# HG changeset patch
# User Maria Panteli <m.x.panteli@gmail.com>
# Date 1506967259 -3600
# Node ID 1d9c96974c3e93176136c2dd2984d8065239cc34
# Parent  4aa0763bf8d805534954c4461275379cef051f4e# Parent  69521f86d931badc195b74ade5635a080c5f668f
merged

diff -r 4aa0763bf8d8 -r 1d9c96974c3e notebooks/correlation_n_outliers.ipynb
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/notebooks/correlation_n_outliers.ipynb	Mon Oct 02 19:00:59 2017 +0100
@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pickle\n",
+    "from scipy.stats import pearsonr\n",
+    "import sys\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "DATA_FILE = '../data/lda_data_8.pickle'\n",
+    "METADATA_FILE = '../data/metadata.csv'\n",
+    "dataset, ddf, w_dict = outliers.load_data(DATA_FILE, METADATA_FILE)\n",
+    "X_list, Y, Yaudio = dataset\n",
+    "X = np.concatenate(X_list, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Outliers</th>\n",
+       "      <th>N_Country</th>\n",
+       "      <th>N_Outliers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lithuania</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cambodia</td>\n",
+       "      <td>0.210526</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ethiopia</td>\n",
+       "      <td>0.257143</td>\n",
+       "      <td>35</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Swaziland</td>\n",
+       "      <td>0.163265</td>\n",
+       "      <td>98</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Outliers  N_Country  N_Outliers\n",
+       "0     Canada  0.050000        100           5\n",
+       "1  Lithuania  0.000000         47           0\n",
+       "2   Cambodia  0.210526         19           4\n",
+       "3   Ethiopia  0.257143         35           9\n",
+       "4  Swaziland  0.163265         98          16"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)\n",
+    "df_global.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pearson correlation between percentage of outliers and number of samples per country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlation -0.0102335874359\n",
+      "p-value 0.905523601988\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bWaKTOvj8LLLtpF4OfC/Y7hCdO6nXk0+nasc4G5adIr9O6ijleSa1Trj1ecTY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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7efedacd46d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr, pval = pearsonr(df_global['Outliers'], df_global['N_Country'])\n",
+    "print 'correlation', corr\n",
+    "print 'p-value', pval\n",
+    "\n",
+    "plt.scatter(df_global['Outliers'], df_global['N_Country'])\n",
+    "plt.xlabel('Outliers');\n",
+    "plt.ylabel('N');"
+   ]
+  },
+  {
+   "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": 2
+}
diff -r 4aa0763bf8d8 -r 1d9c96974c3e notebooks/hubness.ipynb
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/notebooks/hubness.ipynb	Mon Oct 02 19:00:59 2017 +0100
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pickle\n",
+    "from scipy.stats import skew\n",
+    "from sklearn.metrics.pairwise import pairwise_distances\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(8200, 381)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_list, Y, Yaudio = pickle.load(open('../data/lda_data_8.pickle','rb'))\n",
+    "X = np.concatenate(X_list, axis=1)\n",
+    "print X.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pairwise distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "D = pairwise_distances(X, metric='mahalanobis')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f91b4eefc10>"
+      ]
+     },
+     "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": 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,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    }
+   ],
+   "source": [
+    "# k = average number of samples per country\n",
+    "uniq_countries, uniq_counts = np.unique(Y, return_counts=True)\n",
+    "k = np.int(np.round(np.mean(uniq_counts)))\n",
+    "print k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hubness 10.5914304167\n",
+      "median 1.0\n",
+      "std 348.006117132\n",
+      "hubs 129 0.0157317073171\n",
+      "orphans 3332 0.406341463415\n"
+     ]
+    }
+   ],
+   "source": [
+    "N_k = n_occurrence_from_D(D, k=k)\n",
+    "\n",
+    "print 'hubness', skew(N_k)\n",
+    "print 'median', np.median(N_k)\n",
+    "print 'std', np.std(N_k)\n",
+    "print 'hubs', len(np.where(N_k>1000)[0]), len(np.where(N_k>1000)[0]) / float(len(N_k))\n",
+    "print 'orphans', len(np.where(N_k==0)[0]), len(np.where(N_k==0)[0]) / float(len(N_k))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f91173fa350>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f91398c4a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9139a4a390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.hist(N_k, bins=100);\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.sort(N_k))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Large hubs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "129\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f9139b32490>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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p+QJFi2FVk+PyfDM7TNFV6Z/FQne/YhrbOfB7N7MXKy46H5MCjeKCdw3VB+5S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\nOsfTf/uet61hFl4zu9Ldn56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vXXFhvLW736PYT3+guHnSlcxnWLe+qbTUFq2nle29Q9Gi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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f91399f3950>"
+      ]
+     },
+     "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",
+    "\n",
+    "plt.figure(figsize=(15, 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": "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": 2
+}
diff -r 4aa0763bf8d8 -r 1d9c96974c3e notebooks/results_classification.ipynb
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/notebooks/results_classification.ipynb	Mon Oct 02 19:00:59 2017 +0100
@@ -0,0 +1,1078 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle \n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../')\n",
+    "import scripts.map_and_average as mapper\n",
+    "import scripts.classification as classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature learning and write output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "print \"mapping...\"\n",
+    "data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs = mapper.map_and_average_frames(min_variance=0.99)\n",
+    "mapper.write_output(data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "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 0.0742913265128\n",
+      "KNN LDA 0.0547390436205\n",
+      "LDA LDA 0.150312531138\n",
+      "SVM LDA 0.0787628988868\n",
+      "RF LDA 0.0427708629723\n",
+      "KNN LDA 0.0232330458268\n",
+      "LDA LDA 0.0702474072041\n",
+      "SVM LDA 0.050068706152\n",
+      "RF LDA 0.0193967985786\n",
+      "KNN LDA 0.281733731607\n",
+      "LDA LDA 0.198582742899\n",
+      "SVM LDA 0.296355560166\n",
+      "RF LDA 0.132752660164\n",
+      "KNN LDA 0.0857923493684\n",
+      "LDA LDA 0.107355289483\n",
+      "SVM LDA 0.0896098014444\n",
+      "RF LDA 0.0419252843345\n",
+      "KNN PCA 0.140643930221\n",
+      "LDA PCA 0.175099072208\n",
+      "SVM PCA 0.0149273059799\n",
+      "RF PCA 0.044347638128\n",
+      "KNN PCA 0.052516908106\n",
+      "LDA PCA 0.055028942176\n",
+      "SVM PCA 0.0479512645907\n",
+      "RF PCA 0.0325567872284\n",
+      "KNN PCA 0.0268729640269\n",
+      "LDA PCA 0.0459303318699\n",
+      "SVM PCA 0.0386730267598\n",
+      "RF PCA 0.0184694543728\n",
+      "KNN PCA 0.220850433533\n",
+      "LDA PCA 0.161502657527\n",
+      "SVM PCA 0.245790916558\n",
+      "RF PCA 0.131939188698\n",
+      "KNN PCA 0.0814272808267\n",
+      "LDA PCA 0.0839732813486\n",
+      "SVM PCA 0.0918638232782\n",
+      "RF PCA 0.0449817232296\n",
+      "KNN NMF 0.114298949339\n",
+      "LDA NMF 0.178244078869\n",
+      "SVM NMF 0.0164055663008\n",
+      "RF NMF 0.0588656307204\n",
+      "KNN NMF 0.043057794756\n",
+      "LDA NMF 0.0586662842996\n",
+      "SVM NMF 0.00781273342686\n",
+      "RF NMF 0.0285937566916\n",
+      "KNN NMF 0.0285281454673\n",
+      "LDA NMF 0.0463659955869\n",
+      "SVM NMF 0.00768887594564\n",
+      "RF NMF 0.0206293416635\n",
+      "KNN NMF 0.177819886656\n",
+      "LDA NMF 0.166221515627\n",
+      "SVM NMF 0.010788613595\n",
+      "RF NMF 0.111500698621\n",
+      "KNN NMF 0.0795454671166\n",
+      "LDA NMF 0.0856428557896\n",
+      "SVM NMF 0.0116920633048\n",
+      "RF NMF 0.0421056105664\n",
+      "KNN SSNMF 0.14322692821\n",
+      "LDA SSNMF 0.18320247367\n",
+      "SVM SSNMF 0.0205784326384\n",
+      "RF SSNMF 0.0438349321113\n",
+      "KNN SSNMF 0.0431300683181\n",
+      "LDA SSNMF 0.0533449581285\n",
+      "SVM SSNMF 0.0106542141335\n",
+      "RF SSNMF 0.0272971462205\n",
+      "KNN SSNMF 0.0152235481009\n",
+      "LDA SSNMF 0.038872838043\n",
+      "SVM SSNMF 0.00536127803533\n",
+      "RF SSNMF 0.0189951953248\n",
+      "KNN SSNMF 0.227101074174\n",
+      "LDA SSNMF 0.165382484171\n",
+      "SVM SSNMF 0.0184921176111\n",
+      "RF SSNMF 0.105334465578\n",
+      "KNN SSNMF 0.0715413500709\n",
+      "LDA SSNMF 0.0819764377219\n",
+      "SVM SSNMF 0.0138822224913\n",
+      "RF SSNMF 0.035838117053\n",
+      "KNN NA 0.140075287804\n",
+      "LDA NA 0.176953549195\n",
+      "SVM NA 0.0149485545637\n",
+      "RF NA 0.0891679877647\n",
+      "KNN NA 0.0515315452955\n",
+      "LDA NA 0.0599453579616\n",
+      "SVM NA 0.0468615478392\n",
+      "RF NA 0.0440373525097\n",
+      "KNN NA 0.0273364752119\n",
+      "LDA NA 0.0378819151174\n",
+      "SVM NA 0.038290667129\n",
+      "RF NA 0.0256534114754\n",
+      "KNN NA 0.221769305159\n",
+      "LDA NA 0.191217962613\n",
+      "SVM NA 0.250268813953\n",
+      "RF NA 0.118133604659\n",
+      "KNN NA 0.0814734970192\n",
+      "LDA NA 0.0839348156722\n",
+      "SVM NA 0.0881235182136\n",
+      "RF NA 0.0532974158539\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_results = classification.classify_for_filenames(file_list=mapper.OUTPUT_FILES)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Sort results by accuracy of all features ('All' - Column 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{llrrrrr}\n",
+      "\\toprule\n",
+      "     0 &    1 &         2 &         3 &         4 &         5 &         6 \\\\\n",
+      "\\midrule\n",
+      "   LDA &  LDA &  0.320670 &  0.150313 &  0.070247 &  0.198583 &  0.107355 \\\\\n",
+      " SSNMF &  LDA &  0.183202 &  0.053345 &  0.038873 &  0.165382 &  0.081976 \\\\\n",
+      "   NMF &  LDA &  0.178244 &  0.058666 &  0.046366 &  0.166222 &  0.085643 \\\\\n",
+      "    NA &  LDA &  0.176954 &  0.059945 &  0.037882 &  0.191218 &  0.083935 \\\\\n",
+      "   PCA &  LDA &  0.175099 &  0.055029 &  0.045930 &  0.161503 &  0.083973 \\\\\n",
+      "   LDA &  KNN &  0.151978 &  0.054739 &  0.023233 &  0.281734 &  0.085792 \\\\\n",
+      " SSNMF &  KNN &  0.143227 &  0.043130 &  0.015224 &  0.227101 &  0.071541 \\\\\n",
+      "   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 &  0.084140 &  0.045801 &  0.022834 &  0.118752 &  0.052336 \\\\\n",
+      "   LDA &   RF &  0.075053 &  0.040452 &  0.014805 &  0.133543 &  0.052025 \\\\\n",
+      "   NMF &   RF &  0.065347 &  0.036663 &  0.024069 &  0.121136 &  0.049071 \\\\\n",
+      "   PCA &   RF &  0.053220 &  0.029322 &  0.017777 &  0.113936 &  0.046819 \\\\\n",
+      " SSNMF &   RF &  0.031423 &  0.021354 &  0.015184 &  0.100996 &  0.045024 \\\\\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",
+      "    NA &  SVM &  0.014949 &  0.046862 &  0.038291 &  0.250269 &  0.088124 \\\\\n",
+      "   PCA &  SVM &  0.014927 &  0.047951 &  0.038673 &  0.245791 &  0.091864 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_results_sorted = df_results.sort_values(2, ascending=False, inplace=False)\n",
+    "df_results_sorted.head()\n",
+    "print df_results_sorted.to_latex(index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to results above, best classifier = LDA and best transformation = LDA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "accuracy, CF, labels = classification.confusion_matrix_for_dataset(df_results, classifier='LDA', filename=mapper.OUTPUT_FILES[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.320669835863\n"
+     ]
+    }
+   ],
+   "source": [
+    "print accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the figure functionality to zoom in the confusion matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "            fig.waiting = false;\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.send_message('closing', {});\n",
+       "    fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Close figure', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib notebook\n",
+    "plt.figure(figsize=(20, 20))\n",
+    "classification.plot_CF(CF, labels=labels)"
+   ]
+  },
+  {
+   "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": 2
+}
diff -r 4aa0763bf8d8 -r 1d9c96974c3e notebooks/sensitivity_experiment_outliers.ipynb
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/notebooks/sensitivity_experiment_outliers.ipynb	Mon Oct 02 19:00:59 2017 +0100
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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 pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from collections import Counter\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../')\n",
+    "import scripts.classification as classification\n",
+    "import scripts.outliers as outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample 80% of the dataset, for 10 times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's sample only 80% of the recordings each time (in a stratified manner) so that the set of recordings considered for each country is changed every time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "results_file = '../data/lda_data_8.pickle'\n",
+    "n_iters = 10\n",
+    "for n in range(n_iters):\n",
+    "    print \"iteration %d\" % n\n",
+    "    print results_file\n",
+    "    X, Y, Yaudio = classification.load_data_from_pickle(results_file)\n",
+    "    # get only 80% of the dataset.. to vary the choice of outliers\n",
+    "    X, _, Y, _ = train_test_split(X, Y, train_size=0.8, stratify=Y)\n",
+    "    print X.shape, Y.shape\n",
+    "    # outliers\n",
+    "    print \"detecting outliers...\"\n",
+    "    df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)\n",
+    "    outliers.print_most_least_outliers_topN(df_global, N=10)\n",
+    "    \n",
+    "    # write output\n",
+    "    print \"writing file\"\n",
+    "    df_global.to_csv('../data/outliers_'+str(n)+'.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_iters = 10\n",
+    "ranked_countries = pd.DataFrame()\n",
+    "ranked_outliers = pd.DataFrame()\n",
+    "for n in range(n_iters):\n",
+    "    df_global = pd.read_csv('../data/outliers_'+str(n)+'.csv')\n",
+    "    df_global = df_global.sort_values('Outliers', axis=0, ascending=False).reset_index()\n",
+    "    ranked_countries = pd.concat([ranked_countries, df_global['Country']], axis=1)\n",
+    "    ranked_outliers = pd.concat([ranked_outliers, df_global['Outliers']], axis=1)\n",
+    "ranked_countries_arr = ranked_countries.get_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimate precision at K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First get the ground truth from a majority vote on the top K=10 positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# majority voting + precision at K\n",
+    "K_vote = 10\n",
+    "country_vote = Counter(ranked_countries_arr[:K_vote, :].ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Chad</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gambia</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Ivory Coast</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Nepal</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Benin</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Senegal</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>French Guiana</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>El Salvador</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Mozambique</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Uganda</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bhutan</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Liberia</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            index   0\n",
+       "0        Pakistan  10\n",
+       "2            Chad  10\n",
+       "5          Gambia  10\n",
+       "10    Ivory Coast  10\n",
+       "12       Botswana  10\n",
+       "6           Nepal   9\n",
+       "13          Benin   8\n",
+       "8         Senegal   7\n",
+       "9   French Guiana   7\n",
+       "4     El Salvador   5\n",
+       "11     Mozambique   5\n",
+       "7          Uganda   4\n",
+       "1          Bhutan   3\n",
+       "3         Liberia   2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_country_vote = pd.DataFrame.from_dict(country_vote, orient='index').reset_index()\n",
+    "df_country_vote.sort_values(0, ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def precision_at_k(array, gr_truth, k):\n",
+    "    return len(set(array[:k]) & set(gr_truth[:k])) / float(k)\n",
+    "    \n",
+    "k = 10\n",
+    "ground_truth = df_country_vote['index'].get_values()\n",
+    "p_ = []\n",
+    "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_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean 0.67\n",
+      "std 0.0640312423743\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'mean', np.mean(p_) \n",
+    "print 'std', np.std(p_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.6  0.7  0.7  0.6  0.6  0.7  0.8  0.6  0.7  0.7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print p_"
+   ]
+  },
+  {
+   "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": 2
+}
diff -r 4aa0763bf8d8 -r 1d9c96974c3e scripts/classification.py
--- a/scripts/classification.py	Mon Oct 02 19:00:35 2017 +0100
+++ b/scripts/classification.py	Mon Oct 02 19:00:59 2017 +0100
@@ -9,6 +9,7 @@
 import pickle
 from sklearn import metrics
 from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
 
 import map_and_average
 import util_feature_learning
@@ -91,40 +92,40 @@
 def plot_CF(CF, labels=None, figurename=None):
     labels[labels=='United States of America'] = 'United States Amer.'
     plt.imshow(CF, cmap="Greys")
-    plt.xticks(range(len(labels)), labels, rotation='vertical', fontsize=4)
-    plt.yticks(range(len(labels)), labels, fontsize=4)
+    plt.xticks(range(len(labels)), labels, rotation='vertical', fontsize=8)
+    plt.yticks(range(len(labels)), labels, fontsize=8)
+    plt.colorbar()
     if figurename is not None:
         plt.savefig(figurename, bbox_inches='tight')
 
 
-def confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=False, plots=False):
+def confusion_matrix(X_train, Y_train, X_test, Y_test, classifier='LDA'):
     feat_learner = util_feature_learning.Transformer()
+    if classifier=='LDA':
+        model = feat_learner.modelLDA
+    elif classifier=='KNN':
+        model = feat_learner.modelKNN
+    elif classifier=='SVM':
+        model = feat_learner.modelSVM
+    elif classifier=='RF':
+        model = feat_learner.modelRF
     accuracy, predictions = feat_learner.classification_accuracy(X_train, Y_train, 
-                        X_test, Y_test, model=feat_learner.modelLDA)
+                        X_test, Y_test, model=model)
     labels = np.unique(Y_test)  # TODO: countries in geographical proximity
     CF = metrics.confusion_matrix(Y_test, predictions, labels=labels)
-    if saveCF:
-        np.savetxt('data/CFlabels.csv', labels, fmt='%s')
-        np.savetxt('data/CF.csv', CF, fmt='%10.5f')
-    if plots:
-        plot_CF(CF, labels=labels, figurename='data/conf_matrix.pdf')
-    return accuracy, CF 
+    return accuracy, CF, labels 
 
 
-def confusion_matrix_for_best_classification_result(df_results, output_data=False):
-    max_i = np.argmax(df_results[:, 1])
-    feat_learning_i = max_i % 4  # 4 classifiers for each feature learning method
-    filename = FILENAMES[feat_learning_i]
+def confusion_matrix_for_dataset(df_results, filename, classifier='LDA', output_data=False):
     X, Y, Yaudio = load_data_from_pickle(filename)
-    #traininds, testinds = get_train_test_indices(Yaudio)
-    #X_train, Y_train, X_test, Y_test = get_train_test_sets(X, Y, traininds, testinds)
     X_train, X_val_test, Y_train, Y_val_test = train_test_split(X, Y, train_size=0.6, random_state=RANDOM_STATE, stratify=Y)
     X_val, X_test, Y_val, Y_test = train_test_split(X_val_test, Y_val_test, train_size=0.5, random_state=RANDOM_STATE, stratify=Y_val_test)
+    accuracy, CF, labels = confusion_matrix(X_train, Y_train, X_test, Y_test, classifier=classifier)
     if output_data:
-        _, CF = confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=True, plots=True)
-    else:
-        _, CF = confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=False, plots=False)
-    return CF
+        np.savetxt('../data/CFlabels.csv', labels, fmt='%s')
+        np.savetxt('../data/CF.csv', CF, fmt='%10.5f')
+        plot_CF(CF, labels=labels, figurename='../data/conf_matrix.pdf')
+    return accuracy, CF, labels
 
 
 if __name__ == '__main__':
diff -r 4aa0763bf8d8 -r 1d9c96974c3e tests/test_classification.py
--- a/tests/test_classification.py	Mon Oct 02 19:00:35 2017 +0100
+++ b/tests/test_classification.py	Mon Oct 02 19:00:59 2017 +0100
@@ -19,7 +19,7 @@
     X[-50:, :] = X[-50:, :] + 10
     Y = np.concatenate([np.repeat('a', 50), np.repeat('b', 50)])
     X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.6, random_state=1, stratify=Y)
-    accuracy, _ = classification.confusion_matrix(X_train, Y_train, X_test, Y_test)
+    accuracy, _, _ = classification.confusion_matrix(X_train, Y_train, X_test, Y_test)
     # expect perfect accuracy for this 'easy' dataset
     assert accuracy == 1.0