view notebooks/test_hubness.ipynb @ 31:03ff14ba9fa2 branch-tests

merged

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import skew\n",
    "import sys\n",
    "from sklearn.metrics.pairwise import pairwise_distances\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": 3,
   "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"
     ]
    }
   ],
   "source": [
    "X_list, Y, Yaudio = pickle.load(open('../data/lda_data_melodia_8.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)\n",
    "\n",
    "# global outliers\n",
    "df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 380)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = pairwise_distances(X, metric='mahalanobis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
    "collapsed": false
   },
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 8200)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
    "collapsed": false
   },
   "metadata": {},
   "outputs": [
    {
     "data": {
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SnUleSnJ5p/7iJC+0ZXd16s9I8kCrfzrJBZ1la9tr7EhyQ6d+eZJnWpstSU7r4w2RJB2/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6dc44adfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(D.ravel(), bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def n_occurrence_from_D(D, k=10, n_items=None):\n",
    "    if n_items is None:\n",
    "        n_items = len(D)\n",
    "    sort_idx = np.argsort(D, axis=1)\n",
    "    D_k = sort_idx[:, 1:(k+1)]  # nearest neighbour is the item itself\n",
    "    N_k = np.bincount(D_k.astype(int).ravel(), minlength=n_items)\n",
    "    return N_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-0aacb5dec8fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mN_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_occurrence_from_D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mskew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'n_occurrence_from_D' is not defined"
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.18316065981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6d5a4dd550>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6dc44ad2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0ODXrBhblFltI1kPoTuV8ffeZtpVPCmZmjaqonsLAH8u33npr1dY91NFHF/QfWSRpOvB7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\nKZiZWYWTgpmZVTgpmJlZhZOCmZlV/H+LfARR37IDCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5a0d9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_k = n_occurrence_from_D(D, k=100)\n",
    "print skew(N_k)\n",
    "plt.figure()\n",
    "plt.hist(N_k, bins=100);\n",
    "plt.figure()\n",
    "plt.plot(np.sort(N_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_idx = np.argsort(D, axis=1)\n",
    "k = 100\n",
    "D_k = sort_idx[:, 1:(k+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4650, 2942, 3520, ..., 3488, 2864, 6361],\n",
       "       [1933, 6143, 6757, ..., 2346, 3441, 6857],\n",
       "       [3170, 2549, 4860, ..., 2260, 2978, 7433],\n",
       "       ..., \n",
       "       [6016, 2243, 1616, ..., 3486, 3441, 4554],\n",
       "       [7027, 4860, 6346, ..., 7312, 6972, 5532],\n",
       "       [5119, 1563, 4035, ..., 6253, 7433, 3232]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 515 2549 3486 5020 5119]\n",
      "['Nigeria' 'Swaziland' 'Kazakhstan' 'Swaziland' 'Pakistan']\n",
      "['D:/_Audio/Decca-West-African-recordings/025A-1CS0043663XX-0100A0.mp3'\n",
      " 'D:/_Audio/Rycroft/025A-C0811X0005XX-2000A0.mp3'\n",
      " 'D:/_Audio/Colin-Huehns-Pakistan/025A-C0485X0085XX-3100A0.mp3'\n",
      " 'D:/_Audio/Rycroft/025A-C0811X0005XX-1300A0.mp3'\n",
      " 'D:/_Audio/Colin-Huehns-Pakistan/025A-C0485X0031XX-0500A0.mp3']\n"
     ]
    }
   ],
   "source": [
    "large_hubs_idx = np.where(N_k>7000)[0]\n",
    "print large_hubs_idx\n",
    "print Y[large_hubs_idx]\n",
    "print Yaudio[large_hubs_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7160,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(D_k==515)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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