{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"preamble": true
},
"source": [
"(Defining latex commands: not to be shown...)\n",
"$$\n",
"\\newcommand{\\norm}[1]{\\left \\| #1 \\right \\|}\n",
"\\DeclareMathOperator{\\minimize}{minimize}\n",
"\\newcommand{\\real}{\\mathbb{R}}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monster vs. Mouse (50 Points)\n",
"\n",
"While trying to sleep, you hear some noise under your bed. Being a big Science Fiction fan you obviously immediately think it's a monster. A BIG monster... or maybe two?!? Frightened to death, you do not dare to move, but your brain is racing. And it recalls that the other day, you saw a mouse disappearing on the other side of the bedroom... So maybe it is (just?!?) a mouse. Or, just something else... Hmmm. Remembering your Emperical Inference classes, you decide to evaluate the probability that, given some noise under your bed, there is a monster.\n",
"\n",
"You define the random variables:\n",
"\n",
" + n = some noise under your bed (Values 0 or 1)\n",
" + M = 0, 1 or 2 monsters under your bed (Values 0, 1 or 2)\n",
" + m = a mouse under your bed (Values 0 or 1)\n",
" + e = something else (e.g. only air) under your bed (Values 0 or 1),\n",
"\n",
"and express your beliefs about monsters, mice and noise, by assigning numbers to P(M), P(m), P(e), P(n|M), P(n|m), P(n|e). Given that you heard some noise under your bed, you then calculate the Maximum a Posteriori (MAP) of M (i.e., the maximum of P(M|n=1)).\n",
"\n",
"Please share your beliefs and your results with us."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Programming a Hand-Featured SVM (50 points)\n",
"\n",
"The goal of this exercise is to implement a very simple SVM using an off-the-shelf optimizer, in the case of a 2-dimensional input space.\n",
"Most of it has already been implented in an iPython Notebook that you can find on the homepage of the course, and that we reproduce hereafter.\n",
"\n",
"You are asked to hand out a print out of the lines marked \"### CHANGE THIS LINE ###\", as well as your plots."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The SVM equation in the linear separable case\n",
"\n",
"Let $(x_1,t_1), \\ldots, (x_N,t_N) \\in \\real^p \\times \\{-1,1\\}$ be N data points with their binary labels. For this exercise, $p=2$. \n",
"In the lecture, we saw that trying to maximize the margin between two linearly separable classes of data points led to the problem:\n",
"\\begin{equation}\n",
" \\minimize_{\\substack{ \\{ w \\ \\in \\real^2,\\ b \\ \\in \\ \\real \\} }} \\frac{1}{2} \\norm{w}^2 \\quad \\mathrm{subject \\ to} \\quad t_n(w^T x_n + b) \\geq 1 \\quad n = 1 \\ldots N ,\n",
"\\end{equation}\n",
"whereby the points are subsequently classified according to the rule:\n",
"\\begin{equation}\n",
" t = \\left \\{ \n",
" \\begin{array}{cc}\n",
" \\ \\ 1 \\quad \\mathrm{if} \\quad w^T x + b \\geq 0 \\\\\n",
" -1 \\quad \\mathrm{if} \\quad w^T x + b < 0\n",
" \\end{array}\n",
" \\right . .\n",
"\\end{equation}\n",
"The function $\\mathop{l}(w,b) := \\frac{1}{2} \\norm{w}^2$ is called the _objective_ function. The equations $t_n(w^T x_n + b) \\geq 1$ are called the (_inequality_) _constraints_.\n",
"\n",
"In practice, one often preferres to transform this problem into its so-called _dual_ formulation. However, in this exercise, we will stick to the above equations: the _primal_ formulation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Loading the required packages."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from numpy import *\n",
"from scipy import optimize\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of the data point \n",
"\n",
"_X = location of the data points_ $\\in \\real^{N \\times 2}$ \n",
"_t = label class of each point_ $\\in \\real^N$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_ = np.array([[1.,3.],[2.,6.],[2.,3.],[-1.,0],\n",
" [1.,0.],[2.,2.],[3.,1.],[0.,-1.]])\n",
"t_ = np.array([1,1,1,1,-1,-1,-1,-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of the Objective Function and its Gradient\n",
"\n",
"_wb = $(w_1, w_2, b) = (w , b)$_ $\\in \\real^2 \\times \\real$\n",
"\n",
"(Note that the gradient of the objective function (here called 'jac') does not appear in the equations of an SVM. But our off-the-shelf optimizer uses it for its computations.)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def objective(wb):\n",
" ### changed ###\n",
" return 1/2*(wb[0]*wb[0]+wb[1]*wb[1])\n",
"\n",
"def jac(wb):\n",
" return hstack((wb[0:2],array([0])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of the Inequality Constraints and of their Gradient\n",
"\n",
"(Again, the gradient does not appear in the SVM equations, but it is needed for our optimizer.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ineq(wb):\n",
" #writing \"return a-b\" would encode the constraint a-b >= 0\n",
" ### changed ###\n",
" return t_*(list(map(lambda x:np.dot(wb[0:2].T,x),X_))+wb[2])-1\n",
"\n",
"def grad(wb):\n",
" return -1*hstack(((-t*X.T).T, -t[:,newaxis]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimisation of the SVM"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def optimizeSVM(X_,t_):\n",
" global X,t\n",
" X = X_\n",
" t = t_\n",
" wb0 = np.random.randn(3) #initialization\n",
" #wb0 = array([-1.,-2.,0.2])\n",
"\n",
" cons = {'type':'ineq', #constraints\n",
" 'fun': ineq,\n",
" 'jac': grad}\n",
"\n",
" opt = {'disp':False}\n",
"\n",
" opt_result = optimize.minimize(objective, wb0, jac=jac,constraints=cons, #the optimizer\n",
" method='SLSQP', options=opt)\n",
" wb_opt = opt_result[\"x\"] #result of the optimization\n",
" \n",
" return wb_opt\n",
"\n",
"wb_opt = optimizeSVM(X_,t_) #the optimized parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting the Result"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f69572e5c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plotSVM(X,t,wb):\n",
" plt.plot(X[t>0,0],X[t>0,1], 'ro')\n",
" plt.plot(X[t<0,0],X[t<0,1], 'bo')\n",
" plt.axis([-2, 6, -2, 7])\n",
" x = np.arange(-2,6,1)\n",
" y0 = -1*wb[0]/wb[1]*x-wb[2]/wb[1]\n",
" y1 = -1*wb[0]/wb[1]*x-(wb[2]+1)/wb[1]\n",
" y_1 = -1*wb[0]/wb[1]*x-(wb[2]-1)/wb[1]\n",
" plt.plot(x,y0)\n",
" plt.plot(x,y1)\n",
" plt.plot(x,y_1)\n",
" \n",
"plotSVM(X_,t_,wb_opt) ### PROVIDE THIS PLOT ###"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New Data Set\n",
"\n",
"You are now given the following new data set."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Generating the data\n",
"N_half = 20\n",
"u1 = random.rand(N_half)\n",
"x1 = cos(2*pi*u1) + .3*random.rand(N_half)\n",
"y1 = sin(2*pi*u1) + .3*random.rand(N_half)\n",
"u2 = random.rand(N_half)\n",
"x2 = 2.*cos(2*pi*u2) + .3*random.rand(N_half)\n",
"y2 = 2.*sin(2*pi*u2) + .3*random.rand(N_half)\n",
"\n",
"# New Data\n",
"X_c = transpose(vstack((hstack((x1,x2)),hstack((y1,y2)))))\n",
"t_c = hstack((ones(N_half),-ones(N_half)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remark that the data is distributed on two circles, centered on zero, with different radii."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-3, 3, -3, 3]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6957433e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot new data\n",
"plt.plot(X_c[t_c>0,0],X_c[t_c>0,1], 'ro')\n",
"plt.plot(X_c[t_c<0,0],X_c[t_c<0,1], 'bo')\n",
"plt.axis([-3, 3, -3, 3])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-1, 6, -1, 6]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"fz5p+/PqDoveWW5VI3lE0iMR8Y3e471aJ8DLGEJJ4bf6fZJ+yfZ5tk+V9NuSvlByTY1h25I+KelQ\n",
"RNxQdj3jsv1i22f3vj5N0lWS9pdb1Wgi4n0RMRMRL5f0O5K+mlp42z7d9tbe1y+SdLW6jRSVFxGP\n",
"SVqxfUHvW2+U9O1hry+qjTCpRT8R8ZykP5D0JXVn4v8hIobOBFeN7c9I+ndJF9hesf2usmsa0xWS\n",
"3qFu98b+3n8pddW8VNJXe2Pg90q6LSL+peSaJpXicOJLJN3Z9/f/xYj4csk1jWOnpFttf1PdLpQP\n",
"D3shC3kAIFF0oQBAoghwAEgUAQ4AiSLAASBRBDgAJIoAB4BEEeAAkCgCHAAS9f/kGFG7S57jjwAA\n",
"AABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f69572f8668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_transformed = 2.5*transpose(vstack((hstack((np.abs(x1),np.abs(x2))),hstack((np.abs(y1),np.abs(y2)))))) ### CHANGE THIS LINE ####\n",
"### prbly other transormation\n",
"plt.plot(X_transformed[t_c>0,0],X_transformed[t_c>0,1], 'ro')\n",
"plt.plot(X_transformed[t_c<0,0],X_transformed[t_c<0,1], 'bo')\n",
"plt.axis([-1, 6, -1, 6])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(40, 2)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape(X_transformed)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Plotting the result in the transformed space. \n",
"(Lines are commented, because code does not compile, until X has been correctly transformed.)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "all the input array dimensions except for the concatenation axis must match exactly",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-37-ae3c6dd9b647>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwb_opt_c\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptimizeSVM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_transformed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt_c\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#plotSVM(X_transformed,t_c,wb_opt_c) ### PROVIDE THIS PLOT ###s\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-12-49e7f2f9b2de>\u001b[0m in \u001b[0;36moptimizeSVM\u001b[1;34m(X_, t_)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m opt_result = optimize.minimize(objective, wb0, jac=jac,constraints=cons, #the optimizer\n\u001b[1;32m---> 15\u001b[1;33m method='SLSQP', options=opt)\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[0mwb_opt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopt_result\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#result of the optimization\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/opt/anaconda3/lib/python3.4/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m 433\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'slsqp'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 434\u001b[0m return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m--> 435\u001b[1;33m constraints, callback=callback, **options)\n\u001b[0m\u001b[0;32m 436\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dogleg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 437\u001b[0m return _minimize_dogleg(fun, x0, args, jac, hess,\n",
"\u001b[1;32m/opt/anaconda3/lib/python3.4/site-packages/scipy/optimize/slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 403\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma_eq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma_ieq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mla\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 406\u001b[0m \u001b[1;31m# Call SLSQP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
]
}
],
"source": [
"#wb_opt_c = optimizeSVM(X_transformed,t_c)\n",
"#plotSVM(X_transformed,t_c,wb_opt_c) ### PROVIDE THIS PLOT ###s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feel free (not mandatory!) to rewrite the function plotSVM to plot the data and the separation curve, not in the transformed space (as above), but in the original space."
]
}
],
"metadata": {
"annotations": {
"author": "",
"categories": [
"intelligent-systems-1-2015"
],
"date": "2015-04-23",
"location": "Beginning of next lecture",
"parent": "IS_SS2015",
"submission_date": "2015-04-30",
"subtitle": "Exercise Sheet 2, Support Vector Machines",
"tags": [
"IntelligenSystems",
"Course"
],
"title": "Intelligent Systems 1 - Summer Semester 2015"
},
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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Jan-Peter Hohloch