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@@ -1,3 +1,56 @@ 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: + +* `config/*.py`: configuration files for the different models we have experimented with the model which gets the best solution is `mlp_tgtcls_1_cswdtx_alexandre.py` +* `data/*.py` : files related to the data pipeline: + * `__init__.py` contains some general statistics about the data + * `csv_to_hdf5.py` : convert the CSV data file into an HDF5 file usable directly by Fuel + * `hdf5.py` : utility functions for exploiting the HDF5 file + * `init_valid.py` : initializes the HDF5 file for the validation set + * `make_valid_cut.py` : generate a validation set using a list of time cuts. Cut lists are stored in Python files in `data/cuts/` (we used a single cut file) + * `transformers.py` : Fuel pipeline for transforming the training dataset into structures usable by our model +* `data_analysis/*.py` : scripts for various statistical analyses on the dataset + * `cluster_arrival.py` : the script used to generate the mean-shift clustering of the destination points, producing the 3392 target points +* `model/*.py` : source code for the various models we tried + * `__init__.py` contains code common to all the models, including the code for embedding the metadata + * `mlp.py` contains code common to all MLP models + * `dest_mlp_tgtcls.py` containts code for our MLP destination prediction model using target points for the output layer +* `error.py` contains the functions for calculating the error based on the Haversine Distance +* `ext_saveload.py` contains a Blocks extension for saving and reloading the model parameters so that training can be interrupted +* `ext_test.py` contains a Blocks extension that runs the model on the test set and produces an output CSV submission file +* `train.py` contains the main code for the training and testing + +## How to reproduce the winning results? + +There is an helper script `prepare.sh` which might helps you (by performing steps 1-6 and some other checks), but if you encounter an error, the script will re-execute all the steps from the beginning (before the actual training, steps 2, 4 and 5 are quite long). + +Note that some script expect the repository to be in your PYTHONPATH (go to the root of the repository and type `export PYTHONPATH="$PWD:$PYTHONPATH"`). + +1. Set the `TAXI_PATH` environment variable to the path of the folder containing the CSV files. +2. Run `data/csv_to_hdf5.py "$TAXI_PATH" "$TAXI_PATH/data.hdf5"` to generate the HDF5 file (which is generated in `TAXI_PATH`, along the CSV files). This takes around 20 minutes on our machines. +3. Run `data/init_valid.py valid.hdf5` to initialize the validation set HDF5 file. +4. Run `data/make_valid_cut.py test_times_0` to generate the validation set. This can take a few minutes. +5. Run `data_analysis/cluster_arrival.py` to generate the arrival point clustering. This can take a few minutes. +6. Create a folder `model_data` and a folder `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. +7. Run `./train.py dest_mlp_tgtcls_1_cswdtx_alexandre` to train the model. Output solutions are generated in `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: + `THEANO_FLAGS=floatX=float32,device=gpu,optimizer=FAST_RUN` + +*More information in this pdf: https://github.com/adbrebs/taxi/blob/master/doc/short_report.pdf* |