AutoGloun.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM3dLxFB0Z6VGB2lpZq3w7U",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nataliyah123/ibia-Ersilia/blob/main/AutoGloun.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic Autogluon Example "
      ],
      "metadata": {
        "id": "RcoPEr2LdLpA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2mUQmJ_ErN4U"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "!pip install rdkit-pypi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from rdkit import Chem\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "MeLDTv3GrThF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smilescsv = pd.read_csv('dilismiles.csv', names = ['col_smiles'])"
      ],
      "metadata": {
        "id": "x2UH59bLrdGK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('dili_padel_2d.csv')\n",
        "dataframe = pd.DataFrame(data)\n",
        "desc = pd.DataFrame(dataframe.iloc[:, 3:])"
      ],
      "metadata": {
        "id": "lQd-n3l-rlEP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1ec2d0f-1506-4388-fc0f-50c168ee21d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (423,424,425,426,427,428,429,430,431,477,478,479,480,481,482,483,484,485,540,541,542,543,544,545,546,547,548,603,604,605,606,607,608,609,610,611,1012,1013,1050,1052,1074,1083,1084,1121,1123,1145,1217,1218,1219,1220,1222,1223,1224,1226,1227,1229) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install Autogluon"
      ],
      "metadata": {
        "id": "xJK4pC7GdX_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U pip\n",
        "# Upgrade setuptools to be compatible with namespace packages\n",
        "!pip install -U setuptools wheel\n",
        "!pip install -U \"mxnet<2.0.0\"\n",
        "# Install autogluon (Tutorial based on autogluon==0.1.0)\n",
        "!pip install autogluon\n",
        "# Upgrade ipykernel (Necessary for Colab)\n",
        "!pip install -U ipykernel\n",
        "# !pip install autogluon==0.0.15 autogluon.tabular \"mxnet<2.0.0\""
      ],
      "metadata": {
        "id": "nfzec1yqrrBY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "917165f0-814b-4035-82c3-f6b3c93ae987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting ordered-set\n",
            "  Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
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            "Building wheels for collected packages: fairscale, antlr4-python3-runtime, seqeval\n",
            "  Building wheel for fairscale (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fairscale: filename=fairscale-0.4.6-py3-none-any.whl size=307224 sha256=6dd515b63a94e43e0c08e117d4260d33707175a97dd454da2d4c32ba0bc8b47a\n",
            "  Stored in directory: /root/.cache/pip/wheels/60/e8/f1/4f2cc869823c35e834c6cee0552a0605c2bdc89f7da81f1a1d\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141211 sha256=d46be513f34560b8248f7aed97cd22013b56ed482fc3a13ee8e2bea2dd653d98\n",
            "  Stored in directory: /root/.cache/pip/wheels/34/d7/fe/a833ceccaee881c6f8cd49985ee4285bf94c5cf2c66ea5db68\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16164 sha256=9846f44044c91e25d9826df06861d312c381e9d1c52ce8914fc1b21f76aed6f5\n",
            "  Stored in directory: /root/.cache/pip/wheels/e3/30/9b/6b670dac34775f2b7cc4e9b172202e81fbb4f9cdb103c1ca66\n",
            "Successfully built fairscale antlr4-python3-runtime seqeval\n",
            "Installing collected packages: typish, tokenizers, sentencepiece, py4j, distlib, commonmark, antlr4-python3-runtime, yacs, xxhash, virtualenv, urllib3, torch, tensorboardX, smart-open, rich, pyDeprecate, psutil, portalocker, Pillow, ordered-set, omegaconf, nptyping, multiprocess, jmespath, grpcio, deprecated, colorama, autocfg, xgboost, torchmetrics, hyperopt, fairscale, dask, botocore, accelerate, torchvision, torchtext, statsmodels, seqeval, scikit-image, s3transfer, responses, ray, nlpaug, model-index, lightgbm, huggingface-hub, gluonts, gluoncv, distributed, catboost, transformers, timm, sktime, pytorch-metric-learning, pmdarima, openmim, datasets, boto3, albumentations, tbats, evaluate, autogluon.common, pytorch-lightning, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.vision, autogluon.timeseries, autogluon.text, autogluon\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.13.0+cu116\n",
            "    Uninstalling torch-1.13.0+cu116:\n",
            "      Successfully uninstalled torch-1.13.0+cu116\n",
            "  Attempting uninstall: smart-open\n",
            "    Found existing installation: smart-open 6.3.0\n",
            "    Uninstalling smart-open-6.3.0:\n",
            "      Successfully uninstalled smart-open-6.3.0\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
            "  Attempting uninstall: grpcio\n",
            "    Found existing installation: grpcio 1.51.1\n",
            "    Uninstalling grpcio-1.51.1:\n",
            "      Successfully uninstalled grpcio-1.51.1\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "  Attempting uninstall: hyperopt\n",
            "    Found existing installation: hyperopt 0.1.2\n",
            "    Uninstalling hyperopt-0.1.2:\n",
            "      Successfully uninstalled hyperopt-0.1.2\n",
            "  Attempting uninstall: dask\n",
            "    Found existing installation: dask 2022.2.1\n",
            "    Uninstalling dask-2022.2.1:\n",
            "      Successfully uninstalled dask-2022.2.1\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.14.0+cu116\n",
            "    Uninstalling torchvision-0.14.0+cu116:\n",
            "      Successfully uninstalled torchvision-0.14.0+cu116\n",
            "  Attempting uninstall: torchtext\n",
            "    Found existing installation: torchtext 0.14.0\n",
            "    Uninstalling torchtext-0.14.0:\n",
            "      Successfully uninstalled torchtext-0.14.0\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.12.2\n",
            "    Uninstalling statsmodels-0.12.2:\n",
            "      Successfully uninstalled statsmodels-0.12.2\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.18.3\n",
            "    Uninstalling scikit-image-0.18.3:\n",
            "      Successfully uninstalled scikit-image-0.18.3\n",
            "  Attempting uninstall: lightgbm\n",
            "    Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Attempting uninstall: distributed\n",
            "    Found existing installation: distributed 2022.2.1\n",
            "    Uninstalling distributed-2022.2.1:\n",
            "      Successfully uninstalled distributed-2022.2.1\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 1.2.1\n",
            "    Uninstalling albumentations-1.2.1:\n",
            "      Successfully uninstalled albumentations-1.2.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 0.13.0+cu116 requires torch==1.13.0, but you have torch 1.12.1 which is incompatible.\n",
            "grpcio-status 1.48.2 requires grpcio>=1.48.2, but you have grpcio 1.43.0 which is incompatible.\n",
            "google-cloud-bigquery 3.3.6 requires grpcio<2.0dev,>=1.47.0, but you have grpcio 1.43.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Pillow-9.3.0 accelerate-0.13.2 albumentations-1.1.0 antlr4-python3-runtime-4.8 autocfg-0.0.8 autogluon-0.6.1 autogluon.common-0.6.1 autogluon.core-0.6.1 autogluon.features-0.6.1 autogluon.multimodal-0.6.1 autogluon.tabular-0.6.1 autogluon.text-0.6.1 autogluon.timeseries-0.6.1 autogluon.vision-0.6.1 boto3-1.26.36 botocore-1.29.36 catboost-1.1.1 colorama-0.4.6 commonmark-0.9.1 dask-2021.11.2 datasets-2.8.0 deprecated-1.2.13 distlib-0.3.6 distributed-2021.11.2 evaluate-0.3.0 fairscale-0.4.6 gluoncv-0.10.5.post0 gluonts-0.11.6 grpcio-1.43.0 huggingface-hub-0.11.1 hyperopt-0.2.7 jmespath-1.0.1 lightgbm-3.3.3 model-index-0.1.11 multiprocess-0.70.14 nlpaug-1.1.10 nptyping-1.4.4 omegaconf-2.1.2 openmim-0.2.1 ordered-set-4.1.0 pmdarima-1.8.5 portalocker-2.6.0 psutil-5.8.0 py4j-0.10.9.7 pyDeprecate-0.3.2 pytorch-lightning-1.7.7 pytorch-metric-learning-1.3.2 ray-2.0.1 responses-0.18.0 rich-12.6.0 s3transfer-0.6.0 scikit-image-0.19.3 sentencepiece-0.1.97 seqeval-1.2.2 sktime-0.13.4 smart-open-5.2.1 statsmodels-0.13.5 tbats-1.1.2 tensorboardX-2.5.1 timm-0.6.12 tokenizers-0.13.2 torch-1.12.1 torchmetrics-0.8.2 torchtext-0.13.1 torchvision-0.13.1 transformers-4.23.1 typish-1.9.3 urllib3-1.25.11 virtualenv-20.17.1 xgboost-1.7.2 xxhash-3.1.0 yacs-0.1.8\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting ipykernel\n",
            "  Downloading ipykernel-6.19.4-py3-none-any.whl (145 kB)\n",
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            "\u001b[?25hRequirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.8/dist-packages (from ipykernel) (6.1.12)\n",
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            "  Downloading tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (423 kB)\n",
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            "Installing collected packages: pure-eval, executing, tornado, prompt-toolkit, nest-asyncio, matplotlib-inline, jedi, comm, asttokens, stack-data, ipython, ipykernel\n",
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            "    Found existing installation: tornado 6.0.4\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed asttokens-2.2.1 comm-0.1.2 executing-1.2.0 ipykernel-6.19.4 ipython-8.7.0 jedi-0.18.2 matplotlib-inline-0.1.6 nest-asyncio-1.5.6 prompt-toolkit-3.0.36 pure-eval-0.2.2 stack-data-0.6.2 tornado-6.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from autogluon.tabular import TabularDataset, TabularPredictor"
      ],
      "metadata": {
        "id": "5pKJiRp3r-zv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = TabularDataset('dili_padel_2d.csv')\n",
        "print(X.head(), X.shape)"
      ],
      "metadata": {
        "id": "AJOYGQsqsDhg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89adf453-4f8f-449a-d1c1-82a891d705cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               ID         CommonName  Outcome  nAcid  naAromAtom  nAromBond  \\\n",
            "0  AUTOGEN_dili_1    Aurothioglucose        1      0           0          0   \n",
            "1  AUTOGEN_dili_2  DemecariumBromide        1      0          12         12   \n",
            "2  AUTOGEN_dili_3   Dextromethorphan        0      0           6          6   \n",
            "3  AUTOGEN_dili_4         Ergotamine        0      0          15         16   \n",
            "4  AUTOGEN_dili_5     Beclomethasone        0      0           0          0   \n",
            "\n",
            "   nB  nN  nO  nS  ...       AMW      WTPT1     WTPT2      WTPT3      WTPT4  \\\n",
            "0   0   0   5   1  ...  8.479683  23.078977  1.923248  15.422349  12.919448   \n",
            "1   0   4   4   0  ...  6.047814  78.841924  1.971048  24.051089  11.127775   \n",
            "2   0   1   1   0  ...  6.026525  42.041518  2.102076   6.134966   2.806398   \n",
            "3   0   5   5   0  ...  7.452100  90.264234  2.099168  30.004012  13.475249   \n",
            "4   0   0   5   0  ...  7.160883  56.562478  2.020089  15.115392  12.515759   \n",
            "\n",
            "       WTPT5  WPATH  WPOL  XLogP  Zagreb  \n",
            "0   0.000000    182    19 -1.807      58  \n",
            "1  12.923314   8054    55  7.024     194  \n",
            "2   3.328569    682    41  4.207     116  \n",
            "3  16.528762   6191    88  3.848     258  \n",
            "4   0.000000   1670    70  1.275     168  \n",
            "\n",
            "[5 rows x 1447 columns] (588, 1447)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataframe['Outcome']\n",
        "print(type(y),y.shape)"
      ],
      "metadata": {
        "id": "zrXAjYtBsLdR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c70f1732-3029-4c21-a63d-1f09337269ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'> (588,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#deleting incomplete cases rows\n",
        "delrow = [\"GalliumNitrate Hydrate\",\"trichloroethylene\", \"Bromoethanamine\", \"sodiumbicarbonate\", \"carbontetrachloride\", \"Chloroform\", \"cadmiumchloride\", \"thioacetamide\", \"Probucol\",\"dichloroethylene\", \"Hydrazine\", \"nitrosamine\"]\n",
        "print(X.iloc[64]['CommonName'], X.shape)\n",
        "fory = []\n",
        "ind = X.index\n",
        "print(ind)\n",
        "for i in delrow:  \n",
        "  # print(X[X['CommonName']==i].any)  \n",
        "  if(X[X['CommonName']==i].any):     \n",
        "    indexname = X[X['CommonName']==i].index\n",
        "    what = ind[indexname].tolist()\n",
        "    fory.append(what[0])    \n",
        "    X.drop(indexname, inplace=True)\n",
        "    y.drop(indexname, inplace=True)\n",
        "\n",
        "print(X.shape, len(delrow),fory, y.shape)"
      ],
      "metadata": {
        "id": "ZGqNIBwZsQJv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "446da189-1e23-4367-8d54-c211ecfe0cac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GalliumNitrate Hydrate (588, 1447)\n",
            "RangeIndex(start=0, stop=588, step=1)\n",
            "(576, 1447) 12 [64, 84, 85, 94, 105, 106, 296, 301, 313, 435, 554, 555] (576,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# deleting columns with incomplete case\n",
        "X.drop(['BCUTw1l',\"BCUTw1h\",\"BCUTc1l\",\"BCUTc1h\",\"BCUTp1l\",\"BCUTp1h\",\"nHdsCH\",\"ndsCH\",\"nsssCH\",\"maxHdsCH\",\"maxsssCH\",\"maxdsCH\",\"SHdsCH\",\"SdsCH\",\"SsssCH\",\"minHdsCH\",\"minsssCH\",\"nHdsCH\",\"nsCH3\",\"nssCH2\",\"ndsCH\",\"nsssCH\"], axis=1,inplace=True)\n",
        "X.shape"
      ],
      "metadata": {
        "id": "G59VbEyIsb8v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff859643-10a7-4e4e-d09b-049e2421587f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(576, 1428)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.20)"
      ],
      "metadata": {
        "id": "vrLb7a1dszZ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = 'Outcome'"
      ],
      "metadata": {
        "id": "Cq5njrsSs4Ym"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit_args = {}\n",
        "fit_args['time_limit'] = 1200\n",
        "predictor = TabularPredictor(label=label).fit(x_train,presets=\"best_quality\", **fit_args)\n",
        "performance = predictor.evaluate(x_test)"
      ],
      "metadata": {
        "id": "COSZxxxOs8t2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56565254-99b4-42f8-bc90-f053c00cc0df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20221223_052036/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=5, num_bag_sets=20\n",
            "Beginning AutoGluon training ... Time limit = 1200s\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20221223_052036/\"\n",
            "AutoGluon Version:  0.6.1\n",
            "Python Version:     3.8.16\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Fri Aug 26 08:44:51 UTC 2022\n",
            "Train Data Rows:    460\n",
            "Train Data Columns: 1427\n",
            "Label Column: Outcome\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [0, 1]\n",
            "\tIf 'binary' 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",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11549.76 MB\n",
            "\tTrain Data (Original)  Memory Usage: 13.55 MB (0.1% 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",
            "\t\t\tNote: Converting 45 features to boolean dtype as they only contain 2 unique values.\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",
            "\tUseless Original Features (Count: 210): ['nB', 'nBondsQ', 'nHsssNHp', 'nHmisc', 'nsLi', 'nssBe', 'nssssBem', 'nsBH2', 'nssBH', 'nsssB', 'nssssBm', 'nsssNHp', 'nddsN', 'naOm', 'nsSiH3', 'nssSiH2', 'nsssSiH', 'nssssSi', 'nsPH2', 'nssPH', 'nsssP', 'nddsP', 'nsssssP', 'nssssssS', 'nSm', 'nsGeH3', 'nssGeH2', 'nsssGeH', 'nssssGe', 'nsAsH2', 'nssAsH', 'nsssAs', 'ndsssAs', 'nddsAs', 'nsssssAs', 'nsSeH', 'ndSe', 'nssSe', 'naaSe', 'ndssSe', 'nssssssSe', 'nddssSe', 'nsSnH3', 'nssSnH2', 'nsssSnH', 'nssssSn', 'nsPbH3', 'nssPbH2', 'nsssPbH', 'nssssPb', 'SHsssNHp', 'SHmisc', 'SsLi', 'SssBe', 'SssssBem', 'SsBH2', 'SssBH', 'SsssB', 'SssssBm', 'SsssNHp', 'SddsN', 'SaOm', 'SsSiH3', 'SssSiH2', 'SsssSiH', 'SssssSi', 'SsPH2', 'SssPH', 'SsssP', 'SddsP', 'SsssssP', 'SssssssS', 'SSm', 'SsGeH3', 'SssGeH2', 'SsssGeH', 'SssssGe', 'SsAsH2', 'SssAsH', 'SsssAs', 'SdsssAs', 'SddsAs', 'SsssssAs', 'SsSeH', 'SdSe', 'SssSe', 'SaaSe', 'SdssSe', 'SssssssSe', 'SddssSe', 'SsSnH3', 'SssSnH2', 'SsssSnH', 'SssssSn', 'SsPbH3', 'SssPbH2', 'SsssPbH', 'SssssPb', 'minHsssNHp', 'minHmisc', 'minsLi', 'minssBe', 'minssssBem', 'minsBH2', 'minssBH', 'minsssB', 'minssssBm', 'minsssNHp', 'minddsN', 'minaOm', 'minsSiH3', 'minssSiH2', 'minsssSiH', 'minssssSi', 'minsPH2', 'minssPH', 'minsssP', 'minddsP', 'minsssssP', 'minssssssS', 'minSm', 'minsGeH3', 'minssGeH2', 'minsssGeH', 'minssssGe', 'minsAsH2', 'minssAsH', 'minsssAs', 'mindsssAs', 'minddsAs', 'minsssssAs', 'minsSeH', 'mindSe', 'minssSe', 'minaaSe', 'mindssSe', 'minssssssSe', 'minddssSe', 'minsSnH3', 'minssSnH2', 'minsssSnH', 'minssssSn', 'minsPbH3', 'minssPbH2', 'minsssPbH', 'minssssPb', 'maxHsssNHp', 'maxHmisc', 'maxsLi', 'maxssBe', 'maxssssBem', 'maxsBH2', 'maxssBH', 'maxsssB', 'maxssssBm', 'maxsssNHp', 'maxddsN', 'maxaOm', 'maxsSiH3', 'maxssSiH2', 'maxsssSiH', 'maxssssSi', 'maxsPH2', 'maxssPH', 'maxsssP', 'maxdsssP', 'maxddsP', 'maxsssssP', 'maxssS', 'maxaaS', 'maxdssS', 'maxddssS', 'maxssssssS', 'maxSm', 'maxsGeH3', 'maxssGeH2', 'maxsssGeH', 'maxssssGe', 'maxsAsH2', 'maxssAsH', 'maxsssAs', 'maxdsssAs', 'maxddsAs', 'maxsssssAs', 'maxsSeH', 'maxdSe', 'maxssSe', 'maxaaSe', 'maxdssSe', 'maxssssssSe', 'maxddssSe', 'maxsSnH3', 'maxssSnH2', 'maxsssSnH', 'maxssssSn', 'maxsI', 'maxsPbH3', 'maxssPbH2', 'maxsssPbH', 'maxssssPb', 'n8Ring', 'n10Ring', 'nF4Ring', 'nF5Ring', 'n8HeteroRing', 'n10HeteroRing', 'nFHeteroRing', 'nF4HeteroRing', 'nF5HeteroRing', 'nTHeteroRing']\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",
            "\tUnused Original Features (Count: 2): ['ID', 'CommonName']\n",
            "\t\tThese features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.\n",
            "\t\tFeatures can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.\n",
            "\t\tThese features do not need to be present at inference time.\n",
            "\t\t('object', []) : 2 | ['ID', 'CommonName']\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])  : 726 | ['ALogP', 'ALogp2', 'AMR', 'apol', 'ATS0m', ...]\n",
            "\t\t('int', [])    : 187 | ['nAcid', 'naAromAtom', 'nAromBond', 'nN', 'nO', ...]\n",
            "\t\t('object', []) : 302 | ['ATS0s', 'ATS1s', 'ATS2s', 'ATS3s', 'ATS4s', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])  : 302 | ['ATS0s', 'ATS1s', 'ATS2s', 'ATS3s', 'ATS4s', ...]\n",
            "\t\t('float', [])     : 713 | ['ALogP', 'ALogp2', 'AMR', 'apol', 'ATS0m', ...]\n",
            "\t\t('int', [])       : 155 | ['nAcid', 'naAromAtom', 'nAromBond', 'nN', 'nO', ...]\n",
            "\t\t('int', ['bool']) :  45 | ['nHdNH', 'nHaaNH', 'nHsNH3p', 'nHssNH2p', 'nHtCH', ...]\n",
            "\t4.6s = Fit runtime\n",
            "\t1215 features in original data used to generate 1215 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 3.52 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 4.87s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 1195.13s of the 1195.11s of remaining time.\n",
            "\t0.6304\t = Validation score   (accuracy)\n",
            "\t0.13s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 1194.73s of the 1194.7s of remaining time.\n",
            "\t0.6478\t = Validation score   (accuracy)\n",
            "\t0.13s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 1194.29s of the 1194.26s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7326\t = Validation score   (accuracy)\n",
            "\t30.94s\t = Training   runtime\n",
            "\t2.15s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 1158.23s of the 1158.21s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7457\t = Validation score   (accuracy)\n",
            "\t35.02s\t = Training   runtime\n",
            "\t1.24s\t = Validation runtime\n",
            "Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 1118.24s of the 1118.22s of remaining time.\n",
            "\t0.7217\t = Validation score   (accuracy)\n",
            "\t2.36s\t = Training   runtime\n",
            "\t0.39s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 1115.21s of the 1115.2s of remaining time.\n",
            "\t0.7239\t = Validation score   (accuracy)\n",
            "\t2.87s\t = Training   runtime\n",
            "\t0.38s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 1111.67s of the 1111.65s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7391\t = Validation score   (accuracy)\n",
            "\t832.49s\t = Training   runtime\n",
            "\t2.98s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 273.96s of the 273.95s of remaining time.\n",
            "\t0.7391\t = Validation score   (accuracy)\n",
            "\t1.75s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 271.56s of the 271.54s of remaining time.\n",
            "\t0.7348\t = Validation score   (accuracy)\n",
            "\t1.55s\t = Training   runtime\n",
            "\t0.38s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 269.37s of the 269.35s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.737\t = Validation score   (accuracy)\n",
            "\t42.64s\t = Training   runtime\n",
            "\t4.34s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 221.53s of the 221.51s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7413\t = Validation score   (accuracy)\n",
            "\t109.64s\t = Training   runtime\n",
            "\t0.56s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 107.03s of the 107.0s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\tTime limit exceeded... Skipping NeuralNetTorch_BAG_L1.\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 97.08s of the 97.02s of remaining time.\n",
            "2022-12-23 05:39:00,355\tERROR worker.py:399 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7435\t = Validation score   (accuracy)\n",
            "\t82.72s\t = Training   runtime\n",
            "\t1.36s\t = Validation runtime\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 8.49s of remaining time.\n",
            "\t0.7457\t = Validation score   (accuracy)\n",
            "\t0.54s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 1192.13s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221223_052036/\")\n",
            "Evaluation: accuracy on test data: 0.7241379310344828\n",
            "Evaluations on test data:\n",
            "{\n",
            "    \"accuracy\": 0.7241379310344828,\n",
            "    \"balanced_accuracy\": 0.588170865279299,\n",
            "    \"mcc\": 0.22496194417693985,\n",
            "    \"roc_auc\": 0.6893026652062797,\n",
            "    \"f1\": 0.8241758241758242,\n",
            "    \"precision\": 0.7575757575757576,\n",
            "    \"recall\": 0.9036144578313253\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# With Word2Vec"
      ],
      "metadata": {
        "id": "X10es233OUCK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/samoturk/mol2vec"
      ],
      "metadata": {
        "id": "rYmjPLVvtABc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from mol2vec.features import mol2alt_sentence, mol2sentence, MolSentence,DfVec, sentences2vec\n",
        "import numpy as np\n",
        "from gensim.models import word2vec"
      ],
      "metadata": {
        "id": "F80lTz_lO46L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = word2vec.Word2Vec.load('model_300dim_eric.pkl')\n",
        "smilescsv['mol'] = smilescsv['col_smiles'].apply(lambda x: Chem.MolFromSmiles(x)) \n",
        "smilescsv['sentence'] = smilescsv.apply(lambda x: MolSentence(mol2alt_sentence(x['mol'], 1)), axis=1)\n",
        "smilescsv['mol2vec'] = [DfVec(x) for x in sentences2vec(smilescsv['sentence'], model, unseen='UNK')]"
      ],
      "metadata": {
        "id": "JxTLCpHiPZGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_trainw2v, x_testw2v, y_trainw2v, y_testw2v = train_test_split(X,y,test_size=0.20)"
      ],
      "metadata": {
        "id": "RCTaXCrnQ5yU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit_args = {}\n",
        "# fit_args['excluded_model_types'] = ['NN', 'FASTAI']\n",
        "fit_args['time_limit'] = 1200\n",
        "predictor = TabularPredictor(label=label).fit(x_trainw2v,presets=\"best_quality\", **fit_args)\n",
        "performance = predictor.evaluate(x_testw2v)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiOGrYrQRGsR",
        "outputId": "71065873-6f31-488d-c550-5ffd61821eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20221223_054859/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=5, num_bag_sets=20\n",
            "Beginning AutoGluon training ... Time limit = 1200s\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20221223_054859/\"\n",
            "AutoGluon Version:  0.6.1\n",
            "Python Version:     3.8.16\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Fri Aug 26 08:44:51 UTC 2022\n",
            "Train Data Rows:    460\n",
            "Train Data Columns: 1427\n",
            "Label Column: Outcome\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [1, 0]\n",
            "\tIf 'binary' 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",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11502.4 MB\n",
            "\tTrain Data (Original)  Memory Usage: 13.52 MB (0.1% 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",
            "\t\t\tNote: Converting 46 features to boolean dtype as they only contain 2 unique values.\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",
            "\tUseless Original Features (Count: 218): ['nB', 'nBondsQ', 'nHssNH2p', 'nHsssNHp', 'nHmisc', 'nsLi', 'nssBe', 'nssssBem', 'nsBH2', 'nssBH', 'nsssB', 'nssssBm', 'nssNH2p', 'nsssNHp', 'nddsN', 'naOm', 'nsSiH3', 'nssSiH2', 'nsssSiH', 'nssssSi', 'nsPH2', 'nssPH', 'nsssP', 'nddsP', 'nsssssP', 'nssssssS', 'nSm', 'nsGeH3', 'nssGeH2', 'nsssGeH', 'nssssGe', 'nsAsH2', 'nssAsH', 'nsssAs', 'ndsssAs', 'nddsAs', 'nsssssAs', 'nsSeH', 'ndSe', 'nssSe', 'naaSe', 'ndssSe', 'nssssssSe', 'nddssSe', 'nsSnH3', 'nssSnH2', 'nsssSnH', 'nssssSn', 'nsPbH3', 'nssPbH2', 'nsssPbH', 'nssssPb', 'SHsssNHp', 'SHmisc', 'SsLi', 'SssBe', 'SssssBem', 'SsBH2', 'SssBH', 'SsssB', 'SssssBm', 'SsssNHp', 'SddsN', 'SaOm', 'SsSiH3', 'SssSiH2', 'SsssSiH', 'SssssSi', 'SsPH2', 'SssPH', 'SsssP', 'SddsP', 'SsssssP', 'SssssssS', 'SSm', 'SsGeH3', 'SssGeH2', 'SsssGeH', 'SssssGe', 'SsAsH2', 'SssAsH', 'SsssAs', 'SdsssAs', 'SddsAs', 'SsssssAs', 'SsSeH', 'SdSe', 'SssSe', 'SaaSe', 'SdssSe', 'SssssssSe', 'SddssSe', 'SsSnH3', 'SssSnH2', 'SsssSnH', 'SssssSn', 'SsPbH3', 'SssPbH2', 'SsssPbH', 'SssssPb', 'minHsssNHp', 'minHmisc', 'minsLi', 'minssBe', 'minssssBem', 'minsBH2', 'minssBH', 'minsssB', 'minssssBm', 'minsssNHp', 'minddsN', 'minaOm', 'minsSiH3', 'minssSiH2', 'minsssSiH', 'minssssSi', 'minsPH2', 'minssPH', 'minsssP', 'minddsP', 'minsssssP', 'minssssssS', 'minSm', 'minsGeH3', 'minssGeH2', 'minsssGeH', 'minssssGe', 'minsAsH2', 'minssAsH', 'minsssAs', 'mindsssAs', 'minddsAs', 'minsssssAs', 'minsSeH', 'mindSe', 'minssSe', 'minaaSe', 'mindssSe', 'minssssssSe', 'minddssSe', 'minsSnH3', 'minssSnH2', 'minsssSnH', 'minssssSn', 'minsPbH3', 'minssPbH2', 'minsssPbH', 'minssssPb', 'maxHsssNHp', 'maxHmisc', 'maxsLi', 'maxssBe', 'maxssssBem', 'maxsBH2', 'maxssBH', 'maxsssB', 'maxssssBm', 'maxsssNHp', 'maxddsN', 'maxaOm', 'maxsSiH3', 'maxssSiH2', 'maxsssSiH', 'maxssssSi', 'maxsPH2', 'maxssPH', 'maxsssP', 'maxdsssP', 'maxddsP', 'maxsssssP', 'maxssS', 'maxaaS', 'maxdssS', 'maxddssS', 'maxssssssS', 'maxSm', 'maxsGeH3', 'maxssGeH2', 'maxsssGeH', 'maxssssGe', 'maxsAsH2', 'maxssAsH', 'maxsssAs', 'maxdsssAs', 'maxddsAs', 'maxsssssAs', 'maxsSeH', 'maxdSe', 'maxssSe', 'maxaaSe', 'maxdssSe', 'maxssssssSe', 'maxddssSe', 'maxsSnH3', 'maxssSnH2', 'maxsssSnH', 'maxssssSn', 'maxsI', 'maxsPbH3', 'maxssPbH2', 'maxsssPbH', 'maxssssPb', 'n10Ring', 'n11Ring', 'nF4Ring', 'nF5Ring', 'n10HeteroRing', 'n11HeteroRing', 'nFHeteroRing', 'nF4HeteroRing', 'nF5HeteroRing', 'nTHeteroRing', 'SHssNH2p', 'SssNH2p', 'minHssNH2p', 'minssNH2p', 'maxHssNH2p', 'maxssNH2p']\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",
            "\tUnused Original Features (Count: 2): ['ID', 'CommonName']\n",
            "\t\tThese features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.\n",
            "\t\tFeatures can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.\n",
            "\t\tThese features do not need to be present at inference time.\n",
            "\t\t('object', []) : 2 | ['ID', 'CommonName']\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])  : 720 | ['ALogP', 'ALogp2', 'AMR', 'apol', 'ATS0m', ...]\n",
            "\t\t('int', [])    : 185 | ['nAcid', 'naAromAtom', 'nAromBond', 'nN', 'nO', ...]\n",
            "\t\t('object', []) : 302 | ['ATS0s', 'ATS1s', 'ATS2s', 'ATS3s', 'ATS4s', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])  : 302 | ['ATS0s', 'ATS1s', 'ATS2s', 'ATS3s', 'ATS4s', ...]\n",
            "\t\t('float', [])     : 713 | ['ALogP', 'ALogp2', 'AMR', 'apol', 'ATS0m', ...]\n",
            "\t\t('int', [])       : 146 | ['nAcid', 'naAromAtom', 'nAromBond', 'nN', 'nO', ...]\n",
            "\t\t('int', ['bool']) :  46 | ['nBr', 'nBondsT', 'C1SP1', 'nHdNH', 'nHaaNH', ...]\n",
            "\t5.8s = Fit runtime\n",
            "\t1207 features in original data used to generate 1207 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 3.48 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 6.09s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 1193.91s of the 1193.9s of remaining time.\n",
            "\t0.6543\t = Validation score   (accuracy)\n",
            "\t0.13s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 1193.47s of the 1193.44s of remaining time.\n",
            "\t0.6565\t = Validation score   (accuracy)\n",
            "\t0.13s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 1193.03s of the 1193.0s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7478\t = Validation score   (accuracy)\n",
            "\t27.33s\t = Training   runtime\n",
            "\t1.41s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 1160.54s of the 1160.52s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.737\t = Validation score   (accuracy)\n",
            "\t33.06s\t = Training   runtime\n",
            "\t1.32s\t = Validation runtime\n",
            "Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 1119.34s of the 1119.33s of remaining time.\n",
            "\t0.7239\t = Validation score   (accuracy)\n",
            "\t2.34s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 1116.35s of the 1116.32s of remaining time.\n",
            "\t0.7043\t = Validation score   (accuracy)\n",
            "\t2.84s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 1112.85s of the 1112.83s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.737\t = Validation score   (accuracy)\n",
            "\t728.81s\t = Training   runtime\n",
            "\t3.6s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 379.12s of the 379.1s of remaining time.\n",
            "\t0.713\t = Validation score   (accuracy)\n",
            "\t1.71s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 376.73s of the 376.72s of remaining time.\n",
            "\t0.7109\t = Validation score   (accuracy)\n",
            "\t1.48s\t = Training   runtime\n",
            "\t0.38s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 374.6s of the 374.58s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7174\t = Validation score   (accuracy)\n",
            "\t43.37s\t = Training   runtime\n",
            "\t4.6s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 326.05s of the 326.03s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.737\t = Validation score   (accuracy)\n",
            "\t96.73s\t = Training   runtime\n",
            "\t0.48s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 224.18s of the 224.16s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7283\t = Validation score   (accuracy)\n",
            "\t84.36s\t = Training   runtime\n",
            "\t3.24s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 134.69s of the 134.67s of remaining time.\n",
            "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.7304\t = Validation score   (accuracy)\n",
            "\t97.7s\t = Training   runtime\n",
            "\t1.42s\t = Validation runtime\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 31.53s of remaining time.\n",
            "\t0.75\t = Validation score   (accuracy)\n",
            "\t0.53s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 1169.07s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20221223_054859/\")\n",
            "Evaluation: accuracy on test data: 0.7155172413793104\n",
            "Evaluations on test data:\n",
            "{\n",
            "    \"accuracy\": 0.7155172413793104,\n",
            "    \"balanced_accuracy\": 0.6141463414634146,\n",
            "    \"mcc\": 0.335004351548553,\n",
            "    \"roc_auc\": 0.6972357723577235,\n",
            "    \"f1\": 0.8135593220338982,\n",
            "    \"precision\": 0.7058823529411765,\n",
            "    \"recall\": 0.96\n",
            "}\n"
          ]
        }
      ]
    }
  ]
}