FYS-STK4155/doc/LectureNotes/_build/jupyter_execute/week38.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92c39815",
   "metadata": {
    "editable": true
   },
   "source": [
    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
    "doconce format html week38.do.txt --no_mako -->\n",
    "<!-- dom:TITLE: Week 38: Logistic Regression and Optimization -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28ac15e",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Week 38: Logistic Regression and Optimization\n",
    "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n",
    "\n",
    "Date: **September 18-22**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bf16b5",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Plans for week 38\n",
    "\n",
    "**Material for the active learning sessions on Tuesday and Wednesday.**\n",
    "\n",
    "  * Lecture from last week on the bias-variance tradeoff\n",
    "\n",
    "  * Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method\n",
    "\n",
    "  * Exercise for week 38, see also the whiteboard notes from week 37 at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf>\n",
    "\n",
    "  * Work on project 1, in particular resampling methods like cross-validation and bootstrap.\n",
    "\n",
    "  \n",
    "**Material for the lecture on Thursday September 21.**\n",
    "\n",
    "  * Logistic regression as our first encounter of classification methods. From binary cases to several categories.\n",
    "\n",
    "  * Start gradient and optimization methods\n",
    "\n",
    "  * Readings and Videos:\n",
    "\n",
    "    * Hastie et al 4.1, 4.2 and 4.3 on logistic regression\n",
    "\n",
    "    * For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.\n",
    "\n",
    "    * See also the whiteboard notes from week 37 at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf> for a discussion and derivation of the bias-variance tradeoff. \n",
    "\n",
    "    * [Video on Logistic regression](https://www.youtube.com/watch?v=C5268D9t9Ak)\n",
    "\n",
    "    * [Yet another video on logistic regression](https://www.youtube.com/watch?v=yIYKR4sgzI8)\n",
    "\n",
    "    * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47b1a7c",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Material from last week and relevant for the first project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad44c38",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Various steps in cross-validation\n",
    "\n",
    "When the repetitive splitting of the data set is done randomly,\n",
    "samples may accidently end up in a fast majority of the splits in\n",
    "either training or test set. Such samples may have an unbalanced\n",
    "influence on either model building or prediction evaluation. To avoid\n",
    "this $k$-fold cross-validation structures the data splitting. The\n",
    "samples are divided into $k$ more or less equally sized exhaustive and\n",
    "mutually exclusive subsets. In turn (at each split) one of these\n",
    "subsets plays the role of the test set while the union of the\n",
    "remaining subsets constitutes the training set. Such a splitting\n",
    "warrants a balanced representation of each sample in both training and\n",
    "test set over the splits. Still the division into the $k$ subsets\n",
    "involves a degree of randomness. This may be fully excluded when\n",
    "choosing $k=n$. This particular case is referred to as leave-one-out\n",
    "cross-validation (LOOCV)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48181efc",
   "metadata": {
    "editable": true
   },
   "source": [
    "## How to set up the cross-validation for Ridge and/or Lasso\n",
    "\n",
    "* Define a range of interest for the penalty parameter.\n",
    "\n",
    "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
    "\n",
    "* Fit the linear regression model by means of for example Ridge or Lasso regression  for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62106e79",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "\\boldsymbol{\\beta}_{-i}(\\lambda) & =  ( \\boldsymbol{X}_{-i, \\ast}^{T}\n",
    "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n",
    "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d392fb4",
   "metadata": {
    "editable": true
   },
   "source": [
    "* Evaluate the prediction performance of these models on the test set by $C[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
    "\n",
    "* Repeat the first three steps  such that each sample plays the role of the test set once.\n",
    "\n",
    "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883c8ba9",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Cross-validation in brief\n",
    "\n",
    "For the various values of $k$\n",
    "\n",
    "1. shuffle the dataset randomly.\n",
    "\n",
    "2. Split the dataset into $k$ groups.\n",
    "\n",
    "3. For each unique group:\n",
    "\n",
    "a. Decide which group to use as set for test data\n",
    "\n",
    "b. Take the remaining groups as a training data set\n",
    "\n",
    "c. Fit a model on the training set and evaluate it on the test set\n",
    "\n",
    "d. Retain the evaluation score and discard the model\n",
    "\n",
    "5. Summarize the model using the sample of model evaluation scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db06c8a",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Code Example for Cross-validation and $k$-fold Cross-validation\n",
    "\n",
    "The code here uses Ridge regression with cross-validation (CV)  resampling and $k$-fold CV in order to fit a specific polynomial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7487b649",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_10_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# A seed just to ensure that the random numbers are the same for every run.\n",
    "# Useful for eventual debugging.\n",
    "np.random.seed(3155)\n",
    "\n",
    "# Generate the data.\n",
    "nsamples = 100\n",
    "x = np.random.randn(nsamples)\n",
    "y = 3*x**2 + np.random.randn(nsamples)\n",
    "\n",
    "## Cross-validation on Ridge regression using KFold only\n",
    "\n",
    "# Decide degree on polynomial to fit\n",
    "poly = PolynomialFeatures(degree = 6)\n",
    "\n",
    "# Decide which values of lambda to use\n",
    "nlambdas = 500\n",
    "lambdas = np.logspace(-3, 5, nlambdas)\n",
    "\n",
    "# Initialize a KFold instance\n",
    "k = 5\n",
    "kfold = KFold(n_splits = k)\n",
    "\n",
    "# Perform the cross-validation to estimate MSE\n",
    "scores_KFold = np.zeros((nlambdas, k))\n",
    "\n",
    "i = 0\n",
    "for lmb in lambdas:\n",
    "    ridge = Ridge(alpha = lmb)\n",
    "    j = 0\n",
    "    for train_inds, test_inds in kfold.split(x):\n",
    "        xtrain = x[train_inds]\n",
    "        ytrain = y[train_inds]\n",
    "\n",
    "        xtest = x[test_inds]\n",
    "        ytest = y[test_inds]\n",
    "\n",
    "        Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n",
    "        ridge.fit(Xtrain, ytrain[:, np.newaxis])\n",
    "\n",
    "        Xtest = poly.fit_transform(xtest[:, np.newaxis])\n",
    "        ypred = ridge.predict(Xtest)\n",
    "\n",
    "        scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)\n",
    "\n",
    "        j += 1\n",
    "    i += 1\n",
    "\n",
    "\n",
    "estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n",
    "\n",
    "## Cross-validation using cross_val_score from sklearn along with KFold\n",
    "\n",
    "# kfold is an instance initialized above as:\n",
    "# kfold = KFold(n_splits = k)\n",
    "\n",
    "estimated_mse_sklearn = np.zeros(nlambdas)\n",
    "i = 0\n",
    "for lmb in lambdas:\n",
    "    ridge = Ridge(alpha = lmb)\n",
    "\n",
    "    X = poly.fit_transform(x[:, np.newaxis])\n",
    "    estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n",
    "\n",
    "    # cross_val_score return an array containing the estimated negative mse for every fold.\n",
    "    # we have to the the mean of every array in order to get an estimate of the mse of the model\n",
    "    estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
    "\n",
    "    i += 1\n",
    "\n",
    "## Plot and compare the slightly different ways to perform cross-validation\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
    "plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n",
    "\n",
    "plt.xlabel('log10(lambda)')\n",
    "plt.ylabel('mse')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d38a274",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Material for lecture Thursday September 21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cb96b0",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Logistic Regression\n",
    "\n",
    "In linear regression our main interest was centered on learning the\n",
    "coefficients of a functional fit (say a polynomial) in order to be\n",
    "able to predict the response of a continuous variable on some unseen\n",
    "data. The fit to the continuous variable $y_i$ is based on some\n",
    "independent variables $\\boldsymbol{x}_i$. Linear regression resulted in\n",
    "analytical expressions for standard ordinary Least Squares or Ridge\n",
    "regression (in terms of matrices to invert) for several quantities,\n",
    "ranging from the variance and thereby the confidence intervals of the\n",
    "parameters $\\boldsymbol{\\beta}$ to the mean squared error. If we can invert\n",
    "the product of the design matrices, linear regression gives then a\n",
    "simple recipe for fitting our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6120b1c7",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Classification problems\n",
    "\n",
    "Classification problems, however, are concerned with outcomes taking\n",
    "the form of discrete variables (i.e. categories). We may for example,\n",
    "on the basis of DNA sequencing for a number of patients, like to find\n",
    "out which mutations are important for a certain disease; or based on\n",
    "scans of various patients' brains, figure out if there is a tumor or\n",
    "not; or given a specific physical system, we'd like to identify its\n",
    "state, say whether it is an ordered or disordered system (typical\n",
    "situation in solid state physics); or classify the status of a\n",
    "patient, whether she/he has a stroke or not and many other similar\n",
    "situations.\n",
    "\n",
    "The most common situation we encounter when we apply logistic\n",
    "regression is that of two possible outcomes, normally denoted as a\n",
    "binary outcome, true or false, positive or negative, success or\n",
    "failure etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "936083c0",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Optimization and Deep learning\n",
    "\n",
    "Logistic regression will also serve as our stepping stone towards\n",
    "neural network algorithms and supervised deep learning. For logistic\n",
    "learning, the minimization of the cost function leads to a non-linear\n",
    "equation in the parameters $\\boldsymbol{\\beta}$. The optimization of the\n",
    "problem calls therefore for minimization algorithms. This forms the\n",
    "bottle neck of all machine learning algorithms, namely how to find\n",
    "reliable minima of a multi-variable function. This leads us to the\n",
    "family of gradient descent methods. The latter are the working horses\n",
    "of basically all modern machine learning algorithms.\n",
    "\n",
    "We note also that many of the topics discussed here on logistic \n",
    "regression are also commonly used in modern supervised Deep Learning\n",
    "models, as we will see later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32780c62",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Basics\n",
    "\n",
    "We consider the case where the dependent variables, also called the\n",
    "responses or the outcomes, $y_i$ are discrete and only take values\n",
    "from $k=0,\\dots,K-1$ (i.e. $K$ classes).\n",
    "\n",
    "The goal is to predict the\n",
    "output classes from the design matrix $\\boldsymbol{X}\\in\\mathbb{R}^{n\\times p}$\n",
    "made of $n$ samples, each of which carries $p$ features or predictors. The\n",
    "primary goal is to identify the classes to which new unseen samples\n",
    "belong.\n",
    "\n",
    "Let us specialize to the case of two classes only, with outputs\n",
    "$y_i=0$ and $y_i=1$. Our outcomes could represent the status of a\n",
    "credit card user that could default or not on her/his credit card\n",
    "debt. That is"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d08abd",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "y_i = \\begin{bmatrix} 0 & \\mathrm{no}\\\\  1 & \\mathrm{yes} \\end{bmatrix}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b3452f",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Linear classifier\n",
    "\n",
    "Before moving to the logistic model, let us try to use our linear\n",
    "regression model to classify these two outcomes. We could for example\n",
    "fit a linear model to the default case if $y_i > 0.5$ and the no\n",
    "default case $y_i \\leq 0.5$.\n",
    "\n",
    "We would then have our \n",
    "weighted linear combination, namely"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f605f89",
   "metadata": {
    "editable": true
   },
   "source": [
    "<!-- Equation labels as ordinary links -->\n",
    "<div id=\"_auto1\"></div>\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{\\beta} +  \\boldsymbol{\\epsilon},\n",
    "\\label{_auto1} \\tag{1}\n",
    "\\end{equation}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40905342",
   "metadata": {
    "editable": true
   },
   "source": [
    "where $\\boldsymbol{y}$ is a vector representing the possible outcomes, $\\boldsymbol{X}$ is our\n",
    "$n\\times p$ design matrix and $\\boldsymbol{\\beta}$ represents our estimators/predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f09c4f",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Some selected properties\n",
    "\n",
    "The main problem with our function is that it takes values on the\n",
    "entire real axis. In the case of logistic regression, however, the\n",
    "labels $y_i$ are discrete variables. A typical example is the credit\n",
    "card data discussed below here, where we can set the state of\n",
    "defaulting the debt to $y_i=1$ and not to $y_i=0$ for one the persons\n",
    "in the data set (see the full example below).\n",
    "\n",
    "One simple way to get a discrete output is to have sign\n",
    "functions that map the output of a linear regressor to values $\\{0,1\\}$,\n",
    "$f(s_i)=sign(s_i)=1$ if $s_i\\ge 0$ and 0 if otherwise. \n",
    "We will encounter this model in our first demonstration of neural networks.\n",
    "\n",
    "Historically it is called the **perceptron** model in the machine learning\n",
    "literature. This model is extremely simple. However, in many cases it is more\n",
    "favorable to use a ``soft\" classifier that outputs\n",
    "the probability of a given category. This leads us to the logistic function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eea9d16",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Simple example\n",
    "\n",
    "The following example on data for coronary heart disease (CHD) as function of age may serve as an illustration. In the code here we read and plot whether a person has had CHD (output = 1) or not (output = 0). This ouput  is plotted the person's against age. Clearly, the figure shows that attempting to make a standard linear regression fit may not be very meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdccf16e",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Agegroup</th>\n",
       "      <th>CHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>69</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Age  Agegroup  CHD\n",
       "0     1   21         1    0\n",
       "1     2   23         1    0\n",
       "2     3   25         1    1\n",
       "3     4   29         1    0\n",
       "4     5   21         1    0\n",
       "..  ...  ...       ...  ...\n",
       "95   96   61         8    1\n",
       "96   97   69         8    1\n",
       "97   98   65         8    1\n",
       "98   99   64         8    1\n",
       "99  100   63         8    0\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_22_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Common imports\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from IPython.display import display\n",
    "from pylab import plt, mpl\n",
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "\n",
    "# Where to save the figures and data files\n",
    "PROJECT_ROOT_DIR = \"Results\"\n",
    "FIGURE_ID = \"Results/FigureFiles\"\n",
    "DATA_ID = \"DataFiles/\"\n",
    "\n",
    "if not os.path.exists(PROJECT_ROOT_DIR):\n",
    "    os.mkdir(PROJECT_ROOT_DIR)\n",
    "\n",
    "if not os.path.exists(FIGURE_ID):\n",
    "    os.makedirs(FIGURE_ID)\n",
    "\n",
    "if not os.path.exists(DATA_ID):\n",
    "    os.makedirs(DATA_ID)\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(FIGURE_ID, fig_id)\n",
    "\n",
    "def data_path(dat_id):\n",
    "    return os.path.join(DATA_ID, dat_id)\n",
    "\n",
    "def save_fig(fig_id):\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
    "\n",
    "infile = open(data_path(\"chddata.csv\"),'r')\n",
    "\n",
    "# Read the chd data as  csv file and organize the data into arrays with age group, age, and chd\n",
    "chd = pd.read_csv(infile, names=('ID', 'Age', 'Agegroup', 'CHD'))\n",
    "chd.columns = ['ID', 'Age', 'Agegroup', 'CHD']\n",
    "output = chd['CHD']\n",
    "age = chd['Age']\n",
    "agegroup = chd['Agegroup']\n",
    "numberID  = chd['ID'] \n",
    "display(chd)\n",
    "\n",
    "plt.scatter(age, output, marker='o')\n",
    "plt.axis([18,70.0,-0.1, 1.2])\n",
    "plt.xlabel(r'Age')\n",
    "plt.ylabel(r'CHD')\n",
    "plt.title(r'Age distribution and Coronary heart disease')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db91831",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Plotting the mean value for each group\n",
    "\n",
    "What we could attempt however is to plot the mean value for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b570a592",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_24_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agegroupmean = np.array([0.1, 0.133, 0.250, 0.333, 0.462, 0.625, 0.765, 0.800])\n",
    "group = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "plt.plot(group, agegroupmean, \"r-\")\n",
    "plt.axis([0,9,0, 1.0])\n",
    "plt.xlabel(r'Age group')\n",
    "plt.ylabel(r'CHD mean values')\n",
    "plt.title(r'Mean values for each age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e5a12a",
   "metadata": {
    "editable": true
   },
   "source": [
    "We are now trying to find a function $f(y\\vert x)$, that is a function which gives us an expected value for the output $y$ with a given input $x$.\n",
    "In standard linear regression with a linear dependence on $x$, we would write this in terms of our model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d22b13",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "f(y_i\\vert x_i)=\\beta_0+\\beta_1 x_i.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b2b13a",
   "metadata": {
    "editable": true
   },
   "source": [
    "This expression implies however that $f(y_i\\vert x_i)$ could take any\n",
    "value from minus infinity to plus infinity. If we however let\n",
    "$f(y\\vert y)$ be represented by the mean value, the above example\n",
    "shows us that we can constrain the function to take values between\n",
    "zero and one, that is we have $0 \\le f(y_i\\vert x_i) \\le 1$. Looking\n",
    "at our last curve we see also that it has an S-shaped form. This leads\n",
    "us to a very popular model for the function $f$, namely the so-called\n",
    "Sigmoid function or logistic model. We will consider this function as\n",
    "representing the probability for finding a value of $y_i$ with a given\n",
    "$x_i$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038a694a",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The logistic function\n",
    "\n",
    "Another widely studied model, is the so-called \n",
    "perceptron model, which is an example of a \"hard classification\" model. We\n",
    "will encounter this model when we discuss neural networks as\n",
    "well. Each datapoint is deterministically assigned to a category (i.e\n",
    "$y_i=0$ or $y_i=1$). In many cases, and the coronary heart disease data forms one of many such examples, it is favorable to have a \"soft\"\n",
    "classifier that outputs the probability of a given category rather\n",
    "than a single value. For example, given $x_i$, the classifier\n",
    "outputs the probability of being in a category $k$.  Logistic regression\n",
    "is the most common example of a so-called soft classifier. In logistic\n",
    "regression, the probability that a data point $x_i$\n",
    "belongs to a category $y_i=\\{0,1\\}$ is given by the so-called logit function (or Sigmoid) which is meant to represent the likelihood for a given event,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581a37c8",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "p(t) = \\frac{1}{1+\\mathrm \\exp{-t}}=\\frac{\\exp{t}}{1+\\mathrm \\exp{t}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e6ff605",
   "metadata": {
    "editable": true
   },
   "source": [
    "Note that $1-p(t)= p(-t)$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e8c6c7",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Examples of likelihood functions used in logistic regression and nueral networks\n",
    "\n",
    "The following code plots the logistic function, the step function and other functions we will encounter from here and on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82e381c1",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_32_0.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_32_1.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_32_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"The sigmoid function (or the logistic curve) is a\n",
    "function that takes any real number, z, and outputs a number (0,1).\n",
    "It is useful in neural networks for assigning weights on a relative scale.\n",
    "The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n",
    "\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import math as mt\n",
    "\n",
    "z = numpy.arange(-5, 5, .1)\n",
    "sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n",
    "sigma = sigma_fn(z)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(z, sigma)\n",
    "ax.set_ylim([-0.1, 1.1])\n",
    "ax.set_xlim([-5,5])\n",
    "ax.grid(True)\n",
    "ax.set_xlabel('z')\n",
    "ax.set_title('sigmoid function')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\"\"\"Step Function\"\"\"\n",
    "z = numpy.arange(-5, 5, .02)\n",
    "step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n",
    "step = step_fn(z)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(z, step)\n",
    "ax.set_ylim([-0.5, 1.5])\n",
    "ax.set_xlim([-5,5])\n",
    "ax.grid(True)\n",
    "ax.set_xlabel('z')\n",
    "ax.set_title('step function')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\"\"\"tanh Function\"\"\"\n",
    "z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n",
    "t = numpy.tanh(z)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(z, t)\n",
    "ax.set_ylim([-1.0, 1.0])\n",
    "ax.set_xlim([-2*mt.pi,2*mt.pi])\n",
    "ax.grid(True)\n",
    "ax.set_xlabel('z')\n",
    "ax.set_title('tanh function')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffc710d",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Two parameters\n",
    "\n",
    "We assume now that we have two classes with $y_i$ either $0$ or $1$. Furthermore we assume also that we have only two parameters $\\beta$ in our fitting of the Sigmoid function, that is we define probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077523d2",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "p(y_i=1|x_i,\\boldsymbol{\\beta}) &= \\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}},\\nonumber\\\\\n",
    "p(y_i=0|x_i,\\boldsymbol{\\beta}) &= 1 - p(y_i=1|x_i,\\boldsymbol{\\beta}),\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378c1ba1",
   "metadata": {
    "editable": true
   },
   "source": [
    "where $\\boldsymbol{\\beta}$ are the weights we wish to extract from data, in our case $\\beta_0$ and $\\beta_1$. \n",
    "\n",
    "Note that we used"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12641f70",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "p(y_i=0\\vert x_i, \\boldsymbol{\\beta}) = 1-p(y_i=1\\vert x_i, \\boldsymbol{\\beta}).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d4d322",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Maximum likelihood\n",
    "\n",
    "In order to define the total likelihood for all possible outcomes from a  \n",
    "dataset $\\mathcal{D}=\\{(y_i,x_i)\\}$, with the binary labels\n",
    "$y_i\\in\\{0,1\\}$ and where the data points are drawn independently, we use the so-called [Maximum Likelihood Estimation](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) (MLE) principle. \n",
    "We aim thus at maximizing \n",
    "the probability of seeing the observed data. We can then approximate the \n",
    "likelihood in terms of the product of the individual probabilities of a specific outcome $y_i$, that is"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218bad85",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "P(\\mathcal{D}|\\boldsymbol{\\beta})& = \\prod_{i=1}^n \\left[p(y_i=1|x_i,\\boldsymbol{\\beta})\\right]^{y_i}\\left[1-p(y_i=1|x_i,\\boldsymbol{\\beta}))\\right]^{1-y_i}\\nonumber \\\\\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1151b5",
   "metadata": {
    "editable": true
   },
   "source": [
    "from which we obtain the log-likelihood and our **cost/loss** function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31ffe4e",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\mathcal{C}(\\boldsymbol{\\beta}) = \\sum_{i=1}^n \\left( y_i\\log{p(y_i=1|x_i,\\boldsymbol{\\beta})} + (1-y_i)\\log\\left[1-p(y_i=1|x_i,\\boldsymbol{\\beta}))\\right]\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f653cea3",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The cost function rewritten\n",
    "\n",
    "Reordering the logarithms, we can rewrite the **cost/loss** function as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d117384a",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\mathcal{C}(\\boldsymbol{\\beta}) = \\sum_{i=1}^n  \\left(y_i(\\beta_0+\\beta_1x_i) -\\log{(1+\\exp{(\\beta_0+\\beta_1x_i)})}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7763f2",
   "metadata": {
    "editable": true
   },
   "source": [
    "The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to $\\beta$.\n",
    "Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c5d4782",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\mathcal{C}(\\boldsymbol{\\beta})=-\\sum_{i=1}^n  \\left(y_i(\\beta_0+\\beta_1x_i) -\\log{(1+\\exp{(\\beta_0+\\beta_1x_i)})}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47ac025",
   "metadata": {
    "editable": true
   },
   "source": [
    "This equation is known in statistics as the **cross entropy**. Finally, we note that just as in linear regression, \n",
    "in practice we often supplement the cross-entropy with additional regularization terms, usually $L_1$ and $L_2$ regularization as we did for Ridge and Lasso regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0775be1d",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Minimizing the cross entropy\n",
    "\n",
    "The cross entropy is a convex function of the weights $\\boldsymbol{\\beta}$ and,\n",
    "therefore, any local minimizer is a global minimizer. \n",
    "\n",
    "Minimizing this\n",
    "cost function with respect to the two parameters $\\beta_0$ and $\\beta_1$ we obtain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6d8021",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -\\sum_{i=1}^n  \\left(y_i -\\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}}\\right),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934b3029",
   "metadata": {
    "editable": true
   },
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5736ce62",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\sum_{i=1}^n  \\left(y_ix_i -x_i\\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24448336",
   "metadata": {
    "editable": true
   },
   "source": [
    "## A more compact expression\n",
    "\n",
    "Let us now define a vector $\\boldsymbol{y}$ with $n$ elements $y_i$, an\n",
    "$n\\times p$ matrix $\\boldsymbol{X}$ which contains the $x_i$ values and a\n",
    "vector $\\boldsymbol{p}$ of fitted probabilities $p(y_i\\vert x_i,\\boldsymbol{\\beta})$. We can rewrite in a more compact form the first\n",
    "derivative of cost function as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62af3134",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = -\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{p}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75f8b6b",
   "metadata": {
    "editable": true
   },
   "source": [
    "If we in addition define a diagonal matrix $\\boldsymbol{W}$ with elements \n",
    "$p(y_i\\vert x_i,\\boldsymbol{\\beta})(1-p(y_i\\vert x_i,\\boldsymbol{\\beta})$, we can obtain a compact expression of the second derivative as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbcdd5a",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} = \\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4b6b7f",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Extending to more predictors\n",
    "\n",
    "Within a binary classification problem, we can easily expand our model to include multiple predictors. Our ratio between likelihoods is then with $p$ predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0487a05f",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\log{ \\frac{p(\\boldsymbol{\\beta}\\boldsymbol{x})}{1-p(\\boldsymbol{\\beta}\\boldsymbol{x})}} = \\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d00e90",
   "metadata": {
    "editable": true
   },
   "source": [
    "Here we defined $\\boldsymbol{x}=[1,x_1,x_2,\\dots,x_p]$ and $\\boldsymbol{\\beta}=[\\beta_0, \\beta_1, \\dots, \\beta_p]$ leading to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f16948",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "p(\\boldsymbol{\\beta}\\boldsymbol{x})=\\frac{ \\exp{(\\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p)}}{1+\\exp{(\\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p)}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99af8c4d",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Including more classes\n",
    "\n",
    "Till now we have mainly focused on two classes, the so-called binary\n",
    "system. Suppose we wish to extend to $K$ classes.  Let us for the sake\n",
    "of simplicity assume we have only two predictors. We have then following model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba91bb6",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\log{\\frac{p(C=1\\vert x)}{p(K\\vert x)}} = \\beta_{10}+\\beta_{11}x_1,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5833039",
   "metadata": {
    "editable": true
   },
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451f2890",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\log{\\frac{p(C=2\\vert x)}{p(K\\vert x)}} = \\beta_{20}+\\beta_{21}x_1,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae365e3",
   "metadata": {
    "editable": true
   },
   "source": [
    "and so on till the class $C=K-1$ class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3187ffb",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\log{\\frac{p(C=K-1\\vert x)}{p(K\\vert x)}} = \\beta_{(K-1)0}+\\beta_{(K-1)1}x_1,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc72487",
   "metadata": {
    "editable": true
   },
   "source": [
    "and the model is specified in term of $K-1$ so-called log-odds or\n",
    "**logit** transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e550b7a",
   "metadata": {
    "editable": true
   },
   "source": [
    "## More classes\n",
    "\n",
    "In our discussion of neural networks we will encounter the above again\n",
    "in terms of a slightly modified function, the so-called **Softmax** function.\n",
    "\n",
    "The softmax function is used in various multiclass classification\n",
    "methods, such as multinomial logistic regression (also known as\n",
    "softmax regression), multiclass linear discriminant analysis, naive\n",
    "Bayes classifiers, and artificial neural networks.  Specifically, in\n",
    "multinomial logistic regression and linear discriminant analysis, the\n",
    "input to the function is the result of $K$ distinct linear functions,\n",
    "and the predicted probability for the $k$-th class given a sample\n",
    "vector $\\boldsymbol{x}$ and a weighting vector $\\boldsymbol{\\beta}$ is (with two\n",
    "predictors):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2781ed",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "p(C=k\\vert \\mathbf {x} )=\\frac{\\exp{(\\beta_{k0}+\\beta_{k1}x_1)}}{1+\\sum_{l=1}^{K-1}\\exp{(\\beta_{l0}+\\beta_{l1}x_1)}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720ee440",
   "metadata": {
    "editable": true
   },
   "source": [
    "It is easy to extend to more predictors. The final class is"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa0254a",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "p(C=K\\vert \\mathbf {x} )=\\frac{1}{1+\\sum_{l=1}^{K-1}\\exp{(\\beta_{l0}+\\beta_{l1}x_1)}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bc3445",
   "metadata": {
    "editable": true
   },
   "source": [
    "and they sum to one. Our earlier discussions were all specialized to\n",
    "the case with two classes only. It is easy to see from the above that\n",
    "what we derived earlier is compatible with these equations.\n",
    "\n",
    "To find the optimal parameters we would typically use a gradient\n",
    "descent method.  Newton's method and gradient descent methods are\n",
    "discussed in the material on [optimization\n",
    "methods](https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25b0241",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Friday September 23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380d3c17",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Searching for Optimal Regularization Parameters $\\lambda$\n",
    "\n",
    "In project 1, when using Ridge and Lasso regression, we end up\n",
    "searching for the optimal parameter $\\lambda$ which minimizes our\n",
    "selected scores (MSE or $R2$ values for example). The brute force\n",
    "approach, as discussed in the code here for Ridge regression, consists\n",
    "in evaluating the MSE as function of different $\\lambda$ values.\n",
    "Based on these calculations, one tries then to determine the value of the hyperparameter $\\lambda$\n",
    "which results in optimal scores (for example the smallest MSE or an $R2=1$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1e69471",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_72_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "\n",
    "def MSE(y_data,y_model):\n",
    "    n = np.size(y_model)\n",
    "    return np.sum((y_data-y_model)**2)/n\n",
    "# A seed just to ensure that the random numbers are the same for every run.\n",
    "# Useful for eventual debugging.\n",
    "np.random.seed(2021)\n",
    "\n",
    "n = 100\n",
    "x = np.random.rand(n)\n",
    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
    "\n",
    "Maxpolydegree = 5\n",
    "X = np.zeros((n,Maxpolydegree-1))\n",
    "\n",
    "for degree in range(1,Maxpolydegree): #No intercept column\n",
    "    X[:,degree-1] = x**(degree)\n",
    "\n",
    "# We split the data in test and training data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Decide which values of lambda to use\n",
    "nlambdas = 500\n",
    "MSERidgePredict = np.zeros(nlambdas)\n",
    "lambdas = np.logspace(-4, 2, nlambdas)\n",
    "for i in range(nlambdas):\n",
    "    lmb = lambdas[i]\n",
    "    RegRidge = linear_model.Ridge(lmb)\n",
    "    RegRidge.fit(X_train,y_train)\n",
    "    ypredictRidge = RegRidge.predict(X_test)\n",
    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
    "\n",
    "# Now plot the results\n",
    "plt.figure()\n",
    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
    "plt.xlabel('log10(lambda)')\n",
    "plt.ylabel('MSE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82def1d",
   "metadata": {
    "editable": true
   },
   "source": [
    "Here we have performed a rather data greedy calculation as function of the regularization parameter $\\lambda$. There is no resampling here. The latter can easily be added by employing the function **RidgeCV** instead of just calling the **Ridge** function. For **RidgeCV** we need to pass the array of $\\lambda$ values.\n",
    "By inspecting the figure we can in turn determine which is the optimal regularization parameter.\n",
    "This becomes however less functional in the long run."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325e0956",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Grid Search\n",
    "\n",
    "An alternative is to use the so-called grid search functionality\n",
    "included with the library **Scikit-Learn**, as demonstrated for the same\n",
    "example here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "557868b5",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(estimator=Ridge(),\n",
      "             param_grid={'alpha': array([1.00000000e-04, 4.64158883e-04, 2.15443469e-03, 1.00000000e-02,\n",
      "       4.64158883e-02, 2.15443469e-01, 1.00000000e+00, 4.64158883e+00,\n",
      "       2.15443469e+01, 1.00000000e+02])})\n",
      "Best estimated lambda-value: 100.0\n",
      "MSE score: 1.0892144853354966\n",
      "R2 score: -0.0038332550504751595\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def R2(y_data, y_model):\n",
    "    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
    "\n",
    "def MSE(y_data,y_model):\n",
    "    n = np.size(y_model)\n",
    "    return np.sum((y_data-y_model)**2)/n\n",
    "\n",
    "# A seed just to ensure that the random numbers are the same for every run.\n",
    "# Useful for eventual debugging.\n",
    "np.random.seed(2021)\n",
    "\n",
    "n = 100\n",
    "x = np.random.rand(n)\n",
    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
    "\n",
    "Maxpolydegree = 5\n",
    "X = np.zeros((n,Maxpolydegree-1))\n",
    "\n",
    "for degree in range(1,Maxpolydegree): #No intercept column\n",
    "    X[:,degree-1] = x**(degree)\n",
    "\n",
    "# We split the data in test and training data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Decide which values of lambda to use\n",
    "nlambdas = 10\n",
    "lambdas = np.logspace(-4, 2, nlambdas)\n",
    "# create and fit a ridge regression model, testing each alpha\n",
    "model = Ridge()\n",
    "gridsearch = GridSearchCV(estimator=model, param_grid=dict(alpha=lambdas))\n",
    "gridsearch.fit(X_train, y_train)\n",
    "print(gridsearch)\n",
    "ypredictRidge = gridsearch.predict(X_test)\n",
    "# summarize the results of the grid search\n",
    "print(f\"Best estimated lambda-value: {gridsearch.best_estimator_.alpha}\")\n",
    "print(f\"MSE score: {MSE(y_test,ypredictRidge)}\")\n",
    "print(f\"R2 score: {R2(y_test,ypredictRidge)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f0779e",
   "metadata": {
    "editable": true
   },
   "source": [
    "By default the grid search function includes cross validation with\n",
    "five folds. The [Scikit-Learn\n",
    "documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV)\n",
    "contains more information on how to set the different parameters.\n",
    "\n",
    "If we take out the random noise, running the above codes results in $\\lambda=0$ yielding the best fit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f83d4a",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Randomized Grid Search\n",
    "\n",
    "An alternative to the above manual grid set up, is to use a random\n",
    "search where the parameters are tuned from a random distribution\n",
    "(uniform below) for a fixed number of iterations. A model is\n",
    "constructed and evaluated for each combination of chosen parameters.\n",
    "We repeat the previous example but now with a random search.  Note\n",
    "that values of $\\lambda$ are now limited to be within $x\\in\n",
    "[0,1]$. This domain may not be the most relevant one for the specific\n",
    "case under study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fadead70",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13ef4e1c0>})\n",
      "Best estimated lambda-value: 0.9849967686928113\n",
      "MSE score: 1.0853136633465326\n",
      "R2 score: -0.0002382102844775691\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy.stats import uniform as randuniform\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "def R2(y_data, y_model):\n",
    "    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
    "\n",
    "def MSE(y_data,y_model):\n",
    "    n = np.size(y_model)\n",
    "    return np.sum((y_data-y_model)**2)/n\n",
    "\n",
    "# A seed just to ensure that the random numbers are the same for every run.\n",
    "# Useful for eventual debugging.\n",
    "np.random.seed(2021)\n",
    "\n",
    "n = 100\n",
    "x = np.random.rand(n)\n",
    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
    "\n",
    "Maxpolydegree = 5\n",
    "X = np.zeros((n,Maxpolydegree-1))\n",
    "\n",
    "for degree in range(1,Maxpolydegree): #No intercept column\n",
    "    X[:,degree-1] = x**(degree)\n",
    "\n",
    "# We split the data in test and training data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "param_grid = {'alpha': randuniform()}\n",
    "# create and fit a ridge regression model, testing each alpha\n",
    "model = Ridge()\n",
    "gridsearch = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100)\n",
    "gridsearch.fit(X_train, y_train)\n",
    "print(gridsearch)\n",
    "ypredictRidge = gridsearch.predict(X_test)\n",
    "# summarize the results of the grid search\n",
    "print(f\"Best estimated lambda-value: {gridsearch.best_estimator_.alpha}\")\n",
    "print(f\"MSE score: {MSE(y_test,ypredictRidge)}\")\n",
    "print(f\"R2 score: {R2(y_test,ypredictRidge)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42658f49",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Wisconsin Cancer Data\n",
    "\n",
    "We show here how we can use a simple regression case on the breast\n",
    "cancer data using Logistic regression as our algorithm for\n",
    "classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01c1d986",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "Test set accuracy with Logistic Regression: 0.94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Load the data\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, y_train)\n",
    "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86570ee",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Using the correlation matrix\n",
    "\n",
    "In addition to the above scores, we could also study the covariance (and the correlation matrix).\n",
    "We use **Pandas** to compute the correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e241e87e",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x2000 with 30 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_82_0.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_82_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "cancer = load_breast_cancer()\n",
    "import pandas as pd\n",
    "# Making a data frame\n",
    "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "\n",
    "fig, axes = plt.subplots(15,2,figsize=(10,20))\n",
    "malignant = cancer.data[cancer.target == 0]\n",
    "benign = cancer.data[cancer.target == 1]\n",
    "ax = axes.ravel()\n",
    "\n",
    "for i in range(30):\n",
    "    _, bins = np.histogram(cancer.data[:,i], bins =50)\n",
    "    ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)\n",
    "    ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)\n",
    "    ax[i].set_title(cancer.feature_names[i])\n",
    "    ax[i].set_yticks(())\n",
    "ax[0].set_xlabel(\"Feature magnitude\")\n",
    "ax[0].set_ylabel(\"Frequency\")\n",
    "ax[0].legend([\"Malignant\", \"Benign\"], loc =\"best\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "import seaborn as sns\n",
    "correlation_matrix = cancerpd.corr().round(1)\n",
    "# use the heatmap function from seaborn to plot the correlation matrix\n",
    "# annot = True to print the values inside the square\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(data=correlation_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31db566e",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Discussing the correlation data\n",
    "\n",
    "In the above example we note two things. In the first plot we display\n",
    "the overlap of benign and malignant tumors as functions of the various\n",
    "features in the Wisconsing breast cancer data set. We see that for\n",
    "some of the features we can distinguish clearly the benign and\n",
    "malignant cases while for other features we cannot. This can point to\n",
    "us which features may be of greater interest when we wish to classify\n",
    "a benign or not benign tumour.\n",
    "\n",
    "In the second figure we have computed the so-called correlation\n",
    "matrix, which in our case with thirty features becomes a $30\\times 30$\n",
    "matrix.\n",
    "\n",
    "We constructed this matrix using **pandas** via the statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ddb180b",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c6a55b",
   "metadata": {
    "editable": true
   },
   "source": [
    "and then"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3347712",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "correlation_matrix = cancerpd.corr().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60362a3",
   "metadata": {
    "editable": true
   },
   "source": [
    "Diagonalizing this matrix we can in turn say something about which\n",
    "features are of relevance and which are not. This leads  us to\n",
    "the classical Principal Component Analysis (PCA) theorem with\n",
    "applications. This will be discussed later this semester ([week 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232f7e0d",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Other measures in classification studies: Cancer Data  again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "552632a5",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "[1.         0.86666667 1.         0.92857143 1.         0.85714286\n",
      " 1.         0.92857143 0.92857143 1.        ]\n",
      "Test set accuracy with Logistic Regression: 0.94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_89_2.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_89_3.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_89_4.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import  train_test_split \n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Load the data\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_validate\n",
    "#Cross validation\n",
    "accuracy = cross_validate(logreg,X_test,y_test,cv=10)['test_score']\n",
    "print(accuracy)\n",
    "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
    "\n",
    "import scikitplot as skplt\n",
    "y_pred = logreg.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
    "plt.show()\n",
    "y_probas = logreg.predict_proba(X_test)\n",
    "skplt.metrics.plot_roc(y_test, y_probas)\n",
    "plt.show()\n",
    "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7752c5ea",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Optimization, the central part of any Machine Learning algortithm\n",
    "\n",
    "[Overview Video, why do we care about gradient methods?](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/OverarchingAimsWeek39.mp4?vrtx=view-as-webpage)\n",
    "\n",
    "Almost every problem in machine learning and data science starts with\n",
    "a dataset $X$, a model $g(\\beta)$, which is a function of the\n",
    "parameters $\\beta$ and a cost function $C(X, g(\\beta))$ that allows\n",
    "us to judge how well the model $g(\\beta)$ explains the observations\n",
    "$X$. The model is fit by finding the values of $\\beta$ that minimize\n",
    "the cost function. Ideally we would be able to solve for $\\beta$\n",
    "analytically, however this is not possible in general and we must use\n",
    "some approximative/numerical method to compute the minimum."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f307c73e",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Revisiting our Logistic Regression case\n",
    "\n",
    "In our discussion on Logistic Regression we studied the \n",
    "case of\n",
    "two classes, with $y_i$ either\n",
    "$0$ or $1$. Furthermore we assumed also that we have only two\n",
    "parameters $\\beta$ in our fitting, that is we\n",
    "defined probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921fcab7",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "p(y_i=1|x_i,\\boldsymbol{\\beta}) &= \\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}},\\nonumber\\\\\n",
    "p(y_i=0|x_i,\\boldsymbol{\\beta}) &= 1 - p(y_i=1|x_i,\\boldsymbol{\\beta}),\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9863a96d",
   "metadata": {
    "editable": true
   },
   "source": [
    "where $\\boldsymbol{\\beta}$ are the weights we wish to extract from data, in our case $\\beta_0$ and $\\beta_1$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a4b9a3",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The equations to solve\n",
    "\n",
    "Our compact equations used a definition of a vector $\\boldsymbol{y}$ with $n$\n",
    "elements $y_i$, an $n\\times p$ matrix $\\boldsymbol{X}$ which contains the\n",
    "$x_i$ values and a vector $\\boldsymbol{p}$ of fitted probabilities\n",
    "$p(y_i\\vert x_i,\\boldsymbol{\\beta})$. We rewrote in a more compact form\n",
    "the first derivative of the cost function as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f454ef",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = -\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{p}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba81e87",
   "metadata": {
    "editable": true
   },
   "source": [
    "If we in addition define a diagonal matrix $\\boldsymbol{W}$ with elements \n",
    "$p(y_i\\vert x_i,\\boldsymbol{\\beta})(1-p(y_i\\vert x_i,\\boldsymbol{\\beta})$, we can obtain a compact expression of the second derivative as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b87735e",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} = \\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de8fc00",
   "metadata": {
    "editable": true
   },
   "source": [
    "This defines what is called  the Hessian matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a582cfba",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Solving using Newton-Raphson's method\n",
    "\n",
    "If we can set up these equations, Newton-Raphson's iterative method is normally the method of choice. It requires however that we can compute in an efficient way the  matrices that define the first and second derivatives. \n",
    "\n",
    "Our iterative scheme is then given by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb1055f",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T}\\right)^{-1}_{\\boldsymbol{\\beta}^{\\mathrm{old}}}\\times \\left(\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}}\\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}},\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecea7f51",
   "metadata": {
    "editable": true
   },
   "source": [
    "or in matrix form as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98861e3",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X} \\right)^{-1}\\times \\left(-\\boldsymbol{X}^T(\\boldsymbol{y}-\\boldsymbol{p}) \\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd11bf8",
   "metadata": {
    "editable": true
   },
   "source": [
    "The right-hand side is computed with the old values of $\\beta$. \n",
    "\n",
    "If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7f27a4",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Brief reminder on Newton-Raphson's method\n",
    "\n",
    "Let us quickly remind ourselves how we derive the above method.\n",
    "\n",
    "Perhaps the most celebrated of all one-dimensional root-finding\n",
    "routines is Newton's method, also called the Newton-Raphson\n",
    "method. This method  requires the evaluation of both the\n",
    "function $f$ and its derivative $f'$ at arbitrary points. \n",
    "If you can only calculate the derivative\n",
    "numerically and/or your function is not of the smooth type, we\n",
    "normally discourage the use of this method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e2e1f0",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The equations\n",
    "\n",
    "The Newton-Raphson formula consists geometrically of extending the\n",
    "tangent line at a current point until it crosses zero, then setting\n",
    "the next guess to the abscissa of that zero-crossing.  The mathematics\n",
    "behind this method is rather simple. Employing a Taylor expansion for\n",
    "$x$ sufficiently close to the solution $s$, we have"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5605583d",
   "metadata": {
    "editable": true
   },
   "source": [
    "<!-- Equation labels as ordinary links -->\n",
    "<div id=\"eq:taylornr\"></div>\n",
    "\n",
    "$$\n",
    "f(s)=0=f(x)+(s-x)f'(x)+\\frac{(s-x)^2}{2}f''(x) +\\dots.\n",
    "    \\label{eq:taylornr} \\tag{2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ce1a0c",
   "metadata": {
    "editable": true
   },
   "source": [
    "For small enough values of the function and for well-behaved\n",
    "functions, the terms beyond linear are unimportant, hence we obtain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7462cf59",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "f(x)+(s-x)f'(x)\\approx 0,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3609230",
   "metadata": {
    "editable": true
   },
   "source": [
    "yielding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c5804e",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "s\\approx x-\\frac{f(x)}{f'(x)}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6643d0",
   "metadata": {
    "editable": true
   },
   "source": [
    "Having in mind an iterative procedure, it is natural to start iterating with"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58afbcf0",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "x_{n+1}=x_n-\\frac{f(x_n)}{f'(x_n)}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a12296",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Simple geometric interpretation\n",
    "\n",
    "The above is Newton-Raphson's method. It has a simple geometric\n",
    "interpretation, namely $x_{n+1}$ is the point where the tangent from\n",
    "$(x_n,f(x_n))$ crosses the $x$-axis.  Close to the solution,\n",
    "Newton-Raphson converges fast to the desired result. However, if we\n",
    "are far from a root, where the higher-order terms in the series are\n",
    "important, the Newton-Raphson formula can give grossly inaccurate\n",
    "results. For instance, the initial guess for the root might be so far\n",
    "from the true root as to let the search interval include a local\n",
    "maximum or minimum of the function.  If an iteration places a trial\n",
    "guess near such a local extremum, so that the first derivative nearly\n",
    "vanishes, then Newton-Raphson may fail totally"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39144130",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Extending to more than one variable\n",
    "\n",
    "Newton's method can be generalized to systems of several non-linear equations\n",
    "and variables. Consider the case with two equations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98db024",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{array}{cc} f_1(x_1,x_2) &=0\\\\\n",
    "                     f_2(x_1,x_2) &=0,\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79154f84",
   "metadata": {
    "editable": true
   },
   "source": [
    "which we Taylor expand to obtain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b225307",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{array}{cc} 0=f_1(x_1+h_1,x_2+h_2)=&f_1(x_1,x_2)+h_1\n",
    "                     \\partial f_1/\\partial x_1+h_2\n",
    "                     \\partial f_1/\\partial x_2+\\dots\\\\\n",
    "                     0=f_2(x_1+h_1,x_2+h_2)=&f_2(x_1,x_2)+h_1\n",
    "                     \\partial f_2/\\partial x_1+h_2\n",
    "                     \\partial f_2/\\partial x_2+\\dots\n",
    "                       \\end{array}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72363dfd",
   "metadata": {
    "editable": true
   },
   "source": [
    "Defining the Jacobian matrix ${\\bf \\boldsymbol{J}}$ we have"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7608f604",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "{\\bf \\boldsymbol{J}}=\\left( \\begin{array}{cc}\n",
    "                         \\partial f_1/\\partial x_1  & \\partial f_1/\\partial x_2 \\\\\n",
    "                          \\partial f_2/\\partial x_1     &\\partial f_2/\\partial x_2\n",
    "             \\end{array} \\right),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10f9e88",
   "metadata": {
    "editable": true
   },
   "source": [
    "we can rephrase Newton's method as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a79127",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\left(\\begin{array}{c} x_1^{n+1} \\\\ x_2^{n+1} \\end{array} \\right)=\n",
    "\\left(\\begin{array}{c} x_1^{n} \\\\ x_2^{n} \\end{array} \\right)+\n",
    "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8451a71",
   "metadata": {
    "editable": true
   },
   "source": [
    "where we have defined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2c4387",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right)=\n",
    "   -{\\bf \\boldsymbol{J}}^{-1}\n",
    "   \\left(\\begin{array}{c} f_1(x_1^{n},x_2^{n}) \\\\ f_2(x_1^{n},x_2^{n}) \\end{array} \\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc3a90d",
   "metadata": {
    "editable": true
   },
   "source": [
    "We need thus to compute the inverse of the Jacobian matrix and it\n",
    "is to understand that difficulties  may\n",
    "arise in case ${\\bf \\boldsymbol{J}}$ is nearly singular.\n",
    "\n",
    "It is rather straightforward to extend the above scheme to systems of\n",
    "more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee016c8",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Steepest descent\n",
    "\n",
    "The basic idea of gradient descent is\n",
    "that a function $F(\\mathbf{x})$, \n",
    "$\\mathbf{x} \\equiv (x_1,\\cdots,x_n)$, decreases fastest if one goes from $\\bf {x}$ in the\n",
    "direction of the negative gradient $-\\nabla F(\\mathbf{x})$.\n",
    "\n",
    "It can be shown that if"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4340278",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2af5812",
   "metadata": {
    "editable": true
   },
   "source": [
    "with $\\gamma_k > 0$.\n",
    "\n",
    "For $\\gamma_k$ small enough, then $F(\\mathbf{x}_{k+1}) \\leq\n",
    "F(\\mathbf{x}_k)$. This means that for a sufficiently small $\\gamma_k$\n",
    "we are always moving towards smaller function values, i.e a minimum."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8237bc8",
   "metadata": {
    "editable": true
   },
   "source": [
    "## More on Steepest descent\n",
    "\n",
    "The previous observation is the basis of the method of steepest\n",
    "descent, which is also referred to as just gradient descent (GD). One\n",
    "starts with an initial guess $\\mathbf{x}_0$ for a minimum of $F$ and\n",
    "computes new approximations according to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079c64d8",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k), \\ \\ k \\geq 0.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08da6b25",
   "metadata": {
    "editable": true
   },
   "source": [
    "The parameter $\\gamma_k$ is often referred to as the step length or\n",
    "the learning rate within the context of Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1a4917",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The ideal\n",
    "\n",
    "Ideally the sequence $\\{\\mathbf{x}_k \\}_{k=0}$ converges to a global\n",
    "minimum of the function $F$. In general we do not know if we are in a\n",
    "global or local minimum. In the special case when $F$ is a convex\n",
    "function, all local minima are also global minima, so in this case\n",
    "gradient descent can converge to the global solution. The advantage of\n",
    "this scheme is that it is conceptually simple and straightforward to\n",
    "implement. However the method in this form has some severe\n",
    "limitations:\n",
    "\n",
    "In machine learing we are often faced with non-convex high dimensional\n",
    "cost functions with many local minima. Since GD is deterministic we\n",
    "will get stuck in a local minimum, if the method converges, unless we\n",
    "have a very good intial guess. This also implies that the scheme is\n",
    "sensitive to the chosen initial condition.\n",
    "\n",
    "Note that the gradient is a function of $\\mathbf{x} =\n",
    "(x_1,\\cdots,x_n)$ which makes it expensive to compute numerically."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2aabf0",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The sensitiveness of the gradient descent\n",
    "\n",
    "The gradient descent method \n",
    "is sensitive to the choice of learning rate $\\gamma_k$. This is due\n",
    "to the fact that we are only guaranteed that $F(\\mathbf{x}_{k+1}) \\leq\n",
    "F(\\mathbf{x}_k)$ for sufficiently small $\\gamma_k$. The problem is to\n",
    "determine an optimal learning rate. If the learning rate is chosen too\n",
    "small the method will take a long time to converge and if it is too\n",
    "large we can experience erratic behavior.\n",
    "\n",
    "Many of these shortcomings can be alleviated by introducing\n",
    "randomness. One such method is that of Stochastic Gradient Descent\n",
    "(SGD), to be discussed next week."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7377154c",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Convex functions\n",
    "\n",
    "Ideally we want our cost/loss function to be convex(concave).\n",
    "\n",
    "First we give the definition of a convex set: A set $C$ in\n",
    "$\\mathbb{R}^n$ is said to be convex if, for all $x$ and $y$ in $C$ and\n",
    "all $t \\in (0,1)$ , the point $(1 − t)x + ty$ also belongs to\n",
    "C. Geometrically this means that every point on the line segment\n",
    "connecting $x$ and $y$ is in $C$ as discussed below.\n",
    "\n",
    "The convex subsets of $\\mathbb{R}$ are the intervals of\n",
    "$\\mathbb{R}$. Examples of convex sets of $\\mathbb{R}^2$ are the\n",
    "regular polygons (triangles, rectangles, pentagons, etc...)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697326eb",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Convex function\n",
    "\n",
    "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a532b777",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Conditions on convex functions\n",
    "\n",
    "In the following we state first and second-order conditions which\n",
    "ensures convexity of a function $f$. We write $D_f$ to denote the\n",
    "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n",
    "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n",
    "\n",
    "**First order condition.**\n",
    "\n",
    "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n",
    "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n",
    "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n",
    "for all $x,y \\in D_f$. This condition means that for a convex function\n",
    "the first order Taylor expansion (right hand side above) at any point\n",
    "a global under estimator of the function. To convince yourself you can\n",
    "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n",
    "note that it is always below the graph.\n",
    "\n",
    "**Second order condition.**\n",
    "\n",
    "Assume that $f$ is twice\n",
    "differentiable, i.e the Hessian matrix exists at each point in\n",
    "$D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its\n",
    "Hessian is positive semi-definite for all $x\\in D_f$. For a\n",
    "single-variable function this reduces to $f''(x) \\geq 0$. Geometrically this means that $f$ has nonnegative curvature\n",
    "everywhere.\n",
    "\n",
    "This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4152f5",
   "metadata": {
    "editable": true
   },
   "source": [
    "## More on convex functions\n",
    "\n",
    "The next result is of great importance to us and the reason why we are\n",
    "going on about convex functions. In machine learning we frequently\n",
    "have to minimize a loss/cost function in order to find the best\n",
    "parameters for the model we are considering. \n",
    "\n",
    "Ideally we want the\n",
    "global minimum (for high-dimensional models it is hard to know\n",
    "if we have local or global minimum). However, if the cost/loss function\n",
    "is convex the following result provides invaluable information:\n",
    "\n",
    "**Any minimum is global for convex functions.**\n",
    "\n",
    "Consider the problem of finding $x \\in \\mathbb{R}^n$ such that $f(x)$\n",
    "is minimal, where $f$ is convex and differentiable. Then, any point\n",
    "$x^*$ that satisfies $\\nabla f(x^*) = 0$ is a global minimum.\n",
    "\n",
    "This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e48d339b",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Some simple problems\n",
    "\n",
    "1. Show that $f(x)=x^2$ is convex for $x \\in \\mathbb{R}$ using the definition of convexity. Hint: If you re-write the definition, $f$ is convex if the following holds for all $x,y \\in D_f$ and any $\\lambda \\in [0,1]$ $\\lambda f(x)+(1-\\lambda)f(y)-f(\\lambda x + (1-\\lambda) y ) \\geq 0$.\n",
    "\n",
    "2. Using the second order condition show that the following functions are convex on the specified domain.\n",
    "\n",
    " * $f(x) = e^x$ is convex for $x \\in \\mathbb{R}$.\n",
    "\n",
    " * $g(x) = -\\ln(x)$ is convex for $x \\in (0,\\infty)$.\n",
    "\n",
    "3. Let $f(x) = x^2$ and $g(x) = e^x$. Show that $f(g(x))$ and $g(f(x))$ is convex for $x \\in \\mathbb{R}$. Also show that if $f(x)$ is any convex function than $h(x) = e^{f(x)}$ is convex.\n",
    "\n",
    "4. A norm is any function that satisfy the following properties\n",
    "\n",
    " * $f(\\alpha x) = |\\alpha| f(x)$ for all $\\alpha \\in \\mathbb{R}$.\n",
    "\n",
    " * $f(x+y) \\leq f(x) + f(y)$\n",
    "\n",
    " * $f(x) \\leq 0$ for all $x \\in \\mathbb{R}^n$ with equality if and only if $x = 0$\n",
    "\n",
    "Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae5c509",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Revisiting our first homework\n",
    "\n",
    "We will use linear regression as a case study for the gradient descent\n",
    "methods. Linear regression is a great test case for the gradient\n",
    "descent methods discussed in the lectures since it has several\n",
    "desirable properties such as:\n",
    "\n",
    "1. An analytical solution (recall homework set 1).\n",
    "\n",
    "2. The gradient can be computed analytically.\n",
    "\n",
    "3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates\n",
    "\n",
    "We revisit an example similar to what we had in the first homework set. We had a function  of the type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd182f20",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[43mm\u001b[49m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(m,\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
     ]
    }
   ],
   "source": [
    "x = 2*np.random.rand(m,1)\n",
    "y = 4+3*x+np.random.randn(m,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef5bc40",
   "metadata": {
    "editable": true
   },
   "source": [
    "with $x_i \\in [0,1] $ is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution $\\cal {N}(0,1)$. \n",
    "The linear regression model is given by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af1247d",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "h_\\beta(x) = \\boldsymbol{y} = \\beta_0 + \\beta_1 x,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fcf8f2",
   "metadata": {
    "editable": true
   },
   "source": [
    "such that"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b6947aa",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\boldsymbol{y}_i = \\beta_0 + \\beta_1 x_i.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5838344",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Gradient descent example\n",
    "\n",
    "Let $\\mathbf{y} = (y_1,\\cdots,y_n)^T$, $\\mathbf{\\boldsymbol{y}} = (\\boldsymbol{y}_1,\\cdots,\\boldsymbol{y}_n)^T$ and $\\beta = (\\beta_0, \\beta_1)^T$\n",
    "\n",
    "It is convenient to write $\\mathbf{\\boldsymbol{y}} = X\\beta$ where $X \\in \\mathbb{R}^{100 \\times 2} $ is the design matrix given by (we keep the intercept here)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5af1dca",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "X \\equiv \\begin{bmatrix}\n",
    "1 & x_1  \\\\\n",
    "\\vdots & \\vdots  \\\\\n",
    "1 & x_{100} &  \\\\\n",
    "\\end{bmatrix}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f380504f",
   "metadata": {
    "editable": true
   },
   "source": [
    "The cost/loss/risk function is given by ("
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e7530e",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "C(\\beta) = \\frac{1}{n}||X\\beta-\\mathbf{y}||_{2}^{2} = \\frac{1}{n}\\sum_{i=1}^{100}\\left[ (\\beta_0 + \\beta_1 x_i)^2 - 2 y_i (\\beta_0 + \\beta_1 x_i) + y_i^2\\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babaeeee",
   "metadata": {
    "editable": true
   },
   "source": [
    "and we want to find $\\beta$ such that $C(\\beta)$ is minimized."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f25dc02",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The derivative of the cost/loss function\n",
    "\n",
    "Computing $\\partial C(\\beta) / \\partial \\beta_0$ and $\\partial C(\\beta) / \\partial \\beta_1$ we can show  that the gradient can be written as"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58eb4735",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\nabla_{\\beta} C(\\beta) = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n",
    "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n",
    "\\end{bmatrix} = \\frac{2}{n}X^T(X\\beta - \\mathbf{y}),\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f33b17",
   "metadata": {
    "editable": true
   },
   "source": [
    "where $X$ is the design matrix defined above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ffa8c7",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The Hessian matrix\n",
    "The Hessian matrix of $C(\\beta)$ is given by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7009c819",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n",
    "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1}  \\\\\n",
    "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} &  \\\\\n",
    "\\end{bmatrix} = \\frac{2}{n}X^T X.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71cf8211",
   "metadata": {
    "editable": true
   },
   "source": [
    "This result implies that $C(\\beta)$ is a convex function since the matrix $X^T X$ always is positive semi-definite."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41b50aa",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Simple program\n",
    "\n",
    "We can now write a program that minimizes $C(\\beta)$ using the gradient descent method with a constant learning rate $\\gamma$ according to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b52d696",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\beta_{k+1} = \\beta_k - \\gamma \\nabla_\\beta C(\\beta_k), \\ k=0,1,\\cdots\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef629c8b",
   "metadata": {
    "editable": true
   },
   "source": [
    "We can use the expression we computed for the gradient and let use a\n",
    "$\\beta_0$ be chosen randomly and let $\\gamma = 0.001$. Stop iterating\n",
    "when $||\\nabla_\\beta C(\\beta_k) || \\leq \\epsilon = 10^{-8}$. **Note that the code below does not include the latter stop criterion**.\n",
    "\n",
    "And finally we can compare our solution for $\\beta$ with the analytic result given by \n",
    "$\\beta= (X^TX)^{-1} X^T \\mathbf{y}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c30718a",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Gradient Descent Example\n",
    "\n",
    "Here our simple example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84f33bde",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Importing various packages\n",
    "from random import random, seed\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "import sys\n",
    "\n",
    "# the number of datapoints\n",
    "n = 100\n",
    "x = 2*np.random.rand(n,1)\n",
    "y = 4+3*x+np.random.randn(n,1)\n",
    "\n",
    "X = np.c_[np.ones((n,1)), x]\n",
    "# Hessian matrix\n",
    "H = (2.0/n)* X.T @ X\n",
    "# Get the eigenvalues\n",
    "EigValues, EigVectors = np.linalg.eig(H)\n",
    "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n",
    "\n",
    "beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y\n",
    "print(beta_linreg)\n",
    "beta = np.random.randn(2,1)\n",
    "\n",
    "eta = 1.0/np.max(EigValues)\n",
    "Niterations = 1000\n",
    "\n",
    "for iter in range(Niterations):\n",
    "    gradient = (2.0/n)*X.T @ (X @ beta-y)\n",
    "    beta -= eta*gradient\n",
    "\n",
    "print(beta)\n",
    "xnew = np.array([[0],[2]])\n",
    "xbnew = np.c_[np.ones((2,1)), xnew]\n",
    "ypredict = xbnew.dot(beta)\n",
    "ypredict2 = xbnew.dot(beta_linreg)\n",
    "plt.plot(xnew, ypredict, \"r-\")\n",
    "plt.plot(xnew, ypredict2, \"b-\")\n",
    "plt.plot(x, y ,'ro')\n",
    "plt.axis([0,2.0,0, 15.0])\n",
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$y$')\n",
    "plt.title(r'Gradient descent example')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d332552b",
   "metadata": {
    "editable": true
   },
   "source": [
    "## And a corresponding example using **scikit-learn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c46612a1",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Importing various packages\n",
    "from random import random, seed\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "n = 100\n",
    "x = 2*np.random.rand(n,1)\n",
    "y = 4+3*x+np.random.randn(n,1)\n",
    "\n",
    "X = np.c_[np.ones((n,1)), x]\n",
    "beta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n",
    "print(beta_linreg)\n",
    "sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1)\n",
    "sgdreg.fit(x,y.ravel())\n",
    "print(sgdreg.intercept_, sgdreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa00fc2",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Gradient descent and Ridge\n",
    "\n",
    "We have also discussed Ridge regression where the loss function contains a regularized term given by the $L_2$ norm of $\\beta$,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d248a4",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "C_{\\text{ridge}}(\\beta) = \\frac{1}{n}||X\\beta -\\mathbf{y}||^2 + \\lambda ||\\beta||^2, \\ \\lambda \\geq 0.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa969e77",
   "metadata": {
    "editable": true
   },
   "source": [
    "In order to minimize $C_{\\text{ridge}}(\\beta)$ using GD we adjust the gradient as follows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d7d114",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\nabla_\\beta C_{\\text{ridge}}(\\beta)  = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n",
    "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n",
    "\\end{bmatrix} + 2\\lambda\\begin{bmatrix} \\beta_0 \\\\ \\beta_1\\end{bmatrix} = 2 (\\frac{1}{n}X^T(X\\beta - \\mathbf{y})+\\lambda \\beta).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a281f2c0",
   "metadata": {
    "editable": true
   },
   "source": [
    "We can easily extend our program to minimize $C_{\\text{ridge}}(\\beta)$ using gradient descent and compare with the analytical solution given by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c40a890",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\beta_{\\text{ridge}} = \\left(X^T X + n\\lambda I_{2 \\times 2} \\right)^{-1} X^T \\mathbf{y}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e848da6",
   "metadata": {
    "editable": true
   },
   "source": [
    "## The Hessian matrix for Ridge Regression\n",
    "The Hessian matrix of Ridge Regression for our simple example  is given by"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b17645",
   "metadata": {
    "editable": true
   },
   "source": [
    "$$\n",
    "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n",
    "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1}  \\\\\n",
    "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} &  \\\\\n",
    "\\end{bmatrix} = \\frac{2}{n}X^T X+2\\lambda\\boldsymbol{I}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bb3901",
   "metadata": {
    "editable": true
   },
   "source": [
    "This implies that the Hessian matrix  is positive definite, hence the stationary point is a\n",
    "minimum.\n",
    "Note that the Ridge cost function is convex being  a sum of two convex\n",
    "functions. Therefore, the stationary point is a global\n",
    "minimum of this function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ab0a41",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Program example for gradient descent with Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "630a15e8",
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from random import random, seed\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "import sys\n",
    "\n",
    "# the number of datapoints\n",
    "n = 100\n",
    "x = 2*np.random.rand(n,1)\n",
    "y = 4+3*x+np.random.randn(n,1)\n",
    "\n",
    "X = np.c_[np.ones((n,1)), x]\n",
    "XT_X = X.T @ X\n",
    "\n",
    "#Ridge parameter lambda\n",
    "lmbda  = 0.001\n",
    "Id = n*lmbda* np.eye(XT_X.shape[0])\n",
    "\n",
    "# Hessian matrix\n",
    "H = (2.0/n)* XT_X+2*lmbda* np.eye(XT_X.shape[0])\n",
    "# Get the eigenvalues\n",
    "EigValues, EigVectors = np.linalg.eig(H)\n",
    "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n",
    "\n",
    "\n",
    "beta_linreg = np.linalg.inv(XT_X+Id) @ X.T @ y\n",
    "print(beta_linreg)\n",
    "# Start plain gradient descent\n",
    "beta = np.random.randn(2,1)\n",
    "\n",
    "eta = 1.0/np.max(EigValues)\n",
    "Niterations = 100\n",
    "\n",
    "for iter in range(Niterations):\n",
    "    gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta\n",
    "    beta -= eta*gradients\n",
    "\n",
    "print(beta)\n",
    "ypredict = X @ beta\n",
    "ypredict2 = X @ beta_linreg\n",
    "plt.plot(x, ypredict, \"r-\")\n",
    "plt.plot(x, ypredict2, \"b-\")\n",
    "plt.plot(x, y ,'ro')\n",
    "plt.axis([0,2.0,0, 15.0])\n",
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$y$')\n",
    "plt.title(r'Gradient descent example for Ridge')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21abaca5",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Using gradient descent methods, limitations\n",
    "\n",
    "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n",
    "\n",
    "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n",
    "\n",
    "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n",
    "\n",
    "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n",
    "\n",
    "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n",
    "\n",
    "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5753b51d",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Challenge yourself the coming weekend\n",
    "\n",
    "Write a code which implements gradient descent for a logistic regression example."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}