LR with Gradient Descent Variations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project_4.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OTpnyUEJrAjE"
      },
      "source": [
        "# Linear Regression with Gradient Descent Variations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4UwjkNZ3NGk"
      },
      "source": [
        "## Part 1: Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y62qieYVbct2"
      },
      "source": [
        "In Parts 1- 6 of this project, we are designing a controller to manage the ailerons of an F16 Aircraft! \n",
        "\n",
        "![Ailerons](https://raw.githubusercontent.com/aps1070-2019/datasets/master/img/airplane.gif) [Ref](https://www.grc.nasa.gov/www/k-12/BGA/Mike/airplane_parts_act.htm) \n",
        "\n",
        "\n",
        "The features describe the airplane's status, and our goal is to predict the `Goal` column, which is a command that our controller should issue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVI7N2nWbct3"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import copy\n",
        "import math\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from scipy.linalg import inv\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/aps1070-2019/datasets/master/F16L.csv\" , skipinitialspace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKRMb9Ju3NGl"
      },
      "source": [
        "1. Print the dataframe.\n",
        "2. Prepare your dataset as follows: **[1]**\n",
        "*   Split the dataset into training and validation sets (80% training, 20% validation). When splitting, set `random_state=1`.\n",
        "*   Standardize the data using StandardScaler from sklearn.\n",
        "*   Insert a first column of 1s in both the training and validation data sets.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znt_4PSMvO3J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "ee634b23-79b9-4cdf-fa23-64668bd075dd"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>inputs ClimbRate</th>\n",
              "      <th>Sgz</th>\n",
              "      <th>P</th>\n",
              "      <th>Q</th>\n",
              "      <th>CurPitch</th>\n",
              "      <th>CurRoll</th>\n",
              "      <th>AbsRoll</th>\n",
              "      <th>DiffClb</th>\n",
              "      <th>DiffRollRate</th>\n",
              "      <th>DiffDiffClb</th>\n",
              "      <th>SeTime1</th>\n",
              "      <th>SeTime2</th>\n",
              "      <th>SeTime3</th>\n",
              "      <th>SeTime4</th>\n",
              "      <th>SeTime5</th>\n",
              "      <th>SeTime6</th>\n",
              "      <th>SeTime7</th>\n",
              "      <th>SeTime8</th>\n",
              "      <th>SeTime9</th>\n",
              "      <th>SeTime10</th>\n",
              "      <th>SeTime11</th>\n",
              "      <th>SeTime12</th>\n",
              "      <th>SeTime13</th>\n",
              "      <th>SeTime14</th>\n",
              "      <th>DiffSeTime1</th>\n",
              "      <th>DiffSeTime2</th>\n",
              "      <th>DiffSeTime3</th>\n",
              "      <th>DiffSeTime4</th>\n",
              "      <th>DiffSeTime5</th>\n",
              "      <th>DiffSeTime6</th>\n",
              "      <th>DiffSeTime7</th>\n",
              "      <th>DiffSeTime8</th>\n",
              "      <th>DiffSeTime9</th>\n",
              "      <th>DiffSeTime10</th>\n",
              "      <th>DiffSeTime11</th>\n",
              "      <th>DiffSeTime12</th>\n",
              "      <th>DiffSeTime13</th>\n",
              "      <th>DiffSeTime14</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Se</th>\n",
              "      <th>Goal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-17</td>\n",
              "      <td>-6</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.59</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-11</td>\n",
              "      <td>-9</td>\n",
              "      <td>0.015</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.0008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-791</td>\n",
              "      <td>15</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-10</td>\n",
              "      <td>27</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.013</td>\n",
              "      <td>-0.0009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-46</td>\n",
              "      <td>48</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.6</td>\n",
              "      <td>-5</td>\n",
              "      <td>-8</td>\n",
              "      <td>-0.008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>496</td>\n",
              "      <td>-5</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-7</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.017</td>\n",
              "      <td>-0.001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.017</td>\n",
              "      <td>-0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-124</td>\n",
              "      <td>16</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-9</td>\n",
              "      <td>-3</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.018</td>\n",
              "      <td>-0.0006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-253</td>\n",
              "      <td>19</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.17</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-16</td>\n",
              "      <td>-10</td>\n",
              "      <td>-0.012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.033</td>\n",
              "      <td>-0.0016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-420</td>\n",
              "      <td>-7</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.99</td>\n",
              "      <td>-1.7</td>\n",
              "      <td>-20</td>\n",
              "      <td>-6</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.021</td>\n",
              "      <td>-0.0012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-335</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.7</td>\n",
              "      <td>-8</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.009</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.018</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>197</td>\n",
              "      <td>-50</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-12</td>\n",
              "      <td>10</td>\n",
              "      <td>0.016</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.017</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-256</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.77</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-13</td>\n",
              "      <td>-2</td>\n",
              "      <td>0.008</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-0.0010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   inputs ClimbRate  Sgz     P     Q  ...  DiffSeTime14  Alpha     Se    Goal\n",
              "0               -17   -6 -0.03  0.14  ...           0.0    0.6  0.021 -0.0008\n",
              "1              -791   15 -0.28 -0.34  ...           0.0    0.4  0.013 -0.0009\n",
              "2               -46   48 -0.42  0.07  ...           0.0    0.5  0.019 -0.0005\n",
              "3               496   -5 -0.21 -0.04  ...           0.0    0.5  0.017 -0.0005\n",
              "4              -124   16 -0.02  0.05  ...           0.0    0.5  0.018 -0.0006\n",
              "5              -253   19  0.01  0.22  ...           0.0    0.9  0.033 -0.0016\n",
              "6              -420   -7  0.14  0.14  ...           0.0    0.6  0.021 -0.0012\n",
              "7              -335    3 -0.13  0.03  ...           0.0    0.5  0.018 -0.0007\n",
              "8               197  -50  0.27 -0.04  ...           0.0    0.5  0.017 -0.0007\n",
              "9              -256    3 -0.17  0.08  ...           0.0    0.5  0.019 -0.0010\n",
              "\n",
              "[10 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJqrSMWdxXG_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d9b73b4-62b0-48dd-854e-c32f96667277"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13750, 41)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO0IcnG7vn88"
      },
      "source": [
        "## Train test split (80/20)\n",
        "df_copy = df.copy(deep=True)\n",
        "X = df_copy.iloc[:,:-1]\n",
        "y = df_copy.iloc[:,-1]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDp_H-n1voTi"
      },
      "source": [
        "## Standardization\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEpXEqW3voI0"
      },
      "source": [
        "## Insert bias column\n",
        "X_train = np.insert(X_train, 0, np.ones(X_train.shape[0]), axis=1)\n",
        "X_test = np.insert(X_test, 0, np.ones(X_test.shape[0]), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNFmwmgKHC_G"
      },
      "source": [
        "y_train = y_train.to_numpy().reshape(-1,1)\n",
        "y_test = y_test.to_numpy().reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ6KBnbpbcuE"
      },
      "source": [
        "## Part 2: Linear Regression - Direct Solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzLJgLvy5Re4"
      },
      "source": [
        "def rmse(yPred, y):\n",
        "  return np.sqrt(mean_squared_error(yPred, y))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCmSWQ-o3iZf"
      },
      "source": [
        "W_ds = np.dot(inv(np.dot(X_train.T, X_train)), np.dot(X_train.T, y_train))\n",
        "\n",
        "ytrainPred = np.dot(X_train, W_ds)\n",
        "ytestPred = np.dot(X_test, W_ds)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Tc3jyXy4KVN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "595343e8-f72f-4f78-c236-4656fd39d624"
      },
      "source": [
        "print('RMSE for training sets: ', rmse(ytrainPred, y_train))\n",
        "print('RMSE for validation sets: ', rmse(ytestPred, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE for training sets:  0.0001983241902503662\n",
            "RMSE for validation sets:  0.0001874084898605524\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SigDVWIw56yz"
      },
      "source": [
        "rmse_ds = rmse(ytestPred, y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tceGCk1V3NG_"
      },
      "source": [
        "## Part 3: Full-Batch Gradient Descent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9DMAs3I3NHB"
      },
      "source": [
        "Implement full-batch gradient descent for linear regression using a fixed learning rate of $\\alpha= 1e-2$, and by iterating until the model's validation RMSE converges. Here we will consider it converged once validation RMSE is within 0.1% of the Direct Solution RMSE (i.e., $RMSE_{GD} <= RMSE_{Direct Solution}*1.001$). We call this value **Convergence threshold ($CT = RMSE_{Direct Solution}*1.001$)** and will use this value in the next parts too.\n",
        "\n",
        "  *  Record the training time\n",
        "\n",
        "  * Plot the Training RMSE and the Validation RMSE vs epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3KOOJ8m5SAs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "1ab4ab88-9eb2-4b2f-e662-6f5ca4d90c3f"
      },
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time() ## Records current time\n",
        "lr = 0.01\n",
        "ct = rmse_ds * 1.001\n",
        "w = (np.random.random(X_train.shape[1])/10000).reshape(1,-1) ## Initialize weights\n",
        "rmse_train = []\n",
        "rmse_test = []\n",
        "rmse_fb = 1\n",
        "epoch = 0\n",
        "\n",
        "while rmse_fb > ct:\n",
        "    ytrain_fb = np.dot(X_train, w.T).reshape(-1,1)\n",
        "    rmse_train.append(rmse(ytrain_fb, y_train))\n",
        "    gradient = (1/len(ytrain_fb) * np.dot(X_train.T, ytrain_fb-y_train)).reshape(1,-1)\n",
        "    ytest_fb = np.dot(X_test, w.T).reshape(-1,1)\n",
        "    rmse_fb = rmse(ytest_fb, y_test)\n",
        "    rmse_test.append(rmse_fb)\n",
        "    w = w - lr * gradient\n",
        "    epoch += 1\n",
        "\n",
        "plt.plot(range(epoch), rmse_train, label='Training RMSE')\n",
        "plt.plot(range(epoch), rmse_test, label='Validation RMSE')\n",
        "plt.xlim(-5, epoch + 5)\n",
        "plt.legend()\n",
        "plt.title('Training RMSE and Validation RMSE vs. epoch')\n",
        "plt.xlabel('Number of Epoch')\n",
        "plt.ylabel('RMSE')\n",
        "plt.show()\n",
        "\n",
        "print(\"--- Total Training Time: %s (s) ---\" % (time.time() - start_time))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--- Total Training Time: 0.7086071968078613 (s) ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPTpsNEXMIaE"
      },
      "source": [
        "In general, checking RMSE of the model can help us determine if the model is overfitting or underfitting:\n",
        "- If the training RMSE is low while the validation RMSE remains high, then the model is likely overfitting;\n",
        "- If both training RMSE and validation RMSE remain high, then the model is likely underfitting;\n",
        "- We should try to make sure that both training RMSE and validation RMSE are low.\n",
        "\n",
        "For full batch gradient descent, running the algorithm too long is a concern because it means that convergence is hard to reach. This might be due to bad weight-initialization or inappropriate learning rate alpha. Adjusting initial weights should fix this problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3xwmhsr3NHS"
      },
      "source": [
        "## Part 4: Function for Mini-batch and Stochastic Gradient Descent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbItQ_C5Af_-"
      },
      "source": [
        "The function below performs mini-batch gradient descent until the convergence threshold (CT) is reached.\n",
        "\n",
        "The inputs of that function are:\n",
        "  * Training Data\n",
        "  * Validation Data\n",
        "  * Batch size\n",
        "  * Learning Rate\n",
        "  * Convergence Threshold (CT)\n",
        "  * Maximum Iterations = 20000\n",
        "\n",
        "The function will return the following arrays: \n",
        " * The final weights after training.\n",
        " * The training RMSE at each epoch (the number of epochs is the number of passes on the full data set).\n",
        " * The validation RMSE at each epoch.\n",
        " * An array that has the elapsed time from the start of the training process to the end of each epoch (e.g., if each epoch takes exactly 2 seconds the array would look like: [2 4 6 8 ...]).\n",
        " * For certain batchsizes GD might not converge to a solution. For that reason we have a parameter \"Maximum iterations\" where we limit the total number of iterations that this function can perform. \n",
        "\n",
        "We now use the function to investigate the effect of batch size on convergence. When the batch size is 1, we call that stochastic gradient descent. When the batch size equals the # of training data, it is full-batch (ie all data points are used at every iteration). Anywhere in between is mini-batch (we use some of the data).\n",
        "  * Sweep different values for the mini-batch size (at least 10 values, including 1 -- Stochastic GD), each time using a learning rate of $\\alpha= 1e-2$.\n",
        "  * Plot training and validation RMSE vs. **epoch** for all the batchsizes in a figure.\n",
        "  * Plot training and validation RMSE vs. **time** for all the batchsizes in a figure.\n",
        "  * Plot Total training time (y-axis) vs. Batch size (x-axis)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGQ4_OPkleDq"
      },
      "source": [
        "## Main function for Part 4\n",
        "def mini_batch_GD(training_data: np.ndarray, training_target: np.ndarray,\n",
        "                  validation_data: np.ndarray, validation_target: np.ndarray,\n",
        "                  batch_size: int, learning_rate: float, \n",
        "                  convergence_threshold: float, max_iter = 20000):\n",
        "    ## Initialization\n",
        "    np.random.seed(2)\n",
        "    start_time = time.time()\n",
        "    time_arr = []\n",
        "    rmse_train = []\n",
        "    rmse_test = []\n",
        "    n = training_data.shape[0]\n",
        "    w = (np.random.random(training_data.shape[1])/10000).reshape(1,-1)\n",
        "    iter = 0\n",
        "    row_num = 0\n",
        "\n",
        "    ## Start of iteration\n",
        "    while iter <= max_iter:\n",
        "    ## Shuffle training data before each epoch\n",
        "        xShuffled, yShuffled = shuffle(training_data, training_target)\n",
        "\n",
        "        ## Start of each epoch\n",
        "        for i in range(0, xShuffled.shape[0], batch_size):\n",
        "            X_batch = xShuffled[i:i+batch_size]\n",
        "            y_batch = yShuffled[i:i+batch_size]\n",
        "            ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "            gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "            w = w - learning_rate * gradient ## Update weights\n",
        "            iter += 1\n",
        "            row_num = i\n",
        "\n",
        "        if row_num < n: ## Consider cases when the last mini batch has less rows than batch size\n",
        "            X_batch = xShuffled[row_num:]\n",
        "            y_batch = yShuffled[row_num:]\n",
        "            ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "            gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "            w = w - learning_rate * gradient ## Update weights\n",
        "            iter += 1\n",
        "\n",
        "        ## Collect info at the end of each epoch\n",
        "        epoch_time = time.time() - start_time\n",
        "        time_arr.append(epoch_time)\n",
        "\n",
        "        ## Compute training RMSE\n",
        "        ytrain_ep = np.dot(training_data, w.T).reshape(-1,1)\n",
        "        rmse_train_ep = rmse(ytrain_ep, training_target)\n",
        "        rmse_train.append(rmse_train_ep)\n",
        "\n",
        "        ## Compute validation RMSE\n",
        "        ytest_ep = np.dot(validation_data, w.T).reshape(-1,1)\n",
        "        rmse_test_ep = rmse(ytest_ep, validation_target)\n",
        "        rmse_test.append(rmse_test_ep)\n",
        "\n",
        "        ## Check against threshold\n",
        "        if rmse_test_ep <= convergence_threshold:\n",
        "            break\n",
        "\n",
        "    return w, rmse_train, rmse_test, time_arr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gwXvCJNOOCY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "outputId": "1a6f8482-0810-4dce-b559-52e745e20238"
      },
      "source": [
        "## Plotting diverging set of RMSE curves\n",
        "batch_list = []\n",
        "for i in range(0, 7):\n",
        "    batch_list.append(2**i)\n",
        "\n",
        "fig, axes = plt.subplots(3, 2, figsize=(15,15))\n",
        "\n",
        "for i in batch_list:\n",
        "    w, rmse_train, rmse_test, time_arr = mini_batch_GD(X_train, y_train, X_test, \n",
        "                                                    y_test, i, 0.01, ct, 20000)\n",
        "    axes.flat[0].plot(rmse_train, label='Training RMSE with batch size '+ str(i))\n",
        "    axes.flat[0].legend(loc='upper right')\n",
        "    axes.flat[0].set(title='Training RMSE with Different Batch Sizes',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[1].plot(rmse_test, label='Validation RMSE with batch size '+ str(i))\n",
        "    axes.flat[1].legend(loc='upper right')\n",
        "    axes.flat[1].set(title='Validation RMSE with Different Batch Sizes',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[2].plot(time_arr, rmse_train, label='Training RMSE with batch size '+ str(i))\n",
        "    axes.flat[2].legend(loc='upper right')\n",
        "    axes.flat[2].set(title='Training RMSE with Different Batch Sizes',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[3].plot(time_arr, rmse_test, label='Validation RMSE with batch size '+ str(i))\n",
        "    axes.flat[3].legend(loc='upper right')\n",
        "    axes.flat[3].set(title='Validation RMSE with Different Batch Sizes',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[4].scatter(i, time_arr[-1], label='Total Training Time vs. Batch Size '+ str(i))\n",
        "    axes.flat[4].legend(loc='upper left')\n",
        "    axes.flat[4].set(title='Total Training Time vs. Batch Size',\n",
        "                    xlabel='Batch Sizes',\n",
        "                    ylabel='Total Training Time')\n",
        "\n",
        "fig.delaxes(axes[2][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoGHQ3oWOOmd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "outputId": "4df668c5-dbe1-4410-be8a-509cd3d44c02"
      },
      "source": [
        "## Plotting converging set of RMSE curves\n",
        "batch_list = []\n",
        "for i in range(7, 13):\n",
        "    batch_list.append(2**i)\n",
        "\n",
        "batch_list.append(int(11000))\n",
        "fig, axes = plt.subplots(3, 2, figsize=(15,15))\n",
        "\n",
        "for i in batch_list:\n",
        "    w, rmse_train, rmse_test, time_arr = mini_batch_GD(X_train, y_train, X_test, \n",
        "                                                    y_test, i, 0.01, ct, 20000)\n",
        "    axes.flat[0].plot(rmse_train, label='Training RMSE with batch size '+ str(i))\n",
        "    axes.flat[0].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[0].legend(loc='upper right')\n",
        "    axes.flat[0].set(title='Training RMSE with Different Batch Sizes',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[1].plot(rmse_test, label='Validation RMSE with batch size '+ str(i))\n",
        "    axes.flat[1].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[1].legend(loc='upper right')\n",
        "    axes.flat[1].set(title='Validation RMSE with Different Batch Sizes',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[2].plot(time_arr, rmse_train, label='Training RMSE with batch size '+ str(i))\n",
        "    axes.flat[2].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[2].legend(loc='upper right')\n",
        "    axes.flat[2].set(title='Training RMSE with Different Batch Sizes',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[3].plot(time_arr, rmse_test, label='Validation RMSE with batch size '+ str(i))\n",
        "    axes.flat[3].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[3].legend(loc='upper right')\n",
        "    axes.flat[3].set(title='Validation RMSE with Different Batch Sizes',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[4].scatter(i, time_arr[-1], label='Total Training Time vs. Batch Size '+ str(i))\n",
        "    axes.flat[4].legend(loc='upper left')\n",
        "    axes.flat[4].set(title='Total Training Time vs. Batch Size',\n",
        "                    xlabel='Batch Sizes',\n",
        "                    ylabel='Total Training Time')\n",
        "\n",
        "fig.delaxes(axes[2][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QIKN9E6AuWo"
      },
      "source": [
        "Findings and main takeaways:\n",
        "- Since the max iteration is set at 20000, batch sizes from 1 to 64 did not converge within the function limit (using power of 2 list);\n",
        "- Batch sizes larger than 64 when using power of 2 (128 upwards) did converge, while the larger batch sizes (1024, 2048, 4096) have smoother RMSE curves than the ones with smaller batch sizes;\n",
        "- Most of the training RMSE curves and the validation RMSE curves follow very similar trajectories. However, when comparing the RMSE curves with larger training batch sizes, the validation RMSE seems to require a bit more number of epochs to reach the same RMSE as the training one;\n",
        "- In general, larger batch sizes lead to longer training time used;\n",
        "- The best batch size appears to be 128 because it produces a quickly-descending convergence RMSE curve in fewer epochs and less training time than the other batch sizes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0XR0IpjlJwN"
      },
      "source": [
        "## Part 5: Gradient Descent with Variable Learning Rate\n",
        "Use the function  in Part 4 to investigate the effect of learning rate on convergence.\n",
        "\n",
        "Select the best batch size based on the fastest convergence time and sweep the learning rate (at least 10 values) while applying Mini-batch GD. \n",
        "  * Plot the training and validation RMSE vs **epoch** (x-axis) for all the learning rates implemented.\n",
        "  * Plot the training and validation RMSE vs **time** (x-axis) for all the learning rates implemented."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABlVgNHI5Sic",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "outputId": "88ec78e7-8390-470c-d317-b547d35aedf9"
      },
      "source": [
        "## Creating plots for a sweep of alpha from 0.001 to 0.01\n",
        "fig, axes = plt.subplots(2, 2, figsize=(15,15))\n",
        "\n",
        "for i in np.arange(0.001, 0.0101, 0.001):\n",
        "    w, rmse_train, rmse_test, time_arr = mini_batch_GD(X_train, y_train, X_test, \n",
        "                                                    y_test, 128, i, ct, 20000)\n",
        "    str_i = \"{:.3f}\".format(i)\n",
        "    axes.flat[0].plot(rmse_train, label='Training RMSE with lr = ' + str_i)\n",
        "    axes.flat[0].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[0].legend(loc='upper right')\n",
        "    axes.flat[0].set(title='Training RMSE with Batch Size 128 and Different Learning Rates',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[1].plot(rmse_test, label='Validation RMSE with lr = ' + str_i)\n",
        "    axes.flat[1].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[1].legend(loc='upper right')\n",
        "    axes.flat[1].set(title='Validation RMSE with Batch Size 128 and Different Learning Rates',\n",
        "                    xlabel='Number of Epoch',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[2].plot(time_arr, rmse_train, label='Training RMSE with lr = ' + str_i)\n",
        "    axes.flat[2].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[2].legend(loc='upper right')\n",
        "    axes.flat[2].set(title='Training RMSE with Batch Size 128 and Different Learning Rates',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')\n",
        "    axes.flat[3].plot(time_arr, rmse_test, label='Validation RMSE with lr = ' + str_i)\n",
        "    axes.flat[3].axhline(y=ct, linestyle = '--', linewidth = 0.8, alpha = 0.2)\n",
        "    axes.flat[3].legend(loc='upper right')\n",
        "    axes.flat[3].set(title='Validation RMSE with Batch Size 128 and Different Learning Rates',\n",
        "                    xlabel='Time',\n",
        "                    ylabel='RMSE')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZFI_tmK85xW"
      },
      "source": [
        "Findings and main takeaways:\n",
        "- In comparison, we can see that all RMSE curves in both training and validation groups are highly similar to their corresponding counterpart respectively;\n",
        "- As the learning rate increase from 0.001 to 0.01, we can see that the RMSE curves have steeper slopes and faster convergence;\n",
        "- As the learning rate decreases, the respective RMSE curve becomes smoother compared to the curve with a larger alpha;\n",
        "- Larger learning rate within reasonable range leads to steeper descend of RMSE curve and much faster training time to reach convergence threshold."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_APuh43i3NHU"
      },
      "source": [
        "## Part 6: Gradient Descent with Adaptive Learning Rate\n",
        "\n",
        "Implement a simple adaptive learning rate scheme:\n",
        "- Start a mini-batch gradient descent with a large learning rate;\n",
        "- Decrease the learning rate when closer to the local minimum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdkoiE-AftTZ"
      },
      "source": [
        "The scheme/method that I use is as follows:\n",
        "- Check the validation RMSE against the CT threshold after each epoch;\n",
        "- Once the validation RMSE descends to within 5% of the CT, divide the learning rate by half;\n",
        "- If the next epoch does not bring the validation RMSE to less than or equal to CT, divide the learning rate by half again;\n",
        "- Repeat the above two steps until convergence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKQUGV8Pwsp2"
      },
      "source": [
        "## Main function for Part 6\n",
        "def adaptive_lr_MBGD(training_data: np.ndarray, training_target: np.ndarray,\n",
        "                  validation_data: np.ndarray, validation_target: np.ndarray,\n",
        "                  batch_size: int, learning_rate: float, \n",
        "                  convergence_threshold: float, max_iter = 20000):\n",
        "    ## Initialization\n",
        "    np.random.seed(2)\n",
        "    start_time = time.time()\n",
        "    time_arr = []\n",
        "    rmse_train = []\n",
        "    rmse_test = []\n",
        "    n = training_data.shape[0]\n",
        "    w = (np.random.random(training_data.shape[1])/10000).reshape(1,-1)\n",
        "    iter = 0\n",
        "    row_num = 0\n",
        "\n",
        "    ## Start of iteration\n",
        "    while iter <= max_iter:\n",
        "        ## Shuffle training data before each epoch\n",
        "        xShuffled, yShuffled = shuffle(training_data, training_target)\n",
        "\n",
        "        ## Start of each epoch\n",
        "        for i in range(0, xShuffled.shape[0], batch_size):\n",
        "            X_batch = xShuffled[i:i+batch_size]\n",
        "            y_batch = yShuffled[i:i+batch_size]\n",
        "            ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "            gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "            w = w - learning_rate * gradient ## Update weights\n",
        "            iter += 1\n",
        "            row_num = i\n",
        "\n",
        "        if row_num < n: ## Consider cases when the last mini batch has less rows than batch size\n",
        "            X_batch = xShuffled[row_num:]\n",
        "            y_batch = yShuffled[row_num:]\n",
        "            ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "            gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "            w = w - learning_rate * gradient ## Update weights\n",
        "            iter += 1\n",
        "\n",
        "        ## Collect info at the end of each epoch\n",
        "        epoch_time = time.time() - start_time\n",
        "        time_arr.append(epoch_time)\n",
        "\n",
        "        ## Compute training RMSE\n",
        "        ytrain_ep = np.dot(training_data, w.T).reshape(-1,1)\n",
        "        rmse_train_ep = rmse(ytrain_ep, training_target)\n",
        "        rmse_train.append(rmse_train_ep)\n",
        "\n",
        "        ## Compute validation RMSE\n",
        "        ytest_ep = np.dot(validation_data, w.T).reshape(-1,1)\n",
        "        rmse_test_ep = rmse(ytest_ep, validation_target)\n",
        "        rmse_test.append(rmse_test_ep)\n",
        "\n",
        "        ## Increase learning rate each epoch if validation RMSE is far from threshols\n",
        "        if rmse_test_ep >= (3 * convergence_threshold):\n",
        "            learning_rate = learning_rate * 1.2\n",
        "\n",
        "        ## Decrease learning rate each epoch\n",
        "        ## once the validation RMSE comes close within 5% of the threshold\n",
        "        if rmse_test_ep <= (1.05 * convergence_threshold):\n",
        "            learning_rate = learning_rate * 0.6\n",
        "        elif rmse_test_ep <= (1.025 * convergence_threshold):\n",
        "            learning_rate = learning_rate * 0.8\n",
        "        elif rmse_test_ep <= (1.001 * convergence_threshold):\n",
        "            learning_rate = learning_rate * 0.95\n",
        "\n",
        "        ## Check against threshold\n",
        "        if rmse_test_ep <= convergence_threshold:\n",
        "            break\n",
        "\n",
        "    return w, rmse_train, rmse_test, time_arr, learning_rate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDuWJOhVrOfI"
      },
      "source": [
        "# ## Testing RMSprop\n",
        "#\n",
        "# def adaptive_lr_MBGD(training_data, training_target,\n",
        "#                   validation_data, validation_target,\n",
        "#                   batch_size, convergence_threshold, \n",
        "#                   learning_rate, gamma, max_iter = 20000):\n",
        "#   ## Initialization\n",
        "#   np.random.seed(2)\n",
        "#   start_time = time.time()\n",
        "#   time_arr = []\n",
        "#   rmse_train = []\n",
        "#   rmse_test = []\n",
        "#   n = training_data.shape[0]\n",
        "#   w = (np.random.random(training_data.shape[1])/10000).reshape(1,-1)\n",
        "#   iter = 0\n",
        "#   row_num = 0\n",
        "#   mean_square = 0\n",
        "\n",
        "#   ## Start of iteration\n",
        "#   while iter <= max_iter:\n",
        "#     ## Shuffle training data before each epoch\n",
        "#     xShuffled, yShuffled = shuffle(training_data, training_target)\n",
        "    \n",
        "#     ## Start of each epoch\n",
        "#     for i in range(0, xShuffled.shape[0], batch_size):\n",
        "#       X_batch = xShuffled[i:i+batch_size]\n",
        "#       y_batch = yShuffled[i:i+batch_size]\n",
        "#       ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "#       gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "#       mean_square = gamma * mean_square + (1 - gamma) * np.square(gradient)\n",
        "#       w = w - (learning_rate / np.sqrt(mean_square + 1e-8)) * gradient ## Update weights\n",
        "#       iter += 1\n",
        "#       row_num = i\n",
        "\n",
        "#     if row_num < n: ## Consider cases when the last mini batch has less rows than batch size\n",
        "#       X_batch = xShuffled[row_num:]\n",
        "#       y_batch = yShuffled[row_num:]\n",
        "#       ytrain_mb = np.dot(X_batch, w.T).reshape(-1,1)\n",
        "#       gradient = (1/len(ytrain_mb) * np.dot(X_batch.T, ytrain_mb-y_batch)).reshape(1,-1)\n",
        "#       mean_square = gamma * mean_square + (1 - gamma) * np.square(gradient)\n",
        "#       w = w - (learning_rate / np.sqrt(mean_square + 1e-8)) * gradient ## Update weights\n",
        "#       iter += 1\n",
        "\n",
        "#     ## Collect info at the end of each epoch\n",
        "#     epoch_time = time.time() - start_time\n",
        "#     time_arr.append(epoch_time)\n",
        "\n",
        "#     ## Compute training RMSE\n",
        "#     ytrain_ep = np.dot(training_data, w.T).reshape(-1,1)\n",
        "#     rmse_train_ep = rmse(ytrain_ep, training_target)\n",
        "#     rmse_train.append(rmse_train_ep)\n",
        "\n",
        "#     ## Compute validation RMSE\n",
        "#     ytest_ep = np.dot(validation_data, w.T).reshape(-1,1)\n",
        "#     rmse_test_ep = rmse(ytest_ep, validation_target)\n",
        "#     rmse_test.append(rmse_test_ep)\n",
        "\n",
        "#     ## Update learning rate by 0.5 (half of previous) each epoch\n",
        "#     ## once the validation RMSE comes close within 5% of the threshold\n",
        "#     # if rmse_test_ep <= (1.05 * convergence_threshold):\n",
        "#     #   learning_rate = learning_rate * 0.5\n",
        "\n",
        "#     ## Check against threshold\n",
        "#     if rmse_test_ep <= convergence_threshold:\n",
        "#       break\n",
        "    \n",
        "#   return w, rmse_train, rmse_test, time_arr, learning_rate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3r5rcvQXMQi"
      },
      "source": [
        "## Set up appropriate batch size (use batch size upwards of 1024 would show the \n",
        "## differences better) and initial learning rate (ranging from 0.03 to 0.1, cannot be too small) for the adaptive model.\n",
        "## Adaptive model works better with larger batch sizes (for updating learning rate) and larger initial alpha.\n",
        "## Static model is using 0.01 as alpha (fastest convergence from Part 5).\n",
        "\n",
        "## Collect the training and validation results from both models\n",
        "w_lr, rmse_train_lr, rmse_test_lr, time_arr_lr, lr = adaptive_lr_MBGD(X_train, y_train, X_test, y_test, 2048, 0.03, ct, 20000)\n",
        "w, rmse_train, rmse_test, time_arr = mini_batch_GD(X_train, y_train, X_test, y_test, 2048, 0.01, ct, 20000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jL8EBho9AXr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "0d519a7b-c3fe-43ac-95cd-8191e109e9e6"
      },
      "source": [
        "## Compare training and validation RMSE curves vs. epoch for adaptive approach vs. static learning rate approach\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12,6))\n",
        "\n",
        "str_i = \"{:.2f}\".format(0.01) ## Change according to static learning rate\n",
        "\n",
        "axes.flat[0].plot(rmse_train_lr, label='Adaptive learning rate')\n",
        "axes.flat[0].plot(rmse_train, label='Static lr = ' + str_i)\n",
        "axes.flat[0].set(title='Training RMSE vs. Epoch',\n",
        "                 xlabel='Number of Epoch',\n",
        "                 ylabel='RMSE')\n",
        "axes.flat[0].legend(loc='upper right')\n",
        "\n",
        "axes.flat[1].plot(rmse_test_lr, label='Adaptive learning rate')\n",
        "axes.flat[1].plot(rmse_test, label='Static lr = ' + str_i)\n",
        "axes.flat[1].set(title='Validation RMSE vs. Epoch',\n",
        "                 xlabel='Number of Epoch',\n",
        "                 ylabel='RMSE')\n",
        "axes.flat[1].legend(loc='upper right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff9c82f1390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G91UFp2f8jDg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "161c5caf-8e3b-46e7-ac6b-f9effe1982c4"
      },
      "source": [
        "## Compare training and validation RMSE curves vs. time for adaptive approach vs. static learning rate approach\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12,6))\n",
        "\n",
        "str_i = \"{:.2f}\".format(0.01) ## Change according to static learning rate\n",
        "\n",
        "axes.flat[0].plot(time_arr_lr, rmse_train_lr, label='Adaptive learning rate')\n",
        "axes.flat[0].plot(time_arr, rmse_train, label='Static lr = ' + str_i)\n",
        "axes.flat[0].set(title='Training RMSE vs. Time',\n",
        "                 xlabel='Time',\n",
        "                 ylabel='RMSE')\n",
        "axes.flat[0].legend(loc='upper right')\n",
        "\n",
        "axes.flat[1].plot(time_arr_lr, rmse_test_lr, label='Adaptive learning rate')\n",
        "axes.flat[1].plot(time_arr, rmse_test, label='Static lr = ' + str_i)\n",
        "axes.flat[1].set(title='Validation RMSE vs. Time',\n",
        "                 xlabel='Time',\n",
        "                 ylabel='RMSE')\n",
        "axes.flat[1].legend(loc='upper right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff9c9d98cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM4qwZtDaOJF"
      },
      "source": [
        "We can see that using the adaptive learning rate method leads to much faster convergence with fewer epochs required in general. However, the function in this part cannot take an initial learning rate less than 0.03."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-1qJzYLYJTW"
      },
      "source": [
        "## Part 7: Gradient Descent with Additional Features and Regularization\n",
        "\n",
        "Applying linear regresssion using sklearn functions to a toy dataset (`LR_data.csv`), with 10 features `x1`-`x10` and a \"measurement\" `y`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyfNlZsiubVk"
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"https://www.eecg.utoronto.ca/~hadizade/APS1070/LR_data.csv\" , skipinitialspace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4UpvxOmrsL8"
      },
      "source": [
        "1. Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AX7IWwvja4XR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "64ca7c5c-d140-4900-ff60-efe5f2ed4c57"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>x3</th>\n",
              "      <th>x4</th>\n",
              "      <th>x5</th>\n",
              "      <th>x6</th>\n",
              "      <th>x7</th>\n",
              "      <th>x8</th>\n",
              "      <th>x9</th>\n",
              "      <th>x10</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.454481</td>\n",
              "      <td>4.940543</td>\n",
              "      <td>7.116015</td>\n",
              "      <td>3.805131</td>\n",
              "      <td>4.105432</td>\n",
              "      <td>1.832290</td>\n",
              "      <td>5.211909</td>\n",
              "      <td>5.088293</td>\n",
              "      <td>3.841250</td>\n",
              "      <td>6.121133</td>\n",
              "      <td>6.575066e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.907182</td>\n",
              "      <td>1.653654</td>\n",
              "      <td>7.339445</td>\n",
              "      <td>9.065282</td>\n",
              "      <td>7.169233</td>\n",
              "      <td>2.354136</td>\n",
              "      <td>2.893005</td>\n",
              "      <td>8.720131</td>\n",
              "      <td>8.646473</td>\n",
              "      <td>3.806860</td>\n",
              "      <td>8.419881e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.842804</td>\n",
              "      <td>1.183878</td>\n",
              "      <td>4.203206</td>\n",
              "      <td>6.815847</td>\n",
              "      <td>6.655899</td>\n",
              "      <td>8.295816</td>\n",
              "      <td>3.556779</td>\n",
              "      <td>5.794679</td>\n",
              "      <td>3.260723</td>\n",
              "      <td>5.463808</td>\n",
              "      <td>9.745537e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.393457</td>\n",
              "      <td>0.094127</td>\n",
              "      <td>3.208929</td>\n",
              "      <td>0.497443</td>\n",
              "      <td>4.318127</td>\n",
              "      <td>4.999247</td>\n",
              "      <td>9.470238</td>\n",
              "      <td>3.061990</td>\n",
              "      <td>7.312995</td>\n",
              "      <td>5.475240</td>\n",
              "      <td>1.129200e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.093724</td>\n",
              "      <td>3.905293</td>\n",
              "      <td>4.858562</td>\n",
              "      <td>4.465484</td>\n",
              "      <td>0.947091</td>\n",
              "      <td>9.410307</td>\n",
              "      <td>8.137329</td>\n",
              "      <td>9.043838</td>\n",
              "      <td>9.128607</td>\n",
              "      <td>0.790795</td>\n",
              "      <td>3.105448e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.375957</td>\n",
              "      <td>3.489081</td>\n",
              "      <td>7.262106</td>\n",
              "      <td>6.306018</td>\n",
              "      <td>5.907472</td>\n",
              "      <td>0.988894</td>\n",
              "      <td>8.231835</td>\n",
              "      <td>8.265774</td>\n",
              "      <td>8.669483</td>\n",
              "      <td>1.254743</td>\n",
              "      <td>7.735764e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9.577222</td>\n",
              "      <td>9.428066</td>\n",
              "      <td>2.681789</td>\n",
              "      <td>1.625122</td>\n",
              "      <td>5.400703</td>\n",
              "      <td>0.052908</td>\n",
              "      <td>5.310164</td>\n",
              "      <td>4.758203</td>\n",
              "      <td>5.493273</td>\n",
              "      <td>0.461270</td>\n",
              "      <td>3.149807e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.338110</td>\n",
              "      <td>0.583168</td>\n",
              "      <td>6.269942</td>\n",
              "      <td>9.864809</td>\n",
              "      <td>0.729452</td>\n",
              "      <td>3.910690</td>\n",
              "      <td>1.076602</td>\n",
              "      <td>9.200548</td>\n",
              "      <td>4.011550</td>\n",
              "      <td>9.206116</td>\n",
              "      <td>2.388450e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.342222</td>\n",
              "      <td>5.926632</td>\n",
              "      <td>5.755885</td>\n",
              "      <td>5.764966</td>\n",
              "      <td>7.684441</td>\n",
              "      <td>1.975542</td>\n",
              "      <td>2.124600</td>\n",
              "      <td>0.045201</td>\n",
              "      <td>6.406578</td>\n",
              "      <td>3.228309</td>\n",
              "      <td>1.204890e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.554502</td>\n",
              "      <td>5.720743</td>\n",
              "      <td>0.348854</td>\n",
              "      <td>8.459366</td>\n",
              "      <td>4.209461</td>\n",
              "      <td>2.389532</td>\n",
              "      <td>1.436743</td>\n",
              "      <td>9.619647</td>\n",
              "      <td>2.951377</td>\n",
              "      <td>5.458826</td>\n",
              "      <td>1.492802e+02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         x1        x2        x3  ...        x9       x10             y\n",
              "0  1.454481  4.940543  7.116015  ...  3.841250  6.121133  6.575066e+06\n",
              "1  4.907182  1.653654  7.339445  ...  8.646473  3.806860  8.419881e+06\n",
              "2  1.842804  1.183878  4.203206  ...  3.260723  5.463808  9.745537e+04\n",
              "3  2.393457  0.094127  3.208929  ...  7.312995  5.475240  1.129200e+04\n",
              "4  5.093724  3.905293  4.858562  ...  9.128607  0.790795  3.105448e+05\n",
              "5  0.375957  3.489081  7.262106  ...  8.669483  1.254743  7.735764e+06\n",
              "6  9.577222  9.428066  2.681789  ...  5.493273  0.461270  3.149807e+03\n",
              "7  1.338110  0.583168  6.269942  ...  4.011550  9.206116  2.388450e+06\n",
              "8  8.342222  5.926632  5.755885  ...  6.406578  3.228309  1.204890e+06\n",
              "9  7.554502  5.720743  0.348854  ...  2.951377  5.458826  1.492802e+02\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARJ30ZVxa4rm"
      },
      "source": [
        "## Train-Validation split (80/20)\n",
        "df_copy = df.copy(deep=True)\n",
        "X = df_copy.iloc[:,:-1]\n",
        "y = df_copy.iloc[:,-1]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQnRW_OuzLzw"
      },
      "source": [
        "## Standardization\n",
        "sc = StandardScaler()\n",
        "X_train_std = sc.fit_transform(X_train)\n",
        "X_test_std = sc.transform(X_test)\n",
        "\n",
        "## Insert bias column\n",
        "X_train_std = np.insert(X_train_std, 0, np.ones(X_train_std.shape[0]), axis=1)\n",
        "X_test_std = np.insert(X_test_std, 0, np.ones(X_test_std.shape[0]), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xER5V1HsrxJj"
      },
      "source": [
        "2. Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCr8gwkwa418",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d4fc1bf-a5e4-4e05-dda9-df9e0ee884ab"
      },
      "source": [
        "## Linear Regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "LR = LinearRegression()\n",
        "LR.fit(X_train_std, y_train)\n",
        "\n",
        "y_pred_train = LR.predict(X_train_std)\n",
        "y_pred_val = LR.predict(X_test_std)\n",
        "\n",
        "print(\"The RMSE for training data is: \", rmse(y_pred_train, y_train))\n",
        "print(\"The RMSE for validation data is: \", rmse(y_pred_val, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data is:  16296980.655667374\n",
            "The RMSE for validation data is:  14061578.864980092\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCW3LxOmr1zH"
      },
      "source": [
        "3. Linear Regression with Additional Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIzjtTvJa4iN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70798934-8f92-4809-8fdc-27e848c8c585"
      },
      "source": [
        "## Linear Regression with Additional Features\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "## Add poly features\n",
        "poly = PolynomialFeatures(8)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "## Standardization\n",
        "X_train_poly_std = sc.fit_transform(X_train_poly)\n",
        "X_test_poly_std = sc.transform(X_test_poly)\n",
        "\n",
        "## Insert bias column\n",
        "# X_train_poly_std = np.insert(X_train_poly_std, 0, np.ones(X_train_poly_std.shape[0]), axis=1)\n",
        "# X_test_poly_std = np.insert(X_test_poly_std, 0, np.ones(X_test_poly_std.shape[0]), axis=1)\n",
        "\n",
        "LR_poly = LinearRegression()\n",
        "LR_poly.fit(X_train_poly_std, y_train)\n",
        "\n",
        "y_pred_train_poly = LR_poly.predict(X_train_poly_std)\n",
        "y_pred_val_poly = LR_poly.predict(X_test_poly_std)\n",
        "\n",
        "print(\"The RMSE for training data is: \", rmse(y_pred_train_poly, y_train))\n",
        "print(\"The RMSE for validation data is: \", rmse(y_pred_val_poly, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data is:  1.1154878119198678e-07\n",
            "The RMSE for validation data is:  10920908.905406754\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMTv6803r5he"
      },
      "source": [
        "4. Linear Regression with Additional Features and Regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0afRgB_a39R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a7af01d-b67d-42a8-b080-099b8911d29a"
      },
      "source": [
        "## Linear Regression with Additional Features and Regularization\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "ridge_train_rmse = []\n",
        "ridge_val_rmse = []\n",
        "\n",
        "for i in np.arange(-2, 11, dtype=float):\n",
        "    alpha = 10 ** i\n",
        "    ridge_model = Ridge(alpha)\n",
        "    ridge_model.fit(X_train_poly_std, y_train)\n",
        "    y_pred_train_ridge = ridge_model.predict(X_train_poly_std)\n",
        "    y_pred_val_ridge = ridge_model.predict(X_test_poly_std)\n",
        "    rtrain_rmse = rmse(y_pred_train_ridge, y_train)\n",
        "    rval_rmse = rmse(y_pred_val_ridge, y_test)\n",
        "    ridge_train_rmse.append(rtrain_rmse)\n",
        "    ridge_val_rmse.append(rval_rmse)\n",
        "\n",
        "print(ridge_train_rmse)\n",
        "print(ridge_val_rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[321.01240916309644, 3199.3496468416506, 30988.518341657847, 248080.3696494477, 1196923.6228686073, 3609078.2596920887, 7712389.432217537, 14600498.520348776, 20036025.00599003, 22910819.324746832, 23435716.156519353, 23494007.189870406, 23499904.341360077]\n",
            "[10919504.125893764, 10916171.329033064, 10884651.018827904, 10659707.103878867, 9484394.22011948, 5633182.875138311, 4521905.583502402, 10502863.775565729, 16057627.104454402, 18700005.54478621, 19178033.464494962, 19231116.414978746, 19236486.536391094]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDD3dsdZa3yQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "39b73c97-39b6-4f35-90d8-b59769e7d93b"
      },
      "source": [
        "## Plotting the training and validation RMSE curves with Ridge regularization\n",
        "alpha_pow = np.arange(-2, 11, dtype=float)\n",
        "plt.plot(alpha_pow, ridge_train_rmse, label='Training RMSE')\n",
        "plt.plot(alpha_pow, ridge_val_rmse, label='Validation RMSE')\n",
        "plt.title('RMSE with Ridge Regularization at Various Alpha')\n",
        "plt.xlabel('Alpha Values (1E)')\n",
        "plt.ylabel('RMSE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBp-4c-P_uGj"
      },
      "source": [
        "From the above plot of training and validation RMSE curves, it is clear that the Ridge regularizer with an alpha of 1E4 successfully bring down the validation RMSE from around 10920000 to about 4521905, which is a big step down in error. Although larger alpha values lead to higher training and validation RMSE (overfitting) again, we can still see the obvious effect of ridge regularization (it does improve model performance)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cS99Cxw3NHV"
      },
      "source": [
        "## Part 8: Linear Regression with the Boston Housing Dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54s5EnYtLhPq"
      },
      "source": [
        "The dataset used in the analysis below is the Boston House Prices Dataset from sklearn.datasets with 506 rows and 13 features. This dataset has the following features:\n",
        "- CRIM per capita crime rate by town\n",
        "- ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n",
        "- INDUS proportion of non-retail business acres per town\n",
        "- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
        "- NOX nitric oxides concentration (parts per 10 million)\n",
        "- RM average number of rooms per dwelling\n",
        "- AGE proportion of owner-occupied units built prior to 1940\n",
        "- DIS weighted distances to five Boston employment centres\n",
        "- RAD index of accessibility to radial highways\n",
        "- TAX full-value property-tax rate per $10,000\n",
        "- PTRATIO pupil-teacher ratio by town\n",
        "- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
        "- LSTAT % lower status of the population\n",
        "\n",
        "The target (y) is:\n",
        "- MEDV Median value of owner-occupied homes in (by 1000 dollars)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrhdwhilO7k-"
      },
      "source": [
        "I intend to utilize the following methods after 80/20 train-validation split and standardization:\n",
        "- Direct solution (similar to the tutorial code)\n",
        "- Standard linear regression\n",
        "- Stochastic gradient descent regression\n",
        "- Adding polynomial features before linear regression\n",
        "- Optimize the best model from the above list using Ridge regularization\n",
        "\n",
        "After all the above steps, we can see what is the best RMSE scores of the Boston House Prices data using sklearn models learned from this project."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQlikNdORpfS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "1ffdcf43-d3ca-46b1-a21a-f79b151b14b4"
      },
      "source": [
        "## Using Boston Housing Data from sklearn datasets\n",
        "from sklearn.datasets import load_boston\n",
        "\n",
        "boston_dataset = load_boston()\n",
        "X = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
        "X.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.02985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.430</td>\n",
              "      <td>58.7</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.12</td>\n",
              "      <td>5.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.08829</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.012</td>\n",
              "      <td>66.6</td>\n",
              "      <td>5.5605</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>395.60</td>\n",
              "      <td>12.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.14455</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.172</td>\n",
              "      <td>96.1</td>\n",
              "      <td>5.9505</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>19.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.21124</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>5.631</td>\n",
              "      <td>100.0</td>\n",
              "      <td>6.0821</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>386.63</td>\n",
              "      <td>29.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.17004</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.004</td>\n",
              "      <td>85.9</td>\n",
              "      <td>6.5921</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>386.71</td>\n",
              "      <td>17.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n",
              "5  0.02985   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.12   5.21\n",
              "6  0.08829  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  395.60  12.43\n",
              "7  0.14455  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  396.90  19.15\n",
              "8  0.21124  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  386.63  29.93\n",
              "9  0.17004  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  386.71  17.10\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iI_NBmlsO0je",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c84276fd-9401-4213-fb18-1c4635cb834e"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azoEqgCxLGwF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79eb6ffb-71fb-4fa7-ca79-b59f31093471"
      },
      "source": [
        "y = boston_dataset.target\n",
        "y[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATU9zeJlLeMc"
      },
      "source": [
        "# Train-validation split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
        "\n",
        "## Standardization\n",
        "sc = StandardScaler()\n",
        "X_train_std = sc.fit_transform(X_train)\n",
        "X_test_std = sc.transform(X_test)\n",
        "\n",
        "## Insert bias column\n",
        "X_train_std = np.insert(X_train_std, 0, np.ones(X_train_std.shape[0]), axis=1)\n",
        "X_test_std = np.insert(X_test_std, 0, np.ones(X_test_std.shape[0]), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptnI2m15QXSv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "149cfb6f-1155-465f-803a-55ba5a14813d"
      },
      "source": [
        "## using direct solution to check RMSE scores\n",
        "W_ds = np.dot(inv(np.dot(X_train_std.T, X_train_std)), np.dot(X_train_std.T, y_train))\n",
        "\n",
        "ytrainPred = np.dot(X_train_std, W_ds)\n",
        "ytestPred = np.dot(X_test_std, W_ds)\n",
        "\n",
        "## ct = 1.001 * rmse(ytestPred, y_test)\n",
        "\n",
        "print('RMSE for training sets with direct solution: ', rmse(ytrainPred, y_train))\n",
        "print('RMSE for validation sets with direct solution: ', rmse(ytestPred, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE for training sets with direct solution:  4.675766751547773\n",
            "RMSE for validation sets with direct solution:  4.835373458200534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiXIxP76Legp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7d4146a-9e51-4893-faba-e01c5c989101"
      },
      "source": [
        "## Check RMSE using standard Linear Regression\n",
        "LR = LinearRegression()\n",
        "LR.fit(X_train_std, y_train)\n",
        "\n",
        "y_pred_train = LR.predict(X_train_std)\n",
        "y_pred_val = LR.predict(X_test_std)\n",
        "\n",
        "print(\"The RMSE for training data with LR is: \", rmse(y_pred_train, y_train))\n",
        "print(\"The RMSE for validation data with LR is: \", rmse(y_pred_val, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data with LR is:  4.675766751547773\n",
            "The RMSE for validation data with LR is:  4.835373458200538\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4rDkepBLeXn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0926ef24-2329-4695-c87f-ece0a052d08f"
      },
      "source": [
        "## Check RMSE using Stochastic Gradient Descent Regression\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "\n",
        "sgd = SGDRegressor()\n",
        "sgd.fit(X_train_std, y_train)\n",
        "\n",
        "y_pred_train_sgd = sgd.predict(X_train_std)\n",
        "y_pred_val_sgd = sgd.predict(X_test_std)\n",
        "\n",
        "print(\"The RMSE for training data with SGD is: \", rmse(y_pred_train_sgd, y_train))\n",
        "print(\"The RMSE for validation data with SGD is: \", rmse(y_pred_val_sgd, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data with SGD is:  4.685692962749388\n",
            "The RMSE for validation data with SGD is:  4.838108692406656\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6_oaGpcZ4QF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0745346e-1c7c-4e5f-e497-71ce799413bf"
      },
      "source": [
        "## Check RMSE using Polynomial Features\n",
        "def poly_reg(degree):\n",
        "    poly = PolynomialFeatures(degree)\n",
        "    X_train_poly = poly.fit_transform(X_train)\n",
        "    X_test_poly = poly.transform(X_test)\n",
        "\n",
        "    ## Standardization\n",
        "    X_train_poly_std = sc.fit_transform(X_train_poly)\n",
        "    X_test_poly_std = sc.transform(X_test_poly)\n",
        "\n",
        "    ## Insert bias column\n",
        "    # X_train_poly_std = np.insert(X_train_poly_std, 0, np.ones(X_train_poly_std.shape[0]), axis=1)\n",
        "    # X_test_poly_std = np.insert(X_test_poly_std, 0, np.ones(X_test_poly_std.shape[0]), axis=1)\n",
        "\n",
        "    LR_poly = LinearRegression()\n",
        "    LR_poly.fit(X_train_poly_std, y_train)\n",
        "\n",
        "    y_pred_train_poly = LR_poly.predict(X_train_poly_std)\n",
        "    y_pred_val_poly = LR_poly.predict(X_test_poly_std)\n",
        "\n",
        "    print(f\"The RMSE for training data with Polynomial degree of {i} is: {rmse(y_pred_train_poly, y_train)}\")\n",
        "    print(f\"The RMSE for validation data with Polynomial degree of {i} is: {rmse(y_pred_val_poly, y_test)}\")\n",
        "\n",
        "## Test polynomial degree to see which one works better\n",
        "for i in [2, 3, 4]:\n",
        "    poly_reg(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data with Polynomial degree of 2 is: 2.4382638501906464\n",
            "The RMSE for validation data with Polynomial degree of 2 is: 2.9553221974322987\n",
            "The RMSE for training data with Polynomial degree of 3 is: 1.6519041935543564e-11\n",
            "The RMSE for validation data with Polynomial degree of 3 is: 39.92167278242836\n",
            "The RMSE for training data with Polynomial degree of 4 is: 8.831618635521033e-13\n",
            "The RMSE for validation data with Polynomial degree of 4 is: 17.039677434265016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIqVvGj0a71l"
      },
      "source": [
        "Now we can see that the model using a Polynomial degree of 2 produces better training and validation RMSE scores than the standard LR and SGD approaches."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkUpHBUTLeFY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "684bad19-84e9-4aa0-ec06-05c512820c57"
      },
      "source": [
        "## Poly Regression with degree = 2\n",
        "poly = PolynomialFeatures(2)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "## Standardization\n",
        "X_train_poly_std = sc.fit_transform(X_train_poly)\n",
        "X_test_poly_std = sc.transform(X_test_poly)\n",
        "\n",
        "## Insert bias column\n",
        "# X_train_poly_std = np.insert(X_train_poly_std, 0, np.ones(X_train_poly_std.shape[0]), axis=1)\n",
        "# X_test_poly_std = np.insert(X_test_poly_std, 0, np.ones(X_test_poly_std.shape[0]), axis=1)\n",
        "\n",
        "LR_poly = LinearRegression()\n",
        "LR_poly.fit(X_train_poly_std, y_train)\n",
        "\n",
        "y_pred_train_poly = LR_poly.predict(X_train_poly_std)\n",
        "y_pred_val_poly = LR_poly.predict(X_test_poly_std)\n",
        "\n",
        "print(f\"The RMSE for training data with Polynomial degree of 2 is: {rmse(y_pred_train_poly, y_train)}\")\n",
        "print(f\"The RMSE for validation data with Polynomial degree of 2 is: {rmse(y_pred_val_poly, y_test)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE for training data with Polynomial degree of 2 is: 2.4382638501906464\n",
            "The RMSE for validation data with Polynomial degree of 2 is: 2.9553221974322987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qneyVBkFbipE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dd29254-7404-4cfe-fc5d-c8fcc29d11f5"
      },
      "source": [
        "## Optimization using Ridge regularization\n",
        "ridge_train_rmse = []\n",
        "ridge_val_rmse = []\n",
        "alpha_pow = np.arange(0, 2, 0.1, dtype=float)\n",
        "\n",
        "for i in alpha_pow:\n",
        "    ridge_model = Ridge(i)\n",
        "    ridge_model.fit(X_train_poly_std, y_train)\n",
        "    y_pred_train_ridge = ridge_model.predict(X_train_poly_std)\n",
        "    y_pred_val_ridge = ridge_model.predict(X_test_poly_std)\n",
        "    rtrain_rmse = rmse(y_pred_train_ridge, y_train)\n",
        "    rval_rmse = rmse(y_pred_val_ridge, y_test)\n",
        "    ridge_train_rmse.append(rtrain_rmse)\n",
        "    ridge_val_rmse.append(rval_rmse)\n",
        "\n",
        "print(ridge_train_rmse)\n",
        "print(ridge_val_rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.440481050284336, 2.5365723281996755, 2.5955812455752856, 2.639155280289205, 2.674182670252301, 2.7037021609994283, 2.729342252922967, 2.752084712585902, 2.772570637963996, 2.791243697201277, 2.8084248031553507, 2.8243542929631467, 2.8392172826286344, 2.8531596873054363, 2.866298766896739, 2.8787303100009147, 2.8905336725591417, 2.901775401658421, 2.912511899319865, 2.9227914185088584]\n",
            "[2.9546495452273707, 2.8793317172932014, 2.812865994950098, 2.7655718766959483, 2.7323980113324446, 2.7090424866595857, 2.692620407995779, 2.6812100670630814, 2.6735029155765666, 2.668585170458977, 2.665803545783918, 2.664681113859404, 2.6648632099415326, 2.666081755580529, 2.6681311689877387, 2.670851749396949, 2.6741179951534066, 2.677830247853347, 2.6819086218333483, 2.6862885312780094]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3eP5JZQbib0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "85c5f67a-a01c-4812-ad30-1822cbd83e47"
      },
      "source": [
        "print('The lowest validation RMSE is', min(ridge_val_rmse), \n",
        "      'with a training RMSE of', ridge_train_rmse[np.argmin(ridge_val_rmse)],\n",
        "      'at Ridge alpha value at', 0.1*np.argmin(ridge_val_rmse))\n",
        "\n",
        "## Plotting the training and validation RMSE curves with Ridge regularization\n",
        "plt.plot(alpha_pow, ridge_train_rmse, label='Training RMSE')\n",
        "plt.plot(alpha_pow, ridge_val_rmse, label='Validation RMSE')\n",
        "plt.title('RMSE with Ridge Regularization at Various Alpha')\n",
        "plt.xlabel('Alpha Values')\n",
        "plt.ylabel('RMSE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The lowest validation RMSE is 2.664681113859404 with a training RMSE of 2.8243542929631467 at Ridge alpha value at 1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMV3kzOkfv7O"
      },
      "source": [
        "After implementing the above models and Ridge optimization, the minimum validation RMSE is about 2.665, which is achieved by:\n",
        "- Standardization;\n",
        "- Adding polynomial of degree 2 to the linear regression model;\n",
        "- Adding Ridge optimization at alpha = 1.1.\n",
        "\n",
        "However, from the above train vs. validation RMSE plot, we can see that the best model might be somewhere between Ridge values of 0.5 to 1.1 because the two RMSE curves intersect at alpha = 0.5. After that point, the Ridge regularized model seems to be increasingly overfitting (train RMSE larger than validation RMSE)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoPYQQPWugwX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}