Clustering Re-Do.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cf = pd.read_csv(\"notebooks/county_facts_with_2012_winner.csv\")\n",
    "\n",
    "# get array of fipscodes to later assign as labels by county\n",
    "fipscodes = cf[\"fips\"].values\n",
    "winner = cf[\"winner\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list of attributes that are the most important according to weka\n",
    "weka_list = [\"SEX255214\", \"RHI125214\", \"RHI225214\",\"RHI625214\", \"RHI725214\", \n",
    "            \"RHI825214\", \"POP815213\", \"EDU635213\", \"LFE305213\",\"HSG495213\",\n",
    "            \"HSD310213\",\"INC910213\",\"PVY020213\",\"SBO415207\", \"RTN131207\",\"LND110210\", \"POP060210\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242\n",
      "1747\n",
      "56\n",
      "912\n"
     ]
    }
   ],
   "source": [
    "# remember the state abbreviations for later (since we need only numeric values)\n",
    "y = cf[\"state_abbreviation\"].values\n",
    "cf = cf.drop(['state_abbreviation'], axis=1)\n",
    "\n",
    "# our table is now only numeric values\n",
    "cf = cf[weka_list]\n",
    "\n",
    "# ???\n",
    "newcf_norm = (cf - cf.mean()) / (cf.max() - cf.min())\n",
    "newcf_norm = newcf_norm.div(newcf_norm.sum(axis=1), axis=0)\n",
    "\n",
    "X = cf.as_matrix() # take our dataframe as numpy matrix\n",
    "kmeans_model = KMeans(n_clusters=4, random_state=1).fit(X) # build model\n",
    "labels = kmeans_model.labels_ # our cluster labels\n",
    "clusters = pd.DataFrame(labels, fipscodes) # our reformed dataset, matching the clusters to the fips codes\n",
    "clusters = clusters.reset_index() \n",
    "clusters.columns = ['fips', 'cluster']\n",
    "clusters # two columns, [fips, cluster]\n",
    "\n",
    "for i in range(0, 4): # print the size of each cluster\n",
    "    print len(clusters.loc[clusters['cluster'] == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many boolean indices",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
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      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/ops.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[1;32m    993\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    994\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 995\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    996\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    997\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'index'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_combine_series_infer\u001b[0;34m(self, other, func, level, fill_value)\u001b[0m\n\u001b[1;32m   3455\u001b[0m                                      columns=self.columns)\n\u001b[1;32m   3456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3457\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_match_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3459\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_match_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_combine_match_columns\u001b[0;34m(self, other, func, level, fill_value)\u001b[0m\n\u001b[1;32m   3473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3474\u001b[0m         new_data = left._data.eval(\n\u001b[0;32m-> 3475\u001b[0;31m             func=func, other=right, axes=[left.columns, self.index])\n\u001b[0m\u001b[1;32m   3476\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   2838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2839\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2840\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2842\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   2821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2822\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2823\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2824\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, func, other, raise_on_error, try_cast, mgr)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# get the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m         \u001b[0;31m# if we have an invalid shape/broadcast error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(other)\u001b[0m\n\u001b[1;32m   1093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1094\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1095\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m             \u001b[0;31m# mask if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/pandas/core/ops.pyc\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m    927\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myrav\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m                 \u001b[0mxrav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 \u001b[0myrav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    930\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myrav\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                     \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myrav\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: too many boolean indices"
     ]
    }
   ],
   "source": [
    "cf[\"fips\"] = fipscodes\n",
    "cf[\"cluster\"] = clusters['cluster']\n",
    "cf[\"winner\"] = winner\n",
    "\n",
    "fi = pd.DataFrame() # create empty dataframe\n",
    "\n",
    "for i in range(0,4):\n",
    "    clustered_facts_grouped = cf[cf[\"cluster\"]==i] # groupby cluster\n",
    "    \n",
    "#     clustered_facts_grouped = (clustered_facts_grouped - clustered_facts_grouped.mean()) / (clustered_facts_grouped.max() - clustered_facts_grouped.min())\n",
    "#     clustered_facts_grouped = clustered_facts_grouped.div(clustered_facts_grouped.sum(axis=1), axis=0)\n",
    "\n",
    "    y = clustered_facts_grouped[\"winner\"]\n",
    "    clustered_facts_grouped = clustered_facts_grouped.drop(['fips', 'cluster', 'winner'], axis=1)\n",
    "\n",
    "    X = clustered_facts_grouped.as_matrix()\n",
    "        \n",
    "    model = ExtraTreesClassifier() \n",
    "    model.fit(X, y) # find which features, in this cluster, were most beneficial to determining the winner\n",
    "\n",
    "    z = pd.Series(model.feature_importances_)\n",
    "    fi = fi.append(z, ignore_index=True)\n",
    "    \n",
    "fi.columns = weka_list\n",
    "for index, row in fi.iterrows():\n",
    "    a = row.sort_values(ascending=False)\n",
    "    print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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