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Diffstat (limited to 'model.py')
| -rw-r--r-- | model.py | 196 |
1 files changed, 0 insertions, 196 deletions
diff --git a/model.py b/model.py deleted file mode 100644 index 744d877..0000000 --- a/model.py +++ /dev/null @@ -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() - |