gwr/notebooks/GWR_Georgia_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pysal as ps\n",
    "from pysal.contrib.gwr.sel_bw import Sel_BW\n",
    "from pysal.contrib.gwr.gwr import GWR\n",
    "from pysal.contrib.glm.family import Gaussian\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Data\n",
    "\n",
    "data = '/Users/toshan/dev/pysal/pysal/examples/georgia/GData_utm.csv'\n",
    "data = pd.read_csv(data)\n",
    "#shp = gp.read_file('/Users/toshan/dev/pysal/pysal/examples/georgia/G_utm.shp')\n",
    "#shp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "/anaconda/envs/test_gwr/lib/python2.7/site-packages/ipykernel/__main__.py:9: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n"
     ]
    }
   ],
   "source": [
    "# Prep data into design matrix and coordinates\n",
    "\n",
    "#Dependent variable\n",
    "y = data.PctBach.reshape((-1,1))\n",
    "\n",
    "#Design matrix - covariates - intercept added automatically\n",
    "pov = data.PctPov.reshape((-1,1))\n",
    "rural = data.PctRural.reshape((-1,1))\n",
    "blk = data.PctBlack.reshape((-1,1))\n",
    "X = np.hstack([rural, pov, blk])\n",
    "labels = ['Intercept', 'PctPov', 'PctRural', 'PctBlack']\n",
    "\n",
    "#Coordinates for calibration points\n",
    "u = data.X\n",
    "v = data.Y\n",
    "coords = np.array(zip(u,v))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.0\n"
     ]
    }
   ],
   "source": [
    "index = np.arange(len(y))\n",
    "train = index[0:-10]\n",
    "test = index[-10:]\n",
    "\n",
    "y_train = y[train]\n",
    "X_train = X[train]\n",
    "coords_train = list(coords[train])\n",
    "\n",
    "y_test = y[test]\n",
    "X_test = X[test]\n",
    "coords_test = list(coords[test])\n",
    "\n",
    "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
    "bw = bw.search(search='golden_section', criterion='AICc')\n",
    "print bw\n",
    "\n",
    "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
    "results = model.predict(coords_test, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10.51695514],\n",
       "       [  9.93321992],\n",
       "       [  8.92473026],\n",
       "       [  5.47350219],\n",
       "       [  8.61756585],\n",
       "       [ 12.8141851 ],\n",
       "       [  5.55619405],\n",
       "       [ 12.63004172],\n",
       "       [  8.70638418],\n",
       "       [  8.17582599]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.0\n"
     ]
    }
   ],
   "source": [
    "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
    "bw = bw.search(search='golden_section', criterion='AICc')\n",
    "print bw\n",
    "\n",
    "t = GWR(coords, y, X, bw).fit()\n",
    "\n",
    "model = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
    "results = model.predict(coords_test, X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 0.45389284]), array([ 0.18761856]))\n",
      "[[  2.27719806e+01  -2.15093080e-01  -1.02539506e-01  -1.40496359e-02]\n",
      " [  1.91053102e+01  -2.32528744e-01  -9.41767859e-02   7.19130384e-02]\n",
      " [  1.97434217e+01  -3.08929815e-01  -8.04472131e-02   8.32061891e-02]\n",
      " [  1.75057600e+01  -1.87954544e-01  -7.89191503e-02   5.17192228e-02]\n",
      " [  2.77474018e+01  -2.08553416e-01  -1.65334529e-01   4.06743861e-03]\n",
      " [  2.62106267e+01  -3.60514422e-01  -1.38397963e-01   7.21984934e-02]\n",
      " [  1.80348332e+01  -2.60555691e-01  -7.70473064e-02   8.43194028e-02]\n",
      " [  2.84528022e+01  -1.40970116e-01  -1.63408415e-01  -6.30756266e-02]\n",
      " [  2.23530956e+01  -2.26653837e-01  -1.03045606e-01   2.99244694e-03]\n",
      " [  1.82205077e+01  -3.09812424e-01  -7.40336720e-02   1.08635554e-01]]\n",
      "[[  2.27719806e+01  -2.15093080e-01  -1.02539506e-01  -1.40496359e-02]\n",
      " [  1.91053102e+01  -2.32528744e-01  -9.41767859e-02   7.19130384e-02]\n",
      " [  1.97434217e+01  -3.08929815e-01  -8.04472131e-02   8.32061891e-02]\n",
      " [  1.75057600e+01  -1.87954544e-01  -7.89191503e-02   5.17192228e-02]\n",
      " [  2.77474018e+01  -2.08553416e-01  -1.65334529e-01   4.06743861e-03]\n",
      " [  2.62106267e+01  -3.60514422e-01  -1.38397963e-01   7.21984934e-02]\n",
      " [  1.80348332e+01  -2.60555691e-01  -7.70473064e-02   8.43194028e-02]\n",
      " [  2.84528022e+01  -1.40970116e-01  -1.63408415e-01  -6.30756266e-02]\n",
      " [  2.23530956e+01  -2.26653837e-01  -1.03045606e-01   2.99244694e-03]\n",
      " [  1.82205077e+01  -3.09812424e-01  -7.40336720e-02   1.08635554e-01]]\n"
     ]
    }
   ],
   "source": [
    "print pearsonr(results.predictions, y_test)\n",
    "\n",
    "print results.params[0:]\n",
    "print t.params[-10:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.0\n",
      "159\n",
      "0.56813744327\n"
     ]
    }
   ],
   "source": [
    "#Find optimal bandwidth using golden section search to minimize AICc\n",
    "\n",
    "#Instantiate bandwidth selection class - bisquare NN (adaptive)\n",
    "bw = Sel_BW(coords, y, X, kernel='bisquare', fixed=False)\n",
    "\n",
    "#Find optimal bandwidth by minimizing AICc using golden section search algorithm\n",
    "bw = bw.search(search='golden_section', criterion='AICc')\n",
    "print bw\n",
    "\n",
    "model_a = GWR(coords, y, X, bw, family=Gaussian(), fixed=False, kernel='bisquare')\n",
    "results_a = model_a.fit()\n",
    "\n",
    "print len(results_a.localR2)\n",
    "print np.mean(results_a.localR2)\n",
    "exog_scale = results_a.scale\n",
    "exog_resid = results_a.resid_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'exog_scale' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-06cdae655127>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGWR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m93\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGaussian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfixed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bisquare'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoords_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexog_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog_resid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexog_resid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mresults_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_b\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_b\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocalR2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_b\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocalR2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'exog_scale' is not defined"
     ]
    }
   ],
   "source": [
    "model_b = GWR(coords, y, X, 93, family=Gaussian(), fixed=False, kernel='bisquare', points=coords_b, exog_scale=exog_scale, exog_resid=exog_resid)\n",
    "results_b = model_b.fit()\n",
    "\n",
    "print len(results_b.localR2)\n",
    "print np.mean(results_b.localR2)\n",
    "results_b.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.10295359  0.12385708  0.10256179  0.11029685  0.12763962  0.13120828\n",
      "  0.11315837  0.11694384  0.09973951  0.1078668 ]\n",
      "[-2.08922363 -1.87739571 -3.01213362 -1.70407901 -1.63392388 -2.74764987\n",
      " -2.3025755  -1.20545144 -2.27245792 -2.87217588]\n",
      "[[ 0.53068693]\n",
      " [ 0.59582647]\n",
      " [ 0.59700925]\n",
      " [ 0.45769954]\n",
      " [ 0.54634509]\n",
      " [ 0.5494828 ]\n",
      " [ 0.55159604]\n",
      " [ 0.55634237]\n",
      " [ 0.53903842]\n",
      " [ 0.55884954]]\n"
     ]
    }
   ],
   "source": [
    "print results_b.bse[0:10, 1]\n",
    "\n",
    "print results_b.tvalues[0:10, 1]\n",
    "\n",
    "print results_b.localR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-d127818b2d26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Results in a set of mappable results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'results' is not defined"
     ]
    }
   ],
   "source": [
    "#Results in a set of mappable results \n",
    "results.params.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Map Parameter estimates and T-vals for each covariate\n",
    "\n",
    "for param in range(results.params.shape[1]):\n",
    "    shp[str(param)] = results.params[:,param]\n",
    "    vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
    "    ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='YlOrRd')\n",
    "    ax.set_title(labels[param] + ' Estimates')\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
    "    sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='YlOrRd')\n",
    "    sm._A = []\n",
    "    fig.colorbar(sm, cax=cax)\n",
    "    \n",
    "    shp[str(param)] = results.tvalues[:,param]\n",
    "    vmin, vmax = np.min(shp[str(param)]), np.max(shp[str(param)]) \n",
    "    ax = shp.plot(str(param), vmin=vmin, vmax=vmax, figsize=(8,8), cmap='Greys')\n",
    "    ax.set_title(labels[param] + ' T-vals')\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
    "    sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='Greys')\n",
    "    sm._A = []\n",
    "    fig.colorbar(sm, cax=cax)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x136d8b150>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33uHzjzqxafNmBg0a5LIOCoVCkVEoQyGdFC5cmIjbt4mKjMA/MNN91w7v3o5O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Oaly5yV4Qs3MKZLM1ZMcKdQ2SqRjitYpjJVmrZj7cxxeJ0/xfMHt/ns74dKpWbtjDFYWlkTGvKF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\no1YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1358c6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Map local R-square values which is a weighted R-square at each observation location\n",
    "\n",
    "shp['localR2'] = results.localR2\n",
    "vmin, vmax = np.min(shp['localR2']), np.max(shp['localR2']) \n",
    "ax = shp.plot('localR2', vmin=vmin, vmax=vmax, figsize=(8,8), cmap='PuBuGn')\n",
    "ax.set_title('Local R-Squared')\n",
    "fig = ax.get_figure()\n",
    "cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\n",
    "sm = plt.cm.ScalarMappable(norm=plt.Normalize(vmin=vmin, vmax=vmax), cmap='PuBuGn')\n",
    "sm._A = []\n",
    "fig.colorbar(sm, cax=cax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "??GWR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dists = np.zeros((10,159))\n",
    "for x, point1 in enumerate(coords_b):\n",
    "    for y, point2 in enumerate(coords):\n",
    "        \n",
    "        dists[x,y] = np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 135141.51708594,  176669.03854578,  150233.03041745, ...,\n",
       "         101245.68031101,   37465.45393773,  186494.71599871],\n",
       "       [  53079.72071743,   93989.76293746,   56523.70300113, ...,\n",
       "         272750.51071353,  192366.73910219,  185946.3168796 ],\n",
       "       [ 211071.91332835,  179410.69684634,  203731.38642588, ...,\n",
       "         259876.04309332,  157691.66651843,   90086.32599479],\n",
       "       ..., \n",
       "       [ 233459.02913062,  277886.65118938,  250155.19163993, ...,\n",
       "              0.        ,  116483.88542009,  277512.51277944],\n",
       "       [ 142506.80885168,  169996.21028779,  153162.14012036, ...,\n",
       "         116483.88542009,       0.        ,  161268.74061156],\n",
       "       [ 144540.5180503 ,   95782.96853831,  130844.73173151, ...,\n",
       "         277512.51277944,  161268.74061156,       0.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pysal.contrib.gwr.kernels import adapt_bisquare, _Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_coords = np.vstack([coords, coords_b])\n",
    "\n",
    "W = adapt_bisquare(all_coords, 93)\n",
    "\n",
    "i = len(coords_b)\n",
    "j = len(coords)\n",
    "W = W[i:, :-j].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = _Kernel(coords, fixed=False, function='bisquare', truncate=False, k=92, points=coords_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.14343799e-01,   6.75977473e-03,   1.13120557e-01, ...,\n",
       "          4.88014350e-01,   9.19154786e-01,   5.15666694e-04],\n",
       "       [  9.27168014e-01,   7.80855123e-01,   9.17619421e-01, ...,\n",
       "          4.11566468e-04,   2.62825351e-01,   2.96646476e-01],\n",
       "       [  2.55461442e-03,   9.86090262e-02,   1.33250285e-02, ...,\n",
       "          1.92971340e-01,   2.20950550e-01,   6.84004125e-01],\n",
       "       ..., \n",
       "       [  3.01668776e-02,   4.39435726e-01,   1.20801314e-01, ...,\n",
       "          1.00000000e+00,   5.00997875e-01,   4.33523139e-01],\n",
       "       [  1.02455255e-01,   1.05479492e-03,   4.60574281e-02, ...,\n",
       "          2.97821020e-01,   1.00000000e+00,   1.67110298e-02],\n",
       "       [  3.42081437e-01,   6.68642075e-01,   4.35361409e-01, ...,\n",
       "          2.81165199e-01,   2.33510795e-01,   1.00000000e+00]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.17538125,  0.87525431,  0.42934275,  0.80242504,\n",
       "        0.70359714,  0.27695634,  0.        ,  0.03083999,  0.76946793,\n",
       "        0.7311884 ,  0.        ,  0.        ,  0.79126134,  0.70002409,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.00106685,\n",
       "        0.        ,  0.7250386 ,  0.        ,  0.85643026,  0.34265747,\n",
       "        0.01878214,  0.50266628,  0.        ,  0.        ,  0.1835491 ,\n",
       "        0.        ,  0.        ,  0.11051018,  0.        ,  0.43041104,\n",
       "        0.05143613,  0.10487   ,  0.        ,  0.02865854,  0.6171386 ,\n",
       "        0.66370484,  0.        ,  0.58953303,  0.85720095,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.06040493,  0.80528346,  0.        ,  0.17767534,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.04826184,  0.58892109,  0.        ,  0.        ,  0.96186502,\n",
       "        0.18599788,  0.4800177 ,  0.40543209,  0.        ,  0.        ,\n",
       "        0.63230904,  0.42509109,  0.07603503,  0.85398556,  0.        ,\n",
       "        0.88942771,  0.55254905,  0.        ,  0.00509282,  0.8178033 ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.06647948,\n",
       "        0.17309084,  0.        ,  0.85232385,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.94253873,  0.        ,  0.        ,\n",
       "        0.24485917,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.10398147,  0.        ,  0.        ,  0.4884651 ,  0.06003501,\n",
       "        0.        ,  0.        ,  0.        ,  0.00648795,  0.        ,\n",
       "        0.        ,  0.92289548,  0.        ,  0.78258809,  0.01501169,\n",
       "        0.30025862,  0.46065143,  0.9013738 ,  0.        ,  0.68516546,\n",
       "        0.        ,  0.31872397,  0.00733061,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.83474417,  0.00833362,  0.17211238,\n",
       "        0.920988  ,  0.        ,  0.77178798,  0.        ,  0.        ,\n",
       "        0.37447591,  0.        ,  0.05710858,  0.28441712,  0.17211238,\n",
       "        0.920988  ,  0.        ,  0.77178798,  0.        ,  0.        ,\n",
       "        0.37447591,  0.        ,  0.05710858,  0.28441712])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pysal.contrib.gwr.kernels import adapt_bisquare, _Kernel\n",
    "#W = adapt_bisquare(coords, 93, coords_b)\n",
    "w = _Kernel(coords, fixed=False, k=93-1, function='bisquare', points=coords_b, truncate=False)\n",
    "#W = _Kernel(coords, fixed=False, function='bisquare', truncate=False, k=92, points=coords_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 184412.62167661],\n",
       "       [ 275559.98944177],\n",
       "       [ 216617.15163301],\n",
       "       [ 187859.24904361],\n",
       "       [ 267873.107272  ],\n",
       "       [ 313030.23895156],\n",
       "       [ 177791.79358056],\n",
       "       [ 215493.75350882],\n",
       "       [ 172825.86012155],\n",
       "       [ 224337.23222372]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.bandwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "??adapt_bisquare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,1) (159,1) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-4ac9090f9d7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTSS\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mresults_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRSS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,1) (159,1) "
     ]
    }
   ],
   "source": [
    "results_a.TSS / results_a.RSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.55018249],\n",
       "       [ 0.55508304],\n",
       "       [ 0.55067801],\n",
       "       [ 0.56737294],\n",
       "       [ 0.56034496],\n",
       "       [ 0.55163749],\n",
       "       [ 0.55802282],\n",
       "       [ 0.56646836],\n",
       "       [ 0.50958839],\n",
       "       [ 0.5488834 ],\n",
       "       [ 0.57973455],\n",
       "       [ 0.53019011],\n",
       "       [ 0.60128026],\n",
       "       [ 0.56221182],\n",
       "       [ 0.60339059],\n",
       "       [ 0.5763165 ],\n",
       "       [ 0.54286951],\n",
       "       [ 0.5833684 ],\n",
       "       [ 0.57449946],\n",
       "       [ 0.61737272],\n",
       "       [ 0.55380111],\n",
       "       [ 0.61102546],\n",
       "       [ 0.54879739],\n",
       "       [ 0.60489614],\n",
       "       [ 0.61531061],\n",
       "       [ 0.63084783],\n",
       "       [ 0.56313164],\n",
       "       [ 0.56211435],\n",
       "       [ 0.55190234],\n",
       "       [ 0.58028618],\n",
       "       [ 0.59173197],\n",
       "       [ 0.58525895],\n",
       "       [ 0.57748692],\n",
       "       [ 0.5337402 ],\n",
       "       [ 0.55415337],\n",
       "       [ 0.54475232],\n",
       "       [ 0.55563599],\n",
       "       [ 0.61699849],\n",
       "       [ 0.60978055],\n",
       "       [ 0.57534427],\n",
       "       [ 0.55258532],\n",
       "       [ 0.55419367],\n",
       "       [ 0.55921201],\n",
       "       [ 0.57623454],\n",
       "       [ 0.50215786],\n",
       "       [ 0.58823547],\n",
       "       [ 0.56940733],\n",
       "       [ 0.59411556],\n",
       "       [ 0.5705367 ],\n",
       "       [ 0.58869272],\n",
       "       [ 0.59467316],\n",
       "       [ 0.55097965],\n",
       "       [ 0.53283037],\n",
       "       [ 0.57561545],\n",
       "       [ 0.54449461],\n",
       "       [ 0.60433883],\n",
       "       [ 0.57143884],\n",
       "       [ 0.55955218],\n",
       "       [ 0.5486125 ],\n",
       "       [ 0.58597217],\n",
       "       [ 0.54926699],\n",
       "       [ 0.54599256],\n",
       "       [ 0.61630895],\n",
       "       [ 0.55746869],\n",
       "       [ 0.55499711],\n",
       "       [ 0.55931011],\n",
       "       [ 0.56417   ],\n",
       "       [ 0.54673444],\n",
       "       [ 0.5540521 ],\n",
       "       [ 0.56008229],\n",
       "       [ 0.59327121],\n",
       "       [ 0.64547689],\n",
       "       [ 0.54818115],\n",
       "       [ 0.62552538],\n",
       "       [ 0.58738464],\n",
       "       [ 0.57551537],\n",
       "       [ 0.52859729],\n",
       "       [ 0.55451107],\n",
       "       [ 0.56953983],\n",
       "       [ 0.50665376],\n",
       "       [ 0.53630241],\n",
       "       [ 0.54924515],\n",
       "       [ 0.51356766],\n",
       "       [ 0.56402094],\n",
       "       [ 0.60593352],\n",
       "       [ 0.5664703 ],\n",
       "       [ 0.48498454],\n",
       "       [ 0.57497136],\n",
       "       [ 0.603174  ],\n",
       "       [ 0.55005894],\n",
       "       [ 0.60048813],\n",
       "       [ 0.57134237],\n",
       "       [ 0.54907691],\n",
       "       [ 0.54975445],\n",
       "       [ 0.61465079],\n",
       "       [ 0.6060417 ],\n",
       "       [ 0.55180747],\n",
       "       [ 0.62700231],\n",
       "       [ 0.63110394],\n",
       "       [ 0.56627448],\n",
       "       [ 0.55936342],\n",
       "       [ 0.59153842],\n",
       "       [ 0.49072277],\n",
       "       [ 0.56039346],\n",
       "       [ 0.54861849],\n",
       "       [ 0.64223183],\n",
       "       [ 0.56429376],\n",
       "       [ 0.55584156],\n",
       "       [ 0.55414867],\n",
       "       [ 0.58173359],\n",
       "       [ 0.59096694],\n",
       "       [ 0.55677681],\n",
       "       [ 0.5811985 ],\n",
       "       [ 0.61684415],\n",
       "       [ 0.58192915],\n",
       "       [ 0.56583346],\n",
       "       [ 0.5632224 ],\n",
       "       [ 0.59850011],\n",
       "       [ 0.54152829],\n",
       "       [ 0.58753223],\n",
       "       [ 0.54196418],\n",
       "       [ 0.56961293],\n",
       "       [ 0.60674024],\n",
       "       [ 0.56542027],\n",
       "       [ 0.56345589],\n",
       "       [ 0.59930614],\n",
       "       [ 0.54614222],\n",
       "       [ 0.61625657],\n",
       "       [ 0.59130554],\n",
       "       [ 0.63750061],\n",
       "       [ 0.55951303],\n",
       "       [ 0.56151487],\n",
       "       [ 0.62565668],\n",
       "       [ 0.47217495],\n",
       "       [ 0.58182132],\n",
       "       [ 0.55963473],\n",
       "       [ 0.54540066],\n",
       "       [ 0.53159056],\n",
       "       [ 0.54104851],\n",
       "       [ 0.49172269],\n",
       "       [ 0.6391049 ],\n",
       "       [ 0.55938683],\n",
       "       [ 0.54270534],\n",
       "       [ 0.54468759],\n",
       "       [ 0.6266155 ],\n",
       "       [ 0.55445185],\n",
       "       [ 0.55985567],\n",
       "       [ 0.59130847],\n",
       "       [ 0.55217823],\n",
       "       [ 0.53068693],\n",
       "       [ 0.59582647],\n",
       "       [ 0.59700925],\n",
       "       [ 0.45769954],\n",
       "       [ 0.54634509],\n",
       "       [ 0.5494828 ],\n",
       "       [ 0.55159604],\n",
       "       [ 0.55634237],\n",
       "       [ 0.53903842],\n",
       "       [ 0.55884954]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_a.localR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "385.87740857134793"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(results_b.W[0].reshape((-1,1)) * (results_b.y.reshape((-1,1)) - results_a.predy.reshape((-1,1)))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.62264884,  0.79783573, -1.90581472,  0.58585524, -1.77602421])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(results_a.y - results_a.mu)[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.62264884],\n",
       "       [ 0.79783573],\n",
       "       [-1.90581472],\n",
       "       [ 0.58585524],\n",
       "       [-1.77602421]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(results_a.y.reshape((-1,1)) - results_a.predy.reshape((-1,1)))[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.62264884,  0.79783573, -1.90581472,  0.58585524, -1.77602421])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_a.resid_response[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'GWRResults' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-c10354c94228>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'GWRResults' has no len()"
     ]
    }
   ],
   "source": [
    "print len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148]\n"
     ]
    }
   ],
   "source": [
    "print(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159\n"
     ]
    }
   ],
   "source": [
    "print len(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
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