classification_with_missing_generation.yaml

# @package _global_

experiment_name: ${db.name}_${model.name}_${preprocessing.imputer.method}_with_missing_generation # DO NOT CHANGE
pipeline: missing # DO NOT CHANGE

seed: 42 # Seed for randomness control
verbose: 1 # 0 or 1, verbosity of the training

continue_experiment: False # True or False, if the experiment should be continued from where it was interrupted

missing_percentages: [0.0, 0.05, 0.1, 0.25, 0.5, 0.75]

defaults: # DO NOT CHANGE
  - _self_ # DO NOT CHANGE
  - paths@: default # DO NOT CHANGE
  - paths: experiment_paths # DO NOT CHANGE

  - databases@db: adult # Name of the configuration file of the dataset

  - cross_validation@test_cv: stratifiedkfold # Cross-validation strategy for the test set
  - cross_validation@val_cv: holdout          # Cross-validation strategy for the validation set

  - preprocessing/missing_generation@missing_generation.train: MCAR_global # DO NOT CHANGE
  - preprocessing/missing_generation@missing_generation.val: no_generation # DO NOT CHANGE
  - preprocessing/missing_generation@missing_generation.test: MCAR_global  # DO NOT CHANGE

  - preprocessing/numerical: normalize # normalize or standardize
  - preprocessing/categorical: categorical_encode # categorical_encode or one_hot_encode
  - preprocessing/imputer: no_imputation # simple or knn or iterative or no_imputation

  - model_type_params@dl_params: dl_params # DO NOT CHANGE
  - model_type_params@ml_params: ml_params # DO NOT CHANGE

  - model: naim # Name of the model to use

  - model_type_params@train.dl_params: dl_params # DO NOT CHANGE

  - initializer@train.initializer: xavier_normal # DO NOT CHANGE
  - loss@train.loss.CE: cross_entropy             # DO NOT CHANGE
  - regularizer@train.regularizer.l1: l1          # DO NOT CHANGE
  - regularizer@train.regularizer.l2: l2          # DO NOT CHANGE
  - optimizer@train.optimizer: adam               # DO NOT CHANGE
  - train_utils@train.manager: train_manager      # DO NOT REMOVE

  - metric@train.set_metrics.auc: auc # Metric to use for the early stopping

  - metric@performance_metrics.auc: auc # Metric to use for the performance evaluation
  - metric@performance_metrics.accuracy: accuracy # Metric to use for the performance evaluation
  - metric@performance_metrics.recall: recall # Metric to use for the performance evaluation
  - metric@performance_metrics.precision: precision # Metric to use for the performance evaluation
  - metric@performance_metrics.f1_score: f1_score # Metric to use for the performance evaluation