2024_05_11_ez_qsar_model_ipynb.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOQPRnFcvY6pUAM+caEoeAc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chupvl/gcolab/blob/main/2024_05_11_ez_qsar_model_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# A very easy introduction into model building for chemistry\n",
        "by Vladimir Chupakhin"
      ],
      "metadata": {
        "id": "OzgJoRshymUc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Packages"
      ],
      "metadata": {
        "id": "9Po-L2GDy4IO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBowIu0SxM5c",
        "outputId": "49121e10-89e1-4e3f-9b05-f0211eb012ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rdkit in /usr/local/lib/python3.10/dist-packages (2023.9.6)\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (9.4.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.0.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install rdkit catboost"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Libs & functions"
      ],
      "metadata": {
        "id": "BGCZehuZzBr5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import scipy\n",
        "\n",
        "from sklearn.model_selection import KFold\n",
        "from catboost import CatBoostRegressor, Pool\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from scipy.stats import spearmanr\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "_WnxCgPTzDBY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import rdkit\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit.Chem.MolStandardize import rdMolStandardize\n",
        "\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "from rdkit.Chem import Descriptors\n",
        "# not the best solution but rdkit is chatty (for good, not bad)\n",
        "from rdkit import RDLogger\n",
        "RDLogger.DisableLog('rdApp.*')\n",
        "\n"
      ],
      "metadata": {
        "id": "REDHCcb95y-4"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mol_std(smiles):\n",
        "  try:\n",
        "    # we need rdkit mol format\n",
        "    m = Chem.MolFromSmiles(smiles)\n",
        "    # default cleaning procedure\n",
        "    m = rdMolStandardize.Cleanup(m)\n",
        "    # getting the largest compound\n",
        "    m = rdMolStandardize.FragmentParent(m)\n",
        "    # removing charge if any\n",
        "    uncharger = rdMolStandardize.Uncharger()\n",
        "    m = uncharger.uncharge(m)\n",
        "    # this is the most controversional step\n",
        "    # getting the most common tautomeric form\n",
        "    # and the main source of errors\n",
        "    TautomerEnumerator = rdMolStandardize.TautomerEnumerator()\n",
        "    m = TautomerEnumerator.Canonicalize(m)\n",
        "    return m\n",
        "  except:\n",
        "    return None"
      ],
      "metadata": {
        "id": "O9XItdrTVd1E"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getRDKitDescr(lst_mols):\n",
        "    # physchem and 1D descriptors\n",
        "    descriptor_names = [d[0] for d in Descriptors.descList]\n",
        "    descriptor_funcs = [d[1] for d in Descriptors.descList]\n",
        "    # init matrix\n",
        "    fp_matrix = scipy.sparse.lil_matrix((len(lst_mols), 2048))\n",
        "    #\n",
        "    descr2d_lst = []\n",
        "    # calculate descriptors\n",
        "    for i, mol in tqdm(enumerate(lst_mols)):\n",
        "        try:\n",
        "            # let's use Morgan one\n",
        "            fp = rdFingerprintGenerator.GetMorganGenerator(radius=3).GetFingerprint(mol)\n",
        "            for j in fp.GetOnBits():\n",
        "                fp_matrix[i, j] = 1\n",
        "            descr2d = {desc_name: func(mol) for desc_name, func in zip(descriptor_names, descriptor_funcs)}\n",
        "            descr2d_lst.append(descr2d)\n",
        "        except:\n",
        "            descr2d_lst.append(None)\n",
        "            continue\n",
        "    # sparse format will let you save memory and disk space\n",
        "    descr_rdkit_2d = scipy.sparse.csr_matrix(pd.DataFrame(descr2d_lst).to_numpy())\n",
        "    descr_combo = scipy.sparse.hstack((descr_rdkit_2d, fp_matrix))\n",
        "    descr_names = descriptor_names + [['FP'+'str(i)']*2048 for i in range(2048)]\n",
        "    return descr_combo, descr_names\n"
      ],
      "metadata": {
        "id": "qF8_fC1Pbg33"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_cb(X,y):\n",
        "  # CatBoostRegressor\n",
        "  model = CatBoostRegressor(loss_function='RMSEWithUncertainty', verbose=False)\n",
        "  # K-fold cross-validation\n",
        "  kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "  rmse_scores = []\n",
        "  r2_scores = []\n",
        "  spearman_scores = []\n",
        "\n",
        "  # cross-validation\n",
        "  for train_index, test_index in kf.split(X):\n",
        "      X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "      y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "      train_pool = Pool(X_train, y_train)\n",
        "      test_pool = Pool(X_test, y_test)\n",
        "\n",
        "      model.fit(train_pool, eval_set=test_pool)\n",
        "\n",
        "      y_pred = model.predict(X_test)\n",
        "      y_pred_mean = y_pred[:, 0]\n",
        "\n",
        "      # Calculate metrics\n",
        "      rmse = np.sqrt(mean_squared_error(y_test, y_pred_mean))\n",
        "      r2 = r2_score(y_test, y_pred_mean)\n",
        "      spearman_corr, _ = spearmanr(y_test, y_pred_mean)\n",
        "\n",
        "      rmse_scores.append(rmse)\n",
        "      r2_scores.append(r2)\n",
        "      spearman_scores.append(spearman_corr)\n",
        "\n",
        "  print(\"Median RMSE:\", np.median(rmse_scores))\n",
        "  print(\"Median R²:\", np.median(r2_scores))\n",
        "  print(\"Median Spearman Correlation:\", np.median(spearman_scores))\n",
        "  res = {}\n",
        "  res['model'] = model\n",
        "  res['rmse_scores'] = rmse_scores\n",
        "  res['r2_scores'] = r2_scores\n",
        "  res['spearman_scores'] = spearman_scores\n",
        "  return res"
      ],
      "metadata": {
        "id": "2GX0TgW-ioA3"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "7D_Y7Qv0zHlZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's use the most simple example of water solubility prediction.\n",
        "We will take the dataset from Oleg A. Raevsky [publication](https://pubs.acs.org/doi/10.1021/ci400692n) that was curated as a part of the [AqSol database](https://www.nature.com/articles/s41597-019-0151-1). Why? Those compounds are neutral in water at a standard temperature (25C), thus likely will cause least issues."
      ],
      "metadata": {
        "id": "ox2LY6_hViyg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/mcsorkun/AqSolDB/master/results/data_curated.csv')\n",
        "#\n",
        "dfc = df[df['ID'].str.contains('C-')]\n",
        "dfcg1 = dfc[dfc['Group'] == 'G1'].reset_index(drop=True)"
      ],
      "metadata": {
        "id": "XgizYiMwxbdL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfc.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8jty3cnzR07",
        "outputId": "8801b80f-4256-4128-9495-3e629008eb97"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD',\n",
              "       'Occurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount',\n",
              "       'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds',\n",
              "       'NumValenceElectrons', 'NumAromaticRings', 'NumSaturatedRings',\n",
              "       'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ',\n",
              "       'BertzCT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next \"curation\" step is a selection of the compounds that has most stable solubility values: \"The selection is performed by first classifying the compounds into five distinct groups which are defined based on the statistics of occurrence of a compound in the dataset\"[ref](https://www.nature.com/articles/s41597-019-0151-1)."
      ],
      "metadata": {
        "id": "19k9pPTq09s6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Group'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag8yDvOV0wtG",
        "outputId": "ddb0dc45-3124-42e2-9333-9d28982632b0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Group\n",
              "G1    7746\n",
              "G3    1182\n",
              "G5     636\n",
              "G2     235\n",
              "G4     183\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfcg1['Solubility'].plot(kind='hist', bins=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "3KyPs6M71NVc",
        "outputId": "2e88f3fe-43d2-489f-b4fe-efd67c65dff1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Smiles standardization"
      ],
      "metadata": {
        "id": "Rike6EDZ1YlD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's common that for open (public) data we deal with data coming from several data sources. Thus SMILES reprsentation might following different encoding rules, as not all vendors of chemsoftware follow identical rules. Standardization procedure is well described [here](https://www.blopig.com/blog/2022/05/molecular-standardization/) and [here](https://github.com/rdkit/rdkit/blob/master/Docs/Notebooks/MolStandardize.ipynb)."
      ],
      "metadata": {
        "id": "TH_v35pc1hPR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = [mol_std(s) for s in tqdm(dfcg1['SMILES'].to_list())]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynVQimy15Cri",
        "outputId": "80a7c7e4-e5ae-4f14-bcf1-ebf3028bff60"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1073/1073 [01:00<00:00, 17.72it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfcg1['smiles_std'] = result"
      ],
      "metadata": {
        "id": "KJ4ZAnkW57U1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Featurization"
      ],
      "metadata": {
        "id": "VjpnLDyu15gi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "descr, descr_names = getRDKitDescr(dfcg1['smiles_std'].to_list())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "mi3rvuDvbYvI",
        "outputId": "5a472ae2-37ed-4fbc-a971-c46fc1327854"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1073it [00:14, 72.02it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modeling building"
      ],
      "metadata": {
        "id": "VVih3DMmci6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Catboost models are perfect out-of-box solution for classification and regression. So, we will use cross-validation setup. Most importantly you don't need to bother about descriptor scaling or any other transformation."
      ],
      "metadata": {
        "id": "7FZx4Uoaclfz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = descr\n",
        "y = dfcg1['Solubility'].to_numpy()"
      ],
      "metadata": {
        "id": "sgsh8Uq5eez8"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# catboost does not support sparse by default, so we need to use pandas sparse dataframe\n",
        "df_descr = pd.DataFrame.sparse.from_spmatrix(descr)\n",
        "df_descr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "W2EHlxU6gZYt",
        "outputId": "4f11502a-c01b-4487-b044-c3ec9fe67f44"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        0          1         2         3         4          5        6     \\\n",
              "0   9.903750   9.903750  0.363426 -0.695648  0.579480   9.375000  117.148   \n",
              "1   9.487593   9.487593  0.231065 -0.231065  0.571409  31.363636  152.237   \n",
              "2  10.014931  10.014931  0.452546 -0.724537  0.511678  10.875000  117.148   \n",
              "3  10.172060  10.172060  0.646991 -1.018519  0.488091  15.111111  149.215   \n",
              "4  11.858935  11.858935  0.030000 -0.738426  0.652158  27.153846  183.251   \n",
              "\n",
              "      7           8     9     ...  2248  2249  2250  2251  2252  2253  2254  \\\n",
              "0  106.060  117.078979  48.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
              "1  136.109  152.120115  62.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   1.0   \n",
              "2  106.060  117.078979  48.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
              "3  138.127  149.051050  54.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
              "4  166.115  183.125929  74.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
              "\n",
              "   2255  2256  2257  \n",
              "0   0.0   0.0   0.0  \n",
              "1   0.0   0.0   0.0  \n",
              "2   0.0   0.0   0.0  \n",
              "3   0.0   0.0   0.0  \n",
              "4   0.0   0.0   0.0  \n",
              "\n",
              "[5 rows x 2258 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8b94ef4-4c3a-46c8-a55e-96252d5fea7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>2248</th>\n",
              "      <th>2249</th>\n",
              "      <th>2250</th>\n",
              "      <th>2251</th>\n",
              "      <th>2252</th>\n",
              "      <th>2253</th>\n",
              "      <th>2254</th>\n",
              "      <th>2255</th>\n",
              "      <th>2256</th>\n",
              "      <th>2257</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.903750</td>\n",
              "      <td>9.903750</td>\n",
              "      <td>0.363426</td>\n",
              "      <td>-0.695648</td>\n",
              "      <td>0.579480</td>\n",
              "      <td>9.375000</td>\n",
              "      <td>117.148</td>\n",
              "      <td>106.060</td>\n",
              "      <td>117.078979</td>\n",
              "      <td>48.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.487593</td>\n",
              "      <td>9.487593</td>\n",
              "      <td>0.231065</td>\n",
              "      <td>-0.231065</td>\n",
              "      <td>0.571409</td>\n",
              "      <td>31.363636</td>\n",
              "      <td>152.237</td>\n",
              "      <td>136.109</td>\n",
              "      <td>152.120115</td>\n",
              "      <td>62.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.014931</td>\n",
              "      <td>10.014931</td>\n",
              "      <td>0.452546</td>\n",
              "      <td>-0.724537</td>\n",
              "      <td>0.511678</td>\n",
              "      <td>10.875000</td>\n",
              "      <td>117.148</td>\n",
              "      <td>106.060</td>\n",
              "      <td>117.078979</td>\n",
              "      <td>48.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.172060</td>\n",
              "      <td>10.172060</td>\n",
              "      <td>0.646991</td>\n",
              "      <td>-1.018519</td>\n",
              "      <td>0.488091</td>\n",
              "      <td>15.111111</td>\n",
              "      <td>149.215</td>\n",
              "      <td>138.127</td>\n",
              "      <td>149.051050</td>\n",
              "      <td>54.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.858935</td>\n",
              "      <td>11.858935</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>-0.738426</td>\n",
              "      <td>0.652158</td>\n",
              "      <td>27.153846</td>\n",
              "      <td>183.251</td>\n",
              "      <td>166.115</td>\n",
              "      <td>183.125929</td>\n",
              "      <td>74.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2258 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-f8b94ef4-4c3a-46c8-a55e-96252d5fea7e 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-f8b94ef4-4c3a-46c8-a55e-96252d5fea7e');\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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              "        element.appendChild(docLink);\n",
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              "\n",
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              "    </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",
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              "      --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",
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              "  }\n",
              "\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",
              "    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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              "      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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b6b4c8dd-82d1-4854-9274-54bf23c48ff3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_descr"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# different loss metrics will impact the model quality\n",
        "# but let's use RMSEWith\n",
        "aqsol_model = run_cb(df_descr, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkysJUZrf-ie",
        "outputId": "b44cca30-4a0c-4ddc-806f-6a703dc70373"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Median RMSE: 0.9382356930630592\n",
            "Median R²: 0.7882259242285613\n",
            "Median Spearman Correlation: 0.8407696341453892\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No the perfect model, but can be used as a baseline.\n",
        "There are many ways to improve the quality, but that's a totally different story."
      ],
      "metadata": {
        "id": "dgdgRvELiXzk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# picture worth a 1000 words, right...\n",
        "y_pred = aqsol_model['model'].predict(df_descr)\n",
        "dfcg1['y_pred'] = y_pred[:,0]\n",
        "dfcg1['y_pred_uncertainty'] = y_pred[:,1]\n",
        "dfcg1.plot(x='Solubility', y='y_pred', c='y_pred_uncertainty', kind='scatter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "3VMB5gnqih9w",
        "outputId": "14a2d328-ccde-4fd0-dd10-6159364f7932"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Solubility', ylabel='y_pred'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}