FYS-STK4155/doc/LectureNotes/_build/jupyter_execute/chapter4.ipynb

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    "# Logistic Regression\n",
    "\n",
    "\n",
    "\n",
    "## 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 $x_i$. 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",
    "optimal parameters $\\hat{\\beta}$ 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.\n",
    "\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.\n",
    "\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 $\\hat{\\beta}$. 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.\n",
    "\n",
    "\n",
    "\n",
    "## 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"
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    "$$\n",
    "y_i = \\begin{bmatrix} 0 & \\mathrm{no}\\\\  1 & \\mathrm{yes} \\end{bmatrix}.\n",
    "$$"
   ]
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    "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"
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    "<!-- Equation labels as ordinary links -->\n",
    "<div id=\"_auto1\"></div>\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{\\beta} +  \\boldsymbol{\\epsilon},\n",
    "\\label{_auto1} \\tag{1}\n",
    "\\end{equation}\n",
    "$$"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "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.\n",
    "\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. 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.\n",
    "\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."
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       "  <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>8</td>\n",
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       "     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]"
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_5_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# 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",
   "metadata": {},
   "source": [
    "What we could attempt however is to plot the mean value for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_7_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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "$$\n",
    "f(y_i\\vert x_i)=\\beta_0+\\beta_1 x_i.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "$x_i$.\n",
    "\n",
    "\n",
    "## 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",
   "metadata": {},
   "source": [
    "$$\n",
    "p(t) = \\frac{1}{1+\\mathrm \\exp{-t}}=\\frac{\\exp{t}}{1+\\mathrm \\exp{t}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that $1-p(t)= p(-t)$.\n",
    "\n",
    "## Examples of likelihood functions used in logistic regression and nueral networks\n",
    "\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": 3,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_13_0.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_13_1.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_13_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",
   "metadata": {},
   "source": [
    "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "from which we obtain the log-likelihood and our **cost/loss** function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "Reordering the logarithms, we can rewrite the **cost/loss** function as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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.\n",
    "\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",
    "\n",
    "Minimizing this\n",
    "cost function with respect to the two parameters $\\beta_0$ and $\\beta_1$ we obtain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "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",
   "metadata": {},
   "source": [
    "$$\n",
    "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = -\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{p}\\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "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",
   "metadata": {},
   "source": [
    "$$\n",
    "\\log{\\frac{p(C=1\\vert x)}{p(K\\vert x)}} = \\beta_{10}+\\beta_{11}x_1,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\log{\\frac{p(C=2\\vert x)}{p(K\\vert x)}} = \\beta_{20}+\\beta_{21}x_1,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and so on till the class $C=K-1$ class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "and the model is specified in term of $K-1$ so-called log-odds or\n",
    "**logit** transformations.\n",
    "\n",
    "\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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "It is easy to extend to more predictors. The final class is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "metadata": {},
   "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).\n",
    "\n",
    "## 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": 4,
   "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",
      "Test set accuracy Logistic Regression with scaled data: 0.96\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)))\n",
    "#now scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# Logistic Regression\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 5,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_51_0.png"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_51_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",
   "metadata": {},
   "source": [
    "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": 6,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "editable": true
   },
   "outputs": [],
   "source": [
    "correlation_matrix = cancerpd.corr().round(1)"
   ]
  },
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    "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))."
   ]
  },
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     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "Test set accuracy with Logistic Regression: 0.94\n",
      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
      "[1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         0.92857143 0.92857143]\n",
      "Test set accuracy with Logistic Regression  and scaled data: 0.96\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"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'scikitplot'",
     "output_type": "error",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-12adb44b1c20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscikitplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mskplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_scaled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0mskplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scikitplot'"
     ]
    }
   ],
   "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)))\n",
    "#now scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# Logistic Regression\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "\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_scaled,y_test,cv=10)['test_score']\n",
    "print(accuracy)\n",
    "print(\"Test set accuracy with Logistic Regression  and scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
    "\n",
    "\n",
    "import scikitplot as skplt\n",
    "y_pred = logreg.predict(X_test_scaled)\n",
    "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
    "plt.show()\n",
    "y_probas = logreg.predict_proba(X_test_scaled)\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()"
   ]
  }
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