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@@ -1,3 +1,134 @@ Winning entry to the Kaggle ECML/PKDD destination competition. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i + + + +**Dependencies** + +We used the following packages developped at the MILA lab: +⢠Theano. A general GPU-accelerated python math library, with an interface similar to numpy (see [3, 4]). http://deeplearning.net/software/theano/ +⢠Blocks. A deep-learning and neural network framework for Python based on Theano. https://github.com/mila-udem/blocks +⢠Fuel. A data pipelining framework for Blocks. https://github.com/mila-udem/fuel +We also used the scikit-learn Python library for their mean-shift clustering algorithm. numpy, cPickle and h5py are also used at various places. + + + +**Structure** + + Here is a brief description of the Python files in the archive: + + <\itemize> + <item><verbatim|config/*.py> : configuration files for the different + models we have experimented with + + The model which gets the best solution is + <verbatim|mlp_tgtcls_1_cswdtx_alexandre.py> + + <item><verbatim|data/*.py> : files related to the data pipeline: + + <\itemize> + <item><verbatim|__init__.py> contains some general statistics about the + data + + <item><verbatim|csv_to_hdf5.py> : convert the CSV data file into an + HDF5 file usable directly by Fuel + + <item><verbatim|hdf5.py> : utility functions for exploiting the HDF5 + file + + <item><verbatim|init_valid.py> : initializes the HDF5 file for the + validation set + + <item><verbatim|make_valid_cut.py> : generate a validation set using a + list of time cuts. Cut lists are stored in Python files in + <verbatim|data/cuts/> (we used a single cut file) + + <item><verbatim|transformers.py> : Fuel pipeline for transforming the + training dataset into structures usable by our model + </itemize> + + <item><strong|<verbatim|data_analysis/*.py>> : scripts for various + statistical analyses on the dataset + + <\itemize> + <item><verbatim|cluster_arrival.py> : the script used to generate the + mean-shift clustering of the destination points, producing the 3392 + target points + </itemize> + + <item><verbatim|model/*.py> : source code for the various models we tried + + <\itemize> + <item><verbatim|__init__.py> contains code common to all the models, + including the code for embedding the metadata + + <item><verbatim|mlp.py> contains code common to all MLP models + + <item><verbatim|dest_mlp_tgtcls.py> containts code for our MLP + destination prediction model using target points for the output layer + </itemize> + + <item><verbatim|error.py> contains the functions for calculating the + error based on the Haversine Distance + + <item><verbatim|ext_saveload.py> contains a Blocks extension for saving + and reloading the model parameters so that training can be interrupted + + <item><verbatim|ext_test.py> contains a Blocks extension that runs the + model on the test set and produces an output CSV submission file + + <item><verbatim|train.py> contains the main code for the training and + testing + </itemize> + + + **How to reproduce the winning results?** + + + <\enumerate> + <item>Set the <verbatim|TAXI_PATH> environment variable to the path of + the folder containing the CSV files. + + <item>Run <verbatim|data/csv_to_hdf5.py> to generate the HDF5 file (which + is generated in <verbatim|TAXI_PATH>, along the CSV files). This takes + around 20 minutes on our machines. + + <item>Run <verbatim|data/init_valid.py> to initialize the validation set + HDF5 file. + + <item>Run <verbatim|data/make_valid_cut.py test_times_0> to generate the + validation set. This can take a few minutes. + + <item>Run <verbatim|data_analysis/cluster_arrival.py> to generate the + arrival point clustering. This can take a few minutes. + + <item>Create a folder <verbatim|model_data> and a folder + <verbatim|output> (next to the training script), which will receive + respectively a regular save of the model parameters and many submission + files generated from the model at a regular interval. + + <item>Run <verbatim|./train.py dest_mlp_tgtcls_1_cswdtx_alexandre> to + train the model. Output solutions are generated in <verbatim|output/> + every 1000 iterations. Interrupt the model with three consecutive Ctrl+C + at any times. The training script is set to stop training after 10 000 + 000 iterations, but a result file produced after less than 2 000 000 + iterations is already the winning solution. We trained our model on a + GeForce GTX 680 card and it took about an afternoon to generate the + winning solution. + + When running the training script, set the following Theano flags + environment variable to exploit GPU parallelism: + + <verbatim|THEANO_FLAGS=floatX=float32,device=gpu,optimizer=FAST_RUN> + + Theano is only compatible with CUDA, which requires an Nvidia GPU. + Training on the CPU is also possible but much slower. + </enumerate> + + + + + + More information in this pdf: https://github.com/adbrebs/taxi/blob/master/doc/short_report.pdf + |