{ "metadata": { "name": "", "signature": "sha256:2cdf988b0d48fc0c25cc8d3b303fbe139abcf45fa559ec0abc6cb11505084860" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "

# Optimization and Machine Learning with Python

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This part of the advanced tutorial focuses more on some specific packages.\n", "The topics covered in this part are:\n", "\n", "- Optimization in Python with [OpenOpt](http://openopt.org/).\n", "- Easy and effective Machine Learning in Python with [Scikit-Learn](http://scikit-learn.org/stable/).\n", "\n", "**Prerequisites:** *Basic and Intermediate tutorials or some experience with Python, Numpy, Scipy, Matplotlib (optional). Basic knowledge in machine learning and optimization*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Optimization in Python with OpenOpt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this part, we'll learn how to solve optimization problems using the **OpenOpt** package.\n", "\n", "**OpenOpt** is a free-source package (under the license: [BSD](http://openopt.org/license)) for numerical optimization of a variety\n", "of linear, nonlinear problems, least-square problems, network and stochastic problems.\n", "\n", "It contains more than 30 different solvers (both free and commercial) including solvers written in C and Fortran.\n", "Some solvers are already included in the OpenOpt package (e.g. [ralg](http://openopt.org/ralg), \n", "[gsubg](http://openopt.org/gsubg), [interalg](http://openopt.org/interalg)) and other rely on other packagess \n", "(e.g. [scipy](http://www.scipy.org), [IPOPT](http://openopt.org/IPOPT), [ALGENCAN](http://openopt.org/ALGENCAN))\n", "\n", "In this demonstration we are considering solvers existing in OpenOpt and SciPy. The library workstations have already OpenOpt\n", "installed and Scipy is included in the Python distribution.\n", "For instructions how to install OpenOpt on other machines go to: [http://openopt.org/Install](http://openopt.org/Install). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acknowledgements:\n", "\n", "- OpenOpt's [website](http://openopt.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimization formulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The general formulation of an optimization problem is to \n", "An optimization problem can be expressed in the following way:\n", "\n", "- Given: a function $f : A \\to R$ from some set $A$ to the set of real numbers\n", "- Want to find a solution $x_0 \\in A$ such that $f(x_0)$ is the minimum (or maximum) of $f(x)$ for all $x \\in A$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example the standard form of a continuous optimization problem is:\n", "\n", "$\n", " \\text{max (or min) } \\mathbf{f(x)}\n", "$\n", "\n", "$\n", " \\text{subject to } \\\\\n", "$\n", "\n", "$\n", " \\mathbf{lb} \\le \\mathbf{x} \\le \\mathbf{ub} \\\\\n", " \\mathbf{A x} \\le \\mathbf{b} \\\\\n", " \\mathbf{A}_\\mathbf{eq} \\mathbf{x} = \\mathbf{b}_\\mathbf{eq} \\\\\n", " \\mathbf{c_i(x) \\le 0} \\quad \\forall i=0,...,I \\\\\n", " \\mathbf{h_j(x) = 0} \\quad \\forall i=0,...,J \\\\\n", " \\mathbf{x \\in R^n}\n", "$\n", "\n", "where $\\mathbf{f, c_i, h_j} :R^n \\to R$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to the type of the objective function $\\mathbf{f(x)}$ and constraints the optimization problem is either linear, quadratic, nonlinear, unconstrained, constrained etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### OpenOpt syntax\n", "\n", "In *OpenOpt* optimization problems are assigned in the following way:\n", "\n", "> from openopt import NLP\n", "\n", "or other constructor names: LP, MILP, QP, etc according to the problem\n", "\n", "Then we use\n", "\n", "> p = NLP(\\*args, \\**kwargs)\n", "\n", "to assign the constructor to object *p*. \n", "\n", "Each constructor class has its own arguments\n", "\n", "e.g. for NLP it is least *f* and *x0*, the objective function and the initial guess.\n", "\n", "thus using \n", "\n", "> p = NLP(objfun, initialguess)\n", "\n", "will assign *objfun* to f and *initialguess* to x0.\n", "\n", "We can use \n", "\n", "> p.kwargs\n", "\n", "to change any *kwargs* defined in the constructor.\n", "\n", "Finally we solve the problem by\n", "\n", "> r = p.solve(nameOfSolver, otherParameters)\n", "\n", "Object *r* contains all results, i.e. the minimizer, the objective function at solution and output\n", "parameters. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding OpenOpt path to PYTHONPATH\n", "\n", "The best way to see how it works is to go through some examples.\n", "\n", "But since OpenOpt is an additional package not included in the Python distribution we use first we need to include its path to the PYTHONPATH.\n", "\n", "If you are using a library workstation this can be done in the notebook by running:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "sys.path.append('/Library/Python/2.7/site-packages/openopt-0.5404-py2.7.egg')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On other machines (Linux or Mac) add the following line in the .bashrc (Linux) or .bash_profile (Mac):\n", "\n", "> export OPENOPT=path-where-the-OOSuite-is\n", "\n", "> export PYTHONPATH=\\$PYTHONPATH:\\$OPENOPT\n", "\n", "If you don't have such a file then create one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1 : Linear problem\n", "\n", "A linear optimization problem has the following form:\n", "\n", "$\n", " \\text{max (or min) } \\mathbf{c}^T\\mathbf{x}\n", "$\n", "\n", "$\n", " \\text{subject to } \\\\\n", "$\n", "\n", "$\n", " \\mathbf{lb} \\le \\mathbf{x} \\le \\mathbf{ub} \\\\\n", " \\mathbf{A x} \\le \\mathbf{b} \\\\\n", " \\mathbf{A}_\\mathbf{eq} \\mathbf{x} = \\mathbf{b}_\\mathbf{eq} \\\\\n", " \\mathbf{x \\in R^n}\n", "$\n", "\n", "Consider the following problem adapted from OpenOpt examples on [Linear Problems](http://trac.openopt.org/openopt/browser/PythonPackages/OpenOpt/openopt/examples/lp_1.py):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Example:\n", "Let's concider the problem\n", "15x1 + 8x2 + 80x3 -> min (1)\n", "subjected to\n", "x1 + 2x2 + 3x3 <= 15 (2)\n", "8x1 + 15x2 + 80x3 <= 80 (3)\n", "8x1 + 80x2 + 15x3 <=150 (4)\n", "-100x1 - 10x2 - x3 <= -800 (5)\n", "80x1 + 8x2 + 15x3 = 750 (6)\n", "x1 + 10x2 + 100x3 = 80 (7)\n", "x1 >= 4 (8)\n", "-8 >= x2 >= -80 (9)\n", "\"\"\"\n", "\n", "from numpy import *\n", "from openopt import LP\n", "f = array([15, 8, 80])\n", "A = array([[1, 2, 3], [8, 15, 80], [8, 80, 15], [-100, -10, -1]]) \n", "b = array([15, 80, 150, -800]) \n", "Aeq = array([[80, 8, 15], [1, 10, 100]]) \n", "beq = array([750, 80])\n", "\n", "lb = array([4, -80, -inf])\n", "ub = array([inf, -8, inf])\n", "p = LP(f, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub)\n", "#or p = LP(f=f, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub)\n", "\n", "#r = p.minimize('glpk') # CVXOPT must be installed\n", "#r = p.minimize('lpSolve') # lpsolve must be installed\n", "r = p.minimize('pclp') \n", "#search for max: r = p.maximize('glpk') # CVXOPT & glpk must be installed\n", "\n", "print('objFunValue: %f' % r.ff) # should print 204.48841578\n", "print('x_opt: %s' % r.xf) # should print [ 9.89355041 -8. 1.5010645 ]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "------------------------- OpenOpt 0.52 -------------------------\n", "solver: pclp problem: unnamed type: LP goal: minimum\n", " iter objFunVal log10(maxResidual) \n", " 0 0.000e+00 2.90 \n", " 1 0.000e+00 -12.94 \n", "istop: 1000\n", "Solver: Time Elapsed = 0.0 \tCPU Time Elapsed = 0.01\n", "objFunValue: 204.48842 (feasible, MaxResidual = 1.13687e-13)\n", "objFunValue: 204.488416\n", "x_opt: [ 9.89355041 -8. 1.5010645 ]\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily verify that solution satisfies the constraints:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.linalg.norm(np.dot(Aeq,r.xf)- beq)\n", "print np.dot(A,r.xf)- b <= 0" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.21417595911e-13\n", "[ True True True True]\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: Solving nonlinear equations\n", "\n", "Solving nonlinear equations can be formulated as minimizing the 2-norm of the vector of equations.\n", "\n", "Below is a script solving a system of three equations (from OpenOpt examples on [Non-linear least squares](http://trac.openopt.org/openopt/browser/PythonPackages/OpenOpt/openopt/examples/nllsp_1.py))." ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Let us solve the overdetermined nonlinear equations:\n", "a^2 + b^2 = 15\n", "a^4 + b^4 = 100\n", "a = 3.5\n", "\n", "Let us concider the problem as\n", "x[0]**2 + x[1]**2 - 15 = 0\n", "x[0]**4 + x[1]**4 - 100 = 0\n", "x[0] - 3.5 = 0\n", "\n", "Now we will solve the one using solver scipy_leastsq\n", "\"\"\"\n", "%pylab inline --no-import-all\n", "from openopt import NLLSP\n", "from openopt import NLP\n", "\n", "f = lambda x: ((x**2).sum() - 15, (x**4).sum() - 100, x[0]-3.5)\n", "# other possible f assignments:\n", "# f = lambda x: [(x**2).sum() - 15, (x**4).sum() - 100, x[0]-3.5]\n", "#f = [lambda x: (x**2).sum() - 15, lambda x: (x**4).sum() - 100, lambda x: x[0]-3.5]\n", "# f = (lambda x: (x**2).sum() - 15, lambda x: (x**4).sum() - 100, lambda x: x[0]-3.5)\n", "# f = lambda x: asfarray(((x**2).sum() - 15, (x**4).sum() - 100, x[0]-3.5))\n", "#optional: gradient\n", "def df(x):\n", " r = zeros((3,2))\n", " r[0,0] = 2*x[0]\n", " r[0,1] = 2*x[1]\n", " r[1,0] = 4*x[0]**3\n", " r[1,1] = 4*x[1]**3\n", " r[2,0] = 1\n", " return r\n", "\n", "# init esimation of solution - sometimes rather pricise one is very important\n", "x0 = [1.5, 8]\n", "\n", "#p = NLLSP(f, x0, diffInt = 1.5e-8, xtol = 1.5e-8, ftol = 1.5e-8)\n", "# or\n", "# p = NLLSP(f, x0)\n", "# or\n", "p = NLLSP(f, x0, xtol = 1.5e-8, ftol = 1.5e-8)\n", "\n", "#optional: user-supplied gradient check:\n", "#p.checkdf() # requires DerApproximator to be installed\n", "\n", "r = p.solve('scipy_leastsq', plot=1, iprint = 1)\n", "\n", "print 'x_opt:', r.xf # 2.74930862, +/-2.5597651\n", "print 'funcs Values:', p.f(r.xf) # [-0.888904734668, 0.0678251418575, -0.750691380965]\n", "print 'f_opt:', r.ff, '; sum of squares (should be same value):', (p.f(r.xf) ** 2).sum() # 1.35828942657\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "\n", "------------------------- OpenOpt 0.52 -------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "solver: scipy_leastsq problem: unnamed type: NLLSP\n", " iter objFunVal \n", " 0 1.601e+07 \n", " 1 1.358e+00 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 2 1.358e+00 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "istop: 1000 (Both actual and predicted relative reductions in the sum of squares\n", " are at most 0.000000)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Solver: Time Elapsed = -0.05 \tCPU Time Elapsed = -0.051061\n", "Plotting: Time Elapsed = 0.36 \tCPU Time Elapsed = 0.361061\n", "objFunValue: 1.3582894\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "x_opt: [ 2.74931062 2.55976255]\n", "funcs Values: [-0.88890684 0.06781965 -0.75068938]\n", "f_opt: 1.3582894266 ; sum of squares (should be same value): 1.3582894266\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3: Least square problem\n", "\n", "Consider some 2D data. We would like to find a model to the measured data that minimizes the discrepancy between model predictions and the data. This can be formulated by minimizing the sum of the squares of the difference between values of model and measured data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First create some data and plot them:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "n = 200\n", "X = np.linspace(0,2,n)\n", "m = lambda X : 0.8*np.exp(-1.5*X)+1.2*np.exp(-0.8*X)\n", "perturb = 0.2*np.random.uniform(0,1,n)\n", "Y = m(X)*(1.0+perturb)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(15,10), dpi=100) # changing figure's shape\n", "plt.plot(X,Y,'.')\n", "plt.xlabel('$X$',fontsize=16) # horizontal axis name\n", "plt.ylabel('Y',fontsize=16) # vertical axis name\n", "plt.title('Sample data',fontsize=18) # title \n", "plt.grid(True) # enabling grid" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ZBAAA2hhFE7jIEsttZPHWsdRTZpB1kcOAdtQQtKeOoBujaAJPzhE8r/HaRhZvkXHMIzMI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\nNqPzrk9TR9CNC1t+v0XWb/5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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's solve the least square problem:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "In the DFP example we will search for z=(a, b, c, d) \n", "that minimizes Sum_i || F(z, X_i) - Y_i ||^2\n", "for the function\n", "F(x) = a*exp(-b*x) + c*exp(-d*x)\n", "\"\"\"\n", "from openopt import DFP\n", "\n", "f = lambda z, X: z[0]*exp(-z[1]*X[0]) + z[2]*exp(-z[3]*X[0])\n", "initEstimation = [0.2, 0.9, 1.9, 0.9]\n", "lb = [-np.inf, -np.inf, -np.inf, -np.inf]\n", "ub = [np.inf, np.inf, np.inf, np.inf]\n", "p = DFP(f, initEstimation, X, Y, lb=lb, ub=ub)\n", "\n", "p.df = lambda z, X: [exp(-z[1]*X[0]), -z[0]*z[1]*exp(-z[1]*X[0]), exp(-z[3]*X[0]), -z[2]*z[3]*exp(-z[3]*X[0])]\n", "\n", "r = p.solve('nlp:ralg', plot=1, iprint = 10)\n", "f = lambda z, X: z[0]*exp(-z[1]*X) + z[2]*exp(-z[3]*X)\n", "print('solution: '+str(r.xf)+'\\n||residuals||^2 = '+str(r.ff))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "------------------------- OpenOpt 0.52 -------------------------\n", "solver: ralg problem: unnamed type: DFP\n", " iter objFunVal \n", " 0 1.147e+00 \n", " 10 1.460e+00 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 20 6.557e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 30 6.381e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 40 6.384e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 50 6.386e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 60 6.387e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 70 6.384e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 80 6.379e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 90 6.370e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 100 6.380e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 109 6.367e-01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "istop: 4 (|| F[k] - F[k-1] || < ftol)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Solver: Time Elapsed = 2.59 \tCPU Time Elapsed = 2.608965\n", "Plotting: Time Elapsed = 2.36 \tCPU Time Elapsed = 2.341035\n", "objFunValue: 0.63670925\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "solution: [ 0.33837813 2.26118336 1.86071125 0.90294692]\n", "||residuals||^2 = 0.636709253815\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "fun = lambda z, X: z[0]*exp(-z[1]*X) + z[2]*exp(-z[3]*X)\n", "\n", "plt.figure(figsize=(15,10), dpi=100) # changing figure's shape\n", "plt.plot(X,Y,'.')\n", "plt.plot(X,fun(r.xf,X),'r-',linewidth=1.5)\n", "plt.xlabel('$X$',fontsize=16) # horizontal axis name\n", "plt.ylabel('Y',fontsize=16) # vertical axis name\n", "plt.title('Sample data',fontsize=18) # title \n", "plt.grid(True) # enabling grid" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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u+zCkdFxDUnquIykbJoFVJp+Err4+7hjmkgB2m6CFAEccAWecEQ+N2Xpr+OKL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\nE7bH7KH7SErHnkBJklR1cunXK1gIMGQIXHYZPPk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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 4: Nonlinear problem\n", "\n", "Finally, the following is an example of a non-linear problem (from OpenOpt [non-linear examples](http://trac.openopt.org/openopt/browser/PythonPackages/OpenOpt/openopt/examples/nlp_1.py)) as described at the beginning of this part." ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Example:\n", "(x0-5)^2 + (x2-5)^2 + ... +(x149-5)^2 -> min\n", "\n", "subjected to\n", "\n", "# lb<= x <= ub:\n", "x4 <= 4\n", "8 <= x5 <= 15\n", "\n", "# Ax <= b\n", "x0+...+x149 >= 825\n", "x9 + x19 <= 3\n", "x10+x11 <= 9\n", "\n", "# Aeq x = beq\n", "x100+x101 = 11\n", "\n", "# c(x) <= 0\n", "2*x0^4-32 <= 0\n", "x1^2+x2^2-8 <= 0\n", "\n", "# h(x) = 0\n", "(x[149]-1)**6 = 0\n", "(x[148]-1.5)**6 = 0\n", "\"\"\"\n", "\n", "from openopt import NLP\n", "from numpy import cos, arange, ones, asarray, zeros, mat, array\n", "N = 150\n", "\n", "# objective function:\n", "f = lambda x: ((x-5)**2).sum()\n", "\n", "# objective function gradient (optional):\n", "df = lambda x: 2*(x-5)\n", "\n", "# start point (initial estimation)\n", "x0 = 8*cos(arange(N))\n", "\n", "# c(x) <= 0 constraints\n", "c = [lambda x: 2* x[0] **4-32, lambda x: x[1]**2+x[2]**2 - 8]\n", "\n", "# dc(x)/dx: non-lin ineq constraints gradients (optional):\n", "dc0 = lambda x: [8 * x[0]**3] + [0]*(N-1)\n", "dc1 = lambda x: [0, 2 * x[1], 2 * x[2]] + [0]*(N-3)\n", "dc = [dc0, dc1]\n", "\n", "# h(x) = 0 constraints\n", "def h(x):\n", " return (x[N-1]-1)**6, (x[N-2]-1.5)**6\n", " # other possible return types: numpy array, matrix, Python list, tuple\n", "# or just h = lambda x: [(x[149]-1)**6, (x[148]-1.5)**6]\n", "\n", "\n", "# dh(x)/dx: non-lin eq constraints gradients (optional):\n", "def dh(x):\n", " r = zeros((2, N))\n", " r[0, -1] = 6*(x[N-1]-1)**5\n", " r[1, -2] = 6*(x[N-2]-1.5)**5\n", " return r\n", " \n", "# lower and upper bounds on variables\n", "lb = -6*ones(N)\n", "ub = 6*ones(N)\n", "ub[4] = 4\n", "lb[5], ub[5] = 8, 15\n", "\n", "# general linear inequality constraints\n", "A = zeros((3, N))\n", "A[0, 9] = 1\n", "A[0, 19] = 1\n", "A[1, 10:12] = 1\n", "A[2] = -ones(N)\n", "b = [7, 9, -825]\n", "\n", "# general linear equality constraints\n", "Aeq = zeros(N)\n", "Aeq[100:102] = 1\n", "beq = 11\n", "\n", "# required constraints tolerance, default for NLP is 1e-6\n", "contol = 1e-7\n", "\n", "# If you use solver algencan, NB! - it ignores xtol and ftol; using maxTime, maxCPUTime, maxIter, maxFunEvals, fEnough is recommended.\n", "# Note that in algencan gtol means norm of projected gradient of the Augmented Lagrangian\n", "# so it should be something like 1e-3...1e-5\n", "gtol = 1e-7 # (default gtol = 1e-6)\n", "\n", "# Assign problem:\n", "# 1st arg - objective function\n", "# 2nd arg - start point\n", "p = NLP(f, x0, df=df, c=c, dc=dc, h=h, dh=dh, A=A, b=b, Aeq=Aeq, beq=beq, \n", " lb=lb, ub=ub, gtol=gtol, contol=contol, iprint = 50, maxIter = 10000, maxFunEvals = 1e7, name = 'NLP_1')\n", "\n", "#optional: graphic output, requires pylab (matplotlib)\n", "p.plot = True\n", "\n", "solver = 'ralg'\n", "#solver = 'scipy_slsqp'\n", "\n", "# solve the problem\n", "r = p.solve(solver, plot=1) # string argument is solver name\n", "\n", "# r.xf and r.ff are optim point and optim objFun value\n", "# r.ff should be something like 132.05\n", "print r.ff" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "------------------------- OpenOpt 0.52 -------------------------\n", "solver: ralg problem: NLP_1 type: NLP goal: minimum\n", " iter objFunVal log10(maxResidual) \n", " 0 8.596e+03 5.73 \n", " 50 3.033e+02 3.79 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 100 2.047e+02 4.15 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 150 1.863e+02 3.89 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 200 1.685e+02 4.06 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 250 1.798e+02 4.21 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 300 1.670e+02 3.90 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 350 1.652e+02 4.18 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 400 1.524e+02 3.73 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 450 1.713e+02 3.90 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 500 1.518e+02 4.11 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 550 1.633e+02 3.87 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 600 1.603e+02 3.76 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 650 1.553e+02 3.19 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 700 1.632e+02 2.63 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 750 2.084e+02 0.98 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 800 2.076e+02 -1.99 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 850 1.479e+02 -3.01 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 900 1.363e+02 -3.56 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 950 1.321e+02 -7.10 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 1000 1.372e+02 -7.09 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 1050 1.372e+02 -7.08 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 1100 1.372e+02 -7.08 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " 1144 1.321e+02 -7.10 " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "istop: 3 (|| X[k] - X[k-1] || < xtol)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Solver: Time Elapsed = 4.27 \tCPU Time Elapsed = 4.288232\n", "Plotting: Time Elapsed = 5.99 \tCPU Time Elapsed = 5.971768\n", "objFunValue: 132.06825 (feasible, MaxResidual = 7.98482e-08)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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yf2b53neuJcJcfRejry2wOEfbfJpz9ZFPW3tC0O5if//5Psdcn1VN9fG+8vVZ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\nDjwA3KrA1cAkM5tGsEPNrN5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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "132.068251573\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Machine Learning in Python\n", "Machine learning is a hot and popular topic nowadays. It is about building programs with tunable parameters that are adjusted automatically by adapting to previously seen data. It intervenes literally every sphere of our life ranging from science, engineering, and industry to economics, sociology, and even [personal relationships](http://www.wired.com/wiredscience/2014/01/how-to-hack-okcupid/all/). So if it is applicable everywhere, we need to have handy tools for doing machine learning." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Start pylab inline mode, so figures will appear in the notebook\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1. Quick Overview of Scikit-Learn\n", "*Adapted from http://scikit-learn.org/stable/tutorial/basic/tutorial.html*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Load a dataset already available in scikit-learn\n", "from sklearn import datasets\n", "digits = datasets.load_digits()\n", "type(digits)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "sklearn.datasets.base.Bunch" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "digits.data" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "array([[ 0., 0., 5., ..., 0., 0., 0.],\n", " [ 0., 0., 0., ..., 10., 0., 0.],\n", " [ 0., 0., 0., ..., 16., 9., 0.],\n", " ..., \n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 2., ..., 12., 0., 0.],\n", " [ 0., 0., 10., ..., 12., 1., 0.]])" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "digits.target" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "array([0, 1, 2, ..., 8, 9, 8])" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import svm\n", "clf = svm.SVC(gamma=0.001, C=100.)\n", "clf.fit(digits.data[:-1], digits.target[:-1])\n", "\n", "print \"Original label:\", digits.target[5]\n", "print \"Predicted label:\", clf.predict(digits.data[5])\n", "\n", "plt.figure(figsize=(3, 3))\n", "plt.imshow(digits.images[5], interpolation='nearest', cmap=plt.cm.binary)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Original label: 5\n", "Predicted label: [5]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2. Data representation in Scikit-learn\n", "*Adapted from https://github.com/jakevdp/sklearn_scipy2013*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data in scikit-learn, with very few exceptions, is assumed to be stored as a **two-dimensional array**, of size [n_samples, n_features], and most of machine learning algorithms implemented in scikit-learn expect data to be stored this way.\n", "\n", "- **n_samples:** The number of samples: each sample is an item to process (e.g. classify). A sample can be a document, a picture, a sound, a video, an astronomical object, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n", "- **n_features:** The number of features or distinct traits that can be used to describe each item in a quantitative manner. Features are generally real-valued, but may be boolean or discrete-valued in some cases.\n", "\n", "The number of features must be fixed in advance. However it can be very high dimensional (e.g. millions of features) with most of them being zeros for a given sample. This is a case where scipy.sparse matrices can be useful, in that they are much more memory-efficient than numpy arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1.2.1. Example: iris dataset\n", "To understand these simple concepts better, let's work with an example: iris-dataset." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Don't know what iris is? Run this code to generate some pictures.\n", "from IPython.core.display import Image, display\n", "display(Image(filename='figures/iris_setosa.jpg'))\n", "print \"Iris Setosa\\n\"\n", "\n", "display(Image(filename='figures/iris_versicolor.jpg'))\n", "print \"Iris Versicolor\\n\"\n", "\n", "display(Image(filename='figures/iris_virginica.jpg'))\n", "print \"Iris Virginica\"" ], "language": "python", "metadata": {}, "outputs": [ { "jpeg": 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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Setosa\n", "\n" ] }, { "jpeg": 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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI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\nWlJnW5EIgFHOKhvrgKhCnnFfA2xMk80Iv7nJPNC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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Versicolor\n", "\n" ] }, { "jpeg": 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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh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\nfaeNmRyfyr07SPi5ZxX0VkbkI0y74PM4Df7INeG+K/hpq/iHU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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Virginica\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# Load the dataset from scikit-learn\n", "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "type(iris) # The resulting dataset is a Bunch object" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "sklearn.datasets.base.Bunch" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "iris.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "['target_names', 'data', 'target', 'DESCR', 'feature_names']" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "print iris.DESCR" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Iris Plants Database\n", "\n", "Notes\n", "-----\n", "Data Set Characteristics:\n", " :Number of Instances: 150 (50 in each of three classes)\n", " :Number of Attributes: 4 numeric, predictive attributes and the class\n", " :Attribute Information:\n", " - sepal length in cm\n", " - sepal width in cm\n", " - petal length in cm\n", " - petal width in cm\n", " - class:\n", " - Iris-Setosa\n", " - Iris-Versicolour\n", " - Iris-Virginica\n", " :Summary Statistics:\n", " ============== ==== ==== ======= ===== ====================\n", " Min Max Mean SD Class Correlation\n", " ============== ==== ==== ======= ===== ====================\n", " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", " ============== ==== ==== ======= ===== ====================\n", " :Missing Attribute Values: None\n", " :Class Distribution: 33.3% for each of 3 classes.\n", " :Creator: R.A. Fisher\n", " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", " :Date: July, 1988\n", "\n", "This is a copy of UCI ML iris datasets.\n", "http://archive.ics.uci.edu/ml/datasets/Iris\n", "\n", "The famous Iris database, first used by Sir R.A Fisher\n", "\n", "This is perhaps the best known database to be found in the\n", "pattern recognition literature. Fisher's paper is a classic in the field and\n", "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", "data set contains 3 classes of 50 instances each, where each class refers to a\n", "type of iris plant. One class is linearly separable from the other 2; the\n", "latter are NOT linearly separable from each other.\n", "\n", "References\n", "----------\n", " - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n", " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", " Mathematical Statistics\" (John Wiley, NY, 1950).\n", " - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n", " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", " Structure and Classification Rule for Recognition in Partially Exposed\n", " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", " on Information Theory, May 1972, 431-433.\n", " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", " conceptual clustering system finds 3 classes in the data.\n", " - Many, many more ...\n", "\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "# Number of samples and features\n", "iris.data.shape\n", "print \"Number of samples:\", iris.data.shape[0]\n", "print \"Number of features:\", iris.data.shape[1]\n", "print iris.data[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Number of samples: 150\n", "Number of features: 4\n", "[ 5.1 3.5 1.4 0.2]\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "# Target (label) names\n", "print iris.target_names" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['setosa' 'versicolor' 'virginica']\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize the data\n", "x_index = 0\n", "y_index = 1\n", "\n", "# this formatter will label the colorbar with the correct target names\n", "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", "\n", "plt.figure(figsize=(7, 5))\n", "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)\n", "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", "plt.xlabel(iris.feature_names[x_index])\n", "plt.ylabel(iris.feature_names[y_index])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xoUmT5thsUVirl50iO3sfXbp0cVmuW7duiGRgrW+twO9ADIMGDXJZzqpFnuXi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\n5Cfaa5u7W2v12tV127ZtJfoZ/vbCC1ozPl4vd/v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"text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quick Exercise:**\n", "Change x_index and y_index and find the 2D projection of the data which maximally separate all three classes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# your code goes here" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1.2.2. Other available datasets\n", "Scikit-learn makes available a host of datasets for testing learning algorithms. They come in three flavors:\n", "\n", "- **Packaged Data**: these small datasets are packaged with the scikit-learn installation, and can be downloaded using the tools in sklearn.datasets.load_*\n", "- **Downloadable Data**: these larger datasets are available for download, and scikit-learn includes tools which streamline this process. These tools can be found in sklearn.datasets.fetch_*\n", "- **Generated Data**: there are several datasets which are generated from models based on a random seed. These are available in the sklearn.datasets.make_*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Where the downloaded datasets via fetch_scripts are stored\n", "from sklearn.datasets import get_data_home\n", "get_data_home()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "'/Users/hadjimy/scikit_learn_data'" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "# How to fetch an external dataset\n", "from sklearn import datasets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can type datasets.fetch_ and a list of available datasets will pop up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise:\n", "Fetch the dataset olivetti_faces. Visualize various 2D projections for the data. Find the best in terms of separability." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import fetch_olivetti_faces" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "# your code goes here\n", "# fetch the faces data\n", "# Use a script like above to plot the faces image data.\n", "# Hint: plt.cm.bone is a good colormap for this data." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "#%load solutions/sklearn_vis_data.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3. Linear regression and classification\n", "In this section, we explain how to use scikit-learn library for regression and classification. We discuss Estimator class - the base class for many of the scikit-learn models - and work through some examples." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_boston\n", "data = load_boston()\n", "print data.DESCR" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Boston House Prices dataset\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in$1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "http://archive.ics.uci.edu/ml/datasets/Housing\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", "**References**\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", "\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot histogram for real estate prices\n", "plt.hist(data.target)\n", "plt.xlabel('price ($1000s)')\n", "plt.ylabel('count')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quick Exercise:** Try sctter-plot several 2D projections of the data. What you can infer from the plots?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# your code goes here" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every algorithm is exposed in scikit-learn via an Estimator object. For instance a linear regression is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import LinearRegression\n", "clf = LinearRegression()\n", "clf.fit(data.data, data.target)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "predicted = clf.predict(data.data)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the predicted price vs. true price\n", "plt.scatter(data.target, predicted)\n", "plt.plot([0, 50], [0, 50], '--k')\n", "plt.axis('tight')\n", "plt.xlabel('True price ($1000s)')\n", "plt.ylabel('Predicted price (\$1000s)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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FwAZEvgb8Hvi60w7AdxH5BUuXLozzkEhlwboY4sjkymVWGRikEi7hTODfjTHb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\npIkH3YsVgLEJrlM1CcVGpXQ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"text": [ "" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examples of regression type problems in machine learning:\n", "\n", "- **Sales:** given consumer data, predict how much they will spend\n", "- **Advertising:** given information about a user, predict the click-through rate for a web ad.\n", "- **Collaborative Filtering:** given a collection of user-ratings for movies, predict preferences for other movies & users.\n", "- **Astronomy:** given observations of galaxies, predict their mass or redshift" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example of regularized linear regression on a synthetic data set." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sklearn.linear_model import LinearRegression\n", "\n", "rng = np.random.RandomState(0)\n", "x = 2 * rng.rand(100) - 1\n", "\n", "f = lambda t: 1.2 * t ** 2 + .1 * t ** 3 - .4 * t ** 5 - .5 * t ** 9\n", "y = f(x) + .4 * rng.normal(size=100)\n", "\n", "plt.figure(figsize=(7, 5))\n", "plt.scatter(x, y, s=4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "x_test = np.linspace(-1, 1, 100)\n", "\n", "for k in [4, 9]:\n", " X = np.array([x**i for i in range(k)]).T\n", " X_test = np.array([x_test**i for i in range(k)]).T\n", " order4 = LinearRegression()\n", " order4.fit(X, y)\n", " plt.plot(x_test, order4.predict(X_test), label='%i-th order'%(k))\n", "\n", "plt.legend(loc='best')\n", "plt.axis('tight')\n", "plt.title('Fitting a 4th and a 9th order polynomial')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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DK6VUggQFBREUFJToesQYh3n4/kIiJYFlxpgqDvZNBYKMMQtsjw8CTY0x52OU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\nhw7BkCFQrhycO2ddqPb1gjv8GPYOLea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"text": [ "" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's look at the ground truth\n", "plt.figure()\n", "plt.scatter(x, y, s=4)\n", "plt.plot(x_test, f(x_test), label=\"truth\")\n", "plt.axis('tight')\n", "plt.title('Ground truth (9th order polynomial)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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c4O7D3L3JzEYA9xNa9se7++wYix2ny4BJZnYaMBc4BiBzfxFSOndF\n38XOwB/d/YF4ilt5uY4XMzsjev06d59iZkPNbA7wAXBKjEWOVT77Czga+J6ZNQErgWNjK3DMzOxW\n4EBgSzObB1xI6GVUtmNLww+IiKRQGtIyIiLSioK7iEgKKbiLiKSQgruISAopuIuIpJCCu4hICim4\ni4ikkIK7iEgK/T/DYR+D8EQbWgAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use [Ridge regression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) (with l1-norm regularization)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import Ridge\n", "\n", "for k in [20]:\n", " X = np.array([x**i for i in range(k)]).T\n", " X_test = np.array([x_test**i for i in range(k)]).T\n", " order4 = Ridge(alpha=0.3)\n", " order4.fit(X, y)\n", " plt.plot(x_test, order4.predict(X_test), label='%i-th order'%(k))\n", "plt.plot(x_test, f(x_test), label=\"truth\")\n", "plt.scatter(x, y, s=4)\n", "plt.legend(loc='best')\n", "plt.axis('tight')\n", "plt.title('Fitting a 4th and a 9th order polynomial')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+ofNXnfl9ZBduvK4snTpB5845X0awdkqkAk9aWiopKal8+eV8Wrcer8eSFwXD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\nbhE2bYIPPk3m080jSGs4gf5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"text": [ "" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.ylim?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise:\n", "For this exercise, you need to classify iris data with nearest neighbors classifier and with linear support vector classifier (LinearSVC).\n", "In order to measure the performance, calculate the accuracy or just use clf.score function built-in into the Estimator object." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division # turn off division truncation -- division will be floating point by default\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import LinearSVC\n", "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "\n", "# Splitting the data into train and test sets\n", "indices = np.random.permutation(range(iris.data.shape[0]))\n", "train_sz = floor(0.8*iris.data.shape[0])\n", "X, y = iris.data[indices[:train_sz],:], iris.target[indices[:train_sz]]\n", "Xt, yt = iris.data[indices[train_sz:],:], iris.target[indices[train_sz:]]\n", "\n", "# your code goes here" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#%load solutions/iris_classification.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4. Approaches to validation and testing\n", "It is usually an arduous and time consuming task to write cross-validation or other testing wrappers for your models. Scikit-learn allows you validate your models out-of-the-box. Let's have a look at how to do this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import cross_validation\n", "X, Xt, y, yt = cross_validation.train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)\n", "print X.shape, y.shape\n", "print Xt.shape, yt.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(90, 4) (90,)\n", "(60, 4) (60,)\n" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "# Cross-validating your models in one line\n", "scores = cross_validation.cross_val_score(svc, iris.data, iris.target, cv=5,)\n", "print scores\n", "print scores.mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "cross_validation.cross_val_score?" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate F1-measure (or any other score)\n", "from sklearn import metrics\n", "f1_scores = cross_validation.cross_val_score(svc, iris.data, iris.target, cv=5, scoring='f1')\n", "print f1_scores\n", "print f1_scores.mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quick Exercise:** Do the same CV-evaluation of the KNN classifier. Find the best number of nearest neighbors." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.5. What to learn next?\n", "If you would like to start using Scikit-learn for your machine learning tasks, the most right way is to jump into it and refer to [the documentation](http://scikit-learn.org/stable/documentation.html) from time to time. When you acquire enough skills in it, you can go and check out the following resources:\n", "\n", "- [A tutorial from PyCon 2013](https://github.com/jakevdp/sklearn_pycon2013)\n", "- [A tutorial from SciPy 2013 conference](https://github.com/jakevdp/sklearn_scipy2013) (check out the advanced track!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Scikit-learn algorithm cheatsheet](http://scikit-learn.org/dev/_static/ml_map.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Copyright 2015, Yiannis Hadjimichael, Maruan Al-Shedivat, ACM Student Member.*" ] } ], "metadata": {} } ] }