Novartis_Data_Science_Challenge.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Novartis-Data_Science_Challenge.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM7OHDRdtAJIZwHADiV5i2P",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/MachineLearningHackathons/blob/master/Novartis_Data_Science_Challenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNoxjObp6Ct9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "4b5de38c-3209-42df-cb54-c6cbd4b18b3d"
      },
      "source": [
        "!wget https://he-s3.s3.amazonaws.com/media/hackathon/novartis-data-science-hiring-challenge/predict-if-the-server-will-be-hacked-a1889487/6a62a5b4967411ea.zip\n",
        "!unzip 6a62a5b4967411ea.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-06-13 09:01:27--  https://he-s3.s3.amazonaws.com/media/hackathon/novartis-data-science-hiring-challenge/predict-if-the-server-will-be-hacked-a1889487/6a62a5b4967411ea.zip\n",
            "Resolving he-s3.s3.amazonaws.com (he-s3.s3.amazonaws.com)... 52.219.32.216\n",
            "Connecting to he-s3.s3.amazonaws.com (he-s3.s3.amazonaws.com)|52.219.32.216|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 555520 (542K) [application/zip]\n",
            "Saving to: ‘6a62a5b4967411ea.zip’\n",
            "\n",
            "6a62a5b4967411ea.zi 100%[===================>] 542.50K   493KB/s    in 1.1s    \n",
            "\n",
            "2020-06-13 09:01:29 (493 KB/s) - ‘6a62a5b4967411ea.zip’ saved [555520/555520]\n",
            "\n",
            "Archive:  6a62a5b4967411ea.zip\n",
            "   creating: Dataset/\n",
            "  inflating: Dataset/Train.csv       \n",
            "  inflating: Dataset/sample_submission.csv  \n",
            "  inflating: Dataset/Test.csv        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIIStR4T8w7h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "outputId": "bb6d8438-996d-489d-adea-8c0b99fa3100"
      },
      "source": [
        "!pip install h2o\n",
        "import h2o\n",
        "h2o.init()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting h2o\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/25/b0/5a6fbc343043ab6d6062114991fa515b7a408fc1f5339e1c1601e5f3e46e/h2o-3.30.0.4.tar.gz (128.3MB)\n",
            "\u001b[K     |████████████████████████████████| 128.3MB 54kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from h2o) (2.23.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from h2o) (0.8.7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from h2o) (0.16.0)\n",
            "Collecting colorama>=0.3.8\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2020.4.5.1)\n",
            "Building wheels for collected packages: h2o\n",
            "  Building wheel for h2o (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for h2o: filename=h2o-3.30.0.4-py2.py3-none-any.whl size=128397038 sha256=b7d4a9abb7da5b6a7f61a4f249d5fcb0b29ee02b59fdff3b89e2807680487377\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/bd/cc/0a8c7dffc4772df0bbfae4c1c7a9d64525fcd3ada9694dd8c3\n",
            "Successfully built h2o\n",
            "Installing collected packages: colorama, h2o\n",
            "Successfully installed colorama-0.4.3 h2o-3.30.0.4\n",
            "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.7\" 2020-04-14; OpenJDK Runtime Environment (build 11.0.7+10-post-Ubuntu-2ubuntu218.04); OpenJDK 64-Bit Server VM (build 11.0.7+10-post-Ubuntu-2ubuntu218.04, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.6/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpp3cwxtlf\n",
            "  JVM stdout: /tmp/tmpp3cwxtlf/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpp3cwxtlf/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>03 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.30.0.4</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>11 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_o8v5fg</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>3.180 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>accepting new members, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>H2O_API_Extensions:</td>\n",
              "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.6.9 final</td></tr></table></div>"
            ],
            "text/plain": [
              "--------------------------  ------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         03 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.30.0.4\n",
              "H2O_cluster_version_age:    11 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_o8v5fg\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    3.180 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         accepting new members, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
              "Python_version:             3.6.9 final\n",
              "--------------------------  ------------------------------------------------------------------"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISCOkXYz6YyA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "20cfb4dc-d362-43f1-90ec-d0e9104a70da"
      },
      "source": [
        "df = h2o.import_file('/content/Dataset/Train.csv')\n",
        "dft = h2o.import_file('/content/Dataset/Test.csv')\n",
        "df['MULTIPLE_OFFENSE'] = df['MULTIPLE_OFFENSE'].asfactor()\n",
        "print(df.types)\n",
        "print(dft.types)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "{'INCIDENT_ID': 'string', 'DATE': 'time', 'X_1': 'int', 'X_2': 'int', 'X_3': 'int', 'X_4': 'int', 'X_5': 'int', 'X_6': 'int', 'X_7': 'int', 'X_8': 'int', 'X_9': 'int', 'X_10': 'int', 'X_11': 'int', 'X_12': 'int', 'X_13': 'int', 'X_14': 'int', 'X_15': 'int', 'MULTIPLE_OFFENSE': 'enum'}\n",
            "{'INCIDENT_ID': 'string', 'DATE': 'time', 'X_1': 'int', 'X_2': 'int', 'X_3': 'int', 'X_4': 'int', 'X_5': 'int', 'X_6': 'int', 'X_7': 'int', 'X_8': 'int', 'X_9': 'int', 'X_10': 'int', 'X_11': 'int', 'X_12': 'int', 'X_13': 'int', 'X_14': 'int', 'X_15': 'int'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2H-qd4tg9QoL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_val, df_test = df.split_frame(ratios=[.7,.15])\n",
        "y = \"MULTIPLE_OFFENSE\"\n",
        "x = df.columns\n",
        "x.remove(y)\n",
        "ignored_col_list = \"INCIDENT_ID\"\n",
        "#x.remove(ignored_col_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0RQEf1F-TYs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "78c15080-7366-467e-a422-b6f5b8ae5846"
      },
      "source": [
        "from h2o.automl import H2OAutoML\n",
        "aml = H2OAutoML(nfolds=15,\n",
        "                balance_classes=False,\n",
        "                max_runtime_secs=240,\n",
        "                max_models=0,\n",
        "                exclude_algos=['DeepLearning','GLM'],\n",
        "                verbosity = \"info\",\n",
        "                sort_metric='AUC',\n",
        "                stopping_metric='AUC')\n",
        "aml.train(x=x,y=y,training_frame=df_train,validation_frame=df_val)\n",
        "aml.leaderboard"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoML progress: |\n",
            "09:39:05.265: Project: AutoML_20200613_93905265\n",
            "09:39:05.265: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.\n",
            "09:39:05.265: Setting stopping tolerance adaptively based on the training frame: 0.007716315844839853\n",
            "09:39:05.265: Build control seed: -1 (random)\n",
            "09:39:05.265: training frame: Frame key: automl_training_py_538_sid_968a    cols: 18    rows: 16795  chunks: 8    size: 644143  checksum: -6945373353730415299\n",
            "09:39:05.266: validation frame: Frame key: py_539_sid_968a    cols: 18    rows: 3507  chunks: 8    size: 144880  checksum: 316101117747267471\n",
            "09:39:05.266: leaderboard frame: NULL\n",
            "09:39:05.266: blending frame: NULL\n",
            "09:39:05.266: response column: MULTIPLE_OFFENSE\n",
            "09:39:05.266: fold column: null\n",
            "09:39:05.266: weights column: null\n",
            "09:39:05.266: Loading execution steps: [{XGBoost : defaults}, {GLM : defaults}, {DRF : [def_1]}, {GBM : defaults}, {DeepLearning : defaults}, {DRF : [XRT]}, {XGBoost : grids}, {GBM : grids}, {DeepLearning : grids}, {GBM : [lr_annealing]}, {XGBoost : [lr_search]}, {StackedEnsemble : defaults}]\n",
            "09:39:05.266: Disabling Algo: GLM as requested by the user.\n",
            "09:39:05.267: Disabling Algo: DeepLearning as requested by the user.\n",
            "09:39:05.267: AutoML job created: 2020.06.13 09:39:05.265\n",
            "09:39:05.268: AutoML build started: 2020.06.13 09:39:05.268\n",
            "09:39:05.269: AutoML: starting XGBoost_1_AutoML_20200613_093905 model training\n",
            "\n",
            "███\n",
            "09:39:17.279: New leader: XGBoost_1_AutoML_20200613_093905, auc: 0.999362232238908\n",
            "09:39:17.279: AutoML: starting XGBoost_2_AutoML_20200613_093905 model training\n",
            "\n",
            "███\n",
            "09:39:28.284: AutoML: starting XGBoost_3_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:39:39.290: New leader: XGBoost_3_AutoML_20200613_093905, auc: 0.9994639391678898\n",
            "09:39:39.293: AutoML: starting DRF_1_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:39:48.299: AutoML: starting GBM_1_AutoML_20200613_093905 model training\n",
            "\n",
            "███\n",
            "09:39:59.305: New leader: GBM_1_AutoML_20200613_093905, auc: 0.9994664415485444\n",
            "09:39:59.306: AutoML: starting GBM_2_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:40:09.321: AutoML: starting GBM_3_AutoML_20200613_093905 model training\n",
            "\n",
            "███\n",
            "09:40:19.328: New leader: GBM_3_AutoML_20200613_093905, auc: 0.9994698770202907\n",
            "09:40:19.328: AutoML: starting GBM_4_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:40:30.335: AutoML: starting GBM_5_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:40:40.342: AutoML: starting XRT_1_AutoML_20200613_093905 model training\n",
            "\n",
            "██\n",
            "09:40:49.349: AutoML: starting XGBoost_grid__1_AutoML_20200613_093905 hyperparameter search\n",
            "\n",
            "█████\n",
            "09:41:06.985: New leader: XGBoost_grid__1_AutoML_20200613_093905_model_1, auc: 0.9998076559954412\n",
            "\n",
            "██████████████\n",
            "09:42:09.43: AutoML: starting GBM_grid__1_AutoML_20200613_093905 hyperparameter search\n",
            "\n",
            "██████████\n",
            "09:42:54.286: AutoML: starting StackedEnsemble_BestOfFamily_AutoML_20200613_093905 model training\n",
            "\n",
            "███| 100%\n",
            "\n",
            "09:43:11.309: AutoML: starting StackedEnsemble_AllModels_AutoML_20200613_093905 model training\n",
            "09:43:59.326: Actual modeling steps: [{XGBoost : [def_1 (10), def_2 (10), def_3 (10)]}, {DRF : [def_1 (10)]}, {GBM : [def_1 (10), def_2 (10), def_3 (10), def_4 (10), def_5 (10)]}, {DRF : [XRT (10)]}, {XGBoost : [grid_1 (100)]}, {GBM : [grid_1 (60)]}, {StackedEnsemble : [best (10), all (10)]}]\n",
            "09:43:59.326: AutoML build stopped: 2020.06.13 09:43:59.326\n",
            "09:43:59.326: AutoML build done: built 20 models\n",
            "09:43:59.326: AutoML duration:  4 min 54.058 sec\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">   logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200613_093905_model_1     </td><td style=\"text-align: right;\">0.999808</td><td style=\"text-align: right;\">0.00832956</td><td style=\"text-align: right;\">0.999991</td><td style=\"text-align: right;\">            0.00731009</td><td style=\"text-align: right;\">0.040723 </td><td style=\"text-align: right;\">0.00165837</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200613_093905_model_4     </td><td style=\"text-align: right;\">0.999783</td><td style=\"text-align: right;\">0.00740874</td><td style=\"text-align: right;\">0.99999 </td><td style=\"text-align: right;\">            0.0107472 </td><td style=\"text-align: right;\">0.0409587</td><td style=\"text-align: right;\">0.00167762</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_AutoML_20200613_093905   </td><td style=\"text-align: right;\">0.999749</td><td style=\"text-align: right;\">0.00697701</td><td style=\"text-align: right;\">0.999988</td><td style=\"text-align: right;\">            0.00935369</td><td style=\"text-align: right;\">0.0395261</td><td style=\"text-align: right;\">0.00156231</td></tr>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20200613_093905</td><td style=\"text-align: right;\">0.999697</td><td style=\"text-align: right;\">0.006919  </td><td style=\"text-align: right;\">0.999986</td><td style=\"text-align: right;\">            0.00724783</td><td style=\"text-align: right;\">0.0398018</td><td style=\"text-align: right;\">0.00158418</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200613_093905_model_5     </td><td style=\"text-align: right;\">0.999608</td><td style=\"text-align: right;\">0.0159777 </td><td style=\"text-align: right;\">0.999982</td><td style=\"text-align: right;\">            0.0194471 </td><td style=\"text-align: right;\">0.0507228</td><td style=\"text-align: right;\">0.0025728 </td></tr>\n",
              "<tr><td>GBM_3_AutoML_20200613_093905                       </td><td style=\"text-align: right;\">0.99947 </td><td style=\"text-align: right;\">0.034742  </td><td style=\"text-align: right;\">0.999975</td><td style=\"text-align: right;\">            0.02369   </td><td style=\"text-align: right;\">0.0765756</td><td style=\"text-align: right;\">0.00586383</td></tr>\n",
              "<tr><td>GBM_1_AutoML_20200613_093905                       </td><td style=\"text-align: right;\">0.999466</td><td style=\"text-align: right;\">0.0298763 </td><td style=\"text-align: right;\">0.999975</td><td style=\"text-align: right;\">            0.011     </td><td style=\"text-align: right;\">0.0726769</td><td style=\"text-align: right;\">0.00528193</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_20200613_093905                   </td><td style=\"text-align: right;\">0.999464</td><td style=\"text-align: right;\">0.0128156 </td><td style=\"text-align: right;\">0.999975</td><td style=\"text-align: right;\">            0.0148033 </td><td style=\"text-align: right;\">0.049382 </td><td style=\"text-align: right;\">0.00243859</td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_20200613_093905                   </td><td style=\"text-align: right;\">0.999362</td><td style=\"text-align: right;\">0.0191218 </td><td style=\"text-align: right;\">0.99997 </td><td style=\"text-align: right;\">            0.0259515 </td><td style=\"text-align: right;\">0.0584844</td><td style=\"text-align: right;\">0.00342042</td></tr>\n",
              "<tr><td>XGBoost_grid__1_AutoML_20200613_093905_model_2     </td><td style=\"text-align: right;\">0.999342</td><td style=\"text-align: right;\">0.00996801</td><td style=\"text-align: right;\">0.999969</td><td style=\"text-align: right;\">            0.020035  </td><td style=\"text-align: right;\">0.0465005</td><td style=\"text-align: right;\">0.00216229</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMQ6n5Zj__98",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8b031b6d-5a73-48a6-e0c2-30d03ad2ec2e"
      },
      "source": [
        "pred = aml.leader.predict(df_test)\n",
        "aml.leader.model_performance(df_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "xgboost prediction progress: |████████████████████████████████████████████| 100%\n",
            "\n",
            "ModelMetricsBinomial: xgboost\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 0.0021817980877095354\n",
            "RMSE: 0.04670972155461361\n",
            "LogLoss: 0.009876134060017376\n",
            "Mean Per-Class Error: 0.0034886525100048527\n",
            "AUC: 0.9997009959198572\n",
            "AUCPR: 0.9999836279415495\n",
            "Gini: 0.9994019918397143\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.7473490834236145: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.011</td>\n",
              "      <td>(2.0/182.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3368.0</td>\n",
              "      <td>0.0012</td>\n",
              "      <td>(4.0/3372.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>184.0</td>\n",
              "      <td>3370.0</td>\n",
              "      <td>0.0017</td>\n",
              "      <td>(6.0/3554.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              0       1   Error           Rate\n",
              "0      0  180.0     2.0   0.011    (2.0/182.0)\n",
              "1      1    4.0  3368.0  0.0012   (4.0/3372.0)\n",
              "2  Total  184.0  3370.0  0.0017   (6.0/3554.0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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>metric</th>\n",
              "      <th>threshold</th>\n",
              "      <th>value</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.747349</td>\n",
              "      <td>0.999110</td>\n",
              "      <td>246.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.523497</td>\n",
              "      <td>0.999170</td>\n",
              "      <td>252.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.790477</td>\n",
              "      <td>0.999466</td>\n",
              "      <td>244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.790477</td>\n",
              "      <td>0.998312</td>\n",
              "      <td>244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.169197</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.790477</td>\n",
              "      <td>0.982870</td>\n",
              "      <td>244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.915129</td>\n",
              "      <td>0.994505</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.790477</td>\n",
              "      <td>0.996511</td>\n",
              "      <td>244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>3198.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.169197</td>\n",
              "      <td>3372.000000</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>0.948399</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.169197</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         metric  threshold        value    idx\n",
              "0                        max f1   0.747349     0.999110  246.0\n",
              "1                        max f2   0.523497     0.999170  252.0\n",
              "2                  max f0point5   0.790477     0.999466  244.0\n",
              "3                  max accuracy   0.790477     0.998312  244.0\n",
              "4                 max precision   0.999877     1.000000    0.0\n",
              "5                    max recall   0.169197     1.000000  272.0\n",
              "6               max specificity   0.999877     1.000000    0.0\n",
              "7              max absolute_mcc   0.790477     0.982870  244.0\n",
              "8    max min_per_class_accuracy   0.915129     0.994505  231.0\n",
              "9   max mean_per_class_accuracy   0.790477     0.996511  244.0\n",
              "10                      max tns   0.999877   182.000000    0.0\n",
              "11                      max fns   0.999877  3198.000000    0.0\n",
              "12                      max fps   0.000159   182.000000  399.0\n",
              "13                      max tps   0.169197  3372.000000  272.0\n",
              "14                      max tnr   0.999877     1.000000    0.0\n",
              "15                      max fnr   0.999877     0.948399    0.0\n",
              "16                      max fpr   0.000159     1.000000  399.0\n",
              "17                      max tpr   0.169197     1.000000  272.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 94.88 %, avg score: 95.06 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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></th>\n",
              "      <th>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>0.019696</td>\n",
              "      <td>0.999878</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999879</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999879</td>\n",
              "      <td>0.020759</td>\n",
              "      <td>0.020759</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2</td>\n",
              "      <td>0.036016</td>\n",
              "      <td>0.999878</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999878</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999878</td>\n",
              "      <td>0.017200</td>\n",
              "      <td>0.037960</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>3</td>\n",
              "      <td>0.042206</td>\n",
              "      <td>0.999875</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999875</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999878</td>\n",
              "      <td>0.006524</td>\n",
              "      <td>0.044484</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>4</td>\n",
              "      <td>0.050366</td>\n",
              "      <td>0.999861</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999871</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999877</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.053084</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>5</td>\n",
              "      <td>0.102983</td>\n",
              "      <td>0.999853</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999857</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999867</td>\n",
              "      <td>0.055457</td>\n",
              "      <td>0.108541</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>6</td>\n",
              "      <td>0.154474</td>\n",
              "      <td>0.999845</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999849</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999861</td>\n",
              "      <td>0.054270</td>\n",
              "      <td>0.162811</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>7</td>\n",
              "      <td>0.205121</td>\n",
              "      <td>0.999845</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999845</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999857</td>\n",
              "      <td>0.053381</td>\n",
              "      <td>0.216192</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>8</td>\n",
              "      <td>0.300506</td>\n",
              "      <td>0.999832</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999842</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999852</td>\n",
              "      <td>0.100534</td>\n",
              "      <td>0.316726</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>9</td>\n",
              "      <td>0.400113</td>\n",
              "      <td>0.999794</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999814</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999843</td>\n",
              "      <td>0.104982</td>\n",
              "      <td>0.421708</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>10</td>\n",
              "      <td>0.502251</td>\n",
              "      <td>0.999770</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999786</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999831</td>\n",
              "      <td>0.107651</td>\n",
              "      <td>0.529359</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td></td>\n",
              "      <td>11</td>\n",
              "      <td>0.603264</td>\n",
              "      <td>0.999710</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999736</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999815</td>\n",
              "      <td>0.106465</td>\n",
              "      <td>0.635824</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>0.702026</td>\n",
              "      <td>0.999576</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999643</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999791</td>\n",
              "      <td>0.104093</td>\n",
              "      <td>0.739917</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td></td>\n",
              "      <td>13</td>\n",
              "      <td>0.801351</td>\n",
              "      <td>0.999312</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999482</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999753</td>\n",
              "      <td>0.104686</td>\n",
              "      <td>0.844603</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td></td>\n",
              "      <td>14</td>\n",
              "      <td>0.899831</td>\n",
              "      <td>0.992010</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.053974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996523</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.999399</td>\n",
              "      <td>0.103796</td>\n",
              "      <td>0.948399</td>\n",
              "      <td>5.397390</td>\n",
              "      <td>5.39739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td></td>\n",
              "      <td>15</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.515145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.488764</td>\n",
              "      <td>0.512669</td>\n",
              "      <td>0.94879</td>\n",
              "      <td>0.950644</td>\n",
              "      <td>0.051601</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-48.485545</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      group  ...       gain  cumulative_gain\n",
              "0         1  ...   5.397390          5.39739\n",
              "1         2  ...   5.397390          5.39739\n",
              "2         3  ...   5.397390          5.39739\n",
              "3         4  ...   5.397390          5.39739\n",
              "4         5  ...   5.397390          5.39739\n",
              "5         6  ...   5.397390          5.39739\n",
              "6         7  ...   5.397390          5.39739\n",
              "7         8  ...   5.397390          5.39739\n",
              "8         9  ...   5.397390          5.39739\n",
              "9        10  ...   5.397390          5.39739\n",
              "10       11  ...   5.397390          5.39739\n",
              "11       12  ...   5.397390          5.39739\n",
              "12       13  ...   5.397390          5.39739\n",
              "13       14  ...   5.397390          5.39739\n",
              "14       15  ... -48.485545          0.00000\n",
              "\n",
              "[15 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cawDQ1O1__6u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "8ccb658d-6cc9-4165-f923-033996306c44"
      },
      "source": [
        "dft['predict'] = aml.leader.predict(dft)\n",
        "h2o.download_csv(dft, \"dft.csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "xgboost prediction progress: |████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "H2OResponseError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mH2OResponseError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-91a3ba2aa433>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdft\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predict'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdft\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mh2o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dft.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/h2o.py\u001b[0m in \u001b[0;36mdownload_csv\u001b[0;34m(data, filename)\u001b[0m\n\u001b[1;32m   1334\u001b[0m     \u001b[0massert_is_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mH2OFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m     \u001b[0massert_is_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GET /3/DownloadDataset?frame_id=%s&hex_string=false\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/frame.py\u001b[0m in \u001b[0;36mframe_id\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m         \"\"\"\n\u001b[0;32m--> 383\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mframe_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/frame.py\u001b[0m in \u001b[0;36m_frame\u001b[0;34m(self, rows, rows_offset, cols, cols_offset, fill_cache)\u001b[0m\n\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 700\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eager_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    701\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfill_cache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrows_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols_offset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/expr.py\u001b[0m in \u001b[0;36m_eager_frame\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_empty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m  \u001b[0;31m# Data already computed under ID, but not cached locally\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eval_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'frame'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_eager_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# returns a scalar (or a list of scalars)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/expr.py\u001b[0m in \u001b[0;36m_eval_driver\u001b[0;34m(self, top)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \"\"\"\n\u001b[1;32m    114\u001b[0m         \u001b[0mexec_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ast_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExprNode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrapids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'scalar'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scalar'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/expr.py\u001b[0m in \u001b[0;36mrapids\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mreturns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mJSON\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mas\u001b[0m \u001b[0ma\u001b[0m \u001b[0mpython\u001b[0m \u001b[0mdictionary\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mRapids\u001b[0m \u001b[0mexecution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m         \"\"\"\n\u001b[0;32m--> 259\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mh2o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"POST /99/Rapids\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"ast\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"session_id\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh2o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession_id\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/h2o.py\u001b[0m in \u001b[0;36mapi\u001b[0;34m(endpoint, data, json, filename, save_to)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;31m# type checks are performed in H2OConnection class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0m_check_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mh2oconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/backend/connection.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, endpoint, data, json, filename, save_to)\u001b[0m\n\u001b[1;32m    476\u001b[0m                 \u001b[0msave_to\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_end_transaction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 478\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConnectionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h2o/backend/connection.py\u001b[0m in \u001b[0;36m_process_response\u001b[0;34m(response, save_to)\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;31m# Client errors (400 = \"Bad Request\", 404 = \"Not Found\", 412 = \"Precondition Failed\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstatus_code\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m404\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m412\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mH2OErrorV3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mH2OModelBuilderErrorV3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mH2OResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0;31m# Server errors (notably 500 = \"Server Error\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mH2OResponseError\u001b[0m: Server error java.lang.IllegalArgumentException:\n  Error: Can only append one column\n  Request: POST /99/Rapids\n    data: {'ast': \"(tmp= py_1080_sid_968a (append Test4.hex transformation_8693_XGBoost_grid__1_AutoML_20200613_093905_model_1_on_Test4.hex 'predict'))\", 'session_id': '_sid_968a'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQMnQ_Im__2Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGdg5Tt7__0S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwLamvRn__xG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAc8-fHX__uz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApRJc_me__sf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZRv2YVS-mdy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "8f2cd32f-d3f4-494a-83c7-77fa1a7399eb"
      },
      "source": [
        "import pandas as pd\n",
        "sub = pd.read_csv('/content/Dataset/sample_submission.csv')\n",
        "sub.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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>INCIDENT_ID</th>\n",
              "      <th>MULTIPLE_OFFENSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CR_195453</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CR_103520</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>.</td>\n",
              "      <td>.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>.</td>\n",
              "      <td>.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CR_196089</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CR_112195</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CR_149832</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  INCIDENT_ID MULTIPLE_OFFENSE\n",
              "0   CR_195453                1\n",
              "1   CR_103520                0\n",
              "2           .                .\n",
              "3           .                .\n",
              "4   CR_196089                1\n",
              "5   CR_112195                0\n",
              "6   CR_149832                1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmUZ76kE_3qD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}