def lvh_helper()

in analyze_results/tinyimagenet/one_dimensional_subspaces.py [0:0]

• 89 lines of code
• 4 McCabe index (conditional complexity)

def lvh_helper(ax, setxax=True, setyax=True):

    one_dim_subspace_data = utils.read_csv_files(
        [
            f"learning-subspaces-results/tinyimagenet/eval-one-dimesnional-subspaces/results.csv",
        ],
        ["curr_acc1", "ensemble_acc", "m0_acc"],
    )

    ensemble_data = utils.read_csv_files(
        [f"learning-subspaces-results/tinyimagenet/eval-ensemble/results.csv"],
        ["curr_acc1"],
    )

    xarr = [round(i * 0.05, 2) for i in range(0, 21)]
    dh = utils.DataHolder(xarr, r"\alpha", "one-dim-subspaces")

    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "ensemble_acc",
        id="lines",
        **helper("ens_6"),
    )
    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "curr_acc1",
        id="lines",
        **helper("t_6"),
    )

    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "ensemble_acc",
        id="lines-layerwise",
        **helper("ens_4"),
    )
    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "curr_acc1",
        id="lines-layerwise",
        **helper("t_4"),
    )

    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "ensemble_acc",
        id="curves",
        **helper("ens_1"),
    )
    utils.add_data_helper(
        dh,
        ax,
        one_dim_subspace_data,
        "alpha1",
        "curr_acc1",
        id="curves",
        **helper("t_1"),
    )

    baselines = utils.query(
        ensemble_data,
        x="num_models",
        y="curr_acc1",
        outlier=lambda x, y, d: False,
    )

    dh.add(
        *utils.add_baseline(
            ax, xarr, baselines[1], **helper("standard_training")
        )
    )
    dh.add(
        *utils.add_baseline(
            ax, xarr, baselines[2], **helper("standard_ensemble")
        )
    )

    if setxax:
        ax.set_xlabel(r"$\alpha$", fontsize=fs_helper("xlabel"))
    if setyax:
        ax.set_ylabel("Accuracy", fontsize=fs_helper("ylabel"))

    ax.set_title(f"ResNet18 (TinyImageNet)", fontsize=fs_helper("title"))
    ax.set_ylim([0.625, 0.675])