Y_Scrambling_Solubility.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Y-Scrambling-Solubility.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQHZHevuXdEy"
      },
      "source": [
        "# **Y-Scrambling for Modeling of Solubility Dataset**\n",
        "\n",
        "Watch the [**YouTube video**](https://youtu.be/gKCDSiRRyUo) accompanying this Jupyter notebook\n",
        "\n",
        "*Y column is scrambled, shuffled or permutated. In plain English: \"change the order of\".*\n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "*Data Professor YouTube channel, http://youtube.com/dataprofessor*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1qtHa0zXfWM"
      },
      "source": [
        "# Read in data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MdfbvFKXtXq"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nerGP0fCXfgP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "089ac535-f433-4428-ba5f-1ecd0efb5bc9"
      },
      "source": [
        "delaney_with_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n",
        "dataset = pd.read_csv(delaney_with_descriptors_url)\n",
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "      <th>logS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.59540</td>\n",
              "      <td>167.850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-4.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3.60960</td>\n",
              "      <td>308.333</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.695652</td>\n",
              "      <td>-3.893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.521739</td>\n",
              "      <td>-3.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>2.02164</td>\n",
              "      <td>179.219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>-2.581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion   logS\n",
              "0     2.59540  167.850                0.0            0.000000 -2.180\n",
              "1     2.37650  133.405                0.0            0.000000 -2.000\n",
              "2     2.59380  167.850                1.0            0.000000 -1.740\n",
              "3     2.02890  133.405                1.0            0.000000 -1.480\n",
              "4     2.91890  187.375                1.0            0.000000 -3.040\n",
              "...       ...      ...                ...                 ...    ...\n",
              "1139  1.98820  287.343                8.0            0.000000  1.144\n",
              "1140  3.42130  286.114                2.0            0.333333 -4.925\n",
              "1141  3.60960  308.333                4.0            0.695652 -3.893\n",
              "1142  2.56214  354.815                3.0            0.521739 -3.790\n",
              "1143  2.02164  179.219                1.0            0.461538 -2.581\n",
              "\n",
              "[1144 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVue8cSEtYI5"
      },
      "source": [
        "# Y-Scrambling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlL9mp8EdbFm"
      },
      "source": [
        "# Data set\n",
        "X = dataset.drop(['logS'], axis=1)\n",
        "Y = dataset.iloc[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuXAKJhrtbbK"
      },
      "source": [
        "Y.sample(frac=1, replace=False, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVC1BjivfWXo"
      },
      "source": [
        "# Original vs Y-scrambled model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HobSLiRtgWza"
      },
      "source": [
        "### Model from original X-Y pairs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8yWh2hRxDgo",
        "outputId": "d5fa2053-9842-42d9-fd9c-e5fb2fa8c3da"
      },
      "source": [
        "# Model from original X-Y pairs\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# Data set\n",
        "X = dataset.drop(['logS'], axis=1)\n",
        "Y = dataset.iloc[:,-1]\n",
        "# Data split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
        "# Y-Shuffling\n",
        "\n",
        "# Model building\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, Y_train)\n",
        "# Make prediction\n",
        "Y_train_pred = model.predict(X_train)\n",
        "# Compute R2 score\n",
        "r2a = r2_score(Y_train, Y_train_pred)\n",
        "# Print R2 score\n",
        "r2a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7692295963594564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Mxg3d3aRgCTa",
        "outputId": "6bb4a994-23e0-4472-bdb8-15938416e9ef"
      },
      "source": [
        "# Plot of predicted vs actual\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(x=Y_train, y=Y_train_pred, c=\"#7CAE00\", alpha=0.3)\n",
        "\n",
        "# Add trendline\n",
        "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n",
        "z = np.polyfit(Y_train, Y_train_pred, 1)\n",
        "p = np.poly1d(z)\n",
        "\n",
        "plt.plot(Y,p(Y),\"#F8766D\")\n",
        "plt.ylabel('Predicted LogS')\n",
        "plt.xlabel('Experimental LogS')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Experimental LogS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzbvojBOgZSQ"
      },
      "source": [
        "### Build Y-scrambled models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74o3fUjguXdD",
        "outputId": "1f188ec0-f952-4429-b796-f148d2e989ba"
      },
      "source": [
        "# Build Y-scrambled models\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "r2_train_list = []\n",
        "\n",
        "# Data set\n",
        "X = dataset.drop(['logS'], axis=1)\n",
        "Y = dataset.iloc[:,-1]\n",
        "# Data split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
        "\n",
        "for i in range(1000):\n",
        "  # Y-Shuffling\n",
        "  Y_train = Y_train.sample(frac=1, replace=False, random_state=i)\n",
        "  # Model building\n",
        "  model = LinearRegression()\n",
        "  model.fit(X_train, Y_train)\n",
        "  # Make prediction\n",
        "  Y_train_pred = model.predict(X_train)\n",
        "  # Compute R2 score\n",
        "  r2b = r2_score(Y_train, Y_train_pred)\n",
        "  r2_train_list.append(r2b)\n",
        "# Print R2 score\n",
        "r2_train_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.005000466583799712,\n",
              " 0.0007344012911356668,\n",
              " 0.0015350025013735058,\n",
              " 0.008489665717226091,\n",
              " 0.003915607299682988,\n",
              " 0.004548790428902105,\n",
              " 0.0008429663446728464,\n",
              " 0.001615778039415705,\n",
              " 0.006630020361143352,\n",
              " 0.001819193577353495,\n",
              " 0.003317010494101269,\n",
              " 0.0018133191135055737,\n",
              " 0.0030177365815898805,\n",
              " 0.002978056982963273,\n",
              " 0.0032133843178231425,\n",
              " 0.007567690178066466,\n",
              " 0.01087936035200976,\n",
              " 0.008827238527726822,\n",
              " 0.004009406151912254,\n",
              " 0.0025264174317727317,\n",
              " 0.00024635037369347135,\n",
              " 0.0038096144515310515,\n",
              " 0.0021811896733753278,\n",
              " 0.003952612637298625,\n",
              " 0.0007547520675049713,\n",
              " 0.0026415819692209297,\n",
              " 0.004859929867937374,\n",
              " 0.005013421614001201,\n",
              " 0.003921301914299069,\n",
              " 0.008410582384709042,\n",
              " 0.0031573997486421534,\n",
              " 0.0006141583859642363,\n",
              " 0.0037322043369546476,\n",
              " 0.0014226823214904538,\n",
              " 0.002413831601759009,\n",
              " 0.0036567985620454824,\n",
              " 0.005270657997406225,\n",
              " 0.0045796381916292095,\n",
              " 0.00498831882527051,\n",
              " 0.010597172477750827,\n",
              " 0.00871373552960275,\n",
              " 0.003948403001362344,\n",
              " 0.002950385888369267,\n",
              " 0.007037758573460229,\n",
              " 0.010832059826852425,\n",
              " 0.008131938903896963,\n",
              " 0.007999034720979292,\n",
              " 0.008638599076709608,\n",
              " 0.0032013595978932496,\n",
              " 0.0013196311506521452,\n",
              " 0.0013291023473434027,\n",
              " 0.007323949668804786,\n",
              " 0.0018985354897567142,\n",
              " 0.0069323185799359255,\n",
              " 0.006511403844476016,\n",
              " 0.004377721686534275,\n",
              " 0.007408323038023079,\n",
              " 0.003006078037850468,\n",
              " 0.0026539799904796713,\n",
              " 0.0047386951379589926,\n",
              " 0.002689307102032523,\n",
              " 0.004177588941504995,\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "R5kI-S_-4cFF",
        "outputId": "215700ad-e2cb-4dc2-fd0f-1788959b8caa"
      },
      "source": [
        "# Histogram plot\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set_style(\"white\")\n",
        "plt.figure(figsize = (20, 2)) \n",
        "\n",
        "ax = sns.histplot(data=r2_train_list, color=\"skyblue\", kde=True, bins = 10)\n",
        "plt.axvline(r2a)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7Oay5tgnSBD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}