comparison notebooks/test_hubness.ipynb @ 82:4395037087b6 branch-tests

notebooks

 {
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "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"
-     ]
-    }
-   ],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pickle\n",
     "from scipy.stats import pearsonr\n",
     "from scipy.stats import skew\n",

     "import scripts.utils as utils"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [

     "#df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(8200, 380)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [

     "np.savetxt('../data/D_mahal.csv', D)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(8200, 8200)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [

     "D.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb84d839fd0>"
+       "<matplotlib.figure.Figure at 0x7f767464a390>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }

     "## n-occurrence and stats"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [

     "    return N_k"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [

      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb823513f90>]"
-      ]
-     },
-     "execution_count": 14,
+       "[<matplotlib.lines.Line2D at 0x7f75aa2cd2d0>]"
+      ]
+     },
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb849123390>"
+       "<matplotlib.figure.Figure at 0x7f761c147050>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb823a72dd0>"
+       "<matplotlib.figure.Figure at 0x7f75aaa41dd0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "image/png": 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7U7+Z2Zg13mYKU4Y6UNJs4NvAbRFx6vWKEERESCrt867b2trydq1Wo1arlbVrM7NSVXH1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\nrkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb846884750>"
+       "<matplotlib.figure.Figure at 0x7f75aa39ac10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# take k mean of country sample\n",
     "uniq_countries, uniq_counts = np.unique(Y, return_counts=True)\n",
     "k = np.int(np.round(np.mean(uniq_counts)))\n",
-    "#N_k = n_occurrence_from_D(D, k=k)\n",
+    "#k = 200\n",
+    "N_k = n_occurrence_from_D(D, k=k)\n",
     "\n",
     "print k\n",
     "print skew(N_k)\n",
     "print np.median(N_k)\n",
     "print np.mean(N_k)\n",

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[4650, 2942, 3520, ..., 3488, 2864, 6361],\n",

     "D_k"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "142\n",
       "[ 129  186  261  373  432  468  509  515  552  562  627  655  701  710  740\n",
       "  743  861  913  997 1012 1064 1128 1204 1273 1422 1426 1563 1566 1622 1761\n",
       " 1846 1933 1936 1949 1980 1989 2034 2042 2053 2110 2147 2329 2335 2346 2462\n",
       " 2546 2549 2568 2651 2864 2903 3003 3044 3069 3170 3215 3239 3295 3383 3397\n",
       " 3441 3486 3488 3517 3520 3686 3712 3731 3859 3892 3982 4026 4035 4167 4208\n",

      ]
     }
    ],
    "source": [
     "large_hubs_idx = np.where(N_k>1000)[0]\n",
+    "print len(large_hubs_idx)\n",
     "print large_hubs_idx\n",
     "print Y[large_hubs_idx]\n",
     "print Yaudio[large_hubs_idx]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(7160,)"

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(8200, 108)"

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "True"

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([ 'https://sounds.bl.uk/World-and-traditional-music/Decca-West-African-recordings/025M-1CS0043663XX-0100V0',\n",

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [

    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [

    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 1
 }