(outputs=['cost']) def valid_cost(self, **kwargs): - # Only works when batch_size is 1. - return self.cost_matrix(**kwargs)[-1,0] + last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=1) - 1, dtype='int64') + return self.cost_matrix(**kwargs)[last_id].mean() @valid_cost.property('inputs') def valid_cost_inputs(self): @@ -158,7 +168,7 @@ class Stream(object): stream = transformers.add_destination(stream) stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) - stream = Batch(stream, iteration_scheme=ConstantScheme(1)) + stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size)) stream = Padding(stream, mask_sources=['latitude', 'longitude']) stream = transformers.Select(stream, req_vars) return stream @@ -169,7 +179,7 @@ class Stream(object): stream = transformers.taxi_remove_test_only_clients(stream) stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) - stream = Batch(stream, iteration_scheme=ConstantScheme(1)) + stream = Batch(stream, iteration_scheme=ConstantScheme(self.config.batch_size)) stream = Padding(stream, mask_sources=['latitude', 'longitude']) stream = transformers.Select(stream, req_vars) return stream diff --git a/model/rnn_direct.py b/model/rnn_direct.py new file mode 100644 index 0000000..8123327 --- /dev/null +++ b/model/rnn_direct.py @@ -0,0 +1,13 @@ +from blocks.bricks.base import lazy + +from model.rnn import RNN, Stream +import data + + +class Model(RNN): + @lazy() + def __init__(self, config, **kwargs): + super(Model, self).__init__(config, **kwargs) + + def process_rto(self, rto): + return (rto * data.train_gps_std) + data.train_gps_mean diff --git a/model/rnn_lag_tgtcls.py b/model/rnn_lag_tgtcls.py new file mode 100644 index 0000000..49d1f01 --- /dev/null +++ b/model/rnn_lag_tgtcls.py @@ -0,0 +1,31 @@ +import numpy +import theano +from theano import tensor +from blocks.bricks.base import lazy +from blocks.bricks import Softmax + +from model.rnn import RNN, Stream + + +class Model(RNN): + @lazy() + def __init__(self, config, **kwargs): + super(Model, self).__init__(config, rec_input_len=4, output_dim=config.tgtcls.shape[0], **kwargs) + self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes') + self.softmax = Softmax() + self.sequences.extend(['latitude_lag', 'longitude_lag']) + self.children.append(self.softmax) + + def before_predict_all(self, kwargs): + super(Model, self).before_predict_all(kwargs) + kwargs['latitude_lag'] = tensor.extra_ops.repeat(kwargs['latitude'], 2, axis=0) + kwargs['longitude_lag'] = tensor.extra_ops.repeat(kwargs['longitude'], 2, axis=0) + + def process_rto(self, rto): + return tensor.dot(self.softmax.apply(rto), self.classes) + + def rec_input(self, latitude, longitude, latitude_lag, longitude_lag, **kwargs): + return (tensor.shape_padright(latitude), + tensor.shape_padright(longitude), + tensor.shape_padright(latitude_lag), + tensor.shape_padright(longitude_lag)) diff --git a/model/rnn_tgtcls.py b/model/rnn_tgtcls.py new file mode 100644 index 0000000..0c0faf2 --- /dev/null +++ b/model/rnn_tgtcls.py @@ -0,0 +1,19 @@ +import numpy +import theano +from theano import tensor +from blocks.bricks.base import lazy +from blocks.bricks import Softmax + +from model.rnn import RNN, Stream + + +class Model(RNN): + @lazy() + def __init__(self, config, **kwargs): + super(Model, self).__init__(config, output_dim=config.tgtcls.shape[0], **kwargs) + self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes') + self.softmax = Softmax() + self.children.append(self.softmax) + + def process_rto(self, rto): + return tensor.dot(self.softmax.apply(rto), self.classes) |