Morten Hjorth-Jensen
1133 lines
36 KiB
Plaintext
1133 lines
36 KiB
Plaintext
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"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
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"doconce format html chapter4.do.txt -->"
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"# Logistic Regression"
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]
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"## Logistic Regression\n",
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"\n",
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"In linear regression our main interest was centered on learning the\n",
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"coefficients of a functional fit (say a polynomial) in order to be\n",
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"able to predict the response of a continuous variable on some unseen\n",
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"data. The fit to the continuous variable $y_i$ is based on some\n",
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"independent variables $x_i$. Linear regression resulted in\n",
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"analytical expressions for standard ordinary Least Squares or Ridge\n",
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"regression (in terms of matrices to invert) for several quantities,\n",
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"ranging from the variance and thereby the confidence intervals of the\n",
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"optimal parameters $\\hat{\\beta}$ to the mean squared error. If we can invert\n",
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"the product of the design matrices, linear regression gives then a\n",
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"simple recipe for fitting our data.\n",
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"\n",
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"Classification problems, however, are concerned with outcomes taking\n",
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"the form of discrete variables (i.e. categories). We may for example,\n",
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"on the basis of DNA sequencing for a number of patients, like to find\n",
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"out which mutations are important for a certain disease; or based on\n",
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"scans of various patients' brains, figure out if there is a tumor or\n",
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"not; or given a specific physical system, we'd like to identify its\n",
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"state, say whether it is an ordered or disordered system (typical\n",
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"situation in solid state physics); or classify the status of a\n",
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"patient, whether she/he has a stroke or not and many other similar\n",
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"situations.\n",
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"\n",
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"The most common situation we encounter when we apply logistic\n",
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"regression is that of two possible outcomes, normally denoted as a\n",
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"binary outcome, true or false, positive or negative, success or\n",
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"failure etc.\n",
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"\n",
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"Logistic regression will also serve as our stepping stone towards\n",
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"neural network algorithms and supervised deep learning. For logistic\n",
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"learning, the minimization of the cost function leads to a non-linear\n",
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"equation in the parameters $\\hat{\\beta}$. The optimization of the\n",
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"problem calls therefore for minimization algorithms. This forms the\n",
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"bottle neck of all machine learning algorithms, namely how to find\n",
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"reliable minima of a multi-variable function. This leads us to the\n",
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"family of gradient descent methods. The latter are the working horses\n",
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"of basically all modern machine learning algorithms.\n",
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"\n",
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"We note also that many of the topics discussed here on logistic \n",
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"regression are also commonly used in modern supervised Deep Learning\n",
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"models, as we will see later."
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"source": [
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"## Basics\n",
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"\n",
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"We consider the case where the dependent variables, also called the\n",
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"responses or the outcomes, $y_i$ are discrete and only take values\n",
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"from $k=0,\\dots,K-1$ (i.e. $K$ classes).\n",
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"\n",
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"The goal is to predict the\n",
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"output classes from the design matrix $\\boldsymbol{X}\\in\\mathbb{R}^{n\\times p}$\n",
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"made of $n$ samples, each of which carries $p$ features or predictors. The\n",
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"primary goal is to identify the classes to which new unseen samples\n",
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"belong.\n",
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"\n",
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"Let us specialize to the case of two classes only, with outputs\n",
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"$y_i=0$ and $y_i=1$. Our outcomes could represent the status of a\n",
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"credit card user that could default or not on her/his credit card\n",
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"debt. That is"
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]
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"$$\n",
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"y_i = \\begin{bmatrix} 0 & \\mathrm{no}\\\\ 1 & \\mathrm{yes} \\end{bmatrix}.\n",
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"$$"
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"Before moving to the logistic model, let us try to use our linear\n",
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"regression model to classify these two outcomes. We could for example\n",
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"fit a linear model to the default case if $y_i > 0.5$ and the no\n",
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"default case $y_i \\leq 0.5$.\n",
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"\n",
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"We would then have our \n",
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"weighted linear combination, namely"
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"<!-- Equation labels as ordinary links -->\n",
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"<div id=\"_auto1\"></div>\n",
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"\n",
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"$$\n",
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"\\begin{equation}\n",
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"\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon},\n",
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"\\label{_auto1} \\tag{1}\n",
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"\\end{equation}\n",
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"$$"
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"cell_type": "markdown",
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"source": [
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"where $\\boldsymbol{y}$ is a vector representing the possible outcomes, $\\boldsymbol{X}$ is our\n",
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"$n\\times p$ design matrix and $\\boldsymbol{\\beta}$ represents our estimators/predictors.\n",
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"\n",
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"The main problem with our function is that it takes values on the\n",
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"entire real axis. In the case of logistic regression, however, the\n",
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"labels $y_i$ are discrete variables. A typical example is the credit\n",
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"card data discussed below here, where we can set the state of\n",
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"defaulting the debt to $y_i=1$ and not to $y_i=0$ for one the persons\n",
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"in the data set (see the full example below).\n",
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"\n",
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"One simple way to get a discrete output is to have sign\n",
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"functions that map the output of a linear regressor to values $\\{0,1\\}$,\n",
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"$f(s_i)=sign(s_i)=1$ if $s_i\\ge 0$ and 0 if otherwise. \n",
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"We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron\" model in the machine learning\n",
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"literature. This model is extremely simple. However, in many cases it is more\n",
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"favorable to use a ``soft\" classifier that outputs\n",
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"the probability of a given category. This leads us to the logistic function.\n",
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"\n",
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"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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"source": [
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"%matplotlib inline\n",
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"\n",
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"# Common imports\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.utils import resample\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from IPython.display import display\n",
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"from pylab import plt, mpl\n",
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"plt.style.use('seaborn')\n",
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"mpl.rcParams['font.family'] = 'serif'\n",
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"\n",
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"# Where to save the figures and data files\n",
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"PROJECT_ROOT_DIR = \"Results\"\n",
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"FIGURE_ID = \"Results/FigureFiles\"\n",
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"DATA_ID = \"DataFiles/\"\n",
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"\n",
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"if not os.path.exists(PROJECT_ROOT_DIR):\n",
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" os.mkdir(PROJECT_ROOT_DIR)\n",
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"\n",
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"if not os.path.exists(FIGURE_ID):\n",
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" os.makedirs(FIGURE_ID)\n",
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"\n",
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"if not os.path.exists(DATA_ID):\n",
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" os.makedirs(DATA_ID)\n",
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"\n",
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"def image_path(fig_id):\n",
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" return os.path.join(FIGURE_ID, fig_id)\n",
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"\n",
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"def data_path(dat_id):\n",
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" return os.path.join(DATA_ID, dat_id)\n",
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"\n",
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"def save_fig(fig_id):\n",
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" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
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"\n",
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"infile = open(data_path(\"chddata.csv\"),'r')\n",
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"\n",
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"# Read the chd data as csv file and organize the data into arrays with age group, age, and chd\n",
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"chd = pd.read_csv(infile, names=('ID', 'Age', 'Agegroup', 'CHD'))\n",
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"chd.columns = ['ID', 'Age', 'Agegroup', 'CHD']\n",
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"output = chd['CHD']\n",
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"age = chd['Age']\n",
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"agegroup = chd['Agegroup']\n",
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"numberID = chd['ID'] \n",
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"display(chd)\n",
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"\n",
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"plt.scatter(age, output, marker='o')\n",
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"plt.axis([18,70.0,-0.1, 1.2])\n",
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"plt.xlabel(r'Age')\n",
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"plt.ylabel(r'CHD')\n",
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"plt.title(r'Age distribution and Coronary heart disease')\n",
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"plt.show()"
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]
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"source": [
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"What we could attempt however is to plot the mean value for each group."
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"cell_type": "code",
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"source": [
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"agegroupmean = np.array([0.1, 0.133, 0.250, 0.333, 0.462, 0.625, 0.765, 0.800])\n",
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"group = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
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"plt.plot(group, agegroupmean, \"r-\")\n",
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"plt.axis([0,9,0, 1.0])\n",
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"plt.xlabel(r'Age group')\n",
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"plt.ylabel(r'CHD mean values')\n",
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"plt.title(r'Mean values for each age group')\n",
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"plt.show()"
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]
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},
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"cell_type": "markdown",
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"source": [
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"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",
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"In standard linear regression with a linear dependence on $x$, we would write this in terms of our model"
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"source": [
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"$$\n",
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"f(y_i\\vert x_i)=\\beta_0+\\beta_1 x_i.\n",
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"$$"
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"source": [
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"This expression implies however that $f(y_i\\vert x_i)$ could take any\n",
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"value from minus infinity to plus infinity. If we however let\n",
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"$f(y\\vert y)$ be represented by the mean value, the above example\n",
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"shows us that we can constrain the function to take values between\n",
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"zero and one, that is we have $0 \\le f(y_i\\vert x_i) \\le 1$. Looking\n",
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"at our last curve we see also that it has an S-shaped form. This leads\n",
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"us to a very popular model for the function $f$, namely the so-called\n",
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"Sigmoid function or logistic model. We will consider this function as\n",
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"representing the probability for finding a value of $y_i$ with a given\n",
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"$x_i$."
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"source": [
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"## The logistic function\n",
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"\n",
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"Another widely studied model, is the so-called \n",
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"perceptron model, which is an example of a \"hard classification\" model. We\n",
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"will encounter this model when we discuss neural networks as\n",
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"well. Each datapoint is deterministically assigned to a category (i.e\n",
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"$y_i=0$ 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",
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"classifier that outputs the probability of a given category rather\n",
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"than a single value. For example, given $x_i$, the classifier\n",
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"outputs the probability of being in a category $k$. Logistic regression\n",
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"is the most common example of a so-called soft classifier. In logistic\n",
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"regression, the probability that a data point $x_i$\n",
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"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,"
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"source": [
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"$$\n",
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"p(t) = \\frac{1}{1+\\mathrm \\exp{-t}}=\\frac{\\exp{t}}{1+\\mathrm \\exp{t}}.\n",
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"$$"
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"source": [
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"Note that $1-p(t)= p(-t)$."
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]
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},
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"cell_type": "markdown",
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"source": [
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"## Examples of likelihood functions used in logistic regression and neural networks\n",
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"\n",
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"The following code plots the logistic function, the step function and other functions we will encounter from here and on."
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]
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5ecec05e",
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"metadata": {
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"collapsed": false,
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"editable": true
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"outputs": [],
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"source": [
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"\"\"\"The sigmoid function (or the logistic curve) is a\n",
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"function that takes any real number, z, and outputs a number (0,1).\n",
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"It is useful in neural networks for assigning weights on a relative scale.\n",
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"The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n",
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"\n",
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"import numpy\n",
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"import matplotlib.pyplot as plt\n",
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"import math as mt\n",
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"\n",
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"z = numpy.arange(-5, 5, .1)\n",
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"sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n",
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"sigma = sigma_fn(z)\n",
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"\n",
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111)\n",
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"ax.plot(z, sigma)\n",
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"ax.set_ylim([-0.1, 1.1])\n",
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"ax.set_xlim([-5,5])\n",
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"ax.grid(True)\n",
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"ax.set_xlabel('z')\n",
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"ax.set_title('sigmoid function')\n",
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"\n",
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"plt.show()\n",
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"\n",
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"\"\"\"Step Function\"\"\"\n",
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"z = numpy.arange(-5, 5, .02)\n",
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"step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n",
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"step = step_fn(z)\n",
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"\n",
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111)\n",
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"ax.plot(z, step)\n",
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"ax.set_ylim([-0.5, 1.5])\n",
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"ax.set_xlim([-5,5])\n",
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"ax.grid(True)\n",
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"ax.set_xlabel('z')\n",
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"ax.set_title('step function')\n",
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"\n",
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"plt.show()\n",
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"\n",
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"\"\"\"tanh Function\"\"\"\n",
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"z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n",
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"t = numpy.tanh(z)\n",
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"\n",
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111)\n",
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"ax.plot(z, t)\n",
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"ax.set_ylim([-1.0, 1.0])\n",
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"ax.set_xlim([-2*mt.pi,2*mt.pi])\n",
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"ax.grid(True)\n",
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"ax.set_xlabel('z')\n",
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"ax.set_title('tanh function')\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5802efe3",
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"metadata": {
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"source": [
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"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"
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]
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},
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"cell_type": "markdown",
|
|
"id": "f85d14ea",
|
|
"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": "6c4e0334",
|
|
"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": "df7facc9",
|
|
"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": "a920e6d8",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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",
|
|
"id": "a780bfe1",
|
|
"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": "9f810a44",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"from which we obtain the log-likelihood and our **cost/loss** function"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3fac4ef4",
|
|
"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": "75aa2c14",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"Reordering the logarithms, we can rewrite the **cost/loss** function as"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "780f2038",
|
|
"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": "c8c940aa",
|
|
"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": "9d4a527b",
|
|
"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": "c1706606",
|
|
"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.\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": "fe8fb387",
|
|
"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": "bb564e81",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"and"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "700c5443",
|
|
"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": "f442c956",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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",
|
|
"id": "83937f6f",
|
|
"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": "71819c1d",
|
|
"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": "23dfd975",
|
|
"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": "6ca9f74b",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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",
|
|
"id": "ddb3d93a",
|
|
"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": "0cfae560",
|
|
"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": "7c427fc3",
|
|
"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": "6a6814bf",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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",
|
|
"id": "85282137",
|
|
"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": "4864f76d",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"and"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "390e9a55",
|
|
"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": "c3f39c44",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"and so on till the class $C=K-1$ class"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "92f32a03",
|
|
"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": "677a5c4d",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"and the model is specified in term of $K-1$ so-called log-odds or\n",
|
|
"**logit** transformations.\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": "fa70e3bf",
|
|
"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": "4c59400c",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"It is easy to extend to more predictors. The final class is"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f4073652",
|
|
"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": "810e872a",
|
|
"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": "f4ebc1da",
|
|
"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": 4,
|
|
"id": "b7a8cbac",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"editable": true
|
|
},
|
|
"outputs": [],
|
|
"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",
|
|
"id": "10f74b93",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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,
|
|
"id": "d0b8025d",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"editable": true
|
|
},
|
|
"outputs": [],
|
|
"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": "9d84a2aa",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"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,
|
|
"id": "6144ea0a",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"editable": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5627f5ae",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"and then"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "a7c8662a",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"editable": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"correlation_matrix = cancerpd.corr().round(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d03ee2a7",
|
|
"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)).\n",
|
|
"\n",
|
|
"Here we present a further way to present our results in terms of a so-called **confusion matrix**, the cumulative gain and the **ROC** curve.\n",
|
|
"This way of displaying our data are based upon different ways to classify our possible outcomes. Before we proceed we need some definitions.\n",
|
|
"1. **TP**: true positive or in other words, something equivalent with a proper classification\n",
|
|
"\n",
|
|
"2. **TN**: true negative, which is equivalent with a correct rejection\n",
|
|
"\n",
|
|
"3. **FP**: false positive, or in simpler words something that is equivalent with a false alarm\n",
|
|
"\n",
|
|
"4. **FN**: false negative, which is mean to be equivalent with a miss.\n",
|
|
"\n",
|
|
"The total data set is then the sum of the true positive and true negative targets or outputs, labeled by $n$.\n",
|
|
"Based on this we can then define the accuracy score as the sum of correctly predicted **TP** and **TN** cases divided by the sum of true positive and treue negative events in our data set, or as"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44ce5095",
|
|
"metadata": {
|
|
"editable": true
|
|
},
|
|
"source": [
|
|
"$$\n",
|
|
"\\mathrm{Accuracy} = \\frac{\\sum_{i=0}^{n-1}I(y_i=\\tilde{y}_i)}{n}.\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "24ff3dd3",
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"editable": true
|
|
},
|
|
"outputs": [],
|
|
"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()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|