annotate notebooks/test_hubness.ipynb @ 26:29b5ee381305 branch-tests

notebooks rerunning, and changes in load features for melodia

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   "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": 2,
   "metadata": {},
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     "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",
    "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8200, 380)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = pairwise_distances(X, metric='mahalanobis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(D.ravel(), bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sort_idx = np.argsort(D, axis=1)\n",
    "k = 10\n",
    "D_k = sort_idx[:, 1:(k+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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       "array([[4650, 2942, 3520, ..., 1318, 6678, 6056],\n",
       "       [1933, 6143, 6757, ..., 7269, 4321, 1563],\n",
       "       [3170, 2549, 4860, ..., 6678, 7414, 6056],\n",
       "       ..., \n",
       "       [6016, 2243, 1616, ..., 7627, 2018,  515],\n",
       "       [7027, 4860, 6346, ...,  997, 3892, 1846],\n",
       "       [5119, 1563, 4035, ..., 3486, 7617, 3854]])"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "D_k"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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