Défi IA 2022 Kaggle - Local prediction of rainfall using data measure on ground stations and results from MétéoFrance predictive models (Arpège_2D).

This code achieved a MAPE score of 26.57 (16th/84) on the public leaderboard and the competition results were a MAPE of 30.13, ranked 27th place (private leaderboard).

• Create your account on Kaggle
• Get your API credentials

" To use the Kaggle API, sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (https://www.kaggle.com//account) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json "

• Be sure to have the kaggle command installed using pip install kaggle. If the kaggle command cannot be found, add ~/.local/bin to your path.
• Run dowload_data.sh in your working directory. A ./DATA_RAINFALL/ directory will be created containing all the data from ground stations and Arpege.

• Install the xarray python library (plus netcdf4 and h5netcdf if necessary) to collect Arpege_2D data
• Open your terminal in your working directory and run the following command : python preprocess_train.py <your_working_directory_path>. The script fills nans in X_station_train and Y_train, merges X_station_train with 2D_arpege_train and then reshapes the training features to hourly features (all features at each hour). Two files full_X_train.csv and full_Y_train.csv are created in the directory ./DATA_RAINFALL/Train/Train/. Preprocessing the training set is time-consuming (approximately 6 hours).
• Open your terminal in your working directory and run the following command : python preprocess_test.py <your_working_directory_path>. The script fills nans in X_station_test, merges X_station_test with 2D_arpege_test and then reshapes the features to hourly features. A file full_X_test.csv is created in the directory ./DATA_RAINFALL/Test/Test/. Preprocessing the test set could last for around 30 minutes. Note that Y_test is not provided by MeteoFrance.

• Download the "DATA_RAINFALL.zip" file from : https://drive.google.com/file/d/10xF6B2JB-cEftuSWBBWLvF_FETbXBVpc/view?usp=sharing , or run the preprocessing part that creates it. Unzip the file, make sure the folder is next to main_model.py. This folder contains the data after preprocessing.
• Run main_model.py, this file runs the feature engineering before training on MLP and LGBM models, creating prediction .csv files afterwards.