lessons/lesson-08/code/solution-code/solution-code-8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets, neighbors, metrics\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Axes(0.125,0.125;0.775x0.775)\n",
      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
      "count         150.000000        150.000000         150.000000   \n",
      "mean            5.843333          3.054000           3.758667   \n",
      "std             0.828066          0.433594           1.764420   \n",
      "min             4.300000          2.000000           1.000000   \n",
      "25%             5.100000          2.800000           1.600000   \n",
      "50%             5.800000          3.000000           4.350000   \n",
      "75%             6.400000          3.300000           5.100000   \n",
      "max             7.900000          4.400000           6.900000   \n",
      "\n",
      "       petal width (cm)      target  \n",
      "count        150.000000  150.000000  \n",
      "mean           1.198667    1.000000  \n",
      "std            0.763161    0.819232  \n",
      "min            0.100000    0.000000  \n",
      "25%            0.300000    0.000000  \n",
      "50%            1.300000    1.000000  \n",
      "75%            1.800000    2.000000  \n",
      "max            2.500000    2.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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YZnMN4Cxt26aUqe+xP//My6LiFDFfFwg7gFKqHxCptZ6slBoFLAeygd+11ot8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\nBeIT4Eul1CogG3gAQCk1AtirtZ6vlPo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11098bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YZnMN4Cxt26aUqe+xP//My6LiFDFfFwg7gFKqHxCptZ6slBoFLAeygd+11ot8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\nACxBALAEAcAKShuxM9lzVksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11098b810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "irisdf = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "irisdf['target'] = iris.target\n",
    "cmap = {'0': 'r', '1': 'g', '2': 'b' }\n",
    "irisdf['ctarget'] = irisdf.target.apply(lambda x: cmap[str(x)])\n",
    "irisdf.plot('petal length (cm)', 'petal width (cm)', kind='scatter', c=irisdf.ctarget)\n",
    "print irisdf.plot('petal length (cm)', 'petal width (cm)', kind='scatter', c=irisdf.ctarget)\n",
    "print irisdf.describe()\n",
    "\n",
    "def my_classifier(row):\n",
    "    if row['petal length (cm)'] < 2:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "predictions = irisdf.apply(my_classifier, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.666666666667\n"
     ]
    }
   ],
   "source": [
    "irisdf['predictions'] = predictions\n",
    "\n",
    "print float(len(irisdf[irisdf.target == irisdf.predictions])) / len(irisdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More specific solution\n",
    "\n",
    "For the class, this solution is as simple it really needs to be in order to get a very good prediction score. But: Why, or when, does this fail? What attributes make this a great data set for learning classification algorithms? What makes it not as great?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.946666666667\n"
     ]
    }
   ],
   "source": [
    "def my_classifier(row):\n",
    "    if row['petal length (cm)'] < 2:\n",
    "        return 0\n",
    "    elif row['petal length (cm)'] < 5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "predictions = irisdf.apply(my_classifier, axis=1)\n",
    "\n",
    "irisdf['predictions'] = predictions\n",
    "\n",
    "print float(len(irisdf[irisdf.target == irisdf.predictions])) / len(irisdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using distance: KNN implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1\n",
      " 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "0.96\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets, neighbors, metrics\n",
    "import pandas as pd\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "# n_neighbors is our option in KNN. We'll tune this value to attempt to improve our prediction.\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform')\n",
    "knn.fit(iris.data[:,2:], iris.target)\n",
    "print knn.predict(iris.data[:,2:])\n",
    "print iris.target\n",
    "\n",
    "print knn.score(iris.data[:,2:], iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do we see a change in performaance with using the distance weight?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "0.993333333333\n"
     ]
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5, weights='distance')\n",
    "knn.fit(iris.data[:,2:], iris.target)\n",
    "print knn.predict(iris.data[:,2:])\n",
    "print iris.target\n",
    "\n",
    "print knn.score(iris.data[:,2:], iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution to solving K\n",
    "\n",
    "This is only one approach to the problem, but adding in the 'distance' parameter (instead of uniform) would only be additive; note that the code would need some editing to handle it properly if done in the grid search; alternatively, make the change directly in the estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.90667, std: 0.09752, params: {'n_neighbors': 2},\n",
       " mean: 0.90667, std: 0.09286, params: {'n_neighbors': 3},\n",
       " mean: 0.90667, std: 0.09286, params: {'n_neighbors': 4},\n",
       " mean: 0.91333, std: 0.08327, params: {'n_neighbors': 5},\n",
       " mean: 0.90667, std: 0.09286, params: {'n_neighbors': 6},\n",
       " mean: 0.92000, std: 0.08589, params: {'n_neighbors': 7},\n",
       " mean: 0.91333, std: 0.08844, params: {'n_neighbors': 8},\n",
       " mean: 0.92000, std: 0.09092, params: {'n_neighbors': 9},\n",
       " mean: 0.92000, std: 0.09092, params: {'n_neighbors': 10},\n",
       " mean: 0.91333, std: 0.08589, params: {'n_neighbors': 11},\n",
       " mean: 0.89333, std: 0.10625, params: {'n_neighbors': 12},\n",
       " mean: 0.90667, std: 0.08273, params: {'n_neighbors': 13},\n",
       " mean: 0.90000, std: 0.09428, params: {'n_neighbors': 14},\n",
       " mean: 0.90000, std: 0.09428, params: {'n_neighbors': 15},\n",
       " mean: 0.88667, std: 0.11851, params: {'n_neighbors': 16},\n",
       " mean: 0.88000, std: 0.12754, params: {'n_neighbors': 17},\n",
       " mean: 0.86667, std: 0.12111, params: {'n_neighbors': 18},\n",
       " mean: 0.88667, std: 0.11662, params: {'n_neighbors': 19},\n",
       " mean: 0.86667, std: 0.13499, params: {'n_neighbors': 20},\n",
       " mean: 0.86667, std: 0.13499, params: {'n_neighbors': 21},\n",
       " mean: 0.86667, std: 0.13499, params: {'n_neighbors': 22},\n",
       " mean: 0.86667, std: 0.13499, params: {'n_neighbors': 23},\n",
       " mean: 0.84667, std: 0.17075, params: {'n_neighbors': 24},\n",
       " mean: 0.86000, std: 0.14667, params: {'n_neighbors': 25},\n",
       " mean: 0.84667, std: 0.17075, params: {'n_neighbors': 26},\n",
       " mean: 0.84667, std: 0.15434, params: {'n_neighbors': 27},\n",
       " mean: 0.82000, std: 0.18809, params: {'n_neighbors': 28},\n",
       " mean: 0.80000, std: 0.19437, params: {'n_neighbors': 29},\n",
       " mean: 0.78667, std: 0.21250, params: {'n_neighbors': 30},\n",
       " mean: 0.77333, std: 0.21848, params: {'n_neighbors': 31},\n",
       " mean: 0.74000, std: 0.27520, params: {'n_neighbors': 32},\n",
       " mean: 0.75333, std: 0.26043, params: {'n_neighbors': 33},\n",
       " mean: 0.72000, std: 0.30155, params: {'n_neighbors': 34},\n",
       " mean: 0.69333, std: 0.31510, params: {'n_neighbors': 35},\n",
       " mean: 0.68000, std: 0.33506, params: {'n_neighbors': 36},\n",
       " mean: 0.68667, std: 0.30955, params: {'n_neighbors': 37},\n",
       " mean: 0.64000, std: 0.38320, params: {'n_neighbors': 38},\n",
       " mean: 0.64000, std: 0.38320, params: {'n_neighbors': 39},\n",
       " mean: 0.64000, std: 0.38320, params: {'n_neighbors': 40},\n",
       " mean: 0.38667, std: 0.42248, params: {'n_neighbors': 41},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 42},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 43},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 44},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 45},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 46},\n",
       " mean: 0.37333, std: 0.43123, params: {'n_neighbors': 47},\n",
       " mean: 0.36000, std: 0.41655, params: {'n_neighbors': 48},\n",
       " mean: 0.36000, std: 0.41655, params: {'n_neighbors': 49},\n",
       " mean: 0.36000, std: 0.41655, params: {'n_neighbors': 50},\n",
       " mean: 0.36000, std: 0.42864, params: {'n_neighbors': 51},\n",
       " mean: 0.34667, std: 0.42667, params: {'n_neighbors': 52},\n",
       " mean: 0.34667, std: 0.42667, params: {'n_neighbors': 53},\n",
       " mean: 0.33333, std: 0.41312, params: {'n_neighbors': 54},\n",
       " mean: 0.34667, std: 0.42667, params: {'n_neighbors': 55},\n",
       " mean: 0.32000, std: 0.40089, params: {'n_neighbors': 56},\n",
       " mean: 0.32667, std: 0.40683, params: {'n_neighbors': 57},\n",
       " mean: 0.32000, std: 0.40089, params: {'n_neighbors': 58},\n",
       " mean: 0.32667, std: 0.40683, params: {'n_neighbors': 59},\n",
       " mean: 0.24667, std: 0.36246, params: {'n_neighbors': 60},\n",
       " mean: 0.20667, std: 0.28783, params: {'n_neighbors': 61},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 62},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 63},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 64},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 65},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 66},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 67},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 68},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 69},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 70},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 71},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 72},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 73},\n",
       " mean: 0.10667, std: 0.13233, params: {'n_neighbors': 74},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 75},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 76},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 77},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 78},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 79},\n",
       " mean: 0.08667, std: 0.11851, params: {'n_neighbors': 80},\n",
       " mean: 0.07333, std: 0.10414, params: {'n_neighbors': 81},\n",
       " mean: 0.07333, std: 0.10414, params: {'n_neighbors': 82},\n",
       " mean: 0.07333, std: 0.10414, params: {'n_neighbors': 83},\n",
       " mean: 0.07333, std: 0.10414, params: {'n_neighbors': 84},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 85},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 86},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 87},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 88},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 89},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 90},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 91},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 92},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 93},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 94},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 95},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 96},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 97},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 98},\n",
       " mean: 0.06667, std: 0.10328, params: {'n_neighbors': 99}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import grid_search, cross_validation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# some n_list! keep in mind cross validation\n",
    "# recall: what's an effective way to create a numerical list in python?\n",
    "k = range(2, 100)\n",
    "params = {'n_neighbors': k }\n",
    "kf = cross_validation.KFold(len(irisdf), n_folds = 5)\n",
    "gs = grid_search.GridSearchCV(\n",
    "    estimator=neighbors.KNeighborsClassifier(),\n",
    "    param_grid=params,\n",
    "    cv=kf,\n",
    ")\n",
    "gs.fit(iris.data, iris.target)\n",
    "gs.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110d8d6d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27ni9CPhlpBcMBLIjPSRpqBeQkeUFoC2ofnRRH8LUi4GJFPBPAMuMMeWdt28wxlwH+K21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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11098b910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k,[s[1] for s in  gs.grid_scores_],)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in to look at fit before first dive around 25:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110e95450>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uVI4bzyrK/Am/qa1/UQnf5/Pxf3cdpm9onG/cXke5O/n/7ZRaqKRN+NduXklJfjbeJSgZ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\nOVwVNhZwGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d9c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k[:25],[s[1] for s in  gs.grid_scores_][:25],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}