diff options
Diffstat (limited to 'model/bidirectional_tgtcls_window.py')
| -rw-r--r-- | model/bidirectional_tgtcls_window.py | 190 |
1 files changed, 190 insertions, 0 deletions
diff --git a/model/bidirectional_tgtcls_window.py b/model/bidirectional_tgtcls_window.py new file mode 100644 index 0000000..10693ff --- /dev/null +++ b/model/bidirectional_tgtcls_window.py @@ -0,0 +1,190 @@ +from model.bidirectional import SegregatedBidirectional + + +class Model(Initializable): + @lazy() + def __init__(self, config, output_dim=2, **kwargs): + super(Model, self).__init__(**kwargs) + self.config = config + + self.context_embedder = ContextEmbedder(config) + + act = config.rec_activation() if hasattr(config, 'rec_activation') else None + self.rec = SegregatedBidirectional(LSTM(dim=config.hidden_state_dim, activation=act, + name='recurrent')) + + self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], + prototype=Linear(), name='fwd_fork') + self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'], + prototype=Linear(), name='bkwd_fork') + + rto_in = config.hidden_state_dim * 2 + sum(x[2] for x in config.dim_embeddings) + self.rec_to_output = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()], + dims=[rto_in] + config.dim_hidden + [output_dim]) + + self.softmax = Softmax() + + self.sequences = ['latitude', 'latitude_mask', 'longitude'] + self.inputs = self.sequences + self.context_embedder.inputs + + self.children = [ self.context_embedder, self.fwd_fork, self.bkwd_fork, + self.rec, self.rec_to_output, self.softmax ] + + self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), + name='classes') + + def _push_allocation_config(self): + for i, fork in enumerate([self.fwd_fork, self.bkwd_fork]): + fork.input_dim = 2 * self.config.window_size + fork.output_dims = [ self.rec.children[i].get_dim(name) + for name in fork.output_names ] + + def _push_initialization_config(self): + for brick in [self.fwd_fork, self.bkwd_fork, self.rec, self.rec_to_output]: + brick.weights_init = self.config.weights_init + brick.biases_init = self.config.biases_init + + def process_outputs(self, outputs): + return tensor.dot(self.softmax.apply(outputs), self.classes) + + @application(outputs=['destination']) + def predict(self, latitude, longitude, latitude_mask, **kwargs): + latitude = (latitude.dimshuffle(1, 0, 2) - data.train_gps_mean[0]) / data.train_gps_std[0] + longitude = (longitude.dimshuffle(1, 0, 2) - data.train_gps_mean[1]) / data.train_gps_std[1] + latitude_mask = latitude_mask.T + + rec_in = tensor.concatenate((latitude, longitude), axis=2) + + last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64') + + path = self.rec.apply(merge(self.fwd_fork.apply(rec_in, as_dict=True), + {'mask': latitude_mask}), + merge(self.bkwd_fork.apply(rec_in, as_dict=True), + {'mask': latitude_mask}))[0] + + path_representation = (path[0][:, -self.config.hidden_state_dim:], + path[last_id - 1, tensor.arange(latitude_mask.shape[1])] + [:, :self.config.hidden_state_dim]) + + embeddings = tuple(self.context_embedder.apply( + **{k: kwargs[k] for k in self.context_embedder.inputs })) + + inputs = tensor.concatenate(path_representation + embeddings, axis=1) + outputs = self.rec_to_output.apply(inputs) + + return self.process_outputs(outputs) + + @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): + stream = TaxiDataset('train', data.traintest_ds) + + 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)) + + if not data.tvt: + valid = TaxiDataset(data.valid_set, data.valid_ds, sources=('trip_id',)) + valid_trips_ids = valid.get_data(None, slice(0, valid.num_examples))[0] + stream = transformers.TaxiExcludeTrips(stream, valid_trips_ids) + + if hasattr(self.config, 'max_splits'): + stream = transformers.TaxiGenerateSplits(stream, max_splits=self.config.max_splits) + elif not data.tvt: + stream = transformers.add_destination(stream) + + if hasattr(self.config, 'train_max_len'): + idx = stream.sources.index('latitude') + def max_len_filter(x): + return len(x[idx]) <= self.config.train_max_len + stream = Filter(stream, max_len_filter) + + stream = transformers.TaxiExcludeEmptyTrips(stream) + + stream = transformers.window(stream, config.window_size) + + stream = transformers.taxi_add_datetime(stream) + stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) + + stream = transformers.balanced_batch(stream, key='latitude', + batch_size=self.config.batch_size, + batch_sort_size=self.config.batch_sort_size) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + stream = MultiProcessing(stream) + + return stream + + def valid(self, req_vars): + stream = TaxiStream(data.valid_set, data.valid_ds) + + stream = transformers.window(stream, config.window_size) + + stream = transformers.taxi_add_datetime(stream) + stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask'))) + + stream = transformers.balanced_batch(stream, key='latitude', + batch_size=self.config.batch_size, + batch_sort_size=self.config.batch_sort_size) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + stream = MultiProcessing(stream) + + return stream + + def test(self, req_vars): + stream = TaxiStream('test', data.traintest_ds) + + stream = transformers.window(stream, config.window_size) + + stream = transformers.taxi_add_datetime(stream) + 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(self.config.batch_size)) + stream = Padding(stream, mask_sources=['latitude', 'longitude']) + stream = transformers.Select(stream, req_vars) + return stream + + 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.tensor('latitude'), + 'longitude': tensor.tensor('longitude'), + 'latitude_mask': tensor.matrix('latitude_mask'), + 'longitude_mask': tensor.matrix('longitude_mask'), + 'destination_latitude': tensor.vector('destination_latitude'), + 'destination_longitude': tensor.vector('destination_longitude'), + 'travel_time': tensor.ivector('travel_time'), + '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')} + |