view notebooks/correlation_samples_outliers.ipynb @ 9:c4841876a8ff branch-tests

adding notebooks and trying to explain classifier coefficients

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "from scipy.stats import pearsonr\n",
    "import sys\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "sys.path.append('../')\n",
    "import scripts.results as results\n",
    "import scripts.utils_spatial as utils_spatial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: there are 21 disconnected observations\n",
      "Island ids:  [3, 6, 26, 35, 39, 45, 52, 61, 62, 66, 77, 85, 94, 97, 98, 102, 103, 107, 110, 120, 121]\n",
      "Antigua and Barbuda\n",
      "Australia\n",
      "Cuba\n",
      "Fiji\n",
      "French Polynesia\n",
      "Grenada\n",
      "Iceland\n",
      "Jamaica\n",
      "Japan\n",
      "Kiribati\n",
      "Malta\n",
      "New Zealand\n",
      "Philippines\n",
      "Puerto Rico\n",
      "Republic of Serbia\n",
      "Saint Lucia\n",
      "Samoa\n",
      "Solomon Islands\n",
      "South Korea\n",
      "The Bahamas\n",
      "Trinidad and Tobago\n",
      "most outliers \n",
      "           Country  Outliers\n",
      "60            Chad  0.636364\n",
      "86          Gambia  0.540000\n",
      "17   French Guiana  0.535714\n",
      "43           Benin  0.500000\n",
      "78     El Salvador  0.484848\n",
      "136       Botswana  0.477778\n",
      "6          Bolivia  0.457143\n",
      "104         Bhutan  0.454545\n",
      "14         Liberia  0.450000\n",
      "63         Senegal  0.439024\n",
      "least outliers \n",
      "                              Country  Outliers\n",
      "1                           Lithuania  0.000000\n",
      "120                        Kazakhstan  0.000000\n",
      "119                           Denmark  0.000000\n",
      "107                          Kiribati  0.000000\n",
      "109  Democratic Republic of the Congo  0.042553\n",
      "105                             Sudan  0.044118\n",
      "15                        Netherlands  0.044776\n",
      "84                               Iraq  0.045977\n",
      "74                     Czech Republic  0.048780\n",
      "85                       Sierra Leone  0.050000\n"
     ]
    }
   ],
   "source": [
    "X_list, Y, Yaudio = pickle.load(open('../data/lda_data_melodia_8.pickle','rb'))\n",
    "ddf = results.load_metadata(Yaudio, metadata_file='../data/metadata.csv')\n",
    "w, data_countries = utils_spatial.get_neighbors_for_countries_in_dataset(Y)\n",
    "w_dict = utils_spatial.from_weights_to_dict(w, data_countries)\n",
    "Xrhy, Xmel, Xmfc, Xchr = X_list\n",
    "X = np.concatenate((Xrhy, Xmel, Xmfc, Xchr), axis=1)\n",
    "\n",
    "# global outliers\n",
    "df_global, threshold, MD = results.get_outliers_df(X, Y, chi2thr=0.999)\n",
    "results.print_most_least_outliers_topN(df_global, N=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Outliers</th>\n",
       "      <th>N</th>\n",
       "      <th>OutliersN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>    Canada</td>\n",
       "      <td> 0.060000</td>\n",
       "      <td> 100</td>\n",
       "      <td>  6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> Lithuania</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td>  47</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  Cambodia</td>\n",
       "      <td> 0.263158</td>\n",
       "      <td>  19</td>\n",
       "      <td>  5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  Ethiopia</td>\n",
       "      <td> 0.257143</td>\n",
       "      <td>  35</td>\n",
       "      <td>  9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> Swaziland</td>\n",
       "      <td> 0.142857</td>\n",
       "      <td>  98</td>\n",
       "      <td> 14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Outliers    N  OutliersN\n",
       "0     Canada  0.060000  100          6\n",
       "1  Lithuania  0.000000   47          0\n",
       "2   Cambodia  0.263158   19          5\n",
       "3   Ethiopia  0.257143   35          9\n",
       "4  Swaziland  0.142857   98         14"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_global['N'] = np.zeros(len(df_global))\n",
    "df_global['OutliersN'] = np.zeros(len(df_global))\n",
    "for i, country in enumerate(df_global['Country']):\n",
    "    n_counts = len(np.where(Y==country)[0])\n",
    "    df_global['N'].iloc[i] = n_counts\n",
    "    df_global['OutliersN'].iloc[i] = np.round(n_counts * df_global['Outliers'].iloc[i])\n",
    "df_global.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.662469099039 1.18268680985e-18\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x103c730d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ne7KYWTfQD+yjIuVz94+7+9nuvhx4L/C/3f0DVKR8ZvYaMzs1fP1aYICgU0+y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d213d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr, pval = pearsonr(df_global['OutliersN'], df_global['N'])\n",
    "print corr, pval\n",
    "\n",
    "plt.scatter(df_global['OutliersN'], df_global['N'])\n",
    "plt.xlabel('outliers')\n",
    "plt.ylabel('N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.122714085273 0.153126076772\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111dc7510>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h4BHgNcAKd18fNzkAuPse4FkzOz1Y9X7gUeAHHP6PHQRuj/sdImmq3YGMj69mfHw1a9YM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\nX9vUTo3l8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111dd7350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr, pval = pearsonr(df_global['Outliers'], df_global['N'])\n",
    "print corr, pval\n",
    "\n",
    "plt.scatter(df_global['Outliers'], df_global['N'])\n",
    "plt.xlabel('outliers')\n",
    "plt.ylabel('N')"
   ]
  },
  {
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}