Rankings.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Init"
      ],
      "metadata": {
        "id": "ZRfCSReRdNm2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sOh8siuw2zzF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Supervised learning"
      ],
      "metadata": {
        "id": "8hfZhlBidRwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.720, 0.836, 0.557, 0.851, 0.748, 0.801, 0.860],\n",
        "     \"XGBoost\":[0.726, 0.895, 0.587, 0.912, 0.892, 0.821, 0.925],\n",
        "     \"MLP\":[0.643, 0.832, 0.568, 0.904, 0.613, 0.832, 0.893],\n",
        "     \"SNN\":[0.641, 0.880, 0.540, 0.902, 0.621, 0.834, 0.892],\n",
        "     \"TabNet\":[0.585, 0.800, 0.478, 0.904, 0.680, 0.819, 0.896],\n",
        "     \"DCN\":[0.739, 0.870, 0.674, 0.913, 0.848, 0.840, 0.915],\n",
        "     \"AutoInt\":[0.744, 0.866, 0.672, 0.913, 0.808, 0.844, 0.916],\n",
        "     \"TabTrans\":[0.718, 0.860, 0.648, 0.914, 0.855, 0.820, 0.882],\n",
        "     \"FT-Trans\":[0.739, 0.859, 0.657, 0.913, 0.862, 0.841, 0.915],\n",
        "     \"Vime\":[0.735, 0.852, 0.485, 0.912, 0.769, 0.837, 0.908],\n",
        "     \"Scarf\":[0.733, 0.861, 0.663, 0.911, 0.719, 0.833, 0.905],\n",
        "     \"TransTab\":[0.768, 0.881, 0.643, 0.907, 0.851, 0.845, 0.919]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "df.mean(axis=0).sort_values()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9LPvskC7jzX",
        "outputId": "f6f205cd-61ff-46b2-d646-363d576747c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AutoInt      3.142857\n",
              "DCN          3.285714\n",
              "TransTab     3.428571\n",
              "FT-Trans     3.714286\n",
              "XGBoost      4.428571\n",
              "Scarf        6.428571\n",
              "TabTrans     6.571429\n",
              "Vime         6.857143\n",
              "SNN          8.857143\n",
              "MLP          9.571429\n",
              "LR          10.142857\n",
              "TabNet      10.571429\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.720, 0.836, 0.557, 0.851, 0.748, 0.801, 0.860],\n",
        "     \"XGBoost\":[0.726, 0.895, 0.587, 0.912, 0.892, 0.821, 0.925],\n",
        "     \"MLP\":[0.643, 0.832, 0.568, 0.904, 0.613, 0.832, 0.893],\n",
        "     \"SNN\":[0.641, 0.880, 0.540, 0.902, 0.621, 0.834, 0.892],\n",
        "     \"TabNet\":[0.585, 0.800, 0.478, 0.904, 0.680, 0.819, 0.896],\n",
        "     \"DCN\":[0.739, 0.870, 0.674, 0.913, 0.848, 0.840, 0.915],\n",
        "     \"AutoInt\":[0.744, 0.866, 0.672, 0.913, 0.808, 0.844, 0.916],\n",
        "     \"TabTrans\":[0.718, 0.860, 0.648, 0.914, 0.855, 0.820, 0.882],\n",
        "     \"FT-Trans\":[0.739, 0.859, 0.657, 0.913, 0.862, 0.841, 0.915],\n",
        "     \"Vime\":[0.735, 0.852, 0.485, 0.912, 0.769, 0.837, 0.908],\n",
        "     \"Scarf\":[0.733, 0.861, 0.663, 0.911, 0.719, 0.833, 0.905],\n",
        "     \"TransTab\":[0.738, 0.906, 0.628, 0.904, 0.814, 0.835, 0.913]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "df.mean(axis=0).sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBTXTGwDGQpN",
        "outputId": "e32b3791-fb51-4136-af46-a5396088d7bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DCN          2.714286\n",
              "AutoInt      2.714286\n",
              "FT-Trans     3.285714\n",
              "XGBoost      4.571429\n",
              "TransTab     4.857143\n",
              "Scarf        6.428571\n",
              "TabTrans     6.571429\n",
              "Vime         6.714286\n",
              "SNN          8.857143\n",
              "MLP          9.428571\n",
              "LR          10.142857\n",
              "TabNet      10.428571\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature incremental learning"
      ],
      "metadata": {
        "id": "n0KvC8URRodn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.670, 0.773, 0.475, 0.832, 0.727, 0.806, 0.825],\n",
        "     \"XGBoost\":[0.608, 0.817, 0.527, 0.891, 0.778, 0.816, 0.898],\n",
        "     \"MLP\":[0.586, 0.676, 0.516, 0.890, 0.631, 0.825, 0.885],\n",
        "     \"SNN\":[0.583, 0.738, 0.442, 0.888, 0.644, 0.818, 0.881],\n",
        "     \"TabNet\":[0.573, 0.689, 0.419, 0.886, 0.571, 0.837, 0.882],\n",
        "     \"DCN\":[0.674, 0.835, 0.578, 0.893, 0.778, 0.840, 0.891],\n",
        "     \"AutoInt\":[0.671, 0.825, 0.563, 0.893, 0.769, 0.836, 0.887],\n",
        "     \"TabTrans\":[0.653, 0.732, 0.584, 0.856, 0.784, 0.792, 0.828],\n",
        "     \"FT-Trans\":[0.662, 0.824, 0.626, 0.892, 0.768, 0.840, 0.889],\n",
        "     \"Vime\":[0.621, 0.697, 0.571, 0.892, 0.769, 0.803, 0.881],\n",
        "     \"Scarf\":[0.651, 0.753, 0.556, 0.891, 0.703, 0.829,0.887],\n",
        "     \"TransTab\":[0.741, 0.879, 0.665, 0.894, 0.791, 0.841, 0.897]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "print(df)\n",
        "print(df.mean(axis=0).sort_values())\n",
        "print(df.std(axis=0).sort_values())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MY9exPyBRnC6",
        "outputId": "3c31d44f-9a4c-4018-d3f6-e5bf7835f04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     LR  XGBoost   MLP   SNN  TabNet  DCN  AutoInt  TabTrans  FT-Trans  Vime  \\\n",
            "0   4.0      9.0  10.0  11.0    12.0  2.0      3.0       6.0       5.0   8.0   \n",
            "1   6.0      5.0  12.0   8.0    11.0  2.0      3.0       9.0       4.0  10.0   \n",
            "2  10.0      8.0   9.0  11.0    12.0  4.0      6.0       3.0       2.0   5.0   \n",
            "3  12.0      6.0   8.0   9.0    10.0  2.0      2.0      11.0       4.0   4.0   \n",
            "4   8.0      3.0  11.0  10.0    12.0  3.0      5.0       2.0       7.0   5.0   \n",
            "5  10.0      9.0   7.0   8.0     4.0  2.0      5.0      12.0       2.0  11.0   \n",
            "6  12.0      1.0   7.0   9.0     8.0  3.0      5.0      11.0       4.0   9.0   \n",
            "\n",
            "   Scarf  TransTab  \n",
            "0    7.0       1.0  \n",
            "1    7.0       1.0  \n",
            "2    7.0       1.0  \n",
            "3    6.0       1.0  \n",
            "4    9.0       1.0  \n",
            "5    6.0       1.0  \n",
            "6    5.0       2.0  \n",
            "TransTab    1.142857\n",
            "DCN         2.571429\n",
            "FT-Trans    4.000000\n",
            "AutoInt     4.142857\n",
            "XGBoost     5.857143\n",
            "Scarf       6.714286\n",
            "Vime        7.428571\n",
            "TabTrans    7.714286\n",
            "LR          8.857143\n",
            "MLP         9.142857\n",
            "SNN         9.428571\n",
            "TabNet      9.857143\n",
            "dtype: float64\n",
            "TransTab    0.377964\n",
            "DCN         0.786796\n",
            "Scarf       1.253566\n",
            "SNN         1.272418\n",
            "AutoInt     1.463850\n",
            "FT-Trans    1.732051\n",
            "MLP         1.951800\n",
            "Vime        2.760262\n",
            "TabNet      2.968084\n",
            "LR          3.023716\n",
            "XGBoost     3.078342\n",
            "TabTrans    4.070802\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.670, 0.773, 0.475, 0.832, 0.727, 0.806, 0.825],\n",
        "     \"XGBoost\":[0.608, 0.817, 0.527, 0.891, 0.778, 0.816, 0.898],\n",
        "     \"MLP\":[0.586, 0.676, 0.516, 0.890, 0.631, 0.825, 0.885],\n",
        "     \"SNN\":[0.583, 0.738, 0.442, 0.888, 0.644, 0.818, 0.881],\n",
        "     \"TabNet\":[0.573, 0.689, 0.419, 0.886, 0.571, 0.837, 0.882],\n",
        "     \"DCN\":[0.674, 0.835, 0.578, 0.893, 0.778, 0.840, 0.891],\n",
        "     \"AutoInt\":[0.671, 0.825, 0.563, 0.893, 0.769, 0.836, 0.887],\n",
        "     \"TabTrans\":[0.653, 0.732, 0.584, 0.856, 0.784, 0.792, 0.828],\n",
        "     \"FT-Trans\":[0.662, 0.824, 0.626, 0.892, 0.768, 0.840, 0.889],\n",
        "     \"Vime\":[0.621, 0.697, 0.571, 0.892, 0.769, 0.803, 0.881],\n",
        "     \"Scarf\":[0.651, 0.753, 0.556, 0.891, 0.703, 0.829,0.887],\n",
        "     \"TransTab\":[0.733, 0.896, 0.605, 0.904, 0.822, 0.832, 0.912]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "print(df)\n",
        "print(df.mean(axis=0).sort_values())\n",
        "print(df.std(axis=0).sort_values())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_c0ByEMSH_5",
        "outputId": "e63ba531-f419-4232-89b2-157b2030ca89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     LR  XGBoost   MLP   SNN  TabNet  DCN  AutoInt  TabTrans  FT-Trans  Vime  \\\n",
            "0   4.0      9.0  10.0  11.0    12.0  2.0      3.0       6.0       5.0   8.0   \n",
            "1   6.0      5.0  12.0   8.0    11.0  2.0      3.0       9.0       4.0  10.0   \n",
            "2  10.0      8.0   9.0  11.0    12.0  4.0      6.0       3.0       1.0   5.0   \n",
            "3  12.0      6.0   8.0   9.0    10.0  2.0      2.0      11.0       4.0   4.0   \n",
            "4   8.0      3.0  11.0  10.0    12.0  3.0      5.0       2.0       7.0   5.0   \n",
            "5  10.0      9.0   7.0   8.0     3.0  1.0      4.0      12.0       1.0  11.0   \n",
            "6  12.0      2.0   7.0   9.0     8.0  3.0      5.0      11.0       4.0   9.0   \n",
            "\n",
            "   Scarf  TransTab  \n",
            "0    7.0       1.0  \n",
            "1    7.0       1.0  \n",
            "2    7.0       2.0  \n",
            "3    6.0       1.0  \n",
            "4    9.0       1.0  \n",
            "5    6.0       5.0  \n",
            "6    5.0       1.0  \n",
            "TransTab    1.714286\n",
            "DCN         2.428571\n",
            "FT-Trans    3.714286\n",
            "AutoInt     4.000000\n",
            "XGBoost     6.000000\n",
            "Scarf       6.714286\n",
            "Vime        7.428571\n",
            "TabTrans    7.714286\n",
            "LR          8.857143\n",
            "MLP         9.142857\n",
            "SNN         9.428571\n",
            "TabNet      9.714286\n",
            "dtype: float64\n",
            "DCN         0.975900\n",
            "Scarf       1.253566\n",
            "SNN         1.272418\n",
            "AutoInt     1.414214\n",
            "TransTab    1.496026\n",
            "MLP         1.951800\n",
            "FT-Trans    2.138090\n",
            "Vime        2.760262\n",
            "XGBoost     2.828427\n",
            "LR          3.023716\n",
            "TabNet      3.302236\n",
            "TabTrans    4.070802\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.670, 0.773, 0.475, 0.832, 0.727, 0.806, 0.825],\n",
        "     \"XGBoost\":[0.608, 0.817, 0.527, 0.891, 0.778, 0.816, 0.898],\n",
        "     \"MLP\":[0.586, 0.676, 0.516, 0.890, 0.631, 0.825, 0.885],\n",
        "     \"SNN\":[0.583, 0.738, 0.442, 0.888, 0.644, 0.818, 0.881],\n",
        "     \"TabNet\":[0.573, 0.689, 0.419, 0.886, 0.571, 0.837, 0.882],\n",
        "     \"DCN\":[0.674, 0.835, 0.578, 0.893, 0.778, 0.840, 0.891],\n",
        "     \"AutoInt\":[0.671, 0.825, 0.563, 0.893, 0.769, 0.836, 0.887],\n",
        "     \"TabTrans\":[0.653, 0.732, 0.584, 0.856, 0.784, 0.792, 0.828],\n",
        "     \"FT-Trans\":[0.662, 0.824, 0.626, 0.892, 0.768, 0.840, 0.889],\n",
        "     \"Vime\":[0.621, 0.697, 0.571, 0.892, 0.769, 0.803, 0.881],\n",
        "     \"Scarf\":[0.651, 0.753, 0.556, 0.891, 0.703, 0.829,0.887],\n",
        "     \"TransTab\":[0.741, 0.879, 0.665, 0.894, 0.791, 0.841, 0.897]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "df.mean(axis=0).sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GzJeEiAQAXf",
        "outputId": "f1b9dbc2-b870-4031-a68a-ed68eb8dfadf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransTab    1.142857\n",
              "DCN         2.571429\n",
              "FT-Trans    4.000000\n",
              "AutoInt     4.142857\n",
              "XGBoost     5.857143\n",
              "Scarf       6.714286\n",
              "Vime        7.428571\n",
              "TabTrans    7.714286\n",
              "LR          8.857143\n",
              "MLP         9.142857\n",
              "SNN         9.428571\n",
              "TabNet      9.857143\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transfer learning"
      ],
      "metadata": {
        "id": "pFsfi7VsZXAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.69, 0.69, 0.81, 0.82, 0.47, 0.56, 0.81, 0.81, 0.68, 0.78, 0.77, 0.82, 0.81, 0.84],\n",
        "     \"XGBoost\":[0.72,0.71,0.85,0.87,0.46,0.63,0.88,0.89,0.80,0.81,0.76,0.82,0.92,0.91],\n",
        "     \"MLP\":[0.67,0.70,0.82,0.86,0.53,0.67,0.89,0.90,0.73,0.82,0.79,0.83,0.90,0.90],\n",
        "     \"SNN\":[0.66,0.63,0.85,0.83,0.54,0.42,0.87,0.88,0.57,0.54,0.77,0.82,0.87,0.88],\n",
        "     \"TabNet\":[0.60,0.47,0.66,0.68,0.54,0.53,0.87,0.88,0.58,0.62,0.75,0.83,0.88,0.89],\n",
        "     \"DCN\":[0.69,0.70,0.83,0.85,0.51,0.58,0.88,0.74,0.79,0.78,0.79,0.76,0.91,0.90],\n",
        "     \"AutoInt\":[0.70,0.70,0.82,0.86,0.49,0.55,0.88,0.74,0.77,0.79,0.79,0.76,0.91,0.90],\n",
        "     \"TabTrans\":[0.72,0.72,0.84,0.86,0.54,0.57,0.88,0.90,0.73,0.79,0.78,0.81,0.88,0.88],\n",
        "     \"FT-Trans\":[0.72,0.71,0.83,0.85,0.53,0.64,0.89,0.90,0.76,0.79,0.78,0.84,0.91,0.91],\n",
        "     \"Vime\":[0.59,0.70,0.79,0.76,0.45,0.53,0.88,0.90,0.65,0.81,0.58,0.83,0.90,0.90],\n",
        "     \"Scarf\":[0.69,0.72,0.82,0.85,0.55,0.64,0.88,0.89,0.77,0.73,0.78,0.83,0.90,0.89],\n",
        "     \"TransTab\":[0.74,0.76,0.87,0.89,0.55,0.66,0.88,0.90,0.80,0.80,0.79,0.84,0.91,0.91]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "print(df.mean(axis=0).sort_values())\n",
        "print(df.std(axis=0).sort_values())\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "5fFfLgKrZoAN",
        "outputId": "c35d5bff-6331-4897-f8e4-d91738527b60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TransTab    1.500000\n",
            "FT-Trans    3.428571\n",
            "MLP         4.000000\n",
            "XGBoost     4.071429\n",
            "Scarf       5.000000\n",
            "TabTrans    5.071429\n",
            "DCN         5.714286\n",
            "AutoInt     5.714286\n",
            "Vime        7.357143\n",
            "SNN         8.928571\n",
            "TabNet      9.357143\n",
            "LR          9.428571\n",
            "dtype: float64\n",
            "TransTab    0.940540\n",
            "LR          1.869360\n",
            "FT-Trans    2.064882\n",
            "Scarf       2.480695\n",
            "MLP         2.801099\n",
            "TabNet      3.002746\n",
            "DCN         3.049050\n",
            "TabTrans    3.049950\n",
            "SNN         3.149219\n",
            "AutoInt     3.244607\n",
            "XGBoost     3.315796\n",
            "Vime        4.180672\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      LR  XGBoost  MLP   SNN  TabNet   DCN  AutoInt  TabTrans  FT-Trans  Vime  \\\n",
              "0    6.0      2.0  9.0  10.0    11.0   6.0      5.0       2.0       2.0  12.0   \n",
              "1   10.0      4.0  6.0  11.0    12.0   6.0      6.0       2.0       4.0   6.0   \n",
              "2   10.0      2.0  7.0   2.0    12.0   5.0      7.0       4.0       5.0  11.0   \n",
              "3   10.0      2.0  3.0   9.0    12.0   6.0      3.0       3.0       6.0  11.0   \n",
              "4   10.0     11.0  6.0   3.0     3.0   8.0      9.0       3.0       6.0  12.0   \n",
              "5    8.0      5.0  1.0  12.0    10.0   6.0      9.0       7.0       3.0  10.0   \n",
              "6   12.0      3.0  1.0  10.0    10.0   3.0      3.0       3.0       1.0   3.0   \n",
              "7   10.0      6.0  1.0   8.0     8.0  11.0     11.0       1.0       1.0   1.0   \n",
              "8    9.0      1.0  7.0  12.0    11.0   3.0      4.0       7.0       6.0  10.0   \n",
              "9    8.0      2.0  1.0  12.0    11.0   8.0      5.0       5.0       5.0   2.0   \n",
              "10   8.0     10.0  1.0   8.0    11.0   1.0      1.0       5.0       5.0  12.0   \n",
              "11   7.0      7.0  3.0   7.0     3.0  11.0     11.0      10.0       1.0   3.0   \n",
              "12  12.0      1.0  6.0  11.0     9.0   2.0      2.0       9.0       2.0   6.0   \n",
              "13  12.0      1.0  4.0  10.0     8.0   4.0      4.0      10.0       1.0   4.0   \n",
              "\n",
              "    Scarf  TransTab  \n",
              "0     6.0       1.0  \n",
              "1     2.0       1.0  \n",
              "2     7.0       1.0  \n",
              "3     6.0       1.0  \n",
              "4     1.0       1.0  \n",
              "5     3.0       2.0  \n",
              "6     3.0       3.0  \n",
              "7     6.0       1.0  \n",
              "8     4.0       1.0  \n",
              "9    10.0       4.0  \n",
              "10    5.0       1.0  \n",
              "11    3.0       1.0  \n",
              "12    6.0       2.0  \n",
              "13    8.0       1.0  "
            ],
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              "\n",
              "  <div id=\"df-ee0f8c0b-d21e-45dd-abc9-a9f495e4d2d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LR</th>\n",
              "      <th>XGBoost</th>\n",
              "      <th>MLP</th>\n",
              "      <th>SNN</th>\n",
              "      <th>TabNet</th>\n",
              "      <th>DCN</th>\n",
              "      <th>AutoInt</th>\n",
              "      <th>TabTrans</th>\n",
              "      <th>FT-Trans</th>\n",
              "      <th>Vime</th>\n",
              "      <th>Scarf</th>\n",
              "      <th>TransTab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee0f8c0b-d21e-45dd-abc9-a9f495e4d2d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee0f8c0b-d21e-45dd-abc9-a9f495e4d2d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee0f8c0b-d21e-45dd-abc9-a9f495e4d2d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "     \"LR\":[ 0.69, 0.69, 0.81, 0.82, 0.47, 0.56, 0.81, 0.81, 0.68, 0.78, 0.77, 0.82, 0.81, 0.84],\n",
        "     \"XGBoost\":[0.72,0.71,0.85,0.87,0.46,0.63,0.88,0.89,0.80,0.81,0.76,0.82,0.92,0.91],\n",
        "     \"MLP\":[0.67,0.70,0.82,0.86,0.53,0.67,0.89,0.90,0.73,0.82,0.79,0.83,0.90,0.90],\n",
        "     \"SNN\":[0.66,0.63,0.85,0.83,0.54,0.42,0.87,0.88,0.57,0.54,0.77,0.82,0.87,0.88],\n",
        "     \"TabNet\":[0.60,0.47,0.66,0.68,0.54,0.53,0.87,0.88,0.58,0.62,0.75,0.83,0.88,0.89],\n",
        "     \"DCN\":[0.69,0.70,0.83,0.85,0.51,0.58,0.88,0.74,0.79,0.78,0.79,0.76,0.91,0.90],\n",
        "     \"AutoInt\":[0.70,0.70,0.82,0.86,0.49,0.55,0.88,0.74,0.77,0.79,0.79,0.76,0.91,0.90],\n",
        "     \"TabTrans\":[0.72,0.72,0.84,0.86,0.54,0.57,0.88,0.90,0.73,0.79,0.78,0.81,0.88,0.88],\n",
        "     \"FT-Trans\":[0.72,0.71,0.83,0.85,0.53,0.64,0.89,0.90,0.76,0.79,0.78,0.84,0.91,0.91],\n",
        "     \"Vime\":[0.59,0.70,0.79,0.76,0.45,0.53,0.88,0.90,0.65,0.81,0.58,0.83,0.90,0.90],\n",
        "     \"Scarf\":[0.69,0.72,0.82,0.85,0.55,0.64,0.88,0.89,0.77,0.73,0.78,0.83,0.90,0.89],\n",
        "     \"TransTab\":[0.70,0.72,0.83,0.92,0.58,0.62,0.89,0.89,0.81,0.78,0.81,0.83,0.90,0.91]\n",
        "}\n",
        "df = pd.DataFrame(d)\n",
        "x = df.to_numpy()\n",
        "l = np.zeros_like(x)\n",
        "for r in range(x.shape[0]):\n",
        "  ranks = []\n",
        "  for c in range(x.shape[1]):\n",
        "    ranks.append(np.flip(np.sort(x[r, :])).tolist().index(x[r,c]) + 1)\n",
        "  l[r] = ranks\n",
        "\n",
        "df[df.columns] = l\n",
        "print(df.mean(axis=0).sort_values())\n",
        "print(df.std(axis=0).sort_values())\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "TpqU6oBbdXgm",
        "outputId": "cb0b318f-f46c-4e9a-8c9e-60339f602e96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TransTab    2.785714\n",
            "FT-Trans    3.142857\n",
            "XGBoost     3.928571\n",
            "MLP         3.928571\n",
            "Scarf       4.785714\n",
            "TabTrans    4.857143\n",
            "DCN         5.714286\n",
            "AutoInt     5.714286\n",
            "Vime        7.285714\n",
            "SNN         8.857143\n",
            "TabNet      9.285714\n",
            "LR          9.357143\n",
            "dtype: float64\n",
            "LR          1.945691\n",
            "FT-Trans    2.070197\n",
            "TransTab    2.118729\n",
            "Scarf       2.547354\n",
            "MLP         2.730576\n",
            "DCN         2.867016\n",
            "AutoInt     3.148347\n",
            "TabNet      3.172686\n",
            "TabTrans    3.231031\n",
            "XGBoost     3.315796\n",
            "SNN         3.324898\n",
            "Vime        4.232267\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      LR  XGBoost  MLP   SNN  TabNet   DCN  AutoInt  TabTrans  FT-Trans  Vime  \\\n",
              "0    6.0      1.0  9.0  10.0    11.0   6.0      4.0       1.0       1.0  12.0   \n",
              "1   10.0      4.0  6.0  11.0    12.0   6.0      6.0       1.0       4.0   6.0   \n",
              "2   10.0      1.0  7.0   1.0    12.0   4.0      7.0       3.0       4.0  11.0   \n",
              "3   10.0      2.0  3.0   9.0    12.0   6.0      3.0       3.0       6.0  11.0   \n",
              "4   10.0     11.0  6.0   3.0     3.0   8.0      9.0       3.0       6.0  12.0   \n",
              "5    8.0      4.0  1.0  12.0    10.0   6.0      9.0       7.0       2.0  10.0   \n",
              "6   12.0      4.0  1.0  10.0    10.0   4.0      4.0       4.0       1.0   4.0   \n",
              "7   10.0      5.0  1.0   8.0     8.0  11.0     11.0       1.0       1.0   1.0   \n",
              "8    9.0      2.0  7.0  12.0    11.0   3.0      4.0       7.0       6.0  10.0   \n",
              "9    7.0      2.0  1.0  12.0    11.0   7.0      4.0       4.0       4.0   2.0   \n",
              "10   8.0     10.0  2.0   8.0    11.0   2.0      2.0       5.0       5.0  12.0   \n",
              "11   7.0      7.0  2.0   7.0     2.0  11.0     11.0      10.0       1.0   2.0   \n",
              "12  12.0      1.0  5.0  11.0     9.0   2.0      2.0       9.0       2.0   5.0   \n",
              "13  12.0      1.0  4.0  10.0     8.0   4.0      4.0      10.0       1.0   4.0   \n",
              "\n",
              "    Scarf  TransTab  \n",
              "0     6.0       4.0  \n",
              "1     1.0       1.0  \n",
              "2     7.0       4.0  \n",
              "3     6.0       1.0  \n",
              "4     2.0       1.0  \n",
              "5     2.0       5.0  \n",
              "6     4.0       1.0  \n",
              "7     5.0       5.0  \n",
              "8     4.0       1.0  \n",
              "9    10.0       7.0  \n",
              "10    5.0       1.0  \n",
              "11    2.0       2.0  \n",
              "12    5.0       5.0  \n",
              "13    8.0       1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c85eabac-6554-4b8f-b8aa-2fb69c73333a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LR</th>\n",
              "      <th>XGBoost</th>\n",
              "      <th>MLP</th>\n",
              "      <th>SNN</th>\n",
              "      <th>TabNet</th>\n",
              "      <th>DCN</th>\n",
              "      <th>AutoInt</th>\n",
              "      <th>TabTrans</th>\n",
              "      <th>FT-Trans</th>\n",
              "      <th>Vime</th>\n",
              "      <th>Scarf</th>\n",
              "      <th>TransTab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c85eabac-6554-4b8f-b8aa-2fb69c73333a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c85eabac-6554-4b8f-b8aa-2fb69c73333a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c85eabac-6554-4b8f-b8aa-2fb69c73333a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    }
  ]
}