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

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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",
    "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\n",
    "import scripts.utils as utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "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",
    "X = np.concatenate(X_list, 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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 381)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = pairwise_distances(X, metric='mahalanobis')\n",
    "#np.savetxt('../data/D_mahal.csv', D)\n",
    "np.savetxt('../data/D_mahal_30_sec.csv', D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 8200)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#D = np.loadtxt('../data/D_mahal.csv')\n",
    "D = np.loadtxt('../data/D_mahal_30_sec.csv')\n",
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ku4DbIuJI2u71UptyX28B96fyg0BfRAxHxDDQD6xLIbUG2FNj/3OI73I3s9lnwlCRNE/S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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75aa474a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(D.ravel(), bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## n-occurrence and stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "10.5914304167\n",
      "1.0\n",
      "348.006117132\n",
      "129 0.0157317073171\n",
      "3332 0.406341463415\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f761c127950>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75a9e9edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zs2QiTR85FMzMRqneZx+NJYeCmdkoefrIzMxynj4yM7Ocp4/MzCznUDAzs9zBg3DyyWVX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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75aa8ed9d0>"
      ]
     },
     "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",
    "#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.std(N_k)\n",
    "print len(np.where(N_k>1000)[0]), len(np.where(N_k>1000)[0]) / float(len(N_k))\n",
    "print len(np.where(N_k==0)[0]), len(np.where(N_k==0)[0]) / float(len(N_k))\n",
    "\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": {
    "collapsed": true
   },
   "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": {},
   "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": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75aadd3390>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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iuTqPkPTCjDvF6yll8TlL6Tsoy9W9v9zMW29XuvBbXS2C3Mrfzt6k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\neOAqRVljETnr3+5jf7+StBRwDFF29iGgabnlacoGxAPSAsC6kqaNxpL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767424fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "large_hubs_idx = np.where(N_k>1000)[0]\n",
    "print len(large_hubs_idx)\n",
    "labs, counts = np.unique(Y[large_hubs_idx], return_counts=True)\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.bar(range(len(labs)), counts, align='center');\n",
    "plt.xticks(range(len(labs)), labs, rotation=90);\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Country origins for recordings identified as large hubs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## let's get the audio url to listen to tracks identified as large hubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/mp305/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2822: DtypeWarning: Columns (0,1,2,4,5,6,7,8,10,11,12,13,14,15,16,17,19,21,22,23,24,25,26,27,29,31,35,38,39,40,41,44,45,48,55,56,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,93,95,96) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if self.run_code(code, result):\n"
     ]
    }
   ],
   "source": [
    "ddf = outliers.load_metadata(Yaudio, metadata_file='../data/metadata_BLSM_language_all.csv')\n",
    "#ddf = outliers.load_metadata(Yaudio, metadata_file='/Users/mariapanteli/Documents/'+\n",
    "#                             'QMUL/Code/MyPythonCode/MergeBL-Smith/data/metadata_BLSM_language_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 108)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"songurls_Album\" in ddf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### first, fix the url for BL tracks (because it was changed recently and the metadata.csv file is not updated) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:115: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "bl_inds = np.where(np.isnan(ddf['BuyLinkTrackDownload']))[0]\n",
    "for bl_ind in bl_inds:\n",
    "    ddf['songurls_Album'].iloc[bl_ind] = ('https://sounds.bl.uk/World-and-traditional-music/' + \n",
    "                                        ddf['Folder'].iloc[bl_ind] + '/' + \n",
    "                                        ddf['MetaFile'].iloc[bl_ind].split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 'https://sounds.bl.uk/World-and-traditional-music/Decca-West-African-recordings/025M-1CS0043663XX-0100V0',\n",
       "       'https://sounds.bl.uk/World-and-traditional-music/Rycroft/025M-C0811X0005XX-2000V0',\n",
       "       'https://sounds.bl.uk/World-and-traditional-music/Colin-Huehns-Pakistan/025M-C0485X0085XX-3100V0',\n",
       "       'https://sounds.bl.uk/World-and-traditional-music/Rycroft/025M-C0811X0005XX-1300V0',\n",
       "       'https://sounds.bl.uk/World-and-traditional-music/Colin-Huehns-Pakistan/025M-C0485X0031XX-0500V0'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_hubs_idx = np.array([515, 2549, 3486, 5020, 5119])\n",
    "ddf['songurls_Album'].iloc[large_hubs_idx].get_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what if we use a different distance metric?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.20652171267\n"
     ]
    },
    {
     "data": {
      "image/png": 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jAFW1mOQ4sAi8BTw48hBekjQxQ6VxJv6mpnG2aZv6bSSNM9y6ksazFWkczSXTMdK8mvm7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7616b7dd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D_cos = pairwise_distances(X, metric='cosine')\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_cos, k=k)\n",
    "print skew(N_k)\n",
    "plt.figure()\n",
    "plt.hist(N_k, bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## are the extreme hubs same as before?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[232 233 238 246 256] [ 'https://sounds.bl.uk/World-and-traditional-music/Dinka-songs-from-South-Sudan/025M-C1580X0004XX-0001V0'\n",
      " 'https://sounds.bl.uk/World-and-traditional-music/Dinka-songs-from-South-Sudan/025M-C1580X0109XX-0001V0'\n",
      " 'https://sounds.bl.uk/World-and-traditional-music/Terry-Yarnell-Collection/025M-C1005X0002XX-1200V0'\n",
      " 'https://sounds.bl.uk/World-and-traditional-music/Roy-Palmer-collection/025M-C1023X0006XX-0700V0'\n",
      " 'https://sounds.bl.uk/World-and-traditional-music/Terry-Yarnell-Collection/025M-C1005X0004XX-1900V0']\n"
     ]
    }
   ],
   "source": [
    "sort_idx = np.argsort(N_k)\n",
    "print N_k[sort_idx[-5:]], ddf['songurls_Album'].iloc[sort_idx[-5:]].get_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## pairwise Mahalanobis seems to have extreme values, how about Mahalanobis from the whole set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tdjmSdFoYyUCQJC08A0GSBBgIkqTGQJAkAQaCJKkxECRJwIh+D2Eh9G6H0eNtLCTptB4h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\nWLDE1f0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa475850810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold, y_pred, MD = utils.get_outliers_Mahal(X, chi2thr=0.999)\n",
    "plt.figure()\n",
    "plt.hist(MD, bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQt9N9TWBYn/7ln/OV4krQIzrKVgv/ImyQt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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa475850e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold, y_pred, MD = utils.get_outliers_Mahal(X[:, :50], chi2thr=0.999)\n",
    "plt.figure()\n",
    "plt.hist(MD, bins=100);"
   ]
  },
  {
   "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
}