view notebooks/correlation_samples_outliers.ipynb @ 105:edd82eb89b4b branch-tests tip

Merge
author Maria Panteli
date Sun, 15 Oct 2017 13:36:59 +0100
parents e279ccea5f9b
children
line wrap: on
line source
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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.outliers as outliers\n",
    "import scripts.utils_spatial as utils_spatial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/mp305/anaconda/lib/python2.7/site-packages/pysal/weights/weights.py:189: UserWarning: There are 21 disconnected observations\n",
      "  warnings.warn(\"There are %d disconnected observations\" % ni)\n",
      "/homes/mp305/anaconda/lib/python2.7/site-packages/pysal/weights/weights.py:190: UserWarning: Island ids: 3, 6, 26, 35, 39, 45, 52, 61, 62, 66, 77, 85, 94, 97, 98, 102, 103, 107, 110, 120, 121\n",
      "  warnings.warn(\"Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "#X_list, Y, Yaudio = pickle.load(open('../data/lda_data_melodia_8.pickle','rb'))\n",
    "X_list, Y, Yaudio = pickle.load(open('../data/lda_data_melodia_8_30sec.pickle','rb'))\n",
    "ddf = outliers.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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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PF5SZ3V8RCF+h0c3UUe7+3yn9HHff3czOV3T3/GtVhs7ze6tZjRr5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\n3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Z1aRD0i/+gB5Iukfvf2bPm11MfoJagqOKaissqJ3Isr87/x/9WT+QxoQTm1/TaBa/gN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\nIiIiIiIiIibc/wd6q14dUAfvQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb879072dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_country = pd.DataFrame(data=Y, columns=['country'])\n",
    "plt.figure(figsize=(20, 5))\n",
    "df_country['country'].value_counts().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most outliers \n",
      "           Country  Outliers  N_Country  N_Outliers\n",
      "136       Botswana  0.611111         90          55\n",
      "72     Ivory Coast  0.600000         15           9\n",
      "95            Chad  0.545455         11           6\n",
      "43           Benin  0.538462         26          14\n",
      "86          Gambia  0.500000         50          25\n",
      "20        Pakistan  0.494505         91          45\n",
      "106          Nepal  0.473684         95          45\n",
      "78     El Salvador  0.454545         33          15\n",
      "64      Mozambique  0.441176         34          15\n",
      "135  French Guiana  0.428571         28          12\n",
      "least outliers \n",
      "            Country  Outliers  N_Country  N_Outliers\n",
      "1         Lithuania  0.000000         47           0\n",
      "119         Denmark  0.000000         16           0\n",
      "27      South Korea  0.000000         11           0\n",
      "120      Kazakhstan  0.011364         88           1\n",
      "31   Czech Republic  0.024390         41           1\n",
      "15      Netherlands  0.029851         67           2\n",
      "30      Afghanistan  0.041667         24           1\n",
      "105           Sudan  0.044118         68           3\n",
      "102       Nicaragua  0.047619         21           1\n",
      "0            Canada  0.050000        100           5\n"
     ]
    }
   ],
   "source": [
    "# global outliers\n",
    "df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)\n",
    "outliers.print_most_least_outliers_topN(df_global, N=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.659755286693 1.82581829601e-18\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb8759cc510>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/mp305/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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QIJtV+qS731vDofLYw57HmCbF3T3v807M7A3A3cBH3f0Vs8PDvPMev7sfAt5l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\nff9oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb875b45f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr, pval = pearsonr(df_global['N_Outliers'], df_global['N_Country'])\n",
    "print corr, pval\n",
    "\n",
    "plt.scatter(df_global['N_Outliers'], df_global['N_Country'])\n",
    "plt.xlabel('Outliers')\n",
    "plt.ylabel('N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0102335874359 0.905523601988\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb862634550>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ppA4/P5N8G6mPBb4bbneIpRup11JMo+qScTYtO0lxjdRxjucZBI1wa4uIsYs4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\n/x87keK+JSsw6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb86262aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr, pval = pearsonr(df_global['Outliers'], df_global['N_Country'])\n",
    "print corr, pval\n",
    "\n",
    "plt.scatter(df_global['Outliers'], df_global['N_Country'])\n",
    "plt.xlabel('Outliers')\n",
    "plt.ylabel('N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most outliers \n",
      "      Country  Outliers  N_Country  N_Outliers\n",
      "43      Benin  0.500000         26          13\n",
      "136  Botswana  0.488889         90          44\n",
      "106     Nepal  0.421053         95          40\n",
      "84     Belize  0.418605         43          18\n",
      "19      Yemen  0.416667         12           5\n",
      "least outliers \n",
      "                Country  Outliers  N_Country  N_Outliers\n",
      "28           Tajikistan         0         19           0\n",
      "119             Denmark         0         16           0\n",
      "96              Uruguay         0         31           0\n",
      "25   Republic of Serbia         0         16           0\n",
      "27          South Korea         0         11           0\n",
      "most outliers \n",
      "      Country  Outliers  N_Country  N_Outliers\n",
      "117  Zimbabwe  0.533333         15           8\n",
      "96    Uruguay  0.483871         31          15\n",
      "68     Guinea  0.454545         11           5\n",
      "63    Senegal  0.390244         41          16\n",
      "86     Gambia  0.380000         50          19\n",
      "least outliers \n",
      "              Country  Outliers  N_Country  N_Outliers\n",
      "90   French Polynesia  0.000000         15           0\n",
      "37             Rwanda  0.000000         17           0\n",
      "119           Denmark  0.000000         16           0\n",
      "18        New Zealand  0.000000         34           0\n",
      "120        Kazakhstan  0.022727         88           2\n",
      "most outliers \n",
      "           Country  Outliers  N_Country  N_Outliers\n",
      "17   French Guiana  0.678571         28          19\n",
      "136       Botswana  0.477778         90          43\n",
      "72     Ivory Coast  0.400000         15           6\n",
      "23      Azerbaijan  0.384615         13           5\n",
      "106          Nepal  0.347368         95          33\n",
      "least outliers \n",
      "           Country  Outliers  N_Country  N_Outliers\n",
      "68          Guinea         0         11           0\n",
      "55            Mali         0         17           0\n",
      "77         Algeria         0         27           0\n",
      "33     Saint Lucia         0         43           0\n",
      "31  Czech Republic         0         41           0\n",
      "most outliers \n",
      "       Country  Outliers  N_Country  N_Outliers\n",
      "43       Benin  0.538462         26          14\n",
      "20    Pakistan  0.461538         91          42\n",
      "86      Gambia  0.360000         50          18\n",
      "52   Indonesia  0.350000        100          35\n",
      "136   Botswana  0.311111         90          28\n",
      "least outliers \n",
      "       Country  Outliers  N_Country  N_Outliers\n",
      "107   Kiribati         0         17           0\n",
      "1    Lithuania         0         47           0\n",
      "134   Paraguay         0         23           0\n",
      "131    Tunisia         0         39           0\n",
      "19       Yemen         0         12           0\n"
     ]
    }
   ],
   "source": [
    "feat = [Xrhy, Xmel, Xmfc, Xchr]\n",
    "feat_labels = ['rhy', 'mel', 'mfc', 'chr']\n",
    "tabs_feat = []\n",
    "for i in range(len(feat)):\n",
    "    XX = feat[i]\n",
    "    df_feat, threshold, MD = outliers.get_outliers_df(XX, Y, chi2thr=0.999)\n",
    "    outliers.print_most_least_outliers_topN(df_feat, N=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most outliers \n",
      "                         Country  Outliers  N_Country  N_Outliers\n",
      "64                    Mozambique  0.382353         34          13\n",
      "117                     Zimbabwe  0.333333         15           5\n",
      "27                         Kenya  0.288660         97          28\n",
      "67                        Brazil  0.270000        100          27\n",
      "76                          Iran  0.264151         53          14\n",
      "30                        Turkey  0.240000        100          24\n",
      "65                        Uganda  0.211765         85          18\n",
      "4                       Ethiopia  0.200000         35           7\n",
      "126                  South Sudan  0.195652         92          18\n",
      "91   United Republic of Tanzania  0.193548         62          12\n",
      "least outliers \n",
      "             Country  Outliers  N_Country  N_Outliers\n",
      "0             Canada         0        100           0\n",
      "94              Iraq         0         87           0\n",
      "93           Grenada         0         37           0\n",
      "90  French Polynesia         0         15           0\n",
      "89           Croatia         0         31           0\n",
      "88           Morocco         0         40           0\n",
      "87       Philippines         0        100           0\n",
      "86            Gambia         0         50           0\n",
      "85      Sierra Leone         0        100           0\n",
      "84            Belize         0         43           0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "import scripts.utils as utils\n",
    "from collections import Counter\n",
    "#spatial_outliers = utils.get_local_outliers_from_neighbors_dict(X, Y, w_dict, chi2thr=0.999, do_pca=True)\n",
    "spatial_counts = Counter(dict([(ll[0],ll[1]) for ll in spatial_outliers]))\n",
    "df_local = outliers.country_outlier_df(spatial_counts, Y, normalize=True)\n",
    "outliers.print_most_least_outliers_topN(df_local, N=10)"
   ]
  },
  {
   "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": 1
}