diff options
| -rw-r--r-- | .gitignore | 3 | ||||
| -rw-r--r-- | __init__.py | 0 | ||||
| l--------- | data/test.csv | 1 | ||||
| l--------- | data/train.csv | 1 | ||||
| -rw-r--r-- | model.py | 113 |
5 files changed, 116 insertions, 2 deletions
@@ -55,3 +55,6 @@ docs/_build/ # PyBuilder target/ + +# Vim tmpfiles +*.swp diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/__init__.py diff --git a/data/test.csv b/data/test.csv deleted file mode 120000 index b797a91..0000000 --- a/data/test.csv +++ /dev/null @@ -1 +0,0 @@ -/data/lisatmp3/auvolat/taxikaggle/test.csv
\ No newline at end of file diff --git a/data/train.csv b/data/train.csv deleted file mode 120000 index d394bdf..0000000 --- a/data/train.csv +++ /dev/null @@ -1 +0,0 @@ -/data/lisatmp3/auvolat/taxikaggle/train.csv
\ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 0000000..0171a93 --- /dev/null +++ b/model.py @@ -0,0 +1,113 @@ +import logging +import os +from argparse import ArgumentParser + +from theano import tensor +from theano.ifelse import ifelse + +from blocks.bricks import MLP, Rectifier, Linear +from blocks.bricks.lookup import LookupTable + +from blocks.initialization import IsotropicGaussian, Constant +from blocks.model import Model + +from fuel.transformers import Batch +from fuel.streams import DataStream +from fuel.schemes import ConstantScheme + +from blocks.algorithms import GradientDescent, Scale +from blocks.graph import ComputationGraph +from blocks.main_loop import MainLoop +from blocks.extensions import Printing +from blocks.extensions.saveload import Dump, LoadFromDump +from blocks.extensions.monitoring import DataStreamMonitoring + +import data + +n_dow = 7 # number of division for dayofweek/dayofmonth/hourofday +n_dom = 31 +n_hour = 24 + +n_clients = 57106 +n_stands = 63 +n_embed = n_clients + n_stands # embeddings capturing local parameters + +n_begin_pts = 5 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 5 + +dim_embed = 50 +dim_hidden = 200 + +learning_rate = 0.1 +batch_size = 32 + +def main(): + # The input and the targets + x_firstk = tensor.matrix('first_k') + x_lastk = tensor.matrix('last_k') + x_client = tensor.lmatrix('client') + y = tensor.vector('targets') + + # Define the model + client_embed_table = LookupTable(length=n_clients, dim=dim_embed, name='lookup') + hidden_layer = MLP(activations=[Rectifier()], + dims=[(n_begin_pts + n_end_pts) * 2 + dim_embed, dim_hidden]) + output_layer = Linear(input_dim=dim_hidden, output_dim=2) + + # Create the Theano variables + + client_embed = client_embed_table.apply(x_client).flatten(ndim=2) + inputs = tensor.concatenate([x_firstk, x_lastk, client_embed], axis=1) + hidden = hidden_layer.apply(inputs) + outputs = output_layer.apply(hidden) + + # Calculate the cost + cost = (outputs - y).norm(2, axis=1).mean() + + # Initialization + client_embed_table.weights_init = IsotropicGaussian(0.001) + hidden_layer.weights_init = IsotropicGaussian(0.01) + hidden_layer.biases_init = Constant(0.001) + output_layer.weights_init = IsotropicGaussian(0.001) + output_layer.biases_init = Constant(0.001) + + client_embed_table.initialize() + hidden_layer.initialize() + output_layer.initialize() + + # Load the training and test data + train = data.train_data + stream = DataStream(train) + train_stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size)) + + # valid = data.valid_data + # stream = DataStream(valid) + # valid_stream = Batch(stream, iteration_scheme=ConstantScheme(batch_size)) + valid_stream = train_stream + + # Training + cg = ComputationGraph(cost) + algorithm = GradientDescent( + cost=cost, + # step_rule=AdaDelta(decay_rate=0.5), + step_rule=Scale(learning_rate=learning_rate), + params=cg.parameters) + + extensions=[DataStreamMonitoring([cost], valid_stream, + prefix='valid', + every_n_batches=100), + Printing(every_n_batches=100), + Dump('ngram_blocks_model', every_n_batches=100), + LoadFromDump('ngram_blocks_model')] + + main_loop = MainLoop( + model=Model([cost]), + data_stream=train_stream, + algorithm=algorithm, + extensions=extensions) + main_loop.run() + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() + |