fbe9c6d0f43e46674315af432780a0e3961af05d,ludwig/train.py,,kfold_cross_validate,#Any#Any#Any#Any#Any#Any#Any#,403

Before Change

                           output_directory=os.path.join(temp_dir_name,"results"))

                // collect training statistics for this fold
                training_stats = load_json(os.path.join(temp_dir_name,"results","cross_validation_fold_"+str(i),
                                                        "training_statistics.json"))
                kfold_training_stats["fold_"+str(i)] = training_stats

    // save consolidated training statistics from k-fold cv runs
    save_json(os.path.join(output_directory,"kfold_training_statistics.json"), kfold_training_stats)

After Change

                           output_directory=os.path.join(temp_dir_name,"results"))

                // score on hold out fold
                preds = model.predict(preprocessed_data[2],
                                      model_definition["training"]["batch_size"])

                // augment the training statistics with scoring metric fron the hold out fold
                train_stats["fold_metric"] = preds["combined"]

Instances