kaggle-plasticc

Code for the 14th place solution in the Kaggle PLAsTiCC competition.

See Modelling_approach.pdf for a detailed discussion of the modelling approach.

Total runtime is around 5.5 hours on a 24 Gb laptop.

Download the code. Create a subfolder called data and save the csv files there.
To reproduce the results exactly, create an environment with the specific package versions I used. (If you already have numpy, pandas, scikit-learn and lightgbm you can skip this step, but the results may differ slightly if you have different versions.) If you have conda, the easiest option is to build a conda environment using this command:
```
conda env create environment.yml
```
This will create an environment called plasticc-bt. The requirements.txt file is provided as well if you want to build an environment with pip.
Run split_test.py to split the test data into 100 hdf5 files. They will be saved in an automatically created subfolder split_100 of the data folder. Takes around 15 minutes.
Run calculate_features.py to calculate the features. This will generate 3 files in a folder called
features (the folder is created automatically). Takes around 3.5 hours.
Run predict.py to train the model and make predictions on the test set. Takes around 1.5 hours.
Run scale.py to apply regularisation to the class 99 predictions and generate the final submission file. Takes a couple of minutes.

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