changeset 83:a300b571a0b0 branch-tests

merged

--- a/notebooks/results_for_30_seconds.ipynb	Tue Sep 26 14:39:14 2017 +0100
+++ b/notebooks/results_for_30_seconds.ipynb	Tue Sep 26 21:18:54 2017 +0100
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -213,138 +213,127 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/import/c4dm-04/mariap/lda_data_melodia_8_30sec.pickle\n",
+      "/import/c4dm-04/mariap/lda_data_melodia_8_30sec.pickle\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/homes/mp305/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "/homes/mp305/anaconda/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:455: UserWarning: The priors do not sum to 1. Renormalizing\n",
+      "  UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "KNN LDA 0.151978449974\n",
       "LDA LDA 0.320669835863\n",
       "SVM LDA 0.0231101788399\n",
-      "RF LDA 0.0707681464082\n",
+      "RF LDA 0.0750711341437\n",
       "KNN LDA 0.0547390436205\n",
       "LDA LDA 0.150312531138\n",
       "SVM LDA 0.0787628988868\n",
-      "RF LDA 0.0311176089141\n",
+      "RF LDA 0.0325169847578\n",
       "KNN LDA 0.0232330458268\n",
       "LDA LDA 0.0702474072041\n",
       "SVM LDA 0.050068706152\n",
-      "RF LDA 0.0168479682586\n",
+      "RF LDA 0.0221962788765\n",
       "KNN LDA 0.281733731607\n",
       "LDA LDA 0.198582742899\n",
       "SVM LDA 0.296355560166\n",
-      "RF LDA 0.150028594255\n",
+      "RF LDA 0.138929482109\n",
       "KNN LDA 0.0857923493684\n",
       "LDA LDA 0.107355289483\n",
       "SVM LDA 0.0896098014444\n",
-      "RF LDA 0.0507616592735\n",
+      "RF LDA 0.0520582564401\n",
       "/import/c4dm-04/mariap/pca_data_melodia_8_30sec.pickle\n",
       "KNN PCA 0.140643930221\n",
       "LDA PCA 0.175099072208\n",
       "SVM PCA 0.0149273059799\n",
-      "RF PCA 0.0456009299668\n",
+      "RF PCA 0.0436883799804\n",
       "KNN PCA 0.052516908106\n",
       "LDA PCA 0.055028942176\n",
       "SVM PCA 0.0479512645907\n",
-      "RF PCA 0.0263226873341\n",
+      "RF PCA 0.0293191117144\n",
       "KNN PCA 0.0268729640269\n",
       "LDA PCA 0.0459303318699\n",
       "SVM PCA 0.0386730267598\n",
-      "RF PCA 0.0186964838343\n",
+      "RF PCA 0.0200505661024\n",
       "KNN PCA 0.220850433533\n",
       "LDA PCA 0.161502657527\n",
       "SVM PCA 0.245790916558\n",
-      "RF PCA 0.139792959142\n",
+      "RF PCA 0.109652967294\n",
       "KNN PCA 0.0814272808267\n",
       "LDA PCA 0.0839732813486\n",
       "SVM PCA 0.0918638232782\n",
-      "RF PCA 0.0445219060784\n",
+      "RF PCA 0.0512694004704\n",
       "/import/c4dm-04/mariap/nmf_data_melodia_8_30sec.pickle\n",
       "KNN NMF 0.114298949339\n",
       "LDA NMF 0.178244078869\n",
       "SVM NMF 0.0164055663008\n",
-      "RF NMF 0.0629920627191\n",
+      "RF NMF 0.0603986437872\n",
       "KNN NMF 0.043057794756\n",
       "LDA NMF 0.0586662842996\n",
       "SVM NMF 0.00781273342686\n",
-      "RF NMF 0.0322137767606\n",
+      "RF NMF 0.0394888564357\n",
       "KNN NMF 0.0285281454673\n",
       "LDA NMF 0.0463659955869\n",
       "SVM NMF 0.00768887594564\n",
-      "RF NMF 0.020364413503\n",
+      "RF NMF 0.0243236536555\n",
       "KNN NMF 0.177819886656\n",
       "LDA NMF 0.166221515627\n",
       "SVM NMF 0.010788613595\n",
-      "RF NMF 0.125503851218\n",
+      "RF NMF 0.125063096463\n",
       "KNN NMF 0.0795454671166\n",
       "LDA NMF 0.0856428557896\n",
       "SVM NMF 0.0116920633048\n",
-      "RF NMF 0.0415516315034\n",
+      "RF NMF 0.0410278341205\n",
       "/import/c4dm-04/mariap/ssnmf_data_melodia_8_30sec.pickle\n",
       "KNN SSNMF 0.14322692821\n",
       "LDA SSNMF 0.18320247367\n",
       "SVM SSNMF 0.0205784326384\n",
-      "RF SSNMF 0.0453718187022\n",
+      "RF SSNMF 0.0459476869017\n",
       "KNN SSNMF 0.0431300683181\n",
       "LDA SSNMF 0.0533449581285\n",
       "SVM SSNMF 0.0106542141335\n",
-      "RF SSNMF 0.031229373819\n",
+      "RF SSNMF 0.0267217709348\n",
       "KNN SSNMF 0.0152235481009\n",
       "LDA SSNMF 0.038872838043\n",
       "SVM SSNMF 0.00536127803533\n",
-      "RF SSNMF 0.0181117619327\n",
+      "RF SSNMF 0.0196169872436\n",
       "KNN SSNMF 0.227101074174\n",
       "LDA SSNMF 0.165382484171\n",
       "SVM SSNMF 0.0184921176111\n",
-      "RF SSNMF 0.115557523856\n",
+      "RF SSNMF 0.0924443700566\n",
       "KNN SSNMF 0.0715413500709\n",
       "LDA SSNMF 0.0819764377219\n",
       "SVM SSNMF 0.0138822224913\n",
-      "RF SSNMF 0.0350133428793\n",
+      "RF SSNMF 0.0347436738199\n",
       "/import/c4dm-04/mariap/na_data_melodia_8_30sec.pickle\n",
       "KNN NA 0.140075287804\n",
       "LDA NA 0.176953549195\n",
       "SVM NA 0.0149485545637\n",
-      "RF NA 0.0826528174744\n",
+      "RF NA 0.0681836566939\n",
       "KNN NA 0.0515315452955\n",
       "LDA NA 0.0599453579616\n",
       "SVM NA 0.0468615478392\n",
-      "RF NA 0.0398757075984\n",
+      "RF NA 0.045878758841\n",
       "KNN NA 0.0273364752119\n",
       "LDA NA 0.0378819151174\n",
       "SVM NA 0.038290667129\n",
-      "RF NA 0.0317183273408\n",
+      "RF NA 0.0207492176709\n",
       "KNN NA 0.221769305159\n",
       "LDA NA 0.191217962613\n",
       "SVM NA 0.250268813953\n",
-      "RF NA 0.113553333212\n",
+      "RF NA 0.117754824997\n",
       "KNN NA 0.0814734970192\n",
       "LDA NA 0.0839348156722\n",
       "SVM NA 0.0881235182136\n",
-      "RF NA 0.0586010122134\n",
-      "\\begin{tabular}{lllrrrrr}\n",
-      "\\toprule\n",
-      "{} &      0 &    1 &         2 &         3 &         4 &         5 &         6 \\\\\n",
-      "\\midrule\n",
-      "0  &    LDA &  KNN &  0.151978 &  0.054739 &  0.023233 &  0.281734 &  0.085792 \\\\\n",
-      "1  &    LDA &  LDA &  0.320670 &  0.150313 &  0.070247 &  0.198583 &  0.107355 \\\\\n",
-      "2  &    LDA &  SVM &  0.023110 &  0.078763 &  0.050069 &  0.296356 &  0.089610 \\\\\n",
-      "3  &    LDA &   RF &  0.070768 &  0.031118 &  0.016848 &  0.150029 &  0.050762 \\\\\n",
-      "4  &    PCA &  KNN &  0.140644 &  0.052517 &  0.026873 &  0.220850 &  0.081427 \\\\\n",
-      "5  &    PCA &  LDA &  0.175099 &  0.055029 &  0.045930 &  0.161503 &  0.083973 \\\\\n",
-      "6  &    PCA &  SVM &  0.014927 &  0.047951 &  0.038673 &  0.245791 &  0.091864 \\\\\n",
-      "7  &    PCA &   RF &  0.045601 &  0.026323 &  0.018696 &  0.139793 &  0.044522 \\\\\n",
-      "8  &    NMF &  KNN &  0.114299 &  0.043058 &  0.028528 &  0.177820 &  0.079545 \\\\\n",
-      "9  &    NMF &  LDA &  0.178244 &  0.058666 &  0.046366 &  0.166222 &  0.085643 \\\\\n",
-      "10 &    NMF &  SVM &  0.016406 &  0.007813 &  0.007689 &  0.010789 &  0.011692 \\\\\n",
-      "11 &    NMF &   RF &  0.062992 &  0.032214 &  0.020364 &  0.125504 &  0.041552 \\\\\n",
-      "12 &  SSNMF &  KNN &  0.143227 &  0.043130 &  0.015224 &  0.227101 &  0.071541 \\\\\n",
-      "13 &  SSNMF &  LDA &  0.183202 &  0.053345 &  0.038873 &  0.165382 &  0.081976 \\\\\n",
-      "14 &  SSNMF &  SVM &  0.020578 &  0.010654 &  0.005361 &  0.018492 &  0.013882 \\\\\n",
-      "15 &  SSNMF &   RF &  0.045372 &  0.031229 &  0.018112 &  0.115558 &  0.035013 \\\\\n",
-      "16 &     NA &  KNN &  0.140075 &  0.051532 &  0.027336 &  0.221769 &  0.081473 \\\\\n",
-      "17 &     NA &  LDA &  0.176954 &  0.059945 &  0.037882 &  0.191218 &  0.083935 \\\\\n",
-      "18 &     NA &  SVM &  0.014949 &  0.046862 &  0.038291 &  0.250269 &  0.088124 \\\\\n",
-      "19 &     NA &   RF &  0.082653 &  0.039876 &  0.031718 &  0.113553 &  0.058601 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
+      "RF NA 0.0661199464142\n"
      ]
     }
    ],
@@ -354,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -368,23 +357,23 @@
       "   LDA &  KNN &  0.151978 &  0.054739 &  0.023233 &  0.281734 &  0.085792 \\\\\n",
       "   LDA &  LDA &  0.320670 &  0.150313 &  0.070247 &  0.198583 &  0.107355 \\\\\n",
       "   LDA &  SVM &  0.023110 &  0.078763 &  0.050069 &  0.296356 &  0.089610 \\\\\n",
-      "   LDA &   RF &  0.070768 &  0.031118 &  0.016848 &  0.150029 &  0.050762 \\\\\n",
+      "   LDA &   RF &  0.075071 &  0.032517 &  0.022196 &  0.138929 &  0.052058 \\\\\n",
       "   PCA &  KNN &  0.140644 &  0.052517 &  0.026873 &  0.220850 &  0.081427 \\\\\n",
       "   PCA &  LDA &  0.175099 &  0.055029 &  0.045930 &  0.161503 &  0.083973 \\\\\n",
       "   PCA &  SVM &  0.014927 &  0.047951 &  0.038673 &  0.245791 &  0.091864 \\\\\n",
-      "   PCA &   RF &  0.045601 &  0.026323 &  0.018696 &  0.139793 &  0.044522 \\\\\n",
+      "   PCA &   RF &  0.043688 &  0.029319 &  0.020051 &  0.109653 &  0.051269 \\\\\n",
       "   NMF &  KNN &  0.114299 &  0.043058 &  0.028528 &  0.177820 &  0.079545 \\\\\n",
       "   NMF &  LDA &  0.178244 &  0.058666 &  0.046366 &  0.166222 &  0.085643 \\\\\n",
       "   NMF &  SVM &  0.016406 &  0.007813 &  0.007689 &  0.010789 &  0.011692 \\\\\n",
-      "   NMF &   RF &  0.062992 &  0.032214 &  0.020364 &  0.125504 &  0.041552 \\\\\n",
+      "   NMF &   RF &  0.060399 &  0.039489 &  0.024324 &  0.125063 &  0.041028 \\\\\n",
       " SSNMF &  KNN &  0.143227 &  0.043130 &  0.015224 &  0.227101 &  0.071541 \\\\\n",
       " SSNMF &  LDA &  0.183202 &  0.053345 &  0.038873 &  0.165382 &  0.081976 \\\\\n",
       " SSNMF &  SVM &  0.020578 &  0.010654 &  0.005361 &  0.018492 &  0.013882 \\\\\n",
-      " SSNMF &   RF &  0.045372 &  0.031229 &  0.018112 &  0.115558 &  0.035013 \\\\\n",
+      " SSNMF &   RF &  0.045948 &  0.026722 &  0.019617 &  0.092444 &  0.034744 \\\\\n",
       "    NA &  KNN &  0.140075 &  0.051532 &  0.027336 &  0.221769 &  0.081473 \\\\\n",
       "    NA &  LDA &  0.176954 &  0.059945 &  0.037882 &  0.191218 &  0.083935 \\\\\n",
       "    NA &  SVM &  0.014949 &  0.046862 &  0.038291 &  0.250269 &  0.088124 \\\\\n",
-      "    NA &   RF &  0.082653 &  0.039876 &  0.031718 &  0.113553 &  0.058601 \\\\\n",
+      "    NA &   RF &  0.068184 &  0.045879 &  0.020749 &  0.117755 &  0.066120 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
@@ -402,11 +391,11 @@
       "   PCA &  KNN &  0.140644 &  0.052517 &  0.026873 &  0.220850 &  0.081427 \\\\\n",
       "    NA &  KNN &  0.140075 &  0.051532 &  0.027336 &  0.221769 &  0.081473 \\\\\n",
       "   NMF &  KNN &  0.114299 &  0.043058 &  0.028528 &  0.177820 &  0.079545 \\\\\n",
-      "    NA &   RF &  0.082653 &  0.039876 &  0.031718 &  0.113553 &  0.058601 \\\\\n",
-      "   LDA &   RF &  0.070768 &  0.031118 &  0.016848 &  0.150029 &  0.050762 \\\\\n",
-      "   NMF &   RF &  0.062992 &  0.032214 &  0.020364 &  0.125504 &  0.041552 \\\\\n",
-      "   PCA &   RF &  0.045601 &  0.026323 &  0.018696 &  0.139793 &  0.044522 \\\\\n",
-      " SSNMF &   RF &  0.045372 &  0.031229 &  0.018112 &  0.115558 &  0.035013 \\\\\n",
+      "   LDA &   RF &  0.075071 &  0.032517 &  0.022196 &  0.138929 &  0.052058 \\\\\n",
+      "    NA &   RF &  0.068184 &  0.045879 &  0.020749 &  0.117755 &  0.066120 \\\\\n",
+      "   NMF &   RF &  0.060399 &  0.039489 &  0.024324 &  0.125063 &  0.041028 \\\\\n",
+      " SSNMF &   RF &  0.045948 &  0.026722 &  0.019617 &  0.092444 &  0.034744 \\\\\n",
+      "   PCA &   RF &  0.043688 &  0.029319 &  0.020051 &  0.109653 &  0.051269 \\\\\n",
       "   LDA &  SVM &  0.023110 &  0.078763 &  0.050069 &  0.296356 &  0.089610 \\\\\n",
       " SSNMF &  SVM &  0.020578 &  0.010654 &  0.005361 &  0.018492 &  0.013882 \\\\\n",
       "   NMF &  SVM &  0.016406 &  0.007813 &  0.007689 &  0.010789 &  0.011692 \\\\\n",

--- a/notebooks/sensitivity_experiment.ipynb	Tue Sep 26 14:39:14 2017 +0100
+++ b/notebooks/sensitivity_experiment.ipynb	Tue Sep 26 21:18:54 2017 +0100
@@ -3,11 +3,16 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR! Session/line number was not unique in database. History logging moved to new session 32\n"
+     ]
+    },
+    {
      "name": "stderr",
      "output_type": "stream",
      "text": [
@@ -37,9 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "OUTPUT_FILES = load_dataset.OUTPUT_FILES\n",
@@ -49,9 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -72,9 +73,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -284,9 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -455,9 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -480,9 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -741,9 +734,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -842,9 +833,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4591,9 +4580,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4943,9 +4930,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5271,9 +5256,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5613,7 +5596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -5630,10 +5613,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5641,7 +5622,7 @@
        "(137, 10)"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5659,10 +5640,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5702,6 +5681,7 @@
     "first_zero_idx = np.min(zero_idx)\n",
     "ranked_countries = ranked_countries.iloc[:first_zero_idx, :]\n",
     "ranked_outliers = ranked_outliers.iloc[:first_zero_idx, :]\n",
+    "ranked_countries_arr = ranked_countries.get_values()\n",
     "\n",
     "print ranked_countries.head()\n",
     "print ranked_outliers.head()"
@@ -5737,9 +5717,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5756,9 +5734,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5787,9 +5763,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5824,9 +5798,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5844,11 +5816,16 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try precision at K"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# majority voting + precision at K (top5?)\n",
@@ -5859,10 +5836,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -5915,7 +5890,7 @@
        "4   Bhutan   7"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5927,10 +5902,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -6067,7 +6040,7 @@
        "9           Malta   1"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6078,20 +6051,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.51000000000000001"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.51 0.0830662386292\n"
+     ]
     }
    ],
    "source": [
@@ -6104,7 +6072,27 @@
     "for j in range(ranked_countries_arr.shape[1]):\n",
     "    p_.append(precision_at_k(ranked_countries_arr[:, j], ground_truth, k))\n",
     "p_ = np.array(p_)\n",
-    "np.mean(p_)"
+    "print np.mean(p_), np.std(p_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.6,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.4,  0.7,  0.4])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_"
    ]
   },
   {
@@ -6133,7 +6121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

--- a/notebooks/test_hubness.ipynb	Tue Sep 26 14:39:14 2017 +0100
+++ b/notebooks/test_hubness.ipynb	Tue Sep 26 21:18:54 2017 +0100
@@ -2,20 +2,9 @@
  "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",
@@ -37,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -55,10 +44,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -66,7 +53,7 @@
        "(8200, 380)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -96,10 +83,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -107,7 +92,7 @@
        "(8200, 8200)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,16 +104,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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SnUleSnJ5p/7iJC+0ZXd16s9I8kCrfzrJBZ1la9tr7EhyQ6d+eZJnWpstSU7r4w2RJB2/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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb84d839fd0>"
+       "<matplotlib.figure.Figure at 0x7f767464a390>"
       ]
      },
      "metadata": {},
@@ -148,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -165,10 +148,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -186,10 +167,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb823513f90>]"
+       "[<matplotlib.lines.Line2D at 0x7f75aa2cd2d0>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -197,7 +178,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb849123390>"
+       "<matplotlib.figure.Figure at 0x7f761c147050>"
       ]
      },
      "metadata": {},
@@ -207,7 +188,7 @@
      "data": {
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gdVUdav2e6jtGkrQEzOuZQpJbgLcDXwHWVdVk2zUJrGvtG4CTfYedZDpEZtYnWl2StESs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\noSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6vw/qYCC2+xLCD4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb823a72dd0>"
+       "<matplotlib.figure.Figure at 0x7f75aaa41dd0>"
       ]
      },
      "metadata": {},
@@ -217,7 +198,7 @@
      "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": {},
@@ -228,7 +209,8 @@
     "# 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",
@@ -262,9 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -289,15 +269,14 @@
   },
   {
    "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",
@@ -482,6 +461,7 @@
    ],
    "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]"
@@ -490,9 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -519,9 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -548,9 +524,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -570,9 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -599,9 +571,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -626,9 +596,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -660,9 +628,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -702,9 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -735,9 +699,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -759,9 +721,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -806,7 +766,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,