02_kNN_QSPR.ipynb

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   "source": [
    "# 2. kNN for (Q)SPR modeling ⚛️\n",
    "\n",
    "<a href=\"https://githubtocolab.com/edgarsmdn/MLCE_book/blob/main/02_kNN_QSPR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dlaMhdbLXofj"
   },
   "source": [
    "## Goals of this exercise 🌟"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YCWB0IvbXp4O"
   },
   "source": [
    "*   We will learn how to construct a simple SPR model using kNN\n",
    "*   We will learn the importance of data normalization (scaling)\n",
    "*   We will review the concepts of training and test split and cross-validation\n",
    "*   We will review some of the performance metrics for assesing classification models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XI_fHNSEYck6"
   },
   "source": [
    "## A quick reminder ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8wst0cbF39zU"
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   "source": [
    "Probably the simplest data-driven model that you can think of is k-nearest neighbours (kNN). It simply predicts future data as the average (or mode) of the \"k\" nearest neighbours of the queried point.\n",
    "\n",
    "As simple as this idea might be, it works relatively good in various applications. One of them is the generation of (quantitative) structure-property relationships ((Q)SPR) models {cite}`yuan2019developing, shen2003development`. Whether the word \"Quantitative\" is sometimes included or not depends on whether the model in question is a regression model or a classification model. Do you remember the difference?\n",
    "\n",
    "The key question in kNN is what do we consider a neighbour and what not? This indicates us that we need to define a sort of similarity or distance metric that allows us to distinguish neighbouring points from points that are far away. \n",
    "\n",
    "Common distance metrics use the different [mathematical norms](https://en.wikipedia.org/wiki/Norm_(mathematics)). For example, the Euclidean distance:\n",
    "\n",
    "$$\n",
    " d(\\textbf{x}, \\textbf{x'}) = \\sqrt{\\sum_i^D (x_i - x'_i)^2}\n",
    "$$\n",
    "\n",
    "```{figure} media/02_kNN/kNN.png\n",
    ":alt: kNN\n",
    ":width: 75%\n",
    ":align: center\n",
    "\n",
    "Among the k-nearest neighbours (k=5), the majority of points are red 1s. Therefore, the queried point (green x) will be labeled as \"red 1\". Image taken from {cite}`murphy2022probabilistic`. \n",
    "```\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1sWnwvt_FjQa"
   },
   "source": [
    "Let's exemplify the use of kNN by constructing a SPR model that predicts the whether a molecule is mutagenic or not. \n",
    "\n",
    "Mutagenicity is the property of substances to induce genetic mutation. It is one of the most important environmental, health and safety (EHS) properties to check when dealing with novel chemicals (e.g., drugs or solvents). In this case, we are going to use the data of mutagenicity on Salmonella typhimurium (Ames test). This dataset was collected by the [Istituto di Ricerche Farmacologiche Mario Negri](https://www.marionegri.it/), merging experimental data from a benchmark dataset\n",
    "compiled by {cite}`hansen2009benchmark` from a collection of data made available\n",
    "by the [Japan Health Ministry](https://www.nihs.go.jp/dgm/amesqsar.html) within their Ames (Q)SAR project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9-iQHe-XZm6"
   },
   "source": [
    "Let's fist import some libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "V3C3ZNc8XjsA"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oWmicwjH5Ksm"
   },
   "source": [
    "## Get data 📚\n",
    "\n",
    "We have previously computed some molecular descriptors that will serve as input to our model. However, this is also an important step to consider when facing a problem like this: what are the important inputs to model mutagenicity? how do we know if these pre-computed features are enough for modeling mutagenicity? can we generate relevant molecular features automatically? 🤔\n",
    "\n",
    "Ok, let's use [pandas](https://pandas.pydata.org/) to import the data as a DataFrame..."
   ]
  },
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       "      Unnamed: 0    Id          CAS  \\\n",
       "0              0     1     100-00-5   \n",
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       "\n",
       "                                                 SMILES    Status  \\\n",
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       "...                                                 ...       ...   \n",
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       "5763                     O(c1c(cc(cc1Br)C)C(C)(C)C)CC=C  Training   \n",
       "\n",
       "      Experimental value Predicted value  NumValenceElectrons       qed  \\\n",
       "0                      1               1                   52  0.463602   \n",
       "1                      1               1                   52  0.359544   \n",
       "2                      0               1                   52  0.470728   \n",
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       "...                  ...             ...                  ...       ...   \n",
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       "\n",
       "        TPSA     MolMR  BalabanJ      BertzCT    MolWt  MolLogP  \n",
       "0      43.14   38.1064  3.003401   244.429658  157.556  2.24820  \n",
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       "...      ...       ...       ...          ...      ...      ...  \n",
       "5759   78.53   69.3560  2.668492   627.435628  265.269 -1.05750  \n",
       "5760   17.07   40.3720  3.791118    85.425922  315.186  2.59030  \n",
       "5761   12.03   40.4577  2.447473    28.870765  230.931  1.36580  \n",
       "5762  185.59  125.3525  2.552977  1169.342047  485.457  3.84768  \n",
       "5763    9.23   73.2710  3.263016   388.457194  283.209  4.61982  \n",
       "\n",
       "[5764 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if 'google.colab' in str(get_ipython()):\n",
    "  df = pd.read_csv(\"https://raw.githubusercontent.com/edgarsmdn/MLCE_book/main/references/mutagenicity_kNN.csv\")\n",
    "else:\n",
    "  df = pd.read_csv(\"references/mutagenicity_kNN.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ttVnS2-dbxcy"
   },
   "source": [
    "The library pandas has many useful functions for data analytics. For example, we can print the type of the data we have..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IyH-W0ty8-Oi",
    "outputId": "7706cfa4-ad96-4a52-e546-7cc83c005161"
   },
   "outputs": [
    {
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       "Unnamed: 0               int64\n",
       "Id                       int64\n",
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   "source": [
    "df.dtypes    "
   ]
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   "cell_type": "markdown",
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   "source": [
    "And have a look at the first rows of our data to see how it looks like"
   ]
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       "      <th>MolWt</th>\n",
       "      <th>MolLogP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100-00-5</td>\n",
       "      <td>O=[N+]([O-])c1ccc(cc1)Cl</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>0.463602</td>\n",
       "      <td>43.14</td>\n",
       "      <td>38.1064</td>\n",
       "      <td>3.003401</td>\n",
       "      <td>244.429658</td>\n",
       "      <td>157.556</td>\n",
       "      <td>2.2482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100-01-6</td>\n",
       "      <td>O=[N+]([O-])c1ccc(N)cc1</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>0.359544</td>\n",
       "      <td>69.16</td>\n",
       "      <td>37.5088</td>\n",
       "      <td>3.003401</td>\n",
       "      <td>242.429658</td>\n",
       "      <td>138.126</td>\n",
       "      <td>1.1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>100-02-7</td>\n",
       "      <td>O=[N+]([O-])c1ccc(O)cc1</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>0.470728</td>\n",
       "      <td>63.37</td>\n",
       "      <td>34.7612</td>\n",
       "      <td>3.003401</td>\n",
       "      <td>241.674771</td>\n",
       "      <td>139.110</td>\n",
       "      <td>1.3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>100-11-8</td>\n",
       "      <td>O=[N+]([O-])c1ccc(cc1)CBr</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.432586</td>\n",
       "      <td>43.14</td>\n",
       "      <td>45.7274</td>\n",
       "      <td>2.913802</td>\n",
       "      <td>257.648013</td>\n",
       "      <td>216.034</td>\n",
       "      <td>2.4897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>100-12-9</td>\n",
       "      <td>O=[N+]([O-])c1ccc(cc1)CC</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.479785</td>\n",
       "      <td>43.14</td>\n",
       "      <td>42.4744</td>\n",
       "      <td>2.913802</td>\n",
       "      <td>253.299498</td>\n",
       "      <td>151.165</td>\n",
       "      <td>2.1572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Id       CAS                     SMILES    Status  \\\n",
       "0           0   1  100-00-5   O=[N+]([O-])c1ccc(cc1)Cl  Training   \n",
       "1           1   2  100-01-6    O=[N+]([O-])c1ccc(N)cc1  Training   \n",
       "2           2   3  100-02-7    O=[N+]([O-])c1ccc(O)cc1  Training   \n",
       "3           3   4  100-11-8  O=[N+]([O-])c1ccc(cc1)CBr  Training   \n",
       "4           4   5  100-12-9   O=[N+]([O-])c1ccc(cc1)CC  Training   \n",
       "\n",
       "   Experimental value Predicted value  NumValenceElectrons       qed   TPSA  \\\n",
       "0                   1               1                   52  0.463602  43.14   \n",
       "1                   1               1                   52  0.359544  69.16   \n",
       "2                   0               1                   52  0.470728  63.37   \n",
       "3                   1               0                   58  0.432586  43.14   \n",
       "4                   0               0                   58  0.479785  43.14   \n",
       "\n",
       "     MolMR  BalabanJ     BertzCT    MolWt  MolLogP  \n",
       "0  38.1064  3.003401  244.429658  157.556   2.2482  \n",
       "1  37.5088  3.003401  242.429658  138.126   1.1770  \n",
       "2  34.7612  3.003401  241.674771  139.110   1.3004  \n",
       "3  45.7274  2.913802  257.648013  216.034   2.4897  \n",
       "4  42.4744  2.913802  253.299498  151.165   2.1572  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eb8STE1Gc1qN"
   },
   "source": [
    "and access rows by index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "F-vLO_KHeUEy",
    "outputId": "8d1c9199-93b7-4f76-a0e0-fb035b7f9c56",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
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       "      <td>45.7274</td>\n",
       "      <td>2.913802</td>\n",
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       "      <td>2.4897</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Id       CAS                     SMILES    Status  \\\n",
       "0           0   1  100-00-5   O=[N+]([O-])c1ccc(cc1)Cl  Training   \n",
       "1           1   2  100-01-6    O=[N+]([O-])c1ccc(N)cc1  Training   \n",
       "2           2   3  100-02-7    O=[N+]([O-])c1ccc(O)cc1  Training   \n",
       "3           3   4  100-11-8  O=[N+]([O-])c1ccc(cc1)CBr  Training   \n",
       "\n",
       "   Experimental value Predicted value  NumValenceElectrons       qed   TPSA  \\\n",
       "0                   1               1                   52  0.463602  43.14   \n",
       "1                   1               1                   52  0.359544  69.16   \n",
       "2                   0               1                   52  0.470728  63.37   \n",
       "3                   1               0                   58  0.432586  43.14   \n",
       "\n",
       "     MolMR  BalabanJ     BertzCT    MolWt  MolLogP  \n",
       "0  38.1064  3.003401  244.429658  157.556   2.2482  \n",
       "1  37.5088  3.003401  242.429658  138.126   1.1770  \n",
       "2  34.7612  3.003401  241.674771  139.110   1.3004  \n",
       "3  45.7274  2.913802  257.648013  216.034   2.4897  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_rows = df.iloc[:4]\n",
    "first_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ejwwO7ETcTPw"
   },
   "source": [
    "We can access columns in the DataFrame by the column's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4domchYBdi-f",
    "outputId": "967c3ff1-4905-442a-be5e-2a84bb89c037"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       0\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "5759    1\n",
       "5760    1\n",
       "5761    1\n",
       "5762    1\n",
       "5763    1\n",
       "Name: Experimental value, Length: 5764, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_experimental = df['Experimental value']\n",
    "y_experimental"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ro4VEmtzei8w"
   },
   "source": [
    "If we would like to get the subset of data that is labeled as mutagenic (i.e., 'Experimental value' equal to 1), we could do it like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 679
    },
    "id": "p_1NdUU2gVhn",
    "outputId": "10082daf-42b4-4fa9-b260-3e2292777f0f"
   },
   "outputs": [
    {
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       "      <td>149.149</td>\n",
       "      <td>2.23780</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
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       "      <td>85.425922</td>\n",
       "      <td>315.186</td>\n",
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       "      <td>N(CCBr)CCBr</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.568556</td>\n",
       "      <td>12.03</td>\n",
       "      <td>40.4577</td>\n",
       "      <td>2.447473</td>\n",
       "      <td>28.870765</td>\n",
       "      <td>230.931</td>\n",
       "      <td>1.36580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5762</th>\n",
       "      <td>5762</td>\n",
       "      <td>5770</td>\n",
       "      <td>52583-35-4</td>\n",
       "      <td>N#Cc2cc(cc(c2(N=Nc1ccc(cc1(NC(=O)C))N(CCOC)CCO...</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>0.264581</td>\n",
       "      <td>185.59</td>\n",
       "      <td>125.3525</td>\n",
       "      <td>2.552977</td>\n",
       "      <td>1169.342047</td>\n",
       "      <td>485.457</td>\n",
       "      <td>3.84768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5763</th>\n",
       "      <td>5763</td>\n",
       "      <td>5771</td>\n",
       "      <td>188021-38-7</td>\n",
       "      <td>O(c1c(cc(cc1Br)C)C(C)(C)C)CC=C</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0.735392</td>\n",
       "      <td>9.23</td>\n",
       "      <td>73.2710</td>\n",
       "      <td>3.263016</td>\n",
       "      <td>388.457194</td>\n",
       "      <td>283.209</td>\n",
       "      <td>4.61982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3251 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    Id          CAS  \\\n",
       "0              0     1     100-00-5   \n",
       "1              1     2     100-01-6   \n",
       "3              3     4     100-11-8   \n",
       "6              6     7     100-13-0   \n",
       "7              7     8     100-14-1   \n",
       "...          ...   ...          ...   \n",
       "5759        5759  5767   20395-16-8   \n",
       "5760        5760  5768   34718-47-3   \n",
       "5761        5761  5769   43204-63-3   \n",
       "5762        5762  5770   52583-35-4   \n",
       "5763        5763  5771  188021-38-7   \n",
       "\n",
       "                                                 SMILES    Status  \\\n",
       "0                              O=[N+]([O-])c1ccc(cc1)Cl  Training   \n",
       "1                               O=[N+]([O-])c1ccc(N)cc1  Training   \n",
       "3                             O=[N+]([O-])c1ccc(cc1)CBr  Training   \n",
       "6                             O=[N+]([O-])c1ccc(C=C)cc1  Training   \n",
       "7                             O=[N+]([O-])c1ccc(cc1)CCl  Training   \n",
       "...                                                 ...       ...   \n",
       "5759               O=C1N(C(=O)N(C(=O)N1CC=C)CC2OC2)CC=C  Training   \n",
       "5760                                 O=C(C(Br)(Br)Br)Cl  Training   \n",
       "5761                                        N(CCBr)CCBr  Training   \n",
       "5762  N#Cc2cc(cc(c2(N=Nc1ccc(cc1(NC(=O)C))N(CCOC)CCO...  Training   \n",
       "5763                     O(c1c(cc(cc1Br)C)C(C)(C)C)CC=C  Training   \n",
       "\n",
       "      Experimental value Predicted value  NumValenceElectrons       qed  \\\n",
       "0                      1               1                   52  0.463602   \n",
       "1                      1               1                   52  0.359544   \n",
       "3                      1               0                   58  0.432586   \n",
       "6                      1               0                   56  0.477660   \n",
       "7                      1               1                   58  0.389482   \n",
       "...                  ...             ...                  ...       ...   \n",
       "5759                   1               0                  102  0.485090   \n",
       "5760                   1               1                   42  0.495987   \n",
       "5761                   1               1                   44  0.568556   \n",
       "5762                   1               1                  184  0.264581   \n",
       "5763                   1               0                   88  0.735392   \n",
       "\n",
       "        TPSA     MolMR  BalabanJ      BertzCT    MolWt  MolLogP  \n",
       "0      43.14   38.1064  3.003401   244.429658  157.556  2.24820  \n",
       "1      69.16   37.5088  3.003401   242.429658  138.126  1.17700  \n",
       "3      43.14   45.7274  2.913802   257.648013  216.034  2.48970  \n",
       "6      43.14   43.1874  3.000887   276.648462  149.149  2.23780  \n",
       "7      43.14   42.6534  2.913802   257.648013  171.583  2.33360  \n",
       "...      ...       ...       ...          ...      ...      ...  \n",
       "5759   78.53   69.3560  2.668492   627.435628  265.269 -1.05750  \n",
       "5760   17.07   40.3720  3.791118    85.425922  315.186  2.59030  \n",
       "5761   12.03   40.4577  2.447473    28.870765  230.931  1.36580  \n",
       "5762  185.59  125.3525  2.552977  1169.342047  485.457  3.84768  \n",
       "5763    9.23   73.2710  3.263016   388.457194  283.209  4.61982  \n",
       "\n",
       "[3251 rows x 15 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutagenic_data = df[df['Experimental value']==1]\n",
    "mutagenic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the predictions by VEGA which we'll use to compare our results with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "5759    0\n",
       "5760    1\n",
       "5761    1\n",
       "5762    1\n",
       "5763    0\n",
       "Name: Predicted value, Length: 5764, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predicted value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice the data type says `object` even though it should be all `0` and `1`. Let's try to find out why:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   1\n",
       "3                   0\n",
       "1844    Non Predicted\n",
       "Name: Predicted value, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predicted value'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>CAS</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Status</th>\n",
       "      <th>Experimental value</th>\n",
       "      <th>Predicted value</th>\n",
       "      <th>NumValenceElectrons</th>\n",
       "      <th>qed</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>MolMR</th>\n",
       "      <th>BalabanJ</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>MolLogP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>1844</td>\n",
       "      <td>1846</td>\n",
       "      <td>16709-86-7</td>\n",
       "      <td>C=C[Si](C)(C)CCl</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Predicted</td>\n",
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       "      <td>0.401439</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.399</td>\n",
       "      <td>3.575471</td>\n",
       "      <td>68.480406</td>\n",
       "      <td>134.682</td>\n",
       "      <td>2.19800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>2194</td>\n",
       "      <td>2197</td>\n",
       "      <td>2179-59-1</td>\n",
       "      <td>C=CCSSCCC</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Predicted</td>\n",
       "      <td>48</td>\n",
       "      <td>0.333839</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.404</td>\n",
       "      <td>2.616293</td>\n",
       "      <td>52.490225</td>\n",
       "      <td>148.296</td>\n",
       "      <td>2.96380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4118</th>\n",
       "      <td>4118</td>\n",
       "      <td>4125</td>\n",
       "      <td>624-92-0</td>\n",
       "      <td>CSSC</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Predicted</td>\n",
       "      <td>26</td>\n",
       "      <td>0.452840</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.030</td>\n",
       "      <td>1.974745</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>94.204</td>\n",
       "      <td>1.62740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4180</th>\n",
       "      <td>4180</td>\n",
       "      <td>4187</td>\n",
       "      <td>6317-18-6</td>\n",
       "      <td>N#CSCSC#N</td>\n",
       "      <td>Training</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Predicted</td>\n",
       "      <td>36</td>\n",
       "      <td>0.321595</td>\n",
       "      <td>47.58</td>\n",
       "      <td>31.275</td>\n",
       "      <td>2.768386</td>\n",
       "      <td>95.083765</td>\n",
       "      <td>130.197</td>\n",
       "      <td>1.37246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5633</th>\n",
       "      <td>5633</td>\n",
       "      <td>5641</td>\n",
       "      <td>7783-54-2</td>\n",
       "      <td>FN(F)F</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>Non Predicted</td>\n",
       "      <td>26</td>\n",
       "      <td>0.383980</td>\n",
       "      <td>3.24</td>\n",
       "      <td>5.163</td>\n",
       "      <td>2.323790</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>71.001</td>\n",
       "      <td>0.94190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5655</th>\n",
       "      <td>5655</td>\n",
       "      <td>5663</td>\n",
       "      <td>676-83-5</td>\n",
       "      <td>CP(Cl)Cl</td>\n",
       "      <td>Training</td>\n",
       "      <td>1</td>\n",
       "      <td>Non Predicted</td>\n",
       "      <td>26</td>\n",
       "      <td>0.426990</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2.323790</td>\n",
       "      <td>10.754888</td>\n",
       "      <td>116.915</td>\n",
       "      <td>2.40570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    Id         CAS            SMILES    Status  \\\n",
       "1844        1844  1846  16709-86-7  C=C[Si](C)(C)CCl  Training   \n",
       "2194        2194  2197   2179-59-1         C=CCSSCCC  Training   \n",
       "4118        4118  4125    624-92-0              CSSC  Training   \n",
       "4180        4180  4187   6317-18-6         N#CSCSC#N  Training   \n",
       "5633        5633  5641   7783-54-2            FN(F)F  Training   \n",
       "5655        5655  5663    676-83-5          CP(Cl)Cl  Training   \n",
       "\n",
       "      Experimental value Predicted value  NumValenceElectrons       qed  \\\n",
       "1844                   0   Non Predicted                   42  0.401439   \n",
       "2194                   0   Non Predicted                   48  0.333839   \n",
       "4118                   0   Non Predicted                   26  0.452840   \n",
       "4180                   0   Non Predicted                   36  0.321595   \n",
       "5633                   1   Non Predicted                   26  0.383980   \n",
       "5655                   1   Non Predicted                   26  0.426990   \n",
       "\n",
       "       TPSA   MolMR  BalabanJ    BertzCT    MolWt  MolLogP  \n",
       "1844   0.00  38.399  3.575471  68.480406  134.682  2.19800  \n",
       "2194   0.00  45.404  2.616293  52.490225  148.296  2.96380  \n",
       "4118   0.00  27.030  1.974745   6.000000   94.204  1.62740  \n",
       "4180  47.58  31.275  2.768386  95.083765  130.197  1.37246  \n",
       "5633   3.24   5.163  2.323790   8.000000   71.001  0.94190  \n",
       "5655   0.00  24.550  2.323790  10.754888  116.915  2.40570  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ (df['Predicted value'] != \"0\") & (df['Predicted value'] != \"1\") ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some inputs the VEGA model decides to output `non predicted`. As detailed in the documentation, it does this for particularly uncertain predictions. Let's remove these from the data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_predicted = df[ (df['Predicted value'] != \"0\") & (df['Predicted value'] != \"1\") ].index\n",
    "df_clean = df.drop(non_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Si1CwO1Ol9-r"
   },
   "source": [
    "Let's now collect all the input features of our dataset and remove the unnecessary ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "IahR4BJimCJ2",
    "outputId": "d2e62d3c-e82e-4944-c496-8370a07c5a76"
   },
   "outputs": [
    {
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       "      <th>NumValenceElectrons</th>\n",
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       "      <td>138.126</td>\n",
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       "      <td>43.14</td>\n",
       "      <td>45.7274</td>\n",
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       "      <td>265.269</td>\n",
       "      <td>-1.05750</td>\n",
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       "      <th>5760</th>\n",
       "      <td>42</td>\n",
       "      <td>0.495987</td>\n",
       "      <td>17.07</td>\n",
       "      <td>40.3720</td>\n",
       "      <td>3.791118</td>\n",
       "      <td>85.425922</td>\n",
       "      <td>315.186</td>\n",
       "      <td>2.59030</td>\n",
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       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>44</td>\n",
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       "      <td>230.931</td>\n",
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       "      <td>184</td>\n",
       "      <td>0.264581</td>\n",
       "      <td>185.59</td>\n",
       "      <td>125.3525</td>\n",
       "      <td>2.552977</td>\n",
       "      <td>1169.342047</td>\n",
       "      <td>485.457</td>\n",
       "      <td>3.84768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5763</th>\n",
       "      <td>88</td>\n",
       "      <td>0.735392</td>\n",
       "      <td>9.23</td>\n",
       "      <td>73.2710</td>\n",
       "      <td>3.263016</td>\n",
       "      <td>388.457194</td>\n",
       "      <td>283.209</td>\n",
       "      <td>4.61982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5758 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      NumValenceElectrons       qed    TPSA     MolMR  BalabanJ      BertzCT  \\\n",
       "0                      52  0.463602   43.14   38.1064  3.003401   244.429658   \n",
       "1                      52  0.359544   69.16   37.5088  3.003401   242.429658   \n",
       "2                      52  0.470728   63.37   34.7612  3.003401   241.674771   \n",
       "3                      58  0.432586   43.14   45.7274  2.913802   257.648013   \n",
       "4                      58  0.479785   43.14   42.4744  2.913802   253.299498   \n",
       "...                   ...       ...     ...       ...       ...          ...   \n",
       "5759                  102  0.485090   78.53   69.3560  2.668492   627.435628   \n",
       "5760                   42  0.495987   17.07   40.3720  3.791118    85.425922   \n",
       "5761                   44  0.568556   12.03   40.4577  2.447473    28.870765   \n",
       "5762                  184  0.264581  185.59  125.3525  2.552977  1169.342047   \n",
       "5763                   88  0.735392    9.23   73.2710  3.263016   388.457194   \n",
       "\n",
       "        MolWt  MolLogP  \n",
       "0     157.556  2.24820  \n",
       "1     138.126  1.17700  \n",
       "2     139.110  1.30040  \n",
       "3     216.034  2.48970  \n",
       "4     151.165  2.15720  \n",
       "...       ...      ...  \n",
       "5759  265.269 -1.05750  \n",
       "5760  315.186  2.59030  \n",
       "5761  230.931  1.36580  \n",
       "5762  485.457  3.84768  \n",
       "5763  283.209  4.61982  \n",
       "\n",
       "[5758 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_clean.drop(['Unnamed: 0', 'Id','CAS','SMILES','Status','Experimental value','Predicted value'],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ei-EOGnle97C"
   },
   "source": [
    "### Exercise - manipulate a DataFrame ❗❗\n",
    "\n",
    "* How many molecules in our dataset have a `qed` less than 0.5?\n",
    "* What is the molecule with the largest molecular weight `MolWt`?\n",
    "* What is the average number of valance electrons `NumValenceElectrons` of the molecules in our dataset? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "DwAoulsggXUJ"
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KGvYC2i5hW_A"
   },
   "source": [
    "## Feature scaling 📏"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iTfo1fwH7qMv"
   },
   "source": [
    "It is always a good practice to scale your data before starting modeling. This helps the training process of many machine learning models. This is specially true for kNN which works on distances! The distance between two points is naturally afected by the dimensions of the input space. Look for example at the Euclidean distance, if one dimension ranges from 0 to 10,000 and another ranges from 0 to 1, the former one will potentially impact the distance value much more!\n",
    "\n",
    "We do not want this to happen. Therefore, we need to scale all input features in order to give the same importance to all dimensions regardless of their original scale.\n",
    "\n",
    "Here, we will use the method known as [standardization](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler). Here, we move the distribution of the data to have unit variance and a mean equal to zero.\n",
    "\n",
    "$$\n",
    "\\hat{\\textbf{x}} = \\frac{\\textbf{x}-\\mu_x}{\\sigma_x}\n",
    "$$\n",
    "\n",
    "Of course there are other [scaling methods](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e) are available and you might want to review them and check which one is better than the other in which conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "KlCTYKzV7Lja"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nd3K52ulXb1"
   },
   "source": [
    "We initialize our scaler function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "JDrRFz3o7ORI"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sPkVGBadlbgO"
   },
   "source": [
    "and fit it to our data (i.e., get the mean vector $\\mu_x$ and the standard deviation vector $\\sigma_x$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j6tTHY4Al5U-",
    "outputId": "8e2cff7e-8754-4ecd-c82f-0b3a55e99138"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hAMOgj-JprOW"
   },
   "source": [
    "Now, let's scale our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m4TAblST7lIL",
    "outputId": "ca653b6e-0605-47f1-ec43-d752cf4ebe13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.82233916, -0.30469594, -0.24799507, ..., -0.73084025,\n",
       "        -0.72114687, -0.17433149],\n",
       "       [-0.82233916, -0.93207944,  0.35640473, ..., -0.73613157,\n",
       "        -0.88855579, -0.72662586],\n",
       "       [-0.82233916, -0.26173162,  0.221913  , ..., -0.73812874,\n",
       "        -0.88007765, -0.6630027 ],\n",
       "       ...,\n",
       "       [-1.01013559,  0.32808674, -0.97062682, ..., -1.30113579,\n",
       "        -0.08894773, -0.62928346],\n",
       "       [ 2.27630201, -1.50462555,  3.06087321, ...,  1.71616346,\n",
       "         2.10404877,  0.65033598],\n",
       "       [ 0.0227448 ,  1.33396517, -1.035666  , ..., -0.34979236,\n",
       "         0.36147962,  1.04843958]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_hat = scaler.transform(X)\n",
    "X_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g0zIoG5op95Z"
   },
   "source": [
    "### Exercise - read documentation ❗❗\n",
    "\n",
    "* What are exactly the mean an standard deviation vectors that we used to scaled the data? Go to the [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) and learn how you can access these. Reading the documentation of a library is a super important skill to learn! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "8WDOvs_WqVFj"
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h2uOyvKYB_QE"
   },
   "source": [
    "## Training and test split ✂️\n",
    "\n",
    "Now that we have scaled the data, we have to make sure we split it. Remember, our goal is to create a model that **generalize well** to unseen data and not simply fit some seen data perfectly. To achieve this, our goal while splitting the data should be to ensure that the distribution of the test set is as close as possible to the expected distribution of the future data. Hence, often what we want is to make the training and test datasets to have a similar distribution.\n",
    "\n",
    "For example, let's see if how many molecules in our dataset are mutagenic vs. non-mutagenic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QUCpGIgcB0y6",
    "outputId": "82ed55b9-820f-4539-9cb4-8a827906003e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of mutagenic molecules    : 56.42584230635638\n",
      "Percentage of non-mutagenic molecules: 43.57415769364362\n"
     ]
    }
   ],
   "source": [
    "y = df_clean['Experimental value'].to_numpy()\n",
    "perc_mutagenic = y.sum()/len(y)*100\n",
    "print('Percentage of mutagenic molecules    :', perc_mutagenic)\n",
    "print('Percentage of non-mutagenic molecules:', 100-perc_mutagenic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFlMSb6TCZ7S"
   },
   "source": [
    "In this case, the proportion of mutagenic and non-mutagenic data is very similar. Then, we will go ahead and split the data randomly. However, when you have a much more imbalanced dataset, how would you split the data better? You can look for instance at what [stratified splitting](https://scikit-learn.org/stable/modules/cross_validation.html#stratification) is and why is it important.\n",
    "\n",
    "In summary, when splitting your data, always think about the distribution of your splits!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "DQR8ATRjCPCq"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "I09X-mSqCRtN"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_hat, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yeIZ2XI5jwoD",
    "outputId": "32dc2318-453f-4baa-9b3b-285cac037c8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training points:  4606\n",
      "Training points:  1152\n"
     ]
    }
   ],
   "source": [
    "print('Training points: ', len(y_train))\n",
    "print('Training points: ', len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fnRg4cMqDZia"
   },
   "source": [
    "### Exercise - splits distribution ❗❗\n",
    "\n",
    "* Check what is the proportion of mutagenic molecules in your train and test set? Was the random splitting good?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "outc17O_DVKZ"
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ti0h11a7CjPs"
   },
   "source": [
    "## kNN model 🏘️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "fXHhnxgICgJD"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axfX0cUMEWJN"
   },
   "source": [
    "We initialize the kNN model by specifying the parameter \"k\". Later, we will review some ways that help us in determining this parameter better. For now, let's set it to 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "0C18Ne_tCxdn"
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ny_zGEKvEwf5"
   },
   "source": [
    "we train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WYtljFtOCzB7",
    "outputId": "6d8e7c6c-4233-4f71-8752-1e7c4f0372f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J1UYc51DEz3w"
   },
   "source": [
    "we predict the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "8op0R9jbC4R0"
   },
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M2EDTPLAHWit"
   },
   "source": [
    "Let's now evaluate our kNN model!\n",
    "\n",
    "We will use several metrics for classification, for a quick reminder on them check the [documentation](https://scikit-learn.org/stable/modules/classes.html#classification-metrics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "1iStpf04HV_1"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import (confusion_matrix, accuracy_score, precision_score, \n",
    "                             recall_score, f1_score, roc_auc_score, ConfusionMatrixDisplay) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T6ppJg7yGM2R"
   },
   "source": [
    "Let first look at the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "47uV_7PAg808",
    "outputId": "2b304c3a-788c-4d04-8e05-87a58e77337d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[328, 175],\n",
       "       [140, 509]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CD9QYwXEGQAl"
   },
   "source": [
    "to see a prettier confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "zxE7tmAQGaxn",
    "outputId": "d77df237-8655-48d4-9c0d-d4906b1cf61c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x135c942cd48>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xWWS51LpGv75"
   },
   "source": [
    "Now, let's look at the other metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JDQ4clUhG0E2",
    "outputId": "d4ecf921-195b-4110-c224-98df5b600840"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:   0.7265625      \n",
      "Precision:  0.7441520467836257\n",
      "Recall:     0.7842835130970724\n",
      "F1:         0.7636909227306825\n"
     ]
    }
   ],
   "source": [
    "print('{:<10}  {:<15}'.format('Accuracy:', accuracy_score(y_test, y_pred)))\n",
    "print('{:<10}  {:<15}'.format('Precision:', precision_score(y_test, y_pred)))\n",
    "print('{:<10}  {:<15}'.format('Recall:', recall_score(y_test, y_pred)))\n",
    "print('{:<10}  {:<15}'.format('F1:', f1_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M3WtaK81L8iT"
   },
   "source": [
    "### Comparison to VEGA model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tec9purNJ-OK"
   },
   "source": [
    "What do you think about the metrics? Is the kNN model performing good or bad?\n",
    "Let's compare it to the predictions of a kNN model trained on this same dataset and published as part of the [VEGA platform](https://www.vegahub.eu/portfolio-item/vega-qsar/). In the VEGA model, they have choosen k=4 with a similarity threshold of 0.7 (according to an internal similarity metric) Why is such threshold important?\n",
    "\n",
    "Also, the developers of the VEGA kNN model used the [leave-one-out](https://en.wikipedia.org/wiki/Cross-validation_(statistics)#Leave-one-out_cross-validation) approach to assess the performace of their model. Why is this approach specially suitable when using the kNN algorithm? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nyfyVxebUAAX"
   },
   "source": [
    "#### VEGA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "id": "cD_XnbgGJ9PD",
    "outputId": "2b79c535-7ef7-4ae3-f575-75e48cbdb534"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:   0.7999305314345259\n",
      "Precision:  0.8168631006346329\n",
      "Recall:     0.8319482917820868\n",
      "F1:         0.8243366880146386\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_clean = df_clean['Experimental value'].astype(int)\n",
    "y_vega_clean = df_clean['Predicted value'].astype(int)\n",
    "\n",
    "cm = confusion_matrix(y_clean, y_vega_clean)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "print('{:<10}  {:<15}'.format('Accuracy:', accuracy_score(y_clean, y_vega_clean)))\n",
    "print('{:<10}  {:<15}'.format('Precision:', precision_score(y_clean, y_vega_clean)))\n",
    "print('{:<10}  {:<15}'.format('Recall:', recall_score(y_clean, y_vega_clean)))\n",
    "print('{:<10}  {:<15}'.format('F1:', f1_score(y_clean, y_vega_clean)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x-imyMSBUGaE"
   },
   "source": [
    "#### Exercise - leave-one-out cross-validation ❗❗\n",
    "\n",
    "* Can you now assess our previous kNN model configuration using the leave-one-out approach on the cleaned dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uc3jhsfoUZOD",
    "outputId": "e2208f4e-2555-420e-f533-944d6f46e230"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of folds:  5758\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "\n",
    "X_clean = df_clean.drop(['Unnamed: 0', 'Id','CAS','SMILES','Status','Experimental value','Predicted value'],axis=1)\n",
    "\n",
    "loo = LeaveOneOut()\n",
    "print('Number of folds: ', loo.get_n_splits(X_clean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "irPFyGSuW5H-"
   },
   "outputs": [],
   "source": [
    "y_pred_loo_our_model = np.zeros(y_clean.shape[0])\n",
    "for i, (train_index, test_index) in enumerate(loo.split(X_clean)):\n",
    "  # Get training data for the current fold\n",
    "  X_train_loo = X_clean.iloc[train_index]\n",
    "  y_train_loo = y_clean.iloc[train_index]\n",
    "\n",
    "  # Get test data for the current fold\n",
    "  X_test_loo = X_clean.iloc[test_index]\n",
    "\n",
    "  # Train kNN\n",
    "  # Your code here\n",
    "\n",
    "  # Get prediction on the test molecule\n",
    "  # Your code here\n",
    "\n",
    "  # Store the prediction in `y_pred_loo_our_model`\n",
    "  # Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "id": "6FW3Wk0KYWnC",
    "outputId": "ee03b470-b467-4240-a3b9-040330470fcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:   0.4357415769364363\n",
      "Precision:  0.0            \n",
      "Recall:     0.0            \n",
      "F1:         0.0            \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\Anaconda3\\envs\\ML4ChemEng\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_clean, y_pred_loo_our_model)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "print('{:<10}  {:<15}'.format('Accuracy:', accuracy_score(y_clean, y_pred_loo_our_model)))\n",
    "print('{:<10}  {:<15}'.format('Precision:', precision_score(y_clean, y_pred_loo_our_model)))\n",
    "print('{:<10}  {:<15}'.format('Recall:', recall_score(y_clean, y_pred_loo_our_model)))\n",
    "print('{:<10}  {:<15}'.format('F1:', f1_score(y_clean, y_pred_loo_our_model)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uOnXu2eHHgUR"
   },
   "source": [
    "\n",
    "## Finding the best k in kNN 🔎\n",
    "\n",
    "What about the parameter k? How do we find the best k for our model? Why the VEGA model developers used k=4? Let's try to answer this...\n",
    "\n",
    "k is a hyperparameter of the model and should be chosen using a validation set.\n",
    "\n",
    "```{important}\n",
    "Remember to reserve your test set exclusively for assesing your model! Never use it for training or hyperparameter tuning!  \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "131bcbae8f1a4166b7c187e27752a4a1",
      "0d5dd3b8b7914bafb3c57066f2117f4b",
      "da3943a1533d48339cc99add56f296ec",
      "8ab9c22f3c6b407aab7c2b20542caddf",
      "62cc78dd0f354e4bab79b1adcbbb8d51",
      "bbb549238aca44c0bcc7b41a4e0516d4",
      "34b5cde70d7a41ae82eec617f48a33ff",
      "b01c55a604c741d3838d98d412f6098e",
      "99446b25af0a41efb78e8347a0468bc5",
      "536147b20e874c8a820f2ffbfd010b5a",
      "cbeab16ad6e344eea77e78270864e9bc"
     ]
    },
    "id": "GLeXAFEJnQj8",
    "outputId": "59e721c0-50af-4ea9-a904-f99b613cb10e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c2393312c7641ecbc7dac156862911c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "num_ks = np.arange(1, 100, 2).astype(int)\n",
    "\n",
    "X_train_hyp, X_valid, y_train_hyp, y_valid = train_test_split(X_train, y_train, test_size=0.1, random_state=0)\n",
    "\n",
    "train_accuracy = []\n",
    "valid_accuracy = []\n",
    "\n",
    "for i in tqdm(range(len(num_ks))):\n",
    "  knn = KNeighborsClassifier(n_neighbors=num_ks[i])\n",
    "  knn.fit(X_train_hyp, y_train_hyp)\n",
    "\n",
    "  pred_train = knn.predict(X_train_hyp)\n",
    "  pred_valid  = knn.predict(X_valid)\n",
    "\n",
    "  train_accuracy.append(1-accuracy_score(y_train_hyp, pred_train))\n",
    "  valid_accuracy.append(1-accuracy_score(y_valid, pred_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "S4CftpmcHmKM",
    "outputId": "c57e9c1d-6335-4ff0-e7d6-9142bdb64a65"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(num_ks, train_accuracy, 'bs--', label='Train')\n",
    "plt.plot(num_ks, valid_accuracy, 'rx--', label='Validation')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Misclasification rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kGhwzvOOHtd5"
   },
   "source": [
    "This graph is pretty similar to the one that we saw on slide 9 of Lecture 2. Here, we can see the expected general trend of the performance curves. \n",
    "\n",
    "Which k do you think is the best?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7VJZyifr-4nR"
   },
   "source": [
    "### Cross-validation\n",
    "\n",
    "Problem: if validation set is small and noisy, it might be misleading\n",
    "Idea: Increase the size of the validation set\n",
    "Problem: This would reduce the size of the training set\n",
    "\n",
    "Then, let's use all data for training and validation using k-fold cross-validation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "t_6J0cmd_558"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "914eff8c2fae4f248af2dd1c0ab2adec",
      "c90eb39b1d6e4b5ba8136df7e5480a61",
      "b5249ba10245474e8ea18b502bebffb2",
      "3f07d346263c40ae80952f23251b4b74",
      "7976f19eb8f1414e8de608ed280755a7",
      "d23639c2608d4c4bbbb58fbeeff6c934",
      "164fc49ebc3b466f9b4c25260637210a",
      "4bfe0903581b4ca999017a19607380d5",
      "ef2b2ce93832431aac24fdb8dcbaf7f2",
      "d19e4dface4c4701a08ac745a31b8225",
      "24b693f1c368469faf63263a9c520158"
     ]
    },
    "id": "jXOO3iN3-3n2",
    "outputId": "21856a82-4d55-49d0-eb6f-58e9a1645d5c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77278a5275a64da79c9902886869a2a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_ks = np.arange(1, 50, 1).astype(int)\n",
    "\n",
    "train_misclassification = []\n",
    "valid_misclassification = []\n",
    "for i in tqdm(range(len(num_ks))):\n",
    "  knn = KNeighborsClassifier(n_neighbors=num_ks[i])\n",
    "  cv_dict = cross_validate(knn, X_train, y_train, cv=10, \n",
    "                                 scoring='accuracy', return_train_score=True)\n",
    "  \n",
    "  k_fold_train_scored = cv_dict['train_score']\n",
    "  k_fold_valid_scored = cv_dict['test_score']\n",
    "  \n",
    "  train_misclassification.append(1-k_fold_train_scored.mean())\n",
    "  valid_misclassification.append(1-k_fold_valid_scored.mean())\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "fhwmbs0dzoyu",
    "outputId": "8fe30b73-c5af-41cd-9dea-210c37810d43"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(num_ks, train_misclassification, 'bs--', label='Train')\n",
    "plt.plot(num_ks, valid_misclassification, 'rx--', label='Validation')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Misclasification rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1ruje72l3Gcv",
    "outputId": "f8955444-23e4-4014-83e5-f04032f69eeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k with minimum validation misclassification:  17\n"
     ]
    }
   ],
   "source": [
    "print('k with minimum validation misclassification: ', num_ks[np.argmin(valid_misclassification)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "LeGHGE0Z4Iw9",
    "outputId": "10e7209a-766e-4c65-ef65-7359fef9c92c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(num_ks, valid_misclassification, 'rx--', label='Validation')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Misclasification rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "seihHrN44uia"
   },
   "source": [
    "You can now go back to your implementation of leave-one-out and train your kNN model using the best k that you found. How does it perform compared to the VEGA kNN? Do you got a better model? Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IrYX_LjKkjcE"
   },
   "source": [
    "## Challenge - kNN QSPR for predicting BCF 🥇\n",
    "\n",
    "\n",
    "Develop a kNN model for regression to predict the bioconcentration factor (BCF).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "rv1bQTxtVvuz"
   },
   "outputs": [],
   "source": [
    "if 'google.colab' in str(get_ipython()):\n",
    "  df_bcf = pd.read_csv(\"https://raw.githubusercontent.com/edgarsmdn/MLCE_book/main/references/BCF_training.csv\")\n",
    "else:\n",
    "  df_bcf = pd.read_csv(\"references/BCF_training.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "X5a-AgMZ55Rj"
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "```{bibliography}\n",
    ":filter: docname in docnames\n",
    "```"
   ]
  }
 ],
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