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https://github.com/fxia22/seizure_prediction

Kaggle Competition Code
https://github.com/fxia22/seizure_prediction

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Kaggle Competition Code

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README 

        
# Seizure Detection



This repository contains the winning submission for UPenn and Mayo Clinic's Seizure Detection Challenge on Kaggle.



http://www.kaggle.com/c/seizure-detection



This README and repository modelled on https://www.kaggle.com/wiki/ModelSubmissionBestPractices



##Hardware / OS platform used



 * 15" Retina MacBook Pro (Late 2013) 2.7GHz Core i7, 16GB RAM

 * OS X Mavericks



##Dependencies



###Required



 * Python 2.7

 * scikit_learn-0.14.1

 * numpy-1.8.1

 * pandas-0.14.0

 * scipy

 * hickle (plus h5py and hdf5, see https://github.com/telegraphic/hickle for installation details)



###Optional (to try out various data transforms)



 * pywt (for Daubechies wavelet)

 * scikits talkbox (for MFCC)



##Train the model and make predictions



Obtain the competition data and place it in the root directory of the project.

```

seizure-data/

  Dog_1/

    Dog_1_ictal_segment_1.mat

    Dog_1_ictal_segment_2.mat

    ...

    Dog_1_interictal_segment_1.mat

    Dog_1_interictal_segment_2.mat

    ...

    Dog_1_test_segment_1.mat

    Dog_1_test_segment_2.mat

    ...



  Dog_2/

  ...

```



The directory name of the data should match the value in SETTINGS.json under the key `competition-data-dir`.



Then simply run:

```

./train.py

```



One classifier is trained for each patient, and dumped to the data-cache directory.



```

data-cache/classifier_Dog_1_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle

data-cache/classifier_Dog_2_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle

...

data-cache/classifier_Patient_8_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle

```



Although using these classifiers outside the scope of this project is not very straightforward.



More convenient is to run the predict script.



```

./predict.py

```



This will take at least 2 hours. Feel free to update the classifier's `n_jobs` parameter

in `seizure_detection.py`.



A submission file will be created under the directory specified by the `submission-dir` key

in `SETTINGS.json` (default `submissions/`).



Predictions are made using the test segments found in the competition data directory. They

are iterated over starting from 1 counting upwards until no file is found.



i.e.

```

seizure-data/

  Dog_1/

    Dog_1_test_segment_1.mat

    Dog_1_test_segment_2.mat

    ...

    Dog_1_test_segment_3181.mat

```



To make predictions on a new dataset, simply replace these test segments with new ones.

The files must numbered sequentially starting from 1 otherwise it will not find all of

the files.



This project uses a custom task system which caches task results to disk using hickle format and

falling back to pickle. First a task's output will be checked if it is in the data cache on disk,

and if not the task will be executed and the data cached.



See `seizure/tasks.py` for the custom tasks defined for this project. More specifically the

`MakePredictionsTask` depends on `TrainClassifierTask`, which means `predict.py` will train

and dump the classifiers as well as make predictions.



## Run cross-validation



```

./cross_validation.py

```



Cross-validation set is obtained by splitting on entire seizures. For example if there are 4 seizures,

3 seizures are used for training and 1 is used for cross-validation.



## SETTINGS.json



```

{

  "competition-data-dir": "seizure-data",

  "data-cache-dir": "data-cache",

  "submission-dir": "./submissions"

}

```



* `competition-data-dir`: directory containing the downloaded competition data

* `data-cache-dir`: directory the task framework will store cached data

* `submission-dir`: directory submissions are written to



## Model documentation



Available at https://github.com/MichaelHills/seizure-detection/raw/master/seizure-detection.pdf