Organic_up_downsampled.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Organic_up_downsampled.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5F1F4jo94W1j"
      },
      "source": [
        "# Objectives: A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes.An effective way to handle imbalanced data is to downsample and upweight the majority class. Let's start by defining those two new terms:\n",
        "\n",
        "Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.\n",
        "Upweighting means adding an example weight to the downsampled class equal to the factor by which you downsampled.\n",
        "-\n",
        "-\n",
        "\n",
        "### Getting Started:\n",
        "- Colab notebooks consist of text cells (like this one) and program code cells, like the one shown below. Code cells are executed by typing the Cmd+Enter keys (or Ctrl-Enter). You can also execute a code cell by mousing over the [ ] symbol in the upper left hand side of the code cell---when you hover over it it will turn into a \"play\" button, and clicking the play button will execute the code cell. You can find other options for executing groups of cells in the \"Runtime\" menu above.\n",
        "\n",
        "- Start by executing the code cell below (the one that begins with the line import pandas as pd). This loads (\"imports\") the required software modules that will be used in the assignment.\n",
        "## Loading Python Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zslB42cvT8S1"
      },
      "source": [
        "#load the packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder,MinMaxScaler,KBinsDiscretizer,OneHotEncoder,StandardScaler\n",
        " \n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        " \n",
        "from sklearn.metrics import confusion_matrix,recall_score\n",
        "from sklearn.compose import ColumnTransformer\n",
        " \n",
        "from scipy import stats\n",
        " \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ePDbwG95Ux8"
      },
      "source": [
        "Take a moment to look at this code block:\n",
        "- import loads a module\n",
        "- import ... as allows you to assign a short alias to the module\n",
        "- from ... import loads a small portion of a module\n",
        "- observe that the import, as and from keywords are color coded purple.\n",
        "indicates a comment (observe that all of the text following the # is color coded green). This text is not interpreted by the computer, and its goal is to provide the human with some information about what is happening.<BR><BR>\n",
        "What do each of these program modules do? You can think of them as being like a library of books that accomplish program tasks. In general, they can be quite complicated. In most cases, you will never learn all of the functionality of a module, and will have to use the documentation to help you determine the relevant parts for solving your problem. It is useful to have a general sense of the types of tasks that each of modules do, so that you can find the appropriate functionality.\n",
        "\n",
        "- `pandas` is a library for handling datasets\n",
        "- `numpy` and `scipy` are libraries for mathematical and scientific computing\n",
        "- `matplotlib` and `plotly` are libraries for data visualization\n",
        "- `sklearn` and `imblearn` are libraries for machine learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vskQhri6v_o"
      },
      "source": [
        "Importing file:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgZG8gewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwoKICAgICAgbGV0IHBlcmNlbnREb25lID0gZmlsZURhdGEuYnl0ZUxlbmd0aCA9PT0gMCA/CiAgICAgICAgICAxMDAgOgogICAgICAgICAgTWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCk7CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPSBgJHtwZXJjZW50RG9uZX0lIGRvbmVgOwoKICAgIH0gd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCk7CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "VW7y749jnRAG",
        "outputId": "fd9c8746-9855-4b72-dfb9-77696c6ab9c9"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-39339415-122b-4738-8835-95c011b01076\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-39339415-122b-4738-8835-95c011b01076\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ORGANIC.xlsx to ORGANIC.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSgLKZShKiAj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8f900439-f361-4112-fe01-15bf64922614"
      },
      "source": [
        "import io\n",
        "df = pd.read_excel(io.BytesIO(uploaded['ORGANIC.xlsx']))\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-400d183d-9317-4b73-a711-f6fbe90b22c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>shelf</th>\n",
              "      <th>book</th>\n",
              "      <th>page</th>\n",
              "      <th>λ</th>\n",
              "      <th>n</th>\n",
              "      <th>k</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>organic</td>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>Smith</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.196</td>\n",
              "      <td>0.6096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>organic</td>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>Smith</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.209</td>\n",
              "      <td>0.6501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>organic</td>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>Smith</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.228</td>\n",
              "      <td>0.7096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>organic</td>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>Smith</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.235</td>\n",
              "      <td>0.7315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>organic</td>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>Smith</td>\n",
              "      <td>0.1012</td>\n",
              "      <td>1.417</td>\n",
              "      <td>0.4472</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-400d183d-9317-4b73-a711-f6fbe90b22c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-400d183d-9317-4b73-a711-f6fbe90b22c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-400d183d-9317-4b73-a711-f6fbe90b22c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     shelf                                     book   page       λ      n  \\\n",
              "0  organic  (C2ClF3)n - polychlorotrifluoroethylene  Smith  0.0900  1.196   \n",
              "1  organic  (C2ClF3)n - polychlorotrifluoroethylene  Smith  0.0900  1.209   \n",
              "2  organic  (C2ClF3)n - polychlorotrifluoroethylene  Smith  0.0900  1.228   \n",
              "3  organic  (C2ClF3)n - polychlorotrifluoroethylene  Smith  0.0900  1.235   \n",
              "4  organic  (C2ClF3)n - polychlorotrifluoroethylene  Smith  0.1012  1.417   \n",
              "\n",
              "        k  \n",
              "0  0.6096  \n",
              "1  0.6501  \n",
              "2  0.7096  \n",
              "3  0.7315  \n",
              "4  0.4472  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JudwliVZdo9k"
      },
      "source": [
        "# Creating a copy so that our original remain intact.\n",
        "new_df = df.copy()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUYPsQkEdUXa"
      },
      "source": [
        "## Exploring the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tf4eooFedqpa",
        "outputId": "db785ac4-9435-49c0-d394-becee6168927"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(252130, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlD2Qz6O6_qf",
        "outputId": "f1a48515-6de9-4ba7-b213-022c3bc1ecca"
      },
      "source": [
        "# Investigating all the elements within each feature\n",
        "for column in df:\n",
        "    unique_vals = np.unique(df[column].astype(str))\n",
        "    nr_values = len(unique_vals)\n",
        "    \n",
        "    if nr_values < 36:\n",
        "        print('The number of values for feature -  {} : {} -- {}'.format(column, nr_values, unique_vals))\n",
        "    else:\n",
        "        print('The number of values for feature {} : {}'.format(column, nr_values))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of values for feature -  shelf : 1 -- ['organic']\n",
            "The number of values for feature book : 61\n",
            "The number of values for feature page : 59\n",
            "The number of values for feature λ : 54358\n",
            "The number of values for feature n : 41463\n",
            "The number of values for feature k : 195870\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iu9VYSl4dvZB",
        "outputId": "98ac20de-1f98-4178-92ca-b803ff660ebb"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 252130 entries, 0 to 252129\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   shelf   252130 non-null  object \n",
            " 1   book    252130 non-null  object \n",
            " 2   page    252129 non-null  object \n",
            " 3   λ       252129 non-null  float64\n",
            " 4   n       245286 non-null  float64\n",
            " 5   k       242239 non-null  float64\n",
            "dtypes: float64(3), object(3)\n",
            "memory usage: 11.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96-HQoVCd3Ew"
      },
      "source": [
        "#filling missing values in k with '0'\n",
        "df['k'].fillna(0,inplace=True)\n",
        "# Dropping all the null values from n\n",
        "df.dropna(axis=0,inplace=True)   "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GmPQ7gU7owt"
      },
      "source": [
        "We don't require 'Shelf' and 'Page' for the training and texting purpose.\n",
        "Dropping \"Shelf\" and \"Page\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yP600wMP7iI2"
      },
      "source": [
        "df.drop(['shelf','page'],axis=1,inplace=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxIL0iWOePLp",
        "outputId": "9df83fba-5d03-42e7-cf1d-85bc69347beb"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(245286, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kvp5WJmY78Ze"
      },
      "source": [
        "We are down from 252130 rows to 245286, and from 6 columns to 4.<br>\n",
        "We also need to catagories Wavelength into \"UV\", \"visible_light\", \"near_IR\", \"IR_rays\" and \"Far_IR_Rays\", based on the range mentioned below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N1hXouhNUURz",
        "outputId": "4944e4b4-8457-459d-8e92-c1c646d31be5"
      },
      "source": [
        "# Creating a new Column for the wavelength catagory:\n",
        "df['λ_binned'] = pd.cut(df['λ'], [-np.inf,0.4,0.75,1.5,4.0,np.inf],\n",
        "                            labels=['UV','visible_light','near_IR','IR_rays','Far_IR_Rays'])\n",
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-49d621d7-c338-4988-8021-fbf602dd532e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>book</th>\n",
              "      <th>λ</th>\n",
              "      <th>n</th>\n",
              "      <th>k</th>\n",
              "      <th>λ_binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.196</td>\n",
              "      <td>0.6096</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.209</td>\n",
              "      <td>0.6501</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.228</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.235</td>\n",
              "      <td>0.7315</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.1012</td>\n",
              "      <td>1.417</td>\n",
              "      <td>0.4472</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49d621d7-c338-4988-8021-fbf602dd532e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49d621d7-c338-4988-8021-fbf602dd532e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49d621d7-c338-4988-8021-fbf602dd532e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      book       λ      n       k λ_binned\n",
              "0  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.196  0.6096       UV\n",
              "1  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.209  0.6501       UV\n",
              "2  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.228  0.7096       UV\n",
              "3  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.235  0.7315       UV\n",
              "4  (C2ClF3)n - polychlorotrifluoroethylene  0.1012  1.417  0.4472       UV"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VSX7Bept52R",
        "outputId": "f292dd8a-88f2-489a-a249-fc3bca9a56d8"
      },
      "source": [
        "# Distribution of datapoints amongst different wavelengths\n",
        "df.λ_binned.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IR_rays          135408\n",
              "Far_IR_Rays       66678\n",
              "near_IR           35446\n",
              "visible_light      5980\n",
              "UV                 1774\n",
              "Name: λ_binned, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Ew5stpwMWlhZ",
        "outputId": "64f23d26-d684-40e2-aea5-214c72a80235"
      },
      "source": [
        "df.λ_binned.value_counts().plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eRuNqvBZbTE"
      },
      "source": [
        "### There is very high class imbalance - We can see that uv and visible light are having very few samples, compared to the next three.\n",
        "\n",
        "- Dividing the dataframe into two parts : `df_minor` and `df_major`.\n",
        "-`df_minor` will have \"UV\" and \"Visible Light\".\n",
        "- `df_major` will have \"near_IR\", \"IR_rays\" and \"Far_IR_Rays\". "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Seiq23x0ZBA2",
        "outputId": "263d1167-0008-486e-f2d8-55b087fbb908"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(245286, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwzi96I5VZ31",
        "outputId": "404a4615-4b1b-470f-8808-ac67d59f2832"
      },
      "source": [
        "# Creating df_minor and df_major based on the wavelength category mensioned above\n",
        "df_minor = df[ (df.λ_binned=='UV') | (df.λ_binned=='visible_light')]\n",
        "df_major = df[df.λ_binned!='UV']\n",
        "df_major = df_major[df.λ_binned!='visible_light']\n",
        "df_major.λ_binned.unique()\n",
        "print('Length of df_minor : {}\\nLength of df_major : {} '.format(len(df_minor), len(df_major)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of df_minor : 7754\n",
            "Length of df_major : 237532 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZNhkw73gmLk"
      },
      "source": [
        "- Downsampling `majority class` i.e. `df_major`\n",
        "- Upsampling `minority class`, i.e. `df_minor`\n",
        "- Combining the above two in `df_up_down_sampled`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ha4hTs1qU7b1"
      },
      "source": [
        "from sklearn.utils import resample\n",
        "#Downsample majority class\n",
        "df_majority_downsampled = resample(df_major, \n",
        "                                 replace=False,    \n",
        "                                 n_samples=150000,\n",
        "                                 stratify = df_major['λ_binned'],\n",
        "                                 random_state=42)\n",
        "#Upsample minority class\n",
        "df_minority_upsampled = resample(df_minor, \n",
        "                                 replace=True,     \n",
        "                                 n_samples=60000,\n",
        "                                 stratify = df_minor['λ_binned'],\n",
        "                                 random_state=42)\n",
        "# Combine minority class with df_majority_downsampled class\n",
        "df_up_down_sampled = pd.concat([df_majority_downsampled, df_minority_upsampled],ignore_index=True)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aJo9IUXU7qd",
        "outputId": "0bf26a7e-9f8c-4c7f-c769-a057c1e3640d"
      },
      "source": [
        "# Final shape of the combined dataframe\n",
        "print(\"Size of the new combined dataframe 'df_up_down_sampled' : \", end= \"\")\n",
        "print(df_up_down_sampled.shape[0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the new combined dataframe 'df_up_down_sampled' : 210000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWprYr81ov5M",
        "outputId": "afe80f68-a045-49e8-89d7-c6c379332f74"
      },
      "source": [
        "# Distribution of datapoint amongst different Wavelength categories.\n",
        "df_up_down_sampled.λ_binned.value_counts()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IR_rays          85509\n",
              "visible_light    46273\n",
              "Far_IR_Rays      42107\n",
              "near_IR          22384\n",
              "UV               13727\n",
              "Name: λ_binned, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "rw5-O20_Z2mo",
        "outputId": "dc2ce042-550d-4e5d-b658-1973cb775c9f"
      },
      "source": [
        "df_up_down_sampled.λ_binned.value_counts().plot(kind='bar')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8ZYaJ_BWarvJ",
        "outputId": "2acaf1b7-82d0-4806-8a1f-7ba2a7c9421b"
      },
      "source": [
        "df_up_down_sampled.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ebf7216d-59e9-4479-a66d-72484fffde32\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>book</th>\n",
              "      <th>λ</th>\n",
              "      <th>n</th>\n",
              "      <th>k</th>\n",
              "      <th>λ_binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nitrobenzene</td>\n",
              "      <td>2.17319</td>\n",
              "      <td>1.52008</td>\n",
              "      <td>0.000180</td>\n",
              "      <td>IR_rays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>dichloromethane</td>\n",
              "      <td>3.95990</td>\n",
              "      <td>1.41050</td>\n",
              "      <td>0.000301</td>\n",
              "      <td>IR_rays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>3.54851</td>\n",
              "      <td>1.38379</td>\n",
              "      <td>0.045790</td>\n",
              "      <td>IR_rays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>octane</td>\n",
              "      <td>2.60810</td>\n",
              "      <td>1.39480</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>IR_rays</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>nitrobenzene</td>\n",
              "      <td>10.65300</td>\n",
              "      <td>1.51348</td>\n",
              "      <td>0.020930</td>\n",
              "      <td>Far_IR_Rays</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebf7216d-59e9-4479-a66d-72484fffde32')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ebf7216d-59e9-4479-a66d-72484fffde32 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ebf7216d-59e9-4479-a66d-72484fffde32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              book         λ        n         k     λ_binned\n",
              "0     nitrobenzene   2.17319  1.52008  0.000180      IR_rays\n",
              "1  dichloromethane   3.95990  1.41050  0.000301      IR_rays\n",
              "2         Methanol   3.54851  1.38379  0.045790      IR_rays\n",
              "3           octane   2.60810  1.39480  0.000110      IR_rays\n",
              "4     nitrobenzene  10.65300  1.51348  0.020930  Far_IR_Rays"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wz5vqHIIiDpB"
      },
      "source": [
        "## Splitting the modified Data\n",
        "*Creating Input features (attributes) and Target Vectors for Training and testing the ML model.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtGIREUKUUV7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a559ea5-2b1c-4b45-fb16-0609b8492e63"
      },
      "source": [
        "df_up_down_sampled.drop('λ',axis=1,inplace=True)\n",
        "y = df_up_down_sampled['book']               # Target Vector\n",
        "x = df_up_down_sampled.drop('book',axis=1)   # Input Features (attributes)\n",
        "\n",
        "print('x shape: {}'.format(np.shape(x)[0]))\n",
        "print('y shape: {}'.format(np.shape(y)[0]))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x shape: 210000\n",
            "y shape: 210000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viwsKfgo--EA"
      },
      "source": [
        "*`75%` data is for training and `25%` data is for testing.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOAFXJ8aZEHU"
      },
      "source": [
        "#split the data for training and testing\n",
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,random_state=42,stratify=x.λ_binned)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YOulYQGRAVl",
        "outputId": "6b3e8b48-0541-41bc-adcd-d2fe2ca87f00"
      },
      "source": [
        "print('Length of x_test : {}\\nLength of y_test : {}\\nLength of x_train : {}\\nLength of y_train : {}'.format(len(x_test),  len(y_test), len(x_train), len(y_train)))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of x_test : 52500\n",
            "Length of y_test : 52500\n",
            "Length of x_train : 157500\n",
            "Length of y_train : 157500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__wCDjgo_k_5"
      },
      "source": [
        "`λ_binned` is our Catagorical features, whereas, `n` and `k` are Numerical features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SydCA-TUUUYt"
      },
      "source": [
        "cat_features = ['λ_binned']\n",
        "numeric_features = ['n','k']"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsjsG0piUUcI"
      },
      "source": [
        "#preprocessing and training pipeline with our own bins\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "                                      ('scaler', StandardScaler())\n",
        "                                      ])\n",
        "\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "                                          ('onehot',OneHotEncoder())\n",
        "                                          ])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "                  ('numeric', numeric_transformer, numeric_features),   #apply the transformation\n",
        "                  ('category',categorical_transformer,cat_features)\n",
        "                  ]) \n",
        "\n",
        "pipeline = Pipeline(steps = [\n",
        "               ('preprocessor', preprocessor)\n",
        "              ,('classifier',RandomForestClassifier(random_state=42))\n",
        "           ])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6k8WfzWsi4rC"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hI79HLbTUUjL",
        "outputId": "fbb7eac2-99a1-424a-9876-42942cb2135c"
      },
      "source": [
        "# model\n",
        "rf_model = pipeline.fit(x_train, y_train)\n",
        "print (rf_model)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pipeline(steps=[('preprocessor',\n",
            "                 ColumnTransformer(transformers=[('numeric',\n",
            "                                                  Pipeline(steps=[('scaler',\n",
            "                                                                   StandardScaler())]),\n",
            "                                                  ['n', 'k']),\n",
            "                                                 ('category',\n",
            "                                                  Pipeline(steps=[('onehot',\n",
            "                                                                   OneHotEncoder())]),\n",
            "                                                  ['λ_binned'])])),\n",
            "                ('classifier', RandomForestClassifier(random_state=42))])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STLDHnKArmMC",
        "outputId": "ac1d274d-1597-459b-95b6-e79c85c6ce6b"
      },
      "source": [
        "# Training and testing Accuracy\n",
        "print(f'Training accuracy {rf_model.score(x_train,y_train)}')\n",
        "print(f'Test accuracy {rf_model.score(x_test,y_test)}')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy 0.978031746031746\n",
            "Test accuracy 0.8706285714285714\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "UV = x_test[x_test.λ_binned== 'UV']\n",
        "UV_y = y_test[x_test.λ_binned== 'UV']\n",
        "Visible = x_test[x_test.λ_binned== \"Visible\"]\n",
        "Visible_y = y_test[x_test.λ_binned == \"Visible\"]\n",
        "Near_IR = x_test[x_test.λ_binned== 'Near_IR']\n",
        "Near_IR_y = y_test[x_test.λ_binned== 'Near_IR']\n",
        "IR = x_test[x_test.λ_binned== 'IR']\n",
        "IR_y = y_test[x_test.λ_binned== 'IR']\n",
        "Far_IR = x_test[x_test.λ_binned== 'Far_IR']\n",
        "Far_IR_y = y_test[x_test.λ_binned== 'Far_IR']"
      ],
      "metadata": {
        "id": "0Tujf74Aq5V7"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiPoDzSNHU3-"
      },
      "source": [
        "### Precision, Recall, F1-Score of this model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ployDUv2G4hk",
        "outputId": "afb20d6b-37cd-49a6-de74-e5eb3b1c3a67"
      },
      "source": [
        "y_pred = rf_model.predict(x_test)\n",
        "print(len(y_pred), len(x_test))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52500 52500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZGa43hMjpPF",
        "outputId": "74aff505-8a17-450f-de6b-ec5499ccdbaa"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                         precision    recall  f1-score   support\n",
            "\n",
            "(C2ClF3)n - polychlorotrifluoroethylene       0.38      0.44      0.41        54\n",
            "           (C6H12)n - polymethylpentene       0.00      0.00      0.00         7\n",
            "                              Acetylene       1.00      1.00      1.00       200\n",
            "                            Cyclohexane       1.00      1.00      1.00       907\n",
            "                          Ethyl acetate       1.00      0.99      0.99       167\n",
            "                               Ethylene       1.00      1.00      1.00       219\n",
            "                                Heptane       1.00      1.00      1.00       617\n",
            "                                 Hexane       1.00      1.00      1.00       762\n",
            "                               Methanol       0.88      0.90      0.89      2870\n",
            "                 Pentanol, amyl alcohol       0.98      0.98      0.98       144\n",
            "                            acetic_acid       0.99      0.97      0.98       156\n",
            "                                acetone       0.98      0.99      0.99       171\n",
            "                           acetonitrile       0.96      0.97      0.97      2631\n",
            "                                benzene       0.82      0.83      0.83      2450\n",
            "                              bromoform       0.75      0.75      0.75        16\n",
            "                                butanol       1.00      1.00      1.00       290\n",
            "                   carbon_tetrachloride       0.99      0.99      0.99       135\n",
            "                              cellulose       0.98      0.98      0.98       125\n",
            "                             chloroform       1.00      1.00      1.00        66\n",
            "                         cinnamaldehyde       0.98      0.93      0.95        88\n",
            "                        dichloromethane       0.90      0.80      0.85       212\n",
            "                      diethyl_phthalate       0.57      0.40      0.47        93\n",
            "                        diethyl_sulfite       0.85      0.80      0.82       161\n",
            "          diisopropyl_methylphosphonate       0.42      0.19      0.26        78\n",
            "                diisopropylaminoethanol       0.83      0.84      0.83      2392\n",
            "             dimethyl_methylphosphonate       0.82      0.80      0.81      2512\n",
            "                     dimethyl_sulfoxide       1.00      1.00      1.00       209\n",
            "                                dioxane       0.95      0.96      0.96        56\n",
            "                                 ethane       1.00      1.00      1.00       205\n",
            "                                ethanol       0.88      0.88      0.88      3496\n",
            "                        ethyl_cinnamate       1.00      1.00      1.00       210\n",
            "                       ethyl_salicylate       0.96      0.94      0.95        80\n",
            "                        ethylene_glycol       0.97      0.96      0.97      1107\n",
            "                               glycerol       0.94      0.89      0.91       120\n",
            "                                methane       0.91      0.87      0.89       335\n",
            "                      methyl_salicylate       1.00      0.97      0.98        86\n",
            "                           nitrobenzene       0.87      0.87      0.87      2494\n",
            "                                 octane       0.41      0.42      0.42      2586\n",
            "                              oxathiane       0.81      0.80      0.81      2476\n",
            "                                pentane       0.95      0.91      0.93       728\n",
            "                            pentanediol       0.00      0.00      0.00        12\n",
            "            poly(N-isopropylacrylamide)       1.00      1.00      1.00       728\n",
            "              poly(methyl_methacrylate)       0.81      0.79      0.80      1464\n",
            "                          polycarbonate       0.87      0.78      0.82       273\n",
            "                   polydimethylsiloxane       0.96      0.94      0.95      3062\n",
            "                         polyetherimide       0.82      0.79      0.81       160\n",
            "                           polyethylene       1.00      0.33      0.50         3\n",
            "             polyethylene_terephthalate       0.75      0.70      0.72       161\n",
            "                        polylactic_acid       0.98      1.00      0.99       181\n",
            "                      polymethylpentene       0.30      0.75      0.43         4\n",
            "                             polystyren       0.90      0.94      0.92      2661\n",
            "                     polyvinil_chloride       0.76      0.67      0.71       162\n",
            "                      polyvinyl_alcohol       1.00      0.98      0.99       284\n",
            "                   polyvinylpyrrolidone       1.00      1.00      1.00        32\n",
            "           potassium_hydrogen_phthalate       0.95      0.93      0.94       177\n",
            "                               propanol       0.92      0.94      0.93      3517\n",
            "                       propylene_glycol       1.00      1.00      1.00         3\n",
            "                                styrene       0.89      0.90      0.90      2567\n",
            "                                toluene       0.91      0.91      0.91      2770\n",
            "                       trichlorobenzene       0.84      0.83      0.84      2334\n",
            "                                   urea       0.93      0.97      0.95       234\n",
            "\n",
            "                               accuracy                           0.87     52500\n",
            "                              macro avg       0.86      0.84      0.84     52500\n",
            "                           weighted avg       0.87      0.87      0.87     52500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-G5uF3ABKHt"
      },
      "source": [
        "#### Checking the accuracy catagory wise, for: `UV`, `Far_IR_Rays`, `IR_rays`, `near_IR`,`visible_light` , individually\n",
        "\n",
        "### For Training Set :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTi2CBLVszZy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "783a84ed-b260-458a-ec5d-0c9716f044e5"
      },
      "source": [
        "# Here are the accuracies in the tabular format:\n",
        "labls = ['UV','Far_IR_Rays','IR_rays',\"near_IR\",'visible_light']\n",
        "tr_scores = []\n",
        "\n",
        "for l in labls:\n",
        "  train_x = x_train[x_train.λ_binned==l]\n",
        "  train_y = y_train[x_train.λ_binned==l]\n",
        " \n",
        "  tr_scores.append(rf_model.score(train_x,train_y))\n",
        "for i in range(len(labls)):\n",
        "    print(labls[i],\":\", tr_scores[i])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UV : 0.9870811073336572\n",
            "Far_IR_Rays : 0.9981950601646612\n",
            "IR_rays : 0.949120563837086\n",
            "near_IR : 0.9999404336430784\n",
            "visible_light : 0.9998271142486673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Kv1QcYnQtQ3t",
        "outputId": "1c4efc6c-0fb7-46e0-b5bb-33c1df81d109"
      },
      "source": [
        "# Accuracy based on different wavelengths\n",
        "plt.bar(labls,tr_scores)\n",
        "plt.title('Accuracy on Training Data ')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hQBy5KJkXBr"
      },
      "source": [
        "### For Test Set :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dr4gdU-tZ2yv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbe4d8f2-7e68-4b60-827d-34728c5f858c"
      },
      "source": [
        "# Here are the accuracies in the tabular format:\n",
        "labls = ['UV','Far_IR_Rays','IR_rays',\"near_IR\",'visible_light']\n",
        "ts_scores = []\n",
        "\n",
        "for l in labls:\n",
        "  test_x = x_test[x_test.λ_binned==l]\n",
        "  test_y = y_test[x_test.λ_binned==l]\n",
        " \n",
        "  ts_scores.append(rf_model.score(test_x,test_y))\n",
        "for i in range(len(labls)):\n",
        "    print(labls[i],\":\", ts_scores[i])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UV : 0.9784382284382285\n",
            "Far_IR_Rays : 0.7014344067635604\n",
            "IR_rays : 0.8392665013799878\n",
            "near_IR : 0.979807005003574\n",
            "visible_light : 0.9977524204702628\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "xzT4BM9zac_C",
        "outputId": "042fec81-4300-4907-99e6-de656a7c96f1"
      },
      "source": [
        "# Accuracy based on different wavelengths\n",
        "plt.bar(labls,ts_scores)\n",
        "plt.title('Accuracy on testing ')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Saxt9QSscMYo"
      },
      "source": [
        "### Prediction on up-down sampled data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojIcJ91JlFH6"
      },
      "source": [
        "## Splitting the Raw Data\n",
        "*Creating Input features (attributes) and Target Vectors for Training and testing the ML model.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ziev2RvKnX3w"
      },
      "source": [
        "### Prediction Time:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vroa2No5niIv"
      },
      "source": [
        "pred = rf_model.predict(x_test)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "D9wr3ABSz1j7",
        "outputId": "3929a0fa-e2f4-482d-f187-f37367955cfe"
      },
      "source": [
        "# Predicting the Book, based on the the parameters:\n",
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('UV',1,0,rf_model)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is methane'"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f1pbRGpnvwI"
      },
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sghg9rvm4eFl",
        "outputId": "8b105bb6-e290-44f3-949e-cd2cf801d740"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(40,40))\n",
        "cm = confusion_matrix(y_test,pred,labels=list(df.book.unique()))\n",
        "sns.heatmap(cm,annot=True,fmt='.2f',cbar=False)\n",
        "plt.xticks(range(0,63),labels=list(df.book.unique()),rotation=90)\n",
        "plt.yticks(range(0,63),labels=list(df.book.unique()),rotation=45)\n",
        "plt.title('confusion_matrix')         #checking the confusion matrix\n",
        "plt.xlabel('Actual Class')\n",
        "plt.ylabel('predicted class')\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2880x2880 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcRJxY3Jn7OX"
      },
      "source": [
        "### Checking the accuracy on bases of the Wavelength Catagory:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MorTJnrv5Bav"
      },
      "source": [
        "# Creating test set based upon the category of Wavelength they belong to:\n",
        "UV = x_test[x_test.λ_binned=='UV']\n",
        "UV_y = y_test[x_test.λ_binned=='UV']\n",
        "Near_IR = x_test[x_test.λ_binned=='near_IR']\n",
        "Near_IR_y = y_test[x_test.λ_binned=='near_IR']\n",
        "Far_IR = x_test[x_test.λ_binned=='Far_IR_Rays']\n",
        "Far_IR_y = y_test[x_test.λ_binned=='Far_IR_Rays']\n",
        "IR = x_test[x_test.λ_binned=='IR_rays']\n",
        "IR_y = y_test[x_test.λ_binned=='IR_rays']\n",
        "Visible = x_test[x_test.λ_binned=='visible_light']\n",
        "Visible_y = y_test[x_test.λ_binned=='visible_light']"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlTkzZJ-6iUZ",
        "outputId": "80873344-a8f1-494a-d50b-94cb1dad9d7d"
      },
      "source": [
        "# Prediction 1: \n",
        "print(UV[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Actual Class\\n\\n',UV_y[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Predicted class\\n\\n',rf_model.predict(UV[10:19]))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               n         k λ_binned\n",
            "156636  1.385248  0.000000       UV\n",
            "188463  1.459180  0.000000       UV\n",
            "183269  1.515344  0.000000       UV\n",
            "158822  1.459930  0.000000       UV\n",
            "186920  1.434280  0.000014       UV\n",
            "155216  1.414910  0.000001       UV\n",
            "151948  1.491000  0.568100       UV\n",
            "170486  1.420000  0.455100       UV\n",
            "162354  1.493600  0.000001       UV\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Actual Class\n",
            "\n",
            " 156636                 Heptane\n",
            "188463    polydimethylsiloxane\n",
            "183269      dimethyl_sulfoxide\n",
            "158822    polydimethylsiloxane\n",
            "186920                 ethanol\n",
            "155216    polydimethylsiloxane\n",
            "151948                 methane\n",
            "170486                 methane\n",
            "162354         ethylene_glycol\n",
            "Name: book, dtype: object\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Predicted class\n",
            "\n",
            " ['Heptane' 'polydimethylsiloxane' 'dimethyl_sulfoxide'\n",
            " 'polydimethylsiloxane' 'ethanol' 'polydimethylsiloxane' 'methane'\n",
            " '(C2ClF3)n - polychlorotrifluoroethylene' 'ethylene_glycol']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6RGT6P9oXZ5"
      },
      "source": [
        "We can see that the prediction matched the actual class perfectly. \n",
        "### Some more predictions :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "dFjMe5QO9QZU",
        "outputId": "08c19ef6-a53b-4f4a-8654-9e73b427b2e1"
      },
      "source": [
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('UV',1.437401e+00,0.000000e+00,rf_model)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is polydimethylsiloxane'"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9G-hfCT7JlI",
        "outputId": "ed186265-4e1c-42c9-ca94-f0109a24aee5"
      },
      "source": [
        "# Prediction 2:\n",
        "print(Near_IR[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Actual Class\\n\\n',Near_IR_y[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Predicted class\\n\\n',rf_model.predict(Near_IR[10:19]))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              n         k λ_binned\n",
            "144248  1.36225  0.000022  near_IR\n",
            "18911   1.48890  0.000002  near_IR\n",
            "83877   1.31264  0.000033  near_IR\n",
            "119300  1.47454  0.000015  near_IR\n",
            "5615    1.48885  0.000009  near_IR\n",
            "145959  1.55373  0.000056  near_IR\n",
            "66591   1.48876  0.000006  near_IR\n",
            "66441   1.39629  0.000002  near_IR\n",
            "58572   1.34809  0.000045  near_IR\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Actual Class\n",
            "\n",
            " 144248                propanol\n",
            "18911                oxathiane\n",
            "83877                 Methanol\n",
            "119300                 toluene\n",
            "5615                 oxathiane\n",
            "145959              polystyren\n",
            "66591                oxathiane\n",
            "66441     polydimethylsiloxane\n",
            "58572                  ethanol\n",
            "Name: book, dtype: object\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Predicted class\n",
            "\n",
            " ['propanol' 'oxathiane' 'Methanol' 'toluene' 'oxathiane' 'polystyren'\n",
            " 'oxathiane' 'polydimethylsiloxane' 'ethanol']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "v6GImxfn9ntb",
        "outputId": "b3e4eafe-ddb7-43a8-c43f-c805d274dc58"
      },
      "source": [
        "# near_IR, Prediction\n",
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('near_IR',1.462147,0.000000,rf_model)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is cellulose'"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ms2bhds7fje",
        "outputId": "4b43eabe-f15b-455f-aef3-8e054bffe18a"
      },
      "source": [
        "# Prediction 3: Far_IR\n",
        "print(Far_IR[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Actual Class\\n\\n',Far_IR_y[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Predicted class\\n\\n',rf_model.predict(Far_IR[10:19]))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              n         k     λ_binned\n",
            "148832  1.37357  0.003325  Far_IR_Rays\n",
            "6099    1.51515  0.000133  Far_IR_Rays\n",
            "40371   1.33391  0.044694  Far_IR_Rays\n",
            "101443  1.40890  0.000041  Far_IR_Rays\n",
            "101986  1.45838  0.002715  Far_IR_Rays\n",
            "119371  1.47481  0.000174  Far_IR_Rays\n",
            "69866   1.51849  0.012611  Far_IR_Rays\n",
            "117532  1.48608  0.000430  Far_IR_Rays\n",
            "116535  1.53698  0.000371  Far_IR_Rays\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Actual Class\n",
            "\n",
            " 148832    dimethyl_methylphosphonate\n",
            "6099                         styrene\n",
            "40371     dimethyl_methylphosphonate\n",
            "101443               dichloromethane\n",
            "101986       diisopropylaminoethanol\n",
            "119371                       toluene\n",
            "69866                     polystyren\n",
            "117532                     oxathiane\n",
            "116535              trichlorobenzene\n",
            "Name: book, dtype: object\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Predicted class\n",
            "\n",
            " ['dimethyl_methylphosphonate' 'styrene' 'ethanol' 'dichloromethane'\n",
            " 'benzene' 'toluene' 'trichlorobenzene' 'oxathiane' 'trichlorobenzene']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "jOahLeMa-LCF",
        "outputId": "8caacf26-0cc7-4c95-c7c7-f4aea8987f25"
      },
      "source": [
        "# Prediction Far_IR_Rays\n",
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('Far_IR_Rays',1.55675,0.003872,rf_model)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is polystyren'"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qfwShYU74Qm",
        "outputId": "023f5fa8-a8f1-4163-b506-9887f53bd1c0"
      },
      "source": [
        "# Prediction 4 : IR\n",
        "print(IR[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Actual Class\\n\\n',IR_y[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Predicted class\\n\\n',rf_model.predict(IR[10:19]))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              n         k λ_binned\n",
            "82464   1.54737  0.000002  IR_rays\n",
            "1198    1.39937  0.000104  IR_rays\n",
            "120364  1.39959  0.000043  IR_rays\n",
            "457     1.47825  0.000343  IR_rays\n",
            "115559  1.54687  0.000061  IR_rays\n",
            "127263  1.36903  0.070742  IR_rays\n",
            "10730   1.36255  0.008164  IR_rays\n",
            "116756  1.35732  0.000178  IR_rays\n",
            "105484  1.30221  0.000536  IR_rays\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Actual Class\n",
            "\n",
            " 82464               trichlorobenzene\n",
            "1198            polydimethylsiloxane\n",
            "120364    dimethyl_methylphosphonate\n",
            "457                          toluene\n",
            "115559              trichlorobenzene\n",
            "127263                      Methanol\n",
            "10730                       Methanol\n",
            "116756                      propanol\n",
            "105484                      Methanol\n",
            "Name: book, dtype: object\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Predicted class\n",
            "\n",
            " ['trichlorobenzene' 'octane' 'dimethyl_methylphosphonate' 'toluene'\n",
            " 'trichlorobenzene' 'octane' 'Methanol' 'propanol' 'Methanol']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "wD7HqVfr-qU0",
        "outputId": "4e7cf818-e114-4ca3-ef2c-981a0a67ddd2"
      },
      "source": [
        "# Prediction, based on the parameters \n",
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('IR_rays',1.47427,0.000013,rf_model)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is toluene'"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgdCqrgn8jv5",
        "outputId": "132bc3cf-b67c-4290-a442-43ab1d720d8f"
      },
      "source": [
        "# Prediction 5 : Visible rays\n",
        "print(Visible[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Actual Class\\n\\n',Visible_y[10:19])\n",
        "print('-'*90,end='\\n\\n')\n",
        "print('Predicted class\\n\\n',rf_model.predict(Visible[10:19]))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               n             k       λ_binned\n",
            "177678  1.407150  9.320000e-07  visible_light\n",
            "198825  1.409740  0.000000e+00  visible_light\n",
            "167513  1.484673  0.000000e+00  visible_light\n",
            "158779  1.576300  1.350000e-06  visible_light\n",
            "161358  1.492778  0.000000e+00  visible_light\n",
            "153995  1.343125  0.000000e+00  visible_light\n",
            "204281  1.482300  2.480000e-07  visible_light\n",
            "175230  1.357770  1.124520e-07  visible_light\n",
            "179135  1.340532  0.000000e+00  visible_light\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Actual Class\n",
            "\n",
            " 177678            polydimethylsiloxane\n",
            "198825            polydimethylsiloxane\n",
            "167513       poly(methyl_methacrylate)\n",
            "158779      polyethylene_terephthalate\n",
            "161358    potassium_hydrogen_phthalate\n",
            "153995                         pentane\n",
            "204281       poly(methyl_methacrylate)\n",
            "175230                         ethanol\n",
            "179135                    acetonitrile\n",
            "Name: book, dtype: object\n",
            "------------------------------------------------------------------------------------------\n",
            "\n",
            "Predicted class\n",
            "\n",
            " ['polydimethylsiloxane' 'polydimethylsiloxane' 'poly(methyl_methacrylate)'\n",
            " 'polyethylene_terephthalate' 'potassium_hydrogen_phthalate' 'pentane'\n",
            " 'poly(methyl_methacrylate)' 'ethanol' 'acetonitrile']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Og0G29_l9ID2",
        "outputId": "f7ccc44a-b8d0-4756-8be5-594dec13a5ad"
      },
      "source": [
        "# Predictions based on the parameters.\n",
        "def predict(l_binned,n,k,model):\n",
        "  pred_df = pd.DataFrame({'λ_binned':[l_binned],\n",
        "                         'n':[n],\n",
        "                         'k':[k]})\n",
        "  pred = model.predict(pred_df)\n",
        "  return f'Book is {pred[0]}'\n",
        "\n",
        "predict('visible_light',1.360901, 0.000000e+00,rf_model)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Book is ethanol'"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROUND-OFF"
      ],
      "metadata": {
        "id": "W5nYRnjI-ZZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xiOdAO3I-ZDf",
        "outputId": "d9f9f8d8-a498-4292-c461-d2694a474e55"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f1ee6396-d953-47e3-ad7f-0e74d892de1c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>book</th>\n",
              "      <th>λ</th>\n",
              "      <th>n</th>\n",
              "      <th>k</th>\n",
              "      <th>λ_binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.196</td>\n",
              "      <td>0.6096</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.209</td>\n",
              "      <td>0.6501</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.228</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>1.235</td>\n",
              "      <td>0.7315</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(C2ClF3)n - polychlorotrifluoroethylene</td>\n",
              "      <td>0.1012</td>\n",
              "      <td>1.417</td>\n",
              "      <td>0.4472</td>\n",
              "      <td>UV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1ee6396-d953-47e3-ad7f-0e74d892de1c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f1ee6396-d953-47e3-ad7f-0e74d892de1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f1ee6396-d953-47e3-ad7f-0e74d892de1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      book       λ      n       k λ_binned\n",
              "0  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.196  0.6096       UV\n",
              "1  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.209  0.6501       UV\n",
              "2  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.228  0.7096       UV\n",
              "3  (C2ClF3)n - polychlorotrifluoroethylene  0.0900  1.235  0.7315       UV\n",
              "4  (C2ClF3)n - polychlorotrifluoroethylene  0.1012  1.417  0.4472       UV"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df.copy()\n",
        "new_df.drop('λ_binned',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "M7LJsyJg-h-O"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#preprocessing and training pipeline with our own bins\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "                                      ('scaler', StandardScaler())\n",
        "                                      ])\n",
        "\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "                  ('numeric', numeric_transformer, numeric_features),   #apply the transformation\n",
        "                  ]) \n",
        "\n",
        "pipeline = Pipeline(steps = [\n",
        "               ('preprocessor', preprocessor)\n",
        "              ,('regressor',RandomForestClassifier())\n",
        "           ])"
      ],
      "metadata": {
        "id": "_51AeJ3J-pmi"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split the data for training and testing\n",
        "\n",
        "acc_tr = []\n",
        "acc_ts = []\n",
        "\n",
        "recall = []\n",
        "\n",
        "round_place = [1,2,3,4,5]\n",
        "\n",
        "for r in round_place:\n",
        "  y = new_df['book']\n",
        "  x = new_df.drop('book',axis=1).round(r)   #rounding off\n",
        "  x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,random_state=42)\n",
        "  rf_model = pipeline.fit(x_train,y_train)\n",
        "  acc_tr.append(rf_model.score(x_train,y_train))\n",
        "  acc_ts.append(rf_model.score(x_test,y_test))\n",
        "\n",
        "  pred = rf_model.predict(x_test)\n",
        "\n",
        "  recall.append(recall_score(y_test,pred,average='weighted'))"
      ],
      "metadata": {
        "id": "zVNML7BN-tV2"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc_tr,acc_ts,recall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWmcGDjK-wp7",
        "outputId": "115675f8-612d-4dda-c05a-adb9fcd1056a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0.24957056815463896,\n",
              "  0.6157346002478746,\n",
              "  0.7941553782261747,\n",
              "  0.9058022221739036,\n",
              "  0.9484464351720989],\n",
              " [0.25175304132285314,\n",
              "  0.6058184664557581,\n",
              "  0.7411532565800202,\n",
              "  0.7829327158279247,\n",
              "  0.7741919702553732],\n",
              " [0.25175304132285314,\n",
              "  0.6058184664557581,\n",
              "  0.7411532565800202,\n",
              "  0.7829327158279247,\n",
              "  0.7741919702553732])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "plt.title('Train/Test Accuracy with different roundoffs')\n",
        "X_axis = np.arange(len(round_place))\n",
        "ax.bar(X_axis - width/2,acc_tr,width,label='train')\n",
        "ax.bar(X_axis + width/2,acc_ts,width,label='test')\n",
        "plt.xticks(X_axis,round_place)\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "XYyAm-Gn-0N6",
        "outputId": "b169d0de-377c-4d57-cf49-69dc4f776aed"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title('Train/Test Accuracy with different roundoffs')\n",
        "plt.plot(X_axis,acc_tr,color='b',label ='train')\n",
        "plt.plot(X_axis,acc_ts,color='orange',label='test')\n",
        "plt.xlabel('Round offs')\n",
        "plt.ylabel('Accuracy score')\n",
        "plt.xticks(range(len(round_place)),round_place)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ti0La0lD-3e8",
        "outputId": "ab58c7c9-a779-4348-e729-2df7a949454d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(round_place,recall)\n",
        "plt.title('Recall with different roundoffs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "RK5IbzmP-6mO",
        "outputId": "8bcce6b5-8625-43a3-8de0-bc366cfd416d"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}