generations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "\n",
    "import json\n",
    "import codecs\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g = nx.read_gexf(\"datasets/influences.philosophersonly.gexf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>deathyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Syed_Abdul_Hakim</th>\n",
       "      <td>1874.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moses_ibn_Ezra</th>\n",
       "      <td>1055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francisco_Sanches</th>\n",
       "      <td>1550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibawayh</th>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon_ibn_Gabirol</th>\n",
       "      <td>1021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1058.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     birthyear birthdate  deathyear\n",
       "name                                               \n",
       "Syed_Abdul_Hakim        1874.0       NaN     1944.0\n",
       "Moses_ibn_Ezra          1055.0       NaN     1138.0\n",
       "Francisco_Sanches       1550.0       NaN     1623.0\n",
       "Sibawayh                 760.0       NaN      796.0\n",
       "Solomon_ibn_Gabirol     1021.0       NaN     1058.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_csv(\"datasets/dates.birthdeath.csv\", encoding=\"utf-8\")\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "944\n"
     ]
    }
   ],
   "source": [
    "base = list(set(g.nodes()) & set(df[ df[\"birthyear\"].notnull() | df[\"deathyear\"].notnull() ].index))\n",
    "g_base = g.subgraph(base)\n",
    "\n",
    "print len(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_base = df.loc[base, [\"birthyear\", \"deathyear\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>deathyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonaventure__Bonaventure__1</th>\n",
       "      <td>1221.0</td>\n",
       "      <td>1274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muhammad_Mustafa_Jauhar</th>\n",
       "      <td>1895.0</td>\n",
       "      <td>1985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonardo_Polo</th>\n",
       "      <td>1926.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julian_Savulescu</th>\n",
       "      <td>1963.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francis_Fukuyama</th>\n",
       "      <td>1952.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             birthyear  deathyear\n",
       "name                                             \n",
       "Bonaventure__Bonaventure__1     1221.0     1274.0\n",
       "Muhammad_Mustafa_Jauhar         1895.0     1985.0\n",
       "Leonardo_Polo                   1926.0     2013.0\n",
       "Julian_Savulescu                1963.0        NaN\n",
       "Francis_Fukuyama                1952.0        NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = nx.to_numpy_matrix(g_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA().fit(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y = pca.transform(A)\n",
    "Y = zip(*Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7rcLnsaePfQR7Slf0308GjgC/oK9vgO997/tx9Y6uf5fTJj8Ffgvsx/7MRpw67AWuBBYB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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3c4910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(df_base[\"birthyear\"])\n",
    "y = Y[0]\n",
    "\n",
    "time_periods = [[-750, 1000],[1000, 1900],[1900, 2000]]\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b3b5e10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S+vrqTzGM2Nnq+WMjzMwWwtTUPupM7Vg97gmYWaPUnd93T6AeTweZmbWYi0BNOc/pgvOn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3c45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_base[\"birthyear\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d912d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3XsEiZ3nrdZzt6ajV6p1aLk9EPhZB+QWEBgky9rouijYCFrEgmDQK8tT4T1KY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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d91250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(df_base[\"birthyear\"])\n",
    "y = Y[0]\n",
    "\n",
    "time_periods = [[-750, 1000],[1000, 1900],[1900, 2000]]\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "f, ax = plt.subplots(1, 3, sharex=False, sharey=True, figsize=(20,8))\n",
    "\n",
    "for i, time_period in enumerate(time_periods):\n",
    "  ax[i].scatter(x,y)\n",
    "  \n",
    "  ax[i].set_xlim(time_period[0], time_period[1])\n",
    "  \n",
    "  ax[i].spines['right'].set_visible(False)\n",
    "  ax[i].spines['left'].set_visible(False)\n",
    "  ax[i].spines['top'].set_visible(False)\n",
    "  ax[i].xaxis.set_ticks_position('bottom') \n",
    "  ax[i].yaxis.set_ticks_position('none') \n",
    "\n",
    "# f.tight_layout()\n",
    "f.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def colorize(condition):\n",
    "  if condition:\n",
    "    return (1,0,0,1)\n",
    "  else:\n",
    "    return (.5,.5,.5,.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## chronological anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def is_chronological_anomaly(n, g, df):\n",
    "  # print n\n",
    "  successors = g.successors(n)\n",
    "  predecessors = g.predecessors(n)\n",
    "\n",
    "  condition = False\n",
    "  \n",
    "  # check if successors (people that have been given influence)\n",
    "  for n2 in successors:\n",
    "    if df.loc[n2, \"birthyear\"] > df.loc[n, \"deathyear\"]:\n",
    "      condition = True\n",
    "  \n",
    "  # check if predecessors (people that have received influence)\n",
    "  for n2 in predecessors:\n",
    "    if df.loc[n, \"birthyear\"] > df.loc[n2, \"deathyear\"]:\n",
    "      condition = True\n",
    "  \n",
    "  return condition\n",
    "\n",
    "df_base[\"is chronological anomaly\"] = map(lambda n: is_chronological_anomaly(n, g_base, df_base), df_base.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([                           u'Plato',\n",
      "          u'Georg_Wilhelm_Friedrich_Hegel',\n",
      "                              u'Abu_Yusuf',\n",
      "              u'Mirza_Mazhar_Jan-e-Janaan',\n",
      "                              u'Leucippus',\n",
      "                          u'Blaise_Pascal',\n",
      "                                u'Proclus',\n",
      "                   u'Muhammad_al-Shaybani',\n",
      "                             u'Al-Dhahabi',\n",
      "                             u'Democritus',\n",
      "        u'Pseudo-Dionysius_the_Areopagite',\n",
      "       u'Yusuf_ibn_Abd_al-Rahman_al-Mizzi',\n",
      "                    u'Ludwig_Wittgenstein',\n",
      "                                  u'Homer',\n",
      "                            u'Zhuang_Zhou',\n",
      "                         u'Edmund_Husserl',\n",
      "                               u'Plotinus',\n",
      "            u'Burhan_al-Din_al-Marghinani',\n",
      "                         u'Ahmad_Sirhindi',\n",
      "                        u'Ibn_Abd_al-Hadi',\n",
      "                          u'Immanuel_Kant',\n",
      "                             u'Iamblichus',\n",
      "                          u'Predrag_Finci',\n",
      "               u'Qadi_Thanaullah_Panipati',\n",
      "                   u'Jesús_Padilla_Gálvez',\n",
      "                                  u'Laozi'],\n",
      "      dtype='object', name=u'name')\n"
     ]
    }
   ],
   "source": [
    "print df_base[df_base[\"is chronological anomaly\"] == True].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z8V1/r1SPHbWEvFpReP08vh4kO20vK3z0o4v517/+14DjwPjMZ9BeK/PNskxn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\nyPf9PFgql8vXNjfo4OAgH4YdhqF2dnYK349yuay9vb28BXDVKqd1IDA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d7df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = map(colorize, df_base[\"is chronological anomaly\"])\n",
    "  \n",
    "plt.figure(figsize=(20,8))\n",
    "plt.scatter(x, y, lw=0, color=colors)\n",
    "plt.xlim(-700, 2018)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## demographical anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.4228395062\n",
      "15.4076067634\n"
     ]
    },
    {
     "data": {
      "image/png": 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/+cEYoCXDmM7xHMvzLKF93CUFKZlMkkwms7Y/c6/9KfFm1gl4zN0PSb2fByTcvczMSoFn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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f1add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = df_base[ df_base[\"deathyear\"].notnull() & df_base[\"birthyear\"].notnull() ][\"deathyear\"] - df_base[ df_base[\"deathyear\"].notnull() & df_base[\"birthyear\"].notnull()][\"birthyear\"]\n",
    "ages = ages[(ages > 0) & (ages < 130)]\n",
    "\n",
    "mu = ages.mean()\n",
    "sigma = ages.std()\n",
    "\n",
    "print mu\n",
    "print sigma\n",
    "\n",
    "plt.hist(ages, bins=50)\n",
    "\n",
    "x = np.linspace(0,120,100)\n",
    "plt.plot(x,mlab.normpdf(x,mu,sigma)*len(ages), color=\"red\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.58509475361e-07\n",
      "9.87180669183e-05\n",
      "0.0257823840232\n",
      "0.0221759230229\n",
      "0.00463630840226\n",
      "1.29254873316e-06\n"
     ]
    }
   ],
   "source": [
    "def get_prob_normdist(x, mu, sigma):\n",
    "  return mlab.normpdf(x,mu,sigma)\n",
    "\n",
    "print get_prob_normdist(0, mu, sigma)\n",
    "print get_prob_normdist(20, mu, sigma)\n",
    "print get_prob_normdist(70, mu, sigma)\n",
    "print get_prob_normdist(80, mu, sigma)\n",
    "print get_prob_normdist(100, mu, sigma)\n",
    "print get_prob_normdist(140, mu, sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error detected: 19\n",
      "name\n",
      "Speusippus                               747.0\n",
      "Abu_Yusuf                                685.0\n",
      "Philodemus                               150.0\n",
      "Mirza_Mazhar_Jan-e-Janaan                 -1.0\n",
      "Ioane_Petritsi                             1.0\n",
      "Panaetius                                295.0\n",
      "Leucippus                                  0.0\n",
      "David_the_Invincible                       0.0\n",
      "Sufyan_al-Thawri                        -555.0\n",
      "Pseudo-Dionysius_the_Areopagite            1.0\n",
      "Yusuf_ibn_Abd_al-Rahman_al-Mizzi        -672.0\n",
      "Metrodorus_of_Lampsacus_(the_younger)    609.0\n",
      "Muhammad_Usman_Damani                    653.0\n",
      "Burhan_al-Din_al-Marghinani                6.0\n",
      "Arcesilaus                               557.0\n",
      "Ibn_Abi_Asim                             694.0\n",
      "Xenocrates                               710.0\n",
      "Haji_Dost_Muhammad_Qandhari              652.0\n",
      "Laozi                                     -1.0\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_base[\"age\"] = (df_base[\"birthyear\"].notnull() & df_base[\"deathyear\"].notnull()) * (df_base[\"deathyear\"] - df_base[\"birthyear\"])\n",
    "\n",
    "df_base[\"is demographical anomaly\"] = map(lambda x: get_prob_normdist(x, mu, sigma) < .0001, df_base[\"age\"])\n",
    "#df_base.loc[ df_base[\"age\"].isnull().index , \"is demographical anomaly\"] = None\n",
    "\n",
    "print \"error detected: %s\" % len(df_base[ df_base[\"age\"].notnull() & df_base[\"is demographical anomaly\"] ])\n",
    "print df_base[ df_base[\"age\"].notnull() & df_base[\"is demographical anomaly\"] ][\"age\"]\n",
    "\n",
    "#df_base.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "19\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "print len(df_base[df_base[\"is chronological anomaly\"] == True])\n",
    "print len(df_base[df_base[\"is demographical anomaly\"] == True])\n",
    "print len(df_base[df_base[\"is demographical anomaly\"] | df_base[\"is chronological anomaly\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train a predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "g_train = g.subgraph(df_base[df_base[\"birthyear\"].notnull() & (df_base[\"is demographical anomaly\"] == False) & (df_base[\"is chronological anomaly\"] == False)].index)\n",
    "g_test = g.subgraph(df_base[df_base[\"is demographical anomaly\"] | df_base[\"is chronological anomaly\"]].index)\n",
    "\n",
    "print len(g_train.nodes())\n",
    "print len(g_test.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### using only adjacency matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "\n",
    "X = nx.to_numpy_matrix(g_train)\n",
    "Y = list(df_base.loc[ g_train.nodes() , \"birthyear\"])\n",
    "\n",
    "model = model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- min max range mean std predecessors and successors\n",
    "- other models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>deathyear</th>\n",
       "      <th>is chronological anomaly</th>\n",
       "      <th>age</th>\n",
       "      <th>is demographical anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonaventure__Bonaventure__1</th>\n",
       "      <td>1221.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>False</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muhammad_Mustafa_Jauhar</th>\n",
       "      <td>1895.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>False</td>\n",
       "      <td>90.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonardo_Polo</th>\n",
       "      <td>1926.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>False</td>\n",
       "      <td>87.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julian_Savulescu</th>\n",
       "      <td>1963.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francis_Fukuyama</th>\n",
       "      <td>1952.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacques_Ellul</th>\n",
       "      <td>1912.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>False</td>\n",
       "      <td>82.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joseph_Dietzgen</th>\n",
       "      <td>1828.0</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>False</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivo_Urbančič</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakob_Böhme</th>\n",
       "      <td>1575.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richard_David_Precht</th>\n",
       "      <td>1964.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             birthyear  deathyear is chronological anomaly  \\\n",
       "name                                                                         \n",
       "Bonaventure__Bonaventure__1     1221.0     1274.0                    False   \n",
       "Muhammad_Mustafa_Jauhar         1895.0     1985.0                    False   \n",
       "Leonardo_Polo                   1926.0     2013.0                    False   \n",
       "Julian_Savulescu                1963.0        NaN                    False   \n",
       "Francis_Fukuyama                1952.0        NaN                    False   \n",
       "Jacques_Ellul                   1912.0     1994.0                    False   \n",
       "Joseph_Dietzgen                 1828.0     1888.0                    False   \n",
       "Ivo_Urbančič                    1930.0        NaN                    False   \n",
       "Jakob_Böhme                     1575.0     1624.0                    False   \n",
       "Richard_David_Precht            1964.0        NaN                    False   \n",
       "\n",
       "                              age is demographical anomaly  \n",
       "name                                                        \n",
       "Bonaventure__Bonaventure__1  53.0                    False  \n",
       "Muhammad_Mustafa_Jauhar      90.0                    False  \n",
       "Leonardo_Polo                87.0                    False  \n",
       "Julian_Savulescu              NaN                    False  \n",
       "Francis_Fukuyama              NaN                    False  \n",
       "Jacques_Ellul                82.0                    False  \n",
       "Joseph_Dietzgen              60.0                    False  \n",
       "Ivo_Urbančič                  NaN                    False  \n",
       "Jakob_Böhme                  49.0                    False  \n",
       "Richard_David_Precht          NaN                    False  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_base[df_base[\"birthyear\"].notnull() & (df_base[\"is demographical anomaly\"] == False) & (df_base[\"is chronological anomaly\"] == False)].copy()\n",
    "\n",
    "df_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356.92685303572358"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val = df_base[df_base[\"birthyear\"].notnull() & (df_base[\"is demographical anomaly\"] == False) & (df_base[\"is chronological anomaly\"] == False)].copy()\n",
    "df_val[\"predict\"] = model.predict(nx.to_numpy_matrix(g_train))\n",
    "\n",
    "np.mean(np.sqrt((df_val[\"birthyear\"] - df_val[\"predict\"])**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### network statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(following, count)</th>\n",
       "      <th>(following, birth mean)</th>\n",
       "      <th>(following, birth std)</th>\n",
       "      <th>(following, birth min)</th>\n",
       "      <th>(following, birth max)</th>\n",
       "      <th>(following, death mean)</th>\n",
       "      <th>(following, death std)</th>\n",
       "      <th>(followers, count)</th>\n",
       "      <th>(followers, birth mean)</th>\n",
       "      <th>(followers, birth std)</th>\n",
       "      <th>(followers, birth min)</th>\n",
       "      <th>(followers, birth max)</th>\n",
       "      <th>(followers, death mean)</th>\n",
       "      <th>(followers, death std)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Muhammad_Mustafa_Jauhar</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonardo_Polo</th>\n",
       "      <td>3</td>\n",
       "      <td>1033.67</td>\n",
       "      <td>1236.45</td>\n",
       "      <td>-384</td>\n",
       "      <td>1889</td>\n",
       "      <td>1101.33</td>\n",
       "      <td>1243.37</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julian_Savulescu</th>\n",
       "      <td>1</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1946</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francis_Fukuyama</th>\n",
       "      <td>2</td>\n",
       "      <td>1916</td>\n",
       "      <td>19.799</td>\n",
       "      <td>1902</td>\n",
       "      <td>1930</td>\n",
       "      <td>1980</td>\n",
       "      <td>16.9706</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacques_Ellul</th>\n",
       "      <td>4</td>\n",
       "      <td>1828.5</td>\n",
       "      <td>30.556</td>\n",
       "      <td>1809</td>\n",
       "      <td>1874</td>\n",
       "      <td>1887.75</td>\n",
       "      <td>41.8041</td>\n",
       "      <td>4</td>\n",
       "      <td>1923.75</td>\n",
       "      <td>4.78714</td>\n",
       "      <td>1918</td>\n",
       "      <td>1929</td>\n",
       "      <td>1993</td>\n",
       "      <td>7.87401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        (following, count) (following, birth mean)  \\\n",
       "Muhammad_Mustafa_Jauhar                  0                     NaN   \n",
       "Leonardo_Polo                            3                 1033.67   \n",
       "Julian_Savulescu                         1                    1946   \n",
       "Francis_Fukuyama                         2                    1916   \n",
       "Jacques_Ellul                            4                  1828.5   \n",
       "\n",
       "                        (following, birth std) (following, birth min)  \\\n",
       "Muhammad_Mustafa_Jauhar                    NaN                    NaN   \n",
       "Leonardo_Polo                          1236.45                   -384   \n",
       "Julian_Savulescu                           NaN                   1946   \n",
       "Francis_Fukuyama                        19.799                   1902   \n",
       "Jacques_Ellul                           30.556                   1809   \n",
       "\n",
       "                        (following, birth max) (following, death mean)  \\\n",
       "Muhammad_Mustafa_Jauhar                    NaN                     NaN   \n",
       "Leonardo_Polo                             1889                 1101.33   \n",
       "Julian_Savulescu                          1946                     NaN   \n",
       "Francis_Fukuyama                          1930                    1980   \n",
       "Jacques_Ellul                             1874                 1887.75   \n",
       "\n",
       "                        (following, death std) (followers, count)  \\\n",
       "Muhammad_Mustafa_Jauhar                    NaN                  0   \n",
       "Leonardo_Polo                          1243.37                  0   \n",
       "Julian_Savulescu                           NaN                  0   \n",
       "Francis_Fukuyama                       16.9706                  0   \n",
       "Jacques_Ellul                          41.8041                  4   \n",
       "\n",
       "                        (followers, birth mean) (followers, birth std)  \\\n",
       "Muhammad_Mustafa_Jauhar                     NaN                    NaN   \n",
       "Leonardo_Polo                               NaN                    NaN   \n",
       "Julian_Savulescu                            NaN                    NaN   \n",
       "Francis_Fukuyama                            NaN                    NaN   \n",
       "Jacques_Ellul                           1923.75                4.78714   \n",
       "\n",
       "                        (followers, birth min) (followers, birth max)  \\\n",
       "Muhammad_Mustafa_Jauhar                    NaN                    NaN   \n",
       "Leonardo_Polo                              NaN                    NaN   \n",
       "Julian_Savulescu                           NaN                    NaN   \n",
       "Francis_Fukuyama                           NaN                    NaN   \n",
       "Jacques_Ellul                             1918                   1929   \n",
       "\n",
       "                        (followers, death mean) (followers, death std)  \n",
       "Muhammad_Mustafa_Jauhar                     NaN                    NaN  \n",
       "Leonardo_Polo                               NaN                    NaN  \n",
       "Julian_Savulescu                            NaN                    NaN  \n",
       "Francis_Fukuyama                            NaN                    NaN  \n",
       "Jacques_Ellul                              1993                7.87401  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_sociodemographic_stats(nodes, g, df_source):\n",
    "  \n",
    "  cols = [ (c1, c2) for c1 in [\"following\", \"followers\"] for c2 in [\"count\", \"birth mean\", \"birth std\", \"birth min\", \"birth max\", \"death mean\", \"death std\"] ]\n",
    "  \n",
    "  df = pd.DataFrame(index=nodes, columns=cols)\n",
    "  \n",
    "  for n in nodes:\n",
    "    following = g.successors(n)\n",
    "    followers = g.predecessors(n)\n",
    "\n",
    "    df_following = df_source.loc[ [ n2 for n2 in following if n2 in df_source.index ] ]\n",
    "    df_followers = df_source.loc[ [ n2 for n2 in followers if n2 in df_source.index ] ]\n",
    "\n",
    "    df.set_value(n, (\"following\", \"count\"), len(following))    \n",
    "    df.set_value(n, (\"following\", \"birth min\"), df_following[\"birthyear\"].min())\n",
    "    df.set_value(n, (\"following\", \"birth max\"), df_following[\"birthyear\"].max())\n",
    "    df.set_value(n, (\"following\", \"birth mean\"), df_following[\"birthyear\"].mean())\n",
    "    df.set_value(n, (\"following\", \"birth std\"), df_following[\"birthyear\"].std())\n",
    "    df.set_value(n, (\"following\", \"death mean\"), df_following[\"deathyear\"].mean())\n",
    "    df.set_value(n, (\"following\", \"death std\"), df_following[\"deathyear\"].std())\n",
    "    \n",
    "    df.set_value(n, (\"followers\", \"count\"), len(followers))    \n",
    "    df.set_value(n, (\"followers\", \"birth min\"), df_followers[\"birthyear\"].min())\n",
    "    df.set_value(n, (\"followers\", \"birth max\"), df_followers[\"birthyear\"].max())\n",
    "    df.set_value(n, (\"followers\", \"birth mean\"), df_followers[\"birthyear\"].mean())\n",
    "    df.set_value(n, (\"followers\", \"birth std\"), df_followers[\"birthyear\"].std())\n",
    "    df.set_value(n, (\"followers\", \"death mean\"), df_followers[\"deathyear\"].mean())\n",
    "    df.set_value(n, (\"followers\", \"death std\"), df_followers[\"deathyear\"].std())\n",
    "\n",
    "  return df\n",
    "\n",
    "get_sociodemographic_stats(g_train.nodes()[0:5], g_train, df_base.loc[g_train.nodes()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_all = get_sociodemographic_stats(g.nodes(), g, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_all.to_csv(\"datasets/demographics.csv\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = linear_model.LinearRegression(normalize=True)\n",
    "\n",
    "df_train2 = get_sociodemographic_stats(g_train.nodes(), g_train, df_base.loc[g_train.nodes()])\n",
    "\n",
    "X = df_train2.fillna(0).as_matrix()\n",
    "Y = list(df_base.loc[ g_train.nodes() , \"birthyear\"])\n",
    "\n",
    "model = model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train2.to_csv(\"datasets/generations.training_sample.csv\", encoding=\"utf_8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.04790389751327"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.sqrt((Y - model.predict(X))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train2[\"real\"] = df_base.loc[ g_train.nodes() , \"birthyear\"]\n",
    "df_train2[\"predict\"] = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(following, count)</th>\n",
       "      <th>(following, birth mean)</th>\n",
       "      <th>(following, birth std)</th>\n",
       "      <th>(following, birth min)</th>\n",
       "      <th>(following, birth max)</th>\n",
       "      <th>(following, death mean)</th>\n",
       "      <th>(following, death std)</th>\n",
       "      <th>(followers, count)</th>\n",
       "      <th>(followers, birth mean)</th>\n",
       "      <th>(followers, birth std)</th>\n",
       "      <th>(followers, birth min)</th>\n",
       "      <th>(followers, birth max)</th>\n",
       "      <th>(followers, death mean)</th>\n",
       "      <th>(followers, death std)</th>\n",
       "      <th>real</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Muhammad_Mustafa_Jauhar</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1270.527897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonardo_Polo</th>\n",
       "      <td>3</td>\n",
       "      <td>1033.67</td>\n",
       "      <td>1236.45</td>\n",
       "      <td>-384</td>\n",
       "      <td>1889</td>\n",
       "      <td>1101.33</td>\n",
       "      <td>1243.37</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>1841.338699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julian_Savulescu</th>\n",
       "      <td>1</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1946</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>2006.659355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francis_Fukuyama</th>\n",
       "      <td>2</td>\n",
       "      <td>1916</td>\n",
       "      <td>19.799</td>\n",
       "      <td>1902</td>\n",
       "      <td>1930</td>\n",
       "      <td>1980</td>\n",
       "      <td>16.9706</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1888.705803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacques_Ellul</th>\n",
       "      <td>4</td>\n",
       "      <td>1828.5</td>\n",
       "      <td>30.556</td>\n",
       "      <td>1809</td>\n",
       "      <td>1874</td>\n",
       "      <td>1887.75</td>\n",
       "      <td>41.8041</td>\n",
       "      <td>4</td>\n",
       "      <td>1923.75</td>\n",
       "      <td>4.78714</td>\n",
       "      <td>1918</td>\n",
       "      <td>1929</td>\n",
       "      <td>1993</td>\n",
       "      <td>7.87401</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>1934.706175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moses_Mendelssohn</th>\n",
       "      <td>4</td>\n",
       "      <td>1539.5</td>\n",
       "      <td>272.835</td>\n",
       "      <td>1137</td>\n",
       "      <td>1743</td>\n",
       "      <td>1610.75</td>\n",
       "      <td>276.035</td>\n",
       "      <td>3</td>\n",
       "      <td>1798.67</td>\n",
       "      <td>87.0651</td>\n",
       "      <td>1743</td>\n",
       "      <td>1899</td>\n",
       "      <td>1864</td>\n",
       "      <td>94.8736</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>1752.214456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivo_Urbančič</th>\n",
       "      <td>1</td>\n",
       "      <td>1889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1889</td>\n",
       "      <td>1889</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1893.191454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakob_Böhme</th>\n",
       "      <td>1</td>\n",
       "      <td>1493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1493</td>\n",
       "      <td>1493</td>\n",
       "      <td>1541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1812.33</td>\n",
       "      <td>53.799</td>\n",
       "      <td>1775</td>\n",
       "      <td>1874</td>\n",
       "      <td>1887.33</td>\n",
       "      <td>52.6245</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>1780.570887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richard_David_Precht</th>\n",
       "      <td>1</td>\n",
       "      <td>1953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1953</td>\n",
       "      <td>1953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2009.373699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emil_Fackenheim</th>\n",
       "      <td>1</td>\n",
       "      <td>1775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1775</td>\n",
       "      <td>1775</td>\n",
       "      <td>1854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>1854.627410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shibli_Nomani</th>\n",
       "      <td>1</td>\n",
       "      <td>1817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1817</td>\n",
       "      <td>1817</td>\n",
       "      <td>1898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1928</td>\n",
       "      <td>33.9411</td>\n",
       "      <td>1904</td>\n",
       "      <td>1952</td>\n",
       "      <td>1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>1957.065020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hent_de_Vries</th>\n",
       "      <td>1</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1926</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>1998.904087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George_Gurdjieff</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1898</td>\n",
       "      <td>1898</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1866.0</td>\n",
       "      <td>1388.665201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friedrich_Wilhelm_Foerster</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>1270.527897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debiprasad_Chattopadhyaya</th>\n",
       "      <td>1</td>\n",
       "      <td>1818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1818</td>\n",
       "      <td>1818</td>\n",
       "      <td>1883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1947</td>\n",
       "      <td>1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>2036.801437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Svetozar_Marković</th>\n",
       "      <td>1</td>\n",
       "      <td>1818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1818</td>\n",
       "      <td>1818</td>\n",
       "      <td>1883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>2036.974282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gerald_Cohen</th>\n",
       "      <td>3</td>\n",
       "      <td>1875.67</td>\n",
       "      <td>50.1431</td>\n",
       "      <td>1818</td>\n",
       "      <td>1909</td>\n",
       "      <td>1952</td>\n",
       "      <td>60.6712</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1959</td>\n",
       "      <td>1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>2030.007216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeno_of_Citium</th>\n",
       "      <td>4</td>\n",
       "      <td>-402.5</td>\n",
       "      <td>88.5532</td>\n",
       "      <td>-535</td>\n",
       "      <td>-350</td>\n",
       "      <td>-330</td>\n",
       "      <td>96.6954</td>\n",
       "      <td>5</td>\n",
       "      <td>-138.6</td>\n",
       "      <td>167.3</td>\n",
       "      <td>-330</td>\n",
       "      <td>55</td>\n",
       "      <td>-57.4</td>\n",
       "      <td>161.178</td>\n",
       "      <td>-334.0</td>\n",
       "      <td>784.029245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peter_G._Ossorio</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>1270.527897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hans_Reichenbach</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1933.5</td>\n",
       "      <td>10.6066</td>\n",
       "      <td>1926</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1425.215976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           (following, count) (following, birth mean)  \\\n",
       "Muhammad_Mustafa_Jauhar                     0                     NaN   \n",
       "Leonardo_Polo                               3                 1033.67   \n",
       "Julian_Savulescu                            1                    1946   \n",
       "Francis_Fukuyama                            2                    1916   \n",
       "Jacques_Ellul                               4                  1828.5   \n",
       "Moses_Mendelssohn                           4                  1539.5   \n",
       "Ivo_Urbančič                                1                    1889   \n",
       "Jakob_Böhme                                 1                    1493   \n",
       "Richard_David_Precht                        1                    1953   \n",
       "Emil_Fackenheim                             1                    1775   \n",
       "Shibli_Nomani                               1                    1817   \n",
       "Hent_de_Vries                               1                    1926   \n",
       "George_Gurdjieff                            0                     NaN   \n",
       "Friedrich_Wilhelm_Foerster                  0                     NaN   \n",
       "Debiprasad_Chattopadhyaya                   1                    1818   \n",
       "Svetozar_Marković                           1                    1818   \n",
       "Gerald_Cohen                                3                 1875.67   \n",
       "Zeno_of_Citium                              4                  -402.5   \n",
       "Peter_G._Ossorio                            0                     NaN   \n",
       "Hans_Reichenbach                            0                     NaN   \n",
       "\n",
       "                           (following, birth std) (following, birth min)  \\\n",
       "Muhammad_Mustafa_Jauhar                       NaN                    NaN   \n",
       "Leonardo_Polo                             1236.45                   -384   \n",
       "Julian_Savulescu                              NaN                   1946   \n",
       "Francis_Fukuyama                           19.799                   1902   \n",
       "Jacques_Ellul                              30.556                   1809   \n",
       "Moses_Mendelssohn                         272.835                   1137   \n",
       "Ivo_Urbančič                                  NaN                   1889   \n",
       "Jakob_Böhme                                   NaN                   1493   \n",
       "Richard_David_Precht                          NaN                   1953   \n",
       "Emil_Fackenheim                               NaN                   1775   \n",
       "Shibli_Nomani                                 NaN                   1817   \n",
       "Hent_de_Vries                                 NaN                   1926   \n",
       "George_Gurdjieff                              NaN                    NaN   \n",
       "Friedrich_Wilhelm_Foerster                    NaN                    NaN   \n",
       "Debiprasad_Chattopadhyaya                     NaN                   1818   \n",
       "Svetozar_Marković                             NaN                   1818   \n",
       "Gerald_Cohen                              50.1431                   1818   \n",
       "Zeno_of_Citium                            88.5532                   -535   \n",
       "Peter_G._Ossorio                              NaN                    NaN   \n",
       "Hans_Reichenbach                              NaN                    NaN   \n",
       "\n",
       "                           (following, birth max) (following, death mean)  \\\n",
       "Muhammad_Mustafa_Jauhar                       NaN                     NaN   \n",
       "Leonardo_Polo                                1889                 1101.33   \n",
       "Julian_Savulescu                             1946                     NaN   \n",
       "Francis_Fukuyama                             1930                    1980   \n",
       "Jacques_Ellul                                1874                 1887.75   \n",
       "Moses_Mendelssohn                            1743                 1610.75   \n",
       "Ivo_Urbančič                                 1889                    1976   \n",
       "Jakob_Böhme                                  1493                    1541   \n",
       "Richard_David_Precht                         1953                     NaN   \n",
       "Emil_Fackenheim                              1775                    1854   \n",
       "Shibli_Nomani                                1817                    1898   \n",
       "Hent_de_Vries                                1926                     NaN   \n",
       "George_Gurdjieff                              NaN                     NaN   \n",
       "Friedrich_Wilhelm_Foerster                    NaN                     NaN   \n",
       "Debiprasad_Chattopadhyaya                    1818                    1883   \n",
       "Svetozar_Marković                            1818                    1883   \n",
       "Gerald_Cohen                                 1909                    1952   \n",
       "Zeno_of_Citium                               -350                    -330   \n",
       "Peter_G._Ossorio                              NaN                     NaN   \n",
       "Hans_Reichenbach                              NaN                     NaN   \n",
       "\n",
       "                           (following, death std) (followers, count)  \\\n",
       "Muhammad_Mustafa_Jauhar                       NaN                  0   \n",
       "Leonardo_Polo                             1243.37                  0   \n",
       "Julian_Savulescu                              NaN                  0   \n",
       "Francis_Fukuyama                          16.9706                  0   \n",
       "Jacques_Ellul                             41.8041                  4   \n",
       "Moses_Mendelssohn                         276.035                  3   \n",
       "Ivo_Urbančič                                  NaN                  0   \n",
       "Jakob_Böhme                                   NaN                  3   \n",
       "Richard_David_Precht                          NaN                  0   \n",
       "Emil_Fackenheim                               NaN                  0   \n",
       "Shibli_Nomani                                 NaN                  2   \n",
       "Hent_de_Vries                                 NaN                  0   \n",
       "George_Gurdjieff                              NaN                  1   \n",
       "Friedrich_Wilhelm_Foerster                    NaN                  0   \n",
       "Debiprasad_Chattopadhyaya                     NaN                  1   \n",
       "Svetozar_Marković                             NaN                  1   \n",
       "Gerald_Cohen                              60.6712                  1   \n",
       "Zeno_of_Citium                            96.6954                  5   \n",
       "Peter_G._Ossorio                              NaN                  0   \n",
       "Hans_Reichenbach                              NaN                  2   \n",
       "\n",
       "                           (followers, birth mean) (followers, birth std)  \\\n",
       "Muhammad_Mustafa_Jauhar                        NaN                    NaN   \n",
       "Leonardo_Polo                                  NaN                    NaN   \n",
       "Julian_Savulescu                               NaN                    NaN   \n",
       "Francis_Fukuyama                               NaN                    NaN   \n",
       "Jacques_Ellul                              1923.75                4.78714   \n",
       "Moses_Mendelssohn                          1798.67                87.0651   \n",
       "Ivo_Urbančič                                   NaN                    NaN   \n",
       "Jakob_Böhme                                1812.33                 53.799   \n",
       "Richard_David_Precht                           NaN                    NaN   \n",
       "Emil_Fackenheim                                NaN                    NaN   \n",
       "Shibli_Nomani                                 1928                33.9411   \n",
       "Hent_de_Vries                                  NaN                    NaN   \n",
       "George_Gurdjieff                              1898                    NaN   \n",
       "Friedrich_Wilhelm_Foerster                     NaN                    NaN   \n",
       "Debiprasad_Chattopadhyaya                     1947                    NaN   \n",
       "Svetozar_Marković                             1949                    NaN   \n",
       "Gerald_Cohen                                  1959                    NaN   \n",
       "Zeno_of_Citium                              -138.6                  167.3   \n",
       "Peter_G._Ossorio                               NaN                    NaN   \n",
       "Hans_Reichenbach                            1933.5                10.6066   \n",
       "\n",
       "                           (followers, birth min) (followers, birth max)  \\\n",
       "Muhammad_Mustafa_Jauhar                       NaN                    NaN   \n",
       "Leonardo_Polo                                 NaN                    NaN   \n",
       "Julian_Savulescu                              NaN                    NaN   \n",
       "Francis_Fukuyama                              NaN                    NaN   \n",
       "Jacques_Ellul                                1918                   1929   \n",
       "Moses_Mendelssohn                            1743                   1899   \n",
       "Ivo_Urbančič                                  NaN                    NaN   \n",
       "Jakob_Böhme                                  1775                   1874   \n",
       "Richard_David_Precht                          NaN                    NaN   \n",
       "Emil_Fackenheim                               NaN                    NaN   \n",
       "Shibli_Nomani                                1904                   1952   \n",
       "Hent_de_Vries                                 NaN                    NaN   \n",
       "George_Gurdjieff                             1898                   1898   \n",
       "Friedrich_Wilhelm_Foerster                    NaN                    NaN   \n",
       "Debiprasad_Chattopadhyaya                    1947                   1947   \n",
       "Svetozar_Marković                            1949                   1949   \n",
       "Gerald_Cohen                                 1959                   1959   \n",
       "Zeno_of_Citium                               -330                     55   \n",
       "Peter_G._Ossorio                              NaN                    NaN   \n",
       "Hans_Reichenbach                             1926                   1941   \n",
       "\n",
       "                           (followers, death mean) (followers, death std)  \\\n",
       "Muhammad_Mustafa_Jauhar                        NaN                    NaN   \n",
       "Leonardo_Polo                                  NaN                    NaN   \n",
       "Julian_Savulescu                               NaN                    NaN   \n",
       "Francis_Fukuyama                               NaN                    NaN   \n",
       "Jacques_Ellul                                 1993                7.87401   \n",
       "Moses_Mendelssohn                             1864                94.8736   \n",
       "Ivo_Urbančič                                   NaN                    NaN   \n",
       "Jakob_Böhme                                1887.33                52.6245   \n",
       "Richard_David_Precht                           NaN                    NaN   \n",
       "Emil_Fackenheim                                NaN                    NaN   \n",
       "Shibli_Nomani                                 1997                    NaN   \n",
       "Hent_de_Vries                                  NaN                    NaN   \n",
       "George_Gurdjieff                              1974                    NaN   \n",
       "Friedrich_Wilhelm_Foerster                     NaN                    NaN   \n",
       "Debiprasad_Chattopadhyaya                      NaN                    NaN   \n",
       "Svetozar_Marković                              NaN                    NaN   \n",
       "Gerald_Cohen                                   NaN                    NaN   \n",
       "Zeno_of_Citium                               -57.4                161.178   \n",
       "Peter_G._Ossorio                               NaN                    NaN   \n",
       "Hans_Reichenbach                               NaN                    NaN   \n",
       "\n",
       "                              real      predict  \n",
       "Muhammad_Mustafa_Jauhar     1895.0  1270.527897  \n",
       "Leonardo_Polo               1926.0  1841.338699  \n",
       "Julian_Savulescu            1963.0  2006.659355  \n",
       "Francis_Fukuyama            1952.0  1888.705803  \n",
       "Jacques_Ellul               1912.0  1934.706175  \n",
       "Moses_Mendelssohn           1729.0  1752.214456  \n",
       "Ivo_Urbančič                1930.0  1893.191454  \n",
       "Jakob_Böhme                 1575.0  1780.570887  \n",
       "Richard_David_Precht        1964.0  2009.373699  \n",
       "Emil_Fackenheim             1916.0  1854.627410  \n",
       "Shibli_Nomani               1857.0  1957.065020  \n",
       "Hent_de_Vries               1958.0  1998.904087  \n",
       "George_Gurdjieff            1866.0  1388.665201  \n",
       "Friedrich_Wilhelm_Foerster  1869.0  1270.527897  \n",
       "Debiprasad_Chattopadhyaya   1918.0  2036.801437  \n",
       "Svetozar_Marković           1846.0  2036.974282  \n",
       "Gerald_Cohen                1941.0  2030.007216  \n",
       "Zeno_of_Citium              -334.0   784.029245  \n",
       "Peter_G._Ossorio            1926.0  1270.527897  \n",
       "Hans_Reichenbach            1891.0  1425.215976  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train2.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = get_sociodemographic_stats(g_test.nodes(), g, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test.to_csv(\"datasets/generations.test.csv\", encoding=\"utf_8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mirza_Mazhar_Jan-e-Janaan                1505.015440\n",
       "Pseudo-Dionysius_the_Areopagite          1066.166589\n",
       "Speusippus                               1059.480079\n",
       "Leucippus                                1219.229866\n",
       "Muhammad_al-Shaybani                      154.259009\n",
       "Metrodorus_of_Lampsacus_(the_younger)    1132.328644\n",
       "Al-Dhahabi                               1313.272252\n",
       "Abu_Yusuf                                 154.259009\n",
       "Muhammad_Usman_Damani                    1845.208543\n",
       "Jesús_Padilla_Gálvez                     1252.243384\n",
       "Predrag_Finci                            -177.874577\n",
       "Haji_Dost_Muhammad_Qandhari              1418.629357\n",
       "Burhan_al-Din_al-Marghinani              1480.493861\n",
       "Ioane_Petritsi                           1098.185649\n",
       "Xenocrates                               1033.179947\n",
       "Sufyan_al-Thawri                         1319.647382\n",
       "Plato                                     285.838937\n",
       "Democritus                               -671.531161\n",
       "Ahmad_Sirhindi                            127.153371\n",
       "Ludwig_Wittgenstein                      1731.395380\n",
       "Panaetius                                1122.732099\n",
       "David_the_Invincible                     1098.185649\n",
       "Homer                                    -798.459493\n",
       "Arcesilaus                                621.725418\n",
       "Laozi                                    -584.111865\n",
       "Ibn_Abd_al-Hadi                          1944.953866\n",
       "Edmund_Husserl                           1805.627200\n",
       "Plotinus                                  -53.384461\n",
       "Qadi_Thanaullah_Panipati                 1688.237968\n",
       "Yusuf_ibn_Abd_al-Rahman_al-Mizzi         1313.272252\n",
       "Iamblichus                                674.806330\n",
       "Georg_Wilhelm_Friedrich_Hegel            1530.264559\n",
       "Blaise_Pascal                            1830.196773\n",
       "Immanuel_Kant                            1496.904624\n",
       "Philodemus                               1169.614144\n",
       "Ibn_Abi_Asim                             1527.271221\n",
       "Zhuang_Zhou                              1102.185194\n",
       "Proclus                                    26.080759\n",
       "Name: predict, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[\"predict\"] = model.predict(df_test.fillna(0).as_matrix())\n",
    "\n",
    "df_test[\"predict\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 / variance :  0.533342118415\n",
      "Residual sum of squares: 131608.14\n",
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize='True')\n",
      "{'normalize': 'True'}\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.5, random_state=0)\n",
    "\n",
    "tuned_parameters = [{ \"normalize\" : [\"True\", \"False\"] }]\n",
    "\n",
    "clf = GridSearchCV(linear_model.LinearRegression(), tuned_parameters, n_jobs=-1, verbose=0, cv=2)\n",
    "clf.fit(X, Y)\n",
    "\n",
    "print \"r2 / variance : \", clf.best_score_\n",
    "print(\"Residual sum of squares: %.2f\" % np.mean((clf.predict(X_test) - y_test) ** 2))\n",
    "print clf.best_estimator_\n",
    "print clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 / variance :  0.533684149949\n",
      "Residual sum of squares: 131713.25\n",
      "Lasso(alpha=10, copy_X=True, fit_intercept=True, max_iter=5000,\n",
      "   normalize=False, positive=False, precompute=False, random_state=None,\n",
      "   selection='cyclic', tol=0.0001, warm_start=False)\n",
      "{'normalize': False, 'alpha': 10, 'max_iter': 5000, 'fit_intercept': True}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.5, random_state=0)\n",
    "\n",
    "tuned_parameters = [{ \"alpha\" : [0.01, 0.03, 0.08, 0.1, 0.3, 1, 3, 10, 30, 100],\n",
    "                     \"fit_intercept\": [True, False],\n",
    "                     \"max_iter\": [1000, 5000, 10000],\n",
    "                     \"normalize\": [True, False] }]\n",
    "\n",
    "clf = GridSearchCV(linear_model.Lasso(), tuned_parameters, n_jobs=-1, verbose=0, cv=2)\n",
    "clf.fit(X, Y)\n",
    "\n",
    "print \"r2 / variance : \", clf.best_score_\n",
    "print(\"Residual sum of squares: %.2f\" % np.mean((clf.predict(X_test) - y_test) ** 2))\n",
    "print clf.best_estimator_\n",
    "print clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mirza_Mazhar_Jan-e-Janaan                1505.500810\n",
       "Pseudo-Dionysius_the_Areopagite          1067.672233\n",
       "Speusippus                               1063.124800\n",
       "Leucippus                                1221.854036\n",
       "Muhammad_al-Shaybani                      160.200702\n",
       "Metrodorus_of_Lampsacus_(the_younger)    1135.565510\n",
       "Al-Dhahabi                               1312.213425\n",
       "Abu_Yusuf                                 160.200702\n",
       "Muhammad_Usman_Damani                    1844.795243\n",
       "Jesús_Padilla_Gálvez                     1249.412297\n",
       "Predrag_Finci                            -177.285553\n",
       "Haji_Dost_Muhammad_Qandhari              1414.122753\n",
       "Burhan_al-Din_al-Marghinani              1486.989971\n",
       "Ioane_Petritsi                           1104.495776\n",
       "Xenocrates                               1036.377997\n",
       "Sufyan_al-Thawri                         1318.489888\n",
       "Plato                                     264.324796\n",
       "Democritus                               -664.590850\n",
       "Ahmad_Sirhindi                            123.535662\n",
       "Ludwig_Wittgenstein                      1749.422519\n",
       "Panaetius                                1126.027309\n",
       "David_the_Invincible                     1104.495776\n",
       "Homer                                    -790.445495\n",
       "Arcesilaus                                632.662138\n",
       "Laozi                                    -577.316233\n",
       "Ibn_Abd_al-Hadi                          1949.255314\n",
       "Edmund_Husserl                           1808.994994\n",
       "Plotinus                                  -56.746713\n",
       "Qadi_Thanaullah_Panipati                 1693.143280\n",
       "Yusuf_ibn_Abd_al-Rahman_al-Mizzi         1312.213425\n",
       "Iamblichus                                684.523882\n",
       "Georg_Wilhelm_Friedrich_Hegel            1548.790066\n",
       "Blaise_Pascal                            1818.257129\n",
       "Immanuel_Kant                            1514.467681\n",
       "Philodemus                               1175.584583\n",
       "Ibn_Abi_Asim                             1526.862424\n",
       "Zhuang_Zhou                              1107.926498\n",
       "Proclus                                    26.938853\n",
       "Name: predict, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = get_sociodemographic_stats(g_test.nodes(), g, df)\n",
    "\n",
    "df_test[\"predict\"] = clf.predict(df_test.fillna(0).as_matrix())\n",
    "\n",
    "df_test[\"predict\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "\n",
    "pca.fit(X)\n",
    "\n",
    "Z = pca.transform(X)\n",
    "Z = zip(*Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.96920085e-01   3.66344512e-01   6.82075190e-02   4.39469272e-02\n",
      "   1.11399126e-02   9.30551555e-03   2.77136839e-03   7.27370709e-04\n",
      "   2.65867364e-04   2.43006741e-04   7.79369497e-05   4.48580920e-05\n",
      "   4.63810827e-06   4.82114835e-07]\n"
     ]
    },
    {
     "data": {
      "image/png": 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kOM12GpxtkvLyqrxo8jRQvFQearYaJrjNUvGm8r05jK1lVYl1YHfS2bk37REp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11acce410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_) \n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(Z[0], Z[1])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}