04 - RDDs numericos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![RDD key pair](media/05.numeric_rdd.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 04 - RDDs numéricos\n",
    "--------------\n",
    "\n",
    "Funciones de estadística descriptiva implementadas en Spark\n",
    "\n",
    "  Método              |  Descripción                       \n",
    "  ------------------- | ---------------------------------- \n",
    "  stats()             | Resumen de estadísticas            \n",
    "  mean()              | Media aritmética\n",
    "  sum(), max(), min() | Suma, máximo y mínimo\n",
    "  variance()          | Varianza de los elementos\n",
    "  sampleVariance()    | Varianza de una muestra\n",
    "  stdev()             | Desviación estándar\n",
    "  sampleStdev()       | Desviación estándar de una muestra\n",
    "  histogram()         | Histograma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyspark in /home/xergioalex/Envs/tensorflow/lib/python3.5/site-packages\n",
      "Requirement already satisfied: py4j==0.10.7 in /home/xergioalex/Envs/tensorflow/lib/python3.5/site-packages (from pyspark)\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 18.0 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create apache spark context\n",
    "from pyspark import SparkContext\n",
    "sc = SparkContext(master=\"local\", appName=\"Mi app\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop apache spark context\n",
    "sc.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtener un resúmen de estadísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumen de estadísticas:\n",
      "(count: 10000, mean: 0.016565092702759247, stdev: 0.9906881314645754, max: 3.940276682933942, min: -3.9202054258986507)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# STATS\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Un RDD con datos aleatorios de una distribución normal\n",
    "nrdd = sc.parallelize(np.random.normal(size=10000)).cache()\n",
    "\n",
    "# Resumen de estadísticas\n",
    "sts = nrdd.stats()\n",
    "\n",
    "print(\"Resumen de estadísticas:\\n{0}\\n\".format(sts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de outliers: 25\n"
     ]
    }
   ],
   "source": [
    "# MEAN\n",
    "\n",
    "from math import fabs\n",
    "\n",
    "# Filtra outliers\n",
    "stddev = sts.stdev()\n",
    "avg = sts.mean()\n",
    "\n",
    "frdd = nrdd.filter(lambda x: fabs(x - avg) < 3*stddev).cache()\n",
    "\n",
    "print(\"Número de outliers: {0}\".format(sts.count() - frdd.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:  [-2.926630052775228, -2.336983793876538, -1.7473375349778482, -1.1576912760791582, -0.5680450171804683, 0.021601241718221864, 0.6112475006169116, 1.2008937595156013, 1.7905400184142914, 2.3801862773129816, 2.9698325362116718]\n",
      "y:  [69, 299, 846, 1553, 2176, 2273, 1599, 828, 274, 58]\n"
     ]
    },
    {
     "data": {
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HF+jnBQAAhV+hus9RYcV9joBbx32OCg73OQJurljc5wgAAMBqlCMAAAATyhEAAIAJ5QgA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\n5QgAAMCEcgQAAGBCOQIAADD5f54ElaJxmDwUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1265fbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Obtiene un histograma con 10 grupos\n",
    "x,y = frdd.histogram(10)\n",
    "\n",
    "print(\"x: \", x)\n",
    "print(\"y: \", y)\n",
    "\n",
    "# Limpia la gráfica\n",
    "plt.gcf().clear()\n",
    "\n",
    "plt.bar(x[:-1], y, width=0.6)\n",
    "plt.xlabel(u'Valores')\n",
    "plt.ylabel(u'Número de ocurrencias')\n",
    "plt.title(u'Histograma')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo práctico\n",
    "\n",
    "Número de patentes por año de un país"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\"PATENT\",\"GYEAR\",\"GDATE\",\"APPYEAR\",\"COUNTRY\",\"POSTATE\",\"ASSIGNEE\",\"ASSCODE\",\"CLAIMS\",\"NCLASS\",\"CAT\",\"SUBCAT\",\"CMADE\",\"CRECEIVE\",\"RATIOCIT\",\"GENERAL\",\"ORIGINAL\",\"FWDAPLAG\",\"BCKGTLAG\",\"SELFCTUB\",\"SELFCTLB\",\"SECDUPBD\",\"SECDLWBD\"']\n",
      "['6009554,1999,14606,1997,\"US\",\"NY\",219390,2,,714,2,22,9,0,1,,,,12.7778,0.1111,0.1111,,']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import matplotlib.pyplot as plt\n",
    "from operator import add\n",
    "    \n",
    "# Lee los datos como un rdd\n",
    "rdd = sc.textFile(\"data/apat63_99.txt\")\n",
    "print(rdd.take(1))\n",
    "print(rdd.top(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media de patentes entre el año 1963 y el 1999 = 48242.94594594595\n"
     ]
    },
    {
     "data": {
      "image/png": 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W586dtXPnzmuzMQAAQIVRpl/hTZgwQXXr1tWcOXOseREREdb/G2M0depUjRgxQo888ogk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\n3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f78fd04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtiene un rdd que nos proporcione, para las patentes \"US\" el número de \n",
    "# patentes por año (año, número_de_patentes) ordenadas por año\n",
    "yearNum = rdd.filter(lambda l: '\"US\"' in l).\\\n",
    "              map(lambda l: l.split(',')).\\\n",
    "              map(lambda l: (l[1], 1)).\\\n",
    "              reduceByKey(add)\n",
    "              \n",
    "yearNum.take(5)\n",
    "\n",
    "# Calcula la media del número de patentes\n",
    "media = yearNum.values().mean()\n",
    "\n",
    "print(\"Media de patentes entre el año {0} y el {1} = {2}\".format(\n",
    "       yearNum.keys().min(),\n",
    "       yearNum.keys().max(),\n",
    "       media\n",
    "       ))\n",
    "\n",
    "# Crea el gráfico\n",
    "# Obtén los años como una lista\n",
    "years = yearNum.keys().collect()\n",
    "# Obtén el número de patentes como una lista\n",
    "numPats = yearNum.values().collect()\n",
    "\n",
    "# Descomentar para gráfica\n",
    "plt.gcf().clear() \n",
    "yearsRange = np.arange(len(years))\n",
    "plt.bar(yearsRange, numPats, align='center')\n",
    "plt.xticks(yearsRange, years, fontsize=10, rotation=90)\n",
    "plt.ylabel(\"N Patentes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}