proc-sol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de muestra de grafo extraído de facebook\n",
    "\n",
    "## Secuencia de grados\n",
    "\n",
    "1. Importar los paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import FuncionesGrafos as fg\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Cargar la secuencia de grados desde los archivos de nodos y aristas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sg = fg.extrae_secuencia_grados(\n",
    "    './input/facebook_nodos.csv', \n",
    "    './input/facebook_aristas.csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Crear las columnas 'i', y 'fi' de la tabla de distribución de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i, fi = [], []\n",
    "for g  in Sg:\n",
    "    if g in i:\n",
    "        fi[i.index(g)] += 1\n",
    "    else:\n",
    "        i.append(g)\n",
    "        fi.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fi = []\n",
    "Fi.append(fi[0])\n",
    "for u in np.arange(1, len(fi)):\n",
    "    Fi.append(fi[u] + Fi[u - 1])\n",
    "hi = []\n",
    "for u in fi:\n",
    "    hi.append(u / Fi[-1])\n",
    "Hi = []\n",
    "Hi.append(hi[0])\n",
    "for u in np.arange(1, len(hi)):\n",
    "    Hi.append(hi[u] + Hi[u - 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Armar la tabla de distribución de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "d = {\n",
    "    'fi' : pd.Series(fi, index=i),\n",
    "    'Fi' : pd.Series(Fi, index=i),\n",
    "    'hi' : pd.Series(hi, index=i),\n",
    "    'Hi' : pd.Series(Hi, index=i)\n",
    "}\n",
    "tabla = pd.DataFrame(d, columns=['fi', 'hi', 'Fi', 'Hi'])\n",
    "tabla.index.name = 'i'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "4. Obtención de la gráfica de la distribución de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribución de frecuencias de los grados del grafo')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6pm9TrXl4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1976cb7f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(i, fi, 'red')\n",
    "plt.title(u'Distribución de frecuencias de los grados del grafo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Datos estadísticos\n",
    "i = np.array(i)\n",
    "fi = np.array(fi)\n",
    "media = np.mean(Sg)\n",
    "mediana = np.median(Sg)\n",
    "varianza = np.var(Sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43.69101262688784, 25.0, 2747.2395107101647)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media, mediana, varianza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1976ccaf8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histograma = plt.hist(Sg, len(i), histtype='stepfilled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1976cca5a58>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBoAkZcoAkKRMGQCSlCkDQJIy9f8VTBEbdP5HfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1976cea34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grados = pd.DataFrame(Sg)\n",
    "grados.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.0, 25.0, 57.0, 1045.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = np.percentile(Sg, 25)\n",
    "Q2 = np.percentile(Sg, 50)\n",
    "Q3 = np.percentile(Sg, 75)\n",
    "Q4 = np.percentile(Sg, 100)\n",
    "Q1, Q2, Q3, Q4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datos de grafo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nodos = pd.read_csv('./input/facebook_nodos.csv')\n",
    "enlaces = pd.read_csv('./input/facebook_aristas.csv')\n",
    "N = np.array(nodos['Id'])\n",
    "E = []\n",
    "for y in np.arange(len(enlaces)):\n",
    "    E.append((enlaces.iloc[y,0], enlaces.iloc[y,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de nodos: 4039\n",
      "Cantidad de enlaces: 88234\n",
      "Coef. Transitividad: 0.5192\n",
      "Coef. agrupamiento promedio: 0.6055\n",
      "Cantidad de triangulos: 1612010\n"
     ]
    }
   ],
   "source": [
    "G = fg.n_networkx(N, E)\n",
    "fg.mostrar_datos(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "5. Exportar la tabla de distribución de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tabla.to_csv('TablaDistFrec.csv', sep=';', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Experimentación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cantidad: cantidad de nodos\n",
    "cantidad = 500\n",
    "Sg2 = []\n",
    "# hi*N\n",
    "hin = []\n",
    "for h in hi:\n",
    "    hin.append(round(cantidad * h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cantidad de grados diferentes de 0\n",
    "cant_n_0 = 0\n",
    "for h in hin:\n",
    "    if h > 0:\n",
    "        cant_n_0 += 1\n",
    "cant_n_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.792951541850222, 3.5460992907801416)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(N)/len(i), cantidad/cant_n_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4039"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.10567734682406"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cantidad / 17.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RIC = Q3 - Q1\n",
    "bi = Q1 - 1.5 * RIC\n",
    "bs = Q3 + 1.5 * RIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "can_sup_bs = 0\n",
    "for g in Sg:\n",
    "    if g >= bs:\n",
    "        can_sup_bs += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "can_sup_bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}