You will need TensorFlow 1.12.0 to run experiments

Data preparation

1. Put arzta_daten_anonym1.csv, arzta_daten_anonym2.csv, arzta_daten_anonym3.csv and arzta_daten_anonym4.csv to data_path folder.
2. Run python data_prep.py --data_dir data_path

Pre-train your embeddings

1. Be sure to run data preparation script first
2. Run python pretrain_embeddings.py --data_path data_path/full.csv (full.csv is the file created by data_prep.py script)

Training

1. Choose the model swem_aver, swem_max, swem_max_features, gru or gru_feats (see model/model_fn.py) for detailed information)
2. Create experiment folder exp_path
3. Put experiments/config.yaml to exp_path and specify model params inside the yaml file.
4. Run python train.py --model_dir exp_path --data_dir data_path --architecture swem_max --use_pretrained

This command will initialize embeddings from word2vec_filename (specified in exp_path/config.yaml) and train the model (swem_max). After the training exp_path/config.yaml will be updated with ROC AUC and other metrics.

Other scripts

• xgb.py will train XGBClassifier
• search_hyperparams.py will iterate over hyperparams (hard-coded inside the script) and run train.py multiple times
• calculate_metrics.py will calculate metrics