bbbp_RF_Nested_1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "IMA68ETAQ_4B",
        "outputId": "b74c413c-87c2-49af-a09b-9b155f722e06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit->deepchem) (9.4.0)\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.7.1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "!pip install deepchem\n",
        "import deepchem as dc\n",
        "dc.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRfmYdvFU7YI",
        "outputId": "bdab6492-9639-406e-cbd1-49779fd62f25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rdkit in /usr/local/lib/python3.10/dist-packages (2023.9.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.24.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (9.4.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install rdkit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRRpxTq3mZQ8",
        "outputId": "74ac3f1d-17c9-47a7-de11-9e38c3ab7b6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->mols2grid) (2.21)\n"
          ]
        }
      ],
      "source": [
        "!pip install mols2grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ac6vN_tonUK0"
      },
      "outputs": [],
      "source": [
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit.Chem import Descriptors\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit import DataStructs\n",
        "from rdkit.Chem import rdDepictor\n",
        "from rdkit.Chem.Draw import rdMolDraw2D\n",
        "from IPython.display import SVG\n",
        "import numpy as np\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from rdkit import Chem, DataStructs\n",
        "from rdkit.Chem import (\n",
        "    PandasTools,\n",
        "    Draw,\n",
        "    Descriptors,\n",
        "    MACCSkeys,\n",
        "    rdFingerprintGenerator,\n",
        ")\n",
        "import io\n",
        "from google.colab import files\n",
        "import mols2grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "E0K3Vz79VCHb",
        "outputId": "ce60eacf-87a2-45d1-b58b-b5cc1745a6aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-63ce623b-94b4-442b-9ef7-f9e49f8bf5d0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-63ce623b-94b4-442b-9ef7-f9e49f8bf5d0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving bbbp_curated.xlsx to bbbp_curated (1).xlsx\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "U9fY4nl8VH0l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "2c4e4c3a-ca33-47bf-8f76-657a7c898168"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      index   num                               name  y  \\\n",
              "0         1     1               Terbutylchlorambucil  1   \n",
              "1         2     2                              40730  1   \n",
              "2         3     3                                 24  1   \n",
              "3         4     4                        cloxacillin  1   \n",
              "4         5     5                       cefoperazone  1   \n",
              "...     ...   ...                                ... ..   \n",
              "1830   2045  2034                         licostinel  1   \n",
              "1831   2046  2035  ademetionine(adenosyl-methionine)  1   \n",
              "1832   2047  2036                           mesocarb  1   \n",
              "1833   2048  2037                         tofisoline  1   \n",
              "1834   2049  2038                      azidamfenicol  1   \n",
              "\n",
              "                                                    ids  ROMol  \\\n",
              "0              C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl    NaN   \n",
              "1     c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO...    NaN   \n",
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              "3     Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)...    NaN   \n",
              "4     CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(...    NaN   \n",
              "...                                                 ...    ...   \n",
              "1830    C1=C(Cl)C(=C(C2=C1NC(=O)C(N2)=O)[N+](=O)[O-])Cl    NaN   \n",
              "1831  [C@H]3([N]2C1=C(C(=NC=N1)N)N=C2)[C@@H]([C@@H](...    NaN   \n",
              "1832  [O+]1=N[N](C=C1[N-]C(NC2=CC=CC=C2)=O)C(CC3=CC=...    NaN   \n",
              "1833  C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC...    NaN   \n",
              "1834  [N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-]...    NaN   \n",
              "\n",
              "                                                 morgan  w  \n",
              "0     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
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              "3     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "4     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "...                                                 ... ..  \n",
              "1830  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1831  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1832  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1833  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1834  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "\n",
              "[1835 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-427d504c-6b7b-4006-92b1-7c9130cc659f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>index</th>\n",
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              "      <td>Terbutylchlorambucil</td>\n",
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              "      <td>cefoperazone</td>\n",
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              "      <td>CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(...</td>\n",
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              "<p>1835 rows × 8 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 1835,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 588,\n        \"min\": 1,\n        \"max\": 2049,\n        \"num_unique_values\": 1835,\n        \"samples\": [\n          761,\n          397,\n          1138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 584,\n        \"min\": 1,\n        \"max\": 2038,\n        \"num_unique_values\": 1835,\n        \"samples\": [\n          750,\n          394,\n          1127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1833,\n        \"samples\": [\n          \"cyheptamide\",\n          \"aminopromazine\",\n          \"fumoxicillin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ids\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1835,\n        \"samples\": [\n          \"Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CN4C=C(Cl)C(=O)C(=C4)Cl)C3=O)C(O)=O)nn1\",\n          \"CC(=O)S[C@@H]1CC2=CC(=O)CC[C@]2(C)[C@H]3CC[C@@]4(C)[C@@H](CC[C@@]45CCC(=O)O5)[C@H]13\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROMol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morgan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1835,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"w\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df2 = pd.read_excel('bbbp_curated.xlsx')\n",
        "df2 = df2.rename(columns={\"p_np\": \"y\", \"smiles\": \"ids\"})\n",
        "df2[\"w\"] = 1\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Fk9uAebmV49a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "19d0f65a-63e5-4450-ae21-6b7ee2c2b0b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      index   num                               name  y  \\\n",
              "0         1     1               Terbutylchlorambucil  1   \n",
              "1         2     2                              40730  1   \n",
              "2         3     3                                 24  1   \n",
              "3         4     4                        cloxacillin  1   \n",
              "4         5     5                       cefoperazone  1   \n",
              "...     ...   ...                                ... ..   \n",
              "1830   2045  2034                         licostinel  1   \n",
              "1831   2046  2035  ademetionine(adenosyl-methionine)  1   \n",
              "1832   2047  2036                           mesocarb  1   \n",
              "1833   2048  2037                         tofisoline  1   \n",
              "1834   2049  2038                      azidamfenicol  1   \n",
              "\n",
              "                                                    ids  \\\n",
              "0              C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl   \n",
              "1     c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO...   \n",
              "2                      C1CCN(CC1)Cc1cccc(c1)OCCCNC(=O)C   \n",
              "3     Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)...   \n",
              "4     CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(...   \n",
              "...                                                 ...   \n",
              "1830    C1=C(Cl)C(=C(C2=C1NC(=O)C(N2)=O)[N+](=O)[O-])Cl   \n",
              "1831  [C@H]3([N]2C1=C(C(=NC=N1)N)N=C2)[C@@H]([C@@H](...   \n",
              "1832  [O+]1=N[N](C=C1[N-]C(NC2=CC=CC=C2)=O)C(CC3=CC=...   \n",
              "1833  C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC...   \n",
              "1834  [N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-]...   \n",
              "\n",
              "                                                 ROMol  \\\n",
              "0     <rdkit.Chem.rdchem.Mol object at 0x7ec3223f6500>   \n",
              "1     <rdkit.Chem.rdchem.Mol object at 0x7ec3223f61f0>   \n",
              "2     <rdkit.Chem.rdchem.Mol object at 0x7ec3223f6340>   \n",
              "3     <rdkit.Chem.rdchem.Mol object at 0x7ec3223f5af0>   \n",
              "4     <rdkit.Chem.rdchem.Mol object at 0x7ec3221f9380>   \n",
              "...                                                ...   \n",
              "1830  <rdkit.Chem.rdchem.Mol object at 0x7ec32d2cb840>   \n",
              "1831  <rdkit.Chem.rdchem.Mol object at 0x7ec32d2cb8b0>   \n",
              "1832  <rdkit.Chem.rdchem.Mol object at 0x7ec32d2cb920>   \n",
              "1833  <rdkit.Chem.rdchem.Mol object at 0x7ec32d2cb990>   \n",
              "1834  <rdkit.Chem.rdchem.Mol object at 0x7ec32d2cba00>   \n",
              "\n",
              "                                                 morgan  w  \n",
              "0     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "2     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "3     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "4     <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "...                                                 ... ..  \n",
              "1830  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1831  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1832  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1833  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "1834  <rdkit.DataStructs.cDataStructs.ExplicitBitVec...  1  \n",
              "\n",
              "[1835 rows x 8 columns]"
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              "      <th>index</th>\n",
              "      <th>num</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Terbutylchlorambucil</td>\n",
              "      <td>1</td>\n",
              "      <td>C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>40730</td>\n",
              "      <td>1</td>\n",
              "      <td>c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>C1CCN(CC1)Cc1cccc(c1)OCCCNC(=O)C</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>cloxacillin</td>\n",
              "      <td>1</td>\n",
              "      <td>Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>cefoperazone</td>\n",
              "      <td>1</td>\n",
              "      <td>CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img 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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1830</th>\n",
              "      <td>2045</td>\n",
              "      <td>2034</td>\n",
              "      <td>licostinel</td>\n",
              "      <td>1</td>\n",
              "      <td>C1=C(Cl)C(=C(C2=C1NC(=O)C(N2)=O)[N+](=O)[O-])Cl</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1831</th>\n",
              "      <td>2046</td>\n",
              "      <td>2035</td>\n",
              "      <td>ademetionine(adenosyl-methionine)</td>\n",
              "      <td>1</td>\n",
              "      <td>[C@H]3([N]2C1=C(C(=NC=N1)N)N=C2)[C@@H]([C@@H](...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img 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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1832</th>\n",
              "      <td>2047</td>\n",
              "      <td>2036</td>\n",
              "      <td>mesocarb</td>\n",
              "      <td>1</td>\n",
              "      <td>[O+]1=N[N](C=C1[N-]C(NC2=CC=CC=C2)=O)C(CC3=CC=...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1833</th>\n",
              "      <td>2048</td>\n",
              "      <td>2037</td>\n",
              "      <td>tofisoline</td>\n",
              "      <td>1</td>\n",
              "      <td>C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1834</th>\n",
              "      <td>2049</td>\n",
              "      <td>2038</td>\n",
              "      <td>azidamfenicol</td>\n",
              "      <td>1</td>\n",
              "      <td>[N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-]...</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "      <td>&lt;rdkit.DataStructs.cDataStructs.ExplicitBitVec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1835 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2921d84-a493-4159-b685-d8dedc591ae2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "PandasTools.AddMoleculeColumnToFrame(df2, smilesCol=\"ids\")\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "60-Eppy3VMho",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "d949c53a-001e-43ba-b559-529585087dbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-f0dafa6ba427>:15: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
            "  features.set_axis(list_X, axis=1,inplace=True)\n"
          ]
        },
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              "      <th>3</th>\n",
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              "      <td>CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(...</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>C1=C(Cl)C(=C(C2=C1NC(=O)C(N2)=O)[N+](=O)[O-])Cl</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>[C@H]3([N]2C1=C(C(=NC=N1)N)N=C2)[C@@H]([C@@H](...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1832</th>\n",
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              "      <td>[O+]1=N[N](C=C1[N-]C(NC2=CC=CC=C2)=O)C(CC3=CC=...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1833</th>\n",
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              "      <td>C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1834</th>\n",
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              "      <td>[N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-]...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1835 rows × 1027 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5808ce9f-affa-4bd8-bdbd-2e1f957d8a01')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-5808ce9f-affa-4bd8-bdbd-2e1f957d8a01 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5808ce9f-affa-4bd8-bdbd-2e1f957d8a01');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6662b3a2-07cb-41ae-aad4-6b91375d6790')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "  <div id=\"id_0d086c97-5887-4737-8c91-23c67e36d4c0\">\n",
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              "        border: none;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_featurized2')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_0d086c97-5887-4737-8c91-23c67e36d4c0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_featurized2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_featurized2"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "ids_list = df2['ids'].values.tolist()\n",
        "ids = df2['ids']\n",
        "ids = pd.DataFrame(ids)\n",
        "datapoints = len(ids_list)\n",
        "featurizer = dc.feat.CircularFingerprint(size=1024)\n",
        "#featurizer = dc.feat.ConvMolFeaturizer(datapoints)\n",
        "features = featurizer.featurize(ids_list)\n",
        "features = pd.DataFrame(features)\n",
        "features_list = list(features.columns.values)\n",
        "list_X = []\n",
        "for i in range(1,len(features_list)+1):\n",
        "#    list_X.append('X')\n",
        "    list_X.append('X'+str(i))\n",
        "\n",
        "features.set_axis(list_X, axis=1,inplace=True)\n",
        "df_featurized2 = pd.concat([features, ids, df2.y, df2.w], axis=1, join='inner')\n",
        "df_featurized2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_featurized2['y'].value_counts()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZAl05QmyiNg",
        "outputId": "06e95cde-c40e-4f59-beae-39f34f48fc3d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "421"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_featurized2['y'].value_counts()[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwnWv3jH_IFw",
        "outputId": "d35926ad-6774-4695-cb2b-bcacefcd320a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1414"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import resample\n",
        "#create two different dataframe of majority and minority class\n",
        "df_majority = df_featurized2[(df_featurized2['y']==1)]\n",
        "df_minority = df_featurized2[(df_featurized2['y']==0)]\n",
        "# upsample minority class\n",
        "df_minority_upsampled = resample(df_minority,\n",
        "                                 replace=True,    # sample with replacement\n",
        "                                 n_samples= df_featurized2['y'].value_counts()[1], # to match majority class\n",
        "                                 random_state=42)  # reproducible results\n",
        "# Combine majority class with upsampled minority class\n",
        "df_upsampled = pd.concat([df_minority_upsampled, df_majority])"
      ],
      "metadata": {
        "id": "pCvIEFqpxp1m"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_upsampled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "OJT1Q1dm0wwr",
        "outputId": "17feebf4-361e-4fbc-d3ff-7ff7abbaf080"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1834</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-]...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2828 rows × 1027 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-775493fd-5119-4bb0-b037-931f82e1bf41')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-775493fd-5119-4bb0-b037-931f82e1bf41 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-775493fd-5119-4bb0-b037-931f82e1bf41');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0042f837-4cf8-4a5c-96ac-5574617ce713 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_c88a1177-feb3-41ed-b221-e9d718a59cb4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_upsampled')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c88a1177-feb3-41ed-b221-e9d718a59cb4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_upsampled');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_upsampled"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_upsampled['y'].value_counts()[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5pQBMVLLSQz",
        "outputId": "18970435-14b8-4db6-9147-dcfc67c62819"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1414"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_upsampled['y'].value_counts()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTdBDUoqcM3l",
        "outputId": "df8ddc58-df82-4062-f59d-5a3b41508c7b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1414"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertig dataframe concat into NumpyDataset object (datasetnp)\n",
        "datasetnp = dc.data.NumpyDataset.from_dataframe(df_upsampled)"
      ],
      "metadata": {
        "id": "ItM4DjuSYE1k"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# manual nested cross-validation for random forest on a classification dataset\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from scipy.stats import randint\n",
        "# create dataset\n",
        "X, y = datasetnp.X , datasetnp.y\n",
        "# configure the cross-validation procedure\n",
        "cv_outer = KFold(n_splits=10, shuffle=True, random_state=1)\n",
        "# enumerate splits\n",
        "outer_results = list()\n",
        "for train_ix, test_ix in cv_outer.split(X):\n",
        "\t# split data\n",
        "\tX_train, X_test = X[train_ix, :], X[test_ix, :]\n",
        "\ty_train, y_test = y[train_ix], y[test_ix]\n",
        "\t# configure the cross-validation procedure\n",
        "\tcv_inner = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\t# define the model\n",
        "\tmodel = RandomForestClassifier(random_state=1)\n",
        "\t# define search space\n",
        "\tspace = {'n_estimators': randint(50,500), 'max_depth': randint(1,20)}\n",
        "\t# define search\n",
        "\tsearch = RandomizedSearchCV(model, param_distributions = space, n_iter=100, scoring='accuracy', cv=cv_inner, refit=True)\n",
        "\t# execute search\n",
        "\tresult = search.fit(X_train, y_train.ravel())\n",
        "\t# get the best performing model fit on the whole training set\n",
        "\tbest_model = result.best_estimator_\n",
        "\t# evaluate model on the hold out dataset\n",
        "\tyhat = best_model.predict(X_test)\n",
        "\t# evaluate the model\n",
        "\tacc = accuracy_score(y_test, yhat)\n",
        "\t# store the result\n",
        "\touter_results.append(acc)\n",
        "\t# report progress\n",
        "\tprint('>acc=%.3f, est=%.3f, cfg=%s' % (acc, result.best_score_, result.best_params_))\n",
        "# summarize the estimated performance of the model\n",
        "print('Accuracy: %.3f (%.3f)' % (mean(outer_results), std(outer_results)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j914tomzXXH0",
        "outputId": "00a6ea82-46b1-4d3f-aa85-45296dfbba70"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">acc=0.961, est=0.956, cfg={'max_depth': 19, 'n_estimators': 462}\n",
            ">acc=0.958, est=0.960, cfg={'max_depth': 19, 'n_estimators': 191}\n",
            ">acc=0.958, est=0.951, cfg={'max_depth': 19, 'n_estimators': 396}\n",
            ">acc=0.968, est=0.959, cfg={'max_depth': 19, 'n_estimators': 361}\n",
            ">acc=0.961, est=0.950, cfg={'max_depth': 19, 'n_estimators': 491}\n",
            ">acc=0.982, est=0.951, cfg={'max_depth': 19, 'n_estimators': 322}\n",
            ">acc=0.933, est=0.950, cfg={'max_depth': 19, 'n_estimators': 494}\n",
            ">acc=0.979, est=0.954, cfg={'max_depth': 19, 'n_estimators': 497}\n",
            ">acc=0.972, est=0.954, cfg={'max_depth': 19, 'n_estimators': 383}\n",
            ">acc=0.957, est=0.954, cfg={'max_depth': 19, 'n_estimators': 356}\n",
            "Accuracy: 0.963 (0.013)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = dc.data.NumpyDataset(X_train, y_train)\n",
        "valid_dataset = dc.data.NumpyDataset(X_test, y_test)"
      ],
      "metadata": {
        "id": "hnJWaksYcZvA"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "eheik7dHd-GW"
      },
      "outputs": [],
      "source": [
        "n_tasks = 1\n",
        "n_features = train_dataset.X.shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "0ZBrhmMuKU34"
      },
      "outputs": [],
      "source": [
        "# Modelling\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV, KFold\n",
        "from scipy.stats import randint\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "mLXxpNaVKU35"
      },
      "outputs": [],
      "source": [
        "metric = dc.metrics.Metric(\n",
        "dc.metrics.roc_auc_score, np.mean, mode=\"classification\")\n",
        "import sklearn\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = dc.models.SklearnModel(best_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "z5qgs9GNKU36"
      },
      "outputs": [],
      "source": [
        "model.fit(train_dataset)\n",
        "model.save()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save model\n",
        "import pickle\n",
        "\n",
        "with open(\"model.pkl\", \"wb\") as f:\n",
        "     pickle.dump(model, f)\n",
        "\n",
        "#load model\n",
        "with open(\"model.pkl\", \"rb\") as f:\n",
        "     model = pickle.load(f)"
      ],
      "metadata": {
        "id": "BwPw-8LjHZ_1"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#save model\n",
        "import pickle\n",
        "\n",
        "with open(\"best_rf.pkl\", \"wb\") as f:\n",
        "     pickle.dump(best_model, f)\n",
        "\n",
        "#load model\n",
        "with open(\"best_rf.pkl\", \"rb\") as f:\n",
        "     best_rf = pickle.load(f)"
      ],
      "metadata": {
        "id": "k25fgXMeHjWC"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "LsUzpHQLKU36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0976d92f-9b2f-40ac-9c66-04515e4a584b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating model\n",
            "Train scores\n",
            "{'mean-roc_auc_score': 0.9999672946101517}\n",
            "Validation scores\n",
            "{'mean-roc_auc_score': 0.9882796780684104}\n"
          ]
        }
      ],
      "source": [
        "metric = dc.metrics.Metric(\n",
        "dc.metrics.roc_auc_score, np.mean, mode=\"classification\")\n",
        "\n",
        "print(\"Evaluating model\")\n",
        "train_scores = model.evaluate(train_dataset, [metric])\n",
        "valid_scores = model.evaluate(valid_dataset, [metric])\n",
        "print(\"Train scores\")\n",
        "print(train_scores)\n",
        "print(\"Validation scores\")\n",
        "print(valid_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "IWu-GKYkNs0K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "03a38736-d443-40b9-b36b-2cd6b01d77f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7ec3260d34c0>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Generate predictions with the best model\n",
        "y_pred = best_model.predict(valid_dataset.X)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(valid_dataset.y, y_pred)\n",
        "\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "1bI6Vv3oOIh3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bb3432a-b2f4-4b56-dc06-f6cf41813b54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy     :  0.9574468085106383\n",
            "Precision    :  0.9507042253521126\n",
            "Recall       :  0.9642857142857143\n",
            "F1_score     :  0.9574468085106385\n",
            "MCC          :  0.9149899396378269\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "\n",
        "y_pred = best_model.predict(valid_dataset.X)\n",
        "\n",
        "accuracy = accuracy_score(valid_dataset.y, y_pred)\n",
        "precision = precision_score(valid_dataset.y, y_pred)\n",
        "recall = recall_score(valid_dataset.y, y_pred)\n",
        "\n",
        "precision_s = precision_score(valid_dataset.y, y_pred,average='micro')\n",
        "recall_s    = recall_score(valid_dataset.y, y_pred, average='micro')\n",
        "\n",
        "print(\"Accuracy     : \", accuracy)\n",
        "print(\"Precision    : \", precision)\n",
        "print(\"Recall       : \", recall)\n",
        "print(\"F1_score     : \", 2*((precision_s*recall_s)/(precision_s + recall_s)))\n",
        "print(\"MCC          : \", matthews_corrcoef(valid_dataset.y, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "tp78yA2ubk3_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "696fa9fa-2f55-499b-8e78-e770fc7e4c84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.9574468085106383\n",
            "\n",
            "Confusion matrix: \n",
            "[[135   7]\n",
            " [  5 135]]\n",
            "\n",
            "Classification report: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.96       142\n",
            "           1       0.95      0.96      0.96       140\n",
            "\n",
            "    accuracy                           0.96       282\n",
            "   macro avg       0.96      0.96      0.96       282\n",
            "weighted avg       0.96      0.96      0.96       282\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = 3, random_state = 1)\n",
        "dt.fit(train_dataset.X, train_dataset.y)\n",
        "y_pred = best_rf.predict(valid_dataset.X)\n",
        "print(\"Accuracy score: \" + str(accuracy_score(valid_dataset.y, y_pred)))\n",
        "print(\"\\nConfusion matrix: \\n\" + str(confusion_matrix(valid_dataset.y, y_pred)))\n",
        "print(\"\\nClassification report: \\n\" + str(classification_report(valid_dataset.y, y_pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "ZcoCb5NqcD-f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "outputId": "efce3b72-9bd2-402e-a4c9-0759ffbbacef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0.875, 'x[233] <= 0.5\\nentropy = 1.0\\nsamples = 2546\\nvalue = [1272, 1274]'),\n",
              " Text(0.25, 0.625, 'x[807] <= 0.5\\nentropy = 0.951\\nsamples = 1740\\nvalue = [644, 1096]'),\n",
              " Text(0.125, 0.375, 'x[926] <= 0.5\\nentropy = 0.729\\nsamples = 664\\nvalue = [135, 529]'),\n",
              " Text(0.0625, 0.125, 'entropy = 0.932\\nsamples = 308\\nvalue = [107, 201]'),\n",
              " Text(0.1875, 0.125, 'entropy = 0.397\\nsamples = 356\\nvalue = [28, 328]'),\n",
              " Text(0.375, 0.375, 'x[314] <= 0.5\\nentropy = 0.998\\nsamples = 1076\\nvalue = [509, 567]'),\n",
              " Text(0.3125, 0.125, 'entropy = 0.948\\nsamples = 717\\nvalue = [263, 454]'),\n",
              " Text(0.4375, 0.125, 'entropy = 0.899\\nsamples = 359\\nvalue = [246, 113]'),\n",
              " Text(0.75, 0.625, 'x[519] <= 0.5\\nentropy = 0.762\\nsamples = 806\\nvalue = [628, 178]'),\n",
              " Text(0.625, 0.375, 'x[893] <= 0.5\\nentropy = 0.45\\nsamples = 638\\nvalue = [578, 60]'),\n",
              " Text(0.5625, 0.125, 'entropy = 0.858\\nsamples = 149\\nvalue = [107, 42]'),\n",
              " Text(0.6875, 0.125, 'entropy = 0.227\\nsamples = 489\\nvalue = [471, 18]'),\n",
              " Text(0.875, 0.375, 'x[679] <= 0.5\\nentropy = 0.878\\nsamples = 168\\nvalue = [50, 118]'),\n",
              " Text(0.8125, 0.125, 'entropy = 0.82\\nsamples = 43\\nvalue = [32, 11]'),\n",
              " Text(0.9375, 0.125, 'entropy = 0.595\\nsamples = 125\\nvalue = [18, 107]')]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.subplots(figsize=(20, 8))\n",
        "tree.plot_tree(dt, fontsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "mcAfuPpBKHBn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466,
          "referenced_widgets": [
            "ffadf9c5280840f2831496431b3837a6",
            "fb834b98a5ec46d58ded1164fe90d8ad",
            "c48d2b36fb4c4ecc8a5bd5db5806a8d0",
            "b598db9b673a475183409dd6d5d3c57e",
            "e3fad7ee29c24571b7745c63afe8f6c1",
            "7faf384dcbfc4501a24c90697d971392",
            "e43b0dd8879544dc8f983a565f0246df"
          ]
        },
        "outputId": "b4937bc6-13c7-41ad-a21d-f7ad250bfa08"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=1, description='max_depth:', max=6, min=1), Output()), _dom_classes=('wi…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffadf9c5280840f2831496431b3837a6"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x900 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "value_max_depth = widgets.IntSlider(value=1, min=1, max=6, step=1, description='max_depth:')\n",
        "\n",
        "def DecisionTree(valor):\n",
        "  dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = valor, random_state = 1)\n",
        "  dt.fit(train_dataset.X, train_dataset.y)\n",
        "  y_pred = best_rf.predict(valid_dataset.X)\n",
        "  fig = plt.subplots(figsize=(20+4*valor, 8+valor))\n",
        "  tree.plot_tree(dt, fontsize=12)\n",
        "\n",
        "\n",
        "widgets.interact(DecisionTree , valor = value_max_depth);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRBV39BxJJmM"
      },
      "source": [
        "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact\n",
        "\n",
        "The p value, or probability value, is a statistical measurement used to validate a hypothesis against observed data. It tells you how likely it is that your data could have occurred under the null hypothesis. The level of statistical significance is often expressed as a p-value between 0 and 1. A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true. The lower the p-value, the greater the statistical significance of the observed difference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "enV3wU-HBKwB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d6f96f6b-831a-4fa5-b837-71240e18b55e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          p-value\n",
              "0    2.730109e-84\n",
              "1    5.427456e-80\n",
              "2    2.659318e-76\n",
              "3    5.708669e-73\n",
              "4    6.794387e-70\n",
              "..            ...\n",
              "136  4.281822e-67\n",
              "137  5.475185e-70\n",
              "138  4.312527e-73\n",
              "139  1.785633e-76\n",
              "140  2.733112e-80\n",
              "\n",
              "[141 rows x 1 columns]"
            ],
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              "      <td>2.659318e-76</td>\n",
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              "      <td>5.708669e-73</td>\n",
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              "      <th>4</th>\n",
              "      <td>6.794387e-70</td>\n",
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              "      <th>137</th>\n",
              "      <td>5.475185e-70</td>\n",
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              "      <td>4.312527e-73</td>\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d1d1ab39-cde4-482a-b29b-d4f19dff6797 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d1d1ab39-cde4-482a-b29b-d4f19dff6797');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4be8c7a-a8d8-46fb-907f-863a68ea7488')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e4be8c7a-a8d8-46fb-907f-863a68ea7488 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_39719364-421d-41d1-85fe-16b5c019a02b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('fisher')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_39719364-421d-41d1-85fe-16b5c019a02b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('fisher');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "fisher",
              "summary": "{\n  \"name\": \"fisher\",\n  \"rows\": 141,\n  \"fields\": [\n    {\n      \"column\": \"p-value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020694426573045478,\n        \"min\": 2.7301088273400874e-84,\n        \"max\": 0.09412313971899859,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          0.07951430041091204,\n          7.267317242493028e-20,\n          5.755701633874602e-22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "from scipy.stats import hypergeom\n",
        "\n",
        "table = cm\n",
        "M = table.sum()\n",
        "n = table[0].sum()\n",
        "N = table[:, 0].sum()\n",
        "start, end = hypergeom.support(M, n, N)\n",
        "fisher = pd.DataFrame(hypergeom.pmf(np.arange(start, end+1), M, n, N), columns=['p-value'])\n",
        "fisher"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "RouzffTvDsnW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f815141e-901f-4ac8-aa74-14cc0bb72ae4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2721544535532154e-64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "from scipy.stats import fisher_exact\n",
        "pvalue = fisher_exact(table, alternative='two-sided')\n",
        "pvalue[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4O-89nwtM6c"
      },
      "source": [
        "##Explaining model (LIME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "EncCJuFhEnTX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a46c87a-904a-4b64-986e-e33bb196993a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lime\n",
            "  Downloading lime-0.2.0.1.tar.gz (275 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/275.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.1/275.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.7/275.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from lime) (3.7.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lime) (1.24.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lime) (1.8.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from lime) (4.66.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from lime) (1.2.2)\n",
            "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.10/dist-packages (from lime) (0.19.3)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (3.2.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (9.4.0)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2024.2.12)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (1.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (24.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->lime) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->lime) (3.3.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->lime) (1.16.0)\n",
            "Building wheels for collected packages: lime\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283835 sha256=14c5cc02523e162bea14eab9cd092df49fb07439838e593974db7a100941f7b0\n",
            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
            "Successfully built lime\n",
            "Installing collected packages: lime\n",
            "Successfully installed lime-0.2.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install lime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "kzDiO5PwEsaV"
      },
      "outputs": [],
      "source": [
        "from lime import lime_tabular\n",
        "feature_names = [\"fp_%s\"  % x for x in range(n_features)]\n",
        "explainer = lime_tabular.LimeTabularExplainer(train_dataset.X,\n",
        "                                              feature_names=feature_names,\n",
        "                                              categorical_features=feature_names,\n",
        "                                              class_names=['np', 'p'],\n",
        "                                              discretize_continuous=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "8Pm2bXMIoTw-"
      },
      "outputs": [],
      "source": [
        "#FUNÇÕES\n",
        "# We need a function which takes a 2d numpy array (samples, features) and returns predictions (samples,)\n",
        "\n",
        "def eval_model(my_model,i):\n",
        "    def eval_closure(x):\n",
        "        ds = dc.data.NumpyDataset(x, n_tasks=n_tasks)\n",
        "        # The 0th task is NR-AR\n",
        "        predictions = my_model.predict(ds)[:,i]\n",
        "        return predictions\n",
        "    return eval_closure\n",
        "\n",
        "##################################\n",
        "def fp_mol(mol, fp_length=n_features):\n",
        "    \"\"\"\n",
        "    returns: dict of <int:list of string>\n",
        "        dictionary mapping fingerprint index\n",
        "        to list of SMILES strings that activated that fingerprint\n",
        "    \"\"\"\n",
        "    d = {}\n",
        "    feat = dc.feat.CircularFingerprint(sparse=True, smiles=True, size=n_features)\n",
        "    retval = feat._featurize(mol)\n",
        "    for k, v in retval.items():\n",
        "        index = k % fp_length\n",
        "        if index not in d:\n",
        "            d[index] = set()\n",
        "        d[index].add(v['smiles'])\n",
        "    return d\n",
        "#######################\n",
        "def verifTox(l):\n",
        "  somaTox = 0.0\n",
        "  somaNao = 0.0\n",
        "\n",
        "  for peso in l:\n",
        "      if peso > 0:\n",
        "        somaTox += peso\n",
        "      else:\n",
        "        somaNao += peso\n",
        "  if abs(somaTox) > abs(somaNao):\n",
        "    return (\"YES\",somaTox,somaNao)\n",
        "  else:\n",
        "    return (\"NO\",somaTox,somaNao)\n",
        "#########################"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "ATT69I2d6HoG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ce48cde-1af8-479a-cbec-edf26e7aa331"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.88627310e-01 2.83352260e-01 9.15330976e-03 4.75721806e-03\n",
            " 3.46816847e-01 9.69333240e-03 1.52663344e-02 9.20166927e-04\n",
            " 3.32569774e-02 3.10910399e-02 7.84824851e-02 3.89323077e-02\n",
            " 3.71806339e-03 6.49188328e-02 1.88831526e-03 1.93782657e-01\n",
            " 8.34454277e-01 1.03605921e-01 1.47726440e-01 1.55947285e-04\n",
            " 2.53386278e-02 4.53870336e-04 2.09756958e-01 1.87976239e-01\n",
            " 3.68523659e-01 1.57532367e-01 3.89323077e-02 5.58688399e-02\n",
            " 3.42873609e-01 5.93114323e-02 3.30584833e-01 3.67649580e-02\n",
            " 2.16362451e-01 1.28671835e-01 5.58688399e-02 1.49482585e-01\n",
            " 6.48589571e-03 1.47493817e-01 3.29503816e-02 2.73983000e-01\n",
            " 5.43485560e-01 1.40427750e-03 5.58688399e-02 4.14341421e-02\n",
            " 2.58408484e-01 1.93782657e-01 2.13842238e-01 3.64216457e-02\n",
            " 2.06465241e-02 1.08598715e-02 3.25066187e-01 4.60843164e-03\n",
            " 2.66006363e-02 1.57532367e-01 7.64823738e-02 1.92427659e-04\n",
            " 2.17423610e-02 3.23122800e-02 9.15330976e-03 3.91402678e-01\n",
            " 2.16362451e-01 8.04556740e-03 1.76107957e-01 1.40427750e-03\n",
            " 1.54195640e-01 2.87616851e-02 4.33219296e-02 3.19605722e-01\n",
            " 7.35530451e-01 1.72439623e-02 2.13842238e-01 7.68322353e-02\n",
            " 3.50215391e-01 1.44781375e-02 9.73830399e-02 5.37079667e-02\n",
            " 3.91402678e-01 4.97477099e-02 2.36341919e-03 1.64462787e-01\n",
            " 2.65578408e-04 3.33334698e-01 3.26239578e-01 1.69897856e-01\n",
            " 1.19323426e-01 3.49996493e-02 5.37079667e-02 5.06328047e-01\n",
            " 4.09151282e-04 1.82303883e-01 4.52979445e-02 1.50114438e-01\n",
            " 9.29443063e-03 9.13806584e-04 9.52238366e-04 1.65795261e-01\n",
            " 2.26955072e-01 4.34925482e-01 1.73633767e-01 3.16021674e-03\n",
            " 7.40986258e-02 1.78655722e-02 4.12950780e-01 1.51333809e-01\n",
            " 4.33697229e-01 9.15330976e-03 8.32137436e-02 2.10789518e-01\n",
            " 1.11159229e-01 5.36059535e-01 7.68837211e-02 1.55947285e-04\n",
            " 1.77032726e-01 4.97477099e-02 6.59933023e-03 5.82618688e-01\n",
            " 1.49877711e-01 4.82983911e-02 4.34925482e-01 2.73983000e-01\n",
            " 4.52844325e-04 3.02780811e-02 5.30939807e-01 4.11002891e-02\n",
            " 4.52844325e-04 1.57156361e-01 1.73015303e-03 1.77098347e-01\n",
            " 7.90304200e-03 1.48071257e-03 1.65319387e-02 4.53870336e-04\n",
            " 6.62926501e-02 8.91846551e-02 4.82983911e-02 8.62002348e-02\n",
            " 3.89973008e-01 2.25415466e-01 6.10280299e-02 2.89593157e-01\n",
            " 1.88871881e-03 1.05456750e-02 8.28832186e-01 4.87215250e-01\n",
            " 5.99243353e-01 9.31243417e-01 8.15550108e-01 8.08165609e-01\n",
            " 5.82058036e-01 5.85221426e-01 8.85613718e-01 8.69515384e-01\n",
            " 3.48102706e-01 3.66208320e-01 8.72644602e-01 7.41682349e-01\n",
            " 7.13386157e-01 7.01398392e-01 9.18876858e-01 7.84745949e-01\n",
            " 9.70438968e-02 8.56945586e-01 7.70062506e-01 7.19283731e-01\n",
            " 7.88399100e-01 8.91362432e-01 6.61027242e-01 7.59497696e-01\n",
            " 8.16678057e-01 9.50885239e-01 8.79732145e-01 7.65803692e-01\n",
            " 9.77697840e-01 6.65982622e-01 5.15553564e-01 9.12425577e-01\n",
            " 7.33117967e-01 7.82577977e-01 7.15100383e-01 6.97331185e-01\n",
            " 9.29036031e-01 8.94741015e-01 9.33287645e-01 8.78152600e-01\n",
            " 6.83407609e-01 6.78240204e-01 8.57753847e-01 8.19203676e-01\n",
            " 8.61460023e-01 8.77763093e-01 7.55952564e-01 6.64298610e-01\n",
            " 7.52778930e-01 8.18012854e-01 8.58985298e-01 6.24582362e-01\n",
            " 7.95455568e-01 5.81010987e-01 9.35466894e-01 9.52155149e-01\n",
            " 8.09457337e-01 7.92150605e-01 7.85633560e-01 7.17745527e-01\n",
            " 7.76781346e-01 6.91183331e-01 8.74732016e-01 7.92021403e-01\n",
            " 8.61064901e-01 7.57040185e-01 9.25436975e-01 8.92511581e-01\n",
            " 6.70448360e-01 8.27380593e-01 8.36521091e-01 6.29886259e-01\n",
            " 9.67910160e-01 9.04904974e-01 8.61185907e-01 5.96989771e-01\n",
            " 7.25905081e-01 7.81256082e-01 6.83973632e-01 8.92897107e-01\n",
            " 8.85424383e-01 8.92670860e-01 8.42123359e-01 6.86314533e-01\n",
            " 6.78488245e-01 8.06416008e-01 8.03551450e-01 8.62429125e-01\n",
            " 9.34882500e-01 7.06241028e-01 8.33021627e-01 7.58868638e-01\n",
            " 6.34745662e-01 9.00243651e-01 6.62428386e-01 5.72401066e-01\n",
            " 7.83402104e-01 4.06213648e-01 7.85337308e-01 9.40101924e-01\n",
            " 8.98548271e-01 8.26695189e-01 7.58785701e-01 7.65914288e-01\n",
            " 7.94958782e-01 7.79900425e-01 8.86320331e-01 7.42611158e-01\n",
            " 9.37108789e-01 7.63633322e-01 9.54814807e-01 8.80145143e-01\n",
            " 8.11261978e-01 7.57792678e-01 8.18493191e-01 8.12177973e-01\n",
            " 8.97623307e-01 6.52296462e-01 7.73523389e-01 7.62720630e-01\n",
            " 8.97872186e-01 7.42458163e-01 9.07123529e-01 8.94941937e-01\n",
            " 8.57550179e-01 7.47714004e-01 8.42539914e-01 8.54253697e-01\n",
            " 8.07638554e-01 9.15279377e-01 7.51220738e-01 8.06816216e-01\n",
            " 8.66253616e-01 8.72685035e-01 5.73874301e-01 9.38080393e-01\n",
            " 8.00452812e-01 9.49614699e-01]\n"
          ]
        }
      ],
      "source": [
        "print(best_rf.predict_proba(valid_dataset.X)[:,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "usPFU2I9VHdg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b44b3fa4-e57f-4210-a7e7-937ce78c19e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TASK: 0\n",
            "\n",
            "check 142\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "No registered converter was able to produce a C++ rvalue of type std::basic_string<wchar_t, std::char_traits<wchar_t>, std::allocator<wchar_t> > from this Python object of type int",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-d16b4006dbde>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m       \u001b[0;31m# What fragments activated what fingerprints in our active molecule?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m       \u001b[0mmy_fragments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp_mol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolFromSmiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mactive_id\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[0m\u001b[1;32m     23\u001b[0m       \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m       \u001b[0mmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: No registered converter was able to produce a C++ rvalue of type std::basic_string<wchar_t, std::char_traits<wchar_t>, std::allocator<wchar_t> > from this Python object of type int"
          ]
        }
      ],
      "source": [
        "import statistics as st\n",
        "\n",
        "dfAllFragMolPeso = pd.DataFrame()\n",
        "dfAllMolTasksInfo = pd.DataFrame()\n",
        "dfAllMolTox = pd.DataFrame()\n",
        "\n",
        "for i in range(n_tasks):\n",
        "    #model_fn = eval_model(best_rf,i)\n",
        "    model_fn = lambda x: best_rf.predict_proba(x).astype(float)\n",
        "    lista_active_id = np.where((valid_dataset.y[:,i] == 1) * (best_rf.predict_proba(valid_dataset.X)[:,1] > 0.8))[0] #list of molecules which are toxic and (*) are predicted as such by the model\n",
        "    print('\\nTASK:',i)\n",
        "    print()\n",
        "\n",
        "    #Dataframe that indicates if the molecule is toxic or not and how much is it(total)\n",
        "    dfMolTox = pd.DataFrame(columns=[\"Task\",\"Molecule Index\",\"Molecule Id\",\"Result\",\"Penetrate\",\"Non-Penetrate\"])\n",
        "\n",
        "    for active_id in lista_active_id:\n",
        "      exp = explainer.explain_instance(valid_dataset.X[active_id], model_fn , num_features=100, top_labels=1)\n",
        "      print('check', active_id)\n",
        "\n",
        "      # What fragments activated what fingerprints in our active molecule?\n",
        "      my_fragments = fp_mol(Chem.MolFromSmiles(valid_dataset.ids[active_id]))\n",
        "      q = [key for key in exp.as_map().keys()][0]\n",
        "      map = exp.as_map()[q]\n",
        "      dic = dict(map)\n",
        "      lista_pesos = []\n",
        "\n",
        "      #Dataframe with molecule's information, its toxic fragments and weight\n",
        "      dfFragMolPeso = pd.DataFrame(columns=[\"Task\",\"Fingerprint\",\"Fragment SMILES\",\"Weight\",\"Active Molecule\"])\n",
        "\n",
        "      for fp_index in my_fragments:\n",
        "        if fp_index in dic:\n",
        "          peso = dic[fp_index]\n",
        "          frag = my_fragments[fp_index]\n",
        "          lista_pesos.append(peso)\n",
        "\n",
        "          if peso > 0: #Weight that positively contributes to toxicity\n",
        "              dfFragMolPeso.loc[dfFragMolPeso.shape[0]] = [i,fp_index,frag, peso,active_id]#Adding a row at the bottom\n",
        "\n",
        "      #Inserting data at dfMolTox\n",
        "      vt = verifTox(lista_pesos) #variable that contains the sum of all of toxic fragments weight, non toxic fragment weight and the result of the molecule (if it's more toxic or not)\n",
        "      smile = valid_dataset.select([active_id]).ids[0] #smile of the molecule in question\n",
        "      dfMolTox.loc[dfMolTox.shape[0]] = [i,active_id,smile,vt[0],vt[1],vt[2]]\n",
        "      dfAllMolTox = dfAllMolTox.append(dfMolTox)\n",
        "      #dfAllMolTox = pd.concat([dfAllMolTox, dfMolTox])\n",
        "\n",
        "      #Putting together all dfFragMolPeso\n",
        "      if len(lista_pesos) != 0:\n",
        "          #Selecting a minimum toxicity contribution (a minimum weight) to assess which fragments could be discarded. Setting a tolerance\n",
        "          #tol = st.mean(lista_pesos) #weight's mean\n",
        "          tol = 0.0\n",
        "          dfFragMolPeso.drop(dfFragMolPeso.loc[dfFragMolPeso['Weight']<=tol].index,inplace=True) #dropping indexes which have less weight than the tol tolerance\n",
        "\n",
        "\n",
        "          #Unsetting set object of column Fragment Smiles in dfFragMolPeso\n",
        "          for (j,set_smiles) in enumerate(dfFragMolPeso['Fragment SMILES']): #el = set_smiles\n",
        "\n",
        "              if  set_smiles == None or len(set_smiles) == 0:\n",
        "                  dfFragMolPeso['Fragment SMILES'].iloc[j] = np.nan\n",
        "\n",
        "              elif len(set_smiles) > 1: #If fp is activated by more than one fragment\n",
        "                  k = j\n",
        "                  for smi in set_smiles:\n",
        "                    if smi != '': #Passing by possible bug (Empty smile)\n",
        "                      if k == j: #Represents the first smile of the set of smiles\n",
        "                          dfFragMolPeso['Fragment SMILES'].iloc[k] = smi\n",
        "                          k = dfFragMolPeso.shape[0] #From now on k will be always the index of the new adding row\n",
        "                      else:\n",
        "                          dfFragMolPeso.loc[k] = dfFragMolPeso.iloc[j] #copying data of original row (all the information, exept smile)\n",
        "                          dfFragMolPeso['Fragment SMILES'].loc[k] = smi #modifying the fragment smile (which is still in set) to the smile alone (isolated) in the new position k\n",
        "              else:\n",
        "                  dfFragMolPeso['Fragment SMILES'].iloc[j] = list(set_smiles)[0]\n",
        "\n",
        "          #Dropping possible bugs (empty smile)\n",
        "          dfFragMolPeso.dropna(inplace=True)\n",
        "          dfFragMolPeso.drop(dfFragMolPeso.loc[dfFragMolPeso['Fragment SMILES'].isin([''])].index,inplace=True)\n",
        "\n",
        "          dfAllFragMolPeso = dfAllFragMolPeso.append(dfFragMolPeso)\n",
        "          #dfAllFragMolPeso = pd.concat([dfAllFragMolPeso, dfFragMolPeso])\n",
        "\n",
        "\n",
        "    #Exporting data of the molecule's toxicity classification (dfMolTox)\n",
        "    dfMolTox.to_excel('TASK'+str(i)+'-Classificacao das Moleculas.xlsx')\n",
        "    files.download('TASK'+str(i)+'-Classificacao das Moleculas.xlsx')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TslrBBElJCOg"
      },
      "outputs": [],
      "source": [
        "print(lista_active_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "71vR-tbRrdOZ"
      },
      "outputs": [],
      "source": [
        "#Setting task as index\n",
        "dfAllFragMolPeso.set_index('Task',inplace=True)\n",
        "\n",
        "#Grouping dfAllFragMolPeso by its tasks\n",
        "ag = dfAllFragMolPeso.groupby(level=0)\n",
        "\n",
        "#Function to count how much a smile appears in a task\n",
        "def contaSmile (grupoFrag):\n",
        "    dic ={}\n",
        "    for smile in grupoFrag['Fragment SMILES']:\n",
        "      qtdSmile = dic.get(smile,0)\n",
        "      qtdSmile += 1\n",
        "      dic[smile] = qtdSmile\n",
        "    return dic\n",
        "\n",
        "#Funciton to sum the weights of each smile of the task\n",
        "def somaPesos (grupoFrag):\n",
        "  dic = {}\n",
        "  for smile in grupoFrag['Fragment SMILES']:\n",
        "    soma = dic.get(smile,0)\n",
        "    if soma == 0:\n",
        "        pesosSmile = grupoFrag['Weight'].loc[grupoFrag['Fragment SMILES'] == smile]\n",
        "        soma = sum(pesosSmile)\n",
        "        dic[smile] = soma\n",
        "  return dic\n",
        "\n",
        "def pegaMolecula(grupoFrag):\n",
        "  return list(grupoFrag['Active Molecule'])\n",
        "\n",
        "\"\"\"\n",
        "The next command counts how much a fragment repeats in a task\n",
        "\"\"\"\n",
        "srTaskSmiQtd = ag.apply(contaSmile)\n",
        "srTaskSmiWeightTotal = ag.apply(somaPesos)\n",
        "dfAllTasksInfo = pd.DataFrame()\n",
        "\n",
        "dftop10Tasks = pd.DataFrame()\n",
        "\n",
        "for i,task in enumerate(srTaskSmiQtd.index):\n",
        "  dicTask = srTaskSmiQtd.values[i]\n",
        "  dicTaskWeight = srTaskSmiWeightTotal.values[i]\n",
        "\n",
        "  d = pd.DataFrame(dicTask.values(),index=dicTask.keys())\n",
        "  d.rename_axis('Fragment SMILES',inplace=True)\n",
        "  d.rename(columns={0:'Quantity'},inplace=True)\n",
        "  d['Task'] = task\n",
        "  d.reset_index(inplace=True)\n",
        "  PandasTools.AddMoleculeColumnToFrame(d,smilesCol='Fragment SMILES')\n",
        "  d.set_index('Fragment SMILES',inplace=True)\n",
        "\n",
        "  #Adding Total Weight of each smile\n",
        "  d['Total Weight'] = dicTaskWeight.values()\n",
        "\n",
        "  #Grouping Smile and adding a new column with all the active molecules for each specific fragment smile\n",
        "  agFragTot = dfAllFragMolPeso.loc[dfAllFragMolPeso.index==task].groupby(by='Fragment SMILES')\n",
        "  srMolecules = agFragTot.apply(pegaMolecula)\n",
        "  d['Active Molecules'] = srMolecules\n",
        "\n",
        "  #Setting multindex\n",
        "  d.reset_index(inplace=True)\n",
        "  d.set_index(['Task','Fragment SMILES'],inplace=True)\n",
        "  d.sort_values(by='Total Weight',ascending=False,inplace=True)\n",
        "\n",
        "  #Deleting the compounds that does not have ROMol Structures\n",
        "  d.dropna(inplace=True)\n",
        "\n",
        "  dftop10Tasks = pd.concat([dftop10Tasks, d.head(10)])\n",
        "  dfAllTasksInfo = pd.concat([dfAllTasksInfo, d])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSqSP__UtL3p"
      },
      "source": [
        "##Highlights of top10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "437YTGVZ4FiB"
      },
      "outputs": [],
      "source": [
        "#Highlight of the top10 of each task\n",
        "\n",
        "lHighlights = []\n",
        "for ((task,frag),lmolecules) in dftop10Tasks['Active Molecules'].iteritems():\n",
        "  index_mol = lmolecules[0]\n",
        "  molecule_smile = valid_dataset.ids[index_mol]\n",
        "  m = Chem.MolFromSmiles(molecule_smile)\n",
        "  substructure = Chem.MolFromSmiles(frag)\n",
        "  m.GetSubstructMatches(substructure)\n",
        "  lHighlights.append(m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JZA_K5ReI4mk"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks['Highlight First Active Molecule'] = lHighlights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VKDipRUfTu12"
      },
      "outputs": [],
      "source": [
        "valid_dataset.to_dataframe()['ids'].to_excel('valid_dataset_molecules.xlsx')\n",
        "files.download('valid_dataset_molecules.xlsx')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puAMBdt7tKK_"
      },
      "source": [
        "##Exporting Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D1xI60BVJ0x5"
      },
      "outputs": [],
      "source": [
        "!pip install xlsxwriter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJddZtBXMbYv"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "\n",
        "#Exporting dfAllTasksInfo without figures\n",
        "dfAllTasksInfo.to_excel('Contribuição SMILES em Tasks.xlsx')\n",
        "files.download('Contribuição SMILES em Tasks.xlsx')\n",
        "\n",
        "#Exporting dfAllTasksInfo with figures\n",
        "dfAllTasksInfo.reset_index(inplace=True)\n",
        "PandasTools.SaveXlsxFromFrame(dfAllTasksInfo,'Contribuição SMILES em Tasks_COM FIGURA.xlsx', molCol='ROMol',size=(100,100))\n",
        "files.download('Contribuição SMILES em Tasks_COM FIGURA.xlsx')\n",
        "\n",
        "#Exporting dfAllFragMolPeso\n",
        "dfAllFragMolPeso.to_excel('Descrição Fingerprint,SMILES e Moleculas.xlsx')\n",
        "files.download('Descrição Fingerprint,SMILES e Moleculas.xlsx')\n",
        "\n",
        "#Exporting dftop10Tasks\n",
        "dftop10Tasks.to_excel('Highlights Top 10.xlsx')\n",
        "files.download('Highlights Top 10.xlsx')\n",
        "\n",
        "#Exporting dfAllTasksInfo with figures\n",
        "dftop10Tasks.reset_index(inplace=True)\n",
        "PandasTools.SaveXlsxFromFrame(dftop10Tasks,'Highlights Top 10_COM FIGURA.xlsx', molCol='Highlight First Active Molecule',size=(150,150))\n",
        "files.download('Highlights Top 10_COM FIGURA.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_SKk3eO0wNTP"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks.set_index(['Task','Fragment SMILES'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NplJIkmtMYO9"
      },
      "outputs": [],
      "source": [
        "from rdkit.Chem.Descriptors import HeavyAtomCount\n",
        "\n",
        "dftop10Tasks['Heavy Atom Count'] = [HeavyAtomCount(Chem.MolFromSmiles(smiles)) for smiles in dftop10Tasks['Fragment SMILES']]\n",
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zkPjvKrBM5Am"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks = dftop10Tasks.sort_values(by=['Heavy Atom Count'],ascending=False)\n",
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NISSGuX5NLI7"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Wq3fsjZNXS9"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks = dftop10Tasks.loc[(dftop10Tasks['Heavy Atom Count']) >= 3]\n",
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TzK2ZAcJNkAh"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oMUq-oTfNygn"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bYwATKYhOIB9"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks = dftop10Tasks.sort_values(by=['Task'],ascending=True)\n",
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vQxlfahkaG9M"
      },
      "outputs": [],
      "source": [
        "dftop10Tasks = dftop10Tasks.drop(columns = ['Fragment SMILES', 'Quantity', 'Total Weight', 'Active Molecules', 'Heavy Atom Count'])\n",
        "dftop10Tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ar58h_F38Fzq"
      },
      "outputs": [],
      "source": [
        "mols2grid.display(dftop10Tasks, mol_col=\"Highlight First Active Molecule\", prerender = True)"
      ]
    }
  ],
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