Image-ClassPrediction.ipynb

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      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DBd_SBMVKE6p"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5E8pR3EdLAZf",
        "outputId": "e63389d0-c427-4f49-c1a7-c2dbbb6d33fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_KKVvImqK_qM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## train AP"
      ],
      "metadata": {
        "id": "r3IQrAYBTQN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_AP =  pd.read_csv('/content/drive/MyDrive/kaggle/train/train_AP.csv')\n",
        "truth = pd.read_csv('/content/BUU-LSPINE_spondylolisthesis/train/train_ground_truth.csv')\n",
        "\n",
        "# Create a new column in XX dataframe with 'id' format of AP dataframe\n",
        "truth['new_id'] = truth['id'].apply(lambda x: x.split('_')[0])\n",
        "\n",
        "# Merge the dataframes\n",
        "merged_df = pd.merge(train_AP, truth, how='left', left_on='id', right_on='new_id')\n",
        "\n",
        "# If you want to drop the 'new_id' column after merge\n",
        "merged_df = merged_df.drop(columns=['Unnamed: 0','new_id'])\n",
        "merged_df['L'] = merged_df['id_y'].apply(lambda x : x.split('_')[-1])\n",
        "train_AP_finish = merged_df.drop(columns=['id_y'])\n",
        "train_AP_finish = train_AP_finish.rename(columns={'id_x':'id'})\n",
        "train_AP_finish.head(),len(train_AP_finish)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwMcnDiOQJzs",
        "outputId": "b9132c57-f4a4-400b-b635-f2c908617781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
              " 0  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              " 1  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              " 2  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              " 3  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              " 4  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              " \n",
              "      L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r    L5b_2c    L5b_2r  \\\n",
              " 0  0.610531  0.145667  0.467669  ...  0.421466  0.545728  0.619579  0.545728   \n",
              " 1  0.610531  0.145667  0.467669  ...  0.421466  0.545728  0.619579  0.545728   \n",
              " 2  0.610531  0.145667  0.467669  ...  0.421466  0.545728  0.619579  0.545728   \n",
              " 3  0.610531  0.145667  0.467669  ...  0.421466  0.545728  0.619579  0.545728   \n",
              " 4  0.610531  0.145667  0.467669  ...  0.421466  0.545728  0.619579  0.545728   \n",
              " \n",
              "    S1a_1c  S1a_1r  S1a_2c  S1a_2r  class   L  \n",
              " 0     NaN     NaN     NaN     NaN      0  L1  \n",
              " 1     NaN     NaN     NaN     NaN      0  L2  \n",
              " 2     NaN     NaN     NaN     NaN      0  L3  \n",
              " 3     NaN     NaN     NaN     NaN      0  L4  \n",
              " 4     NaN     NaN     NaN     NaN      0  L5  \n",
              " \n",
              " [5 rows x 47 columns],\n",
              " 14230)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_AP_finish['class'] =train_AP_finish['class'].astype(str)"
      ],
      "metadata": {
        "id": "nKmpcTn7j48z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Q3N7ePTdVJjx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_rows = []\n",
        "\n",
        "# Iterate over rows\n",
        "for index, row in tqdm(train_AP_finish.iterrows()):\n",
        "    # Check if 'class' is not equal to '0'\n",
        "    if row['class'] != '5':\n",
        "        # Create 5 new rows for each row with 'class' not equal to '0'\n",
        "        for _ in range(1):\n",
        "            new_row = row.copy()\n",
        "            # Iterate over each column\n",
        "            # Add constant value between (-0.1) and (0.1)\n",
        "            constant_value = np.random.uniform(-0.1, 0.1)\n",
        "            for column in train_AP_finish.columns:\n",
        "                if column not in ['id', 'class','L']:\n",
        "                    # Multiply value between 0.95 and 1.05\n",
        "                    new_row[column] = new_row[column] + constant_value*np.random.uniform(0.95, 1.05)\n",
        "            new_rows.append(new_row)\n",
        "\n",
        "# Append the new rows to the dataframe\n",
        "train_AP_finish = train_AP_finish.append(new_rows, ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "7fd132a9707649158f497afac67c4bcd",
            "8dc190de2e7d4bad947cb3f9f9f59021",
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            "bb75700143644777aa4f550aab68154c",
            "f2d32cbd22ed4945a7b9a8537a18de59",
            "9fa033d3d4094c92bd1b0bdd54fd51c3",
            "b408d89bfa8f43aa8a566f1d79c495e1",
            "988dee2ac90345bc886f73d653cee862",
            "4cf33969834240348b4e3e19a186a7fa",
            "b66ee96147794fe1bbbc5296434bd1b8",
            "52ec8e73c53047d684ac5f1de3085f2f"
          ]
        },
        "id": "-qoarxCVSB3w",
        "outputId": "e61317a3-8576-4bb9-a14b-10f55126f18a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7fd132a9707649158f497afac67c4bcd"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-112-5b3298dd7297>:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  train_AP_finish = train_AP_finish.append(new_rows, ignore_index=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_AP_finish[train_AP_finish['class']=='1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jaQSotS_Wo8u",
        "outputId": "7cad2926-726a-4d4a-d51b-e4db12bc3692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
              "14038  1012.jpg  0.485990  0.248454  0.630059  0.248454  0.485990  0.316294   \n",
              "14043  1020.jpg  0.369167  0.216896  0.611469  0.216896  0.369167  0.283683   \n",
              "14047  1045.jpg  0.429685  0.223671  0.602649  0.223671  0.429685  0.308919   \n",
              "14052   117.jpg  0.495768  0.219124  0.669827  0.219124  0.495768  0.316330   \n",
              "14058  1180.jpg  0.359345  0.094565  0.511979  0.094565  0.359345  0.167398   \n",
              "14062  1199.jpg  0.481794  0.249287  0.645748  0.249287  0.481794  0.328639   \n",
              "14067  1353.jpg  0.415741  0.178879  0.564504  0.178879  0.415741  0.257535   \n",
              "14073  1699.jpg  0.415400  0.246541  0.565054  0.246541  0.415400  0.333858   \n",
              "14078   172.jpg  0.381583  0.158984  0.520958  0.158984  0.381583  0.241414   \n",
              "14083  1903.jpg  0.440196  0.109316  0.606311  0.109316  0.440196  0.170717   \n",
              "14088  1942.jpg  0.483767  0.263567  0.610296  0.263567  0.483767  0.324098   \n",
              "14091  1943.jpg  0.433948  0.189239  0.575040  0.189239  0.433948  0.264251   \n",
              "14098   196.jpg  0.407775  0.196999  0.570724  0.196999  0.407775  0.259639   \n",
              "14103   437.jpg  0.373227  0.184751  0.542427  0.184751  0.373227  0.249310   \n",
              "14106   455.jpg  0.423470  0.298928  0.587336  0.298928  0.423470  0.366696   \n",
              "14113   494.jpg  0.415349  0.227273  0.569397  0.227273  0.415349  0.277111   \n",
              "14117   566.jpg  0.428374  0.294407  0.657458  0.294407  0.428374  0.352727   \n",
              "28268  1012.jpg  0.517665  0.281147  0.662593  0.280845  0.518389  0.346307   \n",
              "28273  1020.jpg  0.329981  0.174994  0.569312  0.173826  0.328155  0.242934   \n",
              "28277  1045.jpg  0.387080  0.183283  0.562701  0.183502  0.389197  0.267229   \n",
              "28282   117.jpg  0.420466  0.146461  0.597241  0.145646  0.425370  0.244658   \n",
              "28288  1180.jpg  0.361015  0.096212  0.513713  0.096154  0.360991  0.169086   \n",
              "28292  1199.jpg  0.552426  0.316188  0.711451  0.317506  0.546803  0.398017   \n",
              "28297  1353.jpg  0.425031  0.188179  0.574091  0.188934  0.424970  0.267041   \n",
              "28303  1699.jpg  0.377146  0.211668  0.530174  0.208756  0.377618  0.297660   \n",
              "28308   172.jpg  0.288252  0.072664  0.431277  0.069461  0.295048  0.153471   \n",
              "28313  1903.jpg  0.512548  0.178291  0.679162  0.184646  0.512172  0.243227   \n",
              "28318  1942.jpg  0.411849  0.192831  0.538395  0.191990  0.415484  0.257403   \n",
              "28321  1943.jpg  0.469789  0.224570  0.609607  0.224713  0.468923  0.300886   \n",
              "28328   196.jpg  0.340682  0.125439  0.501029  0.124188  0.333761  0.192234   \n",
              "28333   437.jpg  0.271778  0.084992  0.438519  0.085620  0.276949  0.149058   \n",
              "28336   455.jpg  0.514464  0.388650  0.677795  0.390687  0.515241  0.454884   \n",
              "28343   494.jpg  0.484227  0.302907  0.638651  0.299805  0.491320  0.350634   \n",
              "28347   566.jpg  0.419638  0.285182  0.648530  0.285579  0.419642  0.343947   \n",
              "\n",
              "         L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r    L5b_2c  \\\n",
              "14038  0.630059  0.316294  0.490848  ...  0.427517  0.641674  0.605888   \n",
              "14043  0.611469  0.283683  0.379000  ...  0.360579  0.606269  0.655843   \n",
              "14047  0.602649  0.308919  0.454273  ...  0.397521  0.617150  0.614357   \n",
              "14052  0.669827  0.316330  0.515273  ...  0.429079  0.608490  0.619207   \n",
              "14058  0.511979  0.167398  0.374215  ...  0.393088  0.466819  0.585391   \n",
              "14062  0.645748  0.328639  0.494720  ...  0.412747  0.629528  0.606956   \n",
              "14067  0.564504  0.257535  0.392027  ...  0.343399  0.567062  0.510789   \n",
              "14073  0.565054  0.333858  0.416336  ...  0.432990  0.605123  0.617675   \n",
              "14078  0.520958  0.241414  0.400485  ...  0.450186  0.524924  0.619922   \n",
              "14083  0.606311  0.170717  0.432788  ...  0.394770  0.515763  0.608658   \n",
              "14088  0.610296  0.324098  0.491050  ...  0.478836  0.627336  0.648991   \n",
              "14091  0.575040  0.264251  0.403959  ...  0.388951  0.562265  0.566190   \n",
              "14098  0.570724  0.259639  0.419638  ...  0.434439  0.524245  0.618851   \n",
              "14103  0.542427  0.249310  0.373523  ...  0.372831  0.582469  0.588217   \n",
              "14106  0.587336  0.366696  0.410798  ...  0.376598  0.689681  0.583778   \n",
              "14113  0.569397  0.277111  0.402745  ...  0.380027  0.583822  0.553202   \n",
              "14117  0.657458  0.352727  0.403787  ...  0.289170  0.634138  0.567235   \n",
              "28268  0.660899  0.348060  0.521721  ...  0.458180  0.673628  0.636369   \n",
              "28273  0.568919  0.242102  0.337390  ...  0.319094  0.565740  0.614490   \n",
              "28277  0.561911  0.266711  0.413019  ...  0.356230  0.575586  0.570696   \n",
              "28282  0.597761  0.245415  0.443952  ...  0.355568  0.534228  0.545659   \n",
              "28288  0.513675  0.169011  0.375878  ...  0.394671  0.468412  0.587082   \n",
              "28292  0.713826  0.396679  0.559704  ...  0.478535  0.699095  0.675907   \n",
              "28297  0.574234  0.266981  0.401750  ...  0.353331  0.576635  0.519930   \n",
              "28303  0.529749  0.295842  0.378880  ...  0.397544  0.568358  0.580736   \n",
              "28308  0.428985  0.156115  0.307099  ...  0.357717  0.438955  0.527025   \n",
              "28313  0.677871  0.243125  0.508463  ...  0.464042  0.590093  0.679807   \n",
              "28318  0.538746  0.254185  0.419181  ...  0.412099  0.560556  0.578908   \n",
              "28321  0.609521  0.298272  0.439104  ...  0.425520  0.598201  0.602103   \n",
              "28328  0.502841  0.188322  0.347690  ...  0.365059  0.454658  0.549039   \n",
              "28333  0.441195  0.149931  0.269477  ...  0.275011  0.480647  0.486949   \n",
              "28336  0.681876  0.454759  0.505150  ...  0.470055  0.782019  0.672057   \n",
              "28343  0.643193  0.352230  0.477106  ...  0.452562  0.658932  0.622264   \n",
              "28347  0.648959  0.344138  0.394560  ...  0.280686  0.624880  0.558057   \n",
              "\n",
              "         L5b_2r  S1a_1c  S1a_1r  S1a_2c  S1a_2r  class   L  \n",
              "14038  0.641674     NaN     NaN     NaN     NaN      1  L4  \n",
              "14043  0.606269     NaN     NaN     NaN     NaN      1  L4  \n",
              "14047  0.617150     NaN     NaN     NaN     NaN      1  L3  \n",
              "14052  0.608490     NaN     NaN     NaN     NaN      1  L3  \n",
              "14058  0.466819     NaN     NaN     NaN     NaN      1  L4  \n",
              "14062  0.629528     NaN     NaN     NaN     NaN      1  L3  \n",
              "14067  0.567062     NaN     NaN     NaN     NaN      1  L3  \n",
              "14073  0.605123     NaN     NaN     NaN     NaN      1  L4  \n",
              "14078  0.524924     NaN     NaN     NaN     NaN      1  L4  \n",
              "14083  0.515763     NaN     NaN     NaN     NaN      1  L4  \n",
              "14088  0.627336     NaN     NaN     NaN     NaN      1  L4  \n",
              "14091  0.562265     NaN     NaN     NaN     NaN      1  L2  \n",
              "14098  0.524245     NaN     NaN     NaN     NaN      1  L4  \n",
              "14103  0.582469     NaN     NaN     NaN     NaN      1  L4  \n",
              "14106  0.689681     NaN     NaN     NaN     NaN      1  L2  \n",
              "14113  0.583822     NaN     NaN     NaN     NaN      1  L4  \n",
              "14117  0.634138     NaN     NaN     NaN     NaN      1  L3  \n",
              "28268  0.673713     NaN     NaN     NaN     NaN      1  L4  \n",
              "28273  0.567105     NaN     NaN     NaN     NaN      1  L4  \n",
              "28277  0.573755     NaN     NaN     NaN     NaN      1  L3  \n",
              "28282  0.535822     NaN     NaN     NaN     NaN      1  L3  \n",
              "28288  0.468495     NaN     NaN     NaN     NaN      1  L4  \n",
              "28292  0.697477     NaN     NaN     NaN     NaN      1  L3  \n",
              "28297  0.576897     NaN     NaN     NaN     NaN      1  L3  \n",
              "28303  0.567824     NaN     NaN     NaN     NaN      1  L4  \n",
              "28308  0.435838     NaN     NaN     NaN     NaN      1  L4  \n",
              "28313  0.586092     NaN     NaN     NaN     NaN      1  L4  \n",
              "28318  0.555939     NaN     NaN     NaN     NaN      1  L4  \n",
              "28321  0.599114     NaN     NaN     NaN     NaN      1  L2  \n",
              "28328  0.457111     NaN     NaN     NaN     NaN      1  L4  \n",
              "28333  0.481445     NaN     NaN     NaN     NaN      1  L4  \n",
              "28336  0.780201     NaN     NaN     NaN     NaN      1  L2  \n",
              "28343  0.655818     NaN     NaN     NaN     NaN      1  L4  \n",
              "28347  0.625498     NaN     NaN     NaN     NaN      1  L3  \n",
              "\n",
              "[34 rows x 47 columns]"
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              "      <th>id</th>\n",
              "      <th>L1a_1c</th>\n",
              "      <th>L1a_1r</th>\n",
              "      <th>L1a_2c</th>\n",
              "      <th>L1a_2r</th>\n",
              "      <th>L1b_1c</th>\n",
              "      <th>L1b_1r</th>\n",
              "      <th>L1b_2c</th>\n",
              "      <th>L1b_2r</th>\n",
              "      <th>L2a_1c</th>\n",
              "      <th>...</th>\n",
              "      <th>L5b_1c</th>\n",
              "      <th>L5b_1r</th>\n",
              "      <th>L5b_2c</th>\n",
              "      <th>L5b_2r</th>\n",
              "      <th>S1a_1c</th>\n",
              "      <th>S1a_1r</th>\n",
              "      <th>S1a_2c</th>\n",
              "      <th>S1a_2r</th>\n",
              "      <th>class</th>\n",
              "      <th>L</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14038</th>\n",
              "      <td>1012.jpg</td>\n",
              "      <td>0.485990</td>\n",
              "      <td>0.248454</td>\n",
              "      <td>0.630059</td>\n",
              "      <td>0.248454</td>\n",
              "      <td>0.485990</td>\n",
              "      <td>0.316294</td>\n",
              "      <td>0.630059</td>\n",
              "      <td>0.316294</td>\n",
              "      <td>0.490848</td>\n",
              "      <td>...</td>\n",
              "      <td>0.427517</td>\n",
              "      <td>0.641674</td>\n",
              "      <td>0.605888</td>\n",
              "      <td>0.641674</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14043</th>\n",
              "      <td>1020.jpg</td>\n",
              "      <td>0.369167</td>\n",
              "      <td>0.216896</td>\n",
              "      <td>0.611469</td>\n",
              "      <td>0.216896</td>\n",
              "      <td>0.369167</td>\n",
              "      <td>0.283683</td>\n",
              "      <td>0.611469</td>\n",
              "      <td>0.283683</td>\n",
              "      <td>0.379000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.360579</td>\n",
              "      <td>0.606269</td>\n",
              "      <td>0.655843</td>\n",
              "      <td>0.606269</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14047</th>\n",
              "      <td>1045.jpg</td>\n",
              "      <td>0.429685</td>\n",
              "      <td>0.223671</td>\n",
              "      <td>0.602649</td>\n",
              "      <td>0.223671</td>\n",
              "      <td>0.429685</td>\n",
              "      <td>0.308919</td>\n",
              "      <td>0.602649</td>\n",
              "      <td>0.308919</td>\n",
              "      <td>0.454273</td>\n",
              "      <td>...</td>\n",
              "      <td>0.397521</td>\n",
              "      <td>0.617150</td>\n",
              "      <td>0.614357</td>\n",
              "      <td>0.617150</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14052</th>\n",
              "      <td>117.jpg</td>\n",
              "      <td>0.495768</td>\n",
              "      <td>0.219124</td>\n",
              "      <td>0.669827</td>\n",
              "      <td>0.219124</td>\n",
              "      <td>0.495768</td>\n",
              "      <td>0.316330</td>\n",
              "      <td>0.669827</td>\n",
              "      <td>0.316330</td>\n",
              "      <td>0.515273</td>\n",
              "      <td>...</td>\n",
              "      <td>0.429079</td>\n",
              "      <td>0.608490</td>\n",
              "      <td>0.619207</td>\n",
              "      <td>0.608490</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14058</th>\n",
              "      <td>1180.jpg</td>\n",
              "      <td>0.359345</td>\n",
              "      <td>0.094565</td>\n",
              "      <td>0.511979</td>\n",
              "      <td>0.094565</td>\n",
              "      <td>0.359345</td>\n",
              "      <td>0.167398</td>\n",
              "      <td>0.511979</td>\n",
              "      <td>0.167398</td>\n",
              "      <td>0.374215</td>\n",
              "      <td>...</td>\n",
              "      <td>0.393088</td>\n",
              "      <td>0.466819</td>\n",
              "      <td>0.585391</td>\n",
              "      <td>0.466819</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14062</th>\n",
              "      <td>1199.jpg</td>\n",
              "      <td>0.481794</td>\n",
              "      <td>0.249287</td>\n",
              "      <td>0.645748</td>\n",
              "      <td>0.249287</td>\n",
              "      <td>0.481794</td>\n",
              "      <td>0.328639</td>\n",
              "      <td>0.645748</td>\n",
              "      <td>0.328639</td>\n",
              "      <td>0.494720</td>\n",
              "      <td>...</td>\n",
              "      <td>0.412747</td>\n",
              "      <td>0.629528</td>\n",
              "      <td>0.606956</td>\n",
              "      <td>0.629528</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14067</th>\n",
              "      <td>1353.jpg</td>\n",
              "      <td>0.415741</td>\n",
              "      <td>0.178879</td>\n",
              "      <td>0.564504</td>\n",
              "      <td>0.178879</td>\n",
              "      <td>0.415741</td>\n",
              "      <td>0.257535</td>\n",
              "      <td>0.564504</td>\n",
              "      <td>0.257535</td>\n",
              "      <td>0.392027</td>\n",
              "      <td>...</td>\n",
              "      <td>0.343399</td>\n",
              "      <td>0.567062</td>\n",
              "      <td>0.510789</td>\n",
              "      <td>0.567062</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14073</th>\n",
              "      <td>1699.jpg</td>\n",
              "      <td>0.415400</td>\n",
              "      <td>0.246541</td>\n",
              "      <td>0.565054</td>\n",
              "      <td>0.246541</td>\n",
              "      <td>0.415400</td>\n",
              "      <td>0.333858</td>\n",
              "      <td>0.565054</td>\n",
              "      <td>0.333858</td>\n",
              "      <td>0.416336</td>\n",
              "      <td>...</td>\n",
              "      <td>0.432990</td>\n",
              "      <td>0.605123</td>\n",
              "      <td>0.617675</td>\n",
              "      <td>0.605123</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14078</th>\n",
              "      <td>172.jpg</td>\n",
              "      <td>0.381583</td>\n",
              "      <td>0.158984</td>\n",
              "      <td>0.520958</td>\n",
              "      <td>0.158984</td>\n",
              "      <td>0.381583</td>\n",
              "      <td>0.241414</td>\n",
              "      <td>0.520958</td>\n",
              "      <td>0.241414</td>\n",
              "      <td>0.400485</td>\n",
              "      <td>...</td>\n",
              "      <td>0.450186</td>\n",
              "      <td>0.524924</td>\n",
              "      <td>0.619922</td>\n",
              "      <td>0.524924</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14083</th>\n",
              "      <td>1903.jpg</td>\n",
              "      <td>0.440196</td>\n",
              "      <td>0.109316</td>\n",
              "      <td>0.606311</td>\n",
              "      <td>0.109316</td>\n",
              "      <td>0.440196</td>\n",
              "      <td>0.170717</td>\n",
              "      <td>0.606311</td>\n",
              "      <td>0.170717</td>\n",
              "      <td>0.432788</td>\n",
              "      <td>...</td>\n",
              "      <td>0.394770</td>\n",
              "      <td>0.515763</td>\n",
              "      <td>0.608658</td>\n",
              "      <td>0.515763</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14088</th>\n",
              "      <td>1942.jpg</td>\n",
              "      <td>0.483767</td>\n",
              "      <td>0.263567</td>\n",
              "      <td>0.610296</td>\n",
              "      <td>0.263567</td>\n",
              "      <td>0.483767</td>\n",
              "      <td>0.324098</td>\n",
              "      <td>0.610296</td>\n",
              "      <td>0.324098</td>\n",
              "      <td>0.491050</td>\n",
              "      <td>...</td>\n",
              "      <td>0.478836</td>\n",
              "      <td>0.627336</td>\n",
              "      <td>0.648991</td>\n",
              "      <td>0.627336</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14091</th>\n",
              "      <td>1943.jpg</td>\n",
              "      <td>0.433948</td>\n",
              "      <td>0.189239</td>\n",
              "      <td>0.575040</td>\n",
              "      <td>0.189239</td>\n",
              "      <td>0.433948</td>\n",
              "      <td>0.264251</td>\n",
              "      <td>0.575040</td>\n",
              "      <td>0.264251</td>\n",
              "      <td>0.403959</td>\n",
              "      <td>...</td>\n",
              "      <td>0.388951</td>\n",
              "      <td>0.562265</td>\n",
              "      <td>0.566190</td>\n",
              "      <td>0.562265</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14098</th>\n",
              "      <td>196.jpg</td>\n",
              "      <td>0.407775</td>\n",
              "      <td>0.196999</td>\n",
              "      <td>0.570724</td>\n",
              "      <td>0.196999</td>\n",
              "      <td>0.407775</td>\n",
              "      <td>0.259639</td>\n",
              "      <td>0.570724</td>\n",
              "      <td>0.259639</td>\n",
              "      <td>0.419638</td>\n",
              "      <td>...</td>\n",
              "      <td>0.434439</td>\n",
              "      <td>0.524245</td>\n",
              "      <td>0.618851</td>\n",
              "      <td>0.524245</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14103</th>\n",
              "      <td>437.jpg</td>\n",
              "      <td>0.373227</td>\n",
              "      <td>0.184751</td>\n",
              "      <td>0.542427</td>\n",
              "      <td>0.184751</td>\n",
              "      <td>0.373227</td>\n",
              "      <td>0.249310</td>\n",
              "      <td>0.542427</td>\n",
              "      <td>0.249310</td>\n",
              "      <td>0.373523</td>\n",
              "      <td>...</td>\n",
              "      <td>0.372831</td>\n",
              "      <td>0.582469</td>\n",
              "      <td>0.588217</td>\n",
              "      <td>0.582469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14106</th>\n",
              "      <td>455.jpg</td>\n",
              "      <td>0.423470</td>\n",
              "      <td>0.298928</td>\n",
              "      <td>0.587336</td>\n",
              "      <td>0.298928</td>\n",
              "      <td>0.423470</td>\n",
              "      <td>0.366696</td>\n",
              "      <td>0.587336</td>\n",
              "      <td>0.366696</td>\n",
              "      <td>0.410798</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376598</td>\n",
              "      <td>0.689681</td>\n",
              "      <td>0.583778</td>\n",
              "      <td>0.689681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14113</th>\n",
              "      <td>494.jpg</td>\n",
              "      <td>0.415349</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.569397</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>0.415349</td>\n",
              "      <td>0.277111</td>\n",
              "      <td>0.569397</td>\n",
              "      <td>0.277111</td>\n",
              "      <td>0.402745</td>\n",
              "      <td>...</td>\n",
              "      <td>0.380027</td>\n",
              "      <td>0.583822</td>\n",
              "      <td>0.553202</td>\n",
              "      <td>0.583822</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14117</th>\n",
              "      <td>566.jpg</td>\n",
              "      <td>0.428374</td>\n",
              "      <td>0.294407</td>\n",
              "      <td>0.657458</td>\n",
              "      <td>0.294407</td>\n",
              "      <td>0.428374</td>\n",
              "      <td>0.352727</td>\n",
              "      <td>0.657458</td>\n",
              "      <td>0.352727</td>\n",
              "      <td>0.403787</td>\n",
              "      <td>...</td>\n",
              "      <td>0.289170</td>\n",
              "      <td>0.634138</td>\n",
              "      <td>0.567235</td>\n",
              "      <td>0.634138</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28268</th>\n",
              "      <td>1012.jpg</td>\n",
              "      <td>0.517665</td>\n",
              "      <td>0.281147</td>\n",
              "      <td>0.662593</td>\n",
              "      <td>0.280845</td>\n",
              "      <td>0.518389</td>\n",
              "      <td>0.346307</td>\n",
              "      <td>0.660899</td>\n",
              "      <td>0.348060</td>\n",
              "      <td>0.521721</td>\n",
              "      <td>...</td>\n",
              "      <td>0.458180</td>\n",
              "      <td>0.673628</td>\n",
              "      <td>0.636369</td>\n",
              "      <td>0.673713</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28273</th>\n",
              "      <td>1020.jpg</td>\n",
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              "      <td>0.174994</td>\n",
              "      <td>0.569312</td>\n",
              "      <td>0.173826</td>\n",
              "      <td>0.328155</td>\n",
              "      <td>0.242934</td>\n",
              "      <td>0.568919</td>\n",
              "      <td>0.242102</td>\n",
              "      <td>0.337390</td>\n",
              "      <td>...</td>\n",
              "      <td>0.319094</td>\n",
              "      <td>0.565740</td>\n",
              "      <td>0.614490</td>\n",
              "      <td>0.567105</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28277</th>\n",
              "      <td>1045.jpg</td>\n",
              "      <td>0.387080</td>\n",
              "      <td>0.183283</td>\n",
              "      <td>0.562701</td>\n",
              "      <td>0.183502</td>\n",
              "      <td>0.389197</td>\n",
              "      <td>0.267229</td>\n",
              "      <td>0.561911</td>\n",
              "      <td>0.266711</td>\n",
              "      <td>0.413019</td>\n",
              "      <td>...</td>\n",
              "      <td>0.356230</td>\n",
              "      <td>0.575586</td>\n",
              "      <td>0.570696</td>\n",
              "      <td>0.573755</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28282</th>\n",
              "      <td>117.jpg</td>\n",
              "      <td>0.420466</td>\n",
              "      <td>0.146461</td>\n",
              "      <td>0.597241</td>\n",
              "      <td>0.145646</td>\n",
              "      <td>0.425370</td>\n",
              "      <td>0.244658</td>\n",
              "      <td>0.597761</td>\n",
              "      <td>0.245415</td>\n",
              "      <td>0.443952</td>\n",
              "      <td>...</td>\n",
              "      <td>0.355568</td>\n",
              "      <td>0.534228</td>\n",
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              "      <td>0.535822</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28288</th>\n",
              "      <td>1180.jpg</td>\n",
              "      <td>0.361015</td>\n",
              "      <td>0.096212</td>\n",
              "      <td>0.513713</td>\n",
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              "      <td>0.513675</td>\n",
              "      <td>0.169011</td>\n",
              "      <td>0.375878</td>\n",
              "      <td>...</td>\n",
              "      <td>0.394671</td>\n",
              "      <td>0.468412</td>\n",
              "      <td>0.587082</td>\n",
              "      <td>0.468495</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28292</th>\n",
              "      <td>1199.jpg</td>\n",
              "      <td>0.552426</td>\n",
              "      <td>0.316188</td>\n",
              "      <td>0.711451</td>\n",
              "      <td>0.317506</td>\n",
              "      <td>0.546803</td>\n",
              "      <td>0.398017</td>\n",
              "      <td>0.713826</td>\n",
              "      <td>0.396679</td>\n",
              "      <td>0.559704</td>\n",
              "      <td>...</td>\n",
              "      <td>0.478535</td>\n",
              "      <td>0.699095</td>\n",
              "      <td>0.675907</td>\n",
              "      <td>0.697477</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28297</th>\n",
              "      <td>1353.jpg</td>\n",
              "      <td>0.425031</td>\n",
              "      <td>0.188179</td>\n",
              "      <td>0.574091</td>\n",
              "      <td>0.188934</td>\n",
              "      <td>0.424970</td>\n",
              "      <td>0.267041</td>\n",
              "      <td>0.574234</td>\n",
              "      <td>0.266981</td>\n",
              "      <td>0.401750</td>\n",
              "      <td>...</td>\n",
              "      <td>0.353331</td>\n",
              "      <td>0.576635</td>\n",
              "      <td>0.519930</td>\n",
              "      <td>0.576897</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28303</th>\n",
              "      <td>1699.jpg</td>\n",
              "      <td>0.377146</td>\n",
              "      <td>0.211668</td>\n",
              "      <td>0.530174</td>\n",
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              "      <td>0.529749</td>\n",
              "      <td>0.295842</td>\n",
              "      <td>0.378880</td>\n",
              "      <td>...</td>\n",
              "      <td>0.397544</td>\n",
              "      <td>0.568358</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28308</th>\n",
              "      <td>172.jpg</td>\n",
              "      <td>0.288252</td>\n",
              "      <td>0.072664</td>\n",
              "      <td>0.431277</td>\n",
              "      <td>0.069461</td>\n",
              "      <td>0.295048</td>\n",
              "      <td>0.153471</td>\n",
              "      <td>0.428985</td>\n",
              "      <td>0.156115</td>\n",
              "      <td>0.307099</td>\n",
              "      <td>...</td>\n",
              "      <td>0.357717</td>\n",
              "      <td>0.438955</td>\n",
              "      <td>0.527025</td>\n",
              "      <td>0.435838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28313</th>\n",
              "      <td>1903.jpg</td>\n",
              "      <td>0.512548</td>\n",
              "      <td>0.178291</td>\n",
              "      <td>0.679162</td>\n",
              "      <td>0.184646</td>\n",
              "      <td>0.512172</td>\n",
              "      <td>0.243227</td>\n",
              "      <td>0.677871</td>\n",
              "      <td>0.243125</td>\n",
              "      <td>0.508463</td>\n",
              "      <td>...</td>\n",
              "      <td>0.464042</td>\n",
              "      <td>0.590093</td>\n",
              "      <td>0.679807</td>\n",
              "      <td>0.586092</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28318</th>\n",
              "      <td>1942.jpg</td>\n",
              "      <td>0.411849</td>\n",
              "      <td>0.192831</td>\n",
              "      <td>0.538395</td>\n",
              "      <td>0.191990</td>\n",
              "      <td>0.415484</td>\n",
              "      <td>0.257403</td>\n",
              "      <td>0.538746</td>\n",
              "      <td>0.254185</td>\n",
              "      <td>0.419181</td>\n",
              "      <td>...</td>\n",
              "      <td>0.412099</td>\n",
              "      <td>0.560556</td>\n",
              "      <td>0.578908</td>\n",
              "      <td>0.555939</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28321</th>\n",
              "      <td>1943.jpg</td>\n",
              "      <td>0.469789</td>\n",
              "      <td>0.224570</td>\n",
              "      <td>0.609607</td>\n",
              "      <td>0.224713</td>\n",
              "      <td>0.468923</td>\n",
              "      <td>0.300886</td>\n",
              "      <td>0.609521</td>\n",
              "      <td>0.298272</td>\n",
              "      <td>0.439104</td>\n",
              "      <td>...</td>\n",
              "      <td>0.425520</td>\n",
              "      <td>0.598201</td>\n",
              "      <td>0.602103</td>\n",
              "      <td>0.599114</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28328</th>\n",
              "      <td>196.jpg</td>\n",
              "      <td>0.340682</td>\n",
              "      <td>0.125439</td>\n",
              "      <td>0.501029</td>\n",
              "      <td>0.124188</td>\n",
              "      <td>0.333761</td>\n",
              "      <td>0.192234</td>\n",
              "      <td>0.502841</td>\n",
              "      <td>0.188322</td>\n",
              "      <td>0.347690</td>\n",
              "      <td>...</td>\n",
              "      <td>0.365059</td>\n",
              "      <td>0.454658</td>\n",
              "      <td>0.549039</td>\n",
              "      <td>0.457111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28333</th>\n",
              "      <td>437.jpg</td>\n",
              "      <td>0.271778</td>\n",
              "      <td>0.084992</td>\n",
              "      <td>0.438519</td>\n",
              "      <td>0.085620</td>\n",
              "      <td>0.276949</td>\n",
              "      <td>0.149058</td>\n",
              "      <td>0.441195</td>\n",
              "      <td>0.149931</td>\n",
              "      <td>0.269477</td>\n",
              "      <td>...</td>\n",
              "      <td>0.275011</td>\n",
              "      <td>0.480647</td>\n",
              "      <td>0.486949</td>\n",
              "      <td>0.481445</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28336</th>\n",
              "      <td>455.jpg</td>\n",
              "      <td>0.514464</td>\n",
              "      <td>0.388650</td>\n",
              "      <td>0.677795</td>\n",
              "      <td>0.390687</td>\n",
              "      <td>0.515241</td>\n",
              "      <td>0.454884</td>\n",
              "      <td>0.681876</td>\n",
              "      <td>0.454759</td>\n",
              "      <td>0.505150</td>\n",
              "      <td>...</td>\n",
              "      <td>0.470055</td>\n",
              "      <td>0.782019</td>\n",
              "      <td>0.672057</td>\n",
              "      <td>0.780201</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28343</th>\n",
              "      <td>494.jpg</td>\n",
              "      <td>0.484227</td>\n",
              "      <td>0.302907</td>\n",
              "      <td>0.638651</td>\n",
              "      <td>0.299805</td>\n",
              "      <td>0.491320</td>\n",
              "      <td>0.350634</td>\n",
              "      <td>0.643193</td>\n",
              "      <td>0.352230</td>\n",
              "      <td>0.477106</td>\n",
              "      <td>...</td>\n",
              "      <td>0.452562</td>\n",
              "      <td>0.658932</td>\n",
              "      <td>0.622264</td>\n",
              "      <td>0.655818</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28347</th>\n",
              "      <td>566.jpg</td>\n",
              "      <td>0.419638</td>\n",
              "      <td>0.285182</td>\n",
              "      <td>0.648530</td>\n",
              "      <td>0.285579</td>\n",
              "      <td>0.419642</td>\n",
              "      <td>0.343947</td>\n",
              "      <td>0.648959</td>\n",
              "      <td>0.344138</td>\n",
              "      <td>0.394560</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280686</td>\n",
              "      <td>0.624880</td>\n",
              "      <td>0.558057</td>\n",
              "      <td>0.625498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34 rows × 47 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da5ee2a8-545b-4c20-9689-030e5e388070')\"\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-da5ee2a8-545b-4c20-9689-030e5e388070 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-da5ee2a8-545b-4c20-9689-030e5e388070');\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": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## train LA"
      ],
      "metadata": {
        "id": "QTJQAqY0TV9k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_LA =  pd.read_csv('/content/drive/MyDrive/kaggle/train/train_LA.csv')\n",
        "truth = pd.read_csv('/content/BUU-LSPINE_spondylolisthesis/train/train_ground_truth.csv')\n",
        "# Create a new column in XX dataframe with 'id' format of AP dataframe\n",
        "truth['new_id'] = truth['id'].apply(lambda x: x.split('_')[0])\n",
        "\n",
        "# Merge the dataframes\n",
        "merged_df = pd.merge(train_LA, truth, how='left', left_on='id', right_on='new_id')\n",
        "\n",
        "# If you want to drop the 'new_id' column after merge\n",
        "merged_df = merged_df.drop(columns=['Unnamed: 0','new_id'])\n",
        "merged_df['L'] = merged_df['id_y'].apply(lambda x : x.split('_')[-1])\n",
        "merged_df = merged_df.drop(columns=['id_y'])\n",
        "merged_df['class'] =  merged_df['class'].astype(str)\n",
        "train_LA_finish = merged_df.copy()\n",
        "train_LA_finish = train_LA_finish.rename(columns={'id_x':'id'})\n",
        "train_LA_finish.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nIPcYyqETWvG",
        "outputId": "bf1a3e63-75ba-4ae0-d462-e42fc7bb1629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id   L1a_1c    L1a_1r    L1a_2c    L1a_2r   L1b_1c    L1b_1r  \\\n",
              "0  3000.jpg  0.39688  0.081835  0.618736  0.081835  0.39688  0.188593   \n",
              "1  3000.jpg  0.39688  0.081835  0.618736  0.081835  0.39688  0.188593   \n",
              "2  3000.jpg  0.39688  0.081835  0.618736  0.081835  0.39688  0.188593   \n",
              "3  3000.jpg  0.39688  0.081835  0.618736  0.081835  0.39688  0.188593   \n",
              "4  3000.jpg  0.39688  0.081835  0.618736  0.081835  0.39688  0.188593   \n",
              "\n",
              "     L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r   L5b_2c    L5b_2r  \\\n",
              "0  0.618736  0.188593  0.308708  ...  0.217434  0.593876  0.42822  0.593876   \n",
              "1  0.618736  0.188593  0.308708  ...  0.217434  0.593876  0.42822  0.593876   \n",
              "2  0.618736  0.188593  0.308708  ...  0.217434  0.593876  0.42822  0.593876   \n",
              "3  0.618736  0.188593  0.308708  ...  0.217434  0.593876  0.42822  0.593876   \n",
              "4  0.618736  0.188593  0.308708  ...  0.217434  0.593876  0.42822  0.593876   \n",
              "\n",
              "     S1a_1c    S1a_1r    S1a_2c    S1a_2r  class   L  \n",
              "0  0.258535  0.642704  0.434374  0.548236      0  L1  \n",
              "1  0.258535  0.642704  0.434374  0.548236      0  L2  \n",
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        "new_rows = []\n",
        "\n",
        "# Iterate over rows\n",
        "for index, row in tqdm(train_LA_finish.iterrows()):\n",
        "    # Check if 'class' is not equal to '0'\n",
        "    if row['class'] != '0':\n",
        "        # Create 5 new rows for each row with 'class' not equal to '0'\n",
        "        for _ in range(50):\n",
        "            new_row = row.copy()\n",
        "            # Add constant value between (-0.1) and (0.1)\n",
        "            constant_value = np.random.uniform(-0.1, 0.1)\n",
        "            # Iterate over each column\n",
        "            for column in train_LA_finish.columns:\n",
        "                if column not in ['id', 'class','L']:\n",
        "\n",
        "                    # Multiply value between 0.95 and 1.05\n",
        "                    new_row[column] = new_row[column] + constant_value* np.random.uniform(0.95, 1.05)\n",
        "\n",
        "            new_rows.append(new_row)\n",
        "\n",
        "# Append the new rows to the dataframe\n",
        "train_LA_finish = train_LA_finish.append(new_rows, ignore_index=True)"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
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          "output_type": "stream",
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          "text": [
            "<ipython-input-115-6bccd3cdaa72>:22: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  train_LA_finish = train_LA_finish.append(new_rows, ignore_index=True)\n"
          ]
        }
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        "train_LA_finish[train_LA_finish['class']=='0']"
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        "id": "GnMVMFoZp9u2",
        "outputId": "5821c976-07cb-4df3-8cfd-3903b97c0e92"
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              "      <td>0.188593</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.308708</td>\n",
              "      <td>...</td>\n",
              "      <td>0.217434</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.428220</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.258535</td>\n",
              "      <td>0.642704</td>\n",
              "      <td>0.434374</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>0</td>\n",
              "      <td>L1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000.jpg</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.308708</td>\n",
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              "      <td>0.217434</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.428220</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.258535</td>\n",
              "      <td>0.642704</td>\n",
              "      <td>0.434374</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000.jpg</td>\n",
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              "      <td>0.081835</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.308708</td>\n",
              "      <td>...</td>\n",
              "      <td>0.217434</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.428220</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.258535</td>\n",
              "      <td>0.642704</td>\n",
              "      <td>0.434374</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>0</td>\n",
              "      <td>L3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3000.jpg</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.308708</td>\n",
              "      <td>...</td>\n",
              "      <td>0.217434</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.428220</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.258535</td>\n",
              "      <td>0.642704</td>\n",
              "      <td>0.434374</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>0</td>\n",
              "      <td>L4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3000.jpg</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.081835</td>\n",
              "      <td>0.396880</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.618736</td>\n",
              "      <td>0.188593</td>\n",
              "      <td>0.308708</td>\n",
              "      <td>...</td>\n",
              "      <td>0.217434</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.428220</td>\n",
              "      <td>0.593876</td>\n",
              "      <td>0.258535</td>\n",
              "      <td>0.642704</td>\n",
              "      <td>0.434374</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>0</td>\n",
              "      <td>L5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1765</th>\n",
              "      <td>3757.jpg</td>\n",
              "      <td>0.498877</td>\n",
              "      <td>0.364351</td>\n",
              "      <td>0.616190</td>\n",
              "      <td>0.364351</td>\n",
              "      <td>0.498877</td>\n",
              "      <td>0.432137</td>\n",
              "      <td>0.616190</td>\n",
              "      <td>0.432137</td>\n",
              "      <td>0.491363</td>\n",
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              "      <td>0.490892</td>\n",
              "      <td>0.738885</td>\n",
              "      <td>0.616933</td>\n",
              "      <td>0.738885</td>\n",
              "      <td>0.495062</td>\n",
              "      <td>0.770242</td>\n",
              "      <td>0.614322</td>\n",
              "      <td>0.716460</td>\n",
              "      <td>0</td>\n",
              "      <td>L1</td>\n",
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              "    <tr>\n",
              "      <th>1766</th>\n",
              "      <td>3757.jpg</td>\n",
              "      <td>0.498877</td>\n",
              "      <td>0.364351</td>\n",
              "      <td>0.616190</td>\n",
              "      <td>0.364351</td>\n",
              "      <td>0.498877</td>\n",
              "      <td>0.432137</td>\n",
              "      <td>0.616190</td>\n",
              "      <td>0.432137</td>\n",
              "      <td>0.491363</td>\n",
              "      <td>...</td>\n",
              "      <td>0.490892</td>\n",
              "      <td>0.738885</td>\n",
              "      <td>0.616933</td>\n",
              "      <td>0.738885</td>\n",
              "      <td>0.495062</td>\n",
              "      <td>0.770242</td>\n",
              "      <td>0.614322</td>\n",
              "      <td>0.716460</td>\n",
              "      <td>0</td>\n",
              "      <td>L2</td>\n",
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              "    <tr>\n",
              "      <th>1770</th>\n",
              "      <td>3793.jpg</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.543501</td>\n",
              "      <td>...</td>\n",
              "      <td>0.487256</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.593914</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.490798</td>\n",
              "      <td>0.704367</td>\n",
              "      <td>0.592929</td>\n",
              "      <td>0.646611</td>\n",
              "      <td>0</td>\n",
              "      <td>L1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1771</th>\n",
              "      <td>3793.jpg</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.543501</td>\n",
              "      <td>...</td>\n",
              "      <td>0.487256</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.593914</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.490798</td>\n",
              "      <td>0.704367</td>\n",
              "      <td>0.592929</td>\n",
              "      <td>0.646611</td>\n",
              "      <td>0</td>\n",
              "      <td>L2</td>\n",
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              "    <tr>\n",
              "      <th>1774</th>\n",
              "      <td>3793.jpg</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.241433</td>\n",
              "      <td>0.537818</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.650506</td>\n",
              "      <td>0.319133</td>\n",
              "      <td>0.543501</td>\n",
              "      <td>...</td>\n",
              "      <td>0.487256</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.593914</td>\n",
              "      <td>0.658165</td>\n",
              "      <td>0.490798</td>\n",
              "      <td>0.704367</td>\n",
              "      <td>0.592929</td>\n",
              "      <td>0.646611</td>\n",
              "      <td>0</td>\n",
              "      <td>L5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1487 rows × 47 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-607208da-e618-4d57-9239-41a47fe537bd')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_LA_finish[train_LA_finish['class']=='3']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "c3kY9eQup-nz",
        "outputId": "9793f7f8-0030-4f18-e83f-f2d8b0f0bd9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
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              "543    2048.jpg  0.324668  0.196992  0.491415  0.196992  0.324668  0.284190   \n",
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              "553    2088.jpg  0.573157  0.049510  0.694902  0.049510  0.573157  0.131982   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "15970  3757.jpg  0.560263  0.430758  0.677683  0.425419  0.562543  0.496584   \n",
              "15971  3757.jpg  0.514671  0.379824  0.630947  0.380276  0.514385  0.447772   \n",
              "15972  3757.jpg  0.426499  0.294510  0.545130  0.294917  0.430182  0.361627   \n",
              "15973  3757.jpg  0.499768  0.365185  0.617094  0.365211  0.499708  0.433004   \n",
              "15974  3757.jpg  0.551127  0.415389  0.667145  0.416289  0.549469  0.483240   \n",
              "\n",
              "         L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r    L5b_2c  \\\n",
              "538    0.710294  0.349588  0.536459  ...  0.486402  0.650122  0.591366   \n",
              "543    0.491415  0.284190  0.282603  ...  0.374377  0.651461  0.562510   \n",
              "547    0.508173  0.324410  0.310154  ...  0.455154  0.631371  0.659037   \n",
              "548    0.508173  0.324410  0.310154  ...  0.455154  0.631371  0.659037   \n",
              "553    0.694902  0.131982  0.536223  ...  0.516590  0.449650  0.628359   \n",
              "...         ...       ...       ...  ...       ...       ...       ...   \n",
              "15970  0.682011  0.498225  0.555740  ...  0.557029  0.805268  0.679964   \n",
              "15971  0.632143  0.446940  0.506865  ...  0.505678  0.754281  0.632070   \n",
              "15972  0.544270  0.361196  0.422255  ...  0.420186  0.664167  0.545211   \n",
              "15973  0.617075  0.432993  0.492248  ...  0.491748  0.739742  0.617766   \n",
              "15974  0.671296  0.486684  0.542691  ...  0.545095  0.792381  0.667954   \n",
              "\n",
              "         L5b_2r    S1a_1c    S1a_1r    S1a_2c    S1a_2r  class   L  \n",
              "538    0.650122  0.487548  0.690257  0.593602  0.634407      3  L4  \n",
              "543    0.651461  0.437675  0.678290  0.594798  0.586274      3  L4  \n",
              "547    0.631371  0.492952  0.656280  0.669753  0.593604      3  L3  \n",
              "548    0.631371  0.492952  0.656280  0.669753  0.593604      3  L4  \n",
              "553    0.449650  0.530984  0.481564  0.632695  0.421570      3  L4  \n",
              "...         ...       ...       ...       ...       ...    ...  ..  \n",
              "15970  0.805488  0.556371  0.836553  0.679317  0.778803      3  L3  \n",
              "15971  0.754436  0.510080  0.785618  0.629307  0.731753      3  L3  \n",
              "15972  0.664631  0.421796  0.698094  0.541574  0.642192      3  L3  \n",
              "15973  0.739711  0.495966  0.771133  0.615219  0.717296      3  L3  \n",
              "15974  0.793130  0.546878  0.825588  0.669018  0.769966      3  L3  \n",
              "\n",
              "[10608 rows x 47 columns]"
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              "      <th>L1a_1c</th>\n",
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          "metadata": {},
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    },
    {
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        "train_LA_finish[train_LA_finish['class']=='1']"
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        "outputId": "ade8c507-1372-4e23-ddc8-f5635a234ee9"
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a419921f-9f54-4b8f-b4ae-14ee633738f4 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-a419921f-9f54-4b8f-b4ae-14ee633738f4');\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": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## test AP"
      ],
      "metadata": {
        "id": "HTWiRcGXTjLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "iAM9o8wnXBqp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_AP = pd.read_csv('/content/drive/MyDrive/kaggle/test/test_AP.csv').drop(columns=['Unnamed: 0'])\n",
        "test_AP_left = test_AP.copy().reindex(test_AP.index.repeat(5)).reset_index(drop=True)\n",
        "\n",
        "# Create new column 'L' with values L1, L2, L3, L4, L5\n",
        "test_AP_left['L'] = np.resize(['L1', 'L2', 'L3', 'L4', 'L5'], len(test_AP_left))\n",
        "test_AP_finish = test_AP_left.copy()\n",
        "test_AP_finish['id'] = test_AP_finish['id'] + '_' +test_AP_finish['L']"
      ],
      "metadata": {
        "id": "vGWnoTVzVi4k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_AP_finish.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "2G7WoKjTWgdk",
        "outputId": "b5dfa273-cf89-4dfc-ea79-76e3290d41fb"
      },
      "execution_count": null,
      "outputs": [
        {
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              "            id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
              "0  t120.jpg_L1  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "1  t120.jpg_L2  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "2  t120.jpg_L3  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "3  t120.jpg_L4  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "4  t120.jpg_L5  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "\n",
              "     L1b_2c    L1b_2r    L2a_1c  ...    L5a_2r    L5b_1c    L5b_1r    L5b_2c  \\\n",
              "0  0.535148  0.226797  0.404185  ...  0.488745  0.397427  0.549606  0.556771   \n",
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              "3  0.535148  0.226797  0.404185  ...  0.488745  0.397427  0.549606  0.556771   \n",
              "4  0.535148  0.226797  0.404185  ...  0.488745  0.397427  0.549606  0.556771   \n",
              "\n",
              "     L5b_2r  S1a_1c  S1a_1r  S1a_2c  S1a_2r   L  \n",
              "0  0.549606     NaN     NaN     NaN     NaN  L1  \n",
              "1  0.549606     NaN     NaN     NaN     NaN  L2  \n",
              "2  0.549606     NaN     NaN     NaN     NaN  L3  \n",
              "3  0.549606     NaN     NaN     NaN     NaN  L4  \n",
              "4  0.549606     NaN     NaN     NaN     NaN  L5  \n",
              "\n",
              "[5 rows x 46 columns]"
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              "      <td>0.401268</td>\n",
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              "      <td>t120.jpg_L3</td>\n",
              "      <td>0.401268</td>\n",
              "      <td>0.165478</td>\n",
              "      <td>0.535148</td>\n",
              "      <td>0.165478</td>\n",
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              "      <th>3</th>\n",
              "      <td>t120.jpg_L4</td>\n",
              "      <td>0.401268</td>\n",
              "      <td>0.165478</td>\n",
              "      <td>0.535148</td>\n",
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              "      <td>0.226797</td>\n",
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              "      <td>0.556771</td>\n",
              "      <td>0.549606</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>t120.jpg_L5</td>\n",
              "      <td>0.401268</td>\n",
              "      <td>0.165478</td>\n",
              "      <td>0.535148</td>\n",
              "      <td>0.165478</td>\n",
              "      <td>0.401268</td>\n",
              "      <td>0.226797</td>\n",
              "      <td>0.535148</td>\n",
              "      <td>0.226797</td>\n",
              "      <td>0.404185</td>\n",
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              "      <td>0.488745</td>\n",
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              "      <td>0.549606</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>L5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-038d0966-3e6f-481d-9138-30e29e78a196')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "      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-038d0966-3e6f-481d-9138-30e29e78a196 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-038d0966-3e6f-481d-9138-30e29e78a196');\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": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## test LA"
      ],
      "metadata": {
        "id": "CFMeGinzXqic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_LA = pd.read_csv('/content/drive/MyDrive/kaggle/test/test_LA.csv')\n",
        "test_LA_left = test_LA.copy().reindex(test_LA.index.repeat(5)).reset_index(drop=True).drop(columns=['Unnamed: 0'])\n",
        "\n",
        "# Create new column 'L' with values L1, L2, L3, L4, L5\n",
        "test_LA_left['L'] = np.resize(['L1', 'L2', 'L3', 'L4', 'L5'], len(test_LA_left))\n",
        "test_LA_finish = test_LA_left.copy()\n",
        "test_LA_finish['id'] = test_LA_finish['id'] + '_' +test_LA_finish['L']\n",
        "print(len(test_LA_finish))\n",
        "test_LA_finish.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "2NHxjQF5Xk-m",
        "outputId": "ac4476ec-8579-4f37-fff2-efa41bd923f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4475\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "          id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
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              "\n",
              "     L1b_2c    L1b_2r    L2a_1c  ...    L5a_2r   L5b_1c    L5b_1r    L5b_2c  \\\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>L1a_1c</th>\n",
              "      <th>L1a_1r</th>\n",
              "      <th>L1a_2c</th>\n",
              "      <th>L1a_2r</th>\n",
              "      <th>L1b_1c</th>\n",
              "      <th>L1b_1r</th>\n",
              "      <th>L1b_2c</th>\n",
              "      <th>L1b_2r</th>\n",
              "      <th>L2a_1c</th>\n",
              "      <th>...</th>\n",
              "      <th>L5a_2r</th>\n",
              "      <th>L5b_1c</th>\n",
              "      <th>L5b_1r</th>\n",
              "      <th>L5b_2c</th>\n",
              "      <th>L5b_2r</th>\n",
              "      <th>S1a_1c</th>\n",
              "      <th>S1a_1r</th>\n",
              "      <th>S1a_2c</th>\n",
              "      <th>S1a_2r</th>\n",
              "      <th>L</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>t0.jpg_L1</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.440128</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577823</td>\n",
              "      <td>0.35886</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.471924</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.372348</td>\n",
              "      <td>0.70469</td>\n",
              "      <td>0.485956</td>\n",
              "      <td>0.638903</td>\n",
              "      <td>L1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>t0.jpg_L2</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.440128</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577823</td>\n",
              "      <td>0.35886</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.471924</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.372348</td>\n",
              "      <td>0.70469</td>\n",
              "      <td>0.485956</td>\n",
              "      <td>0.638903</td>\n",
              "      <td>L2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>t0.jpg_L3</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.440128</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577823</td>\n",
              "      <td>0.35886</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.471924</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.372348</td>\n",
              "      <td>0.70469</td>\n",
              "      <td>0.485956</td>\n",
              "      <td>0.638903</td>\n",
              "      <td>L3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>t0.jpg_L4</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.440128</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577823</td>\n",
              "      <td>0.35886</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.471924</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.372348</td>\n",
              "      <td>0.70469</td>\n",
              "      <td>0.485956</td>\n",
              "      <td>0.638903</td>\n",
              "      <td>L4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>t0.jpg_L5</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.218853</td>\n",
              "      <td>0.502088</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.644152</td>\n",
              "      <td>0.321908</td>\n",
              "      <td>0.440128</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577823</td>\n",
              "      <td>0.35886</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.471924</td>\n",
              "      <td>0.663488</td>\n",
              "      <td>0.372348</td>\n",
              "      <td>0.70469</td>\n",
              "      <td>0.485956</td>\n",
              "      <td>0.638903</td>\n",
              "      <td>L5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train"
      ],
      "metadata": {
        "id": "5kUWifRIYONK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install autogluon.tabular[all]"
      ],
      "metadata": {
        "id": "ZrxumC7UYumP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train AP"
      ],
      "metadata": {
        "id": "ZwfBYvnke-Zp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from autogluon.tabular import TabularDataset, TabularPredictor"
      ],
      "metadata": {
        "id": "UhpuCVOjX7Cg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = 'class'\n",
        "print(\"Summary of class variable: \\n\", merged_df[train_AP_finish].describe())"
      ],
      "metadata": {
        "id": "TuKwLatNY1-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_AP_pre = test_AP_finish.drop(columns=['id'])\n",
        "train_AP_pre = train_AP_finish.drop(columns=['id'])\n"
      ],
      "metadata": {
        "id": "KGDH83wYt11F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictor = TabularPredictor(label='class').fit(train_data=train_AP_pre)\n",
        "predictions = predictor.predict(test_AP_pre)\n",
        "test_AP_finish['class'] = predictions\n",
        "test_AP_finish"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oqxXqC97i7Ce",
        "outputId": "3d44100d-2791-401b-b888-62b1c7f5d6b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20230617_162814/\"\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20230617_162814/\"\n",
            "AutoGluon Version:  0.8.0\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Sat Apr 29 09:15:28 UTC 2023\n",
            "Disk Space Avail:   138.11 GB / 179.07 GB (77.1%)\n",
            "Train Data Rows:    14271\n",
            "Train Data Columns: 45\n",
            "Label Column: class\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n",
            "\t3 unique label values:  ['0', '1', '2']\n",
            "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Train Data Class Count: 3\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    86958.93 MB\n",
            "\tTrain Data (Original)  Memory Usage: 5.87 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tUseless Original Features (Count: 4): ['S1a_1c', 'S1a_1r', 'S1a_2c', 'S1a_2r']\n",
            "\t\tThese features carry no predictive signal and should be manually investigated.\n",
            "\t\tThis is typically a feature which has the same value for all rows.\n",
            "\t\tThese features do not need to be present at inference time.\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])  : 40 | ['L1a_1c', 'L1a_1r', 'L1a_2c', 'L1a_2r', 'L1b_1c', ...]\n",
            "\t\t('object', []) :  1 | ['L']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', []) :  1 | ['L']\n",
            "\t\t('float', [])    : 40 | ['L1a_1c', 'L1a_1r', 'L1a_2c', 'L1a_2r', 'L1b_1c', ...]\n",
            "\t1.0s = Fit runtime\n",
            "\t41 features in original data used to generate 41 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 4.58 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 1.03s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Automatically generating train/validation split with holdout_frac=0.1, Train Rows: 12843, Val Rows: 1428\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': {},\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n",
            "\t'CAT': {},\n",
            "\t'XGB': {},\n",
            "\t'FASTAI': {},\n",
            "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n",
            "}\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t0.03s\t = Training   runtime\n",
            "\t0.3s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t0.03s\t = Training   runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI ...\n",
            "No improvement since epoch 3: early stopping\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t12.46s\t = Training   runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Fitting model: LightGBMXT ...\n",
            "\t0.9958\t = Validation score   (accuracy)\n",
            "\t3.13s\t = Training   runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: LightGBM ...\n",
            "\t0.9951\t = Validation score   (accuracy)\n",
            "\t2.97s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: RandomForestGini ...\n",
            "\t0.9888\t = Validation score   (accuracy)\n",
            "\t1.49s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr ...\n",
            "\t0.9895\t = Validation score   (accuracy)\n",
            "\t1.46s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: CatBoost ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t3.93s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini ...\n",
            "\t0.9937\t = Validation score   (accuracy)\n",
            "\t0.82s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t0.83s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: XGBoost ...\n",
            "\t0.9951\t = Validation score   (accuracy)\n",
            "\t2.7s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t6.55s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge ...\n",
            "\t0.9944\t = Validation score   (accuracy)\n",
            "\t5.42s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.9958\t = Validation score   (accuracy)\n",
            "\t0.87s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 45.1s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20230617_162814/\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
              "0    t120.jpg_L1  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "1    t120.jpg_L2  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "2    t120.jpg_L3  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "3    t120.jpg_L4  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "4    t120.jpg_L5  0.401268  0.165478  0.535148  0.165478  0.401268  0.226797   \n",
              "..           ...       ...       ...       ...       ...       ...       ...   \n",
              "520  t992.jpg_L1  0.284706  0.104945  0.422441  0.104945  0.284706  0.162138   \n",
              "521  t992.jpg_L2  0.284706  0.104945  0.422441  0.104945  0.284706  0.162138   \n",
              "522  t992.jpg_L3  0.284706  0.104945  0.422441  0.104945  0.284706  0.162138   \n",
              "523  t992.jpg_L4  0.284706  0.104945  0.422441  0.104945  0.284706  0.162138   \n",
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          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictor.feature_importance(train_AP_finish)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k2_8cI-FtbAE",
        "outputId": "04950382-7bb0-4760-9995-170a34e63146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "These features in provided data are not utilized by the predictor and will be ignored: ['id', 'S1a_1c', 'S1a_1r', 'S1a_2c', 'S1a_2r']\n",
            "Computing feature importance via permutation shuffling for 41 features using 5000 rows with 5 shuffle sets...\n",
            "\t37.12s\t= Expected runtime (7.42s per shuffle set)\n",
            "\t28.81s\t= Actual runtime (Completed 5 of 5 shuffle sets)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        importance    stddev   p_value  n  p99_high   p99_low\n",
              "L          0.00584  0.001108  0.000148  5  0.008122  0.003558\n",
              "L5b_2r     0.00032  0.000228  0.017460  5  0.000790 -0.000150\n",
              "L5b_2c     0.00020  0.000141  0.017055  5  0.000491 -0.000091\n",
              "L4a_1c     0.00016  0.000167  0.049650  5  0.000505 -0.000185\n",
              "L1b_2r     0.00016  0.000167  0.049650  5  0.000505 -0.000185\n",
              "L4a_2c     0.00016  0.000261  0.120991  5  0.000697 -0.000377\n",
              "L1b_2c     0.00016  0.000167  0.049650  5  0.000505 -0.000185\n",
              "L5a_2c     0.00012  0.000179  0.104000  5  0.000488 -0.000248\n",
              "L5a_1r     0.00012  0.000110  0.035242  5  0.000346 -0.000106\n",
              "L5b_1c     0.00008  0.000110  0.088904  5  0.000306 -0.000146\n",
              "L5a_1c     0.00008  0.000110  0.088904  5  0.000306 -0.000146\n",
              "L1a_1c     0.00008  0.000110  0.088904  5  0.000306 -0.000146\n",
              "L1a_2c     0.00008  0.000179  0.186950  5  0.000448 -0.000288\n",
              "L1b_1c     0.00008  0.000110  0.088904  5  0.000306 -0.000146\n",
              "L2a_1c     0.00008  0.000110  0.088904  5  0.000306 -0.000146\n",
              "L2a_2c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4a_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L1a_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L5b_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L5a_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L1b_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L2a_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4b_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4b_2c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4b_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4b_1c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3b_1c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L2b_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L4a_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L2a_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3b_2c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L1a_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3a_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3a_2c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3a_1r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3a_1c     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L2b_2r     0.00004  0.000089  0.186950  5  0.000224 -0.000144\n",
              "L3b_2r     0.00000  0.000000  0.500000  5  0.000000  0.000000\n",
              "L3b_1r     0.00000  0.000000  0.500000  5  0.000000  0.000000\n",
              "L2b_2c     0.00000  0.000000  0.500000  5  0.000000  0.000000\n",
              "L2b_1c     0.00000  0.000000  0.500000  5  0.000000  0.000000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>importance</th>\n",
              "      <th>stddev</th>\n",
              "      <th>p_value</th>\n",
              "      <th>n</th>\n",
              "      <th>p99_high</th>\n",
              "      <th>p99_low</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>0.00584</td>\n",
              "      <td>0.001108</td>\n",
              "      <td>0.000148</td>\n",
              "      <td>5</td>\n",
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              "      <th>L4a_2c</th>\n",
              "      <td>0.00016</td>\n",
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              "      <th>L1b_2c</th>\n",
              "      <td>0.00016</td>\n",
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              "      <td>5</td>\n",
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              "      <td>-0.000185</td>\n",
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              "      <th>L5a_2c</th>\n",
              "      <td>0.00012</td>\n",
              "      <td>0.000179</td>\n",
              "      <td>0.104000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000488</td>\n",
              "      <td>-0.000248</td>\n",
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              "    <tr>\n",
              "      <th>L5a_1r</th>\n",
              "      <td>0.00012</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.035242</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>-0.000106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L5b_1c</th>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.088904</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000306</td>\n",
              "      <td>-0.000146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L5a_1c</th>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.088904</td>\n",
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              "      <td>0.000306</td>\n",
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              "      <th>L1a_1c</th>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.000110</td>\n",
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              "      <td>0.000306</td>\n",
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              "      <th>L1a_2c</th>\n",
              "      <td>0.00008</td>\n",
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              "      <td>0.000448</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>L1b_1c</th>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.088904</td>\n",
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              "      <td>0.000306</td>\n",
              "      <td>-0.000146</td>\n",
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              "    <tr>\n",
              "      <th>L2a_1c</th>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.088904</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000306</td>\n",
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              "    <tr>\n",
              "      <th>L2a_2c</th>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.186950</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000224</td>\n",
              "      <td>-0.000144</td>\n",
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              "      <th>L4a_2r</th>\n",
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              "      <th>L5b_1r</th>\n",
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              "      <td>0.000224</td>\n",
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              "    <tr>\n",
              "      <th>L4b_2r</th>\n",
              "      <td>0.00004</td>\n",
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              "      <td>0.000224</td>\n",
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              "      <th>L4b_2c</th>\n",
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              "      <th>L3b_1c</th>\n",
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              "      <td>0.00004</td>\n",
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              "      <td>0.186950</td>\n",
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              "      <td>-0.000144</td>\n",
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              "    <tr>\n",
              "      <th>L1a_1r</th>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.000089</td>\n",
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              "      <td>5</td>\n",
              "      <td>0.000224</td>\n",
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              "    <tr>\n",
              "      <th>L3a_2r</th>\n",
              "      <td>0.00004</td>\n",
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              "      <td>-0.000144</td>\n",
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              "    <tr>\n",
              "      <th>L3a_2c</th>\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      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-b9d4296f-10f5-471a-abec-5ae07b3f7971 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-b9d4296f-10f5-471a-abec-5ae07b3f7971');\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": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_AP_finish[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "rfIs5Q0qsNpI",
        "outputId": "1ece3eb6-7216-497e-ca4f-d277c412dd55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
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              "1  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              "2  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              "3  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
              "4  0.jpg  0.475247  0.075482  0.610531  0.075482  0.475247  0.145667   \n",
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              "9  1.jpg  0.493474  0.366567  0.610131  0.366567  0.493474  0.431048   \n",
              "\n",
              "     L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r    L5b_2c    L5b_2r  \\\n",
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              "9  0.610131  0.431048  0.507229  ...  0.471789  0.734705  0.631581  0.734705   \n",
              "\n",
              "   S1a_1c  S1a_1r  S1a_2c  S1a_2r  class   L  \n",
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              "6     NaN     NaN     NaN     NaN      0  L2  \n",
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              "8     NaN     NaN     NaN     NaN      0  L4  \n",
              "9     NaN     NaN     NaN     NaN      0  L5  \n",
              "\n",
              "[10 rows x 47 columns]"
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              "      <td>0.475247</td>\n",
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              "      <td>0.610531</td>\n",
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              "      <td>0.075482</td>\n",
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              "      <td>0.610131</td>\n",
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              "      <td>0.493474</td>\n",
              "      <td>0.366567</td>\n",
              "      <td>0.610131</td>\n",
              "      <td>0.366567</td>\n",
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              "      <td>0.431048</td>\n",
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              "      <td>0.471789</td>\n",
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              "      <td>1.jpg</td>\n",
              "      <td>0.493474</td>\n",
              "      <td>0.366567</td>\n",
              "      <td>0.610131</td>\n",
              "      <td>0.366567</td>\n",
              "      <td>0.493474</td>\n",
              "      <td>0.431048</td>\n",
              "      <td>0.610131</td>\n",
              "      <td>0.431048</td>\n",
              "      <td>0.507229</td>\n",
              "      <td>...</td>\n",
              "      <td>0.471789</td>\n",
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              "      <td>0.631581</td>\n",
              "      <td>0.734705</td>\n",
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              "      <td>L4</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>0.493474</td>\n",
              "      <td>0.366567</td>\n",
              "      <td>0.610131</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>L5</td>\n",
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              "</table>\n",
              "<p>10 rows × 47 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6a2146c-11ef-44d4-a02e-bbd8fac692dc')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a6a2146c-11ef-44d4-a02e-bbd8fac692dc');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_AP_predict = test_AP_finish[['id','class']].copy()\n",
        "test_AP_predict"
      ],
      "metadata": {
        "id": "uEXBcKUOsPlQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "4f1d9e7d-973d-4b3e-b431-8063fcf75239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id class\n",
              "0    t120.jpg_L1     0\n",
              "1    t120.jpg_L2     0\n",
              "2    t120.jpg_L3     0\n",
              "3    t120.jpg_L4     0\n",
              "4    t120.jpg_L5     0\n",
              "..           ...   ...\n",
              "520  t992.jpg_L1     0\n",
              "521  t992.jpg_L2     0\n",
              "522  t992.jpg_L3     0\n",
              "523  t992.jpg_L4     0\n",
              "524  t992.jpg_L5     0\n",
              "\n",
              "[525 rows x 2 columns]"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>id</th>\n",
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              "</table>\n",
              "<p>525 rows × 2 columns</p>\n",
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              "      \n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-59d08f45-9a41-4764-9646-ad2f051cbdb7');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# train LA"
      ],
      "metadata": {
        "id": "khBWO0wjqvuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_LA_pre = train_LA_finish.drop(columns=['id'])\n",
        "train_LA_pre = train_LA_finish.drop(columns=['id'])"
      ],
      "metadata": {
        "id": "9JPp4trjyPtk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictor = TabularPredictor(label='class').fit(train_data=train_LA_pre)\n",
        "predictions = predictor.predict(test_LA_pre)\n",
        "test_LA_finish['class'] = predictions\n",
        "test_LA_finish"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yT6l5jYFqxgB",
        "outputId": "211480f6-578f-4422-da3b-43773e99d6e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20230617_162955/\"\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20230617_162955/\"\n",
            "AutoGluon Version:  0.8.0\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Sat Apr 29 09:15:28 UTC 2023\n",
            "Disk Space Avail:   138.07 GB / 179.07 GB (77.1%)\n",
            "Train Data Rows:    4655\n",
            "Train Data Columns: 45\n",
            "Label Column: class\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).\n",
            "\t3 unique label values:  ['0', '3', '4']\n",
            "\tIf 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Train Data Class Count: 3\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    86348.75 MB\n",
            "\tTrain Data (Original)  Memory Usage: 1.91 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])  : 44 | ['L1a_1c', 'L1a_1r', 'L1a_2c', 'L1a_2r', 'L1b_1c', ...]\n",
            "\t\t('object', []) :  1 | ['L']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', []) :  1 | ['L']\n",
            "\t\t('float', [])    : 44 | ['L1a_1c', 'L1a_1r', 'L1a_2c', 'L1a_2r', 'L1b_1c', ...]\n",
            "\t0.2s = Fit runtime\n",
            "\t45 features in original data used to generate 45 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 1.64 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.21s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Automatically generating train/validation split with holdout_frac=0.10741138560687433, Train Rows: 4155, Val Rows: 500\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': {},\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n",
            "\t'CAT': {},\n",
            "\t'XGB': {},\n",
            "\t'FASTAI': {},\n",
            "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n",
            "}\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif ...\n",
            "\t0.692\t = Validation score   (accuracy)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist ...\n",
            "\t0.782\t = Validation score   (accuracy)\n",
            "\t0.02s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI ...\n",
            "\t0.946\t = Validation score   (accuracy)\n",
            "\t4.73s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: LightGBMXT ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_set's multi_error: 0.12\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\t0.888\t = Validation score   (accuracy)\n",
            "\t5.87s\t = Training   runtime\n",
            "\t0.06s\t = Validation runtime\n",
            "Fitting model: LightGBM ...\n",
            "\t0.886\t = Validation score   (accuracy)\n",
            "\t3.08s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "Fitting model: RandomForestGini ...\n",
            "\t0.834\t = Validation score   (accuracy)\n",
            "\t1.03s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr ...\n",
            "\t0.836\t = Validation score   (accuracy)\n",
            "\t1.37s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: CatBoost ...\n",
            "\t0.882\t = Validation score   (accuracy)\n",
            "\t45.33s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini ...\n",
            "\t0.844\t = Validation score   (accuracy)\n",
            "\t0.74s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr ...\n",
            "\t0.84\t = Validation score   (accuracy)\n",
            "\t0.77s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: XGBoost ...\n",
            "\t0.894\t = Validation score   (accuracy)\n",
            "\t7.83s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch ...\n",
            "\t0.976\t = Validation score   (accuracy)\n",
            "\t21.44s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_set's multi_error: 0.11\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\t0.894\t = Validation score   (accuracy)\n",
            "\t17.33s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.986\t = Validation score   (accuracy)\n",
            "\t0.83s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 112.6s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20230617_162955/\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               id    L1a_1c    L1a_1r    L1a_2c    L1a_2r    L1b_1c    L1b_1r  \\\n",
              "0       t0.jpg_L1  0.502088  0.218853  0.644152  0.218853  0.502088  0.321908   \n",
              "1       t0.jpg_L2  0.502088  0.218853  0.644152  0.218853  0.502088  0.321908   \n",
              "2       t0.jpg_L3  0.502088  0.218853  0.644152  0.218853  0.502088  0.321908   \n",
              "3       t0.jpg_L4  0.502088  0.218853  0.644152  0.218853  0.502088  0.321908   \n",
              "4       t0.jpg_L5  0.502088  0.218853  0.644152  0.218853  0.502088  0.321908   \n",
              "...           ...       ...       ...       ...       ...       ...       ...   \n",
              "4470  t999.jpg_L1  0.506728  0.131119  0.666690  0.131119  0.506728  0.236162   \n",
              "4471  t999.jpg_L2  0.506728  0.131119  0.666690  0.131119  0.506728  0.236162   \n",
              "4472  t999.jpg_L3  0.506728  0.131119  0.666690  0.131119  0.506728  0.236162   \n",
              "4473  t999.jpg_L4  0.506728  0.131119  0.666690  0.131119  0.506728  0.236162   \n",
              "4474  t999.jpg_L5  0.506728  0.131119  0.666690  0.131119  0.506728  0.236162   \n",
              "\n",
              "        L1b_2c    L1b_2r    L2a_1c  ...    L5b_1c    L5b_1r    L5b_2c  \\\n",
              "0     0.644152  0.321908  0.440128  ...  0.358860  0.663488  0.471924   \n",
              "1     0.644152  0.321908  0.440128  ...  0.358860  0.663488  0.471924   \n",
              "2     0.644152  0.321908  0.440128  ...  0.358860  0.663488  0.471924   \n",
              "3     0.644152  0.321908  0.440128  ...  0.358860  0.663488  0.471924   \n",
              "4     0.644152  0.321908  0.440128  ...  0.358860  0.663488  0.471924   \n",
              "...        ...       ...       ...  ...       ...       ...       ...   \n",
              "4470  0.666690  0.236162  0.454731  ...  0.369909  0.634904  0.513259   \n",
              "4471  0.666690  0.236162  0.454731  ...  0.369909  0.634904  0.513259   \n",
              "4472  0.666690  0.236162  0.454731  ...  0.369909  0.634904  0.513259   \n",
              "4473  0.666690  0.236162  0.454731  ...  0.369909  0.634904  0.513259   \n",
              "4474  0.666690  0.236162  0.454731  ...  0.369909  0.634904  0.513259   \n",
              "\n",
              "        L5b_2r    S1a_1c    S1a_1r    S1a_2c    S1a_2r   L  class  \n",
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              "      <td>t0.jpg_L5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4470</th>\n",
              "      <td>t999.jpg_L1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4471</th>\n",
              "      <td>t999.jpg_L2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4472</th>\n",
              "      <td>t999.jpg_L3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4473</th>\n",
              "      <td>t999.jpg_L4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4474</th>\n",
              "      <td>t999.jpg_L5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4475 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bfd8e3b-15f8-4edc-88ed-8a0a53e0d690')\"\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-0bfd8e3b-15f8-4edc-88ed-8a0a53e0d690 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-0bfd8e3b-15f8-4edc-88ed-8a0a53e0d690');\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": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_submit = pd.concat([test_AP_predict, test_LA_predict])"
      ],
      "metadata": {
        "id": "-TRas9hGsql6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_submit[test_submit['class']=='1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "M1YQhJaMs3Lp",
        "outputId": "9e6e14b3-e715-47c4-c47d-d67a6d81b982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, class]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e2a4c0a-840c-4931-8c4b-aa8e5b78972d\">\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>id</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e2a4c0a-840c-4931-8c4b-aa8e5b78972d')\"\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-0e2a4c0a-840c-4931-8c4b-aa8e5b78972d 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-0e2a4c0a-840c-4931-8c4b-aa8e5b78972d');\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": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_submit.to_csv('submission_3_sent_thirftime.csv', index=False)"
      ],
      "metadata": {
        "id": "2qeL4zIVtDlh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_submit[test_submit['class']==2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "eQjL38bDof4K",
        "outputId": "569fe803-5d29-4729-fdca-5780bbfe4bd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, class]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16e67cc9-bc0f-4284-ae96-f37d56648627\">\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>id</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16e67cc9-bc0f-4284-ae96-f37d56648627')\"\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-16e67cc9-bc0f-4284-ae96-f37d56648627 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-16e67cc9-bc0f-4284-ae96-f37d56648627');\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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LGn1YWgroktu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}