def todo_rename()

in domainbed/scripts/list_top_hparams.py [0:0]

• 62 lines of code
• 22 McCabe index (conditional complexity)

def todo_rename(records, selection_method, latex):

    grouped_records = reporting.get_grouped_records(records).map(lambda group:
        { **group, "sweep_acc": selection_method.sweep_acc(group["records"]) }
    ).filter(lambda g: g["sweep_acc"] is not None)

    # read algorithm names and sort (predefined order)
    alg_names = Q(records).select("args.algorithm").unique()
    alg_names = ([n for n in algorithms.ALGORITHMS if n in alg_names] +
        [n for n in alg_names if n not in algorithms.ALGORITHMS])

    # read dataset names and sort (lexicographic order)
    dataset_names = Q(records).select("args.dataset").unique().sorted()
    dataset_names = [d for d in datasets.DATASETS if d in dataset_names]

    for dataset in dataset_names:
        if latex:
            print()
            print("\\subsubsection{{{}}}".format(dataset))
        test_envs = range(datasets.num_environments(dataset))

        table = [[None for _ in [*test_envs, "Avg"]] for _ in alg_names]
        for i, algorithm in enumerate(alg_names):
            means = []
            for j, test_env in enumerate(test_envs):
                trial_accs = (grouped_records
                    .filter_equals(
                        "dataset, algorithm, test_env",
                        (dataset, algorithm, test_env)
                    ).select("sweep_acc"))
                mean, err, table[i][j] = format_mean(trial_accs, latex)
                means.append(mean)
            if None in means:
                table[i][-1] = "X"
            else:
                table[i][-1] = "{:.1f}".format(sum(means) / len(means))

        col_labels = [
            "Algorithm", 
            *datasets.get_dataset_class(dataset).ENVIRONMENTS,
            "Avg"
        ]
        header_text = (f"Dataset: {dataset}, "
            f"model selection method: {selection_method.name}")
        print_table(table, header_text, alg_names, list(col_labels),
            colwidth=20, latex=latex)

    # Print an "averages" table
    if latex:
        print()
        print("\\subsubsection{Averages}")

    table = [[None for _ in [*dataset_names, "Avg"]] for _ in alg_names]
    for i, algorithm in enumerate(alg_names):
        means = []
        for j, dataset in enumerate(dataset_names):
            trial_averages = (grouped_records
                .filter_equals("algorithm, dataset", (algorithm, dataset))
                .group("trial_seed")
                .map(lambda trial_seed, group:
                    group.select("sweep_acc").mean()
                )
            )
            mean, err, table[i][j] = format_mean(trial_averages, latex)
            means.append(mean)
        if None in means:
            table[i][-1] = "X"
        else:
            table[i][-1] = "{:.1f}".format(sum(means) / len(means))

    col_labels = ["Algorithm", *dataset_names, "Avg"]
    header_text = f"Averages, model selection method: {selection_method.name}"
    print_table(table, header_text, alg_names, col_labels, colwidth=25,
        latex=latex)