Export - Main Experiments.ipynb

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            "id": "c1450484-d8eb-4f89-87ba-d0316fe9e7c0",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Python  3.6.13\n",
                        "Python Executable: /home/ubuntu/anaconda3/envs/cfshap22/bin/python\n",
                        "CWD =  /home/ubuntu/air/xai/cfshap-release/cf-shap-facct22\n",
                        "NumPy 1.19.2 | Pandas 1.1.5 | SciPy 1.5.2 | NetworkX 2.5.1 | StatsModels 0.12.2\n",
                        "scikit-learn 0.24.2 | xgboost 1.3.3 \n",
                        "MatPlotLib 3.3.4 | Seaborn 0.11.1 | "
                    ]
                },
                {
                    "data": {
                        "text/html": [
                            "        <script type=\"text/javascript\">\n",
                            "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
                            "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
                            "        if (typeof require !== 'undefined') {\n",
                            "        require.undef(\"plotly\");\n",
                            "        requirejs.config({\n",
                            "            paths: {\n",
                            "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
                            "            }\n",
                            "        });\n",
                            "        require(['plotly'], function(Plotly) {\n",
                            "            window._Plotly = Plotly;\n",
                            "        });\n",
                            "        }\n",
                            "        </script>\n",
                            "        "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.colors.ListedColormap at 0x7fdf44ecff28>"
                        ]
                    },
                    "execution_count": 1,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from emutils.imports import *\n",
                "from emutils.utils import (\n",
                "    attrdict,\n",
                "    in_ipynb,\n",
                "    pandas_max,\n",
                "    load_pickle,\n",
                "    save_pickle,\n",
                ")\n",
                "import statsmodels\n",
                "import statsmodels.stats.proportion\n",
                "from emutils.plt.style import activate_latex, deactivate_latex\n",
                "import matplotlib.ticker as mtick\n",
                "\n",
                "activate_latex()\n",
                "\n",
                "from utils import *\n",
                "\n",
                "# Suppress warnings\n",
                "import warnings\n",
                "\n",
                "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
                "\n",
                "# Suppress scientific notation\n",
                "np.seterr(all='raise')\n",
                "\n",
                "pandas_max(100, 200)\n",
                "\n",
                "sns.set_style(\"whitegrid\")\n",
                "sns.color_palette(\"rocket\", as_cmap=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "f9170a2f-6682-4c1d-82e2-c6db8d26dd66",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "{'data_path': './data',\n",
                        " 'export_path': './export',\n",
                        " 'override': False,\n",
                        " 'results_path': './results',\n",
                        " 'show': True}\n"
                    ]
                }
            ],
            "source": [
                "from constants import DATA_DIR, MODEL_DIR, RESULTS_DIR, EXPORT_DIR\n",
                "\n",
                "parser = ArgumentParser(sys.argv)\n",
                "\n",
                "parser.add_argument('--data_path', type=str, default=DATA_DIR, required=False)\n",
                "parser.add_argument('--results_path', type=str, default=RESULTS_DIR)\n",
                "parser.add_argument('--export_path', type=str, default=EXPORT_DIR)\n",
                "\n",
                "parser.add_argument('--override', dest='override', action='store_true', default=False)\n",
                "\n",
                "args, unknown = parser.parse_known_args()\n",
                "args = attrdict(vars(args))\n",
                "\n",
                "os.makedirs(args.export_path, exist_ok=True)\n",
                "\n",
                "# Show graphs and stuff or not?\n",
                "args.show = in_ipynb()\n",
                "\n",
                "print(args)\n",
                "\n",
                "# -> Results\n",
                "def result_filename(result_name, dataset, data_version, model_version, results_version, ext='pkl'):\n",
                "    DATA_RUN_NAME = f\"{dataset}_D{data_version}\"\n",
                "    MODEL_RUN_NAME = f\"{DATA_RUN_NAME}M{model_version}_xgb\"\n",
                "    return f\"{args.results_path}/{MODEL_RUN_NAME}_{result_name}{results_version}.{ext}\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "33ffb306-5b1b-4110-8618-e86c553e7a6b",
            "metadata": {},
            "outputs": [],
            "source": [
                "MODELS = [\n",
                "    ('heloc', 'v2', 'v5'),\n",
                "    ('lendingclub', 'v2', 'v5'),\n",
                "    ('wines', 'v2', 'v5'),\n",
                "]\n",
                "\n",
                "RESULTS = [\n",
                "    'v5_close',\n",
                "    'v5_far',\n",
                "]\n",
                "\n",
                "def load_results(experiment_name):\n",
                "    results_ = {}\n",
                "    \n",
                "    for dataset, data_version, model_version in tqdm(MODELS):\n",
                "        for results_version in RESULTS:\n",
                "            model_name = model_version_to_name(model_version)\n",
                "            dataset_name = dataset_to_name(dataset)\n",
                "            result_name = result_version_to_name(results_version)\n",
                "            filename = result_filename(experiment_name,\n",
                "                                       results_version=results_version,\n",
                "                                       data_version=data_version,\n",
                "                                       model_version=model_version,\n",
                "                                       dataset=dataset)\n",
                "            \n",
                "            if not os.path.exists(filename):\n",
                "                warnings.warn(\n",
                "                    f'Could not load {experiment_name.upper()} results for {dataset_name} / {model_name} / {result_name}'\n",
                "                )\n",
                "            else:\n",
                "                results_[(dataset_name, model_name, result_name)] = load_pickle(filename)\n",
                "\n",
                "    print(f'Done {experiment_name.upper()}.')\n",
                "    return results_"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "9f1143f6-0a00-4ce7-a00b-e2c14281c18c",
            "metadata": {},
            "outputs": [
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                        "text/plain": [
                            "  0%|          | 0/3 [00:00<?, ?it/s]"
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                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[2022-09-27 13:08:13] INFO:\t Loading PICKLE from ./results/heloc_Dv2Mv5_xgb_costsv5_close.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.2069 seconds.\n",
                        "[2022-09-27 13:08:14] INFO:\t Loading PICKLE from ./results/heloc_Dv2Mv5_xgb_costsv5_far.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.187 seconds.\n",
                        "[2022-09-27 13:08:14] INFO:\t Loading PICKLE from ./results/lendingclub_Dv2Mv5_xgb_costsv5_close.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.1775 seconds.\n",
                        "[2022-09-27 13:08:14] INFO:\t Loading PICKLE from ./results/lendingclub_Dv2Mv5_xgb_costsv5_far.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.1735 seconds.\n",
                        "[2022-09-27 13:08:14] INFO:\t Loading PICKLE from ./results/wines_Dv2Mv5_xgb_costsv5_close.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.1365 seconds.\n",
                        "[2022-09-27 13:08:14] INFO:\t Loading PICKLE from ./results/wines_Dv2Mv5_xgb_costsv5_far.pkl...\n",
                        "[2022-09-27 13:08:14] INFO:\t Load done in 0.1558 seconds.\n",
                        "Done COSTS.\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "[('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'Far (>50%)'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'Far (>50%)'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'Far (>50%)')]"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cdf = load_results('costs')\n",
                "list(cdf.keys())"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "26560e0b-d4f6-4716-9116-bbbf556db170",
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                            "model_id": "7d7560b24e4a49d4b32af92775d09779",
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                            "version_minor": 0
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                        "text/plain": [
                            "  0%|          | 0/3 [00:00<?, ?it/s]"
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                    },
                    "metadata": {},
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                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/heloc_Dv2Mv5_xgb_nnsv5_close.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.1124 seconds.\n",
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/heloc_Dv2Mv5_xgb_nnsv5_far.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.1353 seconds.\n",
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/lendingclub_Dv2Mv5_xgb_nnsv5_close.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.1221 seconds.\n",
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/lendingclub_Dv2Mv5_xgb_nnsv5_far.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.1083 seconds.\n",
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/wines_Dv2Mv5_xgb_nnsv5_close.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.0722 seconds.\n",
                        "[2022-09-27 13:08:16] INFO:\t Loading PICKLE from ./results/wines_Dv2Mv5_xgb_nnsv5_far.pkl...\n",
                        "[2022-09-27 13:08:16] INFO:\t Load done in 0.0739 seconds.\n",
                        "Done NNS.\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "[('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'Far (>50%)'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'Far (>50%)'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'Close (<50%)'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'Far (>50%)')]"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "nndf = load_results('nns')\n",
                "list(nndf.keys())"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "23e09ba8-3563-4db0-9c99-b02e7fa1486a",
            "metadata": {},
            "source": [
                "### Merge ALL results together"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "a22fbae0-c052-43b1-abe6-f72901ff9c06",
            "metadata": {},
            "outputs": [],
            "source": [
                "def generate_closenfar_result(results_, delcols, nindex_cols, drop = True):\n",
                "    \n",
                "    for dataset, model_name in set(tuple(map(tuple, np.array(tuple(results_.keys()))[:, :-1]))):\n",
                "        if (dataset, model_name, 'Close (<50%)') in results_ and (dataset, model_name, 'Far (>50%)') in results_:\n",
                "            \n",
                "            A = results_[(dataset, model_name, 'Close (<50%)')]\n",
                "            B = results_[(dataset, model_name, 'Far (>50%)')]\n",
                "            \n",
                "            if not drop:\n",
                "                A = A.copy()\n",
                "                B = B.copy()\n",
                "                \n",
                "            if np.all(A.columns.values == B.columns.values) and A.shape == B.shape:\n",
                "                index_cols = list(set(A.columns.values) - set(delcols) - set(nindex_cols))\n",
                "                for c in delcols:\n",
                "                    if c in A:\n",
                "                        del A[c]\n",
                "                    if c in B:\n",
                "                        del B[c]\n",
                "                        \n",
                "                A = A.sort_values(index_cols)\n",
                "                B = B.sort_values(index_cols)\n",
                "                col = nindex_cols[0]\n",
                "\n",
                "                if np.all(A[index_cols].values == B[index_cols].values):\n",
                "                    print(f'Generating aggregation for {dataset} / {model_name}')\n",
                "                    C = A[index_cols].copy()\n",
                "                    C[col] = [np.concatenate([a, b]) for a, b in zip(A[col].values, B[col].values)]\n",
                "                    C['len'] = C[col].apply(len)\n",
                "                    C['failure'] = C[col].apply(failure_nan)\n",
                "                    C['mean'] = C[col].apply(safe_mean)\n",
                "\n",
                "                    results_[(dataset, model_name, 'All')] = C\n",
                "                    if drop:\n",
                "                        del results_[(dataset, model_name, 'Close (<50%)')]\n",
                "                        del results_[(dataset, model_name, 'Far (>50%)')]\n",
                "                else:\n",
                "                    logging.error(f'Problem at sorting level. {(dataset, model_name)}')\n",
                "            else:\n",
                "                logging.error(f'Problem at shape level. {(dataset, model_name)}')\n",
                "                print('Shapes:', A.shape, B.shape)\n",
                "                print('Columns:', np.all(A.columns.values == B.columns.values))\n",
                "                display(A.method.unique())\n",
                "                display(B.method.unique())\n",
                "\n",
                "        else:\n",
                "            logging.warning(f'Could not find both CLOSE n FAR. {(dataset, model_name)}')\n",
                "    return results_"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "95d3cd19-5899-442d-a045-63285d36c340",
            "metadata": {},
            "outputs": [],
            "source": [
                "delcols = ['mean', 'mean_pad', 'failure', 'costs_pad', 'xNN']"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "fb2baab6-b2ab-4a39-b236-2759d1f09369",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Generating aggregation for HELOC (Home Equity Line of Credit) / Non-Monotonic\n",
                        "Generating aggregation for Wine Quality (White) / Non-Monotonic\n",
                        "Generating aggregation for Lending Club (2007-2018) / Non-Monotonic\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "[('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'All'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'All'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'All')]"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cdf = generate_closenfar_result(cdf, delcols = delcols, nindex_cols = ['costs'])\n",
                "list(cdf.keys())"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "d35904d8-2196-4ad8-a6e5-10ee9f90fe19",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Generating aggregation for HELOC (Home Equity Line of Credit) / Non-Monotonic\n",
                        "Generating aggregation for Wine Quality (White) / Non-Monotonic\n",
                        "Generating aggregation for Lending Club (2007-2018) / Non-Monotonic\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "[('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'All'),\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'All'),\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'All')]"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "nndf = generate_closenfar_result(nndf, delcols = delcols, nindex_cols = ['NN'])\n",
                "list(nndf.keys())"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "d73e9830-688c-45c6-86da-3f36939a6237",
            "metadata": {},
            "source": [
                "# Results Pre-processing"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "864139c5-2bcc-4381-b903-b40f20517d8c",
            "metadata": {},
            "outputs": [],
            "source": [
                "from joblib import Parallel, delayed\n",
                "from emutils.parallel.utils import max_cpu_count\n",
                "\n",
                "parallel = Parallel(n_jobs = min(64, max_cpu_count() - 1))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "6b08bf5b-475e-42f3-b719-0292575ceeb5",
            "metadata": {},
            "outputs": [],
            "source": [
                "def safe_proportion(a, strict=False, sign='>='):\n",
                "    a = a.copy()\n",
                "    if strict:\n",
                "        assert np.isnan(a).sum() == 0\n",
                "    a = a[~np.isnan(a)]\n",
                "    if sign == '>=':\n",
                "        count = (a >= 0).sum()\n",
                "    elif sign == '><' or sign == '<>':\n",
                "        count = (a > 0).sum() - (a < 0).sum()\n",
                "    else:\n",
                "        raise ValueError('Invalid sign.')\n",
                "    nobs = len(a)\n",
                "    cl, cr = statsmodels.stats.proportion.proportion_confint(count, nobs)\n",
                "    return count / nobs, (cr - cl) / 2\n",
                "\n",
                "\n",
                "def compute_proportion(dif, **kwargs):\n",
                "    return np.array([[safe_proportion(b, **kwargs) for b in a] for a in dif])\n",
                "\n",
                "def compute_dif_safe(vals):\n",
                "    # NaN are infinite cost\n",
                "    vals = np.nan_to_num(vals, nan=np.inf)\n",
                "\n",
                "    # Let's do the difference (we have to ignore inf - inf = nan)\n",
                "    C = np.expand_dims(vals, axis=1) - np.expand_dims(vals, axis=0)\n",
                "\n",
                "    # The difference is NaN if both are NaN: we set it to zero\n",
                "    C = np.nan_to_num(C, nan=0)\n",
                "    return C\n",
                "\n",
                "\n",
                "def pivot_to_proportions(df, pivot_sig, sign):\n",
                "    # Delete 1 dimension\n",
                "    def explode_(x):\n",
                "        if isinstance(x, (np.ndarray, list)) and isinstance(x[0], (np.ndarray, list)):\n",
                "            return x[0]\n",
                "        else:\n",
                "            return x\n",
                "\n",
                "    pivot_sig = pivot_sig.apply(lambda s: s.apply(explode_))\n",
                "\n",
                "    experiments = list(pivot_sig.columns.values)\n",
                "\n",
                "    prev_err_cfg = np.seterr(invalid='ignore')\n",
                "    difs_safe = {\n",
                "        c: compute_dif_safe(\n",
                "            np.vstack(pivot_sig[c].values[pivot_sig[c].apply(lambda x: not isinstance(x, float) or not np.isnan(x))]))\n",
                "        for c in experiments\n",
                "    }\n",
                "    np.seterr(**prev_err_cfg)\n",
                "\n",
                "    prop = dict(\n",
                "        parallel(\n",
                "            delayed(lambda c, dif: (c, compute_proportion(dif, strict=True, sign=sign)))(k, v)\n",
                "            for k, v in tqdm(difs_safe.items())))\n",
                "    prop_ci = {c: p[:, :, 1] for c, p in prop.items()}\n",
                "    prop = {c: p[:, :, 0] for c, p in prop.items()}\n",
                "\n",
                "    return pivot_sig, prop, prop_ci"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "76980d9d-36cc-4e95-b509-6f2335039ab2",
            "metadata": {
                "tags": []
            },
            "source": [
                "## Precompute -  Costs"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "8b43d3d7-ce80-4f19-9adf-da07f704c8ea",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "def costs_to_proportion(cdf, sign=\">=\"):\n",
                "    # Convert into a pivot table (per method/baseline)\n",
                "    pivot_sig = pd.pivot_table(\n",
                "        cdf,\n",
                "        values='costs',\n",
                "        index=['type', 'method', 'action_direction'],  # <- Trend now here\n",
                "        columns=[\n",
                "            'action_normalization',\n",
                "            'action_aggregation',\n",
                "            'action_strategy',\n",
                "            #             'action_direction',\n",
                "            'cost_normalization',\n",
                "            'cost_aggregation',\n",
                "            'k',\n",
                "        ],\n",
                "        aggfunc=lambda X: X.values,\n",
                "    )\n",
                "    return pivot_to_proportions(cdf, pivot_sig, sign)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "e12fefab-7798-454a-a1c1-db420f1d62ff",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "db17c0faca444d589da35cf0571524e0",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "  0%|          | 0/40 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "db7652849dd846cb9d0769001cc36e6b",
                            "version_major": 2,
                            "version_minor": 0
                        },
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                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "cc7f0eec88c5437383f9b0553357bd56",
                            "version_major": 2,
                            "version_minor": 0
                        },
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                            "  0%|          | 0/40 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "pivot_sig2, prop2, prop_ci2 = dict(), dict(), dict()\n",
                "    \n",
                "for name in cdf.keys():\n",
                "    pivot_sig2[name], prop2[name], prop_ci2[name] = costs_to_proportion(cdf[name], sign = '><')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "75d6e4a8-85c2-4a06-bd43-b01e43fbf8d4",
            "metadata": {
                "tags": []
            },
            "source": [
                "## Precompute - NN"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "id": "5b7942a9-edfa-4404-ac94-1ec5e3015e2a",
            "metadata": {},
            "outputs": [],
            "source": [
                "def nn_to_proportions(ynndf, sign):\n",
                "    # Convert into a pivot table (per method/baseline)\n",
                "    pivot_sig = pd.pivot_table(\n",
                "        ynndf,\n",
                "        values='NN',\n",
                "        index=['type', 'method', 'action_direction'],  # The trend is part of the index now\n",
                "        columns=[\n",
                "            'action_normalization',\n",
                "            'action_aggregation',\n",
                "            'action_strategy',\n",
                "            'cost_normalization',\n",
                "            'cost_aggregation',\n",
                "            'k',\n",
                "            'topk',\n",
                "        ],\n",
                "        aggfunc=lambda X: X.values,\n",
                "    )\n",
                "    return pivot_to_proportions(ynndf, pivot_sig, sign)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "id": "63d7e371-4129-4769-ad68-3c571ecfcc4f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "c99dde6d7f2b4ee6ba73464c539704a1",
                            "version_major": 2,
                            "version_minor": 0
                        },
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                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "65b46061b59f4ab3a0afc8d6326fec69",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
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                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "9da89c35904342cf98516dbc6ee920e1",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "  0%|          | 0/40 [00:00<?, ?it/s]"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "pivot_sig_nn2, prop_nn2, prop_ci_nn2 = {}, {}, {}\n",
                "\n",
                "for name in nndf.keys():\n",
                "    pivot_sig_nn2[name], prop_nn2[name], prop_ci_nn2[name] = nn_to_proportions(nndf[name], sign = '><')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "792cab51-9606-4957-a23b-0ff8499a8600",
            "metadata": {},
            "source": [
                "# Export Results"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "62fe087b-c46f-464d-877a-95ccd4a09ec0",
            "metadata": {},
            "source": [
                "### Number of samples"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "id": "560da1b5-0523-4ba0-a244-81fa836d9d20",
            "metadata": {},
            "outputs": [],
            "source": [
                "def safe_len(x):\n",
                "    try:\n",
                "        return len(x)\n",
                "    except:\n",
                "        return -1\n",
                "\n",
                "def compute_sizes(pivot_sig):\n",
                "    def _compute_size(pivot_sig_):\n",
                "        sizes_ = pivot_sig_.apply(lambda s: s.apply(safe_len)).values\n",
                "        max_size = sizes_.max()\n",
                "        assert np.all((sizes_ == max_size) | (sizes_ == -1))\n",
                "        return max_size\n",
                "\n",
                "    return {name: _compute_size(pivot_sig_) for name, pivot_sig_ in pivot_sig.items()}"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "id": "088a5878-4b3c-47ff-ac35-b2648af819df",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'All'): 4000,\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'All'): 2996,\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'All'): 4000}"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "compute_sizes(pivot_sig2)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "id": "442679bd-b6ae-46ed-8ae9-c5b3b20182ea",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{('HELOC (Home Equity Line of Credit)', 'Non-Monotonic', 'All'): 4000,\n",
                            " ('Wine Quality (White)', 'Non-Monotonic', 'All'): 2996,\n",
                            " ('Lending Club (2007-2018)', 'Non-Monotonic', 'All'): 4000}"
                        ]
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "compute_sizes(pivot_sig_nn2)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "f95e4f7e-40b3-4fd0-960e-a188238a2fac",
            "metadata": {},
            "source": [
                "## Plots"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "id": "7f65d4b0-3fe5-45ad-bc4f-f2fa1c0298e6",
            "metadata": {},
            "outputs": [],
            "source": [
                "def print_nonunique_cols(df_, exclude_cols):\n",
                "    for col in df_:\n",
                "        if col not in exclude_cols:\n",
                "            u = df_[col].unique()\n",
                "            if len(u) > 1:\n",
                "                print(col, u)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "id": "1743d560-414b-48c4-84f0-f89b60b4e3be",
            "metadata": {},
            "outputs": [],
            "source": [
                "def __is_value_ok(v, f):\n",
                "    if f is None:\n",
                "        return True\n",
                "    elif v == f:\n",
                "        return True\n",
                "    else:\n",
                "        return False\n",
                "\n",
                "def is_tuple_ok(t, filter_tuple):\n",
                "    return all([__is_value_ok(v, f) for v, f in zip(t, filter_tuple)])\n",
                "\n",
                "def filter_dict(d, filter_tuple):\n",
                "    def __filter_tuple(t):\n",
                "        return tuple([v for v, f in zip(t, filter_tuple) if f is None])\n",
                "\n",
                "    return {__filter_tuple(t): data for t, data in d.items() if is_tuple_ok(t, filter_tuple)}\n",
                "\n",
                "def select_data_to_plot(\n",
                "    matrices_dict,\n",
                "    pivotname,\n",
                "    selected_methods_tuples,\n",
                "    selected_baselines_tuples,\n",
                "    fixed_features,\n",
                "    metric_name,\n",
                "    method_details,\n",
                "    feature_names,\n",
                "):\n",
                "    \n",
                "    methods_names = [method_to_name(method, trend = method_details, norm = method_details, details=method_details) for method in selected_methods_tuples]\n",
                "    baselines_names = [method_to_name(method, trend = method_details, norm = method_details, details=method_details) for method in selected_baselines_tuples]\n",
                "\n",
                "    # print('Methods/Baselines Names', methods_names, baselines_names)\n",
                "    \n",
                "    methods_tuples = pivotname.iloc[:, np.array([is_tuple_ok(k, fixed_features) for k in pivotname.columns.values])].dropna().index.values\n",
                "    \n",
                "    def get_methods_ids(selected_methods_tuples):\n",
                "        return np.array([np.argwhere([m == m_ for m_ in methods_tuples]) for m in selected_methods_tuples]).flatten()\n",
                "    \n",
                "    methods_index = get_methods_ids(selected_methods_tuples)\n",
                "    baselines_index = get_methods_ids(selected_baselines_tuples)\n",
                "    \n",
                "    # print('(All Available) Methods Tuples', methods_tuples)\n",
                "    # print('Selected Methods Tuples', selected_methods_tuples)\n",
                "    # print('Selected Baselines Tuples', selected_baselines_tuples)\n",
                "    # print('Indices', methods_index, baselines_index)\n",
                "    \n",
                "    float_index = [i for i, x in enumerate(fixed_features) if x is None]\n",
                "    fixed_index = [i for i, x in enumerate(fixed_features) if x is not None]\n",
                "    float_names = feature_names[float_index]\n",
                "    fixed_names = feature_names[fixed_index]\n",
                "    \n",
                "    selected_matrices_dict = matrices_dict\n",
                "    \n",
                "    \n",
                "    selected_matrices_dict = filter_dict(selected_matrices_dict, fixed_features)\n",
                "    \n",
                "    for k, v in selected_matrices_dict.items():\n",
                "        assert v.shape[0] == len(methods_tuples), f\"{v.shape} / {len(methods_tuples)}\"\n",
                "        assert v.shape[1] == len(methods_tuples), f\"{v.shape} / {len(methods_tuples)}\"\n",
                "    \n",
                "    selected_matrices_dict = {k: M[baselines_index][:, methods_index] for k, M in selected_matrices_dict.items()}\n",
                "    shape = selected_matrices_dict[list(selected_matrices_dict.keys())[0]].shape\n",
                "    \n",
                "    df_ =  pd.DataFrame(\n",
                "        [\n",
                "            {\n",
                "                **{\n",
                "                    'Baseline': baselines_names[b],\n",
                "                    'Method': methods_names[m],\n",
                "                    metric_name: selected_matrices_dict[floats][b, m]\n",
                "                },\n",
                "                **{\n",
                "                    float_name: float_value\n",
                "                    for float_name, float_value in zip(float_names, floats)\n",
                "                }\n",
                "            }\n",
                "            for b in range(shape[0]) for m in range(shape[1]) for floats in selected_matrices_dict.keys()\n",
                "        ]\n",
                "    )\n",
                "    return df_\n",
                "\n",
                "\n",
                "def generate_dataframe_to_plot(pivot_sig, prop, prop_ci = None, sizes=None, metric_name = \"M > B\", **kwargs):\n",
                "    df_ = dict()\n",
                "    for name in prop.keys():\n",
                "        df_[name] = select_data_to_plot(matrices_dict = prop[name], metric_name=metric_name, pivotname = pivot_sig[name], **kwargs)\n",
                "        # display(df_[name])\n",
                "        df_[name]['CI'] = select_data_to_plot(matrices_dict = prop_ci[name], metric_name='CI', pivotname = pivot_sig[name], **kwargs)['CI'] if prop_ci is not None else 0\n",
                "        \n",
                "    for k in df_:\n",
                "        df_[k]['Dataset'] = k[0]\n",
                "        df_[k]['Model'] = k[1]\n",
                "        df_[k]['Decision Boundary'] = k[2]\n",
                "        df_[k]['#XPs'] = sizes[k] if sizes is not None else np.nan\n",
                "        \n",
                "    df_ = pd.concat(list(df_.values()), axis = 0)\n",
                "    return df_"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "id": "3562d69d-2690-4e0c-acab-dca9c6b590c5",
            "metadata": {},
            "outputs": [],
            "source": [
                "def camera_custom_plot(data, x, y, ci=None, hue=None, style=None):\n",
                "    ax = plt.gca()\n",
                "\n",
                "    us = data[style].unique() if style is not None else [None]\n",
                "    markers = get_markers(len(us))\n",
                "    for s, marker in zip(us, markers):\n",
                "        uh = data[hue].unique() if hue is not None else [None]\n",
                "        colors = get_colors(len(uh))\n",
                "        for h, color in zip(uh, colors):\n",
                "            data_ = data.copy()\n",
                "            if hue is not None:\n",
                "                data_ = data_[data_[hue] == h]\n",
                "            if style is not None:\n",
                "                data_ = data_[data_[style] == s]\n",
                "            label = None\n",
                "            if s is not None:\n",
                "                label = s\n",
                "            if h is not None:\n",
                "                if label is None:\n",
                "                    label = h\n",
                "                else:\n",
                "                    if hue != style:\n",
                "                        label += ' - ' + h\n",
                "\n",
                "            if len(data_) > 0:\n",
                "                kwargs = dict(\n",
                "                    x=data_[x].values,\n",
                "                    y=data_[y].values,\n",
                "                    yerr=data_[ci].values if ci is not None else None,\n",
                "                    color=color,\n",
                "                    marker=marker,\n",
                "                    markersize=4,\n",
                "                )\n",
                "\n",
                "                plt.errorbar(**kwargs, linewidth=.5, alpha=.2)\n",
                "                plt.errorbar(**kwargs,\n",
                "                             linewidth=0,\n",
                "                             label=label.replace('%', '\\%').replace('<', '$<$').replace('>', '$>$'))\n",
                "\n",
                "\n",
                "def camera_figure(\n",
                "    df_,\n",
                "    x=\"k\",\n",
                "    y='Metric Name',\n",
                "    style=\"Baseline\",  #\"Decision Boundary\",\n",
                "    hue=\"Baseline\",\n",
                "    ci=\"CI\",\n",
                "    col='Method',\n",
                "    row=None,  #\"Model\",\n",
                "    figure='Dataset',\n",
                "    filename=None,\n",
                "    width=2.75,\n",
                "    ratio=2.25,\n",
                "    show_table=True,\n",
                "    ylabel_font=11,\n",
                "    ylabel_pad=10,\n",
                "    xlabel_font=11,\n",
                "    ticks_font=11,\n",
                "    suptitle_font=13,\n",
                "    title_font=12,\n",
                "    detached_legend=True,\n",
                "    legend_font=10,\n",
                "    ret_table=False,\n",
                "):\n",
                "\n",
                "    ylim = np.array(\n",
                "        (df_[y].min() - (df_[y].max() - df_[y].min()) / 20, df_[y].max() + (df_[y].max() - df_[y].min()) / 20))\n",
                "    ylim = ylim / .25\n",
                "    ylim = ylim * .25\n",
                "\n",
                "    tables = []\n",
                "    figures = df_[figure].unique() if figure is not None else [None]\n",
                "    for figure_name in figures:\n",
                "        df__ = df_[df_[figure] == figure_name].copy() if figure_name is not None else df_.copy()\n",
                "        cols = df__[col].unique() if col is not None else [None]\n",
                "        rows = df__[row].unique() if row is not None else [None]\n",
                "        fig, axs = plt.subplots(len(rows),\n",
                "                                len(cols),\n",
                "                                sharex=True,\n",
                "                                sharey=True,\n",
                "                                figsize=(len(cols) * width, len(rows) * width / ratio),\n",
                "                                dpi=100,\n",
                "                                gridspec_kw=dict(wspace=0.05, hspace=.1))\n",
                "        for i, c in enumerate(cols):\n",
                "            for j, r in enumerate(rows):\n",
                "                df___ = df__.copy()\n",
                "                if c is not None:\n",
                "                    df___ = df___[df___[col] == c]\n",
                "                if r is not None:\n",
                "                    df___ = df___[df___[row] == r]\n",
                "                if len(rows) == 1 and len(cols) == 1:\n",
                "                    plt.sca(axs)\n",
                "                elif len(rows) == 1:\n",
                "                    plt.sca(axs[i])\n",
                "                elif len(cols) == 1:\n",
                "                    plt.sca(axs[j])\n",
                "                else:\n",
                "                    plt.sca(axs[j, i])\n",
                "                camera_custom_plot(\n",
                "                    data=df___,\n",
                "                    x=x,\n",
                "                    y=y,\n",
                "                    style=style,\n",
                "                    hue=hue,\n",
                "                    ci=ci,\n",
                "                    #                     legend=(i == 0) and (j == 0),\n",
                "                )\n",
                "                xts = np.arange(1, len(df___[x].unique()) + 1)\n",
                "                plt.xticks(xts, fontsize=ticks_font)\n",
                "                #                 plt.xticks(xts, [\"$\\\\bm{\" + str(i) + \"}$\" for i in xts], fontsize = 13)\n",
                "                plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0, decimals=0))\n",
                "                if j == 0:\n",
                "                    plt.title(c, fontsize=title_font)\n",
                "                if ylim[1] - ylim[0] > 1.2:\n",
                "                    yticks = [t for t in np.arange(-1, 1 + .5, .5) if t > ylim[0] and t < ylim[1]]\n",
                "                    plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0, decimals=0))\n",
                "                elif ylim[1] - ylim[0] > .6:\n",
                "                    yticks = [t for t in np.arange(-1, 1 + .25, .25) if t > ylim[0] and t < ylim[1]]\n",
                "                    plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0, decimals=0))\n",
                "                else:\n",
                "                    yticks = [t for t in np.arange(-1, 1 + .125, .125) if t > ylim[0] and t < ylim[1]]\n",
                "                    plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0, decimals=1))\n",
                "                plt.yticks(yticks, fontsize=ticks_font)\n",
                "                if 'r' in y:\n",
                "                    plt.gca().set_facecolor((.995, 1.00, 1.00))\n",
                "                if 'p' in y:\n",
                "                    plt.gca().set_facecolor((1, 1, 0.995))\n",
                "                if j == len(rows) - 1:\n",
                "                    plt.xlabel(x, fontsize=xlabel_font)\n",
                "                if i == 0:\n",
                "                    if len(rows) > 1:\n",
                "                        if j == int(np.floor(len(rows) / 2)):\n",
                "                            plt.ylabel(y.replace('%', '\\%'), fontsize=ylabel_font, labelpad=ylabel_pad, loc='bottom')\n",
                "\n",
                "                    else:\n",
                "                        plt.ylabel(y.replace('%', '\\%'), fontsize=ylabel_font, labelpad=ylabel_pad)\n",
                "                if i == len(cols) - 1:\n",
                "                    if len(rows) > 1:\n",
                "                        ax = plt.gca()\n",
                "                        plt.sca(plt.gca().twinx())\n",
                "                        plt.ylabel(r)\n",
                "                        plt.tick_params(\n",
                "                            axis='y',  # changes apply to the x-axis\n",
                "                            which='both',  # both major and minor ticks are affected\n",
                "                            right=False,  # ticks along the top edge are off\n",
                "                            labelright=False)  # labels along the bottom edge are off\n",
                "                        plt.sca(ax)\n",
                "\n",
                "        plt.ylim(*ylim)\n",
                "        if figure_name is not None:\n",
                "            plt.suptitle(figure_name, fontsize=suptitle_font, y=1.30 - .15 * (len(rows) - 1))\n",
                "\n",
                "        # Create legend\n",
                "        plt.legend(\n",
                "            title=\"Baselines\",\n",
                "            bbox_to_anchor=(1.1 + (len(rows) > 1) * .3, 1),\n",
                "            handletextpad=.2,\n",
                "            fontsize=legend_font,\n",
                "            title_fontsize=legend_font + 1,\n",
                "        )\n",
                "        # Remove legend\n",
                "        if detached_legend:\n",
                "            lgn = plt.gca().get_legend_handles_labels()\n",
                "            plt.gca().get_legend().remove()\n",
                "\n",
                "        # Show/Save main figure\n",
                "        if filename is not None:\n",
                "            fig_name = str(figure_name)[:4] if len(figures) > 1 else 'None'\n",
                "            plt.savefig(f\"export/{filename}_{fig_name}.svg\", bbox_inches='tight', pad_inches=0)\n",
                "        plt.show()\n",
                "\n",
                "        #         display(df__)\n",
                "        if show_table:\n",
                "            table = (pd.pivot_table(df__,\n",
                "                                    columns=[c for c in [col, x] if c is not None],\n",
                "                                    index=[c for c in [row, style] if c is not None],\n",
                "                                    values=y) * 100).round(1)\n",
                "            #             table = table.loc[df__['Method'].unique()]\n",
                "            pandas_max(200, 200)\n",
                "            display(table)\n",
                "            tables.append(table)\n",
                "\n",
                "        # Legend Figure\n",
                "        if detached_legend:\n",
                "            plt.figure(figsize=(len(cols) * 4, 1.25))\n",
                "            plt.legend(\n",
                "                *lgn,\n",
                "                title=\"Baselines\",\n",
                "                # bbox_to_anchor=(1.1, 1),\n",
                "                loc='center',\n",
                "                ncol=3,\n",
                "                columnspacing=0.75,\n",
                "                handletextpad=.05,\n",
                "            )\n",
                "            plt.gca().axis('off')\n",
                "            if filename is not None:\n",
                "                plt.savefig(f\"export/{filename}_{fig_name}_legend.svg\", bbox_inches='tight', pad_inches=0)\n",
                "\n",
                "        plt.show()\n",
                "    if ret_table:\n",
                "        return tables\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6e690111-dba1-41e7-9cc8-4672a18077e8",
            "metadata": {},
            "source": [
                "What we want to plot"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "id": "e01ea598-489d-4173-833c-a9e324ad2daf",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Let's select what method we want to plot\n",
                "\n",
                "KWARGS = dict(\n",
                "    selected_baselines_tuples=[\n",
                "        ('SHAP', 'training_100', 'global'),\n",
                "        ('SHAP', 'diff_pred_100', 'global'),\n",
                "        ('SHAP', 'diff_label_100', 'global'),\n",
                "    ],\n",
                "    selected_methods_tuples=[\n",
                "        ('SHAP', 'knn100_qL1', 'local'),\n",
                "    ],\n",
                "    method_details = False,\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6f3fdeb0-877c-4623-87d3-3d326a170c15",
            "metadata": {
                "tags": []
            },
            "source": [
                "### Main Text - Counterfactual-Ability"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "id": "ebfd6a60-f84e-44a3-8db7-cebebc2af430",
            "metadata": {},
            "outputs": [],
            "source": [
                "FEATURE_NAMES_COSTS = np.array([\n",
                "    'Action Normalization',\n",
                "    'Action Aggregation',\n",
                "    'Action Strategy',\n",
                "    'Cost Normalization',\n",
                "    'Cost Aggregation',\n",
                "    'k',\n",
                "])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "id": "110ada32-f090-45ba-88f8-3e26a80d9820",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.8</td>\n",
                            "      <td>8.0</td>\n",
                            "      <td>12.3</td>\n",
                            "      <td>10.3</td>\n",
                            "      <td>10.3</td>\n",
                            "      <td>7.1</td>\n",
                            "      <td>14.2</td>\n",
                            "      <td>18.6</td>\n",
                            "      <td>23.0</td>\n",
                            "      <td>29.6</td>\n",
                            "      <td>8.7</td>\n",
                            "      <td>17.5</td>\n",
                            "      <td>19.5</td>\n",
                            "      <td>17.7</td>\n",
                            "      <td>15.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>6.3</td>\n",
                            "      <td>5.1</td>\n",
                            "      <td>6.6</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>1.6</td>\n",
                            "      <td>5.0</td>\n",
                            "      <td>9.8</td>\n",
                            "      <td>17.1</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>15.3</td>\n",
                            "      <td>15.2</td>\n",
                            "      <td>12.3</td>\n",
                            "      <td>10.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>5.4</td>\n",
                            "      <td>13.8</td>\n",
                            "      <td>26.2</td>\n",
                            "      <td>26.2</td>\n",
                            "      <td>20.8</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>17.5</td>\n",
                            "      <td>21.7</td>\n",
                            "      <td>28.1</td>\n",
                            "      <td>35.4</td>\n",
                            "      <td>14.8</td>\n",
                            "      <td>33.0</td>\n",
                            "      <td>34.0</td>\n",
                            "      <td>30.8</td>\n",
                            "      <td>25.2</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset     HELOC                         Lending Club                    \\\n",
                            "k               1     2     3     4     5            1     2     3     4   \n",
                            "Baseline                                                                   \n",
                            "SHAP D-LAB    2.8   8.0  12.3  10.3  10.3          7.1  14.2  18.6  23.0   \n",
                            "SHAP D-PRED  -1.0   3.7   6.3   5.1   6.6         -7.8   1.6   5.0   9.8   \n",
                            "SHAP TRAIN    5.4  13.8  26.2  26.2  20.8         11.0  17.5  21.7  28.1   \n",
                            "\n",
                            "Dataset           Wine Quality                          \n",
                            "k               5            1     2     3     4     5  \n",
                            "Baseline                                                \n",
                            "SHAP D-LAB   29.6          8.7  17.5  19.5  17.7  15.3  \n",
                            "SHAP D-PRED  17.1          7.9  15.3  15.2  12.3  10.7  \n",
                            "SHAP TRAIN   35.4         14.8  33.0  34.0  30.8  25.2  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "KWARGS_PLOT = dict(\n",
                "    y = KWARGS['metric_name'],\n",
                "    figure=None,\n",
                "    col='Dataset',    \n",
                "    detached_legend=False,\n",
                ")\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L1', 'random', 'quantile', 'L1', None),\n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'costs_random_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "aa2103b0-0950-4b3e-90d1-3373140ec27a",
            "metadata": {},
            "source": [
                "#### Plausibility - Main Text"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "id": "f9ae4c48-4484-4f6d-8e42-d5e470a17648",
            "metadata": {},
            "outputs": [],
            "source": [
                "FEATURE_NAMES_NN = np.array([\n",
                "    'Action Normalization',\n",
                "    'Action Aggregation',\n",
                "    'Action Strategy',\n",
                "    #     'Action Direction',\n",
                "    'Cost Normalization',\n",
                "    'Cost Aggregation',\n",
                "    'K',\n",
                "    'k',\n",
                "])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "id": "6ab2d869-9b78-46fa-b7ed-26c40029f214",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>1.6</td>\n",
                            "      <td>5.9</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>13.2</td>\n",
                            "      <td>14.6</td>\n",
                            "      <td>3.5</td>\n",
                            "      <td>9.4</td>\n",
                            "      <td>16.1</td>\n",
                            "      <td>26.7</td>\n",
                            "      <td>37.8</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>18.1</td>\n",
                            "      <td>19.2</td>\n",
                            "      <td>23.3</td>\n",
                            "      <td>23.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>1.5</td>\n",
                            "      <td>6.1</td>\n",
                            "      <td>9.8</td>\n",
                            "      <td>10.5</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>-4.7</td>\n",
                            "      <td>9.6</td>\n",
                            "      <td>21.5</td>\n",
                            "      <td>29.1</td>\n",
                            "      <td>36.4</td>\n",
                            "      <td>9.7</td>\n",
                            "      <td>17.9</td>\n",
                            "      <td>17.2</td>\n",
                            "      <td>20.1</td>\n",
                            "      <td>21.8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>3.4</td>\n",
                            "      <td>9.5</td>\n",
                            "      <td>15.2</td>\n",
                            "      <td>19.6</td>\n",
                            "      <td>18.8</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>13.9</td>\n",
                            "      <td>19.4</td>\n",
                            "      <td>31.0</td>\n",
                            "      <td>40.9</td>\n",
                            "      <td>12.7</td>\n",
                            "      <td>27.1</td>\n",
                            "      <td>29.6</td>\n",
                            "      <td>32.1</td>\n",
                            "      <td>31.5</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset     HELOC                        Lending Club                          \\\n",
                            "k               1    2     3     4     5            1     2     3     4     5   \n",
                            "Baseline                                                                        \n",
                            "SHAP D-LAB    1.6  5.9   9.9  13.2  14.6          3.5   9.4  16.1  26.7  37.8   \n",
                            "SHAP D-PRED   1.5  6.1   9.8  10.5  11.0         -4.7   9.6  21.5  29.1  36.4   \n",
                            "SHAP TRAIN    3.4  9.5  15.2  19.6  18.8          7.9  13.9  19.4  31.0  40.9   \n",
                            "\n",
                            "Dataset     Wine Quality                          \n",
                            "k                      1     2     3     4     5  \n",
                            "Baseline                                          \n",
                            "SHAP D-LAB           9.9  18.1  19.2  23.3  23.4  \n",
                            "SHAP D-PRED          9.7  17.9  17.2  20.1  21.8  \n",
                            "SHAP TRAIN          12.7  27.1  29.6  32.1  31.5  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Plausibility\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig_nn2, prop_nn2, prop_ci_nn2,\n",
                "    fixed_features = ('quantile_sum', 'L1', 'random', 'quantile', 'L1', 5, None), \n",
                "    feature_names = FEATURE_NAMES_NN,\n",
                "    **KWARGS\n",
                ")\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'plausability_random_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "de3ed9e7-22eb-436b-8290-2526148f5d3e",
            "metadata": {},
            "source": [
                "### Appendix - KNN* - Projected over the DB"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "id": "e7055edb-f85a-4094-ab56-cf7bf28be0fa",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "Method ['CF-SHAP\\n$100$-NN' 'CF-SHAP\\n$100$-NN$^*$']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x244.444 with 8 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP\\n$100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.8</td>\n",
                            "      <td>11.1</td>\n",
                            "      <td>21.9</td>\n",
                            "      <td>29.2</td>\n",
                            "      <td>29.1</td>\n",
                            "      <td>7.1</td>\n",
                            "      <td>29.8</td>\n",
                            "      <td>41.7</td>\n",
                            "      <td>48.4</td>\n",
                            "      <td>52.8</td>\n",
                            "      <td>8.7</td>\n",
                            "      <td>25.3</td>\n",
                            "      <td>31.8</td>\n",
                            "      <td>36.5</td>\n",
                            "      <td>38.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>7.2</td>\n",
                            "      <td>7.5</td>\n",
                            "      <td>7.2</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>1.5</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>12.5</td>\n",
                            "      <td>15.0</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>24.6</td>\n",
                            "      <td>28.6</td>\n",
                            "      <td>31.7</td>\n",
                            "      <td>32.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>5.4</td>\n",
                            "      <td>17.0</td>\n",
                            "      <td>32.6</td>\n",
                            "      <td>42.1</td>\n",
                            "      <td>43.2</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>37.4</td>\n",
                            "      <td>49.7</td>\n",
                            "      <td>57.0</td>\n",
                            "      <td>63.0</td>\n",
                            "      <td>14.8</td>\n",
                            "      <td>42.0</td>\n",
                            "      <td>50.6</td>\n",
                            "      <td>52.2</td>\n",
                            "      <td>55.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP\\n$100$-NN$^*$</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.2</td>\n",
                            "      <td>14.4</td>\n",
                            "      <td>22.7</td>\n",
                            "      <td>27.8</td>\n",
                            "      <td>29.3</td>\n",
                            "      <td>-3.4</td>\n",
                            "      <td>9.8</td>\n",
                            "      <td>25.2</td>\n",
                            "      <td>31.6</td>\n",
                            "      <td>36.2</td>\n",
                            "      <td>5.3</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>28.7</td>\n",
                            "      <td>31.9</td>\n",
                            "      <td>34.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.6</td>\n",
                            "      <td>6.0</td>\n",
                            "      <td>10.2</td>\n",
                            "      <td>10.6</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>-17.4</td>\n",
                            "      <td>-10.0</td>\n",
                            "      <td>-1.8</td>\n",
                            "      <td>-1.6</td>\n",
                            "      <td>0.3</td>\n",
                            "      <td>4.4</td>\n",
                            "      <td>20.5</td>\n",
                            "      <td>25.6</td>\n",
                            "      <td>26.5</td>\n",
                            "      <td>28.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>4.8</td>\n",
                            "      <td>21.7</td>\n",
                            "      <td>34.8</td>\n",
                            "      <td>41.5</td>\n",
                            "      <td>43.0</td>\n",
                            "      <td>0.7</td>\n",
                            "      <td>19.4</td>\n",
                            "      <td>36.6</td>\n",
                            "      <td>44.6</td>\n",
                            "      <td>49.6</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>39.9</td>\n",
                            "      <td>49.0</td>\n",
                            "      <td>49.2</td>\n",
                            "      <td>54.3</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                           HELOC                         Lending Club  \\\n",
                            "k                                     1     2     3     4     5            1   \n",
                            "Method                Baseline                                                 \n",
                            "CF-SHAP\\n$100$-NN     SHAP D-LAB    2.8  11.1  21.9  29.2  29.1          7.1   \n",
                            "                      SHAP D-PRED  -1.0  -1.0   7.2   7.5   7.2         -7.8   \n",
                            "                      SHAP TRAIN    5.4  17.0  32.6  42.1  43.2         11.0   \n",
                            "CF-SHAP\\n$100$-NN$^*$ SHAP D-LAB    2.2  14.4  22.7  27.8  29.3         -3.4   \n",
                            "                      SHAP D-PRED  -1.6   6.0  10.2  10.6  11.0        -17.4   \n",
                            "                      SHAP TRAIN    4.8  21.7  34.8  41.5  43.0          0.7   \n",
                            "\n",
                            "Dataset                                                   Wine Quality        \\\n",
                            "k                                     2     3     4     5            1     2   \n",
                            "Method                Baseline                                                 \n",
                            "CF-SHAP\\n$100$-NN     SHAP D-LAB   29.8  41.7  48.4  52.8          8.7  25.3   \n",
                            "                      SHAP D-PRED   1.5  11.0  12.5  15.0          7.9  24.6   \n",
                            "                      SHAP TRAIN   37.4  49.7  57.0  63.0         14.8  42.0   \n",
                            "CF-SHAP\\n$100$-NN$^*$ SHAP D-LAB    9.8  25.2  31.6  36.2          5.3  20.0   \n",
                            "                      SHAP D-PRED -10.0  -1.8  -1.6   0.3          4.4  20.5   \n",
                            "                      SHAP TRAIN   19.4  36.6  44.6  49.6         11.0  39.9   \n",
                            "\n",
                            "Dataset                                              \n",
                            "k                                     3     4     5  \n",
                            "Method                Baseline                       \n",
                            "CF-SHAP\\n$100$-NN     SHAP D-LAB   31.8  36.5  38.7  \n",
                            "                      SHAP D-PRED  28.6  31.7  32.4  \n",
                            "                      SHAP TRAIN   50.6  52.2  55.9  \n",
                            "CF-SHAP\\n$100$-NN$^*$ SHAP D-LAB   28.7  31.9  34.2  \n",
                            "                      SHAP D-PRED  25.6  26.5  28.2  \n",
                            "                      SHAP TRAIN   49.0  49.2  54.3  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "selected_methods_tuples_cache = KWARGS['selected_methods_tuples']\n",
                "KWARGS['selected_methods_tuples'] = [\n",
                "    ('SHAP', 'knn100_qL1', 'local'), \n",
                "    ('SHAP', 'cone-knn100_qL1', 'local')\n",
                "]\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "KWARGS_PLOT['row'] = 'Method'\n",
                "KWARGS_PLOT['col'] = 'Dataset'\n",
                "\n",
                "# DataFrame\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L1', 'proportional', 'quantile', 'L1', None), \n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "df_['Method'] = df_['Method'].apply(lambda x : x.replace(' ', '\\n'))\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'cone_vs_default', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "5807c654-0c94-4c0f-b817-3eab8598125b",
            "metadata": {},
            "source": [
                "### Appendix - More experiments on plausibility and counterfactual-ability"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "id": "1fddea65-2108-4091-bfa8-472905ed197c",
            "metadata": {},
            "outputs": [],
            "source": [
                "KWARGS_PLOT['figure'] = 'Setting'\n",
                "KWARGS['selected_methods_tuples'] = selected_methods_tuples_cache"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "fa28a27b-0a8b-49bf-8444-a21fdd13bf3b",
            "metadata": {},
            "source": [
                "##### Counterfactual-Ability"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "id": "df980550-9228-4125-a6b9-af8bc17c3bc9",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.8</td>\n",
                            "      <td>8.4</td>\n",
                            "      <td>12.1</td>\n",
                            "      <td>10.8</td>\n",
                            "      <td>11.8</td>\n",
                            "      <td>7.1</td>\n",
                            "      <td>13.5</td>\n",
                            "      <td>18.8</td>\n",
                            "      <td>24.4</td>\n",
                            "      <td>31.5</td>\n",
                            "      <td>8.7</td>\n",
                            "      <td>18.9</td>\n",
                            "      <td>21.2</td>\n",
                            "      <td>19.2</td>\n",
                            "      <td>15.5</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>6.6</td>\n",
                            "      <td>5.3</td>\n",
                            "      <td>7.8</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>0.8</td>\n",
                            "      <td>4.2</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>19.2</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>16.7</td>\n",
                            "      <td>16.8</td>\n",
                            "      <td>13.5</td>\n",
                            "      <td>10.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>5.4</td>\n",
                            "      <td>14.0</td>\n",
                            "      <td>26.0</td>\n",
                            "      <td>26.6</td>\n",
                            "      <td>23.3</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>16.9</td>\n",
                            "      <td>21.4</td>\n",
                            "      <td>29.4</td>\n",
                            "      <td>37.3</td>\n",
                            "      <td>14.8</td>\n",
                            "      <td>34.6</td>\n",
                            "      <td>36.3</td>\n",
                            "      <td>32.1</td>\n",
                            "      <td>28.0</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                         Lending Club        \\\n",
                            "k                                1     2     3     4     5            1     2   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    2.8   8.4  12.1  10.8  11.8          7.1  13.5   \n",
                            "                 SHAP D-PRED  -1.0   3.7   6.6   5.3   7.8         -7.8   0.8   \n",
                            "                 SHAP TRAIN    5.4  14.0  26.0  26.6  23.3         11.0  16.9   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   18.8  24.4  31.5          8.7  18.9  21.2  19.2   \n",
                            "                 SHAP D-PRED   4.2   9.9  19.2          7.9  16.7  16.8  13.5   \n",
                            "                 SHAP TRAIN   21.4  29.4  37.3         14.8  34.6  36.3  32.1   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   15.5  \n",
                            "                 SHAP D-PRED  10.9  \n",
                            "                 SHAP TRAIN   28.0  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L2', 'random', 'quantile', 'L2', None), \n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Random Recourse ($\\\\bar{A}_k$) / L2 Cost ($c_{L2}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'costs_random_l2_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "id": "0a210453-3e08-4b09-9b62-4393e2422a99",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.8</td>\n",
                            "      <td>11.1</td>\n",
                            "      <td>21.9</td>\n",
                            "      <td>29.2</td>\n",
                            "      <td>29.1</td>\n",
                            "      <td>7.1</td>\n",
                            "      <td>29.8</td>\n",
                            "      <td>41.7</td>\n",
                            "      <td>48.4</td>\n",
                            "      <td>52.8</td>\n",
                            "      <td>8.7</td>\n",
                            "      <td>25.3</td>\n",
                            "      <td>31.8</td>\n",
                            "      <td>36.5</td>\n",
                            "      <td>38.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>7.2</td>\n",
                            "      <td>7.5</td>\n",
                            "      <td>7.2</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>1.5</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>12.5</td>\n",
                            "      <td>15.0</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>24.6</td>\n",
                            "      <td>28.6</td>\n",
                            "      <td>31.7</td>\n",
                            "      <td>32.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>5.4</td>\n",
                            "      <td>17.0</td>\n",
                            "      <td>32.6</td>\n",
                            "      <td>42.1</td>\n",
                            "      <td>43.2</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>37.4</td>\n",
                            "      <td>49.7</td>\n",
                            "      <td>57.0</td>\n",
                            "      <td>63.0</td>\n",
                            "      <td>14.8</td>\n",
                            "      <td>42.0</td>\n",
                            "      <td>50.6</td>\n",
                            "      <td>52.2</td>\n",
                            "      <td>55.9</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                         Lending Club        \\\n",
                            "k                                1     2     3     4     5            1     2   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    2.8  11.1  21.9  29.2  29.1          7.1  29.8   \n",
                            "                 SHAP D-PRED  -1.0  -1.0   7.2   7.5   7.2         -7.8   1.5   \n",
                            "                 SHAP TRAIN    5.4  17.0  32.6  42.1  43.2         11.0  37.4   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   41.7  48.4  52.8          8.7  25.3  31.8  36.5   \n",
                            "                 SHAP D-PRED  11.0  12.5  15.0          7.9  24.6  28.6  31.7   \n",
                            "                 SHAP TRAIN   49.7  57.0  63.0         14.8  42.0  50.6  52.2   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   38.7  \n",
                            "                 SHAP D-PRED  32.4  \n",
                            "                 SHAP TRAIN   55.9  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L1', 'proportional', 'quantile', 'L1', None), \n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Proportional Recourse ($\\\\tilde{A}_{k}$) / L1 Cost ($c_{L1}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'costs_proportional_l1_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "id": "ad342103-da2a-4972-92fb-a79052010cd2",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>2.8</td>\n",
                            "      <td>10.7</td>\n",
                            "      <td>15.6</td>\n",
                            "      <td>15.8</td>\n",
                            "      <td>11.2</td>\n",
                            "      <td>7.1</td>\n",
                            "      <td>28.4</td>\n",
                            "      <td>35.1</td>\n",
                            "      <td>39.6</td>\n",
                            "      <td>42.8</td>\n",
                            "      <td>8.7</td>\n",
                            "      <td>35.9</td>\n",
                            "      <td>40.1</td>\n",
                            "      <td>38.4</td>\n",
                            "      <td>37.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>2.9</td>\n",
                            "      <td>12.2</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>9.4</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>3.2</td>\n",
                            "      <td>10.6</td>\n",
                            "      <td>12.0</td>\n",
                            "      <td>14.4</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>34.6</td>\n",
                            "      <td>36.1</td>\n",
                            "      <td>35.7</td>\n",
                            "      <td>35.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>5.4</td>\n",
                            "      <td>16.1</td>\n",
                            "      <td>26.8</td>\n",
                            "      <td>28.0</td>\n",
                            "      <td>23.1</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>35.0</td>\n",
                            "      <td>40.7</td>\n",
                            "      <td>44.2</td>\n",
                            "      <td>47.2</td>\n",
                            "      <td>14.8</td>\n",
                            "      <td>51.3</td>\n",
                            "      <td>55.3</td>\n",
                            "      <td>52.3</td>\n",
                            "      <td>53.1</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                         Lending Club        \\\n",
                            "k                                1     2     3     4     5            1     2   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    2.8  10.7  15.6  15.8  11.2          7.1  28.4   \n",
                            "                 SHAP D-PRED  -1.0   2.9  12.2  11.0   9.4         -7.8   3.2   \n",
                            "                 SHAP TRAIN    5.4  16.1  26.8  28.0  23.1         11.0  35.0   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   35.1  39.6  42.8          8.7  35.9  40.1  38.4   \n",
                            "                 SHAP D-PRED  10.6  12.0  14.4          7.9  34.6  36.1  35.7   \n",
                            "                 SHAP TRAIN   40.7  44.2  47.2         14.8  51.3  55.3  52.3   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   37.7  \n",
                            "                 SHAP D-PRED  35.4  \n",
                            "                 SHAP TRAIN   53.1  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L2', 'proportional', 'quantile', 'L2', None), \n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Proportional Recourse ($\\\\tilde{A}_{k}$) / L2 Cost ($c_{L2}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'costs_proportional_l2_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "55cc11e3-627e-436a-97e5-e9a571a725c0",
            "metadata": {},
            "source": [
                "##### Plausibility"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "id": "abd18438-c903-4975-87b4-1b9ca059f0e4",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>1.3</td>\n",
                            "      <td>4.5</td>\n",
                            "      <td>8.6</td>\n",
                            "      <td>10.0</td>\n",
                            "      <td>12.6</td>\n",
                            "      <td>3.0</td>\n",
                            "      <td>11.5</td>\n",
                            "      <td>17.5</td>\n",
                            "      <td>24.9</td>\n",
                            "      <td>29.3</td>\n",
                            "      <td>10.2</td>\n",
                            "      <td>20.7</td>\n",
                            "      <td>22.3</td>\n",
                            "      <td>23.5</td>\n",
                            "      <td>22.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>2.1</td>\n",
                            "      <td>4.7</td>\n",
                            "      <td>9.2</td>\n",
                            "      <td>9.7</td>\n",
                            "      <td>11.8</td>\n",
                            "      <td>-4.3</td>\n",
                            "      <td>9.2</td>\n",
                            "      <td>18.2</td>\n",
                            "      <td>26.1</td>\n",
                            "      <td>30.4</td>\n",
                            "      <td>9.6</td>\n",
                            "      <td>20.5</td>\n",
                            "      <td>19.7</td>\n",
                            "      <td>21.5</td>\n",
                            "      <td>21.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>3.6</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>15.2</td>\n",
                            "      <td>16.3</td>\n",
                            "      <td>15.6</td>\n",
                            "      <td>7.6</td>\n",
                            "      <td>14.7</td>\n",
                            "      <td>19.6</td>\n",
                            "      <td>27.5</td>\n",
                            "      <td>31.7</td>\n",
                            "      <td>13.5</td>\n",
                            "      <td>29.8</td>\n",
                            "      <td>31.5</td>\n",
                            "      <td>31.4</td>\n",
                            "      <td>29.7</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                        Lending Club        \\\n",
                            "k                                1    2     3     4     5            1     2   \n",
                            "Method           Baseline                                                      \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    1.3  4.5   8.6  10.0  12.6          3.0  11.5   \n",
                            "                 SHAP D-PRED   2.1  4.7   9.2   9.7  11.8         -4.3   9.2   \n",
                            "                 SHAP TRAIN    3.6  9.0  15.2  16.3  15.6          7.6  14.7   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   17.5  24.9  29.3         10.2  20.7  22.3  23.5   \n",
                            "                 SHAP D-PRED  18.2  26.1  30.4          9.6  20.5  19.7  21.5   \n",
                            "                 SHAP TRAIN   19.6  27.5  31.7         13.5  29.8  31.5  31.4   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   22.2  \n",
                            "                 SHAP D-PRED  21.3  \n",
                            "                 SHAP TRAIN   29.7  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Plausibility\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig_nn2, prop_nn2, prop_ci_nn2,\n",
                "    fixed_features = ('quantile_sum', 'L2', 'random', 'quantile', 'L2', 5, None), \n",
                "    feature_names = FEATURE_NAMES_NN,\n",
                "    **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Random Recourse ($\\\\bar{A}_k$) / L2 Cost and Plausibility ($c_{L2}$ and $p_{L2}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'plausability_random_l2_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "id": "d2570f24-18f2-4c98-ac06-3075bf8a43e7",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>1.6</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>8.1</td>\n",
                            "      <td>10.8</td>\n",
                            "      <td>7.0</td>\n",
                            "      <td>3.5</td>\n",
                            "      <td>11.6</td>\n",
                            "      <td>17.2</td>\n",
                            "      <td>24.9</td>\n",
                            "      <td>33.6</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>26.9</td>\n",
                            "      <td>30.9</td>\n",
                            "      <td>32.3</td>\n",
                            "      <td>34.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>1.5</td>\n",
                            "      <td>13.3</td>\n",
                            "      <td>24.4</td>\n",
                            "      <td>25.7</td>\n",
                            "      <td>25.0</td>\n",
                            "      <td>-4.7</td>\n",
                            "      <td>30.4</td>\n",
                            "      <td>40.4</td>\n",
                            "      <td>45.4</td>\n",
                            "      <td>49.7</td>\n",
                            "      <td>9.7</td>\n",
                            "      <td>28.3</td>\n",
                            "      <td>32.6</td>\n",
                            "      <td>34.5</td>\n",
                            "      <td>36.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>3.4</td>\n",
                            "      <td>6.4</td>\n",
                            "      <td>12.9</td>\n",
                            "      <td>18.0</td>\n",
                            "      <td>12.4</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>13.1</td>\n",
                            "      <td>22.0</td>\n",
                            "      <td>32.1</td>\n",
                            "      <td>41.0</td>\n",
                            "      <td>12.7</td>\n",
                            "      <td>38.6</td>\n",
                            "      <td>40.8</td>\n",
                            "      <td>40.5</td>\n",
                            "      <td>43.2</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                         Lending Club        \\\n",
                            "k                                1     2     3     4     5            1     2   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    1.6   3.7   8.1  10.8   7.0          3.5  11.6   \n",
                            "                 SHAP D-PRED   1.5  13.3  24.4  25.7  25.0         -4.7  30.4   \n",
                            "                 SHAP TRAIN    3.4   6.4  12.9  18.0  12.4          7.9  13.1   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   17.2  24.9  33.6          9.9  26.9  30.9  32.3   \n",
                            "                 SHAP D-PRED  40.4  45.4  49.7          9.7  28.3  32.6  34.5   \n",
                            "                 SHAP TRAIN   22.0  32.1  41.0         12.7  38.6  40.8  40.5   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   34.4  \n",
                            "                 SHAP D-PRED  36.3  \n",
                            "                 SHAP TRAIN   43.2  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Plausibility\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig_nn2, prop_nn2, prop_ci_nn2,\n",
                "    fixed_features = ('quantile_sum', 'L1', 'proportional', 'quantile', 'L1', 5, None), \n",
                "    feature_names = FEATURE_NAMES_NN,\n",
                "    **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Proportional Recourse ($\\\\tilde{A}_{k}$) / L1 Cost and Plausibility ($c_{L1}$ and $p_{L1}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'plausability_proportional_l1_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "id": "692714c7-59b5-41db-9d37-6e3728627445",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Baseline ['SHAP TRAIN' 'SHAP D-PRED' 'SHAP D-LAB']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th></th>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th>Baseline</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">CF-SHAP $100$-NN</th>\n",
                            "      <th>SHAP D-LAB</th>\n",
                            "      <td>1.6</td>\n",
                            "      <td>5.9</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>13.2</td>\n",
                            "      <td>14.6</td>\n",
                            "      <td>3.5</td>\n",
                            "      <td>9.4</td>\n",
                            "      <td>16.1</td>\n",
                            "      <td>26.7</td>\n",
                            "      <td>37.8</td>\n",
                            "      <td>9.9</td>\n",
                            "      <td>18.1</td>\n",
                            "      <td>19.2</td>\n",
                            "      <td>23.3</td>\n",
                            "      <td>23.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP D-PRED</th>\n",
                            "      <td>1.5</td>\n",
                            "      <td>6.1</td>\n",
                            "      <td>9.8</td>\n",
                            "      <td>10.5</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>-4.7</td>\n",
                            "      <td>9.6</td>\n",
                            "      <td>21.5</td>\n",
                            "      <td>29.1</td>\n",
                            "      <td>36.4</td>\n",
                            "      <td>9.7</td>\n",
                            "      <td>17.9</td>\n",
                            "      <td>17.2</td>\n",
                            "      <td>20.1</td>\n",
                            "      <td>21.8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>SHAP TRAIN</th>\n",
                            "      <td>3.4</td>\n",
                            "      <td>9.5</td>\n",
                            "      <td>15.2</td>\n",
                            "      <td>19.6</td>\n",
                            "      <td>18.8</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>13.9</td>\n",
                            "      <td>19.4</td>\n",
                            "      <td>31.0</td>\n",
                            "      <td>40.9</td>\n",
                            "      <td>12.7</td>\n",
                            "      <td>27.1</td>\n",
                            "      <td>29.6</td>\n",
                            "      <td>32.1</td>\n",
                            "      <td>31.5</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset                      HELOC                        Lending Club        \\\n",
                            "k                                1    2     3     4     5            1     2   \n",
                            "Method           Baseline                                                      \n",
                            "CF-SHAP $100$-NN SHAP D-LAB    1.6  5.9   9.9  13.2  14.6          3.5   9.4   \n",
                            "                 SHAP D-PRED   1.5  6.1   9.8  10.5  11.0         -4.7   9.6   \n",
                            "                 SHAP TRAIN    3.4  9.5  15.2  19.6  18.8          7.9  13.9   \n",
                            "\n",
                            "Dataset                                        Wine Quality                    \\\n",
                            "k                                3     4     5            1     2     3     4   \n",
                            "Method           Baseline                                                       \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   16.1  26.7  37.8          9.9  18.1  19.2  23.3   \n",
                            "                 SHAP D-PRED  21.5  29.1  36.4          9.7  17.9  17.2  20.1   \n",
                            "                 SHAP TRAIN   19.4  31.0  40.9         12.7  27.1  29.6  32.1   \n",
                            "\n",
                            "Dataset                             \n",
                            "k                                5  \n",
                            "Method           Baseline           \n",
                            "CF-SHAP $100$-NN SHAP D-LAB   23.4  \n",
                            "                 SHAP D-PRED  21.8  \n",
                            "                 SHAP TRAIN   31.5  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Plausibility\\nImprovement'\n",
                "KWARGS_PLOT['y'] = KWARGS['metric_name']\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig_nn2, prop_nn2, prop_ci_nn2,\n",
                "    feature_names = FEATURE_NAMES_NN,\n",
                "    fixed_features = ('quantile_sum', 'L2', 'random', 'quantile', 'L1', 5, None), **KWARGS\n",
                ")\n",
                "df_['Setting'] = 'Proportional Recourse ($\\\\tilde{A}_{k}$) / L2 Cost and Plausibility ($c_{L2}$ and $p_{L2}$)'\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "camera_figure(df_, filename = 'plausability_proportional_l2_2', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "cc3ce040-bb43-484a-b6a5-9df45886f11f",
            "metadata": {},
            "source": [
                "## Appendix - Effect of $K$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "id": "856d7371-8f30-42a9-ab50-59101f570a61",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Let's select what method we want to plot\n",
                "KWARGS = dict(\n",
                "    selected_baselines_tuples=[\n",
                "        ('SHAP', 'diff_pred_100', 'global'),\n",
                "    ],\n",
                "    selected_methods_tuples=[\n",
                "        ('SHAP', 'knn1_qL1', 'local'),\n",
                "        ('SHAP', 'knn3_qL1', 'local'),\n",
                "        ('SHAP', 'knn5_qL1', 'local'),\n",
                "        ('SHAP', 'knn10_qL1', 'local'),\n",
                "        ('SHAP', 'knn20_qL1', 'local'),\n",
                "        ('SHAP', 'knn50_qL1', 'local'),\n",
                "        ('SHAP', 'knn100_qL1', 'local'),\n",
                "        ('SHAP', 'knn250_qL1', 'local'),\n",
                "        ('SHAP', 'knn500_qL1', 'local'),\n",
                "        ('SHAP', 'knn1000_qL1', 'local'),\n",
                "    ],\n",
                "    method_details = False,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "id": "b0d2b0a6-951e-45ca-86fe-d68f5ce2331a",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Method ['CF-SHAP $1$-NN' 'CF-SHAP $3$-NN' 'CF-SHAP $5$-NN' 'CF-SHAP $10$-NN'\n",
                        " 'CF-SHAP $20$-NN' 'CF-SHAP $50$-NN' 'CF-SHAP $100$-NN' 'CF-SHAP $250$-NN'\n",
                        " 'CF-SHAP $500$-NN' 'CF-SHAP $1000$-NN']\n",
                        "k [1 2 3 4 5]\n",
                        "Dataset ['HELOC' 'Wine Quality' 'Lending Club']\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/home/ubuntu/anaconda3/envs/cfshap22/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning:\n",
                        "\n",
                        "Warning: converting a masked element to nan.\n",
                        "\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 825x122.222 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
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                            "        vertical-align: top;\n",
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                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
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                            "\n",
                            "    .dataframe thead tr:last-of-type th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th>Dataset</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>k</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "      <th>1</th>\n",
                            "      <th>2</th>\n",
                            "      <th>3</th>\n",
                            "      <th>4</th>\n",
                            "      <th>5</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Method</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $1$-NN</th>\n",
                            "      <td>-8.6</td>\n",
                            "      <td>-6.2</td>\n",
                            "      <td>-15.6</td>\n",
                            "      <td>-20.0</td>\n",
                            "      <td>-16.4</td>\n",
                            "      <td>-28.0</td>\n",
                            "      <td>-13.8</td>\n",
                            "      <td>-5.0</td>\n",
                            "      <td>2.9</td>\n",
                            "      <td>8.6</td>\n",
                            "      <td>-0.4</td>\n",
                            "      <td>13.9</td>\n",
                            "      <td>16.2</td>\n",
                            "      <td>27.3</td>\n",
                            "      <td>44.3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $10$-NN</th>\n",
                            "      <td>-1.9</td>\n",
                            "      <td>6.4</td>\n",
                            "      <td>7.0</td>\n",
                            "      <td>4.7</td>\n",
                            "      <td>5.5</td>\n",
                            "      <td>-14.0</td>\n",
                            "      <td>2.4</td>\n",
                            "      <td>6.7</td>\n",
                            "      <td>13.0</td>\n",
                            "      <td>21.6</td>\n",
                            "      <td>9.6</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>17.9</td>\n",
                            "      <td>19.9</td>\n",
                            "      <td>22.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $100$-NN</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>6.3</td>\n",
                            "      <td>5.1</td>\n",
                            "      <td>6.6</td>\n",
                            "      <td>-7.8</td>\n",
                            "      <td>1.6</td>\n",
                            "      <td>5.0</td>\n",
                            "      <td>9.8</td>\n",
                            "      <td>17.1</td>\n",
                            "      <td>7.9</td>\n",
                            "      <td>15.3</td>\n",
                            "      <td>15.2</td>\n",
                            "      <td>12.3</td>\n",
                            "      <td>10.7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $1000$-NN</th>\n",
                            "      <td>-0.7</td>\n",
                            "      <td>0.5</td>\n",
                            "      <td>1.6</td>\n",
                            "      <td>2.2</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>-1.2</td>\n",
                            "      <td>0.8</td>\n",
                            "      <td>1.8</td>\n",
                            "      <td>5.5</td>\n",
                            "      <td>7.7</td>\n",
                            "      <td>0.7</td>\n",
                            "      <td>-13.2</td>\n",
                            "      <td>-26.6</td>\n",
                            "      <td>-34.1</td>\n",
                            "      <td>-37.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $20$-NN</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>6.4</td>\n",
                            "      <td>8.0</td>\n",
                            "      <td>5.4</td>\n",
                            "      <td>5.4</td>\n",
                            "      <td>-12.2</td>\n",
                            "      <td>2.1</td>\n",
                            "      <td>6.2</td>\n",
                            "      <td>12.4</td>\n",
                            "      <td>19.5</td>\n",
                            "      <td>10.5</td>\n",
                            "      <td>18.0</td>\n",
                            "      <td>18.3</td>\n",
                            "      <td>19.2</td>\n",
                            "      <td>20.6</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $250$-NN</th>\n",
                            "      <td>-1.1</td>\n",
                            "      <td>2.0</td>\n",
                            "      <td>4.7</td>\n",
                            "      <td>4.6</td>\n",
                            "      <td>6.0</td>\n",
                            "      <td>-4.4</td>\n",
                            "      <td>2.1</td>\n",
                            "      <td>3.9</td>\n",
                            "      <td>8.9</td>\n",
                            "      <td>13.2</td>\n",
                            "      <td>4.6</td>\n",
                            "      <td>8.6</td>\n",
                            "      <td>5.2</td>\n",
                            "      <td>0.9</td>\n",
                            "      <td>-0.1</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $3$-NN</th>\n",
                            "      <td>-4.9</td>\n",
                            "      <td>2.4</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>-2.0</td>\n",
                            "      <td>-1.6</td>\n",
                            "      <td>-19.2</td>\n",
                            "      <td>-4.2</td>\n",
                            "      <td>3.8</td>\n",
                            "      <td>8.8</td>\n",
                            "      <td>18.3</td>\n",
                            "      <td>6.1</td>\n",
                            "      <td>14.6</td>\n",
                            "      <td>16.9</td>\n",
                            "      <td>21.3</td>\n",
                            "      <td>28.9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $5$-NN</th>\n",
                            "      <td>-3.1</td>\n",
                            "      <td>3.9</td>\n",
                            "      <td>4.2</td>\n",
                            "      <td>2.7</td>\n",
                            "      <td>3.3</td>\n",
                            "      <td>-16.6</td>\n",
                            "      <td>-0.6</td>\n",
                            "      <td>6.6</td>\n",
                            "      <td>10.2</td>\n",
                            "      <td>20.3</td>\n",
                            "      <td>8.2</td>\n",
                            "      <td>17.2</td>\n",
                            "      <td>17.7</td>\n",
                            "      <td>19.6</td>\n",
                            "      <td>25.1</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $50$-NN</th>\n",
                            "      <td>-1.0</td>\n",
                            "      <td>5.2</td>\n",
                            "      <td>7.2</td>\n",
                            "      <td>5.3</td>\n",
                            "      <td>6.4</td>\n",
                            "      <td>-9.5</td>\n",
                            "      <td>2.4</td>\n",
                            "      <td>5.7</td>\n",
                            "      <td>11.2</td>\n",
                            "      <td>18.4</td>\n",
                            "      <td>9.4</td>\n",
                            "      <td>18.3</td>\n",
                            "      <td>17.8</td>\n",
                            "      <td>15.7</td>\n",
                            "      <td>15.2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>CF-SHAP $500$-NN</th>\n",
                            "      <td>-1.2</td>\n",
                            "      <td>1.4</td>\n",
                            "      <td>3.7</td>\n",
                            "      <td>4.0</td>\n",
                            "      <td>5.8</td>\n",
                            "      <td>-2.7</td>\n",
                            "      <td>1.2</td>\n",
                            "      <td>3.2</td>\n",
                            "      <td>8.1</td>\n",
                            "      <td>10.6</td>\n",
                            "      <td>1.2</td>\n",
                            "      <td>-1.6</td>\n",
                            "      <td>-8.2</td>\n",
                            "      <td>-14.8</td>\n",
                            "      <td>-18.9</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset           HELOC                        Lending Club                   \\\n",
                            "k                     1    2     3     4     5            1     2    3     4   \n",
                            "Method                                                                         \n",
                            "CF-SHAP $1$-NN     -8.6 -6.2 -15.6 -20.0 -16.4        -28.0 -13.8 -5.0   2.9   \n",
                            "CF-SHAP $10$-NN    -1.9  6.4   7.0   4.7   5.5        -14.0   2.4  6.7  13.0   \n",
                            "CF-SHAP $100$-NN   -1.0  3.7   6.3   5.1   6.6         -7.8   1.6  5.0   9.8   \n",
                            "CF-SHAP $1000$-NN  -0.7  0.5   1.6   2.2   3.7         -1.2   0.8  1.8   5.5   \n",
                            "CF-SHAP $20$-NN    -1.0  6.4   8.0   5.4   5.4        -12.2   2.1  6.2  12.4   \n",
                            "CF-SHAP $250$-NN   -1.1  2.0   4.7   4.6   6.0         -4.4   2.1  3.9   8.9   \n",
                            "CF-SHAP $3$-NN     -4.9  2.4   1.0  -2.0  -1.6        -19.2  -4.2  3.8   8.8   \n",
                            "CF-SHAP $5$-NN     -3.1  3.9   4.2   2.7   3.3        -16.6  -0.6  6.6  10.2   \n",
                            "CF-SHAP $50$-NN    -1.0  5.2   7.2   5.3   6.4         -9.5   2.4  5.7  11.2   \n",
                            "CF-SHAP $500$-NN   -1.2  1.4   3.7   4.0   5.8         -2.7   1.2  3.2   8.1   \n",
                            "\n",
                            "Dataset                 Wine Quality                          \n",
                            "k                     5            1     2     3     4     5  \n",
                            "Method                                                        \n",
                            "CF-SHAP $1$-NN      8.6         -0.4  13.9  16.2  27.3  44.3  \n",
                            "CF-SHAP $10$-NN    21.6          9.6  19.0  17.9  19.9  22.7  \n",
                            "CF-SHAP $100$-NN   17.1          7.9  15.3  15.2  12.3  10.7  \n",
                            "CF-SHAP $1000$-NN   7.7          0.7 -13.2 -26.6 -34.1 -37.0  \n",
                            "CF-SHAP $20$-NN    19.5         10.5  18.0  18.3  19.2  20.6  \n",
                            "CF-SHAP $250$-NN   13.2          4.6   8.6   5.2   0.9  -0.1  \n",
                            "CF-SHAP $3$-NN     18.3          6.1  14.6  16.9  21.3  28.9  \n",
                            "CF-SHAP $5$-NN     20.3          8.2  17.2  17.7  19.6  25.1  \n",
                            "CF-SHAP $50$-NN    18.4          9.4  18.3  17.8  15.7  15.2  \n",
                            "CF-SHAP $500$-NN   10.6          1.2  -1.6  -8.2 -14.8 -18.9  "
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Settings\n",
                "KWARGS['metric_name'] = 'Counterfactual-Ability\\nImprovement'\n",
                "KWARGS_PLOT = dict(\n",
                "    y = KWARGS['metric_name'],\n",
                "    figure=None,\n",
                "    style=\"Method\", #\"Decision Boundary\",\n",
                "    hue=\"Method\",\n",
                "    col='Dataset',\n",
                "    detached_legend=False,\n",
                "    ret_table=True,\n",
                ")\n",
                "\n",
                "# DataFrame\n",
                "df_ = generate_dataframe_to_plot(\n",
                "    pivot_sig2, prop2, prop_ci2, \n",
                "    fixed_features = ('quantile_sum', 'L1', 'random', 'quantile', 'L1', None), \n",
                "    feature_names = FEATURE_NAMES_COSTS,\n",
                "    **KWARGS\n",
                ")\n",
                "df_ = df_[(df_['Decision Boundary'] == 'All')]\n",
                "df_ = df_[df_['Dataset'].apply(lambda x : 'GMSC' not in x)]\n",
                "df_['Dataset'] = df_['Dataset'].apply(lambda x : x.split('(')[0][:-1])\n",
                "print_nonunique_cols(df_, ['CI', KWARGS['metric_name']])\n",
                "\n",
                "# Plot\n",
                "tables = camera_figure(df_, filename = 'costs_effect_of_K', **KWARGS_PLOT)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "id": "c243cef9-0c1e-4ca5-adfd-dc9cb61292e5",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead tr th {\n",
                            "        text-align: left;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr>\n",
                            "      <th>Dataset</th>\n",
                            "      <th>$K$</th>\n",
                            "      <th>Method</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">HELOC</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Lending Club</th>\n",
                            "      <th colspan=\"5\" halign=\"left\">Wine Quality</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>k</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th>$1$</th>\n",
                            "      <th>$2$</th>\n",
                            "      <th>$3$</th>\n",
                            "      <th>$4$</th>\n",
                            "      <th>$5$</th>\n",
                            "      <th>$1$</th>\n",
                            "      <th>$2$</th>\n",
                            "      <th>$3$</th>\n",
                            "      <th>$4$</th>\n",
                            "      <th>$5$</th>\n",
                            "      <th>$1$</th>\n",
                            "      <th>$2$</th>\n",
                            "      <th>$3$</th>\n",
                            "      <th>$4$</th>\n",
                            "      <th>$5$</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>1</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-8.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-6.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$-15.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-20.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$-16.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$-28.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$-13.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$-5.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-0.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$13.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$16.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{27.3}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{44.3}$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>3</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-4.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$-2.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$-1.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-19.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$-4.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$18.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$14.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$16.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$21.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$28.9$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>5</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-3.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$-16.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-0.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$10.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$20.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$17.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$17.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$19.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$25.1$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>10</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{6.4}$\\!\\!</td>\n",
                            "      <td>\\!\\!$7.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.5$\\!\\!</td>\n",
                            "      <td>\\!\\!$-14.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{2.4}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{6.7}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{13.0}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{21.6}$\\!\\!</td>\n",
                            "      <td>\\!\\!$9.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{19.0}$\\!\\!</td>\n",
                            "      <td>\\!\\!$17.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$19.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$22.7$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>20</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{6.4}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{8.0}$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{5.4}$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$-12.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$12.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$19.5$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{10.5}$\\!\\!</td>\n",
                            "      <td>\\!\\!$18.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{18.3}$\\!\\!</td>\n",
                            "      <td>\\!\\!$19.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$20.6$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>50</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$7.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$-9.5$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{2.4}$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$11.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$18.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$9.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$18.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$17.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$15.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$15.2$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>100</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{6.6}$\\!\\!</td>\n",
                            "      <td>\\!\\!$-7.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$9.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$17.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$7.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$15.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$15.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$12.3$\\!\\!</td>\n",
                            "      <td>\\!\\!$10.7$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>250</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$6.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$-4.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$13.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$0.9$\\!\\!</td>\n",
                            "      <td>\\!\\!$-0.1$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>500</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$-1.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.4$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$4.0$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$-2.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$8.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$10.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$-1.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-8.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$-14.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$-18.9$\\!\\!</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>1000</td>\n",
                            "      <td></td>\n",
                            "      <td>\\!\\!$\\bm{-0.7}$\\!\\!</td>\n",
                            "      <td>\\!\\!$0.5$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$2.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$3.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$\\bm{-1.2}$\\!\\!</td>\n",
                            "      <td>\\!\\!$0.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$1.8$\\!\\!</td>\n",
                            "      <td>\\!\\!$5.5$\\!\\!</td>\n",
                            "      <td>\\!\\!$7.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$0.7$\\!\\!</td>\n",
                            "      <td>\\!\\!$-13.2$\\!\\!</td>\n",
                            "      <td>\\!\\!$-26.6$\\!\\!</td>\n",
                            "      <td>\\!\\!$-34.1$\\!\\!</td>\n",
                            "      <td>\\!\\!$-37.0$\\!\\!</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Dataset   $K$ Method                HELOC                      \\\n",
                            "k                                     $1$                 $2$   \n",
                            "0           1              \\!\\!$-8.6$\\!\\!      \\!\\!$-6.2$\\!\\!   \n",
                            "1           3              \\!\\!$-4.9$\\!\\!       \\!\\!$2.4$\\!\\!   \n",
                            "2           5              \\!\\!$-3.1$\\!\\!       \\!\\!$3.9$\\!\\!   \n",
                            "3          10              \\!\\!$-1.9$\\!\\!  \\!\\!$\\bm{6.4}$\\!\\!   \n",
                            "4          20              \\!\\!$-1.0$\\!\\!  \\!\\!$\\bm{6.4}$\\!\\!   \n",
                            "5          50              \\!\\!$-1.0$\\!\\!       \\!\\!$5.2$\\!\\!   \n",
                            "6         100              \\!\\!$-1.0$\\!\\!       \\!\\!$3.7$\\!\\!   \n",
                            "7         250              \\!\\!$-1.1$\\!\\!       \\!\\!$2.0$\\!\\!   \n",
                            "8         500              \\!\\!$-1.2$\\!\\!       \\!\\!$1.4$\\!\\!   \n",
                            "9        1000         \\!\\!$\\bm{-0.7}$\\!\\!       \\!\\!$0.5$\\!\\!   \n",
                            "\n",
                            "Dataset                                                              \\\n",
                            "k                       $3$                 $4$                 $5$   \n",
                            "0           \\!\\!$-15.6$\\!\\!     \\!\\!$-20.0$\\!\\!     \\!\\!$-16.4$\\!\\!   \n",
                            "1             \\!\\!$1.0$\\!\\!      \\!\\!$-2.0$\\!\\!      \\!\\!$-1.6$\\!\\!   \n",
                            "2             \\!\\!$4.2$\\!\\!       \\!\\!$2.7$\\!\\!       \\!\\!$3.3$\\!\\!   \n",
                            "3             \\!\\!$7.0$\\!\\!       \\!\\!$4.7$\\!\\!       \\!\\!$5.5$\\!\\!   \n",
                            "4        \\!\\!$\\bm{8.0}$\\!\\!  \\!\\!$\\bm{5.4}$\\!\\!       \\!\\!$5.4$\\!\\!   \n",
                            "5             \\!\\!$7.2$\\!\\!       \\!\\!$5.3$\\!\\!       \\!\\!$6.4$\\!\\!   \n",
                            "6             \\!\\!$6.3$\\!\\!       \\!\\!$5.1$\\!\\!  \\!\\!$\\bm{6.6}$\\!\\!   \n",
                            "7             \\!\\!$4.7$\\!\\!       \\!\\!$4.6$\\!\\!       \\!\\!$6.0$\\!\\!   \n",
                            "8             \\!\\!$3.7$\\!\\!       \\!\\!$4.0$\\!\\!       \\!\\!$5.8$\\!\\!   \n",
                            "9             \\!\\!$1.6$\\!\\!       \\!\\!$2.2$\\!\\!       \\!\\!$3.7$\\!\\!   \n",
                            "\n",
                            "Dataset         Lending Club                                          \\\n",
                            "k                        $1$                 $2$                 $3$   \n",
                            "0            \\!\\!$-28.0$\\!\\!     \\!\\!$-13.8$\\!\\!      \\!\\!$-5.0$\\!\\!   \n",
                            "1            \\!\\!$-19.2$\\!\\!      \\!\\!$-4.2$\\!\\!       \\!\\!$3.8$\\!\\!   \n",
                            "2            \\!\\!$-16.6$\\!\\!      \\!\\!$-0.6$\\!\\!       \\!\\!$6.6$\\!\\!   \n",
                            "3            \\!\\!$-14.0$\\!\\!  \\!\\!$\\bm{2.4}$\\!\\!  \\!\\!$\\bm{6.7}$\\!\\!   \n",
                            "4            \\!\\!$-12.2$\\!\\!       \\!\\!$2.1$\\!\\!       \\!\\!$6.2$\\!\\!   \n",
                            "5             \\!\\!$-9.5$\\!\\!  \\!\\!$\\bm{2.4}$\\!\\!       \\!\\!$5.7$\\!\\!   \n",
                            "6             \\!\\!$-7.8$\\!\\!       \\!\\!$1.6$\\!\\!       \\!\\!$5.0$\\!\\!   \n",
                            "7             \\!\\!$-4.4$\\!\\!       \\!\\!$2.1$\\!\\!       \\!\\!$3.9$\\!\\!   \n",
                            "8             \\!\\!$-2.7$\\!\\!       \\!\\!$1.2$\\!\\!       \\!\\!$3.2$\\!\\!   \n",
                            "9        \\!\\!$\\bm{-1.2}$\\!\\!       \\!\\!$0.8$\\!\\!       \\!\\!$1.8$\\!\\!   \n",
                            "\n",
                            "Dataset                                                   Wine Quality  \\\n",
                            "k                        $4$                  $5$                  $1$   \n",
                            "0              \\!\\!$2.9$\\!\\!        \\!\\!$8.6$\\!\\!       \\!\\!$-0.4$\\!\\!   \n",
                            "1              \\!\\!$8.8$\\!\\!       \\!\\!$18.3$\\!\\!        \\!\\!$6.1$\\!\\!   \n",
                            "2             \\!\\!$10.2$\\!\\!       \\!\\!$20.3$\\!\\!        \\!\\!$8.2$\\!\\!   \n",
                            "3        \\!\\!$\\bm{13.0}$\\!\\!  \\!\\!$\\bm{21.6}$\\!\\!        \\!\\!$9.6$\\!\\!   \n",
                            "4             \\!\\!$12.4$\\!\\!       \\!\\!$19.5$\\!\\!  \\!\\!$\\bm{10.5}$\\!\\!   \n",
                            "5             \\!\\!$11.2$\\!\\!       \\!\\!$18.4$\\!\\!        \\!\\!$9.4$\\!\\!   \n",
                            "6              \\!\\!$9.8$\\!\\!       \\!\\!$17.1$\\!\\!        \\!\\!$7.9$\\!\\!   \n",
                            "7              \\!\\!$8.9$\\!\\!       \\!\\!$13.2$\\!\\!        \\!\\!$4.6$\\!\\!   \n",
                            "8              \\!\\!$8.1$\\!\\!       \\!\\!$10.6$\\!\\!        \\!\\!$1.2$\\!\\!   \n",
                            "9              \\!\\!$5.5$\\!\\!        \\!\\!$7.7$\\!\\!        \\!\\!$0.7$\\!\\!   \n",
                            "\n",
                            "Dataset                                                                 \\\n",
                            "k                        $2$                  $3$                  $4$   \n",
                            "0             \\!\\!$13.9$\\!\\!       \\!\\!$16.2$\\!\\!  \\!\\!$\\bm{27.3}$\\!\\!   \n",
                            "1             \\!\\!$14.6$\\!\\!       \\!\\!$16.9$\\!\\!       \\!\\!$21.3$\\!\\!   \n",
                            "2             \\!\\!$17.2$\\!\\!       \\!\\!$17.7$\\!\\!       \\!\\!$19.6$\\!\\!   \n",
                            "3        \\!\\!$\\bm{19.0}$\\!\\!       \\!\\!$17.9$\\!\\!       \\!\\!$19.9$\\!\\!   \n",
                            "4             \\!\\!$18.0$\\!\\!  \\!\\!$\\bm{18.3}$\\!\\!       \\!\\!$19.2$\\!\\!   \n",
                            "5             \\!\\!$18.3$\\!\\!       \\!\\!$17.8$\\!\\!       \\!\\!$15.7$\\!\\!   \n",
                            "6             \\!\\!$15.3$\\!\\!       \\!\\!$15.2$\\!\\!       \\!\\!$12.3$\\!\\!   \n",
                            "7              \\!\\!$8.6$\\!\\!        \\!\\!$5.2$\\!\\!        \\!\\!$0.9$\\!\\!   \n",
                            "8             \\!\\!$-1.6$\\!\\!       \\!\\!$-8.2$\\!\\!      \\!\\!$-14.8$\\!\\!   \n",
                            "9            \\!\\!$-13.2$\\!\\!      \\!\\!$-26.6$\\!\\!      \\!\\!$-34.1$\\!\\!   \n",
                            "\n",
                            "Dataset                       \n",
                            "k                        $5$  \n",
                            "0        \\!\\!$\\bm{44.3}$\\!\\!  \n",
                            "1             \\!\\!$28.9$\\!\\!  \n",
                            "2             \\!\\!$25.1$\\!\\!  \n",
                            "3             \\!\\!$22.7$\\!\\!  \n",
                            "4             \\!\\!$20.6$\\!\\!  \n",
                            "5             \\!\\!$15.2$\\!\\!  \n",
                            "6             \\!\\!$10.7$\\!\\!  \n",
                            "7             \\!\\!$-0.1$\\!\\!  \n",
                            "8            \\!\\!$-18.9$\\!\\!  \n",
                            "9            \\!\\!$-37.0$\\!\\!  "
                        ]
                    },
                    "execution_count": 37,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "table_ = tables[0].reset_index()\n",
                "table_.insert(0, '$K$', table_['Method'].apply(lambda x : int(x.split(' ')[1].split('-')[0][1:-1])))\n",
                "table_ = table_.sort_values(['$K$'], ascending = True).reset_index(drop=True)\n",
                "# del table_['Sort Method']\n",
                "\n",
                "\n",
                "table_['Method'] = ''\n",
                "\n",
                "table_.rename(columns = {i: f\"${i}$\" for i in range(6)}, inplace = True)\n",
                "max_row = table_.max()\n",
                "\n",
                "for c in table_:\n",
                "    if \"$\" in c[1]:\n",
                "        table_[c] = table_[c].apply(lambda x : \"\\!\\!$\\\\bm{\" + f\"{x:.1f}\" + \"}$\\!\\!\" if x == max_row[c] else f\"\\!\\!${x:.1f}$\\!\\!\")\n",
                "table_"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "id": "6786ed95-7b37-4400-8cac-427140fb290c",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\\begin{tabular}{rllllllllllllllll}\n",
                        "\\toprule\n",
                        "  $K$ & Method & \\multicolumn{5}{l}{HELOC} & \\multicolumn{5}{l}{Lending Club} & \\multicolumn{5}{l}{Wine Quality} \\\\\n",
                        "      &                  $1$ &                 $2$ &                 $3$ &                 $4$ &                 $5$ &                  $1$ &                 $2$ &                 $3$ &                  $4$ &                  $5$ &                  $1$ &                  $2$ &                  $3$ &                  $4$ &                  $5$ \\\\\n",
                        "\\midrule\n",
                        "    1 &        &       \\!\\!$-8.6$\\!\\! &      \\!\\!$-6.2$\\!\\! &     \\!\\!$-15.6$\\!\\! &     \\!\\!$-20.0$\\!\\! &     \\!\\!$-16.4$\\!\\! &      \\!\\!$-28.0$\\!\\! &     \\!\\!$-13.8$\\!\\! &      \\!\\!$-5.0$\\!\\! &        \\!\\!$2.9$\\!\\! &        \\!\\!$8.6$\\!\\! &       \\!\\!$-0.4$\\!\\! &       \\!\\!$13.9$\\!\\! &       \\!\\!$16.2$\\!\\! &  \\!\\!$\\bm{27.3}$\\!\\! &  \\!\\!$\\bm{44.3}$\\!\\! \\\\\n",
                        "    3 &        &       \\!\\!$-4.9$\\!\\! &       \\!\\!$2.4$\\!\\! &       \\!\\!$1.0$\\!\\! &      \\!\\!$-2.0$\\!\\! &      \\!\\!$-1.6$\\!\\! &      \\!\\!$-19.2$\\!\\! &      \\!\\!$-4.2$\\!\\! &       \\!\\!$3.8$\\!\\! &        \\!\\!$8.8$\\!\\! &       \\!\\!$18.3$\\!\\! &        \\!\\!$6.1$\\!\\! &       \\!\\!$14.6$\\!\\! &       \\!\\!$16.9$\\!\\! &       \\!\\!$21.3$\\!\\! &       \\!\\!$28.9$\\!\\! \\\\\n",
                        "    5 &        &       \\!\\!$-3.1$\\!\\! &       \\!\\!$3.9$\\!\\! &       \\!\\!$4.2$\\!\\! &       \\!\\!$2.7$\\!\\! &       \\!\\!$3.3$\\!\\! &      \\!\\!$-16.6$\\!\\! &      \\!\\!$-0.6$\\!\\! &       \\!\\!$6.6$\\!\\! &       \\!\\!$10.2$\\!\\! &       \\!\\!$20.3$\\!\\! &        \\!\\!$8.2$\\!\\! &       \\!\\!$17.2$\\!\\! &       \\!\\!$17.7$\\!\\! &       \\!\\!$19.6$\\!\\! &       \\!\\!$25.1$\\!\\! \\\\\n",
                        "   10 &        &       \\!\\!$-1.9$\\!\\! &  \\!\\!$\\bm{6.4}$\\!\\! &       \\!\\!$7.0$\\!\\! &       \\!\\!$4.7$\\!\\! &       \\!\\!$5.5$\\!\\! &      \\!\\!$-14.0$\\!\\! &  \\!\\!$\\bm{2.4}$\\!\\! &  \\!\\!$\\bm{6.7}$\\!\\! &  \\!\\!$\\bm{13.0}$\\!\\! &  \\!\\!$\\bm{21.6}$\\!\\! &        \\!\\!$9.6$\\!\\! &  \\!\\!$\\bm{19.0}$\\!\\! &       \\!\\!$17.9$\\!\\! &       \\!\\!$19.9$\\!\\! &       \\!\\!$22.7$\\!\\! \\\\\n",
                        "   20 &        &       \\!\\!$-1.0$\\!\\! &  \\!\\!$\\bm{6.4}$\\!\\! &  \\!\\!$\\bm{8.0}$\\!\\! &  \\!\\!$\\bm{5.4}$\\!\\! &       \\!\\!$5.4$\\!\\! &      \\!\\!$-12.2$\\!\\! &       \\!\\!$2.1$\\!\\! &       \\!\\!$6.2$\\!\\! &       \\!\\!$12.4$\\!\\! &       \\!\\!$19.5$\\!\\! &  \\!\\!$\\bm{10.5}$\\!\\! &       \\!\\!$18.0$\\!\\! &  \\!\\!$\\bm{18.3}$\\!\\! &       \\!\\!$19.2$\\!\\! &       \\!\\!$20.6$\\!\\! \\\\\n",
                        "   50 &        &       \\!\\!$-1.0$\\!\\! &       \\!\\!$5.2$\\!\\! &       \\!\\!$7.2$\\!\\! &       \\!\\!$5.3$\\!\\! &       \\!\\!$6.4$\\!\\! &       \\!\\!$-9.5$\\!\\! &  \\!\\!$\\bm{2.4}$\\!\\! &       \\!\\!$5.7$\\!\\! &       \\!\\!$11.2$\\!\\! &       \\!\\!$18.4$\\!\\! &        \\!\\!$9.4$\\!\\! &       \\!\\!$18.3$\\!\\! &       \\!\\!$17.8$\\!\\! &       \\!\\!$15.7$\\!\\! &       \\!\\!$15.2$\\!\\! \\\\\n",
                        "  100 &        &       \\!\\!$-1.0$\\!\\! &       \\!\\!$3.7$\\!\\! &       \\!\\!$6.3$\\!\\! &       \\!\\!$5.1$\\!\\! &  \\!\\!$\\bm{6.6}$\\!\\! &       \\!\\!$-7.8$\\!\\! &       \\!\\!$1.6$\\!\\! &       \\!\\!$5.0$\\!\\! &        \\!\\!$9.8$\\!\\! &       \\!\\!$17.1$\\!\\! &        \\!\\!$7.9$\\!\\! &       \\!\\!$15.3$\\!\\! &       \\!\\!$15.2$\\!\\! &       \\!\\!$12.3$\\!\\! &       \\!\\!$10.7$\\!\\! \\\\\n",
                        "  250 &        &       \\!\\!$-1.1$\\!\\! &       \\!\\!$2.0$\\!\\! &       \\!\\!$4.7$\\!\\! &       \\!\\!$4.6$\\!\\! &       \\!\\!$6.0$\\!\\! &       \\!\\!$-4.4$\\!\\! &       \\!\\!$2.1$\\!\\! &       \\!\\!$3.9$\\!\\! &        \\!\\!$8.9$\\!\\! &       \\!\\!$13.2$\\!\\! &        \\!\\!$4.6$\\!\\! &        \\!\\!$8.6$\\!\\! &        \\!\\!$5.2$\\!\\! &        \\!\\!$0.9$\\!\\! &       \\!\\!$-0.1$\\!\\! \\\\\n",
                        "  500 &        &       \\!\\!$-1.2$\\!\\! &       \\!\\!$1.4$\\!\\! &       \\!\\!$3.7$\\!\\! &       \\!\\!$4.0$\\!\\! &       \\!\\!$5.8$\\!\\! &       \\!\\!$-2.7$\\!\\! &       \\!\\!$1.2$\\!\\! &       \\!\\!$3.2$\\!\\! &        \\!\\!$8.1$\\!\\! &       \\!\\!$10.6$\\!\\! &        \\!\\!$1.2$\\!\\! &       \\!\\!$-1.6$\\!\\! &       \\!\\!$-8.2$\\!\\! &      \\!\\!$-14.8$\\!\\! &      \\!\\!$-18.9$\\!\\! \\\\\n",
                        " 1000 &        &  \\!\\!$\\bm{-0.7}$\\!\\! &       \\!\\!$0.5$\\!\\! &       \\!\\!$1.6$\\!\\! &       \\!\\!$2.2$\\!\\! &       \\!\\!$3.7$\\!\\! &  \\!\\!$\\bm{-1.2}$\\!\\! &       \\!\\!$0.8$\\!\\! &       \\!\\!$1.8$\\!\\! &        \\!\\!$5.5$\\!\\! &        \\!\\!$7.7$\\!\\! &        \\!\\!$0.7$\\!\\! &      \\!\\!$-13.2$\\!\\! &      \\!\\!$-26.6$\\!\\! &      \\!\\!$-34.1$\\!\\! &      \\!\\!$-37.0$\\!\\! \\\\\n",
                        "\\bottomrule\n",
                        "\\end{tabular}\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "print(table_.to_latex(escape=False, index = False))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "32246711-47a7-4dc2-87f1-45d29199bbc8",
            "metadata": {},
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "cd22f1fa-74fe-41eb-ac27-cefec6c164f2",
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.13"
        },
        "vscode": {
            "interpreter": {
                "hash": "dfd50bd30bdf3d72f99681a86577124e0e3c98068f0063bb9cdbb8c465ddfedb"
            }
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}