# Pastebin BZAX3Ouv
def evaluate_dataset(eval_job, dataset_dir, storage_dir):
    db.dataset_eval.set_job_status(eval_job["id"], db.dataset_eval.STATUS_RUNNING)

    eval_location = os.path.join(os.path.abspath(dataset_dir), eval_job["id"])
    utils.path.create_path(eval_location)
    temp_dir = tempfile.mkdtemp()

    try:
        snapshot = db.dataset.get_snapshot(eval_job["snapshot_id"])

        train, test = artistfilter.filter(eval_job["snapshot_id"], eval_job["options"])
        db.dataset_eval.add_sets_to_job(eval_job["id"], train, test)

        logging.info("Generating filelist.yaml and copying low-level data for evaluation...")
        filelist_path = os.path.join(eval_location, "filelist.yaml")
        filelist = dump_lowlevel_data(train.keys(), temp_dir)
        with open(filelist_path, "w") as f:
            yaml.dump(filelist, f)

        logging.info("Generating groundtruth.yaml...")
        groundtruth_path = os.path.join(eval_location, "groundtruth.yaml")
        with open(groundtruth_path, "w") as f:
            yaml.dump(create_groundtruth_dict(snapshot["data"]["name"], train), f)

        logging.info("Training GAIA model...")
        evaluate_gaia(eval_job["options"], eval_location, groundtruth_path, filelist_path, storage_dir, eval_job)
        db.dataset_eval.set_job_status(eval_job["id"], db.dataset_eval.STATUS_DONE)
        logging.info("Evaluation job %s has been completed." % eval_job["id"])

    # TODO(roman): Also need to catch exceptions from Gaia.
    except db.exceptions.DatabaseException as e:
        logging.info("Evaluation job %s has failed!" % eval_job["id"])
        db.dataset_eval.set_job_status(
            job_id=eval_job["id"],
            status=db.dataset_eval.STATUS_FAILED,
            status_msg=str(e),
        )
        logging.info(e)

    finally:
        # Clean up the source files used to generate this model.
        # We can recreate them from the database if we need them
        # at a later stage.
        shutil.rmtree(temp_dir)


def evaluate_gaia(options, eval_location, groundtruth_path, filelist_path, storage_dir, eval_job):
    results = gaia_wrapper.train_model(
        project_dir=eval_location,
        groundtruth_file=groundtruth_path,
        filelist_file=filelist_path,
        c_values=options.get("c_values", []),
        gamma_values=options.get("gamma_values", []),
        preprocessing_values=options.get("preprocessing_values", []),
    )
    logging.info("Saving results...")
    save_history_file(storage_dir, results["history_path"], eval_job["id"])
    db.dataset_eval.set_job_result(eval_job["id"], json.dumps({
        "project_path": eval_location,
        "parameters": results["parameters"],
        "accuracy": results["accuracy"],
        "confusion_matrix": results["confusion_matrix"],
        "history_path": results["history_path"],
    }))


def evaluate_sklearn(eval_location, groundtruth_path, filelist_path, storage_dir, eval_job):
    # create_classification_project(ground_truth_directory=groundtruth_path)
    pass