{ "metadata": { "name": "", "signature": "sha256:10575422c11e13cbb683256e6fcb6bcbeb3be8bf5fc8382a80a16cb36e727c1d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import statsmodels.api as sm\n", "import numpy as np\n", "from scipy.stats import logistic\n", "import math\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING: pylab import has clobbered these variables: ['logistic']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "dob = pd.read_csv('snapshot_data/2014-09-17/property_indexes/dob-index.csv', index_col=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "dob.fillna(value=0, inplace=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "dob['total'] = dob.sum(axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "dob.ix[1990:]['total']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "1990 12987\n", "1991 10692\n", "1992 9168\n", "1993 7059\n", "1994 5114\n", "1995 3019\n", "1996 1674\n", "1997 836\n", "1998 471\n", "1999 307\n", "2000 199\n", "2001 122\n", "2002 100\n", "2003 71\n", "2004 57\n", "2005 35\n", "2006 28\n", "2007 24\n", "2008 13\n", "2009 17\n", "2010 23\n", "2011 19\n", "2012 20\n", "2013 32\n", "2014 7\n", "2411 1\n", "2426 2\n", "Name: total, dtype: float64" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "dob['ratio'] = (dob['total'] - dob['male']) / dob['total']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "dob['year'] = dob.index\n", "dob['shift-year'] = dob['year'] - 1800" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "dob.ix[1800:1980]['ratio'].plot(kind='line')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "logit = sm.Logit(dob.ix[1800:1990]['ratio'], dob.ix[1800:1990]['shift-year'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "result = logit.fit()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.344498\n", " Iterations 5\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "result.summary()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Logit Regression Results
Dep. Variable: ratio No. Observations: 191
Model: Logit Df Residuals: 190
Method: MLE Df Model: 0
Date: Thu, 08 Jan 2015 Pseudo R-squ.: -0.6871
Time: 15:17:15 Log-Likelihood: -65.799
converged: True LL-Null: -39.002
LLR p-value: 1.000
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [95.0% Conf. Int.]
shift-year -0.0146 0.002 -7.109 0.000 -0.019 -0.011
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "\n", "\"\"\"\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: ratio No. Observations: 191\n", "Model: Logit Df Residuals: 190\n", "Method: MLE Df Model: 0\n", "Date: Thu, 08 Jan 2015 Pseudo R-squ.: -0.6871\n", "Time: 15:17:15 Log-Likelihood: -65.799\n", "converged: True LL-Null: -39.002\n", " LLR p-value: 1.000\n", "==============================================================================\n", " coef std err z P>|z| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "shift-year -0.0146 0.002 -7.109 0.000 -0.019 -0.011\n", "==============================================================================\n", "\"\"\"" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "result.params[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "-0.014622907346901763" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "result.model" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "def sigmoid(x):\n", " b0 = 1\n", " b1 = -result.params[0]\n", " exponent = (b0 + ((x)*b1))\n", " return 1 / (1 + math.exp(-1 * exponent) )\n", "\n", "def invsigmoid(x):\n", " return 1 / sigmoid(x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "dob['logistic'] = dob['shift-year'].apply(sigmoid)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "dob.ix[1800:1990][['logistic','ratio']].plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jjyAgIACmpqZ4/vnn0alTp4YOS0RGQnAEmc7cTZm61v7iPkWfQwWJjAu/07qlcTw35zYh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\nULRQ6LmVTQcv0jE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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "5*math.e**2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "36.94528049465325" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "import numpy as np\n", "import sympy as sym\n", "\n", "\"\"\"\n", "create a function to fit with your data. a, b, c and d are the coefficients\n", "that curve_fit will calculate for you. \n", "In this part you need to guess and/or use mathematical knowledge to find\n", "a function that resembles your data\n", "\"\"\"\n", "def mypoly(x, a, b, c, d):\n", " return a*x**3 + b*x**2 +c*x + d\n", "\n", "def myexp(x, a, b,c, d):\n", " return (a**((b*x)+c)) +d\n", "\n", "def mypow(x, a,b,c):\n", " return ((x)**(b)) +c\n", "\n", "\n", "\"\"\"\n", "make the curve_fit\n", "\"\"\"\n", "for func in [mypoly, myexp, mypow]:\n", " x = list(dob.ix[1800:1980]['ratio'].index)\n", " y = list(dob.ix[1800:1980]['ratio'])\n", "\n", " \n", " popt, pcov = curve_fit(func, x, y, maxfev=1000000)\n", " print 'pcov', pcov\n", "\n", " \"\"\"\n", " Plot your data\n", " \"\"\"\n", " plt.plot(x, y, 'ro',label=\"Original Data\")\n", "\n", " \"\"\"\n", " brutal force to avoid errors\n", " \"\"\" \n", " x = [float(xn) for xn in x] #every element (xn) in x becomes a float\n", " y = [float(yn) for yn in y] #every element (yn) in y becomes a float\n", " x = np.array(x) #transform your data in a numpy array, \n", " y = np.array(y) #so the curve_fit can work\n", "\n", "\n", "\n", " \"\"\"\n", " The result is:\n", " popt[0] = a , popt[1] = b, popt[2] = c and popt[2] = d of the function,\n", " so f(x) = popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3].\n", " \"\"\"\n", " print \"a = %s , b = %s, c = %s, d = %s\" % (popt[0], popt[1], popt[2], popt[3] if len(popt)==4 else None)\n", "\n", " \"\"\"\n", " Use sympy to generate the latex sintax of the function\n", " \"\"\"\n", " xs = sym.Symbol('\\lambda') \n", " tex = sym.latex(func(xs,*popt)).replace('$', '')\n", " plt.title(r'$f(\\lambda)= %s$' %(tex),fontsize=16)\n", "\n", " \"\"\"\n", " Print the coefficients and plot the funcion.\n", " \"\"\"\n", "\n", " plt.plot(x, func(x, *popt), label=\"Fitted Curve\") #same as line above \\/\n", " #plt.plot(x, popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3], label=\"Fitted Curve\") \n", "\n", " plt.legend(loc='upper left')\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "pcov [[ 2.02482052e-17 -1.14807353e-13 2.16886454e-10 -1.36513136e-07]\n", " [ -1.14807353e-13 6.51000487e-10 -1.22990766e-06 7.74181850e-04]\n", " [ 2.16886454e-10 -1.22990766e-06 2.32376497e-03 -1.46282103e+00]\n", " [ -1.36513136e-07 7.74181850e-04 -1.46282103e+00 9.20913276e+02]]\n", "a = 4.87416698802e-08 , b = -0.000269974433142, c = 0.498921947871, d = -307.558030013" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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M6dOnG999950REhLitny//fab2/L/+uuvhslkcnh731rGn3/+2W35zp07Z4wcOdI4duyY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\nU9t0xpigc8almNqmM8YkIs2UWEuXUVhYyODBg4mIiMDf35/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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pcov [[ 3.01147547e+04 -2.59627896e+06 5.42091393e+09 -1.76199201e-01]\n", " [ -2.59627896e+06 2.23832619e+08 -4.67352462e+11 1.51905520e+01]\n", " [ 5.42091393e+09 -4.67352462e+11 9.75810965e+14 -3.17171987e+04]\n", " [ -1.76199183e-01 1.51905505e+01 -3.17171953e+04 7.07313684e-06]]\n", "a = 0.987088150409 , b = -1.10594731976, c = 2309.16849805, d = 0.0383932763027\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0k9n6lJQUwsPD6dixo7Yutw/dWrVqcfHiRW05NTWVhIQEu8qQ3fDhw2nQoAGn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\nY+bOncugQYOIi4tj9+7dF3SuiIjI95ESkVZQU1PDQw89REZ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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pcov [[ inf inf inf]\n", " [ inf inf inf]\n", " [ inf inf inf]]\n", "a = 1.0 , b = 0.260677743372, c = -7.03174236265, d = None" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ixowZuLm50bdvX15//XXOnTvHs88+i5ubG59//jkAe/fupXPnzri7u9OqVSuT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\nxsFmx2nMp3zIL/TGv340P+3rT40aDyKO75w5Q0X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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "myexp_f(a = 0.987088150409 , b = -1.10594731976, c = 2309.16849805, d = 0.0383932763027)(2034)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def myexp_f( a, b,c, d):\n", " return lambda x: (a**((b*x)+c)) +d\n", "\n", "def mypoly_f(a,b,c,d):\n", " return lambda x: a*x**3 + b*x**2 +c*x + d\n", "\n", "\n", "myexp_at_zero = lambda x: abs(myexp_f(a = 0.987088150409 , b = -1.10594731976, c = 2309.16849805, d = 0.0383932763027)(x) - 0.5)\n", "\n", "mypoly_at_zero = lambda x: abs(mypoly_f(a = 4.87416698802e-08 , b = -0.000269974433142, c = 0.498921947871, d = -307.558030013)(x) - 0.5)\n", "\n", "from scipy.optimize import minimize\n", "\n", "print minimize(myexp_at_zero, (2100))\n", "print minimize(mypoly_at_zero,(2100))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " status: 2\n", " success: False\n", " njev: 44\n", " nfev: 144\n", " hess_inv: array([[ 643.99976828]])\n", " fun: 3.212685673048554e-11\n", " x: array([ 2034.17023164])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", " jac: array([ 0.00232247])\n", " status: 2\n", " success: False\n", " njev: 49\n", " nfev: 159\n", " hess_inv: array([[ 16654.7000672]])\n", " fun: 7.298694981727749e-11\n", " x: array([ 2037.01251129])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", " jac: array([-0.00400543])\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }